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2025,36(4):1413-1434, DOI: 10.13328/j.cnki.jos.007223, CSTR: 32375.14.jos.007223
Abstract:
Formal methods have made significant strides in the field of requirements consistency verification. However, as the complexity of embedded system requirements continues to increase, verifying requirements consistency faces the challenge of dealing with an excessively large state space. To effectively reduce the verification state space, while also considering the strong dependency among devices in embedded system requirements, this study proposes a compositional verification method for ensuring the consistency of requirements in complex embedded systems. This method is based on requirement decomposition and identification of dependencies among requirements. By leveraging these dependencies, it assembles verification subsystems, enabling the compositional verification of complex embedded system requirements and facilitating the initial identification of inconsistencies. Specifically, the problem frames approach is employed for requirement modeling and decomposition, while a domain-specific device knowledge base is utilized for modeling the physical characteristics of devices. During the assembly of verification subsystems, models of expected software behavior are generated and dynamically integrated with physical device models. Finally, the feasibility and effectiveness of this method are validated through a case study of an airborne reconnaissance control system, demonstrating a significant reduction in the verification state space through five case evaluations. This method thus provides a practical solution for verifying the requirements of complex embedded systems.
Formal methods have made significant strides in the field of requirements consistency verification. However, as the complexity of embedded system requirements continues to increase, verifying requirements consistency faces the challenge of dealing with an excessively large state space. To effectively reduce the verification state space, while also considering the strong dependency among devices in embedded system requirements, this study proposes a compositional verification method for ensuring the consistency of requirements in complex embedded systems. This method is based on requirement decomposition and identification of dependencies among requirements. By leveraging these dependencies, it assembles verification subsystems, enabling the compositional verification of complex embedded system requirements and facilitating the initial identification of inconsistencies. Specifically, the problem frames approach is employed for requirement modeling and decomposition, while a domain-specific device knowledge base is utilized for modeling the physical characteristics of devices. During the assembly of verification subsystems, models of expected software behavior are generated and dynamically integrated with physical device models. Finally, the feasibility and effectiveness of this method are validated through a case study of an airborne reconnaissance control system, demonstrating a significant reduction in the verification state space through five case evaluations. This method thus provides a practical solution for verifying the requirements of complex embedded systems.
2025,36(4):1435-1460, DOI: 10.13328/j.cnki.jos.007224, CSTR: 32375.14.jos.007224
Abstract:
The rich development ecosystem of Python provides a lot of third-party libraries, significantly boosting developers’ efficiency and quality. Third-party library developers encapsulate underlying code, enabling upper-layer application developers to swiftly accomplish tasks by calling relevant APIs. However, APIs of third-party libraries are not constant. Owing to fixes, refactoring and feature additions, these libraries undergo continuous updates. Incompatible changes are seen in some APIs after updates, leading to abnormal termination or inconsistent results in upper-layer applications. Therefore, the API compatibility of the Python third-party library has become one of the issues that needs to be solved. There have been related studies focusing on API compatibility issues of Python third-party libraries, of which reasons have yet to be fully classified so that, the fine-grained cause can not be provided. An empirical study is conducted on the symptoms and causes of API compatibility issues with Python third-party library and a targeted static detection method is proposed. Initially, this study gathers 108 pairs of incompatible API versions by combining version update logs and regression tests across 6 version pairs of the flask and pandas libraries. Subsequently, an empirical study is conducted on the collected data, summarizing the symptoms and causes of compatibility issues. Finally, this study proposes a static analysis-based detection method for incompatible Python APIs, providing syntactic-level causes of incompatible API issues. This study conducts experimental evaluations on 12 version pairs of 4 popular Python third-party libraries. The results show that the proposed method is good in effectiveness, generalization, time performance, memory performance, and usefulness.
The rich development ecosystem of Python provides a lot of third-party libraries, significantly boosting developers’ efficiency and quality. Third-party library developers encapsulate underlying code, enabling upper-layer application developers to swiftly accomplish tasks by calling relevant APIs. However, APIs of third-party libraries are not constant. Owing to fixes, refactoring and feature additions, these libraries undergo continuous updates. Incompatible changes are seen in some APIs after updates, leading to abnormal termination or inconsistent results in upper-layer applications. Therefore, the API compatibility of the Python third-party library has become one of the issues that needs to be solved. There have been related studies focusing on API compatibility issues of Python third-party libraries, of which reasons have yet to be fully classified so that, the fine-grained cause can not be provided. An empirical study is conducted on the symptoms and causes of API compatibility issues with Python third-party library and a targeted static detection method is proposed. Initially, this study gathers 108 pairs of incompatible API versions by combining version update logs and regression tests across 6 version pairs of the flask and pandas libraries. Subsequently, an empirical study is conducted on the collected data, summarizing the symptoms and causes of compatibility issues. Finally, this study proposes a static analysis-based detection method for incompatible Python APIs, providing syntactic-level causes of incompatible API issues. This study conducts experimental evaluations on 12 version pairs of 4 popular Python third-party libraries. The results show that the proposed method is good in effectiveness, generalization, time performance, memory performance, and usefulness.
SUN Wei-Song, CHEN Yu-Chen, ZHAO Zi-Han, CHEN Hong, GE Yi-Fei, HAN Ting-Xu, HUANG Sheng-Han, LI Jia-Xun, FANG Chun-Rong, CHEN Zhen-Yu
2025,36(4):1461-1488, DOI: 10.13328/j.cnki.jos.007254, CSTR: 32375.14.jos.007254
Abstract:
With the significant success of deep learning in fields such as computer vision and natural language processing, researchers in software engineering have begun to explore its integration into solving software engineering tasks. Existing research indicates that deep learning exhibits advantages in various code-related tasks, such as code retrieval and code summarization, that traditional methods and machine learning cannot match. Deep learning models trained for code-related tasks are referred to as deep code models. However, similar to natural language processing and image processing models, the security of deep code models faces numerous challenges due to the vulnerability and inexplicability of neural networks. It has become a research focus in software engineering. In recent years, researchers have proposed numerous attack and defense methods for deep code models. Nevertheless, there is a lack of a systematic review of research on deep code model security, hindering the rapid understanding of subsequent researchers in this field. To provide a comprehensive overview of the current research, challenges, and latest findings in this field, this study collects 32 relevant papers and categorizes existing research results into two main classes: backdoor attack and defense techniques, and adversarial attack and defense techniques. This study systematically analyzes and summarizes the collected papers based on the above two categories. Subsequently, it outlines commonly used experimental datasets and evaluation metrics in this field. Finally, it analyzes key challenges in this field and suggests feasible future research directions, aiming to provide valuable guidance for further advancements in the security of deep code models.
With the significant success of deep learning in fields such as computer vision and natural language processing, researchers in software engineering have begun to explore its integration into solving software engineering tasks. Existing research indicates that deep learning exhibits advantages in various code-related tasks, such as code retrieval and code summarization, that traditional methods and machine learning cannot match. Deep learning models trained for code-related tasks are referred to as deep code models. However, similar to natural language processing and image processing models, the security of deep code models faces numerous challenges due to the vulnerability and inexplicability of neural networks. It has become a research focus in software engineering. In recent years, researchers have proposed numerous attack and defense methods for deep code models. Nevertheless, there is a lack of a systematic review of research on deep code model security, hindering the rapid understanding of subsequent researchers in this field. To provide a comprehensive overview of the current research, challenges, and latest findings in this field, this study collects 32 relevant papers and categorizes existing research results into two main classes: backdoor attack and defense techniques, and adversarial attack and defense techniques. This study systematically analyzes and summarizes the collected papers based on the above two categories. Subsequently, it outlines commonly used experimental datasets and evaluation metrics in this field. Finally, it analyzes key challenges in this field and suggests feasible future research directions, aiming to provide valuable guidance for further advancements in the security of deep code models.
2025,36(4):1489-1529, DOI: 10.13328/j.cnki.jos.007268, CSTR: 32375.14.jos.007268
Abstract:
With the advancement of informationalization, the development of a variety of applications and iterative functions inevitably leads to software defects, posing significant threats to program reliability and security. Therefore, detecting and repairing software defects becomes essential yet onerous for developers in maintaining software quality. Accordingly, software engineering researchers have proposed numerous technologies over the past decades to help developers address defect-related issues. However, these technologies face serious challenges and make little progress in industrial implementation. Large language model (LLM), such as the code-based model CodeX and the prestigious ChatGPT, trained on massive datasets, can capture complex patterns and structures in code, process extensive contextual information, and flexibly adapt to various tasks. Their superior performance has attracted considerable attention from researchers. In many software engineering tasks, technologies based on LLM show significant advantages in addressing key challenges previously faced in different domains. Consequently, this study attempts to analyze and explore three defect detection domains where technologies based on LLM have been widely adopted: deep-learning library defect detection, GUI automated testing, and automated test case generation, along with one mature software defect repair domain: automated program repair (APR). This study delves into the progress of these domains and provides an in-depth discussion of their characteristics and challenges. Lastly, based on an analysis of existing research, this study summarizes the key challenges faced by these domains and technologies and offers insights for future research.
With the advancement of informationalization, the development of a variety of applications and iterative functions inevitably leads to software defects, posing significant threats to program reliability and security. Therefore, detecting and repairing software defects becomes essential yet onerous for developers in maintaining software quality. Accordingly, software engineering researchers have proposed numerous technologies over the past decades to help developers address defect-related issues. However, these technologies face serious challenges and make little progress in industrial implementation. Large language model (LLM), such as the code-based model CodeX and the prestigious ChatGPT, trained on massive datasets, can capture complex patterns and structures in code, process extensive contextual information, and flexibly adapt to various tasks. Their superior performance has attracted considerable attention from researchers. In many software engineering tasks, technologies based on LLM show significant advantages in addressing key challenges previously faced in different domains. Consequently, this study attempts to analyze and explore three defect detection domains where technologies based on LLM have been widely adopted: deep-learning library defect detection, GUI automated testing, and automated test case generation, along with one mature software defect repair domain: automated program repair (APR). This study delves into the progress of these domains and provides an in-depth discussion of their characteristics and challenges. Lastly, based on an analysis of existing research, this study summarizes the key challenges faced by these domains and technologies and offers insights for future research.
2025,36(4):1530-1556, DOI: 10.13328/j.cnki.jos.007288, CSTR: 32375.14.jos.007288
Abstract:
Automatic bug localization technologies can significantly alleviate the burden of debugging and maintaining software programs for developers. As a widely studied automatic bug localization technology, information retrieval-based bug localization has yielded promising performance of bug localization. In recent years, the utilization of deep learning for information retrieval-based bug localization has emerged as a research trend due to the widespread adoption of deep learning. This study systematically categorizes and summarizes 52 studies that have introduced deep learning to information retrieval-based bug localization in recent years. Firstly, a summary of datasets and evaluation indexes in this kind of bug localization is provided. Then, the localization performance of these techniques is analyzed from the perspectives of different granularity and transportability. Subsequently, information coding characterization methods and feature extraction methods employed in related studies are summarized. Finally, this study summarizes and compares the most advanced bug localization methods, and provides insights into the future directions of utilizing deep learning in information retrieval-based bug localization methods.
Automatic bug localization technologies can significantly alleviate the burden of debugging and maintaining software programs for developers. As a widely studied automatic bug localization technology, information retrieval-based bug localization has yielded promising performance of bug localization. In recent years, the utilization of deep learning for information retrieval-based bug localization has emerged as a research trend due to the widespread adoption of deep learning. This study systematically categorizes and summarizes 52 studies that have introduced deep learning to information retrieval-based bug localization in recent years. Firstly, a summary of datasets and evaluation indexes in this kind of bug localization is provided. Then, the localization performance of these techniques is analyzed from the perspectives of different granularity and transportability. Subsequently, information coding characterization methods and feature extraction methods employed in related studies are summarized. Finally, this study summarizes and compares the most advanced bug localization methods, and provides insights into the future directions of utilizing deep learning in information retrieval-based bug localization methods.
2025,36(4):1557-1569, DOI: 10.13328/j.cnki.jos.007189, CSTR: 32375.14.jos.007189
Abstract:
This study considers slot filling as a crucial component of task-oriented dialogue systems, which serves downstream tasks by identifying specific slot entities in utterances. However, in a specific domain, it necessitates a large amount of labeled data, which is costly to collect. In this context, cross-domain slot filling emerges and efficiently addresses the issue of data scarcity through transfer learning. However, existing methods overlook the dependencies between slot types in utterances, leading to the suboptimal performance of existing models when transferring to new domains. To address this issue, a cross-domain slot filling method based on slot dependency modeling is proposed in this study. Leveraging the prompt learning approach based on generative pre-trained models, a prompt template integrating slot dependency information is designed, establishing implicit dependency relationships between different slot types and fully exploiting the predictive performance of slot entities in the pre-trained model. Furthermore, to enhance the semantic dependencies between slot types, slot entities, and utterance texts, discourse filling subtask is introduced in this study to strengthen the inherent connections between utterances and slot entities through reverse filling. Transfer experiments across multiple domains demonstrate significant performance improvements in zero-shot and few-shot settings achieved by the proposed model. Additionally, a detailed analysis of the main structures in the model and ablation experiments are conducted in this study to further validate the necessity of each part of the model.
This study considers slot filling as a crucial component of task-oriented dialogue systems, which serves downstream tasks by identifying specific slot entities in utterances. However, in a specific domain, it necessitates a large amount of labeled data, which is costly to collect. In this context, cross-domain slot filling emerges and efficiently addresses the issue of data scarcity through transfer learning. However, existing methods overlook the dependencies between slot types in utterances, leading to the suboptimal performance of existing models when transferring to new domains. To address this issue, a cross-domain slot filling method based on slot dependency modeling is proposed in this study. Leveraging the prompt learning approach based on generative pre-trained models, a prompt template integrating slot dependency information is designed, establishing implicit dependency relationships between different slot types and fully exploiting the predictive performance of slot entities in the pre-trained model. Furthermore, to enhance the semantic dependencies between slot types, slot entities, and utterance texts, discourse filling subtask is introduced in this study to strengthen the inherent connections between utterances and slot entities through reverse filling. Transfer experiments across multiple domains demonstrate significant performance improvements in zero-shot and few-shot settings achieved by the proposed model. Additionally, a detailed analysis of the main structures in the model and ablation experiments are conducted in this study to further validate the necessity of each part of the model.
2025,36(4):1570-1589, DOI: 10.13328/j.cnki.jos.007202, CSTR: 32375.14.jos.007202
Abstract:
In recent years, research achievements in deep learning have found widespread applications globally. To enhance the training efficiency of large-scale deep learning models, industry practices often involve constructing GPU clusters and configuring efficient task schedulers. However, deep learning training tasks exhibit complex performance characteristics such as performance heterogeneity and placement topological sensitivity. Scheduling without considering performance can lead to issues such as low resource utilization and poor training efficiency. In response to this challenge, a great number of schedulers of deep learning training tasks based on performance modeling have emerged. These schedulers, by constructing accurate performance models, delve into the intricate performance characteristics of tasks. Based on this understanding, they design more optimized scheduling algorithms, thereby forming more efficient scheduling solutions. This study begins with a modeling design perspective, providing a categorized review of the performance modeling methods employed by current schedulers. Subsequently, based on the optimized scheduling approaches from performance modeling by schedulers, a systematic analysis of existing task scheduling efforts is presented. Finally, this study outlines prospective research directions for performance modeling and scheduling in the future.
In recent years, research achievements in deep learning have found widespread applications globally. To enhance the training efficiency of large-scale deep learning models, industry practices often involve constructing GPU clusters and configuring efficient task schedulers. However, deep learning training tasks exhibit complex performance characteristics such as performance heterogeneity and placement topological sensitivity. Scheduling without considering performance can lead to issues such as low resource utilization and poor training efficiency. In response to this challenge, a great number of schedulers of deep learning training tasks based on performance modeling have emerged. These schedulers, by constructing accurate performance models, delve into the intricate performance characteristics of tasks. Based on this understanding, they design more optimized scheduling algorithms, thereby forming more efficient scheduling solutions. This study begins with a modeling design perspective, providing a categorized review of the performance modeling methods employed by current schedulers. Subsequently, based on the optimized scheduling approaches from performance modeling by schedulers, a systematic analysis of existing task scheduling efforts is presented. Finally, this study outlines prospective research directions for performance modeling and scheduling in the future.
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Available online: March 26, 2025
, DOI: 10.13328/j.cnki.jos.007318
Abstract:
Blockchain has shown strong vitality in the field of cryptocurrency investment, attracting the participation of a large number of investors. However, due to the anonymity of blockchain, it induces a lot of fraud, among which the Ponzi scheme smart contract is a typical fraudulent investment activity, causing huge economic losses for investors. Therefore, the detection of Ponzi scheme contracts on Ethereum becomes particularly important. Nevertheless, most existing studies have ignored control flow information in the source code of Ponzi scheme contracts. To extract more semantic and structural information from Ponzi scheme contracts, this study proposes a Ponzi scheme contract detection model based on code control flow graph. First, the model constructs the obtained contract source code in the form of a control flow diagram. Then, key features including data flow information and code structure information are extracted by the Word2Vec algorithm. Considering that the functions of each smart contract are different and the length of the code varies significantly, resulting in a large difference in the extracted feature vectors. In this study, feature vectors generated by different smart contracts are aligned so that all feature vectors have the same dimension, which is convenient for subsequent processing. Secondly, the feature learning module based on graph convolution and Transformer is utilized to introduce multi-head attention mechanism to learn the dependency of node features. Finally, the multilayer perceptron is used to identify the Ponzi scheme contract. By comparing the proposed model with the traditional graph feature learning model on the dataset provided by the Xblock website, the performance of the multi-head attention mechanism introduced by the model is verified. Experimental results demonstrate that this model effectively improves the ability to detect Ponzi scheme contracts.
Blockchain has shown strong vitality in the field of cryptocurrency investment, attracting the participation of a large number of investors. However, due to the anonymity of blockchain, it induces a lot of fraud, among which the Ponzi scheme smart contract is a typical fraudulent investment activity, causing huge economic losses for investors. Therefore, the detection of Ponzi scheme contracts on Ethereum becomes particularly important. Nevertheless, most existing studies have ignored control flow information in the source code of Ponzi scheme contracts. To extract more semantic and structural information from Ponzi scheme contracts, this study proposes a Ponzi scheme contract detection model based on code control flow graph. First, the model constructs the obtained contract source code in the form of a control flow diagram. Then, key features including data flow information and code structure information are extracted by the Word2Vec algorithm. Considering that the functions of each smart contract are different and the length of the code varies significantly, resulting in a large difference in the extracted feature vectors. In this study, feature vectors generated by different smart contracts are aligned so that all feature vectors have the same dimension, which is convenient for subsequent processing. Secondly, the feature learning module based on graph convolution and Transformer is utilized to introduce multi-head attention mechanism to learn the dependency of node features. Finally, the multilayer perceptron is used to identify the Ponzi scheme contract. By comparing the proposed model with the traditional graph feature learning model on the dataset provided by the Xblock website, the performance of the multi-head attention mechanism introduced by the model is verified. Experimental results demonstrate that this model effectively improves the ability to detect Ponzi scheme contracts.
Available online: March 26, 2025
, DOI: 10.13328/j.cnki.jos.007320
Abstract:
The application of artificial intelligence technology has extended from relatively static tasks such as classification, translation, and question answering to relatively dynamic tasks that require a series of “interaction-action” with the environment to be completed, like autonomous driving, robotic control, and games. The core of the model for executing such tasks is the sequential decision-making (SDM) algorithm. As it faces higher uncertainties of the environment and interaction and these tasks are often safety-critical systems, the testing techniques are confronted with great challenges. The existing testing technologies for intelligent algorithm models mainly focus on the reliability of a single model, the generation of diverse test scenarios for complex tasks, simulation testing, etc., while no attention is paid to the “interaction-action” decision sequence of the SDM model, leading to unadaptability or low cost-effectiveness. In this study, a fuzz testing method named IIFuzzing for intervening in the execution of inert “interaction-action” decision sequences is proposed. In the fuzz testing framework, by learning the “interaction-action” decision sequence pattern, the inert “interaction-action” decision sequences that will not trigger failure accidents are predicted and the testing execution of such sequences is terminated to improve the testing efficiency. The experimental evaluations are conducted in four common test configurations, and the results show that compared with the latest fuzz testing for SDM models, IIFuzzing can detect 16.7%–54.5% more failure accidents within the same time, and the diversity of accidents is also better than that of the baseline approach.
The application of artificial intelligence technology has extended from relatively static tasks such as classification, translation, and question answering to relatively dynamic tasks that require a series of “interaction-action” with the environment to be completed, like autonomous driving, robotic control, and games. The core of the model for executing such tasks is the sequential decision-making (SDM) algorithm. As it faces higher uncertainties of the environment and interaction and these tasks are often safety-critical systems, the testing techniques are confronted with great challenges. The existing testing technologies for intelligent algorithm models mainly focus on the reliability of a single model, the generation of diverse test scenarios for complex tasks, simulation testing, etc., while no attention is paid to the “interaction-action” decision sequence of the SDM model, leading to unadaptability or low cost-effectiveness. In this study, a fuzz testing method named IIFuzzing for intervening in the execution of inert “interaction-action” decision sequences is proposed. In the fuzz testing framework, by learning the “interaction-action” decision sequence pattern, the inert “interaction-action” decision sequences that will not trigger failure accidents are predicted and the testing execution of such sequences is terminated to improve the testing efficiency. The experimental evaluations are conducted in four common test configurations, and the results show that compared with the latest fuzz testing for SDM models, IIFuzzing can detect 16.7%–54.5% more failure accidents within the same time, and the diversity of accidents is also better than that of the baseline approach.
Available online: March 19, 2025
, DOI: 10.13328/j.cnki.jos.007308
Abstract:
The current mainstream mode of software development is the supply chain-level reuse of open-source software and components. It avoids repetitive development, reduces research and development costs, and enhances development efficiency. However, it inevitably brings about issues such as unknown component sources, unclear component compositions, unidentified component vulnerabilities, and license violations. To address these issues, researchers propose software bill of materials (SBOM). SBOM provides a detailed list of software components and their relationships, reveals potential and known threats, and makes software transparent. Since its proposal, research on SBOM by researchers both at home and abroad mainly focus on its current status, applications, and tools, lacking theoretical and systematic research. This study presents a comprehensive review of the background, basic concepts, generation techniques, tools and performance analysis, applications, challenges, and trends of SBOM. It also proposes the new concept of SBOM+, which integrates fine-grained security vulnerability perception and license conflict detection. The aim is to provide support for researchers engaged in SBOM, software development, and supply chain security from the perspectives of concepts, technologies, tools, applications, and development.
The current mainstream mode of software development is the supply chain-level reuse of open-source software and components. It avoids repetitive development, reduces research and development costs, and enhances development efficiency. However, it inevitably brings about issues such as unknown component sources, unclear component compositions, unidentified component vulnerabilities, and license violations. To address these issues, researchers propose software bill of materials (SBOM). SBOM provides a detailed list of software components and their relationships, reveals potential and known threats, and makes software transparent. Since its proposal, research on SBOM by researchers both at home and abroad mainly focus on its current status, applications, and tools, lacking theoretical and systematic research. This study presents a comprehensive review of the background, basic concepts, generation techniques, tools and performance analysis, applications, challenges, and trends of SBOM. It also proposes the new concept of SBOM+, which integrates fine-grained security vulnerability perception and license conflict detection. The aim is to provide support for researchers engaged in SBOM, software development, and supply chain security from the perspectives of concepts, technologies, tools, applications, and development.
Available online: March 19, 2025
, DOI: 10.13328/j.cnki.jos.007313
Abstract:
Mobile applications, a new computing mode that has emerged in the past decade, significantly impact people’s lifestyles. Mobile applications primarily interact through graphical user interfaces (GUIs) and conducting manual testing for them requires significant manpower and material resources. In response to this, researchers propose automated GUI test generation techniques for mobile applications to enhance testing efficiency and detect potential defects. This study collects 145 relevant papers and systematically sorts out, analyzes, and summarizes existing work. This study proposes a research framework called “Test Generator-Test Environment” to categorize research in this domain based on the modules to which it belongs. Particularly, this study classifies existing methods roughly into five categories according to the methods on which the test generator is based: random-based, heuristic-search-based, model-based, machine-learning-based, and test-migration-based approaches. Furthermore, this study analyzes and discusses existing methods from other classification dimensions, such as defect categories and test action categories. Additionally, influential datasets and open-source tools in this field are compiled. Finally, this study summarizes the current challenges and provides an outlook on future research directions.
Mobile applications, a new computing mode that has emerged in the past decade, significantly impact people’s lifestyles. Mobile applications primarily interact through graphical user interfaces (GUIs) and conducting manual testing for them requires significant manpower and material resources. In response to this, researchers propose automated GUI test generation techniques for mobile applications to enhance testing efficiency and detect potential defects. This study collects 145 relevant papers and systematically sorts out, analyzes, and summarizes existing work. This study proposes a research framework called “Test Generator-Test Environment” to categorize research in this domain based on the modules to which it belongs. Particularly, this study classifies existing methods roughly into five categories according to the methods on which the test generator is based: random-based, heuristic-search-based, model-based, machine-learning-based, and test-migration-based approaches. Furthermore, this study analyzes and discusses existing methods from other classification dimensions, such as defect categories and test action categories. Additionally, influential datasets and open-source tools in this field are compiled. Finally, this study summarizes the current challenges and provides an outlook on future research directions.
Available online: March 12, 2025
, DOI: 10.13328/j.cnki.jos.007317
Abstract:
Advances of IoT (Internet of Thing) generate a sheer volume of floating-point time series data, which poses great challenges in storing and transmitting these data. To this end, floating-point time series data compression is extremely crucial. It can be classified into lossy and lossless compression based on data reversibility. Lossy compression methods achieve a better compression ratio by discarding some data information and are suitable for applications with lower precision requirements. Lossless compression methods, while reducing data size, retain all data information, which is essential for applications that require maintaining data integrity and accuracy. In addition, to meet the requirements of real-time monitoring on edge devices, streaming compression algorithms emerge. Current review studies on time series compression encounter issues such as incomplete sorting, unclear line of thought, single classification standards, and lack of inclusion of relatively new and representative algorithms. Time series compression algorithms over the years are divided into lossy compression and lossless compression. Then, different algorithm frameworks are further distinguished, including those based on data representation, prediction, machine learning, and transformation. Meanwhile, the compression characteristics of streaming and batch processing are summarized. Then, the design ideas of various compression algorithms are deeply analyzed, and the development context diagrams of these algorithms are presented. Next, the advantages and disadvantages of various algorithms are compared with experiments. Finally, common application scenarios are summarized. Future research is envisioned.
Advances of IoT (Internet of Thing) generate a sheer volume of floating-point time series data, which poses great challenges in storing and transmitting these data. To this end, floating-point time series data compression is extremely crucial. It can be classified into lossy and lossless compression based on data reversibility. Lossy compression methods achieve a better compression ratio by discarding some data information and are suitable for applications with lower precision requirements. Lossless compression methods, while reducing data size, retain all data information, which is essential for applications that require maintaining data integrity and accuracy. In addition, to meet the requirements of real-time monitoring on edge devices, streaming compression algorithms emerge. Current review studies on time series compression encounter issues such as incomplete sorting, unclear line of thought, single classification standards, and lack of inclusion of relatively new and representative algorithms. Time series compression algorithms over the years are divided into lossy compression and lossless compression. Then, different algorithm frameworks are further distinguished, including those based on data representation, prediction, machine learning, and transformation. Meanwhile, the compression characteristics of streaming and batch processing are summarized. Then, the design ideas of various compression algorithms are deeply analyzed, and the development context diagrams of these algorithms are presented. Next, the advantages and disadvantages of various algorithms are compared with experiments. Finally, common application scenarios are summarized. Future research is envisioned.
Available online: March 12, 2025
, DOI: 10.13328/j.cnki.jos.007310
Abstract:
With the continuous deepening of research on the security and privacy of deep learning models, researchers find that model stealing attacks pose a tremendous threat to neural networks. A typical data-dependent model stealing attack can use a certain percentage of real data to query the target model and train an alternative model locally to steal the target model. Since 2020, a novel data-free model stealing attack method has been proposed, which can steal and attack deep neural networks simply by using fake query examples generated by generative models. Since it does not rely on real data, the data-free model stealing attack can cause more serious damage. However, the diversity and effectiveness of the query examples constructed by the current data-free model stealing attack methods are insufficient, and there are problems of a large number of queries and a relatively low success rate of the attack during the model stealing process. Therefore, this study proposes a vision feature decoupling-based model stealing attack (VFDA), which decouples and generates the visual features of the query examples generated during the data-free model stealing process by using a multi-decoder structure, thus improving the diversity of query examples and the effectiveness of model stealing. Specifically, VFDA uses three decoders to respectively generate the texture information, region encoding, and smoothing information of query examples to complete the decoupling of visual features of query examples. Secondly, to make the generated query examples more consistent with the visual features of real examples, the sparsity of the texture information is limited and the generated smoothing information is filtered. VFDA exploits the property that the representational tendency of neural networks depends on the image texture features, and can generate query examples with inter-class diversity, thus effectively improving the similarity of model stealing and the success rate of the attack. In addition, VFDA adds intra-class diversity loss to the smoothed information of query samples generated through decoupling to make the query samples more consistent with real sample distribution. By comparing with multiple model stealing attack methods, the VFDA method proposed in this study has better performance in the similarity of model stealing and the success rate of the attack. In particular, on the GTSRB and Tiny-ImageNet datasets with high resolution, the attack success rate is respectively improved by 3.86% and 4.15% on average compared with the currently better EBFA method.
With the continuous deepening of research on the security and privacy of deep learning models, researchers find that model stealing attacks pose a tremendous threat to neural networks. A typical data-dependent model stealing attack can use a certain percentage of real data to query the target model and train an alternative model locally to steal the target model. Since 2020, a novel data-free model stealing attack method has been proposed, which can steal and attack deep neural networks simply by using fake query examples generated by generative models. Since it does not rely on real data, the data-free model stealing attack can cause more serious damage. However, the diversity and effectiveness of the query examples constructed by the current data-free model stealing attack methods are insufficient, and there are problems of a large number of queries and a relatively low success rate of the attack during the model stealing process. Therefore, this study proposes a vision feature decoupling-based model stealing attack (VFDA), which decouples and generates the visual features of the query examples generated during the data-free model stealing process by using a multi-decoder structure, thus improving the diversity of query examples and the effectiveness of model stealing. Specifically, VFDA uses three decoders to respectively generate the texture information, region encoding, and smoothing information of query examples to complete the decoupling of visual features of query examples. Secondly, to make the generated query examples more consistent with the visual features of real examples, the sparsity of the texture information is limited and the generated smoothing information is filtered. VFDA exploits the property that the representational tendency of neural networks depends on the image texture features, and can generate query examples with inter-class diversity, thus effectively improving the similarity of model stealing and the success rate of the attack. In addition, VFDA adds intra-class diversity loss to the smoothed information of query samples generated through decoupling to make the query samples more consistent with real sample distribution. By comparing with multiple model stealing attack methods, the VFDA method proposed in this study has better performance in the similarity of model stealing and the success rate of the attack. In particular, on the GTSRB and Tiny-ImageNet datasets with high resolution, the attack success rate is respectively improved by 3.86% and 4.15% on average compared with the currently better EBFA method.
Available online: March 12, 2025
, DOI: 10.13328/j.cnki.jos.007312
Abstract:
In the face of the severe security risks posed by Android malware, effective Android malware detection has become the focus of common concern in both the industry and academia. However, with the emergence of Android adversarial example techniques, existing malware detection systems are facing unprecedented challenges. Android malware adversarial example attacks can bypass existing malware detection models by perturbing the source code or characteristics of malware while keeping its original functionality inact. Despite substantial research on adversarial example attacks against malware, there is still a lack of a comprehensive review specifically focusing on adversarial example attacks in the Android system at present, and the unique requirements for adversarial example design within the Android system are not studied. Therefore, this study begins by introducing the fundamental concepts of Android malware detection. It then classifies existing Android adversarial example techniques from various perspectives and provides an overview of the development sequence of Android adversarial example techniques. Subsequently, it reviews Android adversarial example techniques in recent years, introduces representative work in different categories and analyzes their pros and cons. Furthermore, it categorizes and introduces common means of code perturbation in Android adversarial example attacks, and analyzes their application scenarios. Finally, it discusses the challenges faced by Android malware adversarial example techniques, and envisions future research directions in this emerging field.
In the face of the severe security risks posed by Android malware, effective Android malware detection has become the focus of common concern in both the industry and academia. However, with the emergence of Android adversarial example techniques, existing malware detection systems are facing unprecedented challenges. Android malware adversarial example attacks can bypass existing malware detection models by perturbing the source code or characteristics of malware while keeping its original functionality inact. Despite substantial research on adversarial example attacks against malware, there is still a lack of a comprehensive review specifically focusing on adversarial example attacks in the Android system at present, and the unique requirements for adversarial example design within the Android system are not studied. Therefore, this study begins by introducing the fundamental concepts of Android malware detection. It then classifies existing Android adversarial example techniques from various perspectives and provides an overview of the development sequence of Android adversarial example techniques. Subsequently, it reviews Android adversarial example techniques in recent years, introduces representative work in different categories and analyzes their pros and cons. Furthermore, it categorizes and introduces common means of code perturbation in Android adversarial example attacks, and analyzes their application scenarios. Finally, it discusses the challenges faced by Android malware adversarial example techniques, and envisions future research directions in this emerging field.
Available online: March 12, 2025
, DOI: 10.13328/j.cnki.jos.007319
Abstract:
Heterogeneous graphs, which can effectively capture the complex and diverse relationships between entities in the real world, play a crucial role in many domains. Heterogeneous graph representation learning aims to map the information in graphs into a low-dimensional space, so as to capture the deep semantic associations between nodes and support downstream tasks such as node classification and clustering. This study presents a comprehensive review of the latest research progress in heterogeneous graph representation learning, covering both methodological advancements and real-world applications. It first formally defines the concept of heterogeneous graphs and discusses the key challenges in heterogeneous graph representation learning. From the perspectives of shallow models and deep models. It then systematically reviews the mainstream methods for heterogeneous graph representation learning, with a particular focus on deep models. Especially for deep models, they are categorized and analyzed from the perspective of heterogeneous graph transformation. The strengths, limitations, and application scenarios of various methods are thoroughly analyzed, aiming to provide readers with a holistic research perspective. Furthermore, the commonly used datasets and tools in the field of heterogeneous graph representation learning are introduced, and their applications in the real world are discussed. Finally, the main contributions of this study are summarized and the outlook on the future research directions in this area is presented. This study intends to offer researchers a comprehensive understanding of the field of heterogeneous graph representation learning, laying a solid foundation for future research and application.
Heterogeneous graphs, which can effectively capture the complex and diverse relationships between entities in the real world, play a crucial role in many domains. Heterogeneous graph representation learning aims to map the information in graphs into a low-dimensional space, so as to capture the deep semantic associations between nodes and support downstream tasks such as node classification and clustering. This study presents a comprehensive review of the latest research progress in heterogeneous graph representation learning, covering both methodological advancements and real-world applications. It first formally defines the concept of heterogeneous graphs and discusses the key challenges in heterogeneous graph representation learning. From the perspectives of shallow models and deep models. It then systematically reviews the mainstream methods for heterogeneous graph representation learning, with a particular focus on deep models. Especially for deep models, they are categorized and analyzed from the perspective of heterogeneous graph transformation. The strengths, limitations, and application scenarios of various methods are thoroughly analyzed, aiming to provide readers with a holistic research perspective. Furthermore, the commonly used datasets and tools in the field of heterogeneous graph representation learning are introduced, and their applications in the real world are discussed. Finally, the main contributions of this study are summarized and the outlook on the future research directions in this area is presented. This study intends to offer researchers a comprehensive understanding of the field of heterogeneous graph representation learning, laying a solid foundation for future research and application.
Available online: March 12, 2025
, DOI: 10.13328/j.cnki.jos.007290
Abstract:
Design pattern detection is an essential research topic in software engineering. Many scholars both domestically and internationally have dedicated their efforts to researching and resolving design pattern detection, thereby yielding fruitful results. This study reviews the current technologies in software design pattern detection and points out their prospects. Firstly, this study briefly introduces the development history of software design pattern detection, discusses the objects of design pattern detection, summarizes the feature types of design patterns, and provides the evaluation indexes of design pattern detection. Then, the existing classification methods for design pattern detection techniques are summarized, and the classification method proposed in this study is introduced. Next, according to the development timeline of design pattern detection technologies, the research status and latest advancements of current software design pattern detection technologies are discussed from three approaches, including non-machine learning design pattern detection, machine learning design pattern detection, and design pattern detection based on pre-trained language models, with the current achievements summarized and compared. Finally, the main problems and challenges in this field are analyzed, and further research directions and potential solutions are pointed out. Covering contents from early non-machine learning methods and utilization of machine learning technologies to the application of modern pre-trained language models, this study comprehensively and systematically presents the development history, latest advancements, and prospects of this field. It provides valuable guidance for future research directions and ideas within this area.
Design pattern detection is an essential research topic in software engineering. Many scholars both domestically and internationally have dedicated their efforts to researching and resolving design pattern detection, thereby yielding fruitful results. This study reviews the current technologies in software design pattern detection and points out their prospects. Firstly, this study briefly introduces the development history of software design pattern detection, discusses the objects of design pattern detection, summarizes the feature types of design patterns, and provides the evaluation indexes of design pattern detection. Then, the existing classification methods for design pattern detection techniques are summarized, and the classification method proposed in this study is introduced. Next, according to the development timeline of design pattern detection technologies, the research status and latest advancements of current software design pattern detection technologies are discussed from three approaches, including non-machine learning design pattern detection, machine learning design pattern detection, and design pattern detection based on pre-trained language models, with the current achievements summarized and compared. Finally, the main problems and challenges in this field are analyzed, and further research directions and potential solutions are pointed out. Covering contents from early non-machine learning methods and utilization of machine learning technologies to the application of modern pre-trained language models, this study comprehensively and systematically presents the development history, latest advancements, and prospects of this field. It provides valuable guidance for future research directions and ideas within this area.
Available online: March 05, 2025
, DOI: 10.13328/j.cnki.jos.007304
Abstract:
Bayesian optimization is a technique for optimizing black-box functions. Due to its high sample utilization efficiency, it is widely applied across various scientific and engineering fields, such as hyperparameters tuning of deep models, compound design, drug development, and material design. However, the performance of Bayesian optimization significantly deteriorates when the input space is of high dimensionality. To overcome this limitation, numerous studies carry out high-dimensional extensions on Bayesian optimization methods. To deeply analyze research methods of high-dimensional Bayesian optimization, this study categorizes these methods into three types based on assumptions and characteristics of different kinds of work: methods based on the effective low-dimensional hypothesis, methods based on additive assumptions, and methods based on local search. Then, this study elaborates on and analyzes these methods. This study first focuses on analyzing the research progress of these three types of methods. Then, the advantages and disadvantages of each method in the application of Bayesian optimization are compared. Finally, the main research trends in high-dimensional Bayesian optimization at the current stage are summarized, and future development directions are discussed.
Bayesian optimization is a technique for optimizing black-box functions. Due to its high sample utilization efficiency, it is widely applied across various scientific and engineering fields, such as hyperparameters tuning of deep models, compound design, drug development, and material design. However, the performance of Bayesian optimization significantly deteriorates when the input space is of high dimensionality. To overcome this limitation, numerous studies carry out high-dimensional extensions on Bayesian optimization methods. To deeply analyze research methods of high-dimensional Bayesian optimization, this study categorizes these methods into three types based on assumptions and characteristics of different kinds of work: methods based on the effective low-dimensional hypothesis, methods based on additive assumptions, and methods based on local search. Then, this study elaborates on and analyzes these methods. This study first focuses on analyzing the research progress of these three types of methods. Then, the advantages and disadvantages of each method in the application of Bayesian optimization are compared. Finally, the main research trends in high-dimensional Bayesian optimization at the current stage are summarized, and future development directions are discussed.
Available online: February 26, 2025
, DOI: 10.13328/j.cnki.jos.007306
Abstract:
Twin support vector machine (TSVM) can effectively tackle data such as cross or XOR data. However, when set-valued data are handled, TSVM usually makes use of statistical information of set-valued objects such as the mean and the median. Unlike TSVM, this study proposes twin support function machine (TSFM) that can directly deal with set-valued data. In terms of support functions defined for set-valued objects, TSFM obtains nonparallel hyperplanes in a Banach space. To suppress outliers in set-valued data, TSFM adopts the pinball loss function and introduce the weights of set-valued objects. Considering that TSFM involves optimization problems in the infinite-dimensional space, the measure is taken in the form of a linear combination of Dirac measures. Thus the optimization model in the finite-dimensional space is constructed. To solve the optimization model effectively, this study employs the sampling strategy to transform the model into quadratic programming (QP) problems. The dual formulations of the QP problems are derived, which provides theoretical foundations for determining which sampling points are support vectors. To classify set-valued data, the distance from the set-valued object to the hyperplane in a Banach space is defined, and the decision rule is derived therefrom. This study also considers the kernelization of support functions to capture the nonlinear features of data, which makes the proposed model available for indefinite kernels. Experimental results demonstrate that TSFM can capture the intrinsic structure of cross-plane set-valued data and obtain good classification performance in the case of outliers or set-valued objects containing a few high-dimensional examples.
Twin support vector machine (TSVM) can effectively tackle data such as cross or XOR data. However, when set-valued data are handled, TSVM usually makes use of statistical information of set-valued objects such as the mean and the median. Unlike TSVM, this study proposes twin support function machine (TSFM) that can directly deal with set-valued data. In terms of support functions defined for set-valued objects, TSFM obtains nonparallel hyperplanes in a Banach space. To suppress outliers in set-valued data, TSFM adopts the pinball loss function and introduce the weights of set-valued objects. Considering that TSFM involves optimization problems in the infinite-dimensional space, the measure is taken in the form of a linear combination of Dirac measures. Thus the optimization model in the finite-dimensional space is constructed. To solve the optimization model effectively, this study employs the sampling strategy to transform the model into quadratic programming (QP) problems. The dual formulations of the QP problems are derived, which provides theoretical foundations for determining which sampling points are support vectors. To classify set-valued data, the distance from the set-valued object to the hyperplane in a Banach space is defined, and the decision rule is derived therefrom. This study also considers the kernelization of support functions to capture the nonlinear features of data, which makes the proposed model available for indefinite kernels. Experimental results demonstrate that TSFM can capture the intrinsic structure of cross-plane set-valued data and obtain good classification performance in the case of outliers or set-valued objects containing a few high-dimensional examples.
Available online: February 26, 2025
, DOI: 10.13328/j.cnki.jos.007299
Abstract:
Large language model (LLM) like ChatGPT has found widespread applications across various fields due to their strong natural language understanding and generation capabilities. However, deep learning models exhibit vulnerability when subjected to adversarial example attacks. In natural language processing, current research on adversarial example generation methods typically employs CNN-based models, RNN-based models, and Transformer-based pre-trained models as target models, with few studies exploring the robustness of LLMs under adversarial attacks and quantifying the evaluation criteria of LLM robustness. Taking ChatGPT against Chinese adversarial attacks as an example, this study introduces a novel concept termed offset average difference (OAD) and proposes a quantifiable LLM robustness evaluation metric based on OAD, named OAD-based robustness score (ORS). In a black-box attack scenario, this study selects nine mainstream Chinese adversarial attack methods based on word importance to generate adversarial texts, which are then employed to attack ChatGPT and yield the attack success rate of each method. The proposed ORS assigns a robustness score to LLMs for each attack method based on the attack success rate. In addition to the ChatGPT that outputs hard labels, this study designs ORS for target models with soft-labeled outputs based on the attack success rate and the proportion of misclassified adversarial texts with high confidence. Meanwhile, this study extends the scoring formula to the fluency assessment of adversarial texts, proposing an OAD-based adversarial text fluency scoring method, named OAD-based fluency score (OFS). Compared to traditional methods requiring human involvement, the proposed OFS greatly reduces evaluation costs. Experiments conducted on real-world Chinese news and sentiment classification datasets to some extent initially demonstrate that, for text classification tasks, the robustness score of ChatGPT against adversarial attacks is nearly 20% higher than that of Chinese BERT. However, the powerful ChatGPT still produces erroneous predictions under adversarial attacks, with the highest attack success rate exceeding 40%.
Large language model (LLM) like ChatGPT has found widespread applications across various fields due to their strong natural language understanding and generation capabilities. However, deep learning models exhibit vulnerability when subjected to adversarial example attacks. In natural language processing, current research on adversarial example generation methods typically employs CNN-based models, RNN-based models, and Transformer-based pre-trained models as target models, with few studies exploring the robustness of LLMs under adversarial attacks and quantifying the evaluation criteria of LLM robustness. Taking ChatGPT against Chinese adversarial attacks as an example, this study introduces a novel concept termed offset average difference (OAD) and proposes a quantifiable LLM robustness evaluation metric based on OAD, named OAD-based robustness score (ORS). In a black-box attack scenario, this study selects nine mainstream Chinese adversarial attack methods based on word importance to generate adversarial texts, which are then employed to attack ChatGPT and yield the attack success rate of each method. The proposed ORS assigns a robustness score to LLMs for each attack method based on the attack success rate. In addition to the ChatGPT that outputs hard labels, this study designs ORS for target models with soft-labeled outputs based on the attack success rate and the proportion of misclassified adversarial texts with high confidence. Meanwhile, this study extends the scoring formula to the fluency assessment of adversarial texts, proposing an OAD-based adversarial text fluency scoring method, named OAD-based fluency score (OFS). Compared to traditional methods requiring human involvement, the proposed OFS greatly reduces evaluation costs. Experiments conducted on real-world Chinese news and sentiment classification datasets to some extent initially demonstrate that, for text classification tasks, the robustness score of ChatGPT against adversarial attacks is nearly 20% higher than that of Chinese BERT. However, the powerful ChatGPT still produces erroneous predictions under adversarial attacks, with the highest attack success rate exceeding 40%.
Available online: February 26, 2025
, DOI: 10.13328/j.cnki.jos.007303
Abstract:
Phrasal visual grounding, a fundamental and critical research task in the field of multimodal studies, aims at predicting fine-grained alignment relationships between textual phrases and image regions. Despite the remarkable progress achieved by existing phrasal visual grounding approaches, they all ignore the implicit alignment relationships between textual phrases and their corresponding image regions, commonly referred to as implicit phrase-region alignment. Predicting such relationships can effectively evaluate the ability of models to understand deep multimodal semantics. Therefore, to effectively model implicit phrase-region alignment relationships, this study proposes an implicit-enhanced causal modeling (ICM) approach for phrasal visual grounding, which employs the intervention strategies of causal reasoning to mitigate the confusion caused by shallow semantics. To evaluate models’ ability to understand deep multimodal semantics, this study annotates a high-quality implicit dataset and conducts a large number of experiments. Multiple sets of comparative experimental results demonstrate the effectiveness of the proposed ICM approach in modeling implicit phrase-region alignment relationships. Furthermore, the proposed ICM approach outperforms some advanced multimodal large language models (MLLMs) on the implicit dataset, further promoting the research of MLLMs towards more implicit scenarios.
Phrasal visual grounding, a fundamental and critical research task in the field of multimodal studies, aims at predicting fine-grained alignment relationships between textual phrases and image regions. Despite the remarkable progress achieved by existing phrasal visual grounding approaches, they all ignore the implicit alignment relationships between textual phrases and their corresponding image regions, commonly referred to as implicit phrase-region alignment. Predicting such relationships can effectively evaluate the ability of models to understand deep multimodal semantics. Therefore, to effectively model implicit phrase-region alignment relationships, this study proposes an implicit-enhanced causal modeling (ICM) approach for phrasal visual grounding, which employs the intervention strategies of causal reasoning to mitigate the confusion caused by shallow semantics. To evaluate models’ ability to understand deep multimodal semantics, this study annotates a high-quality implicit dataset and conducts a large number of experiments. Multiple sets of comparative experimental results demonstrate the effectiveness of the proposed ICM approach in modeling implicit phrase-region alignment relationships. Furthermore, the proposed ICM approach outperforms some advanced multimodal large language models (MLLMs) on the implicit dataset, further promoting the research of MLLMs towards more implicit scenarios.
Available online: February 26, 2025
, DOI: 10.13328/j.cnki.jos.007302
Abstract:
This study discusses the computational complexity of the partition function of the symmetric dual-spin system on regular graphs. Based on # exponential time hypothesis (#ETH) and random exponential time hypothesis (rETH), this study develops the classical dichotomies of this problem class into the exponential dichotomies, also known as the fine-grained dichotomies. In other words, this study proves that when the given tractable conditions are satisfied, then the problem is solvable in polynomial time; otherwise, there is no sub-exponential time algorithm when #ETH holds. This study also proposes two solutions to solve the in-effectiveness of existing interpolation methods on building sqrt-sub-exponential time reductions under the restriction of planar graphs. It also utilizes these two solutions to discuss the related fine-grained complexity and dichotomy of this problem under the planar graph restriction.
This study discusses the computational complexity of the partition function of the symmetric dual-spin system on regular graphs. Based on # exponential time hypothesis (#ETH) and random exponential time hypothesis (rETH), this study develops the classical dichotomies of this problem class into the exponential dichotomies, also known as the fine-grained dichotomies. In other words, this study proves that when the given tractable conditions are satisfied, then the problem is solvable in polynomial time; otherwise, there is no sub-exponential time algorithm when #ETH holds. This study also proposes two solutions to solve the in-effectiveness of existing interpolation methods on building sqrt-sub-exponential time reductions under the restriction of planar graphs. It also utilizes these two solutions to discuss the related fine-grained complexity and dichotomy of this problem under the planar graph restriction.
Available online: February 26, 2025
, DOI: 10.13328/j.cnki.jos.007289
Abstract:
Cross-domain recommendation (CDR) alleviates the cold start problem by transferring the user-item rating patterns from a dataset in a dense rating auxiliary domain to one in a sparse rating target domain, and has been widely studied in recent years. The clustering methods based on single-domain recommendation adopted by most CDR algorithms fail to effectively utilize overlapping information and sufficiently adapt to CDR, resulting in inaccurate clustering results. In CDR, graph convolution network (GCN) methods can fully utilize the associations between nodes to improve recommendation accuracy. However, GCN-based CDR often employs static graph learning for node embedding, ignoring the fact that user preferences may change with different recommendation scenarios, which causes poor model performance across different recommendation tasks and ineffective mitigation of data sparsity. To this end, a multi-layer recurrent GCN CDR model based on a pseudo-overlap detection mechanism is proposed. Firstly, by fully leveraging overlapping data based on the community clustering algorithm Louvain, a pseudo-overlap detection mechanism is designed to mine user trust relationships and similar user communities, thereby enhancing the adaptability and accuracy of clustering algorithms in CDR. Secondly, a multi-layer recurrent GCN consisting of an embedding learning module and a graph learning module is proposed to learn dynamic domain-shared features, domain-specific features, and dynamic graph structures. By conducting iterative enhancement of the two modules, the latest user preferences are obtained to alleviate data sparsity. Finally, a multi-layer perceptron (MLP) is employed to model user-item interactions and obtain predicted ratings. Comparative results with 12 related models across four groups of data domains demonstrate the effectiveness of the proposed method, with average improvements of 5.47%, 3.44%, and 2.38% in MRR, NDCG, and HR metrics respectively.
Cross-domain recommendation (CDR) alleviates the cold start problem by transferring the user-item rating patterns from a dataset in a dense rating auxiliary domain to one in a sparse rating target domain, and has been widely studied in recent years. The clustering methods based on single-domain recommendation adopted by most CDR algorithms fail to effectively utilize overlapping information and sufficiently adapt to CDR, resulting in inaccurate clustering results. In CDR, graph convolution network (GCN) methods can fully utilize the associations between nodes to improve recommendation accuracy. However, GCN-based CDR often employs static graph learning for node embedding, ignoring the fact that user preferences may change with different recommendation scenarios, which causes poor model performance across different recommendation tasks and ineffective mitigation of data sparsity. To this end, a multi-layer recurrent GCN CDR model based on a pseudo-overlap detection mechanism is proposed. Firstly, by fully leveraging overlapping data based on the community clustering algorithm Louvain, a pseudo-overlap detection mechanism is designed to mine user trust relationships and similar user communities, thereby enhancing the adaptability and accuracy of clustering algorithms in CDR. Secondly, a multi-layer recurrent GCN consisting of an embedding learning module and a graph learning module is proposed to learn dynamic domain-shared features, domain-specific features, and dynamic graph structures. By conducting iterative enhancement of the two modules, the latest user preferences are obtained to alleviate data sparsity. Finally, a multi-layer perceptron (MLP) is employed to model user-item interactions and obtain predicted ratings. Comparative results with 12 related models across four groups of data domains demonstrate the effectiveness of the proposed method, with average improvements of 5.47%, 3.44%, and 2.38% in MRR, NDCG, and HR metrics respectively.
Available online: February 26, 2025
, DOI: 10.13328/j.cnki.jos.007321
Abstract:
Visual-language pre-training (VLP) aims to obtain a powerful multimodal representation by learning on a large-scale image-text multimodal dataset. Multimodal feature fusion and alignment is a key challenge in multimodal model training. In most of the existing visual-language pre-training models, for the multimodal feature fusion and alignment problem, the main approach is that the extracted visual features and text features are directly input into the Transformer model. Since the attention mechanism in the Transformer calculates the similarity between pairs, it is difficult to achieve the alignment among multiple entities. Considering that the hyperedges of hypergraph neural networks possess the characteristics of connecting multiple entities and encoding high-order entity correlations, thus enabling the establishment of relationships among multiple entities. In this study, a visual-language multimodal model pre-training method based on multi-entity alignment of hypergraph neural networks is proposed. In this method, the hypergraph neural network learning module is introduced into the Transformer multi-modal fusion encoder to learn the alignment relationship of multi-modal entities, thereby enhancing the entity alignment ability of the multi-modal fusion encoder in the pre-training model. The proposed visual-language pre-training model is pre-trained on the large-scale image-text datasets and fine-tuned on multiple visual-language downstream tasks such as visual question answering, image-text retrieval, visual grounding, and natural language visual reasoning. The experimental results indicate that compared with the baseline method, the proposed method has performance improvements in multiple downstream tasks, among which the accuracy is improved by 1.8% on the NLVR2 task.
Visual-language pre-training (VLP) aims to obtain a powerful multimodal representation by learning on a large-scale image-text multimodal dataset. Multimodal feature fusion and alignment is a key challenge in multimodal model training. In most of the existing visual-language pre-training models, for the multimodal feature fusion and alignment problem, the main approach is that the extracted visual features and text features are directly input into the Transformer model. Since the attention mechanism in the Transformer calculates the similarity between pairs, it is difficult to achieve the alignment among multiple entities. Considering that the hyperedges of hypergraph neural networks possess the characteristics of connecting multiple entities and encoding high-order entity correlations, thus enabling the establishment of relationships among multiple entities. In this study, a visual-language multimodal model pre-training method based on multi-entity alignment of hypergraph neural networks is proposed. In this method, the hypergraph neural network learning module is introduced into the Transformer multi-modal fusion encoder to learn the alignment relationship of multi-modal entities, thereby enhancing the entity alignment ability of the multi-modal fusion encoder in the pre-training model. The proposed visual-language pre-training model is pre-trained on the large-scale image-text datasets and fine-tuned on multiple visual-language downstream tasks such as visual question answering, image-text retrieval, visual grounding, and natural language visual reasoning. The experimental results indicate that compared with the baseline method, the proposed method has performance improvements in multiple downstream tasks, among which the accuracy is improved by 1.8% on the NLVR2 task.
Available online: February 26, 2025
, DOI: 10.13328/j.cnki.jos.007322
Abstract:
There are numerous and miscellaneous sources of online information. Judging whether it is a rumor in a timely and accurate manner is a crucial issue in the research of the cognitive domain of social media. Most of the previous studies have mainly concentrated on the text content of rumors, user characteristics, or the inherent features confined to the propagation mode, ignoring the key clues of the collective emotions generated by users’ participation in event discussions and the emotional steady-state characteristics hidden in the spread of rumors. In this study, a social network rumor detection method that is oriented by collective emotional stabilization and integrates temporal and spatial steady-state features is proposed. Based on the text features and user behaviors in rumor propagation, the temporal and spatial relationship steady-state features of collective emotions are combined for the first time, which can achieve strong expressiveness and detection accuracy. Specifically, this method takes the emotional keywords of users’ attitude towards a certain event or topic as the basis and uses recurrent neural networks to construct emotional steady-state features of the temporal relationship, enabling the collective emotions to have temporally consistent features with strong expressiveness, which can reflect the convergence effect of the collective emotions over time. The heterogeneous graph neural network is utilized to establish the connections between users and keywords, as well as between texts and keywords so that the collective emotions possess the fine-grained collective emotional steady-state features of the spatial relationship. Finally, the two types of local steady-state features are fused, possessing globality and improving the feature expression. Further classification can obtain the rumor detection results. The proposed method is run on two internationally publicly available and widely used Twitter datasets. Compared with the best-performing method in the baselines, the accuracy is improved by 3.4% and 3.2% respectively; the T-F1 value is improved by 3.0% and 1.8% respectively; the N-F1 value is improved by 2.7% and 2.3% respectively; the U-F1 value is improved by 2.3% and 1.0% respectively.
There are numerous and miscellaneous sources of online information. Judging whether it is a rumor in a timely and accurate manner is a crucial issue in the research of the cognitive domain of social media. Most of the previous studies have mainly concentrated on the text content of rumors, user characteristics, or the inherent features confined to the propagation mode, ignoring the key clues of the collective emotions generated by users’ participation in event discussions and the emotional steady-state characteristics hidden in the spread of rumors. In this study, a social network rumor detection method that is oriented by collective emotional stabilization and integrates temporal and spatial steady-state features is proposed. Based on the text features and user behaviors in rumor propagation, the temporal and spatial relationship steady-state features of collective emotions are combined for the first time, which can achieve strong expressiveness and detection accuracy. Specifically, this method takes the emotional keywords of users’ attitude towards a certain event or topic as the basis and uses recurrent neural networks to construct emotional steady-state features of the temporal relationship, enabling the collective emotions to have temporally consistent features with strong expressiveness, which can reflect the convergence effect of the collective emotions over time. The heterogeneous graph neural network is utilized to establish the connections between users and keywords, as well as between texts and keywords so that the collective emotions possess the fine-grained collective emotional steady-state features of the spatial relationship. Finally, the two types of local steady-state features are fused, possessing globality and improving the feature expression. Further classification can obtain the rumor detection results. The proposed method is run on two internationally publicly available and widely used Twitter datasets. Compared with the best-performing method in the baselines, the accuracy is improved by 3.4% and 3.2% respectively; the T-F1 value is improved by 3.0% and 1.8% respectively; the N-F1 value is improved by 2.7% and 2.3% respectively; the U-F1 value is improved by 2.3% and 1.0% respectively.
Available online: February 19, 2025
, DOI: 10.13328/j.cnki.jos.007307
Abstract:
In terms of point-in-polygon tests, a grid method proposed recently exhibits high computational efficiency. This method organizes the polygon fragments within each grid cell into stripe structures, ensuring that edges in each stripe intersect with both the left and right boundaries of the stripe. In this way, localization computation is enhanced, and GPUs are used for convenient parallel computation, resulting in a detection efficiency superior to that of various previous methods. However, stripe structures constructed based on grid cells generate redundant stripes. Besides, the method has a high space requirement for stripe construction, making it inconvenient to construct stripe structures on GPUs. In response to this, this study proposes to construct stripe structures via grid rows. Thus, redundant strips can be eliminated, and the space requirement for the creation of computation is reduced, due to which stripe structures can be constructed on GPUs, and work efficiency is improved. Experimental results show that, compared with the original method, the new method significantly accelerates the construction of stripe structures, even by over 40 times. Moreover, it has a faster detection speed and can handle dynamic polygons more efficiently.
In terms of point-in-polygon tests, a grid method proposed recently exhibits high computational efficiency. This method organizes the polygon fragments within each grid cell into stripe structures, ensuring that edges in each stripe intersect with both the left and right boundaries of the stripe. In this way, localization computation is enhanced, and GPUs are used for convenient parallel computation, resulting in a detection efficiency superior to that of various previous methods. However, stripe structures constructed based on grid cells generate redundant stripes. Besides, the method has a high space requirement for stripe construction, making it inconvenient to construct stripe structures on GPUs. In response to this, this study proposes to construct stripe structures via grid rows. Thus, redundant strips can be eliminated, and the space requirement for the creation of computation is reduced, due to which stripe structures can be constructed on GPUs, and work efficiency is improved. Experimental results show that, compared with the original method, the new method significantly accelerates the construction of stripe structures, even by over 40 times. Moreover, it has a faster detection speed and can handle dynamic polygons more efficiently.
Available online: February 19, 2025
, DOI: 10.13328/j.cnki.jos.007305
Abstract:
PKI system is currently an important facility for users to securely access basic resources. It ensures the security of users’ access to resources through public third-party authentication. With the gradual deployment and application of PKI technology, various security issues in deployment arise. Attackers can steal user information and disrupt user access by attacking the PKI system. This study starts from the basic working principle of PKI and comprehensively introduces all the elements involved in the practical deployment and application of the PKI system, including PKI architecture, workflow, certificates, certificate chains, certificate revocation, and CI log services. Based on the basic working principles of PKI, this study focuses on comprehensively sorting out and summarizing the security issues that the PKI system faces during its operation from the perspective of PKI system security, including operational and technical risks, measurement and risk detection of PKI system, and various risk prevention technologies for PKI systems. Finally, future research directions in the field of PKI are prospected.
PKI system is currently an important facility for users to securely access basic resources. It ensures the security of users’ access to resources through public third-party authentication. With the gradual deployment and application of PKI technology, various security issues in deployment arise. Attackers can steal user information and disrupt user access by attacking the PKI system. This study starts from the basic working principle of PKI and comprehensively introduces all the elements involved in the practical deployment and application of the PKI system, including PKI architecture, workflow, certificates, certificate chains, certificate revocation, and CI log services. Based on the basic working principles of PKI, this study focuses on comprehensively sorting out and summarizing the security issues that the PKI system faces during its operation from the perspective of PKI system security, including operational and technical risks, measurement and risk detection of PKI system, and various risk prevention technologies for PKI systems. Finally, future research directions in the field of PKI are prospected.
Available online: February 19, 2025
, DOI: 10.13328/j.cnki.jos.007296
Abstract:
Offline reinforcement learning has yielded significant results in tasks with continuous and intensive rewards. However, since the training process does not interact with the environment, the generalization ability is reduced, and the performance is difficult to guarantee in a discrete and sparse reward environment. The diffusion model combines the information in the neighborhood of the sample data with noise addition to generate actions that are close to the distribution of the sample data, which strengthens the learning and generalization ability of the agents. To this end, offline reinforcement learning with diffusion models and expectation maximization (DMEM) is proposed. The method updates the objective function by maximizing the expectation of the maximum likelihood logarithm to make the strategy more generalizable. Additionally, the diffusion model is introduced into the strategy network to utilize the diffusion characteristics to enhance the ability of the strategy to learn data samples. Meanwhile, the expectile regression is employed to update the value function from the perspective of high-dimensional space, and a penalty term is introduced to make the evaluation of the value function more accurate. DMEM is applied to a series of tasks with discrete and sparse rewards, and experiments show that DMEM has a large advantage in performance over other classical offline reinforcement learning methods.
Offline reinforcement learning has yielded significant results in tasks with continuous and intensive rewards. However, since the training process does not interact with the environment, the generalization ability is reduced, and the performance is difficult to guarantee in a discrete and sparse reward environment. The diffusion model combines the information in the neighborhood of the sample data with noise addition to generate actions that are close to the distribution of the sample data, which strengthens the learning and generalization ability of the agents. To this end, offline reinforcement learning with diffusion models and expectation maximization (DMEM) is proposed. The method updates the objective function by maximizing the expectation of the maximum likelihood logarithm to make the strategy more generalizable. Additionally, the diffusion model is introduced into the strategy network to utilize the diffusion characteristics to enhance the ability of the strategy to learn data samples. Meanwhile, the expectile regression is employed to update the value function from the perspective of high-dimensional space, and a penalty term is introduced to make the evaluation of the value function more accurate. DMEM is applied to a series of tasks with discrete and sparse rewards, and experiments show that DMEM has a large advantage in performance over other classical offline reinforcement learning methods.
Available online: February 19, 2025
, DOI: 10.13328/j.cnki.jos.007297
Abstract:
In recent years, as an algorithm for identifying bug-introducing changes, SZZ has been widely employed in just-in-time software defect prediction. Previous studies show that the SZZ algorithm may mislabel data during data annotation, which could influence the dataset quality and consequently the performance of the defect prediction model. Therefore, researchers have made improvements to the SZZ algorithm and proposed multiple variants of SZZ. However, there is no empirical study to explore the effect of data annotation quality by SZZ on the performance and interpretability of just-in-time defect prediction for mobile APP. To investigate the influence of mislabeled changes by SZZ on just-in-time defect prediction for mobile APP, this study conducts an extensive and in-depth empirical comparison of four SZZ algorithms. Firstly, 17 large-scale mobile APP projects are selected from the GitHub repository, and software metrics are extracted by adopting the PyDriller tool. Then, B-SZZ (original SZZ), AG-SZZ, MA-SZZ, and RA-SZZ are employed for data annotation. Then, the just-in-time defect prediction models are built with random forest, naive Bayes, and logistic regression classifiers based on the time-series data partitioning. Finally, the performance of the models is evaluated by traditional measures of AUC, MCC, and G-mean, and effort-aware measures of F-measure@20% and IFA, and a statistical significance test and interpretability analysis are conducted on the results by employing SKESD and SHAP respectively. By comparing the annotation performance of the four SZZ algorithms, the results are as follows. (1) The data annotation quality conforms to the progressive relationship among SZZ variants. (2) The mislabeled changes by B-SZZ, AG-SZZ, and MA-SZZ can cause performance reduction of AUC and MCC of different levels, but cannot lead to performance reduction of G-mean. (3) B-SZZ is likely to cause a performance reduction of F-measure@20%, while B-SZZ, AG-SZZ, and MA-SZZ are unlikely to increase effort during code inspection. (4) In terms of model interpretation, different SZZ algorithms will influence the three metrics with the largest contribution during the prediction, and the la metric has a significant influence on the prediction results.
In recent years, as an algorithm for identifying bug-introducing changes, SZZ has been widely employed in just-in-time software defect prediction. Previous studies show that the SZZ algorithm may mislabel data during data annotation, which could influence the dataset quality and consequently the performance of the defect prediction model. Therefore, researchers have made improvements to the SZZ algorithm and proposed multiple variants of SZZ. However, there is no empirical study to explore the effect of data annotation quality by SZZ on the performance and interpretability of just-in-time defect prediction for mobile APP. To investigate the influence of mislabeled changes by SZZ on just-in-time defect prediction for mobile APP, this study conducts an extensive and in-depth empirical comparison of four SZZ algorithms. Firstly, 17 large-scale mobile APP projects are selected from the GitHub repository, and software metrics are extracted by adopting the PyDriller tool. Then, B-SZZ (original SZZ), AG-SZZ, MA-SZZ, and RA-SZZ are employed for data annotation. Then, the just-in-time defect prediction models are built with random forest, naive Bayes, and logistic regression classifiers based on the time-series data partitioning. Finally, the performance of the models is evaluated by traditional measures of AUC, MCC, and G-mean, and effort-aware measures of F-measure@20% and IFA, and a statistical significance test and interpretability analysis are conducted on the results by employing SKESD and SHAP respectively. By comparing the annotation performance of the four SZZ algorithms, the results are as follows. (1) The data annotation quality conforms to the progressive relationship among SZZ variants. (2) The mislabeled changes by B-SZZ, AG-SZZ, and MA-SZZ can cause performance reduction of AUC and MCC of different levels, but cannot lead to performance reduction of G-mean. (3) B-SZZ is likely to cause a performance reduction of F-measure@20%, while B-SZZ, AG-SZZ, and MA-SZZ are unlikely to increase effort during code inspection. (4) In terms of model interpretation, different SZZ algorithms will influence the three metrics with the largest contribution during the prediction, and the la metric has a significant influence on the prediction results.
Available online: January 24, 2025
, DOI: 10.13328/j.cnki.jos.007291
Abstract:
Deep stochastic configuration network (DSCN) adopts a feedforward learning approach and randomly assigns node parameters based on a unique supervisory mechanism, which has universal approximation. However, in actual scenarios, the potential outliers and noise during data collection can negatively affect the classification results. To improve the performance of DSCN in solving binary classification problems, this study introduces the idea of intuitionistic fuzzy numbers based on DSCN and proposes an intuitionistic fuzzy deep stochastic configuration network (IFDSCN). Different from the standard DSCN, IFDSCN assigns an intuitionistic fuzzy number to each sample by calculating the sample membership and non-membership, and generates the optimal classifier by a weighting method to overcome the negative effect of noise and outliers on data classification. The experimental results on eight benchmark datasets show that compared to other learning models including the intuitionistic fuzzy twin support vector machine (IFTWSVM), kernel ridge regression (KRR), intuitionistic fuzzy kernel ridge regression (IFKRR), random vector functional link neural network (RVFL), and SCN, IFDSCN has better binary classification performance.
Deep stochastic configuration network (DSCN) adopts a feedforward learning approach and randomly assigns node parameters based on a unique supervisory mechanism, which has universal approximation. However, in actual scenarios, the potential outliers and noise during data collection can negatively affect the classification results. To improve the performance of DSCN in solving binary classification problems, this study introduces the idea of intuitionistic fuzzy numbers based on DSCN and proposes an intuitionistic fuzzy deep stochastic configuration network (IFDSCN). Different from the standard DSCN, IFDSCN assigns an intuitionistic fuzzy number to each sample by calculating the sample membership and non-membership, and generates the optimal classifier by a weighting method to overcome the negative effect of noise and outliers on data classification. The experimental results on eight benchmark datasets show that compared to other learning models including the intuitionistic fuzzy twin support vector machine (IFTWSVM), kernel ridge regression (KRR), intuitionistic fuzzy kernel ridge regression (IFKRR), random vector functional link neural network (RVFL), and SCN, IFDSCN has better binary classification performance.
Available online: January 24, 2025
, DOI: 10.13328/j.cnki.jos.007300
Abstract:
Existing adversarial example detection methods based on image transformation employ the characteristic that the image transformation can significantly change the feature distribution of adversarial examples but slightly change the feature distribution of benign examples. Adversarial examples can be detected by calculating the feature distance before and after image transformation. However, with the deepening research on adversarial attacks, researchers pay more attention to enhancing the robustness of adversarial examples, so that some attacks can be “immune” to the effect exerted by image transformation. Existing methods are difficult to detect robust adversarial examples effectively. This paper observes that the existing adversarial examples are too robust, and the feature distribution distance of robust adversarial examples under image transformation is much smaller than that of benign examples, which is not consistent with the feature distribution laws of benign examples. Based on this key observation, this study proposes a dual-threshold adversarial example detection based on image transformation, which sets a lower threshold combining existing single-threshold methods to form a dual-threshold detection interval. An example whose feature distribution is not within the dual-threshold detection interval will be judged as an adversarial example. Additionally, this study conducts extensive experiments on VGG19, DenseNet, and ConvNeXt models for image classification. The results show that the proposed approach is compatible with the detection ability of existing single-threshold detection schemes, and yields outstanding detection performance against robust adversarial examples.
Existing adversarial example detection methods based on image transformation employ the characteristic that the image transformation can significantly change the feature distribution of adversarial examples but slightly change the feature distribution of benign examples. Adversarial examples can be detected by calculating the feature distance before and after image transformation. However, with the deepening research on adversarial attacks, researchers pay more attention to enhancing the robustness of adversarial examples, so that some attacks can be “immune” to the effect exerted by image transformation. Existing methods are difficult to detect robust adversarial examples effectively. This paper observes that the existing adversarial examples are too robust, and the feature distribution distance of robust adversarial examples under image transformation is much smaller than that of benign examples, which is not consistent with the feature distribution laws of benign examples. Based on this key observation, this study proposes a dual-threshold adversarial example detection based on image transformation, which sets a lower threshold combining existing single-threshold methods to form a dual-threshold detection interval. An example whose feature distribution is not within the dual-threshold detection interval will be judged as an adversarial example. Additionally, this study conducts extensive experiments on VGG19, DenseNet, and ConvNeXt models for image classification. The results show that the proposed approach is compatible with the detection ability of existing single-threshold detection schemes, and yields outstanding detection performance against robust adversarial examples.
Available online: January 24, 2025
, DOI: 10.13328/j.cnki.jos.007301
Abstract:
Scalar multiplication is the core operation in traditional elliptic curve cryptography (ECC). Scalar representations determine the iterations in scalar multiplication algorithms, which directly affect the security and efficiency of the algorithms. This study proposes two new scalar representation algorithms. One algorithm is ordered window width non-adjacent form (OWNAF) which combines traditional window non-adjacent form with random key segmentation and can resist energy analysis attacks while yielding better efficiency. The other is called window joint regular form (wJRF), which is improved from the traditional joint regular form. The wJRF algorithm is applicable to multi-scalar multiplication algorithms, which can reduce computational costs and ensure sound security compared with the existing algorithms.
Scalar multiplication is the core operation in traditional elliptic curve cryptography (ECC). Scalar representations determine the iterations in scalar multiplication algorithms, which directly affect the security and efficiency of the algorithms. This study proposes two new scalar representation algorithms. One algorithm is ordered window width non-adjacent form (OWNAF) which combines traditional window non-adjacent form with random key segmentation and can resist energy analysis attacks while yielding better efficiency. The other is called window joint regular form (wJRF), which is improved from the traditional joint regular form. The wJRF algorithm is applicable to multi-scalar multiplication algorithms, which can reduce computational costs and ensure sound security compared with the existing algorithms.
Available online: January 24, 2025
, DOI: 10.13328/j.cnki.jos.007267
Abstract:
Bug triaging is the process of assigning bug reports to developers suitable for resolving the reported bugs, ensuring timely fixes. Current research in bug triaging mainly focuses on the text classification of bug reports. However, according to the Pareto principle, the data distribution of bug reports used for classification is unbalanced, which may lead to ineffective triaging for inactive developers. Additionally, existing classification models often neglect to model developers and struggle to capture the correlations between bugs and developers, affecting the efficiency of bug triaging. To address these issues, this study proposes a collaborative bug triaging method based on multimodal fusion (CBT-MF). This method first preprocesses bug reports and constructs a bug-developer bipartite graph. To mitigate the impact of the unbalanced distribution of bug fix records, the bipartite graph data is enhanced using K-means clustering and positive-negative sampling. To represent developer information, node features are extracted from the bipartite graph using a graph convolutional network model. Finally, correlations between bugs and developers are captured by matching inner products, and Bayesian personalized ranking (BPR) is utilized for bug report recommendation and triaging. Comprehensive experiments conducted on publicly available datasets demonstrate that CBT-MF outperforms several state-of-the-art methods in bug triaging.
Bug triaging is the process of assigning bug reports to developers suitable for resolving the reported bugs, ensuring timely fixes. Current research in bug triaging mainly focuses on the text classification of bug reports. However, according to the Pareto principle, the data distribution of bug reports used for classification is unbalanced, which may lead to ineffective triaging for inactive developers. Additionally, existing classification models often neglect to model developers and struggle to capture the correlations between bugs and developers, affecting the efficiency of bug triaging. To address these issues, this study proposes a collaborative bug triaging method based on multimodal fusion (CBT-MF). This method first preprocesses bug reports and constructs a bug-developer bipartite graph. To mitigate the impact of the unbalanced distribution of bug fix records, the bipartite graph data is enhanced using K-means clustering and positive-negative sampling. To represent developer information, node features are extracted from the bipartite graph using a graph convolutional network model. Finally, correlations between bugs and developers are captured by matching inner products, and Bayesian personalized ranking (BPR) is utilized for bug report recommendation and triaging. Comprehensive experiments conducted on publicly available datasets demonstrate that CBT-MF outperforms several state-of-the-art methods in bug triaging.
Available online: January 24, 2025
, DOI: 10.13328/j.cnki.jos.007269
Abstract:
Rollup is an emerging off-chain transaction processing solution for blockchains. With the continuous development of applications, the need for interoperability among different types of Rollups is increasingly growing. Existing cross-Rollup interoperability solutions typically rely on third-party service providers to assist in completion, which brings about security risks such as new trust assumptions and single-point-of-failure issues. Completing interoperability among Rollups based on the native chain does not require introducing new trust assumptions, but will consume the computing and storage resources of the native chain, reduce the transaction throughput of the native chain, and thus seriously affect the performance of cross-Rollup. Based on this, this study proposes a cross-Rollup mechanism based on a native blockchain. By aggregating and processing transactions in batches, it effectively reduces the on-chain average computation and storage resource costs of individual transactions. Specifically, a transaction validity proof scheme based on zero-knowledge proof is proposed to significantly reduce the on-chain computation overhead of transaction validity verification. A transaction storage scheme based on index table data compression is proposed, reducing the average on-chain storage overhead of cross-Rollup transactions. An aggregation scale balance adjustment algorithm is proposed, which obtains the optimal aggregation scale, achieving a balance between on-chain resource consumption and processing latency. Finally, this study validates the proposed solution through experiments. The experimental results demonstrate that under the condition of complete trustlessness, the proposed solution reduces on-chain computing and storage overheads while achieving a balance between on-chain resource consumption and processing latency. Moreover, compared to existing cross-Rollup solutions, the proposed solution exhibits good system throughput.
Rollup is an emerging off-chain transaction processing solution for blockchains. With the continuous development of applications, the need for interoperability among different types of Rollups is increasingly growing. Existing cross-Rollup interoperability solutions typically rely on third-party service providers to assist in completion, which brings about security risks such as new trust assumptions and single-point-of-failure issues. Completing interoperability among Rollups based on the native chain does not require introducing new trust assumptions, but will consume the computing and storage resources of the native chain, reduce the transaction throughput of the native chain, and thus seriously affect the performance of cross-Rollup. Based on this, this study proposes a cross-Rollup mechanism based on a native blockchain. By aggregating and processing transactions in batches, it effectively reduces the on-chain average computation and storage resource costs of individual transactions. Specifically, a transaction validity proof scheme based on zero-knowledge proof is proposed to significantly reduce the on-chain computation overhead of transaction validity verification. A transaction storage scheme based on index table data compression is proposed, reducing the average on-chain storage overhead of cross-Rollup transactions. An aggregation scale balance adjustment algorithm is proposed, which obtains the optimal aggregation scale, achieving a balance between on-chain resource consumption and processing latency. Finally, this study validates the proposed solution through experiments. The experimental results demonstrate that under the condition of complete trustlessness, the proposed solution reduces on-chain computing and storage overheads while achieving a balance between on-chain resource consumption and processing latency. Moreover, compared to existing cross-Rollup solutions, the proposed solution exhibits good system throughput.
Available online: January 24, 2025
, DOI: 10.13328/j.cnki.jos.007259
Abstract:
High-quality training data is instrumental in pre-trained language models (PLMs), yet privacy concerns often preclude the centralized collection of data from many professional domains. Federated learning offers a solution by enabling model training while safeguarding data privacy. However, the limited resources of federated learning clients pose a challenge to the training of pre-trained language models. This study addresses this issue through several steps. Firstly, it defines the problem of completing model training with limited resources and explores strategies to balance computational and communication costs for optimizing training efficiency. Secondly, it introduces an efficient federated learning framework for BERT further pre-training and fine-tuning (FedBT). FedBT facilitates the training of the BERT model on federated learning clients, encompassing both further pre-training and downstream task fine-tuning. Depending on the application context, FedBT selectively trains key parameters of the BERT model at the clients, uploading only the updated parameters to the server for aggregation. This approach significantly reduces both computational and communication overhead during training. Finally, extensive experiments are conducted on datasets from multiple professional domains. Results demonstrate that FedBT reduces client-side computational costs to 34.31% and communication costs to 7.04% during further pre-training. In downstream task fine-tuning, it reduces client-side computational costs to 48.26% and communication costs to 20.19%. The accuracy achieved in both pre-training and downstream task fine-tuning is comparable to traditional federated learning methods that train the entire model.
High-quality training data is instrumental in pre-trained language models (PLMs), yet privacy concerns often preclude the centralized collection of data from many professional domains. Federated learning offers a solution by enabling model training while safeguarding data privacy. However, the limited resources of federated learning clients pose a challenge to the training of pre-trained language models. This study addresses this issue through several steps. Firstly, it defines the problem of completing model training with limited resources and explores strategies to balance computational and communication costs for optimizing training efficiency. Secondly, it introduces an efficient federated learning framework for BERT further pre-training and fine-tuning (FedBT). FedBT facilitates the training of the BERT model on federated learning clients, encompassing both further pre-training and downstream task fine-tuning. Depending on the application context, FedBT selectively trains key parameters of the BERT model at the clients, uploading only the updated parameters to the server for aggregation. This approach significantly reduces both computational and communication overhead during training. Finally, extensive experiments are conducted on datasets from multiple professional domains. Results demonstrate that FedBT reduces client-side computational costs to 34.31% and communication costs to 7.04% during further pre-training. In downstream task fine-tuning, it reduces client-side computational costs to 48.26% and communication costs to 20.19%. The accuracy achieved in both pre-training and downstream task fine-tuning is comparable to traditional federated learning methods that train the entire model.
Available online: January 16, 2025
, DOI: 10.13328/j.cnki.jos.007287
Abstract:
To conduct necessary aggregation on varying-quality sensed data uploaded by workers in mobile crowdsensing, truth discovery technology has emerged as the cornerstone for providing precise data support for subsequent applications. Existing studies tend to adopt local differential privacy for protection against potential privacy breaches, but often ignore the influence of outliers in the sensed data on the truth discovery accuracy under local differential privacy. These outliers often have a large range of values, resulting in a large amount of noise in the injected data. Additionally, due to workers’ concerns about privacy breaches, mobile crowdsensing servers cannot preprocess data without privacy protection. To this end, this study proposes NATURE, which meets local differential privacy based on adaptive pruning. The core idea of the algorithm is to consider the noise types in the data to adaptively prune all unnecessary workers’ values or certain task values. In NATURE, the noise-aware weight and importance estimation (NWIE) method based on a formalized constraint optimization problem is designed to facilitate data pruning. Based on proving the optimal pruning problem is NP-hard, this study designs the utility-aware adaptive pruning (UAP) method with polynomial time complexity to conduct pruning. Furthermore, a theoretical analysis of NATURE’s privacy, utility, and complexity is carried out. Experimental results on two real-world datasets and one synthetic dataset demonstrate that NATURE achieves an accuracy improvement of at least 20% in obtaining “truth” compared to its comparative algorithms.
To conduct necessary aggregation on varying-quality sensed data uploaded by workers in mobile crowdsensing, truth discovery technology has emerged as the cornerstone for providing precise data support for subsequent applications. Existing studies tend to adopt local differential privacy for protection against potential privacy breaches, but often ignore the influence of outliers in the sensed data on the truth discovery accuracy under local differential privacy. These outliers often have a large range of values, resulting in a large amount of noise in the injected data. Additionally, due to workers’ concerns about privacy breaches, mobile crowdsensing servers cannot preprocess data without privacy protection. To this end, this study proposes NATURE, which meets local differential privacy based on adaptive pruning. The core idea of the algorithm is to consider the noise types in the data to adaptively prune all unnecessary workers’ values or certain task values. In NATURE, the noise-aware weight and importance estimation (NWIE) method based on a formalized constraint optimization problem is designed to facilitate data pruning. Based on proving the optimal pruning problem is NP-hard, this study designs the utility-aware adaptive pruning (UAP) method with polynomial time complexity to conduct pruning. Furthermore, a theoretical analysis of NATURE’s privacy, utility, and complexity is carried out. Experimental results on two real-world datasets and one synthetic dataset demonstrate that NATURE achieves an accuracy improvement of at least 20% in obtaining “truth” compared to its comparative algorithms.
Available online: January 15, 2025
, DOI: 10.13328/j.cnki.jos.007286
Abstract:
Social network link prediction can help to reveal the potential connections between network nodes, and has important practical application value in friend recommendation and cooperation prediction. However, existing link prediction algorithms ignore the medium and long-term development trend of social network time series, and do not consider the interaction relationship between nodes in the network from a long-term perspective. To address the above-mentioned problems, a spatiotemporal attention-based multi-granularity link prediction algorithm is proposed, which can integrate the spatiotemporal features of social network time series with different granularities to improve the accuracy of link prediction. Firstly, the weights of the social network snapshot graph are constructed with the time decay function, and a graph-weighted moving average strategy is proposed to generate social network time series with different granularities reflecting short-term, medium-term, and long-term trends. Then, a neural network based on the multi-head attention mechanism is used to extract the global temporal features of social network sequences. Next, the historical interaction information of nodes within social network sequences is combined, and the neural network based on the mask attention mechanism is used to adaptively construct the network topology from a long-term perspective to dynamically adjust the interactions between nodes and is combined with graph convolutional network to model spatial information. Finally, the fusion attention neural network is proposed to extract useful short-term, medium-term and long-term information from short-term, medium-term and long-term spatiotemporal features, and perform feature fusion to accurately predict the future links of social networks. Experimental comparisons with seven existing link prediction algorithms on four social network public datasets confirm the effectiveness and superiority of the proposed method.
Social network link prediction can help to reveal the potential connections between network nodes, and has important practical application value in friend recommendation and cooperation prediction. However, existing link prediction algorithms ignore the medium and long-term development trend of social network time series, and do not consider the interaction relationship between nodes in the network from a long-term perspective. To address the above-mentioned problems, a spatiotemporal attention-based multi-granularity link prediction algorithm is proposed, which can integrate the spatiotemporal features of social network time series with different granularities to improve the accuracy of link prediction. Firstly, the weights of the social network snapshot graph are constructed with the time decay function, and a graph-weighted moving average strategy is proposed to generate social network time series with different granularities reflecting short-term, medium-term, and long-term trends. Then, a neural network based on the multi-head attention mechanism is used to extract the global temporal features of social network sequences. Next, the historical interaction information of nodes within social network sequences is combined, and the neural network based on the mask attention mechanism is used to adaptively construct the network topology from a long-term perspective to dynamically adjust the interactions between nodes and is combined with graph convolutional network to model spatial information. Finally, the fusion attention neural network is proposed to extract useful short-term, medium-term and long-term information from short-term, medium-term and long-term spatiotemporal features, and perform feature fusion to accurately predict the future links of social networks. Experimental comparisons with seven existing link prediction algorithms on four social network public datasets confirm the effectiveness and superiority of the proposed method.
Available online: January 15, 2025
, DOI: 10.13328/j.cnki.jos.007264
Abstract:
Mainstream methods for scene text detection often use complex networks with plenty of layers to improve detection accuracy, which requires high computational costs and large storage space, thus making them difficult to deploy on embedded devices with limited computing resources. Knowledge distillation assists in training lightweight student networks by introducing soft target information related to teacher networks, thus achieving model compression. However, existing knowledge distillation methods are mostly designed for image classification and extract the soft probability distributions from teacher networks as knowledge. The amount of information carried by such methods is highly correlated with the number of categories, resulting in insufficient information when directly applied to the binary classification task in text detection. To address the problem of scene text detection, this study introduces a novel concept of information entropy and proposes a knowledge distillation method based on mask entropy transfer (MaskET). MaskET combines information entropy with traditional knowledge distillation methods to increase the amount of information transferred to student networks. Moreover, to eliminate the interference of background information in images, MaskET only extracts the knowledge within the text area by adding mask operations. Experiments conducted on six public benchmark datasets, namely ICDAR 2013, ICDAR 2015, TD500, TD-TR, Total-Text and CASIA-10K, show that MaskET outperforms the baseline model and other knowledge distillation methods. For example, MaskET improves the F1 score of MobileNetV3-based DBNet from 65.3% to 67.2% on the CASIA-10K dataset.
Mainstream methods for scene text detection often use complex networks with plenty of layers to improve detection accuracy, which requires high computational costs and large storage space, thus making them difficult to deploy on embedded devices with limited computing resources. Knowledge distillation assists in training lightweight student networks by introducing soft target information related to teacher networks, thus achieving model compression. However, existing knowledge distillation methods are mostly designed for image classification and extract the soft probability distributions from teacher networks as knowledge. The amount of information carried by such methods is highly correlated with the number of categories, resulting in insufficient information when directly applied to the binary classification task in text detection. To address the problem of scene text detection, this study introduces a novel concept of information entropy and proposes a knowledge distillation method based on mask entropy transfer (MaskET). MaskET combines information entropy with traditional knowledge distillation methods to increase the amount of information transferred to student networks. Moreover, to eliminate the interference of background information in images, MaskET only extracts the knowledge within the text area by adding mask operations. Experiments conducted on six public benchmark datasets, namely ICDAR 2013, ICDAR 2015, TD500, TD-TR, Total-Text and CASIA-10K, show that MaskET outperforms the baseline model and other knowledge distillation methods. For example, MaskET improves the F1 score of MobileNetV3-based DBNet from 65.3% to 67.2% on the CASIA-10K dataset.
Available online: January 08, 2025
, DOI: 10.13328/j.cnki.jos.007265
Abstract:
Image-level weakly supervised semantic segmentation usually uses convolutional neural networks (CNNs) to generate class activation maps to accurately locate targets. However, CNNs have a limited capacity to perceive global information, which results in excessively narrow foregrounds. Recently, Transformer-based weakly supervised semantic segmentation has utilized self-attention mechanisms to capture global dependencies, addressing the inherent defects of CNNs. Nevertheless, the initial class activation map generated by a Transformer often introduces a lot of background noise around the target area, resulting in unsatisfactory performance if used directly. This study comprehensively utilizes both class-to-patch and patch-to-patch attention generated by a Transformer to optimize the initial class activation map. At the same time, a semantic modulation strategy is designed to correct errors in the class-to-patch attention, using the semantic context information of the patch-to-patch attention. Finally, a class activation map that accurately covers more target areas is obtained. On this basis, a novel model for weakly supervised semantic segmentation based on a Transformer is constructed. The mIoU of the proposed method reaches 72.7% and 71.9% on the PASCAL VOC 2012 validation and test sets, respectively, and 42.3% on the MS COCO 2014 validation set, demonstrating that the proposed method achieves improved performance in weakly supervised semantic segmentation.
Image-level weakly supervised semantic segmentation usually uses convolutional neural networks (CNNs) to generate class activation maps to accurately locate targets. However, CNNs have a limited capacity to perceive global information, which results in excessively narrow foregrounds. Recently, Transformer-based weakly supervised semantic segmentation has utilized self-attention mechanisms to capture global dependencies, addressing the inherent defects of CNNs. Nevertheless, the initial class activation map generated by a Transformer often introduces a lot of background noise around the target area, resulting in unsatisfactory performance if used directly. This study comprehensively utilizes both class-to-patch and patch-to-patch attention generated by a Transformer to optimize the initial class activation map. At the same time, a semantic modulation strategy is designed to correct errors in the class-to-patch attention, using the semantic context information of the patch-to-patch attention. Finally, a class activation map that accurately covers more target areas is obtained. On this basis, a novel model for weakly supervised semantic segmentation based on a Transformer is constructed. The mIoU of the proposed method reaches 72.7% and 71.9% on the PASCAL VOC 2012 validation and test sets, respectively, and 42.3% on the MS COCO 2014 validation set, demonstrating that the proposed method achieves improved performance in weakly supervised semantic segmentation.
Available online: January 08, 2025
, DOI: 10.13328/j.cnki.jos.007258
Abstract:
Molecular dynamics simulation plays an important role in material simulation, biopharmaceuticals, and other areas. In recent years, the development of AI-for-Science has greatly improved the accuracy of neural network force fields in predicting energy, force, and other properties, compared to traditional methods using potential functions. Neural network force field models may challenges such as hyperparameter settings and gradient explosion when trained by the first-order method. Based on an optimizer named reorganized layer extended Kalman filtering, this study provides several strategies to avoid hyperparameters and offers theoretical evidence for preventing gradient explosion. This study also proposes an alternate training method and analyzes its accuracy gains and time costs. A performance model of block thresholding is proposed, and its effectiveness is explored. Additionally, the property of preventing gradient explosion is proven, and the optimizer’s robustness with respect to activation functions and weight initialization is validated. Four typical neural network force field models are tested on 11 representative datasets. Results show that when the proposed optimizer and the first-order optimizer achieve comparable prediction accuracy, the proposed optimizer is 8 to 10 times faster than the first-order optimizer. It is believed that the proposed training method can inspire other AI-for-Science applications.
Molecular dynamics simulation plays an important role in material simulation, biopharmaceuticals, and other areas. In recent years, the development of AI-for-Science has greatly improved the accuracy of neural network force fields in predicting energy, force, and other properties, compared to traditional methods using potential functions. Neural network force field models may challenges such as hyperparameter settings and gradient explosion when trained by the first-order method. Based on an optimizer named reorganized layer extended Kalman filtering, this study provides several strategies to avoid hyperparameters and offers theoretical evidence for preventing gradient explosion. This study also proposes an alternate training method and analyzes its accuracy gains and time costs. A performance model of block thresholding is proposed, and its effectiveness is explored. Additionally, the property of preventing gradient explosion is proven, and the optimizer’s robustness with respect to activation functions and weight initialization is validated. Four typical neural network force field models are tested on 11 representative datasets. Results show that when the proposed optimizer and the first-order optimizer achieve comparable prediction accuracy, the proposed optimizer is 8 to 10 times faster than the first-order optimizer. It is believed that the proposed training method can inspire other AI-for-Science applications.
Available online: January 08, 2025
, DOI: 10.13328/j.cnki.jos.007285
Abstract:
Face anti-spoofing is a powerful guarantee for the practical security of facial recognition technology. However, the constant evolution of live attack methods poses significant challenges to existing detection methods. To address the increasing number of unknown scenarios and attack methods, a two-stream face anti-spoofing model based on visual attention and domain feature fusion is proposed. First, a visual attention-based feature extraction module is proposed to strengthen the model’s capacity to extract content features based on global information. Second, a novel style feature fusion module is designed to optimize the feature representation of the sample by fusing content features with low-level textural style features. Third, a feature mapping strategy based on the Siamese network is developed and the contrast loss function is modified to improve the model robustness and avoid easy gradient oscillation during training, respectively. Furthermore, domain adversarial training (DAT) is used to reduce the sensitivity of the model to differences between sample data domains and further improve its generalization. Extensive experimental results verify the generality and strong robustness of the proposed method, demonstrating that it outperforms existing models in cross-domain performance on mainstream datasets.
Face anti-spoofing is a powerful guarantee for the practical security of facial recognition technology. However, the constant evolution of live attack methods poses significant challenges to existing detection methods. To address the increasing number of unknown scenarios and attack methods, a two-stream face anti-spoofing model based on visual attention and domain feature fusion is proposed. First, a visual attention-based feature extraction module is proposed to strengthen the model’s capacity to extract content features based on global information. Second, a novel style feature fusion module is designed to optimize the feature representation of the sample by fusing content features with low-level textural style features. Third, a feature mapping strategy based on the Siamese network is developed and the contrast loss function is modified to improve the model robustness and avoid easy gradient oscillation during training, respectively. Furthermore, domain adversarial training (DAT) is used to reduce the sensitivity of the model to differences between sample data domains and further improve its generalization. Extensive experimental results verify the generality and strong robustness of the proposed method, demonstrating that it outperforms existing models in cross-domain performance on mainstream datasets.
Available online: December 31, 2024
, DOI: 10.13328/j.cnki.jos.007262
Abstract:
Commonsense knowledge is usually not explicitly expressed in natural languages but is implicitly understood in human cognition. Providing machines with commonsense knowledge has been a longstanding aim in artificial intelligence. Initially, this study manually constructs a high-precision, event-centric commonsense knowledge graph (ECKG) for seed events in Chinese. It contains 26 606 commonsense event triples encompassing causal, temporal, conditional, and other common event relationships. Although the constructed ECKG holds considerable value, its limited scale curtails practical applications. Besides, large-scale event commonsense knowledge graphs are rare in current studies. To overcome these challenges, this paper uses large language models from the GPT series to expand the above-mentioned three event relationships and sub-events of the proposed ECKG. The expansion method involves three primary steps. Firstly, specific prompts for event knowledge (ek-prompts) are designed by combining the events in the ECKG with four relationships, and GPT-4-Turbo is used to generate corresponding event triples. Secondly, the triples of the ECKG are integrated with accurate triples obtained by ek-prompts to create a specialized dataset. Additionally, GPT-3.5-Turbo is fine-tuned on the dataset to generate more specific event triples and validate the accuracy of new triples. Lastly, by analyzing the similarities among events in the ECKG and implementing an event-sharing mechanism, similar events within the same relationship are interconnected, ensuring consistency across similar event triples. Experimental results show that the newly acquired triples are of high quality, particularly those of the temporal relationships, with an accuracy rate of 98.2%. Ultimately, the proposed expansion method appends 2 433 012 commonsense event triples to the original ECKG, significantly expanding its scale and providing more commonsense knowledge for many applications in artificial intelligence.
Commonsense knowledge is usually not explicitly expressed in natural languages but is implicitly understood in human cognition. Providing machines with commonsense knowledge has been a longstanding aim in artificial intelligence. Initially, this study manually constructs a high-precision, event-centric commonsense knowledge graph (ECKG) for seed events in Chinese. It contains 26 606 commonsense event triples encompassing causal, temporal, conditional, and other common event relationships. Although the constructed ECKG holds considerable value, its limited scale curtails practical applications. Besides, large-scale event commonsense knowledge graphs are rare in current studies. To overcome these challenges, this paper uses large language models from the GPT series to expand the above-mentioned three event relationships and sub-events of the proposed ECKG. The expansion method involves three primary steps. Firstly, specific prompts for event knowledge (ek-prompts) are designed by combining the events in the ECKG with four relationships, and GPT-4-Turbo is used to generate corresponding event triples. Secondly, the triples of the ECKG are integrated with accurate triples obtained by ek-prompts to create a specialized dataset. Additionally, GPT-3.5-Turbo is fine-tuned on the dataset to generate more specific event triples and validate the accuracy of new triples. Lastly, by analyzing the similarities among events in the ECKG and implementing an event-sharing mechanism, similar events within the same relationship are interconnected, ensuring consistency across similar event triples. Experimental results show that the newly acquired triples are of high quality, particularly those of the temporal relationships, with an accuracy rate of 98.2%. Ultimately, the proposed expansion method appends 2 433 012 commonsense event triples to the original ECKG, significantly expanding its scale and providing more commonsense knowledge for many applications in artificial intelligence.
Available online: December 31, 2024
, DOI: 10.13328/j.cnki.jos.007255
Abstract:
As the Internet of Things and mobile Internet technologies continue to advance, a wide range of mobile devices are connected to the Internet. To identify and authenticate these devices, it is necessary to verify the digital signatures they submit. However, many mobile devices have limited computing power and typically use software modules to store keys locally or on smart chips, which increases the risk of key exposure. To avoid this risk, threshold signatures are commonly employed in real-world applications. These signatures rely on multi-party cooperation to decentralize risks and enhance device availability. The SM2 digital signature algorithm, an elliptic curve public key cryptographic algorithm developed independently by China, was adopted as the national cryptography standard in 2016. It finds extensive use in various sectors including government agencies, financial institutions, and electronic authentication service providers. While there has been interest in constructing SM2 threshold signatures with high availability, there are still limited schemes available, and participant weights have not been adequately considered. This study proposes a flexible SM2 weighted threshold signature scheme. In this scheme, signers are assigned different weights, and multiple signers collaborate to generate a valid signature. The key of the SM2 digital signature is divided based on the weighted threshold secret sharing of the Chinese remainder theorem. Participants do not acquire a signing key only by meeting the threshold value. They have to meet the corresponding secret threshold t and the reconstruction threshold T by calculating the sum of the weights of participants to obtain part of the key information or recover the signing key. During secret segmentation, the private signing key of the SM2 digital signature algorithm is transformed to complete the inversion of the SM2 key during the signing stage. Finally, the proposed scheme is compared with other schemes such as SM2 threshold signatures and joint SM2 signatures. The proposed scheme not only reduces computational overhead but also enhances the functionality of the SM2 signature.
As the Internet of Things and mobile Internet technologies continue to advance, a wide range of mobile devices are connected to the Internet. To identify and authenticate these devices, it is necessary to verify the digital signatures they submit. However, many mobile devices have limited computing power and typically use software modules to store keys locally or on smart chips, which increases the risk of key exposure. To avoid this risk, threshold signatures are commonly employed in real-world applications. These signatures rely on multi-party cooperation to decentralize risks and enhance device availability. The SM2 digital signature algorithm, an elliptic curve public key cryptographic algorithm developed independently by China, was adopted as the national cryptography standard in 2016. It finds extensive use in various sectors including government agencies, financial institutions, and electronic authentication service providers. While there has been interest in constructing SM2 threshold signatures with high availability, there are still limited schemes available, and participant weights have not been adequately considered. This study proposes a flexible SM2 weighted threshold signature scheme. In this scheme, signers are assigned different weights, and multiple signers collaborate to generate a valid signature. The key of the SM2 digital signature is divided based on the weighted threshold secret sharing of the Chinese remainder theorem. Participants do not acquire a signing key only by meeting the threshold value. They have to meet the corresponding secret threshold t and the reconstruction threshold T by calculating the sum of the weights of participants to obtain part of the key information or recover the signing key. During secret segmentation, the private signing key of the SM2 digital signature algorithm is transformed to complete the inversion of the SM2 key during the signing stage. Finally, the proposed scheme is compared with other schemes such as SM2 threshold signatures and joint SM2 signatures. The proposed scheme not only reduces computational overhead but also enhances the functionality of the SM2 signature.
Available online: December 31, 2024
, DOI: 10.13328/j.cnki.jos.007256
Abstract:
In recent years, streaming graph analysis has gained increasing importance in both research and industry. A streaming graph is a continuous sequence of edges received from a data source at a high speed. Those edges form a dynamic graph that is continuously changing. Various analyses can be performed on streaming graphs. Among them, triangle counting is one of the most basic operations. However, the large volume and high update speed of streaming graphs make it inefficient to count triangles accurately on them. It is also unnecessary, as most applications for triangle counting can tolerate small errors. Therefore, approximate triangle counting in streaming graphs has been a hot research topic. This study focuses on sample-based approximate triangle counting in streaming graphs with a sliding window model. Sliding window models focus on the most recent edges in a streaming graph and consider older edges as expired. They are widely applied in various industrial scenarios and research. This study combines a count-before-sample strategy with the state-of-the-art approximate triangle counting algorithm and designs a set of novel strategies to deal with the difficulty brought by edge expiration. Extensive experiments are conducted on real-world datasets to evaluate the proposed algorithm. Results prove that the algorithm decreases the estimation error of the state-of-the-art method by more than 70%.
In recent years, streaming graph analysis has gained increasing importance in both research and industry. A streaming graph is a continuous sequence of edges received from a data source at a high speed. Those edges form a dynamic graph that is continuously changing. Various analyses can be performed on streaming graphs. Among them, triangle counting is one of the most basic operations. However, the large volume and high update speed of streaming graphs make it inefficient to count triangles accurately on them. It is also unnecessary, as most applications for triangle counting can tolerate small errors. Therefore, approximate triangle counting in streaming graphs has been a hot research topic. This study focuses on sample-based approximate triangle counting in streaming graphs with a sliding window model. Sliding window models focus on the most recent edges in a streaming graph and consider older edges as expired. They are widely applied in various industrial scenarios and research. This study combines a count-before-sample strategy with the state-of-the-art approximate triangle counting algorithm and designs a set of novel strategies to deal with the difficulty brought by edge expiration. Extensive experiments are conducted on real-world datasets to evaluate the proposed algorithm. Results prove that the algorithm decreases the estimation error of the state-of-the-art method by more than 70%.
Available online: December 31, 2024
, DOI: 10.13328/j.cnki.jos.007241
Abstract:
The minimum load coloring problem (MLCP) is an important NP-complete problem arising from wavelength division multiplexing (WDM), a technology used for building optical communication networks. The solutions to NP-complete problems grow exponentially as the size of the problems expands, so heuristic algorithms are often used to solve such problems. Analysis of research at home and abroad shows that among the existing heuristic algorithms for solving the MLCP, local search algorithms exhibit the best performance. This study proposes two optimization strategies to overcome the limitations of existing local search algorithms in data preprocessing and neighborhood space search. First, during data preprocessing, a one-degree vertex rule is proposed to reduce the size of data and thus reduce the search space of the MLCP. Second, in the search phase of the algorithm, a strategy termed two-stage best from multiple selections (TSBMS) is proposed to help local search algorithms efficiently select a high-quality neighborhood solution for neighborhood space with different sizes, which effectively improves the performance of local search algorithms for processing data of different sizes. This optimized local search algorithm is named IRLTS. Seventy-four classic test instances are adopted to validate the effectiveness of the IRLTS algorithm. Experimental results demonstrate that the IRLTS algorithm outperforms the three best local search algorithms on most test instances in terms of both optimal and average solutions. Furthermore, the effectiveness of the proposed strategy is validated through experiments, and the influence of key parameters on the IRLTS algorithm is analyzed.
The minimum load coloring problem (MLCP) is an important NP-complete problem arising from wavelength division multiplexing (WDM), a technology used for building optical communication networks. The solutions to NP-complete problems grow exponentially as the size of the problems expands, so heuristic algorithms are often used to solve such problems. Analysis of research at home and abroad shows that among the existing heuristic algorithms for solving the MLCP, local search algorithms exhibit the best performance. This study proposes two optimization strategies to overcome the limitations of existing local search algorithms in data preprocessing and neighborhood space search. First, during data preprocessing, a one-degree vertex rule is proposed to reduce the size of data and thus reduce the search space of the MLCP. Second, in the search phase of the algorithm, a strategy termed two-stage best from multiple selections (TSBMS) is proposed to help local search algorithms efficiently select a high-quality neighborhood solution for neighborhood space with different sizes, which effectively improves the performance of local search algorithms for processing data of different sizes. This optimized local search algorithm is named IRLTS. Seventy-four classic test instances are adopted to validate the effectiveness of the IRLTS algorithm. Experimental results demonstrate that the IRLTS algorithm outperforms the three best local search algorithms on most test instances in terms of both optimal and average solutions. Furthermore, the effectiveness of the proposed strategy is validated through experiments, and the influence of key parameters on the IRLTS algorithm is analyzed.
Available online: December 31, 2024
, DOI: 10.13328/j.cnki.jos.007244
Abstract:
With the rapid development of Transformer-based large models, computing power has gradually become a bottleneck in the development of this field. Research hotspots rely on how to accelerate and optimize the training performance of large language models based on the structural characteristics of accelerator hardware. This study proposes and implements MTTorch, a PyTorch extension library for the CPU+DSP heterogeneous architecture, which is applicable to the MT-3000 accelerator chip of the new generation of the Tianhe supercomputer. The core of MTTorch is a multi-core parallel operator library that vectorizes and optimizes the core operators during the training of Transformer-based models. Additionally, this study innovatively proposes a high-performance reduction algorithm and a ping-pong algorithm for multi-core DSP, significantly improving the computational performance of the operators. MTTorch also has good generality as it can be loaded as a dynamic link library for different versions of PyTorch without changing the native implementation of PyTorch. Extensive experiments show that the core operators implemented in this study have excellent performance on MT-3000 chip, achieving 8 times acceleration on a single DSP cluster. Using MTTorch for training tasks on multiple nodes achieves nearly linear acceleration, greatly improving the training efficiency of Transformer-based models on MT-3000 chip.
With the rapid development of Transformer-based large models, computing power has gradually become a bottleneck in the development of this field. Research hotspots rely on how to accelerate and optimize the training performance of large language models based on the structural characteristics of accelerator hardware. This study proposes and implements MTTorch, a PyTorch extension library for the CPU+DSP heterogeneous architecture, which is applicable to the MT-3000 accelerator chip of the new generation of the Tianhe supercomputer. The core of MTTorch is a multi-core parallel operator library that vectorizes and optimizes the core operators during the training of Transformer-based models. Additionally, this study innovatively proposes a high-performance reduction algorithm and a ping-pong algorithm for multi-core DSP, significantly improving the computational performance of the operators. MTTorch also has good generality as it can be loaded as a dynamic link library for different versions of PyTorch without changing the native implementation of PyTorch. Extensive experiments show that the core operators implemented in this study have excellent performance on MT-3000 chip, achieving 8 times acceleration on a single DSP cluster. Using MTTorch for training tasks on multiple nodes achieves nearly linear acceleration, greatly improving the training efficiency of Transformer-based models on MT-3000 chip.
Available online: December 31, 2024
, DOI: 10.13328/j.cnki.jos.007247
Abstract:
As large language models (LLMs) continue to evolve, they have shown impressive performance in open-domain tasks. However, they exhibit limited effectiveness in domain-specific question-answering due to a lack of domain-specific knowledge. This limitation has attracted widespread attention from researchers in the field. Current research attempts to infuse domain knowledge into LLMs through a retrieve-answer approach to enhance their performance. However, this method often retrieves additional, irrelevant data, leading to a degradation in LLM effectiveness. Therefore, this study proposes a method for knowledge graph question answering based on the relevance of knowledge. This method focuses on distinguishing essential knowledge required for specific questions from noisy data. Under a framework of retrieval-relevance assessment-answering, this method guides LLMs to select appropriate knowledge for accurate answers. Moreover, this study introduces a dataset named Mecha-QA for question-answering using a mechanical domain knowledge graph, covering traditional machinery manufacturing and additive manufacturing, to promote research that integrates LLMs with knowledge graph question answering in this field. To validate the effectiveness of the proposed method, experiments are conducted on the Aero-QA dataset in the aerospace domain and the Mecha-QA dataset. Results demonstrate that the proposed method significantly improves the performance of LLMs in knowledge graph question answering in vertical domains.
As large language models (LLMs) continue to evolve, they have shown impressive performance in open-domain tasks. However, they exhibit limited effectiveness in domain-specific question-answering due to a lack of domain-specific knowledge. This limitation has attracted widespread attention from researchers in the field. Current research attempts to infuse domain knowledge into LLMs through a retrieve-answer approach to enhance their performance. However, this method often retrieves additional, irrelevant data, leading to a degradation in LLM effectiveness. Therefore, this study proposes a method for knowledge graph question answering based on the relevance of knowledge. This method focuses on distinguishing essential knowledge required for specific questions from noisy data. Under a framework of retrieval-relevance assessment-answering, this method guides LLMs to select appropriate knowledge for accurate answers. Moreover, this study introduces a dataset named Mecha-QA for question-answering using a mechanical domain knowledge graph, covering traditional machinery manufacturing and additive manufacturing, to promote research that integrates LLMs with knowledge graph question answering in this field. To validate the effectiveness of the proposed method, experiments are conducted on the Aero-QA dataset in the aerospace domain and the Mecha-QA dataset. Results demonstrate that the proposed method significantly improves the performance of LLMs in knowledge graph question answering in vertical domains.
Available online: December 31, 2024
, DOI: 10.13328/j.cnki.jos.007249
Abstract:
Traditional detection and defense mechanisms for distributed denial-of-service (DDoS) attacks require traffic mirroring, collection, and centralized remote analysis, which introduces extra performance overhead and fails to achieve real-time protection in high-performance networks. With the development of network devices such as programmable switches, the programmable data plane has emerged as a solid foundation for achieving high-performance DDoS attack detection. However, existing detection methods based on the programmable data plane cannot guarantee accuracy and are difficult to deploy directly in programmable switches (such as Intel Tofino) due to programming constraints. To this end, this paper proposes a programmable switch-based mechanism for detecting and defending against DDoS attacks. First, the mechanism uses the difference between the entropy of source and destination addresses to determine whether DDoS attacks occur. When DDoS attacks occur, a traffic filtration mechanism based on the difference in counts between source and destination address will defend against DDoS attacks in real time. Experimental results indicate that the proposed mechanism effectively identifies and defends against DDoS attacks. Compared with the benchmark method, the accuracy of this mechanism in window-level attack detection is increased by 17.75% on average, and the accuracy of packet-level attack filtration is increased by 3.7% on average.
Traditional detection and defense mechanisms for distributed denial-of-service (DDoS) attacks require traffic mirroring, collection, and centralized remote analysis, which introduces extra performance overhead and fails to achieve real-time protection in high-performance networks. With the development of network devices such as programmable switches, the programmable data plane has emerged as a solid foundation for achieving high-performance DDoS attack detection. However, existing detection methods based on the programmable data plane cannot guarantee accuracy and are difficult to deploy directly in programmable switches (such as Intel Tofino) due to programming constraints. To this end, this paper proposes a programmable switch-based mechanism for detecting and defending against DDoS attacks. First, the mechanism uses the difference between the entropy of source and destination addresses to determine whether DDoS attacks occur. When DDoS attacks occur, a traffic filtration mechanism based on the difference in counts between source and destination address will defend against DDoS attacks in real time. Experimental results indicate that the proposed mechanism effectively identifies and defends against DDoS attacks. Compared with the benchmark method, the accuracy of this mechanism in window-level attack detection is increased by 17.75% on average, and the accuracy of packet-level attack filtration is increased by 3.7% on average.
Available online: December 25, 2024
, DOI: 10.13328/j.cnki.jos.007246
Abstract:
Cross-lingual sentiment classification is very important in natural language processing and has been widely studied. It uses label information from the source language to construct a sentiment classification system for the target language, thereby greatly reducing the laborious labeling work in the target language. A fundamental challenge in cross-lingual sentiment classification is the obvious difference in the expressions of different languages. This study proposes a method for cross-lingual sentiment classification based on a bilingual dependency graph model. Although the expressions in different languages are various, their internal syntactic dependencies are similar. By establishing edges among word nodes in different languages to represent the semantic relevance of bilingual comment instances, the bilingual dependency graph can explicitly model the similarity of the dependency relationships among words in different languages, allowing graph neural networks to integrate syntactic structure information within and across languages for cross-lingual sentiment classification. Experiments conducted on datasets in both English and Chinese show that the proposed method achieves an improvement of 3% over the baseline method. It is proven that bilingual dependency graphs can effectively model the correlation of comment instances in different languages, thereby significantly improving the accuracy of cross-lingual sentiment classification.
Cross-lingual sentiment classification is very important in natural language processing and has been widely studied. It uses label information from the source language to construct a sentiment classification system for the target language, thereby greatly reducing the laborious labeling work in the target language. A fundamental challenge in cross-lingual sentiment classification is the obvious difference in the expressions of different languages. This study proposes a method for cross-lingual sentiment classification based on a bilingual dependency graph model. Although the expressions in different languages are various, their internal syntactic dependencies are similar. By establishing edges among word nodes in different languages to represent the semantic relevance of bilingual comment instances, the bilingual dependency graph can explicitly model the similarity of the dependency relationships among words in different languages, allowing graph neural networks to integrate syntactic structure information within and across languages for cross-lingual sentiment classification. Experiments conducted on datasets in both English and Chinese show that the proposed method achieves an improvement of 3% over the baseline method. It is proven that bilingual dependency graphs can effectively model the correlation of comment instances in different languages, thereby significantly improving the accuracy of cross-lingual sentiment classification.
Available online: December 25, 2024
, DOI: 10.13328/j.cnki.jos.007257
Abstract:
Due to the difficulty in determining the structure and training the parameters of recurrent neural network (RNN), an incremental-construction random RNN (IRRNN) is proposed to realize the incremental construction of RNN structures and the random learning of network parameters. The IRRNN establishes an incremental constraint mechanism for hidden nodes and uses the candidate node pool strategy to realize the optimal selection of hidden nodes, avoiding the blindness of random construction of the network. Two incremental random learning methods, termed IR-1 and IR-2, are designed for local and global optimization of model parameters. Additionally, their universal approximation property is proved. Meanwhile, the dynamic property of the IRRNN model is studied to analyze its generalization performance. Experiments validated that the IRRNN exhibits favorable dynamic properties, compactness, and accuracy.
Due to the difficulty in determining the structure and training the parameters of recurrent neural network (RNN), an incremental-construction random RNN (IRRNN) is proposed to realize the incremental construction of RNN structures and the random learning of network parameters. The IRRNN establishes an incremental constraint mechanism for hidden nodes and uses the candidate node pool strategy to realize the optimal selection of hidden nodes, avoiding the blindness of random construction of the network. Two incremental random learning methods, termed IR-1 and IR-2, are designed for local and global optimization of model parameters. Additionally, their universal approximation property is proved. Meanwhile, the dynamic property of the IRRNN model is studied to analyze its generalization performance. Experiments validated that the IRRNN exhibits favorable dynamic properties, compactness, and accuracy.
Available online: December 25, 2024
, DOI: 10.13328/j.cnki.jos.007260
Abstract:
Fuzz testing automatically uncovers vulnerabilities in software. However, existing fuzz testing tools for network protocols are not able to fully explore their internal state space, resulting in limited coverage. Finite state machines comprehensively model the implementation of network protocols to provide an in-depth understanding of their system behavior and internal state space. This study proposes a fuzz testing method for network protocols based on finite state machines. It focuses on the commonly used TLS protocol, using finite state machine learning to model the implementation of the TLS protocol, reflecting the protocol’s internal state space and system behavior. Subsequently, guided by finite state machines, the fuzz testing of the TLS protocol achieves deeper depth and broader code coverage. This study also implements a prototype system, SNETFuzzer, which outperforms existing methods in important metrics such as coverage in a series of comparative experiments. SNETFuzzer successfully discovers multiple vulnerabilities, including two new ones, demonstrating its practicality and effectiveness.
Fuzz testing automatically uncovers vulnerabilities in software. However, existing fuzz testing tools for network protocols are not able to fully explore their internal state space, resulting in limited coverage. Finite state machines comprehensively model the implementation of network protocols to provide an in-depth understanding of their system behavior and internal state space. This study proposes a fuzz testing method for network protocols based on finite state machines. It focuses on the commonly used TLS protocol, using finite state machine learning to model the implementation of the TLS protocol, reflecting the protocol’s internal state space and system behavior. Subsequently, guided by finite state machines, the fuzz testing of the TLS protocol achieves deeper depth and broader code coverage. This study also implements a prototype system, SNETFuzzer, which outperforms existing methods in important metrics such as coverage in a series of comparative experiments. SNETFuzzer successfully discovers multiple vulnerabilities, including two new ones, demonstrating its practicality and effectiveness.
Available online: December 25, 2024
, DOI: 10.13328/j.cnki.jos.007261
Abstract:
Causal discovery aims to uncover causal relationships among variables from observational data, serving as a crucial method for understanding various phenomena and changes in natural, social, and technological systems. A mainstream approach for causal discovery is a constraint-based algorithm, which determines the causal structure among variables by examining their conditional independence. However, data collection in the real world often faces challenges such as limited sample sizes and high variance among nodes due to resource or technical constraints. In these scenarios, the accuracy of conditional independence tests is greatly affected, leading to erroneous deletion of causal edges of some variables in learned causal graphs, thereby impacting the accuracy of the algorithm’s output. To address this issue, this study proposes an enhanced method for conditional independence testing, which focuses on minimizing the interference of irrelevant external noise on the variables being tested, thereby improving the accuracy of conditional independence tests. Based on this enhanced method, the paper introduces a structure learning algorithm based on heuristic search, which iteratively searches for mistakenly deleted causal edges on a graph with an initial structure. This algorithm reconstructs the causal structure by combining enhanced conditional independence tests with score optimization. Experimental results show that, compared to existing methods, the proposed algorithm significantly improves both the F1 score and the structural Hamming distance (SHD) on simulated, Bayesian network, and real data, demonstrating its ability to more accurately reveal underlying causal structures in observational data with limited samples and high-variance nodes.
Causal discovery aims to uncover causal relationships among variables from observational data, serving as a crucial method for understanding various phenomena and changes in natural, social, and technological systems. A mainstream approach for causal discovery is a constraint-based algorithm, which determines the causal structure among variables by examining their conditional independence. However, data collection in the real world often faces challenges such as limited sample sizes and high variance among nodes due to resource or technical constraints. In these scenarios, the accuracy of conditional independence tests is greatly affected, leading to erroneous deletion of causal edges of some variables in learned causal graphs, thereby impacting the accuracy of the algorithm’s output. To address this issue, this study proposes an enhanced method for conditional independence testing, which focuses on minimizing the interference of irrelevant external noise on the variables being tested, thereby improving the accuracy of conditional independence tests. Based on this enhanced method, the paper introduces a structure learning algorithm based on heuristic search, which iteratively searches for mistakenly deleted causal edges on a graph with an initial structure. This algorithm reconstructs the causal structure by combining enhanced conditional independence tests with score optimization. Experimental results show that, compared to existing methods, the proposed algorithm significantly improves both the F1 score and the structural Hamming distance (SHD) on simulated, Bayesian network, and real data, demonstrating its ability to more accurately reveal underlying causal structures in observational data with limited samples and high-variance nodes.
Access Control Structure Based on Byzantine Fault Tolerance in Cross-trust-domain Internet Scenarios
Available online: December 25, 2024
, DOI: 10.13328/j.cnki.jos.007274
Abstract:
In the industrial field, currently used access permission control technologies are increasingly struggling to address access control issues of distributed systems deployed in wide-area internet scenarios. This situation is particularly exacerbated when dealing with large-scale information systems distributed across multiple trust domains, thereby engendering an escalating proliferation of vulnerabilities. Consensus-based access control policy sharing technologies can facilitate the secure and expeditious attainment of consensus decisions among access control nodes deployed across trust domains. This study first proposes a consensus-based access permission control model for multiple nodes and presents the Super-Dumbo consensus algorithm for access control engines, which features robust security and high performance. Super-Dumbo surmounts the performance bottlenecks of Dumbo2 by optimizing the design of key steps encompassing message broadcasting, random coin toss procedures, and consensus algorithm constructs. Notably, it reduces computational overhead such as digital signature verification, thereby effectively enhancing bandwidth utilization. This achieves a substantial improvement in performance metrics, such as throughput and latency, aligning seamlessly with the performance prerequisites of the CBAC access control model, which demands low latency and high throughput from the underlying consensus algorithm.
In the industrial field, currently used access permission control technologies are increasingly struggling to address access control issues of distributed systems deployed in wide-area internet scenarios. This situation is particularly exacerbated when dealing with large-scale information systems distributed across multiple trust domains, thereby engendering an escalating proliferation of vulnerabilities. Consensus-based access control policy sharing technologies can facilitate the secure and expeditious attainment of consensus decisions among access control nodes deployed across trust domains. This study first proposes a consensus-based access permission control model for multiple nodes and presents the Super-Dumbo consensus algorithm for access control engines, which features robust security and high performance. Super-Dumbo surmounts the performance bottlenecks of Dumbo2 by optimizing the design of key steps encompassing message broadcasting, random coin toss procedures, and consensus algorithm constructs. Notably, it reduces computational overhead such as digital signature verification, thereby effectively enhancing bandwidth utilization. This achieves a substantial improvement in performance metrics, such as throughput and latency, aligning seamlessly with the performance prerequisites of the CBAC access control model, which demands low latency and high throughput from the underlying consensus algorithm.
Available online: December 11, 2024
, DOI: 10.13328/j.cnki.jos.007271
Abstract:
Cloud storage has become an important part of the digital economy as it brings great convenience to users’ data management. However, complex and diverse network environments and third parties that are not fully trusted pose great threats to users' privacy. To protect users’ privacy, data is usually encrypted before storage, but the ciphertext generated by traditional encryption techniques hinders subsequent data retrieval. Public-key encryption with keyword search (PEKS) technology can provide a confidential retrieval function while guaranteeing data encryption, but the traditional PEKS scheme is vulnerable to keyword guessing attacks due to the small number of common keywords. Public-key authenticated encryption with keyword search (PAEKS) introduces authentication technology based on PEKS, which can further improve security. However, most of the existing PAEKS schemes are designed based on foreign cryptographic algorithms, which do not meet the development needs of independent innovation of cryptography in China. This study proposes an SM9-PAEKS scheme, which can effectively improve user-side retrieval efficiency by redesigning algorithm structure and transferring time-consuming operations to a resource-rich cloud server. Scheme security is also proved under the random oracle model based on q-BDHI and Gap-q-BCCA1 security assumptions. Finally, theoretical analysis and experimental results show that compared with the optimal communication cost among similar schemes, SM9-PAEKS can reduce the total computational overhead by at least 59.34% with only 96 bytes of additional communication cost, and the computational overhead reduction of keyword trapdoor generation is particularly significant, about 77.55%. This study not only helps to enrich national security algorithm applications but also provides theoretical and technical support for data encryption and retrieval in cloud storage.
Cloud storage has become an important part of the digital economy as it brings great convenience to users’ data management. However, complex and diverse network environments and third parties that are not fully trusted pose great threats to users' privacy. To protect users’ privacy, data is usually encrypted before storage, but the ciphertext generated by traditional encryption techniques hinders subsequent data retrieval. Public-key encryption with keyword search (PEKS) technology can provide a confidential retrieval function while guaranteeing data encryption, but the traditional PEKS scheme is vulnerable to keyword guessing attacks due to the small number of common keywords. Public-key authenticated encryption with keyword search (PAEKS) introduces authentication technology based on PEKS, which can further improve security. However, most of the existing PAEKS schemes are designed based on foreign cryptographic algorithms, which do not meet the development needs of independent innovation of cryptography in China. This study proposes an SM9-PAEKS scheme, which can effectively improve user-side retrieval efficiency by redesigning algorithm structure and transferring time-consuming operations to a resource-rich cloud server. Scheme security is also proved under the random oracle model based on q-BDHI and Gap-q-BCCA1 security assumptions. Finally, theoretical analysis and experimental results show that compared with the optimal communication cost among similar schemes, SM9-PAEKS can reduce the total computational overhead by at least 59.34% with only 96 bytes of additional communication cost, and the computational overhead reduction of keyword trapdoor generation is particularly significant, about 77.55%. This study not only helps to enrich national security algorithm applications but also provides theoretical and technical support for data encryption and retrieval in cloud storage.
Available online: December 11, 2024
, DOI: 10.13328/j.cnki.jos.007266
Abstract:
With the proliferation of massive data and the ever-growing demand for intelligent applications, ensuring data security has become a critical measure for enhancing data quality and realizing data value. The cloud-edge-client architecture has emerged as a promising technology for efficient data processing and optimization. Federated learning (FL), an efficient decentralized machine learning paradigm that can provide privacy protection for data, has garnered extensive attention from academia and industry in recent years. However, FL has demonstrated inherent vulnerabilities that render it highly susceptible to poisoning attacks. Most existing methods for defending against poisoning attacks rely on continuously updated space, but in practical scenarios, those methods may be less robust when facing flexible attack strategies and varied attack scenarios. Therefore, this study proposes FedDiscrete, a defense method for resisting poisoning attacks in cloud-edge FL (CEFL) systems. The key idea is to compute local rankings on the client side using the scores of network model edges to create discrete update space. To ensure fairness among clients participating in the FL task, this study also introduces a contribution metric. In this way, FedDiscrete can penalize potential attackers by allocating updated global rankings. Extensive experiments demonstrate that the proposed method exhibits significant advantages and robustness against poisoning attacks, and is applicable to both independent and identically distributed (IID) and non-IID scenarios, providing protection for CEFL systems.
With the proliferation of massive data and the ever-growing demand for intelligent applications, ensuring data security has become a critical measure for enhancing data quality and realizing data value. The cloud-edge-client architecture has emerged as a promising technology for efficient data processing and optimization. Federated learning (FL), an efficient decentralized machine learning paradigm that can provide privacy protection for data, has garnered extensive attention from academia and industry in recent years. However, FL has demonstrated inherent vulnerabilities that render it highly susceptible to poisoning attacks. Most existing methods for defending against poisoning attacks rely on continuously updated space, but in practical scenarios, those methods may be less robust when facing flexible attack strategies and varied attack scenarios. Therefore, this study proposes FedDiscrete, a defense method for resisting poisoning attacks in cloud-edge FL (CEFL) systems. The key idea is to compute local rankings on the client side using the scores of network model edges to create discrete update space. To ensure fairness among clients participating in the FL task, this study also introduces a contribution metric. In this way, FedDiscrete can penalize potential attackers by allocating updated global rankings. Extensive experiments demonstrate that the proposed method exhibits significant advantages and robustness against poisoning attacks, and is applicable to both independent and identically distributed (IID) and non-IID scenarios, providing protection for CEFL systems.
Available online: December 11, 2024
, DOI: 10.13328/j.cnki.jos.007216
Abstract:
Cloud storage auditing guarantees the security of data stored in the cloud, enabling data owners to easily verify the integrity of data. However, a vast amount of data in the cloud can lead to significant computational overhead during cloud storage auditing when verifying data integrity and modifying data ownership. To solve this problem as well as provide practical solutions, this study proposes a dynamic auditing scheme for cloud storage with efficient data ownership sharing. An efficient validation structure is constructed to aggregate information for data verification, avoiding a large number of bilinear pairing operations that incur high computational costs. An efficient mechanism for data ownership sharing is designed based on the chameleon hash function’s ability to generate new collisions. It allows updating the secret key of the corresponding user for shared data ownership without modifying the ciphertexts stored in the cloud. In addition, the proposed scheme achieves fine-grained data sharing, encrypted data auditing, and dynamic data modification. The security and performance analyses show that the proposed scheme ensures the security of data in the cloud without affecting its performance, which means it is a practical scheme.
Cloud storage auditing guarantees the security of data stored in the cloud, enabling data owners to easily verify the integrity of data. However, a vast amount of data in the cloud can lead to significant computational overhead during cloud storage auditing when verifying data integrity and modifying data ownership. To solve this problem as well as provide practical solutions, this study proposes a dynamic auditing scheme for cloud storage with efficient data ownership sharing. An efficient validation structure is constructed to aggregate information for data verification, avoiding a large number of bilinear pairing operations that incur high computational costs. An efficient mechanism for data ownership sharing is designed based on the chameleon hash function’s ability to generate new collisions. It allows updating the secret key of the corresponding user for shared data ownership without modifying the ciphertexts stored in the cloud. In addition, the proposed scheme achieves fine-grained data sharing, encrypted data auditing, and dynamic data modification. The security and performance analyses show that the proposed scheme ensures the security of data in the cloud without affecting its performance, which means it is a practical scheme.
Available online: December 11, 2024
, DOI: 10.13328/j.cnki.jos.007229
Abstract:
Multi-label text classification aims to assign several predefined labels or categories to text. To fully explore the correlations among labels, current methods typically utilize a label relation graph and integrate it with graph neural networks to obtain the representations of label features. However, such methods often overly rely on the initial graph construction, overlooking the inherent label correlations in the current text. Consequently, classification results heavily depend on the statistics of datasets and may overlook label-related information within the text. Therefore, this study proposes an algorithm for multi-label text classification based on feature-fused dynamic graph networks. It designs dynamic graphs to model label correlations within the current text and integrates feature fusion with graph neural networks to form label representations based on the current text, thus achieving more accurate multi-label text classifications. Experimental results on three datasets demonstrate the effectiveness and feasibility of the proposed model as it shows excellent performance in multi-label text classifications.
Multi-label text classification aims to assign several predefined labels or categories to text. To fully explore the correlations among labels, current methods typically utilize a label relation graph and integrate it with graph neural networks to obtain the representations of label features. However, such methods often overly rely on the initial graph construction, overlooking the inherent label correlations in the current text. Consequently, classification results heavily depend on the statistics of datasets and may overlook label-related information within the text. Therefore, this study proposes an algorithm for multi-label text classification based on feature-fused dynamic graph networks. It designs dynamic graphs to model label correlations within the current text and integrates feature fusion with graph neural networks to form label representations based on the current text, thus achieving more accurate multi-label text classifications. Experimental results on three datasets demonstrate the effectiveness and feasibility of the proposed model as it shows excellent performance in multi-label text classifications.
Available online: December 11, 2024
, DOI: 10.13328/j.cnki.jos.007197
Abstract:
Stochastic block models can fit the generation of various networks, mining implicit structures and potential connections within these networks. Thus, they have significant advantages in community detection. General stochastic block (GSB) models discover general communities based on link communities, but they are only applicable to directed non-attributed networks. This study proposes a degree corrected general stochastic block (DCGSB) model for undirected attributed networks which models both network topology information and node attributes. In the DCGSB model, it is assumed that the generation of network topology information and node attributes follows a distribution in the form of power functions. Node degrees are introduced to characterize the scale-free property of networks, which allows the model to better fit the generation of real networks. The expectation-maximization algorithm is employed to estimate the parameters of the DCGSB model, and node-community memberships are obtained by hard partition to complete community detection. Experiments are conducted on three real attributed network datasets containing different network structures, and the proposed model is compared with ten existing community detection algorithms. Results show that the DCGSB model not only inherits the advantages of GSB models in identifying general communities but also outperforms the ten algorithms in community detection due to the introduction of attribute information and node degrees.
Stochastic block models can fit the generation of various networks, mining implicit structures and potential connections within these networks. Thus, they have significant advantages in community detection. General stochastic block (GSB) models discover general communities based on link communities, but they are only applicable to directed non-attributed networks. This study proposes a degree corrected general stochastic block (DCGSB) model for undirected attributed networks which models both network topology information and node attributes. In the DCGSB model, it is assumed that the generation of network topology information and node attributes follows a distribution in the form of power functions. Node degrees are introduced to characterize the scale-free property of networks, which allows the model to better fit the generation of real networks. The expectation-maximization algorithm is employed to estimate the parameters of the DCGSB model, and node-community memberships are obtained by hard partition to complete community detection. Experiments are conducted on three real attributed network datasets containing different network structures, and the proposed model is compared with ten existing community detection algorithms. Results show that the DCGSB model not only inherits the advantages of GSB models in identifying general communities but also outperforms the ten algorithms in community detection due to the introduction of attribute information and node degrees.
Available online: December 04, 2024
, DOI: 10.13328/j.cnki.jos.007251
Abstract:
Dynamic searchable symmetric encryption has attracted much attention because it allows users to securely search and dynamically update encrypted documents stored in a semi-trusted cloud server. However, most searchable symmetric encryption schemes only support single-keyword search, failing to achieve conjunctive search while protecting forward and backward privacy. In addition, most schemes are not robust, which means that they cannot handle irrational update requests from a client, such as adding or deleting a certain keyword/file identifier pair, or deleting non-existent keywords/file identifier pairs. To address these challenges, this study proposes a robust scheme for conjunctive dynamic symmetric searchable encryption that preserves both forward and backward privacy, called RFBC. In this scheme, the server constructs two Bloom filters for each keyword, which are used to store the relevant hash values of the keyword/file identifier pair to be added and deleted, respectively. When the client sends update requests, the server uses the two Bloom filters to determine and filter irrational update requests, so as to guarantee the robustness of the scheme. In addition, by combining the status information of the lowest frequency keywords among multiple keywords, the Bloom filters, and the update counter, RFBC realizes conjunctive search by filtering out file identifiers that do not contain the rest keywords. Finally, by defining the leakage function, RFBC is proved to be forward private and Type-III backward private through a series of security analyses. Experimental results show that compared with related schemes, RFBC greatly improves computation and communication efficiency. Specifically, the computational overhead of update operations in RFBC is about 28% and 61.7% of that in ODXT and BDXT, respectively. The computational overhead of search operations in RFBC is about 21.9% and 27.3% of that in ODXT and BDXT, respectively. The communication overhead of search operations in RFBC is about 19.7% and 31.6% of that in ODXT and BDXT, respectively. Moreover, as the proportion of irrational updates gradually increases, RFBC exhibits significantly higher improvement in search efficiency compared to both BDXT and ODXT.
Dynamic searchable symmetric encryption has attracted much attention because it allows users to securely search and dynamically update encrypted documents stored in a semi-trusted cloud server. However, most searchable symmetric encryption schemes only support single-keyword search, failing to achieve conjunctive search while protecting forward and backward privacy. In addition, most schemes are not robust, which means that they cannot handle irrational update requests from a client, such as adding or deleting a certain keyword/file identifier pair, or deleting non-existent keywords/file identifier pairs. To address these challenges, this study proposes a robust scheme for conjunctive dynamic symmetric searchable encryption that preserves both forward and backward privacy, called RFBC. In this scheme, the server constructs two Bloom filters for each keyword, which are used to store the relevant hash values of the keyword/file identifier pair to be added and deleted, respectively. When the client sends update requests, the server uses the two Bloom filters to determine and filter irrational update requests, so as to guarantee the robustness of the scheme. In addition, by combining the status information of the lowest frequency keywords among multiple keywords, the Bloom filters, and the update counter, RFBC realizes conjunctive search by filtering out file identifiers that do not contain the rest keywords. Finally, by defining the leakage function, RFBC is proved to be forward private and Type-III backward private through a series of security analyses. Experimental results show that compared with related schemes, RFBC greatly improves computation and communication efficiency. Specifically, the computational overhead of update operations in RFBC is about 28% and 61.7% of that in ODXT and BDXT, respectively. The computational overhead of search operations in RFBC is about 21.9% and 27.3% of that in ODXT and BDXT, respectively. The communication overhead of search operations in RFBC is about 19.7% and 31.6% of that in ODXT and BDXT, respectively. Moreover, as the proportion of irrational updates gradually increases, RFBC exhibits significantly higher improvement in search efficiency compared to both BDXT and ODXT.
Available online: December 04, 2024
, DOI: 10.13328/j.cnki.jos.007252
Abstract:
Code comment generation is an important research task in software engineering. Mainstream methods for comment generation train deep learning models to generate comments, relying on metrics such as BLEU to evaluate comment quality on open code comment datasets. These evaluations mainly reflect the similarity between generated comments and manual reference comments in the datasets. However, the quality of the manual reference comments in open comment datasets varies widely, which leads to more and more doubts about the effectiveness of these metrics. Therefore, for code comment generation tasks, there is an urgent need for direct and effective methods to evaluate code comment quality. Such methods can improve the quality of open comment datasets and enhance the evaluation of generated comments. This study conducts research and analysis on existing quantifiable methods for code comment quality evaluation and applies a set of multi-dimensional metrics to directly evaluate the quality of code comments in mainstream open datasets, comments generated by traditional methods, and comments generated by ChatGPT. The study reveals the following findings. 1) The quality of code comments in mainstream open datasets needs improvement, with issues such as inaccuracy, poor readability, excessive simplicity, and a lack of useful information. 2) Comments generated by traditional methods are more lexically and semantically similar to the code but lack information that is more useful to developers, such as high-level intentions of the code. 3) One important reason for the low BLEU scores of generated comments is the large number of poor-quality reference comments in datasets, which lack relevance with the code or exhibit poor naturalness. These kinds of reference comments should be filtered or improved. 4) Comments generated by LLMs like ChatGPT are rich in content but tend to be lengthy. Their quality evaluation needs to be tailored to developer intentions and specific scenarios. Based on these findings, this study provides several suggestions for future research in code comment generation and comment quality evaluation.
Code comment generation is an important research task in software engineering. Mainstream methods for comment generation train deep learning models to generate comments, relying on metrics such as BLEU to evaluate comment quality on open code comment datasets. These evaluations mainly reflect the similarity between generated comments and manual reference comments in the datasets. However, the quality of the manual reference comments in open comment datasets varies widely, which leads to more and more doubts about the effectiveness of these metrics. Therefore, for code comment generation tasks, there is an urgent need for direct and effective methods to evaluate code comment quality. Such methods can improve the quality of open comment datasets and enhance the evaluation of generated comments. This study conducts research and analysis on existing quantifiable methods for code comment quality evaluation and applies a set of multi-dimensional metrics to directly evaluate the quality of code comments in mainstream open datasets, comments generated by traditional methods, and comments generated by ChatGPT. The study reveals the following findings. 1) The quality of code comments in mainstream open datasets needs improvement, with issues such as inaccuracy, poor readability, excessive simplicity, and a lack of useful information. 2) Comments generated by traditional methods are more lexically and semantically similar to the code but lack information that is more useful to developers, such as high-level intentions of the code. 3) One important reason for the low BLEU scores of generated comments is the large number of poor-quality reference comments in datasets, which lack relevance with the code or exhibit poor naturalness. These kinds of reference comments should be filtered or improved. 4) Comments generated by LLMs like ChatGPT are rich in content but tend to be lengthy. Their quality evaluation needs to be tailored to developer intentions and specific scenarios. Based on these findings, this study provides several suggestions for future research in code comment generation and comment quality evaluation.
Available online: December 04, 2024
, DOI: 10.13328/j.cnki.jos.007253
Abstract:
Software concept drift means that the structure and composition of the same type of software will change over time. In malware classification, concept drift means that the structure and composition characteristics of malware samples from the same family can change over time. This will cause a decline in the performance of fixed-mode malware classification algorithms over time. Existing methods for static malware classification experience significant performance degradation when faced with concept drift scenarios, making it difficult to meet the needs of practical applications. To address this problem, given the commonalities between natural language understanding and binary byte stream analysis, a highly accurate and robust malware classification method is proposed based on BERT and a custom autoencoder architecture. This method extracts execution-oriented malware opcode sequences through disassembly analysis to reduce redundant information. Then, it uses BERT to understand the contextual semantics of the sequences and perform vector embedding to effectively understand the deep program semantics of the malware samples. It also screens effective task-related features through the geometric median subspace projection and bottleneck autoencoders. Finally, a classifier composed of fully connected layers is used to output the classification results. The practical effectiveness of the proposed method is validated through comparative experiments with nine state-of-the-art malware classification methods in both normal and concept drift scenarios. Experimental results show that the proposed method achieves an F1 score of 99.49% in normal scenarios, outperforming those nine methods. Moreover, in concept drift scenarios, the F1 score is improved by 10.78% to 43.71% compared to the nine methods.
Software concept drift means that the structure and composition of the same type of software will change over time. In malware classification, concept drift means that the structure and composition characteristics of malware samples from the same family can change over time. This will cause a decline in the performance of fixed-mode malware classification algorithms over time. Existing methods for static malware classification experience significant performance degradation when faced with concept drift scenarios, making it difficult to meet the needs of practical applications. To address this problem, given the commonalities between natural language understanding and binary byte stream analysis, a highly accurate and robust malware classification method is proposed based on BERT and a custom autoencoder architecture. This method extracts execution-oriented malware opcode sequences through disassembly analysis to reduce redundant information. Then, it uses BERT to understand the contextual semantics of the sequences and perform vector embedding to effectively understand the deep program semantics of the malware samples. It also screens effective task-related features through the geometric median subspace projection and bottleneck autoencoders. Finally, a classifier composed of fully connected layers is used to output the classification results. The practical effectiveness of the proposed method is validated through comparative experiments with nine state-of-the-art malware classification methods in both normal and concept drift scenarios. Experimental results show that the proposed method achieves an F1 score of 99.49% in normal scenarios, outperforming those nine methods. Moreover, in concept drift scenarios, the F1 score is improved by 10.78% to 43.71% compared to the nine methods.
Available online: November 20, 2024
, DOI: 10.13328/j.cnki.jos.007235
Abstract:
With the advent of the big data era, massive volumes of user data have empowered numerous data-driven industry applications, such as smart grids, intelligent transportation, and product recommendations. In scenarios where real-time data is crucial, the business value embedded within data rapidly diminishes over time. Consequently, data analysis systems require high throughput and low latency. Stream processing systems in big data, exemplified by Apache Flink, have been widely applied. Flink enhances system throughput by parallelizing computing tasks across cluster nodes. However, current research indicates that Flink has weak single-point performance and poor cluster scalability. To improve the throughput of stream processing systems, researchers have focused on optimizations in designing control planes, implementing system operators, and improving vertical scalability. However, there is still a lack of attention to the data flow in streaming analysis applications. These applications are driven by event streams and employ stateful processing functions, including low voltage detection in smart grids and advertising recommendation. This study analyzes the data flow characteristics of typical streaming analysis applications, identifies three bottlenecks in optimizing scalability, and proposes corresponding optimization strategies: the key-level watermark strategy, the dynamic load distribution strategy, and the the key-value based exchange strategy. Based on these optimization strategies, this study implements Trilink based on Flink and applies it to various applications such as low voltage detection, bridge arch crowns monitoring, and the Yahoo Streaming Benchmark. Experimental results show that the modified system, Trilink, achieves more than a 5-fold increase in throughput in a single-machine environment and over a 1.6-fold improvement in horizontal scalability acceleration in an 8-node setup, compared to Flink.
With the advent of the big data era, massive volumes of user data have empowered numerous data-driven industry applications, such as smart grids, intelligent transportation, and product recommendations. In scenarios where real-time data is crucial, the business value embedded within data rapidly diminishes over time. Consequently, data analysis systems require high throughput and low latency. Stream processing systems in big data, exemplified by Apache Flink, have been widely applied. Flink enhances system throughput by parallelizing computing tasks across cluster nodes. However, current research indicates that Flink has weak single-point performance and poor cluster scalability. To improve the throughput of stream processing systems, researchers have focused on optimizations in designing control planes, implementing system operators, and improving vertical scalability. However, there is still a lack of attention to the data flow in streaming analysis applications. These applications are driven by event streams and employ stateful processing functions, including low voltage detection in smart grids and advertising recommendation. This study analyzes the data flow characteristics of typical streaming analysis applications, identifies three bottlenecks in optimizing scalability, and proposes corresponding optimization strategies: the key-level watermark strategy, the dynamic load distribution strategy, and the the key-value based exchange strategy. Based on these optimization strategies, this study implements Trilink based on Flink and applies it to various applications such as low voltage detection, bridge arch crowns monitoring, and the Yahoo Streaming Benchmark. Experimental results show that the modified system, Trilink, achieves more than a 5-fold increase in throughput in a single-machine environment and over a 1.6-fold improvement in horizontal scalability acceleration in an 8-node setup, compared to Flink.
Available online: November 20, 2024
, DOI: 10.13328/j.cnki.jos.007184
Abstract:
Reinforcement learning has achieved remarkable results in decision-making tasks like intelligent dialogue systems, yet its efficiency diminishes notably in scenarios with intricate structures and scarce rewards. Researchers have integrated the skill discovery framework into reinforcement learning, aiming to maximize skill disparities to establish policies and boost agent performance in such tasks. However, the constraint posed by the limited diversity of sampled trajectory data confines existing skill discovery methods to learning a single skill per reinforcement learning episode. Consequently, this limitation results in subpar performance in complex tasks requiring sequential skill combinations within a single episode. To address this challenge, a group-wise contrastive learning based sequence-aware skill discovery method (GCSSD) is proposed, which integrates contrastive learning into the skill discovery framework. Initially, to augment trajectory data diversity, the complete trajectories interacting with the environment are segmented and grouped, employing contrastive loss to learn skill embedding representations from grouped trajectories. Subsequently, skill policy training is conducted by combining the skill embedding representation with reinforcement learning. Lastly, to enhance performance in tasks featuring diverse sequential skill combinations, the sampled trajectories are segmented into skill representations and embedded into the learned policy network, facilitating the sequential combination of learned skill policies. Experimental results demonstrate the efficacy of the GCSSD method in tasks characterized by sparse rewards and sequential skill combinations, showcasing its capability to swiftly adapt to tasks with varying sequential skill combinations using learned skills.
Reinforcement learning has achieved remarkable results in decision-making tasks like intelligent dialogue systems, yet its efficiency diminishes notably in scenarios with intricate structures and scarce rewards. Researchers have integrated the skill discovery framework into reinforcement learning, aiming to maximize skill disparities to establish policies and boost agent performance in such tasks. However, the constraint posed by the limited diversity of sampled trajectory data confines existing skill discovery methods to learning a single skill per reinforcement learning episode. Consequently, this limitation results in subpar performance in complex tasks requiring sequential skill combinations within a single episode. To address this challenge, a group-wise contrastive learning based sequence-aware skill discovery method (GCSSD) is proposed, which integrates contrastive learning into the skill discovery framework. Initially, to augment trajectory data diversity, the complete trajectories interacting with the environment are segmented and grouped, employing contrastive loss to learn skill embedding representations from grouped trajectories. Subsequently, skill policy training is conducted by combining the skill embedding representation with reinforcement learning. Lastly, to enhance performance in tasks featuring diverse sequential skill combinations, the sampled trajectories are segmented into skill representations and embedded into the learned policy network, facilitating the sequential combination of learned skill policies. Experimental results demonstrate the efficacy of the GCSSD method in tasks characterized by sparse rewards and sequential skill combinations, showcasing its capability to swiftly adapt to tasks with varying sequential skill combinations using learned skills.
Available online: November 18, 2024
, DOI: 10.13328/j.cnki.jos.007236
Abstract:
The rise of video platforms has led to the rapid dissemination of videos, integrating them into various aspects of social life. Videos transmitted in the network may include harmful content, highlighting an urgent need for cyberspace security supervision to accurately identify harmful videos that are encrypted and transmitted in the network. The existing methods collect traffic data at main network access points to extract the features of encrypted video traffic and identify the harmful videos by matching the traffic features based on harmful video databases. However, with the progress of encryption protocol for video transmission, HTTP/2 using new multiplexing technologies has been widely applied, which makes the traditional traffic analysis method based on HTTP/1.1 features fail to identify encrypted videos using HTTP/2. Moreover, the current research mostly focuses on videos with a fixed resolution during playback. Few studies have considered the impact of resolution switching in video identification. To address the above problems, this study analyzes the factors that cause offsets in the length of the audio/video data during the HTTP/2 transmission process and proposes a method to precisely reconstruct corrected fingerprints for encrypted videos by calculating the size of the combined audio and video segments in the encrypted traffic. The study also proposes an encrypted video identification model based on the hidden Markov model and the Viterbi algorithm by using the corrected fingerprints of encrypted videos and a large plaintext fingerprint database for videos. The model applies dynamic planning to solve the problems caused by adaptive video resolution switching. The proposed model achieves identification accuracy of 98.41% and 97.91% respectively for encrypted videos with fixed and adaptive resolutions in 400000-level fingerprint databases, namely Facebook and Instagram. The study validates the generality and generalization of the proposed method using three video platforms: Triller, Twitter, and Mango TV. The higher application value of the proposed method has been validated through comparisons with similar work in terms of recognition effectiveness, generalization, and time overhead.
The rise of video platforms has led to the rapid dissemination of videos, integrating them into various aspects of social life. Videos transmitted in the network may include harmful content, highlighting an urgent need for cyberspace security supervision to accurately identify harmful videos that are encrypted and transmitted in the network. The existing methods collect traffic data at main network access points to extract the features of encrypted video traffic and identify the harmful videos by matching the traffic features based on harmful video databases. However, with the progress of encryption protocol for video transmission, HTTP/2 using new multiplexing technologies has been widely applied, which makes the traditional traffic analysis method based on HTTP/1.1 features fail to identify encrypted videos using HTTP/2. Moreover, the current research mostly focuses on videos with a fixed resolution during playback. Few studies have considered the impact of resolution switching in video identification. To address the above problems, this study analyzes the factors that cause offsets in the length of the audio/video data during the HTTP/2 transmission process and proposes a method to precisely reconstruct corrected fingerprints for encrypted videos by calculating the size of the combined audio and video segments in the encrypted traffic. The study also proposes an encrypted video identification model based on the hidden Markov model and the Viterbi algorithm by using the corrected fingerprints of encrypted videos and a large plaintext fingerprint database for videos. The model applies dynamic planning to solve the problems caused by adaptive video resolution switching. The proposed model achieves identification accuracy of 98.41% and 97.91% respectively for encrypted videos with fixed and adaptive resolutions in 400000-level fingerprint databases, namely Facebook and Instagram. The study validates the generality and generalization of the proposed method using three video platforms: Triller, Twitter, and Mango TV. The higher application value of the proposed method has been validated through comparisons with similar work in terms of recognition effectiveness, generalization, and time overhead.
Available online: November 18, 2024
, DOI: 10.13328/j.cnki.jos.007238
Abstract:
As the scale of cities continues to increase, urban transportation systems are facing more and more challenges, such as traffic congestion and traffic safety. Traffic simulation is a method to solve urban traffic problems. It uses virtual and real computing technologies to process real-time traffic data and optimize urban traffic efficiency. It is an important method to achieve the parallel city theory in intelligent transportation. However, traditional computing systems often encounter problems such as insufficient computing resources and long simulation delays when running large-scale urban traffic simulations. To solve the above problems, this study proposes a parallel algorithm for traffic simulation of parallel cities based on the parallel city theory and the heterogeneous architecture of China’s new-generation supercomputer, Tianhe. This algorithm accurately simulates traffic elements such as vehicles, roads, and traffic signals, and applies methods such as road network division, parallel driving of vehicles, and parallel control of signal lights to achieve high-performance traffic simulation. The algorithm runs on Tianhe, a supercomputing platform with 16 nodes and more than 25 000 cores, and simulates real traffic scenarios involving 2.4 million vehicles, 7 797 intersections, and 170 000 lanes within the Fifth Ring Road in Beijing. Compared with traditional single-node simulation, the proposed algorithm reduces the simulation time of each step from 2.21 s to 0.37 s, achieving nearly 6 times acceleration. An urban traffic simulation with a scale of one million vehicles has been successfully implemented on a domestic heterogeneous supercomputing platform.
As the scale of cities continues to increase, urban transportation systems are facing more and more challenges, such as traffic congestion and traffic safety. Traffic simulation is a method to solve urban traffic problems. It uses virtual and real computing technologies to process real-time traffic data and optimize urban traffic efficiency. It is an important method to achieve the parallel city theory in intelligent transportation. However, traditional computing systems often encounter problems such as insufficient computing resources and long simulation delays when running large-scale urban traffic simulations. To solve the above problems, this study proposes a parallel algorithm for traffic simulation of parallel cities based on the parallel city theory and the heterogeneous architecture of China’s new-generation supercomputer, Tianhe. This algorithm accurately simulates traffic elements such as vehicles, roads, and traffic signals, and applies methods such as road network division, parallel driving of vehicles, and parallel control of signal lights to achieve high-performance traffic simulation. The algorithm runs on Tianhe, a supercomputing platform with 16 nodes and more than 25 000 cores, and simulates real traffic scenarios involving 2.4 million vehicles, 7 797 intersections, and 170 000 lanes within the Fifth Ring Road in Beijing. Compared with traditional single-node simulation, the proposed algorithm reduces the simulation time of each step from 2.21 s to 0.37 s, achieving nearly 6 times acceleration. An urban traffic simulation with a scale of one million vehicles has been successfully implemented on a domestic heterogeneous supercomputing platform.
Available online: November 18, 2024
, DOI: 10.13328/j.cnki.jos.007239
Abstract:
Traffic flow prediction is an important foundation and a hot research direction for traffic management in intelligent transportation systems (ITS). Traditional methods for traffic flow prediction typically rely on a large amount of high-quality historical observation data to achieve accurate predictions, but the prediction accuracy significantly decreases in more common scenarios with data scarcity in traffic networks. To address this problem, a transfer learning model is proposed based on spatial-temporal graph convolutional networks (TL-STGCN), which leverages traffic flow features from a source network with abundant data to assist in predicting future traffic flow in a target network with data scarcity. Firstly, a spatial-temporal graph convolutional network based on time attention is employed to learn the spatial and temporal features of the traffic flow data in both the source and target networks. Secondly, domain-invariant spatial-temporal features are extracted from the representations of the two networks using transfer learning techniques. Lastly, these domain-invariant features are utilized to predict the future traffic flow in the target network. To validate the effectiveness of the proposed model, experiments are conducted on real-world datasets. The results demonstrate that TL-STGCN outperforms existing methods by achieving the highest accuracy in mean absolute error, root mean square error, and mean absolute percentage error, which proves that TL-STGCN provides more accurate traffic flow predictions for scenarios with data scarcity in traffic networks.
Traffic flow prediction is an important foundation and a hot research direction for traffic management in intelligent transportation systems (ITS). Traditional methods for traffic flow prediction typically rely on a large amount of high-quality historical observation data to achieve accurate predictions, but the prediction accuracy significantly decreases in more common scenarios with data scarcity in traffic networks. To address this problem, a transfer learning model is proposed based on spatial-temporal graph convolutional networks (TL-STGCN), which leverages traffic flow features from a source network with abundant data to assist in predicting future traffic flow in a target network with data scarcity. Firstly, a spatial-temporal graph convolutional network based on time attention is employed to learn the spatial and temporal features of the traffic flow data in both the source and target networks. Secondly, domain-invariant spatial-temporal features are extracted from the representations of the two networks using transfer learning techniques. Lastly, these domain-invariant features are utilized to predict the future traffic flow in the target network. To validate the effectiveness of the proposed model, experiments are conducted on real-world datasets. The results demonstrate that TL-STGCN outperforms existing methods by achieving the highest accuracy in mean absolute error, root mean square error, and mean absolute percentage error, which proves that TL-STGCN provides more accurate traffic flow predictions for scenarios with data scarcity in traffic networks.
Available online: November 06, 2024
, DOI: 10.13328/j.cnki.jos.007181
Abstract:
With the popularity of mobile devices and the enhancement of users’ requirements for privacy protection, studies of user authentication on mobile devices have attracted widespread attention. Recently, the audio infrastructures of mobile devices have provided greater flexibility and scalability for the design of novel user authentication schemes with excellent performance. After surveying a large number of related works, this study first classifies acoustic sensing-based user authentication schemes on mobile devices according to the difference in authentication metrics and sensing methods and describes the corresponding attack model. Then, it analyzes and compares single authentication metric-based and acoustic sensing-based user authentication schemes on mobile devices. Finally, combined with the problems of existing works, this study gives two metrics (security and practicability) to measure the performance of the user authentication system and discuss future research directions.
With the popularity of mobile devices and the enhancement of users’ requirements for privacy protection, studies of user authentication on mobile devices have attracted widespread attention. Recently, the audio infrastructures of mobile devices have provided greater flexibility and scalability for the design of novel user authentication schemes with excellent performance. After surveying a large number of related works, this study first classifies acoustic sensing-based user authentication schemes on mobile devices according to the difference in authentication metrics and sensing methods and describes the corresponding attack model. Then, it analyzes and compares single authentication metric-based and acoustic sensing-based user authentication schemes on mobile devices. Finally, combined with the problems of existing works, this study gives two metrics (security and practicability) to measure the performance of the user authentication system and discuss future research directions.
Available online: November 06, 2024
, DOI: 10.13328/j.cnki.jos.007176
Abstract:
Continuous dynamical systems safety verification is an important research issue, and over the years, various verification methods have been very limited in the scale of the problems they can handle. For a given continuous dynamical system, this study proposes an algorithm to generate a set of compositional probably approximately correct (PAC) barrier certificates through a counterexample-guided approach. A formal description of the infinite-time domain safety verification problem is given in terms of probability and statistics. By establishing and solving a mixed-integer programming method based on the Big-M method, the barrier certificate problem is transformed into a constrained optimization problem. Nonlinear inequalities are linearized in intervals using the mean value theorem of differentiation. Finally, this study implements the compositional PAC barrier certificate generator CPBC and evaluates its performance on 11 benchmark systems. The experimental results show that CPBC can successfully verify the safety of each dynamical system under specified different safety requirement thresholds. Compared with existing methods, the proposed method can more efficiently generate reliable probabilistic barrier certificates for complex or high-dimensional systems, with the verified example scale reaching up to hundreds of dimensions.
Continuous dynamical systems safety verification is an important research issue, and over the years, various verification methods have been very limited in the scale of the problems they can handle. For a given continuous dynamical system, this study proposes an algorithm to generate a set of compositional probably approximately correct (PAC) barrier certificates through a counterexample-guided approach. A formal description of the infinite-time domain safety verification problem is given in terms of probability and statistics. By establishing and solving a mixed-integer programming method based on the Big-M method, the barrier certificate problem is transformed into a constrained optimization problem. Nonlinear inequalities are linearized in intervals using the mean value theorem of differentiation. Finally, this study implements the compositional PAC barrier certificate generator CPBC and evaluates its performance on 11 benchmark systems. The experimental results show that CPBC can successfully verify the safety of each dynamical system under specified different safety requirement thresholds. Compared with existing methods, the proposed method can more efficiently generate reliable probabilistic barrier certificates for complex or high-dimensional systems, with the verified example scale reaching up to hundreds of dimensions.
Available online: October 12, 2024
, DOI: 10.13328/j.cnki.jos.007243
Abstract:
Dynamic information networks (DIN), which contain evolving objects in the real world and the links among them, are often modeled as a series of static undirected graph snapshots. A community consists of a group of well-connected objects in an information network. In a DIN, there is often a community whose size increases over time but its members always keep well-connected during that period of time. The evolving trajectory of such a community over time forms a sequence of the community on several snapshots of the DIN, which is termed a lasting enlarging community sequence in this study. It is meaningful to search for lasting enlarging community sequences in a DIN. However, no previous research has paid attention to such community sequences. This study formally defines the q-based lasting enlarging community sequence (qLEC) in a DIN by combining set inclusion with the triangle-connected k-truss model. A two-phase search algorithm is developed, which includes computing candidate vertex sets of communities from the beginning to the end of the time window and performing community sequence search from the end to the beginning of the time window. This study also provides optimization strategies based on early termination and TCP index compression to reduce time and space costs. Sufficient experiments demonstrate that the qLEC model has specific practical significance compared to existing dynamic community models. The two-phase search algorithm effectively finds qLEC-based lasting enlarging community sequences. The proposed optimization strategies significantly reduce the spatiotemporal cost of the two-phase algorithm.
Dynamic information networks (DIN), which contain evolving objects in the real world and the links among them, are often modeled as a series of static undirected graph snapshots. A community consists of a group of well-connected objects in an information network. In a DIN, there is often a community whose size increases over time but its members always keep well-connected during that period of time. The evolving trajectory of such a community over time forms a sequence of the community on several snapshots of the DIN, which is termed a lasting enlarging community sequence in this study. It is meaningful to search for lasting enlarging community sequences in a DIN. However, no previous research has paid attention to such community sequences. This study formally defines the q-based lasting enlarging community sequence (qLEC) in a DIN by combining set inclusion with the triangle-connected k-truss model. A two-phase search algorithm is developed, which includes computing candidate vertex sets of communities from the beginning to the end of the time window and performing community sequence search from the end to the beginning of the time window. This study also provides optimization strategies based on early termination and TCP index compression to reduce time and space costs. Sufficient experiments demonstrate that the qLEC model has specific practical significance compared to existing dynamic community models. The two-phase search algorithm effectively finds qLEC-based lasting enlarging community sequences. The proposed optimization strategies significantly reduce the spatiotemporal cost of the two-phase algorithm.
Available online: September 30, 2024
, DOI: 10.13328/j.cnki.jos.007234
Abstract:
The minimum weakly connected dominating set problem is a classic NP-hard problem that has wide applications in various fields. This study proposes an efficient local search algorithm to solve this problem. The algorithm employs a method to construct an initial solution based on locked vertices and frequency feedback. This method ensures the inclusion of vertices that are certain or highly likely to be in the optimal solution, resulting in a high-quality initial solution. Furthermore, the study introduces a method to avoid cycling based on two-hop configuration checking, age properties, and tabu strategies. A perturbation strategy is also proposed to enable the algorithm to effectively escape from the local optimum. Additionally, effective vertex selection methods are presented to assist the algorithm in choosing vertices suitable for addition to or removal from the candidate solution by combining two scoring functions, Dscore and Nscore, with strategies for avoiding cycling. Finally, the proposed local search algorithm is evaluated on four benchmark test instances and compared with four state-of-the-art algorithms and the CPELX solver. Experimental results demonstrate that the proposed algorithm achieves better performance.
The minimum weakly connected dominating set problem is a classic NP-hard problem that has wide applications in various fields. This study proposes an efficient local search algorithm to solve this problem. The algorithm employs a method to construct an initial solution based on locked vertices and frequency feedback. This method ensures the inclusion of vertices that are certain or highly likely to be in the optimal solution, resulting in a high-quality initial solution. Furthermore, the study introduces a method to avoid cycling based on two-hop configuration checking, age properties, and tabu strategies. A perturbation strategy is also proposed to enable the algorithm to effectively escape from the local optimum. Additionally, effective vertex selection methods are presented to assist the algorithm in choosing vertices suitable for addition to or removal from the candidate solution by combining two scoring functions, Dscore and Nscore, with strategies for avoiding cycling. Finally, the proposed local search algorithm is evaluated on four benchmark test instances and compared with four state-of-the-art algorithms and the CPELX solver. Experimental results demonstrate that the proposed algorithm achieves better performance.
Available online: September 30, 2024
, DOI: 10.13328/j.cnki.jos.007218
Abstract:
In the Q1 model, this paper proposes a low-data quantum key-recovery attack against Lai-Massey structures, Misty structures, Type-1 generalized Feistel structures, SMS4-like generalized Feistel structures and MARS-like generalized Feistel structures. This attack only needs to select constant-sized plain-ciphertexts, analyze the encryption process of block cipher structures, and recover the key by searching and calculating some intermediate states and round keys using Grover’s algorithm. This attack belongs to the Q1 model, which is more practical than the Q2 model since no quantum superposition query is required. For the 3-round Lai-Massey structure, compared with other quantum attacks, this attack requires only $ {\rm O}(1) $ data and belongs to the Q1 model, and is even reduced by the $ n{2^{n/4}} $ factor on the evaluation of the complexity product (time×data×classical memory×quantum bits). For the 6-round Misty structure, this attack still retains the advantage of low data complexity, and especially for the 6-round Misty L/R-FK structure, this attack is reduced by the$ {2^{n/2}} $factor on the evaluation of the complexity product. For the 9-round 3-branch Type-1 generalized Feistel structure, in line with other quantum attacks on the evaluation of the complexity product, this attack still retains the advantage of low data complexity and belongs to the chosen plaintext attack. In addition, a low-data quantum key-recovery attack for SMS4-like generalized Feistel structures and MARS-like generalized Feistel structures are also given in this study, complementing their security evaluation in the Q1 model.
In the Q1 model, this paper proposes a low-data quantum key-recovery attack against Lai-Massey structures, Misty structures, Type-1 generalized Feistel structures, SMS4-like generalized Feistel structures and MARS-like generalized Feistel structures. This attack only needs to select constant-sized plain-ciphertexts, analyze the encryption process of block cipher structures, and recover the key by searching and calculating some intermediate states and round keys using Grover’s algorithm. This attack belongs to the Q1 model, which is more practical than the Q2 model since no quantum superposition query is required. For the 3-round Lai-Massey structure, compared with other quantum attacks, this attack requires only $ {\rm O}(1) $ data and belongs to the Q1 model, and is even reduced by the $ n{2^{n/4}} $ factor on the evaluation of the complexity product (time×data×classical memory×quantum bits). For the 6-round Misty structure, this attack still retains the advantage of low data complexity, and especially for the 6-round Misty L/R-FK structure, this attack is reduced by the$ {2^{n/2}} $factor on the evaluation of the complexity product. For the 9-round 3-branch Type-1 generalized Feistel structure, in line with other quantum attacks on the evaluation of the complexity product, this attack still retains the advantage of low data complexity and belongs to the chosen plaintext attack. In addition, a low-data quantum key-recovery attack for SMS4-like generalized Feistel structures and MARS-like generalized Feistel structures are also given in this study, complementing their security evaluation in the Q1 model.
Available online: September 14, 2024
, DOI: 10.13328/j.cnki.jos.007186
Abstract:
In the field of federated learning, incentive mechanisms play a crucial role in enticing high-quality data contributors to engage in federated learning and acquire superior models. However, existing research in federated learning often neglects the potential misuse of these incentive mechanisms. Specifically, participants may manipulate their locally trained models to dishonestly maximize their rewards. This issue is thoroughly examined in this study. Firstly, the problem of rewards fraud in federated learning is clearly defined, and the concept of reward-cost ratio is introduced to assess the effectiveness of various rewards fraud techniques and defense mechanisms. Following this, an attack method named the “gradient scale-up attack” is proposed, focusing on manipulating model gradients to exploit the incentive system. This attack method calculates corresponding scaling factors and utilizes them to increase the contribution of the local model to gain more rewards. Finally, an efficient defense mechanism is proposed, which identifies malicious participants by examining the L2-norms of model updates, effectively thwarting gradient scale-up attacks. Through extensive analysis and experimental validation on datasets such as MNIST, the findings of this research demonstrate that the proposed attack method significantly increases rewards, while the corresponding defense method effectively mitigates fraudulent behavior by participants.
In the field of federated learning, incentive mechanisms play a crucial role in enticing high-quality data contributors to engage in federated learning and acquire superior models. However, existing research in federated learning often neglects the potential misuse of these incentive mechanisms. Specifically, participants may manipulate their locally trained models to dishonestly maximize their rewards. This issue is thoroughly examined in this study. Firstly, the problem of rewards fraud in federated learning is clearly defined, and the concept of reward-cost ratio is introduced to assess the effectiveness of various rewards fraud techniques and defense mechanisms. Following this, an attack method named the “gradient scale-up attack” is proposed, focusing on manipulating model gradients to exploit the incentive system. This attack method calculates corresponding scaling factors and utilizes them to increase the contribution of the local model to gain more rewards. Finally, an efficient defense mechanism is proposed, which identifies malicious participants by examining the L2-norms of model updates, effectively thwarting gradient scale-up attacks. Through extensive analysis and experimental validation on datasets such as MNIST, the findings of this research demonstrate that the proposed attack method significantly increases rewards, while the corresponding defense method effectively mitigates fraudulent behavior by participants.
Available online: September 11, 2024
, DOI: 10.13328/j.cnki.jos.007187
Abstract:
Point cloud self-supervised representation learning is conducted in an unlabeled pre-training manner, exploring the structural relationships of 3D topological geometric spaces and capturing feature representations. This approach can be applied to downstream tasks, such as point cloud classification, segmentation, and object detection. To enhance the generalization and robustness of the pretrained models, this study proposes a multi-modal self-supervised method for learning point cloud representations. The method is based on bidirectional fit mask reconstruction and comprises three main components: (1) The “bad teacher” model, guided by the inverse density scale, employs a bidirectional fit strategy that utilizes inverse density noise representation and global feature representation to expedite the convergence of the mask region towards the true value. (2) The StyleGAN-based auxiliary point cloud generation model, grounded in local geometric information, generates stylized point clouds and fuses them with mask reconstruction results while adhering to threshold constraints. The objective is to mitigate the adverse effects of noise on representation learning during the reconstruction process. (3) The multi-modal teacher model aims to enhance the diversity of the 3D feature space and prevent the collapse of modal information. It relies on the triple feature contrast loss function to fully extract the latent information contained in the point cloud-image-text sample space. The proposed method is evaluated on ModelNet, ScanObjectNN, and ShapeNet datasets for fine-tuning tasks. Experimental results demonstrate that the pretrained model achieves state-of-the-art performance in various point cloud recognition tasks, including point cloud classification, linear support vector machine classification, few-shot classification, zero-shot classification, and part segmentation.
Point cloud self-supervised representation learning is conducted in an unlabeled pre-training manner, exploring the structural relationships of 3D topological geometric spaces and capturing feature representations. This approach can be applied to downstream tasks, such as point cloud classification, segmentation, and object detection. To enhance the generalization and robustness of the pretrained models, this study proposes a multi-modal self-supervised method for learning point cloud representations. The method is based on bidirectional fit mask reconstruction and comprises three main components: (1) The “bad teacher” model, guided by the inverse density scale, employs a bidirectional fit strategy that utilizes inverse density noise representation and global feature representation to expedite the convergence of the mask region towards the true value. (2) The StyleGAN-based auxiliary point cloud generation model, grounded in local geometric information, generates stylized point clouds and fuses them with mask reconstruction results while adhering to threshold constraints. The objective is to mitigate the adverse effects of noise on representation learning during the reconstruction process. (3) The multi-modal teacher model aims to enhance the diversity of the 3D feature space and prevent the collapse of modal information. It relies on the triple feature contrast loss function to fully extract the latent information contained in the point cloud-image-text sample space. The proposed method is evaluated on ModelNet, ScanObjectNN, and ShapeNet datasets for fine-tuning tasks. Experimental results demonstrate that the pretrained model achieves state-of-the-art performance in various point cloud recognition tasks, including point cloud classification, linear support vector machine classification, few-shot classification, zero-shot classification, and part segmentation.
Available online: September 11, 2024
, DOI: 10.13328/j.cnki.jos.007185
Abstract:
The service descriptions provide limited information about application scenarios, creating a gap between Mashup service component Web API recommendations based on functional similarity calculation and desired expectations. Consequently, there is a need to enhance the accuracy of function matching. While some researchers utilize collaborative associations among Web APIs to enhance recommendation compatibility, they overlook the adverse effects of functional associations on Mashup service creation, thereby limiting the enhancement of recommendation diversity. To address this issue, this study proposes a Web API recommendation method for Mashup service components that integrates latent related words and heterogeneous association compatibility. The study extracts latent related words associated with application scenarios for both Mashup requirements and Web APIs, integrating them into the generation of function vectors. By enhancing the accuracy of functional similarity matching, it obtains a high-quality candidate set of Web API components. Function association and collaboration association are modeled as heterogeneous service association. The study utilizes heterogeneous association compatibility to replace collaboration compatibility in traditional methods, thus enhancing the recommendation diversity of Web APIs. In comparison, the proposed approach demonstrates improvements in evaluation indicators, with Recall, Precision, and NCDG enhanced by 4.17% to 16.05%, 4.46% to 16.62%, and 5.57% to 17.26%, respectively. Additionally, the diversity index ILS is reduced by 8.22% to 15.23%. The Recall and Precision values for cold-start Web API recommendation are 47.71% and 46.58% of those for non-cold-start Web API recommendation, respectively. Experimental results demonstrate that the proposed method not only enhances the quality of Web API recommendation but also yields favorable results for cold-start Web API recommendations.
The service descriptions provide limited information about application scenarios, creating a gap between Mashup service component Web API recommendations based on functional similarity calculation and desired expectations. Consequently, there is a need to enhance the accuracy of function matching. While some researchers utilize collaborative associations among Web APIs to enhance recommendation compatibility, they overlook the adverse effects of functional associations on Mashup service creation, thereby limiting the enhancement of recommendation diversity. To address this issue, this study proposes a Web API recommendation method for Mashup service components that integrates latent related words and heterogeneous association compatibility. The study extracts latent related words associated with application scenarios for both Mashup requirements and Web APIs, integrating them into the generation of function vectors. By enhancing the accuracy of functional similarity matching, it obtains a high-quality candidate set of Web API components. Function association and collaboration association are modeled as heterogeneous service association. The study utilizes heterogeneous association compatibility to replace collaboration compatibility in traditional methods, thus enhancing the recommendation diversity of Web APIs. In comparison, the proposed approach demonstrates improvements in evaluation indicators, with Recall, Precision, and NCDG enhanced by 4.17% to 16.05%, 4.46% to 16.62%, and 5.57% to 17.26%, respectively. Additionally, the diversity index ILS is reduced by 8.22% to 15.23%. The Recall and Precision values for cold-start Web API recommendation are 47.71% and 46.58% of those for non-cold-start Web API recommendation, respectively. Experimental results demonstrate that the proposed method not only enhances the quality of Web API recommendation but also yields favorable results for cold-start Web API recommendations.
Available online: September 04, 2024
, DOI: 10.13328/j.cnki.jos.007167
Abstract:
In recent years, research on the interconnect technology of superconducting qubits has made important progress, providing an effective way to build a distributed computing architecture for superconducting quantum computers. The distributed superconducting architecture imposes strict constraints on the execution of quantum circuits in terms of network topology, qubit connectivity, and quantum state transfer protocols. To execute and schedule quantum circuits on a distributed architecture, the circuit mapping process is required to transform the quantum circuits to adapt to the underlying architecture and then to distribute the transformed circuits to multiple QPUs. The distributed circuit mapping process necessitates the insertion of additional quantum operations into the original circuit. Such operations, especially the inter-QPU state transfer operations, are susceptible to noise, leading to high error rates. Therefore, minimizing the number of such additional operations inserted by the mapping process is critical to improving the overall computation success rate. This study constructs an abstract model of distributed quantum computing based on the technical features of the interconnect technology of superconducting qubits and today’s superconducting QPUs. Moreover, this study proposes a distributed quantum circuit mapping approach based on this abstract model. The proposed approach consists of two main components the distributed qubit mapping algorithm and the qubit state routing algorithm. The former formulates the problem of distributing qubits to different QPUs as a combinatorial optimization problem and employs simulated annealing enhanced with local search to find the initial mapping that brings the optimal total routing cost. The latter constructs several heuristic qubit routing rules for different scenarios and integrates them systematically to minimize the additional operations inserted by the mapping process. The abstract model shields any technical details of the underlying architecture that are irrelevant to circuit mapping, which makes the mapping method applicable to a class of such networks rather than a specific one. Moreover, the approach proposed in this study can be used as an ancillary tool to design and evaluate the network topology of distributed systems. The experimental results show that, compared to the baseline approach, the proposed approach reduces the number of intra-chip operations (SWAP gates) and inter-chip operations (ST gates) by 69.69% and 85.88% on average, respectively, with a time overhead similar to existing algorithms.
In recent years, research on the interconnect technology of superconducting qubits has made important progress, providing an effective way to build a distributed computing architecture for superconducting quantum computers. The distributed superconducting architecture imposes strict constraints on the execution of quantum circuits in terms of network topology, qubit connectivity, and quantum state transfer protocols. To execute and schedule quantum circuits on a distributed architecture, the circuit mapping process is required to transform the quantum circuits to adapt to the underlying architecture and then to distribute the transformed circuits to multiple QPUs. The distributed circuit mapping process necessitates the insertion of additional quantum operations into the original circuit. Such operations, especially the inter-QPU state transfer operations, are susceptible to noise, leading to high error rates. Therefore, minimizing the number of such additional operations inserted by the mapping process is critical to improving the overall computation success rate. This study constructs an abstract model of distributed quantum computing based on the technical features of the interconnect technology of superconducting qubits and today’s superconducting QPUs. Moreover, this study proposes a distributed quantum circuit mapping approach based on this abstract model. The proposed approach consists of two main components the distributed qubit mapping algorithm and the qubit state routing algorithm. The former formulates the problem of distributing qubits to different QPUs as a combinatorial optimization problem and employs simulated annealing enhanced with local search to find the initial mapping that brings the optimal total routing cost. The latter constructs several heuristic qubit routing rules for different scenarios and integrates them systematically to minimize the additional operations inserted by the mapping process. The abstract model shields any technical details of the underlying architecture that are irrelevant to circuit mapping, which makes the mapping method applicable to a class of such networks rather than a specific one. Moreover, the approach proposed in this study can be used as an ancillary tool to design and evaluate the network topology of distributed systems. The experimental results show that, compared to the baseline approach, the proposed approach reduces the number of intra-chip operations (SWAP gates) and inter-chip operations (ST gates) by 69.69% and 85.88% on average, respectively, with a time overhead similar to existing algorithms.
Available online: September 04, 2024
, DOI: 10.13328/j.cnki.jos.007178
Abstract:
Software traceability is considered critical to trustworthy software engineering, ensuring software reliability through the tracking of the software development process. Despite significant progress in automatic software traceability recovery techniques in recent years, their application in real-world commercial software projects does not meet expectations. An investigation into the application of learning-based software traceability recovery classifier models in commercial software projects is conducted. It uncovers three critical challenges faced in industrial settings. These challenges contribute to underperforming traceability models: low-quality raw data, data sparsity, and class imbalance. In response to these challenges, STRACE(AL+SSL) is proposed. It is a software traceability recovery framework that integrates active learning and semi-supervised learning. By strategically selecting valuable annotated samples and generating high-quality pseudo-labeled samples, STRACE(AL+SSL) effectively harnesses unlabeled data to address data-related challenges. Multiple comparative experiments are conducted with nearly one million issue-commit trace pair samples from 10 different enterprise projects. The results of these experiments validate the effectiveness of the proposed framework for real-world software traceability recovery tasks. The ablation results show that the unlabeled samples selected by the active learning in STRACE(AL+SSL) play a crucial role in the traceability recovery task. Additionally, the optimal combination of sample selection strategies in STRACE(AL+SSL) is confirmed. This includes CBST-Adjust for the semi-supervised sample rebalancing strategy and SMI_Flqmi, which is recognized for its cost-effectiveness and efficiency in active learning.
Software traceability is considered critical to trustworthy software engineering, ensuring software reliability through the tracking of the software development process. Despite significant progress in automatic software traceability recovery techniques in recent years, their application in real-world commercial software projects does not meet expectations. An investigation into the application of learning-based software traceability recovery classifier models in commercial software projects is conducted. It uncovers three critical challenges faced in industrial settings. These challenges contribute to underperforming traceability models: low-quality raw data, data sparsity, and class imbalance. In response to these challenges, STRACE(AL+SSL) is proposed. It is a software traceability recovery framework that integrates active learning and semi-supervised learning. By strategically selecting valuable annotated samples and generating high-quality pseudo-labeled samples, STRACE(AL+SSL) effectively harnesses unlabeled data to address data-related challenges. Multiple comparative experiments are conducted with nearly one million issue-commit trace pair samples from 10 different enterprise projects. The results of these experiments validate the effectiveness of the proposed framework for real-world software traceability recovery tasks. The ablation results show that the unlabeled samples selected by the active learning in STRACE(AL+SSL) play a crucial role in the traceability recovery task. Additionally, the optimal combination of sample selection strategies in STRACE(AL+SSL) is confirmed. This includes CBST-Adjust for the semi-supervised sample rebalancing strategy and SMI_Flqmi, which is recognized for its cost-effectiveness and efficiency in active learning.
Available online: August 28, 2024
, DOI: 10.13328/j.cnki.jos.007233
Abstract:
Domain adaptation (DA) is a group of machine learning tasks where the training set (source domain) and the test set (target domain) exhibit different distributions. Its key idea lies in how to overcome the negative impact given by these distributional differences, in other words, how to design an effective training strategy to obtain a classifier with high generalization performance by minimizing the difference between data domains. This study focuses on the tasks of unsupervised DA (UDA), where annotations are available in the source domain but absent in the target domain. This problem can be considered as how to use partially annotated data and unannotated data to train a classifier in a semi-supervised learning framework. Then, two kinds of semi-supervised learning techniques, namely pseudo labels (PLs) and consistent regularization (CR), are used to augment and annotate data in the observed domain for learning the classifier. Consequently, the classifier can obtain better generalization performance in the tasks of UDA. This study proposes augmentation-based UDA (A-UDA), in which the unannotated data in the target domain are augmented by random augmentation, and the high-confident data are annotated by adding pseudo-labels based on the predicted output of the model. The classifier is trained on the augmented data set. The distribution distance between the source domain and the target domain is calculated by using the maximum mean difference (MMD). By minimizing this distance, the classifier achieves high generalization performance. The proposed method is evaluated on multiple UDA tasks, including MNIST-USPS, Office-Home, and ImageCLEF-DA. Compared to other existing methods, it achieves better performance on these tasks.
Domain adaptation (DA) is a group of machine learning tasks where the training set (source domain) and the test set (target domain) exhibit different distributions. Its key idea lies in how to overcome the negative impact given by these distributional differences, in other words, how to design an effective training strategy to obtain a classifier with high generalization performance by minimizing the difference between data domains. This study focuses on the tasks of unsupervised DA (UDA), where annotations are available in the source domain but absent in the target domain. This problem can be considered as how to use partially annotated data and unannotated data to train a classifier in a semi-supervised learning framework. Then, two kinds of semi-supervised learning techniques, namely pseudo labels (PLs) and consistent regularization (CR), are used to augment and annotate data in the observed domain for learning the classifier. Consequently, the classifier can obtain better generalization performance in the tasks of UDA. This study proposes augmentation-based UDA (A-UDA), in which the unannotated data in the target domain are augmented by random augmentation, and the high-confident data are annotated by adding pseudo-labels based on the predicted output of the model. The classifier is trained on the augmented data set. The distribution distance between the source domain and the target domain is calculated by using the maximum mean difference (MMD). By minimizing this distance, the classifier achieves high generalization performance. The proposed method is evaluated on multiple UDA tasks, including MNIST-USPS, Office-Home, and ImageCLEF-DA. Compared to other existing methods, it achieves better performance on these tasks.
Available online: August 21, 2024
, DOI: 10.13328/j.cnki.jos.007230
Abstract:
As both Android frameworks and malware continue to evolve, the performance of existing malware classifiers degrades significantly over time. This study proposes droid slow aging (DroidSA), a method for Android malware detection based on API clustering and call graph optimization. Firstly, API clustering is performed before malware detection to generate cluster centers that reflect API functionality. To make clustering results more accurate, this study obtains embeddings fully reflecting the semantic similarity of APIs by designing API sentences to summarize vital features such as API names and permissions and using NLP tools to mine the semantic information of API sentences. Then, call graphs are extracted from apps and optimized by removing unknown methods while preserving the connectivity among API nodes. Call graph optimization enables detection methods to extract more robust contextual information of APIs which reflects the mode of app behavior. DroidSA extracts pairs of function calls from the optimized call graphs and abstracts the APIs in the pairs into cluster centers obtained in API clustering to better adjust to the changes in Android frameworks and malware. Finally, one-hot encoding is used to generate feature vectors, and the best-performing classifier is selected from random forests, support vector machines, and the k-nearest neighbors algorithm for malware detection. Experimental results demonstrate that DroidSA achieves an average F1-Measure of 96.7% for malware detection. Under the experimental setup where temporal bias is eliminated, DroidSA trained with apps from 2012 to 2013 achieves an average F1-Measure of 82.6% when detecting malware developed from 2014 to 2018. Compared with the state-of-the-art detection methods MaMaDroid and MalScan, DroidSA stably maintains high detection metrics with minimal impact from temporal changes and effectively detects evolved malware.
As both Android frameworks and malware continue to evolve, the performance of existing malware classifiers degrades significantly over time. This study proposes droid slow aging (DroidSA), a method for Android malware detection based on API clustering and call graph optimization. Firstly, API clustering is performed before malware detection to generate cluster centers that reflect API functionality. To make clustering results more accurate, this study obtains embeddings fully reflecting the semantic similarity of APIs by designing API sentences to summarize vital features such as API names and permissions and using NLP tools to mine the semantic information of API sentences. Then, call graphs are extracted from apps and optimized by removing unknown methods while preserving the connectivity among API nodes. Call graph optimization enables detection methods to extract more robust contextual information of APIs which reflects the mode of app behavior. DroidSA extracts pairs of function calls from the optimized call graphs and abstracts the APIs in the pairs into cluster centers obtained in API clustering to better adjust to the changes in Android frameworks and malware. Finally, one-hot encoding is used to generate feature vectors, and the best-performing classifier is selected from random forests, support vector machines, and the k-nearest neighbors algorithm for malware detection. Experimental results demonstrate that DroidSA achieves an average F1-Measure of 96.7% for malware detection. Under the experimental setup where temporal bias is eliminated, DroidSA trained with apps from 2012 to 2013 achieves an average F1-Measure of 82.6% when detecting malware developed from 2014 to 2018. Compared with the state-of-the-art detection methods MaMaDroid and MalScan, DroidSA stably maintains high detection metrics with minimal impact from temporal changes and effectively detects evolved malware.
Available online: August 21, 2024
, DOI: 10.13328/j.cnki.jos.007179
Abstract:
Local differential privacy (LDP) is widely used to collect and analyze sensitive data while protecting user privacy. However, it is vulnerable to data poisoning attacks by malicious users. The k-subset mechanism and the wheel mechanism are LDP schemes with optimal utility for frequency estimation. Yet, their resistance to data poisoning attacks lacks in-depth analysis and evaluation. Therefore, data poisoning attack methods are designed to assess the resistance to data poisoning attacks of both the k-subset mechanism and the wheel mechanism. First, the random perturbed-value attack and random item attack are discussed, and then the maximal gain attack methods against the k-subset mechanism and the wheel mechanism are constructed. The attack methods can be exploited to maximize the frequencies of target items selected by attackers, which is achieved by sending carefully crafted poisoning data to the data collector via fake users. Theoretically, the attack gains are rigorously analyzed and compared, and the effects of data poisoning attacks are experimentally evaluated, demonstrating their impact on the k-subset mechanism and the wheel mechanism. Finally, defensive measures are proposed to mitigate the effects of data poisoning attacks.
Local differential privacy (LDP) is widely used to collect and analyze sensitive data while protecting user privacy. However, it is vulnerable to data poisoning attacks by malicious users. The k-subset mechanism and the wheel mechanism are LDP schemes with optimal utility for frequency estimation. Yet, their resistance to data poisoning attacks lacks in-depth analysis and evaluation. Therefore, data poisoning attack methods are designed to assess the resistance to data poisoning attacks of both the k-subset mechanism and the wheel mechanism. First, the random perturbed-value attack and random item attack are discussed, and then the maximal gain attack methods against the k-subset mechanism and the wheel mechanism are constructed. The attack methods can be exploited to maximize the frequencies of target items selected by attackers, which is achieved by sending carefully crafted poisoning data to the data collector via fake users. Theoretically, the attack gains are rigorously analyzed and compared, and the effects of data poisoning attacks are experimentally evaluated, demonstrating their impact on the k-subset mechanism and the wheel mechanism. Finally, defensive measures are proposed to mitigate the effects of data poisoning attacks.
Available online: August 21, 2024
, DOI: 10.13328/j.cnki.jos.007180
Abstract:
The most significant feature of social network sentiment data is its dynamic nature. Tackling public sentiment drift analysis, this study proposes a Gaussian mixture based hierarchical variational auto-encoder (GHVAE) model for detecting sentiment drifts. Specifically, the GHVAE applies Gaussian mixture distribution as the prior assumption of latent distribution, which corresponds to the multi-center of the latent distribution property to improve model performances. Moreover, the built-in drift measurement algorithm in the original HVAE model is revised to enlarge the distances among big drift scores and improve the classification performance. Several contrast and ablation experiments are implemented to validate the performance of the GHVAE. The results indicate the novelties of the GHVAE bring improvement in sentiment drift detection.
The most significant feature of social network sentiment data is its dynamic nature. Tackling public sentiment drift analysis, this study proposes a Gaussian mixture based hierarchical variational auto-encoder (GHVAE) model for detecting sentiment drifts. Specifically, the GHVAE applies Gaussian mixture distribution as the prior assumption of latent distribution, which corresponds to the multi-center of the latent distribution property to improve model performances. Moreover, the built-in drift measurement algorithm in the original HVAE model is revised to enlarge the distances among big drift scores and improve the classification performance. Several contrast and ablation experiments are implemented to validate the performance of the GHVAE. The results indicate the novelties of the GHVAE bring improvement in sentiment drift detection.
Available online: August 21, 2024
, DOI: 10.13328/j.cnki.jos.007204
Abstract:
During the path coverage testing of a message passing interface (MPI) program based on evolutionary optimization, the fitness of evolutionary individuals needs to be evaluated by repeatedly executing the MPI program. However, repeated execution of an MPI program often requires high computational costs. Therefore, this study proposes an approach to generate test cases for path coverage of MPI programs guided by surrogate-assisted multi-task evolutionary optimization, which significantly reduces the actual execution times of MPI programs, thereby improving testing efficiency. Firstly, surrogate models are trained for each target sub-path in the target path of an MPI program. Then, the fitness of evolutionary individuals is estimated using the surrogate model corresponding to each target sub-path, and a candidate set of test cases is formed. Finally, all surrogate models are updated based on the candidate set and the actual fitness for each target sub-path. The proposed approach is applied to the basis path coverage testing of seven benchmark MPI programs and compared with several state-of-the-art approaches. The experimental results show that the proposed approach significantly improves testing efficiency while ensuring high effectiveness in generating test cases.
During the path coverage testing of a message passing interface (MPI) program based on evolutionary optimization, the fitness of evolutionary individuals needs to be evaluated by repeatedly executing the MPI program. However, repeated execution of an MPI program often requires high computational costs. Therefore, this study proposes an approach to generate test cases for path coverage of MPI programs guided by surrogate-assisted multi-task evolutionary optimization, which significantly reduces the actual execution times of MPI programs, thereby improving testing efficiency. Firstly, surrogate models are trained for each target sub-path in the target path of an MPI program. Then, the fitness of evolutionary individuals is estimated using the surrogate model corresponding to each target sub-path, and a candidate set of test cases is formed. Finally, all surrogate models are updated based on the candidate set and the actual fitness for each target sub-path. The proposed approach is applied to the basis path coverage testing of seven benchmark MPI programs and compared with several state-of-the-art approaches. The experimental results show that the proposed approach significantly improves testing efficiency while ensuring high effectiveness in generating test cases.
Available online: July 03, 2024
, DOI: 10.13328/j.cnki.jos.007228
Abstract:
In the field of software engineering, code repositories contain a wealth of knowledge resources, which can provide developers with examples of programming practices. If repetitive patterns, frequently occurring in source code, can be effectively extracted in the form of code templates, programming efficiency could be significantly improved. In current practice, developers often reuse existing solutions by searching through source code. However, this method typically generates a large number of similar and redundant results, increasing the burden of subsequent filtering. Moreover, template mining techniques based on cloned code often fail to cover extensive patterns constructed from dispersed small clones, thereby limiting the practicality of the templates. A method is proposed for extracting and retrieving code templates based on code clone detection. This method achieves more efficient function-level code template extraction by stitching together multiple fragment-level clones and extracting and aggregating the shared parts of method-level clones and addresses the issue of template quality. Based on the mined code templates, this study comes up with a triplet representation method for code structural features that effectively supplements plain text features, and implements an efficient and concise structural representation. In addition, this study presents a template feature retrieval method that combines structural and textual search to retrieve these templates by matching features of the programming context. The tool implemented based on this method, CodeSculptor, demonstrates its significant capability to extract high-quality code templates in a test against a codebase containing 45 high-quality Java open-source projects. The results show that the templates mined by the tool achieve an average code reduction of 60.87%, with 92.09% produced by stitching fragment-level clones, a proportion of templates that is not identifiable by traditional method. It proves the superior performance of the method in recognizing and constructing code templates. Furthermore, the accuracy of the top-5 search results in the code template search and recommendation is 96.87%. A preliminary case study on 9600 randomly selected templates reveals that most of the sampled code templates are complete and coherent in semantics, thus affirming their practicality. Nonetheless, there are a few meaningless templates, highlighting the future potential to refine the proposed template extraction strategy. The user research further shows that code development tasks can be done more efficiently with CodeSculptor.
In the field of software engineering, code repositories contain a wealth of knowledge resources, which can provide developers with examples of programming practices. If repetitive patterns, frequently occurring in source code, can be effectively extracted in the form of code templates, programming efficiency could be significantly improved. In current practice, developers often reuse existing solutions by searching through source code. However, this method typically generates a large number of similar and redundant results, increasing the burden of subsequent filtering. Moreover, template mining techniques based on cloned code often fail to cover extensive patterns constructed from dispersed small clones, thereby limiting the practicality of the templates. A method is proposed for extracting and retrieving code templates based on code clone detection. This method achieves more efficient function-level code template extraction by stitching together multiple fragment-level clones and extracting and aggregating the shared parts of method-level clones and addresses the issue of template quality. Based on the mined code templates, this study comes up with a triplet representation method for code structural features that effectively supplements plain text features, and implements an efficient and concise structural representation. In addition, this study presents a template feature retrieval method that combines structural and textual search to retrieve these templates by matching features of the programming context. The tool implemented based on this method, CodeSculptor, demonstrates its significant capability to extract high-quality code templates in a test against a codebase containing 45 high-quality Java open-source projects. The results show that the templates mined by the tool achieve an average code reduction of 60.87%, with 92.09% produced by stitching fragment-level clones, a proportion of templates that is not identifiable by traditional method. It proves the superior performance of the method in recognizing and constructing code templates. Furthermore, the accuracy of the top-5 search results in the code template search and recommendation is 96.87%. A preliminary case study on 9600 randomly selected templates reveals that most of the sampled code templates are complete and coherent in semantics, thus affirming their practicality. Nonetheless, there are a few meaningless templates, highlighting the future potential to refine the proposed template extraction strategy. The user research further shows that code development tasks can be done more efficiently with CodeSculptor.
Available online: July 03, 2024
, DOI: 10.13328/j.cnki.jos.007220
Abstract:
Learned indexes are assisting or gradually replacing traditional index structures due to their low memory usage and high query performance. However, the online retraining caused by data updates makes it unable to adapt to the scenario of frequent data updates. To avoid index reconstruction due to frequent data updates without significantly increasing memory consumption, this study proposes an adaptive update-distribution-aware learned index named DRAMA. It uses an LSM-Tree-like delayed learning method to actively learn the characteristics of the data update distribution, approximate fitting techniques to quickly establish the update-distribution model, a model merging strategy to replace the frequent retraining, and a hybrid compression technique to reduce the memory usage of model parameters in the index. The index is constructed and validated on both real and synthetic datasets. The results show that, compared to traditional indexes and state-of-the-art learned indexes, the proposed index can effectively reduce query delay in a data update environment without additional memory consumption.
Learned indexes are assisting or gradually replacing traditional index structures due to their low memory usage and high query performance. However, the online retraining caused by data updates makes it unable to adapt to the scenario of frequent data updates. To avoid index reconstruction due to frequent data updates without significantly increasing memory consumption, this study proposes an adaptive update-distribution-aware learned index named DRAMA. It uses an LSM-Tree-like delayed learning method to actively learn the characteristics of the data update distribution, approximate fitting techniques to quickly establish the update-distribution model, a model merging strategy to replace the frequent retraining, and a hybrid compression technique to reduce the memory usage of model parameters in the index. The index is constructed and validated on both real and synthetic datasets. The results show that, compared to traditional indexes and state-of-the-art learned indexes, the proposed index can effectively reduce query delay in a data update environment without additional memory consumption.
Available online: July 03, 2024
, DOI: 10.13328/j.cnki.jos.007226
Abstract:
When writing code, software developers often refer to code snippets that implement similar functions in the project. The code generation model shares similar features when generating code fragments and uses the code context provided in the input as a reference. The code completion technology based on retrieval augmentation is akin to this idea. The external code retrieved from the retrieval library is used as additional context information to prompt the generation model so as to complete the unfinished code fragments. The existing code completion method based on retrieval augmentation directly splices the input code and retrieval results together as the input of the generated model. This method brings a risk that the retrieved code fragments may not prompt the model, but mislead the model, resulting in inaccurate or irrelevant code results. In addition, whether the retrieved external code is completely related to the input code or not, it will be spliced with the input code and input to the model. Consequently, the effect of this method largely depends on the accuracy of the code retrieval stage. If the available code fragments cannot be returned in the retrieval phase, the subsequent code completion effect may also be affected. An empirical study is conducted on the retrieval augmentation strategies in the existing code completion methods. Through qualitative and quantitative experiments, the impact of each stage of retrieval augmentation on the code completion effect is analyzed. The empirical study focuses on identifying three factors for the effect of retrieval augmentation, namely, code granularity, code retrieval methods, and post-processing methods. Based on the conclusion of the empirical research, an improved method is designed, and a code completion method MAGIC (multi-stage optimization for retrieval augmented code completion) is proposed to improve the retrieval augmentation by optimizing the code retrieval strategy in stages. The improved strategies such as code segmentation, retrieval-reranking, and template prompt generation are designed to effectively enhance the auxiliary generation effect of the code retrieval module on the code completion model. Meanwhile, those strategies can also reduce the interference of irrelevant code in the code generation phase of the model and improve the quality of generated code. The experimental results on the Java code dataset show that, compared with the existing code completion methods based on retrieval augmentation, this method increases the editing similarity and perfect matching index by 6.76% and 7.81%, respectively. Compared with the large code model with 6B parameters, this method can save 94.5% of the video memory and 73.8% of the inference time, and improve the editing similarity and complete matching index by 5.62% and 4.66% respectively.
When writing code, software developers often refer to code snippets that implement similar functions in the project. The code generation model shares similar features when generating code fragments and uses the code context provided in the input as a reference. The code completion technology based on retrieval augmentation is akin to this idea. The external code retrieved from the retrieval library is used as additional context information to prompt the generation model so as to complete the unfinished code fragments. The existing code completion method based on retrieval augmentation directly splices the input code and retrieval results together as the input of the generated model. This method brings a risk that the retrieved code fragments may not prompt the model, but mislead the model, resulting in inaccurate or irrelevant code results. In addition, whether the retrieved external code is completely related to the input code or not, it will be spliced with the input code and input to the model. Consequently, the effect of this method largely depends on the accuracy of the code retrieval stage. If the available code fragments cannot be returned in the retrieval phase, the subsequent code completion effect may also be affected. An empirical study is conducted on the retrieval augmentation strategies in the existing code completion methods. Through qualitative and quantitative experiments, the impact of each stage of retrieval augmentation on the code completion effect is analyzed. The empirical study focuses on identifying three factors for the effect of retrieval augmentation, namely, code granularity, code retrieval methods, and post-processing methods. Based on the conclusion of the empirical research, an improved method is designed, and a code completion method MAGIC (multi-stage optimization for retrieval augmented code completion) is proposed to improve the retrieval augmentation by optimizing the code retrieval strategy in stages. The improved strategies such as code segmentation, retrieval-reranking, and template prompt generation are designed to effectively enhance the auxiliary generation effect of the code retrieval module on the code completion model. Meanwhile, those strategies can also reduce the interference of irrelevant code in the code generation phase of the model and improve the quality of generated code. The experimental results on the Java code dataset show that, compared with the existing code completion methods based on retrieval augmentation, this method increases the editing similarity and perfect matching index by 6.76% and 7.81%, respectively. Compared with the large code model with 6B parameters, this method can save 94.5% of the video memory and 73.8% of the inference time, and improve the editing similarity and complete matching index by 5.62% and 4.66% respectively.
Available online: July 03, 2024
, DOI: 10.13328/j.cnki.jos.007227
Abstract:
In the field of model-based diagnosis, all minimal hitting sets (MHSs) of minimal conflict sets (MCSs) are the candidate diagnoses of the device to be diagnosed, so the calculation of MHS is a key step for generating candidate diagnoses. MHS is a classic NP-hard constraint solving problem. The bigger the problems get, the harder it becomes exponentially to solve them. Boolean algorithm is typical in calculating MHS. However, in the process of solving, most of the runtime is taken up by the minimization of the intermediate solution sets. This study proposes BWSS (Boolean with suspicious sets) algorithm combined with suspicious set clusters for calculating MHS. By analyzing the spanning tree rule of Boolean algorithm in depth, the set that causes the candidate solution to become a superset is found. When extending elements to the root node, the candidate solution, if discovered to share at least one empty set with the suspicious set cluster, shall be minimal. Otherwise, the solution will be removed. The recursive strategy will be employed to ensure that all and only MHS are generated at the end of the algorithm. In addition, each candidate solution has at least m (m≥1) elements or even the entire solution in no need of complex minimization. Theoretically, BWSS algorithm is far less complex than Boolean algorithm. According to random data and mass reference circuit data, experimental results show that compared with many other state-of-the-art methods, the proposed algorithm reduces several orders of magnitude in runtime.
In the field of model-based diagnosis, all minimal hitting sets (MHSs) of minimal conflict sets (MCSs) are the candidate diagnoses of the device to be diagnosed, so the calculation of MHS is a key step for generating candidate diagnoses. MHS is a classic NP-hard constraint solving problem. The bigger the problems get, the harder it becomes exponentially to solve them. Boolean algorithm is typical in calculating MHS. However, in the process of solving, most of the runtime is taken up by the minimization of the intermediate solution sets. This study proposes BWSS (Boolean with suspicious sets) algorithm combined with suspicious set clusters for calculating MHS. By analyzing the spanning tree rule of Boolean algorithm in depth, the set that causes the candidate solution to become a superset is found. When extending elements to the root node, the candidate solution, if discovered to share at least one empty set with the suspicious set cluster, shall be minimal. Otherwise, the solution will be removed. The recursive strategy will be employed to ensure that all and only MHS are generated at the end of the algorithm. In addition, each candidate solution has at least m (m≥1) elements or even the entire solution in no need of complex minimization. Theoretically, BWSS algorithm is far less complex than Boolean algorithm. According to random data and mass reference circuit data, experimental results show that compared with many other state-of-the-art methods, the proposed algorithm reduces several orders of magnitude in runtime.
Available online: July 03, 2024
, DOI: 10.13328/j.cnki.jos.007221
Abstract:
A cuckoo filter is a space-efficient approximate membership query data structure, widely used in network systems for applications such as network routing, network measurement, and network caching. However, the traditional design of cuckoo filters has not adequately considered the scenario in network systems where some or all queries in the collection are known, and these queries come with associated costs. This limitation results in the suboptimal performance of existing cuckoo filters in such situations. To address this, the variable hashing-fingerprint cuckoo filter (VHCF) has been developed. VHCF introduces variable fingerprint hashing technology, taking into account the known query collection and their associated costs. By searching for the optimal fingerprint hash function for each hash bucket, the overall cost of false positives is significantly reduced. In addition, this study proposes a single-hash technology to reduce the additional computational overhead caused by the variable-hash technology. A theoretical analysis of the operational complexity and false positive rate of VHCF is also provided. Finally, experimental and theoretical results both demonstrate that VHCF achieves a significantly lower false positive rate than existing cuckoo filters and their variants while ensuring comparable query throughput. Specifically, VHCF only needs to allocate 1–2 bits for each hash index unit, which can reduce the false positive rate to 12.5%–50% of the original.
A cuckoo filter is a space-efficient approximate membership query data structure, widely used in network systems for applications such as network routing, network measurement, and network caching. However, the traditional design of cuckoo filters has not adequately considered the scenario in network systems where some or all queries in the collection are known, and these queries come with associated costs. This limitation results in the suboptimal performance of existing cuckoo filters in such situations. To address this, the variable hashing-fingerprint cuckoo filter (VHCF) has been developed. VHCF introduces variable fingerprint hashing technology, taking into account the known query collection and their associated costs. By searching for the optimal fingerprint hash function for each hash bucket, the overall cost of false positives is significantly reduced. In addition, this study proposes a single-hash technology to reduce the additional computational overhead caused by the variable-hash technology. A theoretical analysis of the operational complexity and false positive rate of VHCF is also provided. Finally, experimental and theoretical results both demonstrate that VHCF achieves a significantly lower false positive rate than existing cuckoo filters and their variants while ensuring comparable query throughput. Specifically, VHCF only needs to allocate 1–2 bits for each hash index unit, which can reduce the false positive rate to 12.5%–50% of the original.
Available online: July 03, 2024
, DOI: 10.13328/j.cnki.jos.007222
Abstract:
Smart contracts are scripts running on the Ethereum blockchain capable of handling intricate business logic with most written in the Solidity. As security concerns surrounding smart contracts intensify, a formal verification method employing the modeling, simulation, and verification language (MSVL) alongside propositional projection temporal logic (PPTL) is proposed. A SOL2M converter is developed, facilitating semi-automatic modeling from the Solidity to MSVL programs. However, the proof of operational semantic equivalence of Solidity and MSVL is lacking. This study initially defines Solidity’s operational semantics using big-step semantics across four levels: semantic elements, evaluation rules, expressions, and statements. Subsequently, it establishes equivalence relations between states, expressions, and statements in Solidity and MSVL. In addition, leveraging the operational semantics of both languages, it employs structural induction to prove expression equivalence and rule induction to establish statement equivalence.
Smart contracts are scripts running on the Ethereum blockchain capable of handling intricate business logic with most written in the Solidity. As security concerns surrounding smart contracts intensify, a formal verification method employing the modeling, simulation, and verification language (MSVL) alongside propositional projection temporal logic (PPTL) is proposed. A SOL2M converter is developed, facilitating semi-automatic modeling from the Solidity to MSVL programs. However, the proof of operational semantic equivalence of Solidity and MSVL is lacking. This study initially defines Solidity’s operational semantics using big-step semantics across four levels: semantic elements, evaluation rules, expressions, and statements. Subsequently, it establishes equivalence relations between states, expressions, and statements in Solidity and MSVL. In addition, leveraging the operational semantics of both languages, it employs structural induction to prove expression equivalence and rule induction to establish statement equivalence.
Available online: June 20, 2024
, DOI: 10.13328/j.cnki.jos.007199
Abstract:
Social media text summarization aims to provide concise summaries for large-scale social media short texts (referred to as posts) targeting specific topics. Given the brief and informal contents of posts, traditional methods confront the challenges of sparse features and insufficient information. Recent research endeavors have leveraged social relationships among posts to refine post contents and remove redundant information, but these efforts neglect the presence of unreliable noise relationships in real social media contexts, leading to erroneous assessments of post importance and diversity. Therefore, this study proposes a novel unsupervised model DSNSum, which improves summarization performance by removing noise relationships in the social networks. Firstly, the noise relationships in real social relationship networks are statistically verified. Secondly, two noise functions are designed based on sociological theories, and a denoising graph auto-encoder (DGAE) is constructed to mitigate the influence of noise relationships and cultivate post contents of credible social relationships. Finally, a sparse reconstruction framework is utilized to select posts that maintain coverage, importance, and diversity to form a summary of a certain length. Experimental results on a total of 22 topics from two real social media platforms (Twitter and Sina Weibo) demonstrate the efficacy of the proposed model and provide new insights for subsequent research in related fields.
Social media text summarization aims to provide concise summaries for large-scale social media short texts (referred to as posts) targeting specific topics. Given the brief and informal contents of posts, traditional methods confront the challenges of sparse features and insufficient information. Recent research endeavors have leveraged social relationships among posts to refine post contents and remove redundant information, but these efforts neglect the presence of unreliable noise relationships in real social media contexts, leading to erroneous assessments of post importance and diversity. Therefore, this study proposes a novel unsupervised model DSNSum, which improves summarization performance by removing noise relationships in the social networks. Firstly, the noise relationships in real social relationship networks are statistically verified. Secondly, two noise functions are designed based on sociological theories, and a denoising graph auto-encoder (DGAE) is constructed to mitigate the influence of noise relationships and cultivate post contents of credible social relationships. Finally, a sparse reconstruction framework is utilized to select posts that maintain coverage, importance, and diversity to form a summary of a certain length. Experimental results on a total of 22 topics from two real social media platforms (Twitter and Sina Weibo) demonstrate the efficacy of the proposed model and provide new insights for subsequent research in related fields.
Available online: June 20, 2024
, DOI: 10.13328/j.cnki.jos.007200
Abstract:
As the human pose estimation (HPE) method based on graph convolutional network (GCN) cannot sufficiently aggregate spatiotemporal features of skeleton joints and restrict discriminative features extraction, in this paper, a parallel multi-scale spatio-temporal graph convolutional network (PMST-GNet) model is built to improve the performance of 3D HPE. Firstly, a diagonally dominant spatiotemporal attention graph convolutional layer (DDA-STGConv) is designed to construct a cross-domain spatiotemporal adjacency matrix and model the joint features based on self-constraint and attention mechanism constrain, therefore enhancing information interaction among nodes. Then, a graph topology aggregation function is devised to construct different graph topologies, and a parallel multi-scale sub-graph network module (PM-SubGNet) is constructed with DDA-STGConv as the basic unit. Finally, a multi-scale feature cross fusion block (MFEB) is designed, by which multi-scale information among PM-SubGNets can interact to improve the feature representation of GCN, therefore better extracting the context information of skeleton joints. The experimental results on the mainstream 3D HPE datasets Human3.6M and MPI-INF-3DHP show that the proposed PMST-GNet model has a good effect in 3D HPE and is superior to the current mainstream GCN-based algorithms such as Sem-GCN, GraphSH, and UGCN.
As the human pose estimation (HPE) method based on graph convolutional network (GCN) cannot sufficiently aggregate spatiotemporal features of skeleton joints and restrict discriminative features extraction, in this paper, a parallel multi-scale spatio-temporal graph convolutional network (PMST-GNet) model is built to improve the performance of 3D HPE. Firstly, a diagonally dominant spatiotemporal attention graph convolutional layer (DDA-STGConv) is designed to construct a cross-domain spatiotemporal adjacency matrix and model the joint features based on self-constraint and attention mechanism constrain, therefore enhancing information interaction among nodes. Then, a graph topology aggregation function is devised to construct different graph topologies, and a parallel multi-scale sub-graph network module (PM-SubGNet) is constructed with DDA-STGConv as the basic unit. Finally, a multi-scale feature cross fusion block (MFEB) is designed, by which multi-scale information among PM-SubGNets can interact to improve the feature representation of GCN, therefore better extracting the context information of skeleton joints. The experimental results on the mainstream 3D HPE datasets Human3.6M and MPI-INF-3DHP show that the proposed PMST-GNet model has a good effect in 3D HPE and is superior to the current mainstream GCN-based algorithms such as Sem-GCN, GraphSH, and UGCN.
Available online: June 20, 2024
, DOI: 10.13328/j.cnki.jos.007203
Abstract:
As too many redundant events included in crash test sequences generated by Android automated test tools may result in test replay, defect comprehension, and repairing difficulty, a great number of test sequence reduction works have been proposed. While current works only focus on the application interface changes and ignore the internal state changes during program execution. Moreover, current works only model application states at a single and abstract granularity, such as control widget granularity or activity granularity, resulting in long test sequences after reduction or inefficient reduction. This study proposes an Android test sequence reduction method combined with multi-granularity based on event labeling. By taking into account the Android lifecycle management mechanism and data flow analysis to label critical events that trigger crashes, this method can narrow the sequence reduction space and design a strategy of rough selection under low granularity and detailed reduction under high granularity. At last, a crash test sequence set containing complex scenarios such as inter-application interaction and user input is collected, and the comparison with other test sequence reduction works on this set verifies the effectiveness of the method proposed in this study.
As too many redundant events included in crash test sequences generated by Android automated test tools may result in test replay, defect comprehension, and repairing difficulty, a great number of test sequence reduction works have been proposed. While current works only focus on the application interface changes and ignore the internal state changes during program execution. Moreover, current works only model application states at a single and abstract granularity, such as control widget granularity or activity granularity, resulting in long test sequences after reduction or inefficient reduction. This study proposes an Android test sequence reduction method combined with multi-granularity based on event labeling. By taking into account the Android lifecycle management mechanism and data flow analysis to label critical events that trigger crashes, this method can narrow the sequence reduction space and design a strategy of rough selection under low granularity and detailed reduction under high granularity. At last, a crash test sequence set containing complex scenarios such as inter-application interaction and user input is collected, and the comparison with other test sequence reduction works on this set verifies the effectiveness of the method proposed in this study.
Available online: June 20, 2024
, DOI: 10.13328/j.cnki.jos.007206
Abstract:
In the task of numerical question-answering with texts and tables, the models are required to perform numerical reasoning based on given texts and tables. The goal is to generate a computational program consisting of multi-step numerical calculations, and the program’s results are used as the answer to the question. To model the texts and tables, the current work linearizes the table into a series of cell sentences through templates and then designs a generator based on the texts and cell sentences to produce the computational program. However, this approach faces a specific problem: the differences between cell sentences generated by templates are minimal, making it difficult for the generator to distinguish between cell sentences that are essential for answering the question (supporting cell sentences) and those irrelevant to the question (distracting cell sentences). Ultimately, the model generates incorrect computational programs based on distracting cell sentences. To tackle this issue, this study proposes an approach called multi-granularity cell semantic contrast (MGCC) for our generator. The main purpose of this approach is to enhance the representation distances between supporting and distracting cell sentences, thereby helping the generator differentiate between them. Specifically, this contrast mechanism is composed of coarse-grained cell semantic contrasts and fine-grained constituent element contrasts, including contrasts in row names, column names, and cell values. The experimental results validate that the proposed MGCC approach enables the generator to achieve better performance than the benchmark model on the FinQA and MultiHiertt numerical reasoning datasets. On the FinQA dataset, it leads to an improvement of up to 3.38% in answer accuracy. Notably, on the more challenging hierarchical table dataset MultiHiertt, it yields a 7.8% increase in the accuracy of the generator. Compared with GPT-3 combined with chain of chain of thought (CoT), MGCC results in respective improvements of 5.44% and 1.69% on the FinQA and MultiHiertt datasets. The subsequent analytical experiments further verify that the multi-granularity cell semantic contrast approach contributes to the model’s improved differentiation between supporting and distracting cell sentences.
In the task of numerical question-answering with texts and tables, the models are required to perform numerical reasoning based on given texts and tables. The goal is to generate a computational program consisting of multi-step numerical calculations, and the program’s results are used as the answer to the question. To model the texts and tables, the current work linearizes the table into a series of cell sentences through templates and then designs a generator based on the texts and cell sentences to produce the computational program. However, this approach faces a specific problem: the differences between cell sentences generated by templates are minimal, making it difficult for the generator to distinguish between cell sentences that are essential for answering the question (supporting cell sentences) and those irrelevant to the question (distracting cell sentences). Ultimately, the model generates incorrect computational programs based on distracting cell sentences. To tackle this issue, this study proposes an approach called multi-granularity cell semantic contrast (MGCC) for our generator. The main purpose of this approach is to enhance the representation distances between supporting and distracting cell sentences, thereby helping the generator differentiate between them. Specifically, this contrast mechanism is composed of coarse-grained cell semantic contrasts and fine-grained constituent element contrasts, including contrasts in row names, column names, and cell values. The experimental results validate that the proposed MGCC approach enables the generator to achieve better performance than the benchmark model on the FinQA and MultiHiertt numerical reasoning datasets. On the FinQA dataset, it leads to an improvement of up to 3.38% in answer accuracy. Notably, on the more challenging hierarchical table dataset MultiHiertt, it yields a 7.8% increase in the accuracy of the generator. Compared with GPT-3 combined with chain of chain of thought (CoT), MGCC results in respective improvements of 5.44% and 1.69% on the FinQA and MultiHiertt datasets. The subsequent analytical experiments further verify that the multi-granularity cell semantic contrast approach contributes to the model’s improved differentiation between supporting and distracting cell sentences.
Available online: June 20, 2024
, DOI: 10.13328/j.cnki.jos.007207
Abstract:
The interactions between elements in contemporary software systems are notably intricate, encompassing relationships between packages, classes, and functions. Accurate comprehension of these relationships is pivotal for optimizing system structures and enhancing software quality. Analyzing inter-package relationships can help unveil dependencies between modules, thereby assisting developers in more effectively managing and organizing software architectures. On the other hand, a clear understanding of inter-class relationships contributes to the creation of code repositories that are more scalable and maintainable. Moreover, a clear understanding of inter-function relationships facilitates rapid identification and resolution of logical errors within programs, consequently enhancing the robustness and reliability of the software. However, current predictions of software system interaction confront challenges such as granularity disparities, inadequate features, and version changes. To address this challenge, this study constructs corresponding software network models based on the three granularities, including software packages, classes, and functions. It introduces a novel approach combining local and global features to reinforce the analysis and prediction of software systems through feature extraction and link prediction of software networks. This approach is based on the construction and handling of software networks, involving specific steps such as leveraging the node2vec method to learn local features of software networks and combining Laplacian feature vector encoding to comprehensively represent the global positional information of nodes. Subsequently, the Graph Transformer model is employed to further optimize the feature vectors of node attributes, culminating in the completion of the interaction prediction task of the software system. Extensive experimental validations are conducted on three Java open-source projects, encompassing within-version and cross-version interaction prediction tasks. The experimental results demonstrate that, compared to benchmark methods, the proposed approach achieves an average increase of 8.2% and 8.5% in AUC and AP values, respectively in within-version prediction tasks. This approach reaches an average rise of 3.5% and 2.4% in AUC and AP values, respectively, in cross-version prediction tasks.
The interactions between elements in contemporary software systems are notably intricate, encompassing relationships between packages, classes, and functions. Accurate comprehension of these relationships is pivotal for optimizing system structures and enhancing software quality. Analyzing inter-package relationships can help unveil dependencies between modules, thereby assisting developers in more effectively managing and organizing software architectures. On the other hand, a clear understanding of inter-class relationships contributes to the creation of code repositories that are more scalable and maintainable. Moreover, a clear understanding of inter-function relationships facilitates rapid identification and resolution of logical errors within programs, consequently enhancing the robustness and reliability of the software. However, current predictions of software system interaction confront challenges such as granularity disparities, inadequate features, and version changes. To address this challenge, this study constructs corresponding software network models based on the three granularities, including software packages, classes, and functions. It introduces a novel approach combining local and global features to reinforce the analysis and prediction of software systems through feature extraction and link prediction of software networks. This approach is based on the construction and handling of software networks, involving specific steps such as leveraging the node2vec method to learn local features of software networks and combining Laplacian feature vector encoding to comprehensively represent the global positional information of nodes. Subsequently, the Graph Transformer model is employed to further optimize the feature vectors of node attributes, culminating in the completion of the interaction prediction task of the software system. Extensive experimental validations are conducted on three Java open-source projects, encompassing within-version and cross-version interaction prediction tasks. The experimental results demonstrate that, compared to benchmark methods, the proposed approach achieves an average increase of 8.2% and 8.5% in AUC and AP values, respectively in within-version prediction tasks. This approach reaches an average rise of 3.5% and 2.4% in AUC and AP values, respectively, in cross-version prediction tasks.
Available online: June 20, 2024
, DOI: 10.13328/j.cnki.jos.007209
Abstract:
In this study, the problem of mining cluster frequent patterns in time-ordered transaction data is discussed for the first time. To deal with redundant operations when the Naive algorithm solves this problem, the improved cluster frequent pattern mining (ICFPM) algorithm is proposed. The algorithm uses two optimization strategies. On the one hand, it can use the defined parameter minCF to effectively reduce the search space of mining results; on the other hand, it can refer to the discriminative results of (n–1)-itemsets to accelerate the discriminative process of cluster frequent n-itemset. The algorithm also applies the ICFPM-list structure to reduce the overhead of the candidate n-itemsets construction. Simulation experiments based on two real-world datasets demonstrate the effectiveness of the ICFPM algorithm. Compared with the Naive algorithm, the ICFPM algorithm improves substantially in terms of time and space efficiency, which makes it an effective method for solving clustered frequent pattern mining.
In this study, the problem of mining cluster frequent patterns in time-ordered transaction data is discussed for the first time. To deal with redundant operations when the Naive algorithm solves this problem, the improved cluster frequent pattern mining (ICFPM) algorithm is proposed. The algorithm uses two optimization strategies. On the one hand, it can use the defined parameter minCF to effectively reduce the search space of mining results; on the other hand, it can refer to the discriminative results of (n–1)-itemsets to accelerate the discriminative process of cluster frequent n-itemset. The algorithm also applies the ICFPM-list structure to reduce the overhead of the candidate n-itemsets construction. Simulation experiments based on two real-world datasets demonstrate the effectiveness of the ICFPM algorithm. Compared with the Naive algorithm, the ICFPM algorithm improves substantially in terms of time and space efficiency, which makes it an effective method for solving clustered frequent pattern mining.
Available online: June 20, 2024
, DOI: 10.13328/j.cnki.jos.007210
Abstract:
DEFAULT, a new lightweight cryptosystem presented at Asiacrypt in 2021, is designed to protect the information security of Internet of Things (IoT) devices, such as microchips, microcontrollers, and sensors. Based on the ciphertext-only attack assumption, the statistical fault analysis of the DEFAULT cipher with the algebraic relationship is proposed. The statistical fault analysis uses the random nibble-oriented fault model. It not only combines statistical distributions of the intermediate states before and after the fault injections but also takes advantage of the algebraic relationship and novel distinguishers, including Anderson Darling test-square Euclidean imbalance (AD-SEI), Anderson Darling test-maximum likelihood estimate (AD-MLE), and Anderson Darling test-Hamming weight (AD-HW). The analysis requires at least 1344 faults to achieve the reliability of 99% in the recovery of the 128-bit secret key of DEFAULT. The theoretical analysis and experimental results show that the DEFAULT lightweight cryptosystem is not resistant to the statistical fault attack based on the algebraic relationship. This study provides an important reference for the security analysis of the other lightweight cryptosystems.
DEFAULT, a new lightweight cryptosystem presented at Asiacrypt in 2021, is designed to protect the information security of Internet of Things (IoT) devices, such as microchips, microcontrollers, and sensors. Based on the ciphertext-only attack assumption, the statistical fault analysis of the DEFAULT cipher with the algebraic relationship is proposed. The statistical fault analysis uses the random nibble-oriented fault model. It not only combines statistical distributions of the intermediate states before and after the fault injections but also takes advantage of the algebraic relationship and novel distinguishers, including Anderson Darling test-square Euclidean imbalance (AD-SEI), Anderson Darling test-maximum likelihood estimate (AD-MLE), and Anderson Darling test-Hamming weight (AD-HW). The analysis requires at least 1344 faults to achieve the reliability of 99% in the recovery of the 128-bit secret key of DEFAULT. The theoretical analysis and experimental results show that the DEFAULT lightweight cryptosystem is not resistant to the statistical fault attack based on the algebraic relationship. This study provides an important reference for the security analysis of the other lightweight cryptosystems.
Available online: June 20, 2024
, DOI: 10.13328/j.cnki.jos.007211
Abstract:
In real-world scenarios, rich interaction relationships often exist among users on different platforms such as e-commerce, consumer reviews, and social networks. Constructing these relationships into a graph structure and applying graph neural network (GNN) for malicious user detection has become a research trend in related fields in recent years. However, due to the small proportion of malicious users, as well as their disguises and high labeling costs, traditional GNN methods are limited by problems suchas data imbalance, data inconsistency, and label scarcity. This study proposes a semi-supervised graph representation learning-based method for detecting malicious nodes. The method improves the GNN method for node representation learning and classification. Specifically, a class-aware malicious node detection (CAMD) method is constructed, which introduces a class-aware attention mechanism, inconsistent GNN encoders, and class-aware imbalance loss functions to solve the problems of data inconsistency and imbalance. Furthermore, to address the limitation of CAMD in detecting malicious nodes with scarce labels, a graph contrastive learning-based method, CAMD+, is proposed. CAMD+ introduces data augmentation, self-supervised graph contrastive learning, and class-aware graph contrastive learning to enable the model to learn more information from unlabeled data and fully utilize scarce label information. Finally, a large number of experimental results on real-world datasets verify that the proposed methods outperform all baseline methods and demonstrate good detection performance in situations with different degrees of label scarcity.
In real-world scenarios, rich interaction relationships often exist among users on different platforms such as e-commerce, consumer reviews, and social networks. Constructing these relationships into a graph structure and applying graph neural network (GNN) for malicious user detection has become a research trend in related fields in recent years. However, due to the small proportion of malicious users, as well as their disguises and high labeling costs, traditional GNN methods are limited by problems suchas data imbalance, data inconsistency, and label scarcity. This study proposes a semi-supervised graph representation learning-based method for detecting malicious nodes. The method improves the GNN method for node representation learning and classification. Specifically, a class-aware malicious node detection (CAMD) method is constructed, which introduces a class-aware attention mechanism, inconsistent GNN encoders, and class-aware imbalance loss functions to solve the problems of data inconsistency and imbalance. Furthermore, to address the limitation of CAMD in detecting malicious nodes with scarce labels, a graph contrastive learning-based method, CAMD+, is proposed. CAMD+ introduces data augmentation, self-supervised graph contrastive learning, and class-aware graph contrastive learning to enable the model to learn more information from unlabeled data and fully utilize scarce label information. Finally, a large number of experimental results on real-world datasets verify that the proposed methods outperform all baseline methods and demonstrate good detection performance in situations with different degrees of label scarcity.
Available online: June 20, 2024
, DOI: 10.13328/j.cnki.jos.007214
Abstract:
The cuckoo filter is an efficient probabilistic data structure that can quickly determine whether an element exists in a given set. The cuckoo filter is widely used in computer networks, IoT applications, and database systems. These systems usually involve the handling of massive amounts of data and numerous concurrent requests in practice. A cuckoo filter that supports high concurrency can significantly improve system throughput and data processing capabilities, which is crucial to system performance enhancement. Therefore, a cuckoo filter that supports lock-free concurrency is designed. The filter achieves high-performance lookup, insertion, and deletion through the two-stage query, separation of path exploration and element migration, and atomic migration based on multi-word compare-and-swap. Theoretical analysis and experimental results indicate that the lock-free concurrent cuckoo filter significantly improves the concurrent performance of the most cutting-edge algorithms in current times. The lookup throughput of a lock-free concurrent cuckoo filter is on average 1.94 times that of a cuckoo filter using fine-grained locks.
The cuckoo filter is an efficient probabilistic data structure that can quickly determine whether an element exists in a given set. The cuckoo filter is widely used in computer networks, IoT applications, and database systems. These systems usually involve the handling of massive amounts of data and numerous concurrent requests in practice. A cuckoo filter that supports high concurrency can significantly improve system throughput and data processing capabilities, which is crucial to system performance enhancement. Therefore, a cuckoo filter that supports lock-free concurrency is designed. The filter achieves high-performance lookup, insertion, and deletion through the two-stage query, separation of path exploration and element migration, and atomic migration based on multi-word compare-and-swap. Theoretical analysis and experimental results indicate that the lock-free concurrent cuckoo filter significantly improves the concurrent performance of the most cutting-edge algorithms in current times. The lookup throughput of a lock-free concurrent cuckoo filter is on average 1.94 times that of a cuckoo filter using fine-grained locks.
Available online: June 14, 2024
, DOI: 10.13328/j.cnki.jos.007198
Abstract:
Persistent memory (PM), serving as a supplement and potential replacement for main memory, offers a lower cost for data storage while ensuring data persistence. However, traditional index structures tailored for PM like B+ trees fail to fully exploit the distribution characteristics of data for optimizing reading and writing performance on PM. Recent research endeavors have sought to enhance indexes’ reading and writing performance on PM and support index persistence through the data distribution awareness of learning indexes. Nonetheless, existing designs of persistent learning index structures suffer from additional PM accesses and poor performance when confronted with real-world data. To address the performance degradation of persistent learning indexes in the face of real data distributions, this study proposes a learning index PLTree, a DRAM/PM hybrid architecture. PLTree optimizes reading and writing performance under real data distributions through the following approaches: (1) a two-stage approach to construct the index, eliminating last-mile search in internal nodes and reducing the access of PM, (2) model-based search for efficient query performance on PM and accelerated query by leveraging metadata in DRAM, and (3) a log-based hierarchical overflow buffer structure tailored to PM characteristics to optimize writing performance. The results show that, compared with the existing persistent memory indexes (APEX, FPTree, uTree, NBTree, and DPTree), PLTree achieves significantly better performance in index construction 1.9× to 34× across various datasets. In single-threaded scenarios, PLTree exhibits an average query and insertion performance improvement of 1.26× to 4.45× and 2.63× to 6.83×, respectively. In multi-threaded scenarios, PLTree surpasses the baseline by up to 10.2× and 23.7× in query and insertion performance, respectively.
Persistent memory (PM), serving as a supplement and potential replacement for main memory, offers a lower cost for data storage while ensuring data persistence. However, traditional index structures tailored for PM like B+ trees fail to fully exploit the distribution characteristics of data for optimizing reading and writing performance on PM. Recent research endeavors have sought to enhance indexes’ reading and writing performance on PM and support index persistence through the data distribution awareness of learning indexes. Nonetheless, existing designs of persistent learning index structures suffer from additional PM accesses and poor performance when confronted with real-world data. To address the performance degradation of persistent learning indexes in the face of real data distributions, this study proposes a learning index PLTree, a DRAM/PM hybrid architecture. PLTree optimizes reading and writing performance under real data distributions through the following approaches: (1) a two-stage approach to construct the index, eliminating last-mile search in internal nodes and reducing the access of PM, (2) model-based search for efficient query performance on PM and accelerated query by leveraging metadata in DRAM, and (3) a log-based hierarchical overflow buffer structure tailored to PM characteristics to optimize writing performance. The results show that, compared with the existing persistent memory indexes (APEX, FPTree, uTree, NBTree, and DPTree), PLTree achieves significantly better performance in index construction 1.9× to 34× across various datasets. In single-threaded scenarios, PLTree exhibits an average query and insertion performance improvement of 1.26× to 4.45× and 2.63× to 6.83×, respectively. In multi-threaded scenarios, PLTree surpasses the baseline by up to 10.2× and 23.7× in query and insertion performance, respectively.
Available online: June 14, 2024
, DOI: 10.13328/j.cnki.jos.007188
Abstract:
Although convolutional neural networks (CNNs) are widely used in image recognition due to their excellent generalization performance, adversarial samples contaminated by noise can easily deceive fully trained network models, posing security risks. Many existing defense methods improve the robustness of models, but most inevitably sacrifice model generalization. To alleviate this issue, a label-filtered weight parameter regularization method is proposed to balance the generalization and robustness of models using the label information of samples during model training. Many previous robust model training methods suffer from two main issues: 1) The robustness of models is mainly enhanced by increasing the quantity or complexity of training set samples, which not only diminishes the dominant role of clean samples in model training but also significantly increases the workload of training tasks. 2) The label information of samples is used only to compare with model predictions to control the direction of model parameter updates, neglecting the additional information hidden in sample labels. The proposed method selects weight parameters that play a decisive role in classifying samples by filtering the correct labels of samples and the classification labels of adversarial samples and optimizes these parameters regularly to achieve a balance between model generalization and robustness. Experiments and analysis on the MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate that the proposed method achieves good training results.
Although convolutional neural networks (CNNs) are widely used in image recognition due to their excellent generalization performance, adversarial samples contaminated by noise can easily deceive fully trained network models, posing security risks. Many existing defense methods improve the robustness of models, but most inevitably sacrifice model generalization. To alleviate this issue, a label-filtered weight parameter regularization method is proposed to balance the generalization and robustness of models using the label information of samples during model training. Many previous robust model training methods suffer from two main issues: 1) The robustness of models is mainly enhanced by increasing the quantity or complexity of training set samples, which not only diminishes the dominant role of clean samples in model training but also significantly increases the workload of training tasks. 2) The label information of samples is used only to compare with model predictions to control the direction of model parameter updates, neglecting the additional information hidden in sample labels. The proposed method selects weight parameters that play a decisive role in classifying samples by filtering the correct labels of samples and the classification labels of adversarial samples and optimizes these parameters regularly to achieve a balance between model generalization and robustness. Experiments and analysis on the MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate that the proposed method achieves good training results.
Available online: June 14, 2024
, DOI: 10.13328/j.cnki.jos.007191
Abstract:
Serverless computing is an emerging paradigm of cloud computing, allowing developers to focus only on application logic development without the need to manage complex underlying tasks. This paradigm allows developers to quickly build smaller-granularity applications, the one at the function level. With the increasing popularity of serverless computing, major cloud computing vendors have introduced their commercial serverless platforms one after another. However, the characteristics of these platforms have yet to be systematically studied and reliably compared. A comprehensive analysis of these characteristics can help developers choose an appropriate serverless platform while developing and executing serverless applications in the right way. To this end, an empirical study is conducted on the characteristics of mainstream commercial serverless platforms. This study involves such mainstream serverless platforms as AWS Lambda, Google Cloud Functions, Microsoft Azure Functions, and Alibaba Function Compute. This study is divided into two major parts: feature summarization and runtime performance analysis. In the feature summarization, the official documents of these serverless platforms are discussed and their key features are summarized and compared in terms of development, deployment, and runtime. In the runtime performance analysis, representative benchmarks are applied to analyze the runtime performance offered by these serverless platforms on a multidimensional basis. Specifically, key factors for the cold-start performance of the applications are first analyzed, such as programming languages and memory sizes. Furthermore, the tasks-executing performance of serverless platforms is discussed. Based on the results of feature summarization and runtime performance analysis, this study sums up a series of findings and provides practical insights and potential research opportunities for developers, cloud computing vendors, and researchers.
Serverless computing is an emerging paradigm of cloud computing, allowing developers to focus only on application logic development without the need to manage complex underlying tasks. This paradigm allows developers to quickly build smaller-granularity applications, the one at the function level. With the increasing popularity of serverless computing, major cloud computing vendors have introduced their commercial serverless platforms one after another. However, the characteristics of these platforms have yet to be systematically studied and reliably compared. A comprehensive analysis of these characteristics can help developers choose an appropriate serverless platform while developing and executing serverless applications in the right way. To this end, an empirical study is conducted on the characteristics of mainstream commercial serverless platforms. This study involves such mainstream serverless platforms as AWS Lambda, Google Cloud Functions, Microsoft Azure Functions, and Alibaba Function Compute. This study is divided into two major parts: feature summarization and runtime performance analysis. In the feature summarization, the official documents of these serverless platforms are discussed and their key features are summarized and compared in terms of development, deployment, and runtime. In the runtime performance analysis, representative benchmarks are applied to analyze the runtime performance offered by these serverless platforms on a multidimensional basis. Specifically, key factors for the cold-start performance of the applications are first analyzed, such as programming languages and memory sizes. Furthermore, the tasks-executing performance of serverless platforms is discussed. Based on the results of feature summarization and runtime performance analysis, this study sums up a series of findings and provides practical insights and potential research opportunities for developers, cloud computing vendors, and researchers.
Available online: June 14, 2024
, DOI: 10.13328/j.cnki.jos.007192
Abstract:
Microkernels migrate system services to user mode. Thanks to the isolated framework, microkernels are superior in high reliability, which meets the needs of the aerospace field. SPARC processors are widely applied on the control equipment of spacecraft, satellite payloads, and planetary vehicles. The register window mechanism of SPARC will affect the performance of inter-process communication (IPC) on microkernels. Besides, its inter-processor interrupt (IPI) also seriously affects the efficiency of cross-core IPC. As a key mechanism, IPC is vital to the overall performance of applications on microkernels. Through observing the register window mechanism, this study redesigns and implements the register bank mechanism, where the register window is allocated and managed by the kernel. Thus BankedIPC on SPARC is implemented. At the same time, as IPI underperforms on SPARC, FlexIPC is designed to optimize the performance of cross-core IPC. These approaches are employed to optimize the general synchronous IPC implemented on a self-developed microkernel ChCore. According to the test, the average IPC performance of microkernels on the optimized SPARC is about two times better with the application performance up to 15%.
Microkernels migrate system services to user mode. Thanks to the isolated framework, microkernels are superior in high reliability, which meets the needs of the aerospace field. SPARC processors are widely applied on the control equipment of spacecraft, satellite payloads, and planetary vehicles. The register window mechanism of SPARC will affect the performance of inter-process communication (IPC) on microkernels. Besides, its inter-processor interrupt (IPI) also seriously affects the efficiency of cross-core IPC. As a key mechanism, IPC is vital to the overall performance of applications on microkernels. Through observing the register window mechanism, this study redesigns and implements the register bank mechanism, where the register window is allocated and managed by the kernel. Thus BankedIPC on SPARC is implemented. At the same time, as IPI underperforms on SPARC, FlexIPC is designed to optimize the performance of cross-core IPC. These approaches are employed to optimize the general synchronous IPC implemented on a self-developed microkernel ChCore. According to the test, the average IPC performance of microkernels on the optimized SPARC is about two times better with the application performance up to 15%.
Available online: October 11, 2023
, DOI: 10.13328/j.cnki.jos.006978
Abstract:
There are a lot of two-party threshold schemes for SM2 digital signatures proposed in recent years, which can significantly enhance the security of private keys for SM2 digital signatures. According to different methods of key splitting, public schemes can be divided into two types: multiplicative key splitting and additive key splitting. Further, these public schemes can be subdivided into various two-party threshold schemes according to different constructions of the signature random number. This study proposes the framework of two-party threshold schemes for SM2 digital signature, which provides a safe basic calculation process of two-party threshold schemes and introduces the signature random number that can be constructed variously. With the proposed framework and various constructions of the random number, this study achieves the instantiation of the framework, obtaining a variety of two-party threshold schemes for SM2 digital signature. The instantiation includes 23 known two-party threshold schemes, as well as a variety of new schemes.
There are a lot of two-party threshold schemes for SM2 digital signatures proposed in recent years, which can significantly enhance the security of private keys for SM2 digital signatures. According to different methods of key splitting, public schemes can be divided into two types: multiplicative key splitting and additive key splitting. Further, these public schemes can be subdivided into various two-party threshold schemes according to different constructions of the signature random number. This study proposes the framework of two-party threshold schemes for SM2 digital signature, which provides a safe basic calculation process of two-party threshold schemes and introduces the signature random number that can be constructed variously. With the proposed framework and various constructions of the random number, this study achieves the instantiation of the framework, obtaining a variety of two-party threshold schemes for SM2 digital signature. The instantiation includes 23 known two-party threshold schemes, as well as a variety of new schemes.
Available online: October 18, 2017
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表Proceedings of the 11th Joint Meeting of the European Software Engineering Conference and the ACM SigSoft Symposium on The Foundations of Software Engineering (ESEC/FSE)，ACM，2017年9月，315-325页. 原文链接如下：https://doi.org/10.1145/3106237.3106242， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表Proceedings of the 11th Joint Meeting of the European Software Engineering Conference and the ACM SigSoft Symposium on The Foundations of Software Engineering (ESEC/FSE)，ACM，2017年9月，315-325页. 原文链接如下：https://doi.org/10.1145/3106237.3106242， 读者如需引用该文请标引原文出处。
Available online: October 18, 2017
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表Proceedings of the 11th Joint Meeting of the European Software Engineering Conference and the ACM SigSoft Symposium on The Foundations of Software Engineering (ESEC/FSE)，ACM，2017年9月，303-314页. 原文链接如下：https://doi.org/10.1145/3106237.3106239， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表Proceedings of the 11th Joint Meeting of the European Software Engineering Conference and the ACM SigSoft Symposium on The Foundations of Software Engineering (ESEC/FSE)，ACM，2017年9月，303-314页. 原文链接如下：https://doi.org/10.1145/3106237.3106239， 读者如需引用该文请标引原文出处。
Available online: September 11, 2017
Abstract:
GitHub, a popular social-software-development platform, has fostered a variety of software ecosystems where projects depend on one another and practitioners interact with each other. Projects within an ecosystem often have complex inter-dependencies that impose new challenges in bug reporting and fixing. In this paper, we conduct an empirical study on cross-project correlated bugs, i.e., causally related bugs reported to different projects, focusing on two aspects: 1) how developers track the root causes across projects; and 2) how the downstream developers coordinate to deal with upstream bugs. Through manual inspection of bug reports collected from the scientific Python ecosystem and an online survey with developers, this study reveals the common practices of developers and the various factors in fixing cross-project bugs. These findings provide implications for future software bug analysis in the scope of ecosystem, as well as shed light on the requirements of issue trackers for such bugs.
GitHub, a popular social-software-development platform, has fostered a variety of software ecosystems where projects depend on one another and practitioners interact with each other. Projects within an ecosystem often have complex inter-dependencies that impose new challenges in bug reporting and fixing. In this paper, we conduct an empirical study on cross-project correlated bugs, i.e., causally related bugs reported to different projects, focusing on two aspects: 1) how developers track the root causes across projects; and 2) how the downstream developers coordinate to deal with upstream bugs. Through manual inspection of bug reports collected from the scientific Python ecosystem and an online survey with developers, this study reveals the common practices of developers and the various factors in fixing cross-project bugs. These findings provide implications for future software bug analysis in the scope of ecosystem, as well as shed light on the requirements of issue trackers for such bugs.
Available online: June 21, 2017
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在IEEE Transactions on Software Engineering 2017 已录用待发表. 原文链接如下：http://ieeexplore.ieee.org/document/7792694， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在IEEE Transactions on Software Engineering 2017 已录用待发表. 原文链接如下：http://ieeexplore.ieee.org/document/7792694， 读者如需引用该文请标引原文出处。
Available online: June 13, 2017
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 39th International Conference on Software Engineering, Pages 27-37, Buenos Aires, Argentina — May 20 - 28, 2017, IEEE Press Piscataway, NJ, USA ?2017, ISBN: 978-1-5386-3868-2 原文链接如下：http://dl.acm.org/citation.cfm?id=3097373， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 39th International Conference on Software Engineering, Pages 27-37, Buenos Aires, Argentina — May 20 - 28, 2017, IEEE Press Piscataway, NJ, USA ?2017, ISBN: 978-1-5386-3868-2 原文链接如下：http://dl.acm.org/citation.cfm?id=3097373， 读者如需引用该文请标引原文出处。
Available online: January 25, 2017
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). ACM, New York, NY, USA, 871-882. DOI: https://doi.org/10.1145/2950290.2950364 原文链接如下：http://dl.acm.org/citation.cfm?id=2950364， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). ACM, New York, NY, USA, 871-882. DOI: https://doi.org/10.1145/2950290.2950364 原文链接如下：http://dl.acm.org/citation.cfm?id=2950364， 读者如需引用该文请标引原文出处。
Available online: January 18, 2017
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, Pages 133—143, Seattle WA, USA, November 2016. 原文链接如下：http://dl.acm.org/citation.cfm?id=2950327， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, Pages 133—143, Seattle WA, USA, November 2016. 原文链接如下：http://dl.acm.org/citation.cfm?id=2950327， 读者如需引用该文请标引原文出处。
Available online: January 04, 2017
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE'16), 810 – 821, November 13 - 18, 2016. 原文链接如下：https://doi.org/10.1145/2950290.2950310， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE'16), 810 – 821, November 13 - 18, 2016. 原文链接如下：https://doi.org/10.1145/2950290.2950310， 读者如需引用该文请标引原文出处。
Available online: January 04, 2017
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在FSE 2016, 原文链接如下：http://dl.acm.org/citation.cfm?doid=2950290.2950313, 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在FSE 2016, 原文链接如下：http://dl.acm.org/citation.cfm?doid=2950290.2950313, 读者如需引用该文请标引原文出处。
Available online: December 12, 2016
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在FSE'16会议上Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering， 原文链接如下：http://dl.acm.org/citation.cfm?id=2950340， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在FSE'16会议上Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering， 原文链接如下：http://dl.acm.org/citation.cfm?id=2950340， 读者如需引用该文请标引原文出处。
Available online: September 30, 2016
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在ASE2016会议上http://ase2016.org/ 原文链接如下：http://dl.acm.org/citation.cfm?id=2970366 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在ASE2016会议上http://ase2016.org/ 原文链接如下：http://dl.acm.org/citation.cfm?id=2970366 读者如需引用该文请标引原文出处。
Available online: September 09, 2016
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 俊杰的文章发表在ASE2016会议上，http://ase2016.org/。 原文链接如下：http://dl.acm.org/citation.cfm?doid=2970276.2970300 请读者标引时请引注原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 俊杰的文章发表在ASE2016会议上，http://ase2016.org/。 原文链接如下：http://dl.acm.org/citation.cfm?doid=2970276.2970300 请读者标引时请引注原文出处。
Available online: September 07, 2016
Abstract:
CCF 软件工程专业委员会白晓颖教授（清华大学）推荐。 原文发表在 ASE 2016 Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering。 全文链接：http://dx.doi.org/10.1145/2970276.2970307。 重要提示：读者如引用该文时请标注原文出处。
CCF 软件工程专业委员会白晓颖教授（清华大学）推荐。 原文发表在 ASE 2016 Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering。 全文链接：http://dx.doi.org/10.1145/2970276.2970307。 重要提示：读者如引用该文时请标注原文出处。
Available online: August 29, 2016
Abstract:
CCF软件工程专业委员会白晓颖教授（清华大学）推荐。 该论文发表在ACM Transactions on Software Engineering and Methodology （TOSEM, Vol. 25, No. 2, Article 13, May 2016），被ICSE 2016主会邀请为“Journal first”报告， 全文参见http://dl.acm.org/citation.cfm?id=2876443。 论文作者包括北京大学的周明辉,马秀娟,张路和梅宏，以及田纳西大学的Audris Mockus。 重要提示：读者如引用该文时请标注原文出处。
CCF软件工程专业委员会白晓颖教授（清华大学）推荐。 该论文发表在ACM Transactions on Software Engineering and Methodology （TOSEM, Vol. 25, No. 2, Article 13, May 2016），被ICSE 2016主会邀请为“Journal first”报告， 全文参见http://dl.acm.org/citation.cfm?id=2876443。 论文作者包括北京大学的周明辉,马秀娟,张路和梅宏，以及田纳西大学的Audris Mockus。 重要提示：读者如引用该文时请标注原文出处。
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2003,14(7):1282-1291
Abstract:
Sensor network, which is made by the convergence of sensor, micro-electro-mechanism system and networks technologies, is a novel technology about acquiring and processing information. In this paper, the architecture of wireless sensor network is briefly introduced. Next, some valuable applications are explained and forecasted. Combining with the existing work, the hot spots including power-aware routing and media access control schemes are discussed and presented in detail. Finally, taking account of application requirements, several future research directions are put forward.
Sensor network, which is made by the convergence of sensor, micro-electro-mechanism system and networks technologies, is a novel technology about acquiring and processing information. In this paper, the architecture of wireless sensor network is briefly introduced. Next, some valuable applications are explained and forecasted. Combining with the existing work, the hot spots including power-aware routing and media access control schemes are discussed and presented in detail. Finally, taking account of application requirements, several future research directions are put forward.
2010,21(3):427-437
Abstract:
Automatic generation of poetry has always been considered a hard nut in natural language generation.This paper reports some pioneering research on a possible generic algorithm and its automatic generation of SONGCI. In light of the characteristics of Chinese ancient poetry, this paper designed the level and oblique tones-based coding method, the syntactic and semantic weighted function of fitness, the elitism and roulette-combined selection operator, and the partially mapped crossover operator and the heuristic mutation operator. As shown by tests, the system constructed on the basis of the computing model designed in this paper is basically capable of generating Chinese SONGCI with some aesthetic merit. This work represents progress in the field of Chinese poetry automatic generation.
Automatic generation of poetry has always been considered a hard nut in natural language generation.This paper reports some pioneering research on a possible generic algorithm and its automatic generation of SONGCI. In light of the characteristics of Chinese ancient poetry, this paper designed the level and oblique tones-based coding method, the syntactic and semantic weighted function of fitness, the elitism and roulette-combined selection operator, and the partially mapped crossover operator and the heuristic mutation operator. As shown by tests, the system constructed on the basis of the computing model designed in this paper is basically capable of generating Chinese SONGCI with some aesthetic merit. This work represents progress in the field of Chinese poetry automatic generation.
2011,22(1):71-83
, DOI: 10.3724/SP.J.1001.2011.03958
Abstract:
Cloud Computing is the fundamental change happening in the field of Information Technology. It is a representation of a movement towards the intensive, large scale specialization. On the other hand, it brings about not only convenience and efficiency problems, but also great challenges in the field of data security and privacy protection. Currently, security has been regarded as one of the greatest problems in the development of Cloud Computing. This paper describes the great requirements in Cloud Computing, security key technology, standard and regulation etc., and provides a Cloud Computing security framework. This paper argues that the changes in the above aspects will result in a technical revolution in the field of information security.
Cloud Computing is the fundamental change happening in the field of Information Technology. It is a representation of a movement towards the intensive, large scale specialization. On the other hand, it brings about not only convenience and efficiency problems, but also great challenges in the field of data security and privacy protection. Currently, security has been regarded as one of the greatest problems in the development of Cloud Computing. This paper describes the great requirements in Cloud Computing, security key technology, standard and regulation etc., and provides a Cloud Computing security framework. This paper argues that the changes in the above aspects will result in a technical revolution in the field of information security.
2016,27(1):45-71
, DOI: 10.13328/j.cnki.jos.004914
Abstract:
Android is a modern and most popular software platform for smartphones. According to report, Android accounted for a huge 81% of all smartphones in 2014 and shipped over 1 billion units worldwide for the first time ever. Apple, Microsoft, Blackberry and Firefox trailed a long way behind. At the same time, increased popularity of the Android smartphones has attracted hackers, leading to massive increase of Android malware applications. This paper summarizes and analyzes the latest advances in Android security from multidimensional perspectives, covering Android architecture, design principles, security mechanisms, major security threats, classification and detection of malware, static and dynamic analyses, machine learning approaches, and security extension proposals.
Android is a modern and most popular software platform for smartphones. According to report, Android accounted for a huge 81% of all smartphones in 2014 and shipped over 1 billion units worldwide for the first time ever. Apple, Microsoft, Blackberry and Firefox trailed a long way behind. At the same time, increased popularity of the Android smartphones has attracted hackers, leading to massive increase of Android malware applications. This paper summarizes and analyzes the latest advances in Android security from multidimensional perspectives, covering Android architecture, design principles, security mechanisms, major security threats, classification and detection of malware, static and dynamic analyses, machine learning approaches, and security extension proposals.
2008,19(1):48-61
Abstract:
The research actuality and new progress in clustering algorithm in recent years are summarized in this paper. First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining.
The research actuality and new progress in clustering algorithm in recent years are summarized in this paper. First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining.
2009,20(5):1337-1348
Abstract:
This paper surveys the current technologies adopted in cloud computing as well as the systems in enterprises. Cloud computing can be viewed from two different aspects. One is about the cloud infrastructure which is the building block for the up layer cloud application. The other is of course the cloud application. This paper focuses on the cloud infrastructure including the systems and current research. Some attractive cloud applications are also discussed. Cloud computing infrastructure has three distinct characteristics. First, the infrastructure is built on top of large scale clusters which contain a large number of cheap PC servers. Second, the applications are co-designed with the fundamental infrastructure that the computing resources can be maximally utilized. Third, the reliability of the whole system is achieved by software building on top of redundant hardware instead of mere hardware. All these technologies are for the two important goals for distributed system: high scalability and high availability. Scalability means that the cloud infrastructure can be expanded to very large scale even to thousands of nodes. Availability means that the services are available even when quite a number of nodes fail. From this paper, readers will capture the current status of cloud computing as well as its future trends.
This paper surveys the current technologies adopted in cloud computing as well as the systems in enterprises. Cloud computing can be viewed from two different aspects. One is about the cloud infrastructure which is the building block for the up layer cloud application. The other is of course the cloud application. This paper focuses on the cloud infrastructure including the systems and current research. Some attractive cloud applications are also discussed. Cloud computing infrastructure has three distinct characteristics. First, the infrastructure is built on top of large scale clusters which contain a large number of cheap PC servers. Second, the applications are co-designed with the fundamental infrastructure that the computing resources can be maximally utilized. Third, the reliability of the whole system is achieved by software building on top of redundant hardware instead of mere hardware. All these technologies are for the two important goals for distributed system: high scalability and high availability. Scalability means that the cloud infrastructure can be expanded to very large scale even to thousands of nodes. Availability means that the services are available even when quite a number of nodes fail. From this paper, readers will capture the current status of cloud computing as well as its future trends.
2009,20(2):271-289
Abstract:
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.
2005,16(1):1-7
Abstract:
The paper gives some thinking according to the following four aspects: 1) from the law of things development, revealing the development history of software engineering technology; 2) from the point of software natural characteristic, analyzing the construction of every abstraction layer of virtual machine; 3) from the point of software development, proposing the research content of software engineering discipline, and research the pattern of industrialized software production; 4) based on the appearance of Internet technology, exploring the development trend of software technology.
The paper gives some thinking according to the following four aspects: 1) from the law of things development, revealing the development history of software engineering technology; 2) from the point of software natural characteristic, analyzing the construction of every abstraction layer of virtual machine; 3) from the point of software development, proposing the research content of software engineering discipline, and research the pattern of industrialized software production; 4) based on the appearance of Internet technology, exploring the development trend of software technology.
2010,21(8):1834-1848
Abstract:
This paper surveys the state of the art of sentiment analysis. First, three important tasks of sentiment analysis are summarized and analyzed in detail, including sentiment extraction, sentiment classification, sentiment retrieval and summarization. Then, the evaluation and corpus for sentiment analysis are introduced. Finally, the applications of sentiment analysis are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, making detailed comparison and analysis.
This paper surveys the state of the art of sentiment analysis. First, three important tasks of sentiment analysis are summarized and analyzed in detail, including sentiment extraction, sentiment classification, sentiment retrieval and summarization. Then, the evaluation and corpus for sentiment analysis are introduced. Finally, the applications of sentiment analysis are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, making detailed comparison and analysis.
2004,15(3):428-442
Abstract:
With the rapid development of e-business, web applications based on the Web are developed from localization to globalization, from B2C(business-to-customer) to B2B(business-to-business), from centralized fashion to decentralized fashion. Web service is a new application model for decentralized computing, and it is also an effective mechanism for the data and service integration on the web. Thus, web service has become a solution to e-business. It is important and necessary to carry out the research on the new architecture of web services, on the combinations with other good techniques, and on the integration of services. In this paper, a survey presents on various aspects of the research of web services from the basic concepts to the principal research problems and the underlying techniques, including data integration in web services, web service composition, semantic web service, web service discovery, web service security, the solution to web services in the P2P (Peer-to-Peer) computing environment, and the grid service, etc. This paper also presents a summary of the current art of the state of these techniques, a discussion on the future research topics, and the challenges of the web services.
With the rapid development of e-business, web applications based on the Web are developed from localization to globalization, from B2C(business-to-customer) to B2B(business-to-business), from centralized fashion to decentralized fashion. Web service is a new application model for decentralized computing, and it is also an effective mechanism for the data and service integration on the web. Thus, web service has become a solution to e-business. It is important and necessary to carry out the research on the new architecture of web services, on the combinations with other good techniques, and on the integration of services. In this paper, a survey presents on various aspects of the research of web services from the basic concepts to the principal research problems and the underlying techniques, including data integration in web services, web service composition, semantic web service, web service discovery, web service security, the solution to web services in the P2P (Peer-to-Peer) computing environment, and the grid service, etc. This paper also presents a summary of the current art of the state of these techniques, a discussion on the future research topics, and the challenges of the web services.
2005,16(5):857-868
Abstract:
Wireless Sensor Networks, a novel technology about acquiring and processing information, have been proposed for a multitude of diverse applications. The problem of self-localization, that is, determining where a given node is physically or relatively located in the networks, is a challenging one, and yet extremely crucial for many applications. In this paper, the evaluation criterion of the performance and the taxonomy for wireless sensor networks self-localization systems and algorithms are described, the principles and characteristics of recent representative localization approaches are discussed and presented, and the directions of research in this area are introduced.
Wireless Sensor Networks, a novel technology about acquiring and processing information, have been proposed for a multitude of diverse applications. The problem of self-localization, that is, determining where a given node is physically or relatively located in the networks, is a challenging one, and yet extremely crucial for many applications. In this paper, the evaluation criterion of the performance and the taxonomy for wireless sensor networks self-localization systems and algorithms are described, the principles and characteristics of recent representative localization approaches are discussed and presented, and the directions of research in this area are introduced.
2009,20(1):54-66
Abstract:
Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.
Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.
2012,23(4):962-986
, DOI: 10.3724/SP.J.1001.2012.04175
Abstract:
Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing area and draws great attention from both academia and industry fields. Under cloud computing environment, data center consist of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data, which may easily lead to the failure of the computers or data. The large amount of computers composition not only leads to great challenges to the scalability of the data center and its storage system, but also results in high hardware infrastructure cost and power cost. Therefore, fault-tolerance, scalability, and power consumption of the distributed storage for a data center becomes key part in the technology of cloud computing, in order to ensure the data availability and reliability. In this paper, a survey is made on the state of art of the key technologies in cloud computing in the following aspects: Design of data center network, organization and arrangement of data, strategies to improve fault-tolerance, methods to save storage space, and energy. Firstly, many kinds of classical topologies of data center network are introduced and compared. Secondly, kinds of current fault-tolerant storage techniques are discussed, and data replication and erasure code strategies are especially compared. Thirdly, the main current energy saving technology is addressed and analyzed. Finally, challenges in distributed storage are reviewed as well as future research trends are predicted.
Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing area and draws great attention from both academia and industry fields. Under cloud computing environment, data center consist of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data, which may easily lead to the failure of the computers or data. The large amount of computers composition not only leads to great challenges to the scalability of the data center and its storage system, but also results in high hardware infrastructure cost and power cost. Therefore, fault-tolerance, scalability, and power consumption of the distributed storage for a data center becomes key part in the technology of cloud computing, in order to ensure the data availability and reliability. In this paper, a survey is made on the state of art of the key technologies in cloud computing in the following aspects: Design of data center network, organization and arrangement of data, strategies to improve fault-tolerance, methods to save storage space, and energy. Firstly, many kinds of classical topologies of data center network are introduced and compared. Secondly, kinds of current fault-tolerant storage techniques are discussed, and data replication and erasure code strategies are especially compared. Thirdly, the main current energy saving technology is addressed and analyzed. Finally, challenges in distributed storage are reviewed as well as future research trends are predicted.
2012,23(1):32-45
, DOI: 10.3724/SP.J.1001.2012.04091
Abstract:
In many areas such as science, simulation, Internet, and e-commerce, the volume of data to be analyzed grows rapidly. Parallel techniques which could be expanded cost-effectively should be invented to deal with the big data. Relational data management technique has gone through a history of nearly 40 years. Now it encounters the tough obstacle of scalability, which relational techniques can not handle large data easily. In the mean time, none relational techniques, such as MapReduce as a typical representation, emerge as a new force, and expand their application from Web search to territories that used to be occupied by relational database systems. They confront relational technique with high availability, high scalability and massive parallel processing capability. Relational technique community, after losing the big deal of Web search, begins to learn from MapReduce. MapReduce also borrows valuable ideas from relational technique community to improve performance. Relational technique and MapReduce compete with each other, and learn from each other; new data analysis platform and new data analysis eco-system are emerging. Finally the two camps of techniques will find their right places in the new eco-system of big data analysis.
In many areas such as science, simulation, Internet, and e-commerce, the volume of data to be analyzed grows rapidly. Parallel techniques which could be expanded cost-effectively should be invented to deal with the big data. Relational data management technique has gone through a history of nearly 40 years. Now it encounters the tough obstacle of scalability, which relational techniques can not handle large data easily. In the mean time, none relational techniques, such as MapReduce as a typical representation, emerge as a new force, and expand their application from Web search to territories that used to be occupied by relational database systems. They confront relational technique with high availability, high scalability and massive parallel processing capability. Relational technique community, after losing the big deal of Web search, begins to learn from MapReduce. MapReduce also borrows valuable ideas from relational technique community to improve performance. Relational technique and MapReduce compete with each other, and learn from each other; new data analysis platform and new data analysis eco-system are emerging. Finally the two camps of techniques will find their right places in the new eco-system of big data analysis.
2009,20(3):524-545
Abstract:
Nowadays it has been widely accepted that the quality of software highly depends on the process that iscarried out in an organization. As part of the effort to support software process engineering activities, the researchon software process modeling and analysis is to provide an effective means to represent and analyze a process and,by doing so, to enhance the understanding of the modeled process. In addition, an enactable process model canprovide a direct guidance for the actual development process. Thus, the enforcement of the process model candirectly contribute to the improvement of the software quality. In this paper, a systematic review is carried out tosurvey the recent development in software process modeling. 72 papers from 20 conference proceedings and 7journals are identified as the evidence. The review aims to promote a better understanding of the literature byanswering the following three questions: 1) What kinds of paradigms are existing methods based on? 2) What kinds of purposes does the existing research have? 3) What kinds of new trends are reflected in the current research? Afterproviding the systematic review, we present our software process modeling method based on a multi-dimensionaland integration methodology that is intended to address several core issues facing the community.
Nowadays it has been widely accepted that the quality of software highly depends on the process that iscarried out in an organization. As part of the effort to support software process engineering activities, the researchon software process modeling and analysis is to provide an effective means to represent and analyze a process and,by doing so, to enhance the understanding of the modeled process. In addition, an enactable process model canprovide a direct guidance for the actual development process. Thus, the enforcement of the process model candirectly contribute to the improvement of the software quality. In this paper, a systematic review is carried out tosurvey the recent development in software process modeling. 72 papers from 20 conference proceedings and 7journals are identified as the evidence. The review aims to promote a better understanding of the literature byanswering the following three questions: 1) What kinds of paradigms are existing methods based on? 2) What kinds of purposes does the existing research have? 3) What kinds of new trends are reflected in the current research? Afterproviding the systematic review, we present our software process modeling method based on a multi-dimensionaland integration methodology that is intended to address several core issues facing the community.
2009,20(1):124-137
Abstract:
The appearance of plenty of intelligent devices equipped for short-range wireless communications boosts the fast rise of wireless ad hoc networks application. However, in many realistic application environments, nodes form a disconnected network for most of the time due to nodal mobility, low density, lossy link, etc. Conventional communication model of mobile ad hoc network (MANET) requires at least one path existing from source to destination nodes, which results in communication failure in these scenarios. Opportunistic networks utilize the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implement communications between nodes based on the "store-carry-forward" routing pattern. This networking approach, totally different from the traditional communication model, captures great interests from researchers. This paper first introduces the conceptions and theories of opportunistic networks and some current typical applications. Then it elaborates the popular research problems including opportunistic forwarding mechanism, mobility model and opportunistic data dissemination and retrieval. Some other interesting research points such as communication middleware, cooperation and security problem and new applications are stated briefly. Finally, the paper concludes and looks forward to the possible research focuses for opportunistic networks in the future.
The appearance of plenty of intelligent devices equipped for short-range wireless communications boosts the fast rise of wireless ad hoc networks application. However, in many realistic application environments, nodes form a disconnected network for most of the time due to nodal mobility, low density, lossy link, etc. Conventional communication model of mobile ad hoc network (MANET) requires at least one path existing from source to destination nodes, which results in communication failure in these scenarios. Opportunistic networks utilize the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implement communications between nodes based on the "store-carry-forward" routing pattern. This networking approach, totally different from the traditional communication model, captures great interests from researchers. This paper first introduces the conceptions and theories of opportunistic networks and some current typical applications. Then it elaborates the popular research problems including opportunistic forwarding mechanism, mobility model and opportunistic data dissemination and retrieval. Some other interesting research points such as communication middleware, cooperation and security problem and new applications are stated briefly. Finally, the paper concludes and looks forward to the possible research focuses for opportunistic networks in the future.
2015,26(1):62-81
, DOI: 10.13328/j.cnki.jos.004701
Abstract:
Network abstraction brings about the naissance of software-defined networking. SDN decouples data plane and control plane, and simplifies network management. The paper starts with a discussion on the background in the naissance and developments of SDN, combing its architecture that includes data layer, control layer and application layer. Then their key technologies are elaborated according to the hierarchical architecture of SDN. The characteristics of consistency, availability, and tolerance are especially analyzed. Moreover, latest achievements for profiled scenes are introduced. The future works are summarized in the end.
Network abstraction brings about the naissance of software-defined networking. SDN decouples data plane and control plane, and simplifies network management. The paper starts with a discussion on the background in the naissance and developments of SDN, combing its architecture that includes data layer, control layer and application layer. Then their key technologies are elaborated according to the hierarchical architecture of SDN. The characteristics of consistency, availability, and tolerance are especially analyzed. Moreover, latest achievements for profiled scenes are introduced. The future works are summarized in the end.
2004,15(8):1208-1219
Abstract:
With the explosive growth of network applications and complexity, the threat of Internet worms against network security becomes increasingly serious. Especially under the environment of Internet, the variety of the propagation ways and the complexity of the application environment result in worm with much higher frequency of outbreak, much deeper latency and more wider coverage, and Internet worms have been a primary issue faced by malicious code researchers. In this paper, the concept and research situation of Internet worms, exploration function component and execution mechanism are first presented, then the scanning strategies and propagation model are discussed, and finally the critical techniques of Internet worm prevention are given. Some major problems and research trends in this area are also addressed.
With the explosive growth of network applications and complexity, the threat of Internet worms against network security becomes increasingly serious. Especially under the environment of Internet, the variety of the propagation ways and the complexity of the application environment result in worm with much higher frequency of outbreak, much deeper latency and more wider coverage, and Internet worms have been a primary issue faced by malicious code researchers. In this paper, the concept and research situation of Internet worms, exploration function component and execution mechanism are first presented, then the scanning strategies and propagation model are discussed, and finally the critical techniques of Internet worm prevention are given. Some major problems and research trends in this area are also addressed.
2009,20(2):350-362
Abstract:
This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field. First the research background is introduced, including commercial application demands, academic institutes, conferences and journals. After formally and informally describing the recommendation problem, a comparison study is conducted based on categorized algorithms. In addition, the commonly adopted benchmarked datasets and evaluation methods are exhibited and most difficulties and future directions are concluded.
This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field. First the research background is introduced, including commercial application demands, academic institutes, conferences and journals. After formally and informally describing the recommendation problem, a comparison study is conducted based on categorized algorithms. In addition, the commonly adopted benchmarked datasets and evaluation methods are exhibited and most difficulties and future directions are concluded.
2009,20(11):2965-2976
Abstract:
This paper studies uncertain graph data mining and especially investigates the problem of mining frequent subgraph patterns from uncertain graph data. A data model is introduced for representing uncertainties in graphs, and an expected support is employed to evaluate the significance of subgraph patterns. By using the apriori property of expected support, a depth-first search-based mining algorithm is proposed with an efficient method for computing expected supports and a technique for pruning search space, which reduces the number of subgraph isomorphism testings needed by computing expected support from the exponential scale to the linear scale. Experimental results show that the proposed algorithm is 3 to 5 orders of magnitude faster than a na?ve depth-first search algorithm, and is efficient and scalable.
This paper studies uncertain graph data mining and especially investigates the problem of mining frequent subgraph patterns from uncertain graph data. A data model is introduced for representing uncertainties in graphs, and an expected support is employed to evaluate the significance of subgraph patterns. By using the apriori property of expected support, a depth-first search-based mining algorithm is proposed with an efficient method for computing expected supports and a technique for pruning search space, which reduces the number of subgraph isomorphism testings needed by computing expected support from the exponential scale to the linear scale. Experimental results show that the proposed algorithm is 3 to 5 orders of magnitude faster than a na?ve depth-first search algorithm, and is efficient and scalable.
2009,20(5):1226-1240
Abstract:
This paper introduces the concrete details of combining the automated reasoning techniques with planning methods, which includes planning as satisfiability using propositional logic, Conformant planning using modal logic and disjunctive reasoning, planning as nonmonotonic logic, and Flexible planning as fuzzy description logic. After considering experimental results of International Planning Competition and relevant papers, it concludes that planning methods based on automated reasoning techniques is helpful and can be adopted. It also proposes the challenges and possible hotspots.
This paper introduces the concrete details of combining the automated reasoning techniques with planning methods, which includes planning as satisfiability using propositional logic, Conformant planning using modal logic and disjunctive reasoning, planning as nonmonotonic logic, and Flexible planning as fuzzy description logic. After considering experimental results of International Planning Competition and relevant papers, it concludes that planning methods based on automated reasoning techniques is helpful and can be adopted. It also proposes the challenges and possible hotspots.
2003,14(10):1717-1727
Abstract:
Sensor networks are integration of sensor techniques, nested computation techniques, distributed computation techniques and wireless communication techniques. They can be used for testing, sensing, collecting and processing information of monitored objects and transferring the processed information to users. Sensor network is a new research area of computer science and technology and has a wide application future. Both academia and industries are very interested in it. The concepts and characteristics of the sensor networks and the data in the networks are introduced, and the issues of the sensor networks and the data management of sensor networks are discussed. The advance of the research on sensor networks and the data management of sensor networks are also presented.
Sensor networks are integration of sensor techniques, nested computation techniques, distributed computation techniques and wireless communication techniques. They can be used for testing, sensing, collecting and processing information of monitored objects and transferring the processed information to users. Sensor network is a new research area of computer science and technology and has a wide application future. Both academia and industries are very interested in it. The concepts and characteristics of the sensor networks and the data in the networks are introduced, and the issues of the sensor networks and the data management of sensor networks are discussed. The advance of the research on sensor networks and the data management of sensor networks are also presented.
2014,25(4):839-862
, DOI: 10.13328/j.cnki.jos.004558
Abstract:
Batch computing and stream computing are two important forms of big data computing. The research and discussions on batch computing in big data environment are comparatively sufficient. But how to efficiently deal with stream computing to meet many requirements, such as low latency, high throughput and continuously reliable running, and how to build efficient stream big data computing systems, are great challenges in the big data computing research. This paper provides a research of the data computing architecture and the key issues in stream computing in big data environments. Firstly, the research gives a brief summary of three application scenarios of stream computing in business intelligence, marketing and public service. It also shows distinctive features of the stream computing in big data environment, such as real time, volatility, burstiness, irregularity and infinity. A well-designed stream computing system always optimizes in system structure, data transmission, application interfaces, high-availability, and so on. Subsequently, the research offers detailed analyses and comparisons of five typical and open-source stream computing systems in big data environment. Finally, the research specifically addresses some new challenges of the stream big data systems, such as scalability, fault tolerance, consistency, load balancing and throughput.
Batch computing and stream computing are two important forms of big data computing. The research and discussions on batch computing in big data environment are comparatively sufficient. But how to efficiently deal with stream computing to meet many requirements, such as low latency, high throughput and continuously reliable running, and how to build efficient stream big data computing systems, are great challenges in the big data computing research. This paper provides a research of the data computing architecture and the key issues in stream computing in big data environments. Firstly, the research gives a brief summary of three application scenarios of stream computing in business intelligence, marketing and public service. It also shows distinctive features of the stream computing in big data environment, such as real time, volatility, burstiness, irregularity and infinity. A well-designed stream computing system always optimizes in system structure, data transmission, application interfaces, high-availability, and so on. Subsequently, the research offers detailed analyses and comparisons of five typical and open-source stream computing systems in big data environment. Finally, the research specifically addresses some new challenges of the stream big data systems, such as scalability, fault tolerance, consistency, load balancing and throughput.
2012,23(1):1-20
, DOI: 10.3724/SP.J.1001.2012.04100
Abstract:
Context-Aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. This paper presents an overview of the field of context-aware recommender systems from a process-oriented perspective, including system frameworks, key techniques, main models, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.
Context-Aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. This paper presents an overview of the field of context-aware recommender systems from a process-oriented perspective, including system frameworks, key techniques, main models, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.
2009,20(10):2729-2743
Abstract:
In a multi-hop wireless sensor network (WSN), the sensors closest to the sink tend to deplete their energy faster than other sensors, which is known as an energy hole around the sink. No more data can be delivered to the sink after an energy hole appears, while a considerable amount of energy is wasted and the network lifetime ends prematurely. This paper investigates the energy hole problem, and based on the improved corona model with levels, it concludes that the assignment of transmission ranges of nodes in different coronas is an effective approach for achieving energy-efficient network. It proves that the optimal transmission ranges for all areas is a multi-objective optimization problem (MOP), which is NP hard. The paper proposes an ACO (ant colony optimization)-based distributed algorithm to prolong the network lifetime, which can help nodes in different areas to adaptively find approximate optimal transmission range based on the node distribution. Furthermore, the simulation results indicate that the network lifetime under this solution approximates to that using the optimal list. Compared with existing algorithms, this ACO-based algorithm can not only make the network lifetime be extended more than two times longer, but also have good performance in the non-uniform node distribution.
In a multi-hop wireless sensor network (WSN), the sensors closest to the sink tend to deplete their energy faster than other sensors, which is known as an energy hole around the sink. No more data can be delivered to the sink after an energy hole appears, while a considerable amount of energy is wasted and the network lifetime ends prematurely. This paper investigates the energy hole problem, and based on the improved corona model with levels, it concludes that the assignment of transmission ranges of nodes in different coronas is an effective approach for achieving energy-efficient network. It proves that the optimal transmission ranges for all areas is a multi-objective optimization problem (MOP), which is NP hard. The paper proposes an ACO (ant colony optimization)-based distributed algorithm to prolong the network lifetime, which can help nodes in different areas to adaptively find approximate optimal transmission range based on the node distribution. Furthermore, the simulation results indicate that the network lifetime under this solution approximates to that using the optimal list. Compared with existing algorithms, this ACO-based algorithm can not only make the network lifetime be extended more than two times longer, but also have good performance in the non-uniform node distribution.
2012,23(5):1148-1166
, DOI: 10.3724/SP.J.1001.2012.04195
Abstract:
With the recent development of cloud computing, the importance of cloud databases has been widely acknowledged. Here, the features, influence and related products of cloud databases are first discussed. Then, research issues of cloud databases are presented in detail, which include data model, architecture, consistency, programming model, data security, performance optimization, benchmark, and so on. Finally, some future trends in this area are discussed.
With the recent development of cloud computing, the importance of cloud databases has been widely acknowledged. Here, the features, influence and related products of cloud databases are first discussed. Then, research issues of cloud databases are presented in detail, which include data model, architecture, consistency, programming model, data security, performance optimization, benchmark, and so on. Finally, some future trends in this area are discussed.
2000,11(11):1460-1466
Abstract:
Intrusion detection is a highlighted topic of network security research in recent years. In this paper, first the necessity o f intrusion detection is presented, and its concepts and models are described. T hen, many intrusion detection techniques and architectures are summarized. Final ly, the existing problems and the future direction in this field are discussed.
Intrusion detection is a highlighted topic of network security research in recent years. In this paper, first the necessity o f intrusion detection is presented, and its concepts and models are described. T hen, many intrusion detection techniques and architectures are summarized. Final ly, the existing problems and the future direction in this field are discussed.
2015,26(1):26-39
, DOI: 10.13328/j.cnki.jos.004631
Abstract:
In recent years, transfer learning has provoked vast amount of attention and research. Transfer learning is a new machine learning method that applies the knowledge from related but different domains to target domains. It relaxes the two basic assumptions in traditional machine learning: (1) the training (also referred as source domain) and test data (also referred target domain) follow the independent and identically distributed (i.i.d.) condition; (2) there are enough labeled samples to learn a good classification model, aiming to solve the problems that there are few or even not any labeled data in target domains. This paper surveys the research progress of transfer learning and introduces its own works, especially the ones in building transfer learning models by applying generative model on the concept level. Finally, the paper introduces the applications of transfer learning, such as text classification and collaborative filtering, and further suggests the future research direction of transfer learning.
In recent years, transfer learning has provoked vast amount of attention and research. Transfer learning is a new machine learning method that applies the knowledge from related but different domains to target domains. It relaxes the two basic assumptions in traditional machine learning: (1) the training (also referred as source domain) and test data (also referred target domain) follow the independent and identically distributed (i.i.d.) condition; (2) there are enough labeled samples to learn a good classification model, aiming to solve the problems that there are few or even not any labeled data in target domains. This paper surveys the research progress of transfer learning and introduces its own works, especially the ones in building transfer learning models by applying generative model on the concept level. Finally, the paper introduces the applications of transfer learning, such as text classification and collaborative filtering, and further suggests the future research direction of transfer learning.
2002,13(7):1228-1237
Abstract:
Software architecture (SA) is emerging as one of the primary research areas in software engineering recently and one of the key technologies to the development of large-scale software-intensive system and software product line system. The history and the major direction of SA are summarized, and the concept of SA is brought up based on analyzing and comparing the several classical definitions about SA. Based on summing up the activities about SA, two categories of study about SA are extracted out, and the advancements of researches on SA are subsequently introduced from seven aspects.Additionally,some disadvantages of study on SA are discussed,and the causes are explained at the same.Finally,it is concluded with some singificantly promising tendency about research on SA.
Software architecture (SA) is emerging as one of the primary research areas in software engineering recently and one of the key technologies to the development of large-scale software-intensive system and software product line system. The history and the major direction of SA are summarized, and the concept of SA is brought up based on analyzing and comparing the several classical definitions about SA. Based on summing up the activities about SA, two categories of study about SA are extracted out, and the advancements of researches on SA are subsequently introduced from seven aspects.Additionally,some disadvantages of study on SA are discussed,and the causes are explained at the same.Finally,it is concluded with some singificantly promising tendency about research on SA.
2013,24(8):1786-1803
, DOI: 10.3724/SP.J.1001.2013.04416
Abstract:
Many specific application oriented NoSQL database systems are developed for satisfying the new requirement of big data management. This paper surveys researches on typical NoSQL database based on key-value data model. First, the characteristics of big data, and the key technique issues supporting big data management are introduced. Then frontier efforts and research challenges are given, including system architecture, data model, access mode, index, transaction, system elasticity, load balance, replica strategy, data consistency, flash cache, MapReduce based data process and new generation data management system etc. Finally, research prospects are given.
Many specific application oriented NoSQL database systems are developed for satisfying the new requirement of big data management. This paper surveys researches on typical NoSQL database based on key-value data model. First, the characteristics of big data, and the key technique issues supporting big data management are introduced. Then frontier efforts and research challenges are given, including system architecture, data model, access mode, index, transaction, system elasticity, load balance, replica strategy, data consistency, flash cache, MapReduce based data process and new generation data management system etc. Finally, research prospects are given.
2011,22(1):115-131
, DOI: 10.3724/SP.J.1001.2011.03950
Abstract:
The Internet traffic model is the key issue for network performance management, Quality of Service management, and admission control. The paper first summarizes the primary characteristics of Internet traffic, as well as the metrics of Internet traffic. It also illustrates the significance and classification of traffic modeling. Next, the paper chronologically categorizes the research activities of traffic modeling into three phases: 1) traditional Poisson modeling; 2) self-similar modeling; and 3) new research debates and new progress. Thorough reviews of the major research achievements of each phase are conducted. Finally, the paper identifies some open research issue and points out possible future research directions in traffic modeling area.
The Internet traffic model is the key issue for network performance management, Quality of Service management, and admission control. The paper first summarizes the primary characteristics of Internet traffic, as well as the metrics of Internet traffic. It also illustrates the significance and classification of traffic modeling. Next, the paper chronologically categorizes the research activities of traffic modeling into three phases: 1) traditional Poisson modeling; 2) self-similar modeling; and 3) new research debates and new progress. Thorough reviews of the major research achievements of each phase are conducted. Finally, the paper identifies some open research issue and points out possible future research directions in traffic modeling area.
2006,17(7):1588-1600
Abstract:
Routing technology at the network layer is pivotal in the architecture of wireless sensor networks. As an active branch of routing technology, cluster-based routing protocols excel in network topology management, energy minimization, data aggregation and so on. In this paper, cluster-based routing mechanisms for wireless sensor networks are analyzed. Cluster head selection, cluster formation and data transmission are three key techniques in cluster-based routing protocols. As viewed from the three techniques, recent representative cluster-based routing protocols are presented, and their characteristics and application areas are compared. Finally, the future research issues in this area are pointed out.
Routing technology at the network layer is pivotal in the architecture of wireless sensor networks. As an active branch of routing technology, cluster-based routing protocols excel in network topology management, energy minimization, data aggregation and so on. In this paper, cluster-based routing mechanisms for wireless sensor networks are analyzed. Cluster head selection, cluster formation and data transmission are three key techniques in cluster-based routing protocols. As viewed from the three techniques, recent representative cluster-based routing protocols are presented, and their characteristics and application areas are compared. Finally, the future research issues in this area are pointed out.
2004,15(4):571-583
Abstract:
For most peer-to-peer file-swapping applications, sharing is a volunteer action, and peers are not responsible for their irresponsible bartering history. This situation indicates the trust between participants can not be set up simply on the traditional trust mechanism. A reasonable trust construction approach comes from the social network analysis, in which trust relations between individuals are set up upon recommendations of other individuals. Current p2p trust model could not promise the convergence of iteration for trust computation, and takes no consideration for model security problems, such as sybil attack and slandering. This paper presents a novel recommendation-based global trust model and gives a distributed implementation method. Mathematic analyses and simulations show that, compared to the current global trust model, the proposed model is more robust on trust security problems and more complete on iteration for computing peer trust.
For most peer-to-peer file-swapping applications, sharing is a volunteer action, and peers are not responsible for their irresponsible bartering history. This situation indicates the trust between participants can not be set up simply on the traditional trust mechanism. A reasonable trust construction approach comes from the social network analysis, in which trust relations between individuals are set up upon recommendations of other individuals. Current p2p trust model could not promise the convergence of iteration for trust computation, and takes no consideration for model security problems, such as sybil attack and slandering. This paper presents a novel recommendation-based global trust model and gives a distributed implementation method. Mathematic analyses and simulations show that, compared to the current global trust model, the proposed model is more robust on trust security problems and more complete on iteration for computing peer trust.
2009,20(1):11-29
Abstract:
Constrained optimization problems (COPs) are mathematical programming problems frequently encountered in the disciplines of science and engineering application. Solving COPs has become an important research area of evolutionary computation in recent years. In this paper, the state-of-the-art of constrained optimization evolutionary algorithms (COEAs) is surveyed from two basic aspects of COEAs (i.e., constraint-handling techniques and evolutionary algorithms). In addition, this paper discusses some important issues of COEAs. More specifically, several typical algorithms are analyzed in detail. Based on the analyses, it concluded that to obtain competitive results, a proper constraint-handling technique needs to be considered in conjunction with an appropriate search algorithm. Finally, the open research issues in this field are also pointed out.
Constrained optimization problems (COPs) are mathematical programming problems frequently encountered in the disciplines of science and engineering application. Solving COPs has become an important research area of evolutionary computation in recent years. In this paper, the state-of-the-art of constrained optimization evolutionary algorithms (COEAs) is surveyed from two basic aspects of COEAs (i.e., constraint-handling techniques and evolutionary algorithms). In addition, this paper discusses some important issues of COEAs. More specifically, several typical algorithms are analyzed in detail. Based on the analyses, it concluded that to obtain competitive results, a proper constraint-handling technique needs to be considered in conjunction with an appropriate search algorithm. Finally, the open research issues in this field are also pointed out.
2008,19(zk):112-120
Abstract:
An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure or centralized administration. Due to bandwidth constraint and dynamic topology of mobile ad hoc networks, multipath supported routing is a very important research issue. In this paper, we present an entropy-based metric to support stability multipath on-demand routing (SMDR). The key idea of SMDR protocol is to construct the new metric-entropy and select the stability multipath with the help of entropy metric to reduce the number of route reconstruction so as to provide QoS guarantee in the ad hoc network whose topology changes continuously. Simulation results show that, with the proposed multipath routing protocol, packet delivery ratio, end-to-end delay, and routing overhead ratio can be improved in most of cases. It is an available approach to multipath routing decision.
An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure or centralized administration. Due to bandwidth constraint and dynamic topology of mobile ad hoc networks, multipath supported routing is a very important research issue. In this paper, we present an entropy-based metric to support stability multipath on-demand routing (SMDR). The key idea of SMDR protocol is to construct the new metric-entropy and select the stability multipath with the help of entropy metric to reduce the number of route reconstruction so as to provide QoS guarantee in the ad hoc network whose topology changes continuously. Simulation results show that, with the proposed multipath routing protocol, packet delivery ratio, end-to-end delay, and routing overhead ratio can be improved in most of cases. It is an available approach to multipath routing decision.
2013,24(1):50-66
, DOI: 10.3724/SP.J.1001.2013.04276
Abstract:
As an important application of acceleration in the cloud, the distributed caching technology has received considerable attention in industry and academia. This paper starts with a discussion on the combination of cloud computing and distributed caching technology, giving an analysis of its characteristics, typical application scenarios, stages of development, standards, and several key elements, which have promoted its development. In order to systematically know the state of art progress and weak points of the distributed caching technology, the paper builds a multi-dimensional framework, DctAF. This framework is constituted of 6 dimensions through analyzing the characteristics of cloud computing and boundary of the caching techniques. Based on DctAF, current techniques have been analyzed and summarized; comparisons among several influential products have also been made. Finally, the paper describes and highlights the several challenges that the cache system faces and examines the current research through in-depth analysis and comparison.
As an important application of acceleration in the cloud, the distributed caching technology has received considerable attention in industry and academia. This paper starts with a discussion on the combination of cloud computing and distributed caching technology, giving an analysis of its characteristics, typical application scenarios, stages of development, standards, and several key elements, which have promoted its development. In order to systematically know the state of art progress and weak points of the distributed caching technology, the paper builds a multi-dimensional framework, DctAF. This framework is constituted of 6 dimensions through analyzing the characteristics of cloud computing and boundary of the caching techniques. Based on DctAF, current techniques have been analyzed and summarized; comparisons among several influential products have also been made. Finally, the paper describes and highlights the several challenges that the cache system faces and examines the current research through in-depth analysis and comparison.
2002,13(10):1952-1961
Abstract:
The crucial technologies related to personalization are introduced in this paper, which include the representation and modification of user profile, the representation of resource, the recommendation technology, and the architecture of personalization. By comparing with some existing prototype systems, the key technologies about how to implement personalization are discussed in detail. In addition, three representative personalization systems are analyzed. At last, some research directions for personalization are presented.
The crucial technologies related to personalization are introduced in this paper, which include the representation and modification of user profile, the representation of resource, the recommendation technology, and the architecture of personalization. By comparing with some existing prototype systems, the key technologies about how to implement personalization are discussed in detail. In addition, three representative personalization systems are analyzed. At last, some research directions for personalization are presented.
2003,14(9):1621-1628
Abstract:
Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.
Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.
2003,14(9):1635-1644
Abstract:
Computer forensics is the technology field that attempts to prove thorough, efficient, and secure means to investigate computer crime. Computer evidence must be authentic, accurate, complete and convincing to juries. In this paper, the stages of computer forensics are presented, and the theories and the realization of the forensics software are described. An example about forensic practice is also given. The deficiency of computer forensics technique and anti-forensics are also discussed. The result comes out that it is as the improvement of computer science technology, the forensics technique will become more integrated and thorough.
Computer forensics is the technology field that attempts to prove thorough, efficient, and secure means to investigate computer crime. Computer evidence must be authentic, accurate, complete and convincing to juries. In this paper, the stages of computer forensics are presented, and the theories and the realization of the forensics software are described. An example about forensic practice is also given. The deficiency of computer forensics technique and anti-forensics are also discussed. The result comes out that it is as the improvement of computer science technology, the forensics technique will become more integrated and thorough.
2008,19(7):1565-1580
Abstract:
Software defect prediction has been one of the active parts of software engineering since it was developed in 1970's. It plays a very important role in the analysis of software quality and balance of software cost. This paper investigates and discusses the motivation, evolvement, solutions and challenges of software defect prediction technologies, and it also categorizes, analyzes and compares the representatives of these prediction technologies. Some case studies for software defect distribution models are given to help understanding.
Software defect prediction has been one of the active parts of software engineering since it was developed in 1970's. It plays a very important role in the analysis of software quality and balance of software cost. This paper investigates and discusses the motivation, evolvement, solutions and challenges of software defect prediction technologies, and it also categorizes, analyzes and compares the representatives of these prediction technologies. Some case studies for software defect distribution models are given to help understanding.
2012,23(1):82-96
, DOI: 10.3724/SP.J.1001.2012.04101
Abstract:
Botnets are one of the most serious threats to the Internet. Researchers have done plenty of research and made significant progress. However, botnets keep evolving and have become more and more sophisticated. Due to the underlying security limitation of current system and Internet architecture, and the complexity of botnet itself, how to effectively counter the global threat of botnets is still a very challenging issue. This paper first introduces the evolving of botnet’s propagation, attack, command, and control mechanisms. Then the paper summarizes recent advances of botnet defense research and categorizes into five areas: Botnet monitoring, botnet infiltration, analysis of botnet characteristics, botnet detection and botnet disruption. The limitation of current botnet defense techniques, the evolving trend of botnet, and some possible directions for future research are also discussed.
Botnets are one of the most serious threats to the Internet. Researchers have done plenty of research and made significant progress. However, botnets keep evolving and have become more and more sophisticated. Due to the underlying security limitation of current system and Internet architecture, and the complexity of botnet itself, how to effectively counter the global threat of botnets is still a very challenging issue. This paper first introduces the evolving of botnet’s propagation, attack, command, and control mechanisms. Then the paper summarizes recent advances of botnet defense research and categorizes into five areas: Botnet monitoring, botnet infiltration, analysis of botnet characteristics, botnet detection and botnet disruption. The limitation of current botnet defense techniques, the evolving trend of botnet, and some possible directions for future research are also discussed.
2008,19(8):1947-1964
Abstract:
Wide-Spread deployment for interactive information visualization is difficult. Non-Specialist users need a general development method and a toolkit to support the generic data structures suited to tree, network and multi-dimensional data, special visualization techniques and interaction techniques, and well-known generic information tasks. This paper presents a model driven development method for interactive information visualization. First, an interactive information visualization interface model (IIVM) is proposed. Then, the development method for interactive information visualization based on IIVM is presented. The Daisy toolkit is introduced, which includes Daisy model builder, Daisy IIV generator and runtime framework with Daisy library. Finally, an application example is given. Experimental results show that Daisy can provide a general solution for development for interactive information visualization.
Wide-Spread deployment for interactive information visualization is difficult. Non-Specialist users need a general development method and a toolkit to support the generic data structures suited to tree, network and multi-dimensional data, special visualization techniques and interaction techniques, and well-known generic information tasks. This paper presents a model driven development method for interactive information visualization. First, an interactive information visualization interface model (IIVM) is proposed. Then, the development method for interactive information visualization based on IIVM is presented. The Daisy toolkit is introduced, which includes Daisy model builder, Daisy IIV generator and runtime framework with Daisy library. Finally, an application example is given. Experimental results show that Daisy can provide a general solution for development for interactive information visualization.
2008,19(8):1902-1919
Abstract:
Visual language techniques have exhibited more advantages in describing various software artifacts than one-dimensional textual languages during software development, ranging from the requirement analysis and design to testing and maintenance, as diagrammatic and graphical notations have been well applied in modeling system. In addition to an intuitive appearance, graph grammars provide a well-established foundation for defining visual languages with the power of precise modeling and verification on computers. This paper discusses the issues and techniques for a formal foundation of visual languages, reviews related practical graphical environments, presents a spatial graph grammar formalism, and applies the spatial graph grammar to defining behavioral semantics of UML diagrams and developing a style-driven framework for software architecture design.
Visual language techniques have exhibited more advantages in describing various software artifacts than one-dimensional textual languages during software development, ranging from the requirement analysis and design to testing and maintenance, as diagrammatic and graphical notations have been well applied in modeling system. In addition to an intuitive appearance, graph grammars provide a well-established foundation for defining visual languages with the power of precise modeling and verification on computers. This paper discusses the issues and techniques for a formal foundation of visual languages, reviews related practical graphical environments, presents a spatial graph grammar formalism, and applies the spatial graph grammar to defining behavioral semantics of UML diagrams and developing a style-driven framework for software architecture design.
2006,17(9):1848-1859
Abstract:
In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given.
In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given.
2010,21(2):231-247
Abstract:
In this paper, a framework is proposed for handling fault of service composition through analyzing fault requirements. Petri nets are used in the framework for fault detecting and its handling, which focuses on targeting the failure of available services, component failure and network failure. The corresponding fault models are given. Based on the model, the correctness criterion of fault handling is given to analyze fault handling model, and its correctness is proven. Finally, CTL (computational tree logic) is used to specify the related properties and enforcement algorithm of fault analysis. The simulation results show that this method can ensure the reliability and consistency of service composition.
In this paper, a framework is proposed for handling fault of service composition through analyzing fault requirements. Petri nets are used in the framework for fault detecting and its handling, which focuses on targeting the failure of available services, component failure and network failure. The corresponding fault models are given. Based on the model, the correctness criterion of fault handling is given to analyze fault handling model, and its correctness is proven. Finally, CTL (computational tree logic) is used to specify the related properties and enforcement algorithm of fault analysis. The simulation results show that this method can ensure the reliability and consistency of service composition.
2017,28(1):1-16
, DOI: 10.13328/j.cnki.jos.005139
Abstract:
Knapsack problem (KP) is a well-known combinatorial optimization problem which includes 0-1 KP, bounded KP, multi-constraint KP, multiple KP, multiple-choice KP, quadratic KP, dynamic knapsack KP, discounted KP and other types of KPs. KP can be considered as a mathematical model extracted from variety of real fields and therefore has wide applications. Evolutionary algorithms (EAs) are universally considered as an efficient tool to solve KP approximately and quickly. This paper presents a survey on solving KP by EAs over the past ten years. It not only discusses various KP encoding mechanism and the individual infeasible solution processing but also provides useful guidelines for designing new EAs to solve KPs.
Knapsack problem (KP) is a well-known combinatorial optimization problem which includes 0-1 KP, bounded KP, multi-constraint KP, multiple KP, multiple-choice KP, quadratic KP, dynamic knapsack KP, discounted KP and other types of KPs. KP can be considered as a mathematical model extracted from variety of real fields and therefore has wide applications. Evolutionary algorithms (EAs) are universally considered as an efficient tool to solve KP approximately and quickly. This paper presents a survey on solving KP by EAs over the past ten years. It not only discusses various KP encoding mechanism and the individual infeasible solution processing but also provides useful guidelines for designing new EAs to solve KPs.
2010,21(7):1620-1634
Abstract:
As an application of mobile ad hoc networks (MANET) on Intelligent Transportation Information System, the most important goal of vehicular ad hoc networks (VANET) is to reduce the high number of accidents and fatal consequences dramatically. One of the most important factors that would contribute to the realization of this goal is the design of effective broadcast protocols. This paper introduces the characteristics and application fields of VANET briefly. Then, it discusses the characteristics, performance, and application areas with analysis and comparison of various categories of broadcast protocols in VANET. According to the characteristic of VANET and its application requirement, the paper proposes the ideas and breakthrough direction of information broadcast model design of inter-vehicle communication.
As an application of mobile ad hoc networks (MANET) on Intelligent Transportation Information System, the most important goal of vehicular ad hoc networks (VANET) is to reduce the high number of accidents and fatal consequences dramatically. One of the most important factors that would contribute to the realization of this goal is the design of effective broadcast protocols. This paper introduces the characteristics and application fields of VANET briefly. Then, it discusses the characteristics, performance, and application areas with analysis and comparison of various categories of broadcast protocols in VANET. According to the characteristic of VANET and its application requirement, the paper proposes the ideas and breakthrough direction of information broadcast model design of inter-vehicle communication.
2010,21(5):916-929
Abstract:
Data deduplication technologies can be divided into two categories: a) identical data detection techniques, and b) similar data detection and encoding techniques. This paper presents a systematic survey on these two categories of data deduplication technologies and analyzes their advantages and disadvantages. Besides, since data deduplication technologies can affect the reliability and performance of storage systems, this paper also surveys various kinds of technologies proposed to cope with these two aspects of problems. Based on the analysis of the current state of research on data deduplication technologies, this paper makes several conclusions as follows: a) How to mine data characteristic information in data deduplication has not been completely solved, and how to use data characteristic information to effectively eliminate duplicate data also needs further study; b) From the perspective of storage system design, it still needs further study how to introduce proper mechanisms to overcome the reliability limitations of data deduplication techniques and reduce the additional system overheads caused by data deduplication techniques.
Data deduplication technologies can be divided into two categories: a) identical data detection techniques, and b) similar data detection and encoding techniques. This paper presents a systematic survey on these two categories of data deduplication technologies and analyzes their advantages and disadvantages. Besides, since data deduplication technologies can affect the reliability and performance of storage systems, this paper also surveys various kinds of technologies proposed to cope with these two aspects of problems. Based on the analysis of the current state of research on data deduplication technologies, this paper makes several conclusions as follows: a) How to mine data characteristic information in data deduplication has not been completely solved, and how to use data characteristic information to effectively eliminate duplicate data also needs further study; b) From the perspective of storage system design, it still needs further study how to introduce proper mechanisms to overcome the reliability limitations of data deduplication techniques and reduce the additional system overheads caused by data deduplication techniques.
2009,20(6):1393-1405
Abstract:
Combinatorial testing can use a small number of test cases to test systems while preserving fault detection ability. However, the complexity of test case generation problem for combinatorial testing is NP-complete. The efficiency and complexity of this testing method have attracted many researchers from the area of combinatorics and software engineering. This paper summarizes the research works on this topic in recent years. They include: various combinatorial test criteria, the relations between the test generation problem and other NP-complete problems, the mathematical methods for constructing test cases, the computer search techniques for test generation and fault localization techniques based on combinatorial testing.
Combinatorial testing can use a small number of test cases to test systems while preserving fault detection ability. However, the complexity of test case generation problem for combinatorial testing is NP-complete. The efficiency and complexity of this testing method have attracted many researchers from the area of combinatorics and software engineering. This paper summarizes the research works on this topic in recent years. They include: various combinatorial test criteria, the relations between the test generation problem and other NP-complete problems, the mathematical methods for constructing test cases, the computer search techniques for test generation and fault localization techniques based on combinatorial testing.
2008,19(10):2706-2719
Abstract:
Web search engine has become a very important tool for finding information efficiently from the massive Web data. With the explosive growth of the Web data, traditional centralized search engines become harder to catch up with the growing step of people's information needs. With the rapid development of peer-to-peer (P2P) technology, the notion of P2P Web search has been proposed and quickly becomes a research focus. The goal of this paper is to give a brief summary of current P2P Web search technologies in order to facilitate future research. First, some main challenges for P2P Web search are presented. Then, key techniques for building a feasible and efficient P2P Web search engine are reviewed, including system topology, data placement, query routing, index partitioning, collection selection, relevance ranking and Web crawling. Finally, three recently proposed novel P2P Web search prototypes are introduced.
Web search engine has become a very important tool for finding information efficiently from the massive Web data. With the explosive growth of the Web data, traditional centralized search engines become harder to catch up with the growing step of people's information needs. With the rapid development of peer-to-peer (P2P) technology, the notion of P2P Web search has been proposed and quickly becomes a research focus. The goal of this paper is to give a brief summary of current P2P Web search technologies in order to facilitate future research. First, some main challenges for P2P Web search are presented. Then, key techniques for building a feasible and efficient P2P Web search engine are reviewed, including system topology, data placement, query routing, index partitioning, collection selection, relevance ranking and Web crawling. Finally, three recently proposed novel P2P Web search prototypes are introduced.
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2003,14(7):1282-1291
Abstract:
Sensor network, which is made by the convergence of sensor, micro-electro-mechanism system and networks technologies, is a novel technology about acquiring and processing information. In this paper, the architecture of wireless sensor network is briefly introduced. Next, some valuable applications are explained and forecasted. Combining with the existing work, the hot spots including power-aware routing and media access control schemes are discussed and presented in detail. Finally, taking account of application requirements, several future research directions are put forward.
Sensor network, which is made by the convergence of sensor, micro-electro-mechanism system and networks technologies, is a novel technology about acquiring and processing information. In this paper, the architecture of wireless sensor network is briefly introduced. Next, some valuable applications are explained and forecasted. Combining with the existing work, the hot spots including power-aware routing and media access control schemes are discussed and presented in detail. Finally, taking account of application requirements, several future research directions are put forward.
2008,19(1):48-61
Abstract:
The research actuality and new progress in clustering algorithm in recent years are summarized in this paper. First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining.
The research actuality and new progress in clustering algorithm in recent years are summarized in this paper. First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining.
2010,21(8):1834-1848
Abstract:
This paper surveys the state of the art of sentiment analysis. First, three important tasks of sentiment analysis are summarized and analyzed in detail, including sentiment extraction, sentiment classification, sentiment retrieval and summarization. Then, the evaluation and corpus for sentiment analysis are introduced. Finally, the applications of sentiment analysis are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, making detailed comparison and analysis.
This paper surveys the state of the art of sentiment analysis. First, three important tasks of sentiment analysis are summarized and analyzed in detail, including sentiment extraction, sentiment classification, sentiment retrieval and summarization. Then, the evaluation and corpus for sentiment analysis are introduced. Finally, the applications of sentiment analysis are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, making detailed comparison and analysis.
2011,22(1):71-83
, DOI: 10.3724/SP.J.1001.2011.03958
Abstract:
Cloud Computing is the fundamental change happening in the field of Information Technology. It is a representation of a movement towards the intensive, large scale specialization. On the other hand, it brings about not only convenience and efficiency problems, but also great challenges in the field of data security and privacy protection. Currently, security has been regarded as one of the greatest problems in the development of Cloud Computing. This paper describes the great requirements in Cloud Computing, security key technology, standard and regulation etc., and provides a Cloud Computing security framework. This paper argues that the changes in the above aspects will result in a technical revolution in the field of information security.
Cloud Computing is the fundamental change happening in the field of Information Technology. It is a representation of a movement towards the intensive, large scale specialization. On the other hand, it brings about not only convenience and efficiency problems, but also great challenges in the field of data security and privacy protection. Currently, security has been regarded as one of the greatest problems in the development of Cloud Computing. This paper describes the great requirements in Cloud Computing, security key technology, standard and regulation etc., and provides a Cloud Computing security framework. This paper argues that the changes in the above aspects will result in a technical revolution in the field of information security.
2009,20(1):54-66
Abstract:
Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.
Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.
2009,20(5):1337-1348
Abstract:
This paper surveys the current technologies adopted in cloud computing as well as the systems in enterprises. Cloud computing can be viewed from two different aspects. One is about the cloud infrastructure which is the building block for the up layer cloud application. The other is of course the cloud application. This paper focuses on the cloud infrastructure including the systems and current research. Some attractive cloud applications are also discussed. Cloud computing infrastructure has three distinct characteristics. First, the infrastructure is built on top of large scale clusters which contain a large number of cheap PC servers. Second, the applications are co-designed with the fundamental infrastructure that the computing resources can be maximally utilized. Third, the reliability of the whole system is achieved by software building on top of redundant hardware instead of mere hardware. All these technologies are for the two important goals for distributed system: high scalability and high availability. Scalability means that the cloud infrastructure can be expanded to very large scale even to thousands of nodes. Availability means that the services are available even when quite a number of nodes fail. From this paper, readers will capture the current status of cloud computing as well as its future trends.
This paper surveys the current technologies adopted in cloud computing as well as the systems in enterprises. Cloud computing can be viewed from two different aspects. One is about the cloud infrastructure which is the building block for the up layer cloud application. The other is of course the cloud application. This paper focuses on the cloud infrastructure including the systems and current research. Some attractive cloud applications are also discussed. Cloud computing infrastructure has three distinct characteristics. First, the infrastructure is built on top of large scale clusters which contain a large number of cheap PC servers. Second, the applications are co-designed with the fundamental infrastructure that the computing resources can be maximally utilized. Third, the reliability of the whole system is achieved by software building on top of redundant hardware instead of mere hardware. All these technologies are for the two important goals for distributed system: high scalability and high availability. Scalability means that the cloud infrastructure can be expanded to very large scale even to thousands of nodes. Availability means that the services are available even when quite a number of nodes fail. From this paper, readers will capture the current status of cloud computing as well as its future trends.
2009,20(2):271-289
Abstract:
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.
2014,25(9):1889-1908
, DOI: 10.13328/j.cnki.jos.004674
Abstract:
This paper first introduces the key features of big data in different processing modes and their typical application scenarios, as well as corresponding representative processing systems. It then summarizes three development trends of big data processing systems. Next, the paper gives a brief survey on system supported analytic technologies and applications (including deep learning, knowledge computing, social computing, and visualization), and summarizes the key roles of individual technologies in big data analysis and understanding. Finally, the paper lays out three grand challenges of big data processing and analysis, i.e., data complexity, computation complexity, and system complexity. Potential ways for dealing with each complexity are also discussed.
This paper first introduces the key features of big data in different processing modes and their typical application scenarios, as well as corresponding representative processing systems. It then summarizes three development trends of big data processing systems. Next, the paper gives a brief survey on system supported analytic technologies and applications (including deep learning, knowledge computing, social computing, and visualization), and summarizes the key roles of individual technologies in big data analysis and understanding. Finally, the paper lays out three grand challenges of big data processing and analysis, i.e., data complexity, computation complexity, and system complexity. Potential ways for dealing with each complexity are also discussed.
2009,20(2):350-362
Abstract:
This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field. First the research background is introduced, including commercial application demands, academic institutes, conferences and journals. After formally and informally describing the recommendation problem, a comparison study is conducted based on categorized algorithms. In addition, the commonly adopted benchmarked datasets and evaluation methods are exhibited and most difficulties and future directions are concluded.
This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field. First the research background is introduced, including commercial application demands, academic institutes, conferences and journals. After formally and informally describing the recommendation problem, a comparison study is conducted based on categorized algorithms. In addition, the commonly adopted benchmarked datasets and evaluation methods are exhibited and most difficulties and future directions are concluded.
2004,15(10):1493-1504
Abstract:
Graphics processing unit (GPU) has been developing rapidly in recent years at a speed over Moor抯 law, and as a result, various applications associated with computer graphics advance greatly. At the same time, the highly processing power, parallelism and programmability available nowadays on the contemporary GPU provide an ideal platform on which the general-purpose computation could be made. Starting from an introduction to the development history and the architecture of GPU, the technical fundamentals of GPU are described in the paper. Then in the main part of the paper, the development of various applications on general purpose computation on GPU is introduced, and among those applications, fluid dynamics, algebraic computation, database operations, and spectrum analysis are introduced in detail. The experience of our work on fluid dynamics has been also given, and the development of software tools in this area is introduced. Finally, a conclusion is made, and the future development and the new challenge on both hardware and software in this subject are discussed.
Graphics processing unit (GPU) has been developing rapidly in recent years at a speed over Moor抯 law, and as a result, various applications associated with computer graphics advance greatly. At the same time, the highly processing power, parallelism and programmability available nowadays on the contemporary GPU provide an ideal platform on which the general-purpose computation could be made. Starting from an introduction to the development history and the architecture of GPU, the technical fundamentals of GPU are described in the paper. Then in the main part of the paper, the development of various applications on general purpose computation on GPU is introduced, and among those applications, fluid dynamics, algebraic computation, database operations, and spectrum analysis are introduced in detail. The experience of our work on fluid dynamics has been also given, and the development of software tools in this area is introduced. Finally, a conclusion is made, and the future development and the new challenge on both hardware and software in this subject are discussed.
2010,21(3):427-437
Abstract:
Automatic generation of poetry has always been considered a hard nut in natural language generation.This paper reports some pioneering research on a possible generic algorithm and its automatic generation of SONGCI. In light of the characteristics of Chinese ancient poetry, this paper designed the level and oblique tones-based coding method, the syntactic and semantic weighted function of fitness, the elitism and roulette-combined selection operator, and the partially mapped crossover operator and the heuristic mutation operator. As shown by tests, the system constructed on the basis of the computing model designed in this paper is basically capable of generating Chinese SONGCI with some aesthetic merit. This work represents progress in the field of Chinese poetry automatic generation.
Automatic generation of poetry has always been considered a hard nut in natural language generation.This paper reports some pioneering research on a possible generic algorithm and its automatic generation of SONGCI. In light of the characteristics of Chinese ancient poetry, this paper designed the level and oblique tones-based coding method, the syntactic and semantic weighted function of fitness, the elitism and roulette-combined selection operator, and the partially mapped crossover operator and the heuristic mutation operator. As shown by tests, the system constructed on the basis of the computing model designed in this paper is basically capable of generating Chinese SONGCI with some aesthetic merit. This work represents progress in the field of Chinese poetry automatic generation.
2021,32(2):349-369
, DOI: 10.13328/j.cnki.jos.006138
Abstract:
Few-shot learning is defined as learning models to solve problems from small samples. In recent years, under the trend of training model with big data, machine learning and deep learning have achieved success in many fields. However, in many application scenarios in the real world, there is not a large amount of data or labeled data for model training, and labeling a large number of unlabeled samples will cost a lot of manpower. Therefore, how to use a small number of samples for learning has become a problem that needs to be paid attention to at present. This paper systematically combs the current approaches of few-shot learning. It introduces each kind of corresponding model from the three categories: fine-tune based, data augmentation based, and transfer learning based. Then, the data augmentation based approaches are subdivided into unlabeled data based, data generation based, and feature augmentation based approaches. The transfer learning based approaches are subdivided into metric learning based, meta-learning based, and graph neural network based methods. In the following, the paper summarizes the few-shot datasets and the results in the experiments of the aforementioned models. Next, the paper summarizes the current situation and challenges in few-shot learning. Finally, the future technological development of few-shot learning is prospected.
Few-shot learning is defined as learning models to solve problems from small samples. In recent years, under the trend of training model with big data, machine learning and deep learning have achieved success in many fields. However, in many application scenarios in the real world, there is not a large amount of data or labeled data for model training, and labeling a large number of unlabeled samples will cost a lot of manpower. Therefore, how to use a small number of samples for learning has become a problem that needs to be paid attention to at present. This paper systematically combs the current approaches of few-shot learning. It introduces each kind of corresponding model from the three categories: fine-tune based, data augmentation based, and transfer learning based. Then, the data augmentation based approaches are subdivided into unlabeled data based, data generation based, and feature augmentation based approaches. The transfer learning based approaches are subdivided into metric learning based, meta-learning based, and graph neural network based methods. In the following, the paper summarizes the few-shot datasets and the results in the experiments of the aforementioned models. Next, the paper summarizes the current situation and challenges in few-shot learning. Finally, the future technological development of few-shot learning is prospected.
2022,33(7):2464-2481
, DOI: 10.13328/j.cnki.jos.006585
Abstract:
Symbolic propagation methods based on linear abstraction play a significant role in neural network verification. This study proposes the notion of multi-path back-propagation for these methods. Existing methods are viewed as using only a single back-propagation path to calculate the upper and lower bounds of each node in a given neural network, being specific instances of the proposed notion. Leveraging multiple back-propagation paths effectively improves the accuracy of this kind of method. For evaluation, the proposed method is quantitatively compared using multiple back-propagation paths with the state-of-the-art tool DeepPoly on benchmarks ACAS Xu, MNIST, and CIFAR10. The experiment results show that the proposed method achieves significant accuracy improvement while introducing only a low extra time cost. In addition, the multi-path back-propagation method is compared with the Optimized LiRPA based on global optimization, on the dataset MNIST. The results show that the proposed method still has an accuracy advantage.
Symbolic propagation methods based on linear abstraction play a significant role in neural network verification. This study proposes the notion of multi-path back-propagation for these methods. Existing methods are viewed as using only a single back-propagation path to calculate the upper and lower bounds of each node in a given neural network, being specific instances of the proposed notion. Leveraging multiple back-propagation paths effectively improves the accuracy of this kind of method. For evaluation, the proposed method is quantitatively compared using multiple back-propagation paths with the state-of-the-art tool DeepPoly on benchmarks ACAS Xu, MNIST, and CIFAR10. The experiment results show that the proposed method achieves significant accuracy improvement while introducing only a low extra time cost. In addition, the multi-path back-propagation method is compared with the Optimized LiRPA based on global optimization, on the dataset MNIST. The results show that the proposed method still has an accuracy advantage.
2013,24(11):2476-2497
, DOI: 10.3724/SP.J.1001.2013.04486
Abstract:
Probabilistic graphical models are powerful tools for compactly representing complex probability distributions, efficiently computing (approximate) marginal and conditional distributions, and conveniently learning parameters and hyperparameters in probabilistic models. As a result, they have been widely used in applications that require some sort of automated probabilistic reasoning, such as computer vision and natural language processing, as a formal approach to deal with uncertainty. This paper surveys the basic concepts and key results of representation, inference and learning in probabilistic graphical models, and demonstrates their uses in two important probabilistic models. It also reviews some recent advances in speeding up classic approximate inference algorithms, followed by a discussion of promising research directions.
Probabilistic graphical models are powerful tools for compactly representing complex probability distributions, efficiently computing (approximate) marginal and conditional distributions, and conveniently learning parameters and hyperparameters in probabilistic models. As a result, they have been widely used in applications that require some sort of automated probabilistic reasoning, such as computer vision and natural language processing, as a formal approach to deal with uncertainty. This paper surveys the basic concepts and key results of representation, inference and learning in probabilistic graphical models, and demonstrates their uses in two important probabilistic models. It also reviews some recent advances in speeding up classic approximate inference algorithms, followed by a discussion of promising research directions.
2018,29(5):1471-1514
, DOI: 10.13328/j.cnki.jos.005519
Abstract:
Computer aided detection/diagnosis (CAD) can improve the accuracy of diagnosis,reduce false positive,and provide decision supports for doctors.The main purpose of this paper is to analyze the latest development of computer aided diagnosis tools.Focusing on the top four fatal cancer's incidence positions,major recent publications on CAD applications in different medical imaging areas are reviewed in this survey according to different imaging techniques and diseases.Further more,multidimentional analysis is made on the researches from image data sets,algorithms and evaluation methods.Finally,existing problems,research trend and development direction in the field of medical image CAD system are discussed.
Computer aided detection/diagnosis (CAD) can improve the accuracy of diagnosis,reduce false positive,and provide decision supports for doctors.The main purpose of this paper is to analyze the latest development of computer aided diagnosis tools.Focusing on the top four fatal cancer's incidence positions,major recent publications on CAD applications in different medical imaging areas are reviewed in this survey according to different imaging techniques and diseases.Further more,multidimentional analysis is made on the researches from image data sets,algorithms and evaluation methods.Finally,existing problems,research trend and development direction in the field of medical image CAD system are discussed.
2012,23(4):962-986
, DOI: 10.3724/SP.J.1001.2012.04175
Abstract:
Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing area and draws great attention from both academia and industry fields. Under cloud computing environment, data center consist of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data, which may easily lead to the failure of the computers or data. The large amount of computers composition not only leads to great challenges to the scalability of the data center and its storage system, but also results in high hardware infrastructure cost and power cost. Therefore, fault-tolerance, scalability, and power consumption of the distributed storage for a data center becomes key part in the technology of cloud computing, in order to ensure the data availability and reliability. In this paper, a survey is made on the state of art of the key technologies in cloud computing in the following aspects: Design of data center network, organization and arrangement of data, strategies to improve fault-tolerance, methods to save storage space, and energy. Firstly, many kinds of classical topologies of data center network are introduced and compared. Secondly, kinds of current fault-tolerant storage techniques are discussed, and data replication and erasure code strategies are especially compared. Thirdly, the main current energy saving technology is addressed and analyzed. Finally, challenges in distributed storage are reviewed as well as future research trends are predicted.
Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing area and draws great attention from both academia and industry fields. Under cloud computing environment, data center consist of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data, which may easily lead to the failure of the computers or data. The large amount of computers composition not only leads to great challenges to the scalability of the data center and its storage system, but also results in high hardware infrastructure cost and power cost. Therefore, fault-tolerance, scalability, and power consumption of the distributed storage for a data center becomes key part in the technology of cloud computing, in order to ensure the data availability and reliability. In this paper, a survey is made on the state of art of the key technologies in cloud computing in the following aspects: Design of data center network, organization and arrangement of data, strategies to improve fault-tolerance, methods to save storage space, and energy. Firstly, many kinds of classical topologies of data center network are introduced and compared. Secondly, kinds of current fault-tolerant storage techniques are discussed, and data replication and erasure code strategies are especially compared. Thirdly, the main current energy saving technology is addressed and analyzed. Finally, challenges in distributed storage are reviewed as well as future research trends are predicted.
2012,23(1):1-20
, DOI: 10.3724/SP.J.1001.2012.04100
Abstract:
Context-Aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. This paper presents an overview of the field of context-aware recommender systems from a process-oriented perspective, including system frameworks, key techniques, main models, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.
Context-Aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. This paper presents an overview of the field of context-aware recommender systems from a process-oriented perspective, including system frameworks, key techniques, main models, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.
2016,27(1):45-71
, DOI: 10.13328/j.cnki.jos.004914
Abstract:
Android is a modern and most popular software platform for smartphones. According to report, Android accounted for a huge 81% of all smartphones in 2014 and shipped over 1 billion units worldwide for the first time ever. Apple, Microsoft, Blackberry and Firefox trailed a long way behind. At the same time, increased popularity of the Android smartphones has attracted hackers, leading to massive increase of Android malware applications. This paper summarizes and analyzes the latest advances in Android security from multidimensional perspectives, covering Android architecture, design principles, security mechanisms, major security threats, classification and detection of malware, static and dynamic analyses, machine learning approaches, and security extension proposals.
Android is a modern and most popular software platform for smartphones. According to report, Android accounted for a huge 81% of all smartphones in 2014 and shipped over 1 billion units worldwide for the first time ever. Apple, Microsoft, Blackberry and Firefox trailed a long way behind. At the same time, increased popularity of the Android smartphones has attracted hackers, leading to massive increase of Android malware applications. This paper summarizes and analyzes the latest advances in Android security from multidimensional perspectives, covering Android architecture, design principles, security mechanisms, major security threats, classification and detection of malware, static and dynamic analyses, machine learning approaches, and security extension proposals.
2012,23(1):32-45
, DOI: 10.3724/SP.J.1001.2012.04091
Abstract:
In many areas such as science, simulation, Internet, and e-commerce, the volume of data to be analyzed grows rapidly. Parallel techniques which could be expanded cost-effectively should be invented to deal with the big data. Relational data management technique has gone through a history of nearly 40 years. Now it encounters the tough obstacle of scalability, which relational techniques can not handle large data easily. In the mean time, none relational techniques, such as MapReduce as a typical representation, emerge as a new force, and expand their application from Web search to territories that used to be occupied by relational database systems. They confront relational technique with high availability, high scalability and massive parallel processing capability. Relational technique community, after losing the big deal of Web search, begins to learn from MapReduce. MapReduce also borrows valuable ideas from relational technique community to improve performance. Relational technique and MapReduce compete with each other, and learn from each other; new data analysis platform and new data analysis eco-system are emerging. Finally the two camps of techniques will find their right places in the new eco-system of big data analysis.
In many areas such as science, simulation, Internet, and e-commerce, the volume of data to be analyzed grows rapidly. Parallel techniques which could be expanded cost-effectively should be invented to deal with the big data. Relational data management technique has gone through a history of nearly 40 years. Now it encounters the tough obstacle of scalability, which relational techniques can not handle large data easily. In the mean time, none relational techniques, such as MapReduce as a typical representation, emerge as a new force, and expand their application from Web search to territories that used to be occupied by relational database systems. They confront relational technique with high availability, high scalability and massive parallel processing capability. Relational technique community, after losing the big deal of Web search, begins to learn from MapReduce. MapReduce also borrows valuable ideas from relational technique community to improve performance. Relational technique and MapReduce compete with each other, and learn from each other; new data analysis platform and new data analysis eco-system are emerging. Finally the two camps of techniques will find their right places in the new eco-system of big data analysis.
2015,26(1):62-81
, DOI: 10.13328/j.cnki.jos.004701
Abstract:
Network abstraction brings about the naissance of software-defined networking. SDN decouples data plane and control plane, and simplifies network management. The paper starts with a discussion on the background in the naissance and developments of SDN, combing its architecture that includes data layer, control layer and application layer. Then their key technologies are elaborated according to the hierarchical architecture of SDN. The characteristics of consistency, availability, and tolerance are especially analyzed. Moreover, latest achievements for profiled scenes are introduced. The future works are summarized in the end.
Network abstraction brings about the naissance of software-defined networking. SDN decouples data plane and control plane, and simplifies network management. The paper starts with a discussion on the background in the naissance and developments of SDN, combing its architecture that includes data layer, control layer and application layer. Then their key technologies are elaborated according to the hierarchical architecture of SDN. The characteristics of consistency, availability, and tolerance are especially analyzed. Moreover, latest achievements for profiled scenes are introduced. The future works are summarized in the end.
2005,16(5):857-868
Abstract:
Wireless Sensor Networks, a novel technology about acquiring and processing information, have been proposed for a multitude of diverse applications. The problem of self-localization, that is, determining where a given node is physically or relatively located in the networks, is a challenging one, and yet extremely crucial for many applications. In this paper, the evaluation criterion of the performance and the taxonomy for wireless sensor networks self-localization systems and algorithms are described, the principles and characteristics of recent representative localization approaches are discussed and presented, and the directions of research in this area are introduced.
Wireless Sensor Networks, a novel technology about acquiring and processing information, have been proposed for a multitude of diverse applications. The problem of self-localization, that is, determining where a given node is physically or relatively located in the networks, is a challenging one, and yet extremely crucial for many applications. In this paper, the evaluation criterion of the performance and the taxonomy for wireless sensor networks self-localization systems and algorithms are described, the principles and characteristics of recent representative localization approaches are discussed and presented, and the directions of research in this area are introduced.
2011,22(1):115-131
, DOI: 10.3724/SP.J.1001.2011.03950
Abstract:
The Internet traffic model is the key issue for network performance management, Quality of Service management, and admission control. The paper first summarizes the primary characteristics of Internet traffic, as well as the metrics of Internet traffic. It also illustrates the significance and classification of traffic modeling. Next, the paper chronologically categorizes the research activities of traffic modeling into three phases: 1) traditional Poisson modeling; 2) self-similar modeling; and 3) new research debates and new progress. Thorough reviews of the major research achievements of each phase are conducted. Finally, the paper identifies some open research issue and points out possible future research directions in traffic modeling area.
The Internet traffic model is the key issue for network performance management, Quality of Service management, and admission control. The paper first summarizes the primary characteristics of Internet traffic, as well as the metrics of Internet traffic. It also illustrates the significance and classification of traffic modeling. Next, the paper chronologically categorizes the research activities of traffic modeling into three phases: 1) traditional Poisson modeling; 2) self-similar modeling; and 3) new research debates and new progress. Thorough reviews of the major research achievements of each phase are conducted. Finally, the paper identifies some open research issue and points out possible future research directions in traffic modeling area.
2013,24(1):77-90
, DOI: 10.3724/SP.J.1001.2013.04339
Abstract:
Task parallel programming model is a widely used parallel programming model on multi-core platforms. With the intention of simplifying parallel programming and improving the utilization of multiple cores, this paper provides an introduction to the essential programming interfaces and the supporting mechanism used in task parallel programming models and discusses issues and the latest achievements from three perspectives: Parallelism expression, data management and task scheduling. In the end, some future trends in this area are discussed.
Task parallel programming model is a widely used parallel programming model on multi-core platforms. With the intention of simplifying parallel programming and improving the utilization of multiple cores, this paper provides an introduction to the essential programming interfaces and the supporting mechanism used in task parallel programming models and discusses issues and the latest achievements from three perspectives: Parallelism expression, data management and task scheduling. In the end, some future trends in this area are discussed.
2017,28(4):959-992
, DOI: 10.13328/j.cnki.jos.005143
Abstract:
The development of mobile internet and the popularity of mobile terminals produce massive trajectory data of moving objects under the era of big data. Trajectory data has spatio-temporal characteristics and rich information. Trajectory data processing techniques can be used to mine the patterns of human activities and behaviors, the moving patterns of vehicles in the city and the changes of atmospheric environment. However, trajectory data also can be exploited to disclose moving objects' privacy information (e.g., behaviors, hobbies and social relationships). Accordingly, attackers can easily access moving objects' privacy information by digging into their trajectory data such as activities and check-in locations. In another front of research, quantum computation presents an important theoretical direction to mine big data due to its scalable and powerful storage and computing capacity. Applying quantum computing approaches to handle trajectory big data could make some complex problem solvable and achieve higher efficiency. This paper reviews the key technologies of processing trajectory data. First the concept and characteristics of trajectory data is introduced, and the pre-processing methods, including noise filtering and data compression, are summarized. Then, the trajectory indexing and querying techniques, and the current achievements of mining trajectory data, such as pattern mining and trajectory classification, are reviewed. Next, an overview of the basic theories and characteristics of privacy preserving with respect to trajectory data is provided. The supporting techniques of trajectory big data mining, such as processing framework and data visualization, are presented in detail. Some possible ways of applying quantum computation into trajectory data processing, as well as the implementation of some core trajectory mining algorithms by quantum computation are also described. Finally, the challenges of trajectory data processing and promising future research directions are discussed.
The development of mobile internet and the popularity of mobile terminals produce massive trajectory data of moving objects under the era of big data. Trajectory data has spatio-temporal characteristics and rich information. Trajectory data processing techniques can be used to mine the patterns of human activities and behaviors, the moving patterns of vehicles in the city and the changes of atmospheric environment. However, trajectory data also can be exploited to disclose moving objects' privacy information (e.g., behaviors, hobbies and social relationships). Accordingly, attackers can easily access moving objects' privacy information by digging into their trajectory data such as activities and check-in locations. In another front of research, quantum computation presents an important theoretical direction to mine big data due to its scalable and powerful storage and computing capacity. Applying quantum computing approaches to handle trajectory big data could make some complex problem solvable and achieve higher efficiency. This paper reviews the key technologies of processing trajectory data. First the concept and characteristics of trajectory data is introduced, and the pre-processing methods, including noise filtering and data compression, are summarized. Then, the trajectory indexing and querying techniques, and the current achievements of mining trajectory data, such as pattern mining and trajectory classification, are reviewed. Next, an overview of the basic theories and characteristics of privacy preserving with respect to trajectory data is provided. The supporting techniques of trajectory big data mining, such as processing framework and data visualization, are presented in detail. Some possible ways of applying quantum computation into trajectory data processing, as well as the implementation of some core trajectory mining algorithms by quantum computation are also described. Finally, the challenges of trajectory data processing and promising future research directions are discussed.
2010,21(2):344-358
Abstract:
In this paper, the existing intrusion tolerance and self-destruction technology are integrated into autonomic computing in order to construct an autonomic dependability model based on SM-PEPA (semi-Markov performance evaluation process algebra) which is capable of formal analysis and verification. It can hierarchically anticipate Threats to dependability (TtD) at different levels in a self-management manner to satisfy the special requirements for dependability of mission-critical systems. Based on this model, a quantification approach is proposed on the view of steady-state probability to evaluate autonomic dependability. Finally, this paper analyzes the impacts of parameters of the model on autonomic dependability in a case study, and the experimental results demonstrate that improving the detection rate of TtD as well as the successful rate of self-healing will greatly increase the autonomic dependability.
In this paper, the existing intrusion tolerance and self-destruction technology are integrated into autonomic computing in order to construct an autonomic dependability model based on SM-PEPA (semi-Markov performance evaluation process algebra) which is capable of formal analysis and verification. It can hierarchically anticipate Threats to dependability (TtD) at different levels in a self-management manner to satisfy the special requirements for dependability of mission-critical systems. Based on this model, a quantification approach is proposed on the view of steady-state probability to evaluate autonomic dependability. Finally, this paper analyzes the impacts of parameters of the model on autonomic dependability in a case study, and the experimental results demonstrate that improving the detection rate of TtD as well as the successful rate of self-healing will greatly increase the autonomic dependability.
2014,25(1):37-50
, DOI: 10.13328/j.cnki.jos.004497
Abstract:
This paper surveys the state of the art of speech emotion recognition (SER), and presents an outlook on the trend of future SER technology. First, the survey summarizes and analyzes SER in detail from five perspectives, including emotion representation models, representative emotional speech corpora, emotion-related acoustic features extraction, SER methods and applications. Then, based on the survey, the challenges faced by current SER research are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, and presents detailed comparison and analysis between these methods.
This paper surveys the state of the art of speech emotion recognition (SER), and presents an outlook on the trend of future SER technology. First, the survey summarizes and analyzes SER in detail from five perspectives, including emotion representation models, representative emotional speech corpora, emotion-related acoustic features extraction, SER methods and applications. Then, based on the survey, the challenges faced by current SER research are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, and presents detailed comparison and analysis between these methods.
2011,22(6):1299-1315
, DOI: 10.3724/SP.J.1001.2011.03993
Abstract:
Attribute-Based encryption (ABE) scheme takes attributes as the public key and associates the ciphertext and user’s secret key with attributes, so that it can support expressive access control policies. This dramatically reduces the cost of network bandwidth and sending node’s operation in fine-grained access control of data sharing. Therefore, ABE has a broad prospect of application in the area of fine-grained access control. After analyzing the basic ABE system and its two variants, Key-Policy ABE (KP-ABE) and Ciphertext-Policy ABE (CP-ABE), this study elaborates the research problems relating to ABE systems, including access structure design for CP-ABE, attribute key revocation, key abuse and multi-authorities ABE with an extensive comparison of their functionality and performance. Finally, this study discusses the need-to-be solved problems and main research directions in ABE.
Attribute-Based encryption (ABE) scheme takes attributes as the public key and associates the ciphertext and user’s secret key with attributes, so that it can support expressive access control policies. This dramatically reduces the cost of network bandwidth and sending node’s operation in fine-grained access control of data sharing. Therefore, ABE has a broad prospect of application in the area of fine-grained access control. After analyzing the basic ABE system and its two variants, Key-Policy ABE (KP-ABE) and Ciphertext-Policy ABE (CP-ABE), this study elaborates the research problems relating to ABE systems, including access structure design for CP-ABE, attribute key revocation, key abuse and multi-authorities ABE with an extensive comparison of their functionality and performance. Finally, this study discusses the need-to-be solved problems and main research directions in ABE.
2009,20(3):524-545
Abstract:
Nowadays it has been widely accepted that the quality of software highly depends on the process that iscarried out in an organization. As part of the effort to support software process engineering activities, the researchon software process modeling and analysis is to provide an effective means to represent and analyze a process and,by doing so, to enhance the understanding of the modeled process. In addition, an enactable process model canprovide a direct guidance for the actual development process. Thus, the enforcement of the process model candirectly contribute to the improvement of the software quality. In this paper, a systematic review is carried out tosurvey the recent development in software process modeling. 72 papers from 20 conference proceedings and 7journals are identified as the evidence. The review aims to promote a better understanding of the literature byanswering the following three questions: 1) What kinds of paradigms are existing methods based on? 2) What kinds of purposes does the existing research have? 3) What kinds of new trends are reflected in the current research? Afterproviding the systematic review, we present our software process modeling method based on a multi-dimensionaland integration methodology that is intended to address several core issues facing the community.
Nowadays it has been widely accepted that the quality of software highly depends on the process that iscarried out in an organization. As part of the effort to support software process engineering activities, the researchon software process modeling and analysis is to provide an effective means to represent and analyze a process and,by doing so, to enhance the understanding of the modeled process. In addition, an enactable process model canprovide a direct guidance for the actual development process. Thus, the enforcement of the process model candirectly contribute to the improvement of the software quality. In this paper, a systematic review is carried out tosurvey the recent development in software process modeling. 72 papers from 20 conference proceedings and 7journals are identified as the evidence. The review aims to promote a better understanding of the literature byanswering the following three questions: 1) What kinds of paradigms are existing methods based on? 2) What kinds of purposes does the existing research have? 3) What kinds of new trends are reflected in the current research? Afterproviding the systematic review, we present our software process modeling method based on a multi-dimensionaland integration methodology that is intended to address several core issues facing the community.
2018,29(10):2966-2994
, DOI: 10.13328/j.cnki.jos.005551
Abstract:
In recent years, the rapid development of Internet technology and Web applications has triggered the explosion of various data on the Internet, which generates a large amount of valuable knowledge. How to organize, represent and analyze these knowledge has attracted much attention. Knowledge graph was thus developed to organize these knowledge in a semantical and visualized manner. Knowledge reasoning over knowledge graph then becomes one of the hot research topics and plays an important role in many applications such as vertical search and intelligent question-answer. The goal of knowledge reasoning over knowledge graph is to infer new facts or identify erroneous facts according to existing ones. Unlike traditional knowledge reasoning, knowledge reasoning over knowledge graph is more diversified, due to the simplicity, intuitiveness, flexibility, and richness of knowledge representation in knowledge graph. Starting with the basic concept of knowledge reasoning, this paper presents a survey on the recently developed methods for knowledge reasoning over knowledge graph. Specifically, the research progress is reviewed in detail from two aspects:One-Step reasoning and multi-step reasoning, each including rule based reasoning, distributed embedding based reasoning, neural network based reasoning and hybrid reasoning. Finally, future research directions and outlook of knowledge reasoning over knowledge graph are discussed.
In recent years, the rapid development of Internet technology and Web applications has triggered the explosion of various data on the Internet, which generates a large amount of valuable knowledge. How to organize, represent and analyze these knowledge has attracted much attention. Knowledge graph was thus developed to organize these knowledge in a semantical and visualized manner. Knowledge reasoning over knowledge graph then becomes one of the hot research topics and plays an important role in many applications such as vertical search and intelligent question-answer. The goal of knowledge reasoning over knowledge graph is to infer new facts or identify erroneous facts according to existing ones. Unlike traditional knowledge reasoning, knowledge reasoning over knowledge graph is more diversified, due to the simplicity, intuitiveness, flexibility, and richness of knowledge representation in knowledge graph. Starting with the basic concept of knowledge reasoning, this paper presents a survey on the recently developed methods for knowledge reasoning over knowledge graph. Specifically, the research progress is reviewed in detail from two aspects:One-Step reasoning and multi-step reasoning, each including rule based reasoning, distributed embedding based reasoning, neural network based reasoning and hybrid reasoning. Finally, future research directions and outlook of knowledge reasoning over knowledge graph are discussed.
2009,20(1):124-137
Abstract:
The appearance of plenty of intelligent devices equipped for short-range wireless communications boosts the fast rise of wireless ad hoc networks application. However, in many realistic application environments, nodes form a disconnected network for most of the time due to nodal mobility, low density, lossy link, etc. Conventional communication model of mobile ad hoc network (MANET) requires at least one path existing from source to destination nodes, which results in communication failure in these scenarios. Opportunistic networks utilize the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implement communications between nodes based on the "store-carry-forward" routing pattern. This networking approach, totally different from the traditional communication model, captures great interests from researchers. This paper first introduces the conceptions and theories of opportunistic networks and some current typical applications. Then it elaborates the popular research problems including opportunistic forwarding mechanism, mobility model and opportunistic data dissemination and retrieval. Some other interesting research points such as communication middleware, cooperation and security problem and new applications are stated briefly. Finally, the paper concludes and looks forward to the possible research focuses for opportunistic networks in the future.
The appearance of plenty of intelligent devices equipped for short-range wireless communications boosts the fast rise of wireless ad hoc networks application. However, in many realistic application environments, nodes form a disconnected network for most of the time due to nodal mobility, low density, lossy link, etc. Conventional communication model of mobile ad hoc network (MANET) requires at least one path existing from source to destination nodes, which results in communication failure in these scenarios. Opportunistic networks utilize the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implement communications between nodes based on the "store-carry-forward" routing pattern. This networking approach, totally different from the traditional communication model, captures great interests from researchers. This paper first introduces the conceptions and theories of opportunistic networks and some current typical applications. Then it elaborates the popular research problems including opportunistic forwarding mechanism, mobility model and opportunistic data dissemination and retrieval. Some other interesting research points such as communication middleware, cooperation and security problem and new applications are stated briefly. Finally, the paper concludes and looks forward to the possible research focuses for opportunistic networks in the future.
2013,24(4):825-842
, DOI: 10.3724/SP.J.1001.2013.04369
Abstract:
Honeypot is a proactive defense technology, introduced by the defense side to change the asymmetric situation of a network attack and defensive game. Through the deployment of the honeypots, i.e. security resources without any production purpose, the defenders can deceive attackers to illegally take advantage of the honeypots and capture and analyze the attack behaviors to understand the attack tools and methods, and to learn the intentions and motivations. Honeypot technology has won the sustained attention of the security community to make considerable progress and get wide application, and has become one of the main technical means of the Internet security threat monitoring and analysis. In this paper, the origin and evolution process of the honeypot technology are presented first. Next, the key mechanisms of honeypot technology are comprehensively analyzed, the development process of the honeypot deployment structure is also reviewed, and the latest applications of honeypot technology in the directions of Internet security threat monitoring, analysis and prevention are summarized. Finally, the problems of honeypot technology, development trends and further research directions are discussed.
Honeypot is a proactive defense technology, introduced by the defense side to change the asymmetric situation of a network attack and defensive game. Through the deployment of the honeypots, i.e. security resources without any production purpose, the defenders can deceive attackers to illegally take advantage of the honeypots and capture and analyze the attack behaviors to understand the attack tools and methods, and to learn the intentions and motivations. Honeypot technology has won the sustained attention of the security community to make considerable progress and get wide application, and has become one of the main technical means of the Internet security threat monitoring and analysis. In this paper, the origin and evolution process of the honeypot technology are presented first. Next, the key mechanisms of honeypot technology are comprehensively analyzed, the development process of the honeypot deployment structure is also reviewed, and the latest applications of honeypot technology in the directions of Internet security threat monitoring, analysis and prevention are summarized. Finally, the problems of honeypot technology, development trends and further research directions are discussed.
2019,30(2):440-468
, DOI: 10.13328/j.cnki.jos.005659
Abstract:
Recent years, applying Deep Learning (DL) into Image Semantic Segmentation (ISS) has been widely used due to its state-of-the-art performances and high-quality results. This paper systematically reviews the contribution of DL to the field of ISS. Different methods of ISS based on DL (ISSbDL) are summarized. These methods are divided into ISS based on the Regional Classification (ISSbRC) and ISS based on the Pixel Classification (ISSbPC) according to the image segmentation characteristics and segmentation granularity. Then, the methods of ISSbPC are surveyed from two points of view:ISS based on Fully Supervised Learning (ISSbFSL) and ISS based on Weakly Supervised Learning (ISSbWSL). The representative algorithms of each method are introduced and analyzed, as well as the basic workflow, framework, advantages and disadvantages of these methods are detailedly analyzed and compared. In addition, the related experiments of ISS are analyzed and summarized, and the common data sets and performance evaluation indexes in ISS experiments are introduced. Finally, possible research directions and trends are given and analyzed.
Recent years, applying Deep Learning (DL) into Image Semantic Segmentation (ISS) has been widely used due to its state-of-the-art performances and high-quality results. This paper systematically reviews the contribution of DL to the field of ISS. Different methods of ISS based on DL (ISSbDL) are summarized. These methods are divided into ISS based on the Regional Classification (ISSbRC) and ISS based on the Pixel Classification (ISSbPC) according to the image segmentation characteristics and segmentation granularity. Then, the methods of ISSbPC are surveyed from two points of view:ISS based on Fully Supervised Learning (ISSbFSL) and ISS based on Weakly Supervised Learning (ISSbWSL). The representative algorithms of each method are introduced and analyzed, as well as the basic workflow, framework, advantages and disadvantages of these methods are detailedly analyzed and compared. In addition, the related experiments of ISS are analyzed and summarized, and the common data sets and performance evaluation indexes in ISS experiments are introduced. Finally, possible research directions and trends are given and analyzed.
2018,29(10):3068-3090
, DOI: 10.13328/j.cnki.jos.005607
Abstract:
Designing problems are ubiquitous in science research and industry applications. In recent years, Bayesian optimization, which acts as a very effective global optimization algorithm, has been widely applied in designing problems. By structuring the probabilistic surrogate model and the acquisition function appropriately, Bayesian optimization framework can guarantee to obtain the optimal solution under a few numbers of function evaluations, thus it is very suitable to solve the extremely complex optimization problems in which their objective functions could not be expressed, or the functions are non-convex, multimodal and computational expensive. This paper provides a detailed analysis on Bayesian optimization in methodology and application areas, and discusses its research status and the problems in future researches. This work is hopefully beneficial to the researchers from the related communities.
Designing problems are ubiquitous in science research and industry applications. In recent years, Bayesian optimization, which acts as a very effective global optimization algorithm, has been widely applied in designing problems. By structuring the probabilistic surrogate model and the acquisition function appropriately, Bayesian optimization framework can guarantee to obtain the optimal solution under a few numbers of function evaluations, thus it is very suitable to solve the extremely complex optimization problems in which their objective functions could not be expressed, or the functions are non-convex, multimodal and computational expensive. This paper provides a detailed analysis on Bayesian optimization in methodology and application areas, and discusses its research status and the problems in future researches. This work is hopefully beneficial to the researchers from the related communities.
2020,31(7):2245-2282
, DOI: 10.13328/j.cnki.jos.006037
Abstract:
Ultrasonography is the first choice of imaging examination and preoperative evaluation for thyroid and breast cancer. However, ultrasonic characteristics of benign and malignant nodules are commonly overlapped. The diagnosis heavily relies on operator's experience other than quantitative and stable methods. In recent years, medical imaging analysis based on computer technology has developed rapidly, and a series of landmark breakthroughs have been made, which provides effective decision supports for medical imaging diagnosis. In this work, the research progress of computer vision and image recognition technologies in thyroid and breast ultrasound images is studied. A series of key technologies involved in automatic diagnosis of ultrasound images is the main lines of the work. The major algorithms in recent years are summarized and analyzed, such as ultrasound image preprocessing, lesion localization and segmentation, feature extraction and classification. Moreover, multi-dimensional analysis is made on the algorithms, data sets, and evaluation methods. Finally, existing problems related to automatic analysis of those two kinds of ultrasound imaging are discussed, research trend and development direction in the field of ultrasound images analysis are discussed.
Ultrasonography is the first choice of imaging examination and preoperative evaluation for thyroid and breast cancer. However, ultrasonic characteristics of benign and malignant nodules are commonly overlapped. The diagnosis heavily relies on operator's experience other than quantitative and stable methods. In recent years, medical imaging analysis based on computer technology has developed rapidly, and a series of landmark breakthroughs have been made, which provides effective decision supports for medical imaging diagnosis. In this work, the research progress of computer vision and image recognition technologies in thyroid and breast ultrasound images is studied. A series of key technologies involved in automatic diagnosis of ultrasound images is the main lines of the work. The major algorithms in recent years are summarized and analyzed, such as ultrasound image preprocessing, lesion localization and segmentation, feature extraction and classification. Moreover, multi-dimensional analysis is made on the algorithms, data sets, and evaluation methods. Finally, existing problems related to automatic analysis of those two kinds of ultrasound imaging are discussed, research trend and development direction in the field of ultrasound images analysis are discussed.
2004,15(11):1583-1594
Abstract:
Uncertainty exists widely in the subjective and objective world. In all kinds of uncertainty, randomness and fuzziness are the most important and fundamental. In this paper, the relationship between randomness and fuzziness is discussed. Uncertain states and their changes can be measured by entropy and hyper-entropy respectively. Taken advantage of entropy and hyper-entropy, the uncertainty of chaos, fractal and complex networks by their various evolution and differentiation are further studied. A simple and effective way is proposed to simulate the uncertainty by means of knowledge representation which provides a basis for the automation of both logic and image thinking with uncertainty. The AI (artificial intelligence) with uncertainty is a new cross-discipline, which covers computer science, physics, mathematics, brain science, psychology, cognitive science, biology and philosophy, and results in the automation of representation, process and thinking for uncertain information and knowledge.
Uncertainty exists widely in the subjective and objective world. In all kinds of uncertainty, randomness and fuzziness are the most important and fundamental. In this paper, the relationship between randomness and fuzziness is discussed. Uncertain states and their changes can be measured by entropy and hyper-entropy respectively. Taken advantage of entropy and hyper-entropy, the uncertainty of chaos, fractal and complex networks by their various evolution and differentiation are further studied. A simple and effective way is proposed to simulate the uncertainty by means of knowledge representation which provides a basis for the automation of both logic and image thinking with uncertainty. The AI (artificial intelligence) with uncertainty is a new cross-discipline, which covers computer science, physics, mathematics, brain science, psychology, cognitive science, biology and philosophy, and results in the automation of representation, process and thinking for uncertain information and knowledge.
2014,25(4):839-862
, DOI: 10.13328/j.cnki.jos.004558
Abstract:
Batch computing and stream computing are two important forms of big data computing. The research and discussions on batch computing in big data environment are comparatively sufficient. But how to efficiently deal with stream computing to meet many requirements, such as low latency, high throughput and continuously reliable running, and how to build efficient stream big data computing systems, are great challenges in the big data computing research. This paper provides a research of the data computing architecture and the key issues in stream computing in big data environments. Firstly, the research gives a brief summary of three application scenarios of stream computing in business intelligence, marketing and public service. It also shows distinctive features of the stream computing in big data environment, such as real time, volatility, burstiness, irregularity and infinity. A well-designed stream computing system always optimizes in system structure, data transmission, application interfaces, high-availability, and so on. Subsequently, the research offers detailed analyses and comparisons of five typical and open-source stream computing systems in big data environment. Finally, the research specifically addresses some new challenges of the stream big data systems, such as scalability, fault tolerance, consistency, load balancing and throughput.
Batch computing and stream computing are two important forms of big data computing. The research and discussions on batch computing in big data environment are comparatively sufficient. But how to efficiently deal with stream computing to meet many requirements, such as low latency, high throughput and continuously reliable running, and how to build efficient stream big data computing systems, are great challenges in the big data computing research. This paper provides a research of the data computing architecture and the key issues in stream computing in big data environments. Firstly, the research gives a brief summary of three application scenarios of stream computing in business intelligence, marketing and public service. It also shows distinctive features of the stream computing in big data environment, such as real time, volatility, burstiness, irregularity and infinity. A well-designed stream computing system always optimizes in system structure, data transmission, application interfaces, high-availability, and so on. Subsequently, the research offers detailed analyses and comparisons of five typical and open-source stream computing systems in big data environment. Finally, the research specifically addresses some new challenges of the stream big data systems, such as scalability, fault tolerance, consistency, load balancing and throughput.
2005,16(1):1-7
Abstract:
The paper gives some thinking according to the following four aspects: 1) from the law of things development, revealing the development history of software engineering technology; 2) from the point of software natural characteristic, analyzing the construction of every abstraction layer of virtual machine; 3) from the point of software development, proposing the research content of software engineering discipline, and research the pattern of industrialized software production; 4) based on the appearance of Internet technology, exploring the development trend of software technology.
The paper gives some thinking according to the following four aspects: 1) from the law of things development, revealing the development history of software engineering technology; 2) from the point of software natural characteristic, analyzing the construction of every abstraction layer of virtual machine; 3) from the point of software development, proposing the research content of software engineering discipline, and research the pattern of industrialized software production; 4) based on the appearance of Internet technology, exploring the development trend of software technology.
2006,17(9):1848-1859
Abstract:
In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given.
In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given.
2012,23(8):2058-2072
, DOI: 10.3724/SP.J.1001.2012.04237
Abstract:
The Distributed denial of service (DDoS) attack is a major threat to the current network. Based on the attack packet level, the study divides DDoS attacks into network-level DDoS attacks and application-level DDoS attacks. Next, the study analyzes the detection and control methods of these two kinds of DDoS attacks in detail, and it also analyzes the drawbacks of different control methods implemented in different network positions. Finally, the study analyzes the drawbacks of the current detection and control methods, the development trend of the DDoS filter system, and corresponding technological challenges are also proposed.
The Distributed denial of service (DDoS) attack is a major threat to the current network. Based on the attack packet level, the study divides DDoS attacks into network-level DDoS attacks and application-level DDoS attacks. Next, the study analyzes the detection and control methods of these two kinds of DDoS attacks in detail, and it also analyzes the drawbacks of different control methods implemented in different network positions. Finally, the study analyzes the drawbacks of the current detection and control methods, the development trend of the DDoS filter system, and corresponding technological challenges are also proposed.
2005,16(10):1743-1756
Abstract:
This paper presents a survey on the theory of provable security and its applications to the design and analysis of security protocols. It clarifies what the provable security is, explains some basic notions involved in the theory of provable security and illustrates the basic idea of random oracle model. It also reviews the development and advances of provably secure public-key encryption and digital signature schemes, in the random oracle model or the standard model, as well as the applications of provable security to the design and analysis of session-key distribution protocols and their advances.
This paper presents a survey on the theory of provable security and its applications to the design and analysis of security protocols. It clarifies what the provable security is, explains some basic notions involved in the theory of provable security and illustrates the basic idea of random oracle model. It also reviews the development and advances of provably secure public-key encryption and digital signature schemes, in the random oracle model or the standard model, as well as the applications of provable security to the design and analysis of session-key distribution protocols and their advances.
2023,34(2):625-654
, DOI: 10.13328/j.cnki.jos.006696
Abstract:
Source code bug (vulnerability) detection is a process of judging whether there are unexpected behaviors in the program code. It is widely used in software engineering tasks such as software testing and software maintenance, and plays a vital role in software functional assurance and application security. Traditional vulnerability detection research is based on program analysis, which usually requires strong domain knowledge and complex calculation rules, and faces the problem of state explosion, resulting in limited detection performance, and there is room for greater improvement in the rate of false positives and false negatives. In recent years, the open source community's vigorous development has accumulated massive amounts of data with open source code as the core. In this context, the feature learning capabilities of deep learning can automatically learn semantically rich code representations, thereby providing a new way for vulnerability detection. This study collected the latest high-level papers in this field, systematically summarized and explained the current methods from two aspects:vulnerability code dataset and deep learning vulnerability detection model. Finally, it summarizes the main challenges faced by the research in this field, and looks forward to the possible future research focus.
Source code bug (vulnerability) detection is a process of judging whether there are unexpected behaviors in the program code. It is widely used in software engineering tasks such as software testing and software maintenance, and plays a vital role in software functional assurance and application security. Traditional vulnerability detection research is based on program analysis, which usually requires strong domain knowledge and complex calculation rules, and faces the problem of state explosion, resulting in limited detection performance, and there is room for greater improvement in the rate of false positives and false negatives. In recent years, the open source community's vigorous development has accumulated massive amounts of data with open source code as the core. In this context, the feature learning capabilities of deep learning can automatically learn semantically rich code representations, thereby providing a new way for vulnerability detection. This study collected the latest high-level papers in this field, systematically summarized and explained the current methods from two aspects:vulnerability code dataset and deep learning vulnerability detection model. Finally, it summarizes the main challenges faced by the research in this field, and looks forward to the possible future research focus.
2013,24(2):295-316
, DOI: 10.3724/SP.J.1001.2013.04336
Abstract:
Under the new application mode, the traditional hierarchy data centers face several limitations in size, bandwidth, scalability, and cost. In order to meet the needs of new applications, data center network should fulfill the requirements with low-cost, such as high scalability, low configuration overhead, robustness and energy-saving. First, the shortcomings of the traditional data center network architecture are summarized, and new requirements are pointed out. Secondly, the existing proposals are divided into two categories, i.e. server-centric and network-centric. Then, several representative architectures of these two categories are overviewed and compared in detail. Finally, the future directions of data center network are discussed.
Under the new application mode, the traditional hierarchy data centers face several limitations in size, bandwidth, scalability, and cost. In order to meet the needs of new applications, data center network should fulfill the requirements with low-cost, such as high scalability, low configuration overhead, robustness and energy-saving. First, the shortcomings of the traditional data center network architecture are summarized, and new requirements are pointed out. Secondly, the existing proposals are divided into two categories, i.e. server-centric and network-centric. Then, several representative architectures of these two categories are overviewed and compared in detail. Finally, the future directions of data center network are discussed.
LI Xu-Rong, JI Shou-Ling, WU Chun-Ming, LIU Zhen-Guang, DENG Shui-Guang, CHENG Peng, YANG Min, KONG Xiang-Wei
2021,32(2):496-518
, DOI: 10.13328/j.cnki.jos.006140
Abstract:
Deep learning has achieved great success in the field of computer vision, surpassing many traditional methods. However, in recent years, deep learning technology has been abused in the production of fake videos, making fake videos represented by Deepfakes flooding on the Internet. This technique produces pornographic movies, fake news, political rumors by tampering or replacing the face information of the original videos and synthesizes fake speech. In order to eliminate the negative effects brought by such forgery technologies, many researchers have conducted in-depth research on the identification of fake videos and proposed a series of detection methods to help institutions or communities to identify such fake videos. Nevertheless, the current detection technology still has many limitations such as specific distribution data, specific compression ratio, and so on, far behind the generation technology of fake video. In addition, different researchers handle the problem from different angles. The data sets and evaluation indicators used are not uniform. So far, the academic community still lacks a unified understanding of deep forgery and detection technology. The architecture of deep forgery and detection technology research is not clear. In this review, the development of deep forgery and detection technologies are reviewed. Besides, existing research works are systematically summarize and scientifically classified. Finally, the social risks posed by the spread of Deepfakes technology are discussed, the limitations of detection technology are analyzed, and the challenges and potential research directions of detection technology are discussed, aiming to provide guidance for follow-up researchers to further promote the development and deployment of Deepfakes detection technology.
Deep learning has achieved great success in the field of computer vision, surpassing many traditional methods. However, in recent years, deep learning technology has been abused in the production of fake videos, making fake videos represented by Deepfakes flooding on the Internet. This technique produces pornographic movies, fake news, political rumors by tampering or replacing the face information of the original videos and synthesizes fake speech. In order to eliminate the negative effects brought by such forgery technologies, many researchers have conducted in-depth research on the identification of fake videos and proposed a series of detection methods to help institutions or communities to identify such fake videos. Nevertheless, the current detection technology still has many limitations such as specific distribution data, specific compression ratio, and so on, far behind the generation technology of fake video. In addition, different researchers handle the problem from different angles. The data sets and evaluation indicators used are not uniform. So far, the academic community still lacks a unified understanding of deep forgery and detection technology. The architecture of deep forgery and detection technology research is not clear. In this review, the development of deep forgery and detection technologies are reviewed. Besides, existing research works are systematically summarize and scientifically classified. Finally, the social risks posed by the spread of Deepfakes technology are discussed, the limitations of detection technology are analyzed, and the challenges and potential research directions of detection technology are discussed, aiming to provide guidance for follow-up researchers to further promote the development and deployment of Deepfakes detection technology.
2003,14(9):1621-1628
Abstract:
Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.
Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.
2018,29(7):2092-2115
, DOI: 10.13328/j.cnki.jos.005589
Abstract:
Blockchain is a distributed public ledger technology that originates from the digital cryptocurrency, bitcoin. Its development has attracted wide attention in industry and academia fields. Blockchain has the advantages of de-centralization, trustworthiness, anonymity and immutability. It breaks through the limitation of traditional center-based technology and has broad development prospect. This paper introduces the research progress of blockchain technology and its application in the field of information security. Firstly, the basic theory and model of blockchain are introduced from five aspects:Basic framework, key technology, technical feature, and application mode and area. Secondly, from the perspective of current research situation of blockchain in the field of information security, this paper summarizes the research progress of blockchain in authentication technology, access control technology and data protection technology, and compares the characteristics of various researches. Finally, the application challenges of blockchain technology are analyzed, and the development outlook of blockchain in the field of information security is highlighted. This study intends to provide certain reference value for future research work.
Blockchain is a distributed public ledger technology that originates from the digital cryptocurrency, bitcoin. Its development has attracted wide attention in industry and academia fields. Blockchain has the advantages of de-centralization, trustworthiness, anonymity and immutability. It breaks through the limitation of traditional center-based technology and has broad development prospect. This paper introduces the research progress of blockchain technology and its application in the field of information security. Firstly, the basic theory and model of blockchain are introduced from five aspects:Basic framework, key technology, technical feature, and application mode and area. Secondly, from the perspective of current research situation of blockchain in the field of information security, this paper summarizes the research progress of blockchain in authentication technology, access control technology and data protection technology, and compares the characteristics of various researches. Finally, the application challenges of blockchain technology are analyzed, and the development outlook of blockchain in the field of information security is highlighted. This study intends to provide certain reference value for future research work.
2010,21(7):1620-1634
Abstract:
As an application of mobile ad hoc networks (MANET) on Intelligent Transportation Information System, the most important goal of vehicular ad hoc networks (VANET) is to reduce the high number of accidents and fatal consequences dramatically. One of the most important factors that would contribute to the realization of this goal is the design of effective broadcast protocols. This paper introduces the characteristics and application fields of VANET briefly. Then, it discusses the characteristics, performance, and application areas with analysis and comparison of various categories of broadcast protocols in VANET. According to the characteristic of VANET and its application requirement, the paper proposes the ideas and breakthrough direction of information broadcast model design of inter-vehicle communication.
As an application of mobile ad hoc networks (MANET) on Intelligent Transportation Information System, the most important goal of vehicular ad hoc networks (VANET) is to reduce the high number of accidents and fatal consequences dramatically. One of the most important factors that would contribute to the realization of this goal is the design of effective broadcast protocols. This paper introduces the characteristics and application fields of VANET briefly. Then, it discusses the characteristics, performance, and application areas with analysis and comparison of various categories of broadcast protocols in VANET. According to the characteristic of VANET and its application requirement, the paper proposes the ideas and breakthrough direction of information broadcast model design of inter-vehicle communication.
2016,27(11):2855-2869
, DOI: 10.13328/j.cnki.jos.004932
Abstract:
With the proliferation of the Chinese social network (especially the rise of weibo), the productivity and lifestyle of the country's society is more and more profoundly influenced by the Chinese internet public events. Due to the lack of the effective technical means, the efficiency of information processing is limited. This paper proposes a public event information entropy calculation method. First, a mathematical modeling of event information content is built. Then, multidimensional random variable information entropy of the public events is calculated based on Shannon information theory. Furthermore, a new technical index of quantitative analysis to the internet public events is put forward, laying out a foundation for further research work.
With the proliferation of the Chinese social network (especially the rise of weibo), the productivity and lifestyle of the country's society is more and more profoundly influenced by the Chinese internet public events. Due to the lack of the effective technical means, the efficiency of information processing is limited. This paper proposes a public event information entropy calculation method. First, a mathematical modeling of event information content is built. Then, multidimensional random variable information entropy of the public events is calculated based on Shannon information theory. Furthermore, a new technical index of quantitative analysis to the internet public events is put forward, laying out a foundation for further research work.
2010,21(7):1605-1619
Abstract:
The rapid development of Internet leads to an increase in system complexity and uncertainty. Traditional network management can not meet the requirement, and it shall evolve to fusion based Cyberspace Situational Awareness (CSA). Based on the analysis of function shortage and development requirement, this paper introduces CSA as well as its origin, conception, objective and characteristics. Firstly, a CSA research framework is proposed and the research history is investigated, based on which the main aspects and the existing issues of the research are analyzed. Meanwhile, assessment methods are divided into three categories: Mathematics model, knowledge reasoning and pattern recognition. Then, this paper discusses CSA from three aspects: Model, knowledge representation and assessment methods, and then goes into detail about main idea, assessment process, merits and shortcomings of novel methods. Many typical methods are compared. The current application research of CSA in the fields of security, transmission, survivable, system evaluation and so on is presented. Finally, this paper points the development directions of CSA and offers the conclusions from issue system, technical system and application system.
The rapid development of Internet leads to an increase in system complexity and uncertainty. Traditional network management can not meet the requirement, and it shall evolve to fusion based Cyberspace Situational Awareness (CSA). Based on the analysis of function shortage and development requirement, this paper introduces CSA as well as its origin, conception, objective and characteristics. Firstly, a CSA research framework is proposed and the research history is investigated, based on which the main aspects and the existing issues of the research are analyzed. Meanwhile, assessment methods are divided into three categories: Mathematics model, knowledge reasoning and pattern recognition. Then, this paper discusses CSA from three aspects: Model, knowledge representation and assessment methods, and then goes into detail about main idea, assessment process, merits and shortcomings of novel methods. Many typical methods are compared. The current application research of CSA in the fields of security, transmission, survivable, system evaluation and so on is presented. Finally, this paper points the development directions of CSA and offers the conclusions from issue system, technical system and application system.
2009,20(6):1393-1405
Abstract:
Combinatorial testing can use a small number of test cases to test systems while preserving fault detection ability. However, the complexity of test case generation problem for combinatorial testing is NP-complete. The efficiency and complexity of this testing method have attracted many researchers from the area of combinatorics and software engineering. This paper summarizes the research works on this topic in recent years. They include: various combinatorial test criteria, the relations between the test generation problem and other NP-complete problems, the mathematical methods for constructing test cases, the computer search techniques for test generation and fault localization techniques based on combinatorial testing.
Combinatorial testing can use a small number of test cases to test systems while preserving fault detection ability. However, the complexity of test case generation problem for combinatorial testing is NP-complete. The efficiency and complexity of this testing method have attracted many researchers from the area of combinatorics and software engineering. This paper summarizes the research works on this topic in recent years. They include: various combinatorial test criteria, the relations between the test generation problem and other NP-complete problems, the mathematical methods for constructing test cases, the computer search techniques for test generation and fault localization techniques based on combinatorial testing.
2018,29(2):225-250
, DOI: 10.13328/j.cnki.jos.005424
Abstract:
Map matching is a key preprocessing step in the location-based service to match GPS points into a digital road network. Data analysis on the map matched trajectory data can be used to facilitate many real city computing applications such as intelligent transportation system and trip planning. This survey provides a systematic summary of existing research achievements of map matching. With the rapid development of urban traffic, the cost of acquiring and processing vehicle location information is increasing, low-sampling-rate GPS tracking data is growing, and the accuracy of existing algorithms is not adequate. In recent years, map matching algorithm based on hidden Markov model (HMM) has been widely studies. HMM can smoothly assimilate noisy data with path constraints by choosing a maximum likelihood path. The accuracy of HMM-based algorithms can reach 90% under certain conditions, which confirms the validity of map matching algorithm based on HMM at low sampling rate. A perspective of future work in this research area is also discussed.
Map matching is a key preprocessing step in the location-based service to match GPS points into a digital road network. Data analysis on the map matched trajectory data can be used to facilitate many real city computing applications such as intelligent transportation system and trip planning. This survey provides a systematic summary of existing research achievements of map matching. With the rapid development of urban traffic, the cost of acquiring and processing vehicle location information is increasing, low-sampling-rate GPS tracking data is growing, and the accuracy of existing algorithms is not adequate. In recent years, map matching algorithm based on hidden Markov model (HMM) has been widely studies. HMM can smoothly assimilate noisy data with path constraints by choosing a maximum likelihood path. The accuracy of HMM-based algorithms can reach 90% under certain conditions, which confirms the validity of map matching algorithm based on HMM at low sampling rate. A perspective of future work in this research area is also discussed.
2025,36(3):1040-1064
, DOI: 10.13328/j.cnki.jos.007276
, CSTR: 32375.14.jos.007276
Abstract:
2025,36(3):1390-1412
, DOI: 10.13328/j.cnki.jos.007263
, CSTR: 32375.14.jos.007263
Abstract:
2025,36(3):1152-1185
, DOI: 10.13328/j.cnki.jos.007242
, CSTR: 32375.14.jos.007242
Abstract:
2025,36(2):886-914
, DOI: 10.13328/j.cnki.jos.007168
, CSTR: 32375.14.jos.007168
Abstract:
2025,36(1):446-468
, DOI: 10.13328/j.cnki.jos.007240
, CSTR: 32375.14.jos.007240
Abstract:
2025,36(1):371-398
, DOI: 10.13328/j.cnki.jos.007231
, CSTR: 32375.14.jos.007231
Abstract:
2025,36(1):424-445
, DOI: 10.13328/j.cnki.jos.007225
, CSTR: 32375.14.jos.007225
Abstract:
2025,36(1):152-183
, DOI: 10.13328/j.cnki.jos.007217
, CSTR: 32375.14.jos.007217
Abstract:
2025,36(1):107-151
, DOI: 10.13328/j.cnki.jos.007212
, CSTR: 32375.14.jos.007212
Abstract:
2025,36(1):341-370
, DOI: 10.13328/j.cnki.jos.007196
, CSTR: 32375.14.jos.007196
Abstract:
2025,36(1):321-340
, DOI: 10.13328/j.cnki.jos.007194
, CSTR: 32375.14.jos.007194
Abstract:
2025,36(1):289-320
, DOI: 10.13328/j.cnki.jos.007193
, CSTR: 32375.14.jos.007193
Abstract:
2025,36(1):47-78
, DOI: 10.13328/j.cnki.jos.007190
, CSTR: 32375.14.jos.007190
Abstract:
2025,36(1):253-288
, DOI: 10.13328/j.cnki.jos.007182
, CSTR: 32375.14.jos.007182
Abstract:
2025,36(1):203-252
, DOI: 10.13328/j.cnki.jos.007162
, CSTR: 32375.14.jos.007162
Abstract:
2025,36(1):27-46
, DOI: 10.13328/j.cnki.jos.007157
, CSTR: 32375.14.jos.007157
Abstract:
2025,36(1):184-202
, DOI: 10.13328/j.cnki.jos.007148
, CSTR: 32375.14.jos.007148
Abstract:
2025,36(1):1-26
, DOI: 10.13328/j.cnki.jos.007143
, CSTR: 32375.14.jos.007143
Abstract:
2025,36(1):79-106
, DOI: 10.13328/j.cnki.jos.007141
, CSTR: 32375.14.jos.007141
Abstract:
2025,36(1):399-423
, DOI: 10.13328/j.cnki.jos.007048
, CSTR: 32375.14.jos.007048
Abstract:
2024,35(12):5487-5508
, DOI: 10.13328/j.cnki.jos.007232
, CSTR: 32375.14.jos.007232
Abstract:
2024,35(12):5582-5608
, DOI: 10.13328/j.cnki.jos.007089
, CSTR: 32375.14.jos.007089
Abstract:
2024,35(9):4069-4099
, DOI: 10.13328/j.cnki.jos.007138
Abstract:
2024,35(9):4038-4068
, DOI: 10.13328/j.cnki.jos.007127
Abstract:
2024,35(9):4013-4037
, DOI: 10.13328/j.cnki.jos.007126
Abstract:
2024,35(8):3980-4010
, DOI: 10.13328/j.cnki.jos.007095
Abstract:
2024,35(8):3923-3951
, DOI: 10.13328/j.cnki.jos.007093
Abstract:
2024,35(8):3897-3922
, DOI: 10.13328/j.cnki.jos.007087
Abstract:
2024,35(8):3785-3808
, DOI: 10.13328/j.cnki.jos.007080
Abstract:
2024,35(8):3752-3784
, DOI: 10.13328/j.cnki.jos.007059
Abstract:
2024,35(8):3952-3979
, DOI: 10.13328/j.cnki.jos.007045
Abstract:
2024,35(8):3843-3877
, DOI: 10.13328/j.cnki.jos.007042
Abstract:
2024,35(7):3265-3290
, DOI: 10.13328/j.cnki.jos.007112
Abstract:
2024,35(7):3497-3530
, DOI: 10.13328/j.cnki.jos.007053
Abstract:
2024,35(6):2687-2723
, DOI: 10.13328/j.cnki.jos.007099
Abstract:
2024,35(5):2235-2267
, DOI: 10.13328/j.cnki.jos.007046
Abstract:
2024,35(5):2268-2288
, DOI: 10.13328/j.cnki.jos.007038
Abstract:
2024,35(5):2379-2399
, DOI: 10.13328/j.cnki.jos.006804
Abstract:
2024,35(4):1682-1702
, DOI: 10.13328/j.cnki.jos.007012
Abstract:
2024,35(4):1618-1650
, DOI: 10.13328/j.cnki.jos.007011
Abstract:
2024,35(3):1207-1230
, DOI: 10.13328/j.cnki.jos.007075
Abstract:
2024,35(3):1231-1256
, DOI: 10.13328/j.cnki.jos.007061
Abstract:
2024,35(3):1377-1402
, DOI: 10.13328/j.cnki.jos.006807
Abstract:
2024,35(2):711-738
, DOI: 10.13328/j.cnki.jos.007006
Abstract:
2024,35(2):927-974
, DOI: 10.13328/j.cnki.jos.006996
Abstract:
2024,35(2):773-799
, DOI: 10.13328/j.cnki.jos.006985
Abstract:
2024,35(2):1028-1047
, DOI: 10.13328/j.cnki.jos.006984
Abstract:
2024,35(2):629-674
, DOI: 10.13328/j.cnki.jos.006983
Abstract:
2024,35(2):604-628
, DOI: 10.13328/j.cnki.jos.006981
Abstract:
2024,35(2):581-603
, DOI: 10.13328/j.cnki.jos.006975
Abstract:
2024,35(2):828-851
, DOI: 10.13328/j.cnki.jos.006954
Abstract:
2024,35(2):532-580
, DOI: 10.13328/j.cnki.jos.006953
Abstract:
2024,35(2):899-926
, DOI: 10.13328/j.cnki.jos.006952
Abstract:
2024,35(2):800-827
, DOI: 10.13328/j.cnki.jos.006950
Abstract:
2024,35(2):975-1009
, DOI: 10.13328/j.cnki.jos.006946
Abstract:
2024,35(2):513-531
, DOI: 10.13328/j.cnki.jos.006944
Abstract:
2024,35(2):1010-1027
, DOI: 10.13328/j.cnki.jos.006943
Abstract:
2024,35(2):872-898
, DOI: 10.13328/j.cnki.jos.006942
Abstract:
2024,35(2):852-871
, DOI: 10.13328/j.cnki.jos.006938
Abstract:
2024,35(2):675-710
, DOI: 10.13328/j.cnki.jos.006933
Abstract:
2024,35(1):19-37
, DOI: 10.13328/j.cnki.jos.006935
Abstract:
2024,35(1):430-454
, DOI: 10.13328/j.cnki.jos.006917
Abstract:
2024,35(1):405-429
, DOI: 10.13328/j.cnki.jos.006916
Abstract:
2024,35(1):236-265
, DOI: 10.13328/j.cnki.jos.006913
Abstract:
2024,35(1):356-404
, DOI: 10.13328/j.cnki.jos.006911
Abstract:
2024,35(1):1-18
, DOI: 10.13328/j.cnki.jos.006908
Abstract:
2024,35(1):87-117
, DOI: 10.13328/j.cnki.jos.006902
Abstract:
2024,35(1):309-332
, DOI: 10.13328/j.cnki.jos.006898
Abstract:
2024,35(1):333-355
, DOI: 10.13328/j.cnki.jos.006891
Abstract:
2024,35(1):220-235
, DOI: 10.13328/j.cnki.jos.006844
Abstract:
2024,35(1):63-86
, DOI: 10.13328/j.cnki.jos.006835
Abstract:
2024,35(1):185-219
, DOI: 10.13328/j.cnki.jos.006834
Abstract:
2024,35(1):136-158
, DOI: 10.13328/j.cnki.jos.006828
Abstract:
2024,35(1):118-135
, DOI: 10.13328/j.cnki.jos.006827
Abstract:
2024,35(1):455-480
, DOI: 10.13328/j.cnki.jos.006822
Abstract:
2024,35(1):288-308
, DOI: 10.13328/j.cnki.jos.006818
Abstract:
2024,35(1):38-62
, DOI: 10.13328/j.cnki.jos.006810
Abstract:
2024,35(1):481-512
, DOI: 10.13328/j.cnki.jos.006777
Abstract:
2024,35(1):159-184
, DOI: 10.13328/j.cnki.jos.006758
Abstract:
2024,35(1):266-287
, DOI: 10.13328/j.cnki.jos.006753
Abstract:
2023,34(12):5597-5613
, DOI: 10.13328/j.cnki.jos.006806
Abstract:
2023,34(12):5862-5886
, DOI: 10.13328/j.cnki.jos.006800
Abstract:
2023,34(12):5527-5551
, DOI: 10.13328/j.cnki.jos.006765
Abstract:
2023,34(12):5822-5847
, DOI: 10.13328/j.cnki.jos.006764
Abstract:
2023,34(12):5501-5526
, DOI: 10.13328/j.cnki.jos.006749
Abstract:
2023,34(11):5058-5083
, DOI: 10.13328/j.cnki.jos.006735
Abstract:
2023,34(11):4989-5007
, DOI: 10.13328/j.cnki.jos.006684
Abstract:
2023,34(11):5008-5028
, DOI: 10.13328/j.cnki.jos.006679
Abstract:
2023,34(11):5330-5354
, DOI: 10.13328/j.cnki.jos.006669
Abstract:
2023,34(10):4439-4462
, DOI: 10.13328/j.cnki.jos.006884
Abstract:
2023,34(10):4870-4915
, DOI: 10.13328/j.cnki.jos.006680
Abstract:
2023,34(10):4607-4635
, DOI: 10.13328/j.cnki.jos.006677
Abstract:
2023,34(10):4743-4771
, DOI: 10.13328/j.cnki.jos.006672
Abstract:
2023,34(10):4772-4803
, DOI: 10.13328/j.cnki.jos.006671
Abstract:
2023,34(10):4636-4660
, DOI: 10.13328/j.cnki.jos.006666
Abstract:
2023,34(9):4310-4335
, DOI: 10.13328/j.cnki.jos.006657
Abstract:
2023,34(9):4167-4194
, DOI: 10.13328/j.cnki.jos.006650
Abstract:
2023,34(8):3905-3923
, DOI: 10.13328/j.cnki.jos.006645
Abstract:
2023,34(8):3870-3890
, DOI: 10.13328/j.cnki.jos.006635
Abstract:
2023,34(8):3774-3792
, DOI: 10.13328/j.cnki.jos.006592
Abstract:
2023,34(8):3605-3636
, DOI: 10.13328/j.cnki.jos.006558
Abstract:
2023,34(7):3134-3166
, DOI: 10.13328/j.cnki.jos.006863
Abstract:
2023,34(6):2606-2627
, DOI: 10.13328/j.cnki.jos.006850
Abstract:
2023,34(6):2833-2864
, DOI: 10.13328/j.cnki.jos.006658
Abstract:
2023,34(5):2365-2391
, DOI: 10.13328/j.cnki.jos.006808
Abstract:
2023,34(5):2000-2023
, DOI: 10.13328/j.cnki.jos.006774
Abstract:
2023,34(5):2300-2316
, DOI: 10.13328/j.cnki.jos.006485
Abstract:
2023,34(4):1944-1961
, DOI: 10.13328/j.cnki.jos.006725
Abstract:
2023,34(4):1711-1731
, DOI: 10.13328/j.cnki.jos.006700
Abstract:
2023,34(4):1879-1906
, DOI: 10.13328/j.cnki.jos.006692
Abstract:
2023,34(3):1168-1192
, DOI: 10.13328/j.cnki.jos.006786
Abstract:
2023,34(3):1236-1258
, DOI: 10.13328/j.cnki.jos.006785
Abstract:
2023,34(3):1470-1490
, DOI: 10.13328/j.cnki.jos.006778
Abstract:
2023,34(3):1396-1424
, DOI: 10.13328/j.cnki.jos.006751
Abstract:
2023,34(3):1330-1364
, DOI: 10.13328/j.cnki.jos.006717
Abstract:
2023,34(3):1365-1395
, DOI: 10.13328/j.cnki.jos.006715
Abstract:
2023,34(2):582-624
, DOI: 10.13328/j.cnki.jos.006714
Abstract:
2023,34(2):761-785
, DOI: 10.13328/j.cnki.jos.006713
Abstract:
2023,34(2):565-581
, DOI: 10.13328/j.cnki.jos.006711
Abstract:
2023,34(2):690-711
, DOI: 10.13328/j.cnki.jos.006709
Abstract:
2023,34(2):985-1006
, DOI: 10.13328/j.cnki.jos.006707
Abstract:
2023,34(2):733-760
, DOI: 10.13328/j.cnki.jos.006706
Abstract:
2023,34(2):539-564
, DOI: 10.13328/j.cnki.jos.006705
Abstract:
2023,34(2):509-538
, DOI: 10.13328/j.cnki.jos.006704
Abstract:
2023,34(2):712-732
, DOI: 10.13328/j.cnki.jos.006699
Abstract:
2023,34(2):625-654
, DOI: 10.13328/j.cnki.jos.006696
Abstract:
2023,34(2):964-984
, DOI: 10.13328/j.cnki.jos.006693
Abstract:
2023,34(1):404-420
, DOI: 10.13328/j.cnki.jos.006513
Abstract:
2023,34(1):351-380
, DOI: 10.13328/j.cnki.jos.006510
Abstract:
2023,34(1):50-102
, DOI: 10.13328/j.cnki.jos.006503
Abstract:
2023,34(1):463-488
, DOI: 10.13328/j.cnki.jos.006502
Abstract:
2023,34(1):381-403
, DOI: 10.13328/j.cnki.jos.006501
Abstract:
2023,34(1):171-196
, DOI: 10.13328/j.cnki.jos.006500
Abstract:
2023,34(1):444-462
, DOI: 10.13328/j.cnki.jos.006488
Abstract:
2023,34(1):489-508
, DOI: 10.13328/j.cnki.jos.006436
Abstract:
2023,34(1):334-350
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Abstract:
2023,34(1):421-443
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Abstract:
2023,34(1):150-170
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Abstract:
2023,34(1):277-311
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Abstract:
2023,34(1):130-149
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Abstract:
2023,34(1):33-49
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Abstract:
2023,34(1):255-276
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Abstract:
2023,34(1):230-254
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Abstract:
2023,34(1):197-229
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Abstract:
2023,34(1):103-129
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Abstract:
2023,34(1):1-32
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Abstract:
2023,34(1):312-333
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Abstract:
2022,33(12):4668-4687
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Abstract:
2022,33(12):4644-4667
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Abstract:
2022,33(12):4616-4643
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Abstract:
2022,33(12):4590-4615
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2022,33(11):4107-4136
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Abstract:
2022,33(11):4084-4106
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Abstract:
2022,33(11):4334-4355
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Abstract:
2022,33(11):4217-4238
, DOI: 10.13328/j.cnki.jos.006391
Abstract:
2022,33(11):4173-4191
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2022,33(11):4192-4216
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Abstract:
2022,33(10):3635-3655
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Abstract:
2022,33(10):3582-3618
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Abstract:
2022,33(10):3673-3699
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Abstract:
2022,33(10):3732-3753
, DOI: 10.13328/j.cnki.jos.006365
Abstract:
2022,33(9):3370-3390
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Abstract:
2022,33(8):3059-3085
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Abstract:
2022,33(8):3015-3034
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Abstract:
2022,33(7):2411-2446
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Abstract:
2022,33(7):2367-2410
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Abstract:
2022,33(7):2729-2752
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Abstract:
2022,33(6):2288-2311
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Abstract:
2022,33(5):1612-1634
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Abstract:
2022,33(5):1800-1816
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Abstract:
2022,33(4):1244-1266
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Abstract:
2022,33(4):1287-1314
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Abstract:
2022,33(3):1057-1092
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Abstract:
2022,33(2):751-769
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Abstract:
2022,33(2):598-621
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Abstract:
2022,33(2):498-523
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Abstract:
2022,33(1):78-101
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Abstract:
2022,33(1):297-323
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Abstract:
2022,33(1):193-210
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Abstract:
2022,33(1):274-296
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Abstract:
2022,33(1):55-77
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Abstract:
2022,33(1):356-376
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Abstract:
2022,33(1):171-192
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Abstract:
2022,33(1):150-170
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Abstract:
2022,33(1):129-149
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Abstract:
2022,33(1):102-128
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Abstract:
2022,33(1):254-273
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Abstract:
2022,33(1):26-54
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Abstract:
2022,33(1):324-355
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Abstract:
2022,33(1):1-25
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Abstract:
2022,33(1):233-253
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Abstract:
2022,33(1):211-232
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Abstract:
2021,32(12):3992-4024
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Abstract:
2021,32(10):3051-3067
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Abstract:
2021,32(10):3176-3202
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Abstract:
2021,32(9):2642-2664
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Abstract:
2021,32(9):2665-2690
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Abstract:
2021,32(8):2522-2544
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Abstract:
2021,32(8):2469-2504
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Abstract:
2021,32(7):2260-2286
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Abstract:
2021,32(7):2078-2102
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Abstract:
2021,32(7):1926-1956
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Abstract:
2021,32(7):2056-2077
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Abstract:
2021,32(7):2016-2038
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Abstract:
2021,32(7):2118-2141
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Abstract:
2021,32(5):1404-1426
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Abstract:
2021,32(5):1495-1525
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Abstract:
2021,32(5):1461-1479
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Abstract:
2021,32(5):1427-1460
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Abstract:
2021,32(5):1526-1546
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Abstract:
2021,32(5):1373-1384
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Abstract:
2021,32(4):1082-1115
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Abstract:
2021,32(4):1129-1150
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Abstract:
2021,32(4):1067-1081
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Abstract:
2021,32(4):1201-1227
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Abstract:
2021,32(4):1165-1185
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Abstract:
2021,32(4):1151-1164
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Abstract:
2021,32(3):604-621
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Abstract:
2021,32(3):859-885
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Abstract:
2021,32(2):579-600
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Abstract:
2021,32(2):300-326
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Abstract:
2021,32(2):277-299
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Abstract:
2021,32(2):406-423
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Abstract:
2021,32(2):370-405
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Abstract:
2021,32(2):424-444
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Abstract:
2021,32(2):496-518
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Abstract:
2021,32(2):349-369
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Abstract:
2021,32(2):551-578
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Abstract:
2021,32(2):475-495
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Abstract:
2021,32(2):247-276
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Abstract:
2021,32(2):327-348
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Abstract:
2021,32(2):445-474
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Abstract:
2021,32(2):519-550
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Abstract:
2021,32(1):41-67
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Abstract:
2021,32(1):21-40
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Abstract:
2021,32(1):118-136
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Abstract:
2021,32(1):1-20
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Abstract:
2021,32(1):194-217
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Abstract:
2021,32(1):218-245
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Abstract:
2021,32(1):167-193
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Abstract:
2021,32(1):68-92
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Abstract:
2021,32(1):137-166
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Abstract:
2021,32(1):93-117
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Abstract:
Survey of Research on Protection Mechanisms of Operating System against Board Level Physical Attacks
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Abstract:
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Abstract:
2020,31(10):3087-3099
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Abstract:
2020,31(9):2903-2925
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Abstract:
2020,31(9):2826-2854
, DOI: 10.13328/j.cnki.jos.006087
Abstract:
2020,31(9):2802-2825
, DOI: 10.13328/j.cnki.jos.006085
Abstract:
2020,31(9):2883-2902
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Abstract:
2020,31(9):2654-2677
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Abstract:
2020,31(9):2627-2653
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Abstract:
2020,31(8):2574-2587
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Abstract:
2020,31(8):2432-2452
, DOI: 10.13328/j.cnki.jos.006081
Abstract:
2020,31(8):2603-2624
, DOI: 10.13328/j.cnki.jos.006080
Abstract:
2020,31(8):2465-2491
, DOI: 10.13328/j.cnki.jos.006078
Abstract:
2020,31(8):2404-2431
, DOI: 10.13328/j.cnki.jos.006076
Abstract:
2020,31(7):2040-2061
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Abstract:
2020,31(7):2127-2156
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Abstract:
2020,31(7):2221-2244
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Abstract:
2020,31(7):2095-2126
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Abstract:
2020,31(7):2019-2039
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Abstract:
2020,31(7):2205-2220
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Abstract:
2020,31(7):1997-2018
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Abstract:
2020,31(7):2062-2094
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Abstract:
2020,31(7):2184-2204
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Abstract:
2020,31(7):2245-2282
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Abstract:
2020,31(6):1761-1785
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Abstract:
2020,31(6):1723-1746
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Abstract:
2020,31(6):1681-1702
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Abstract:
2020,31(5):1465-1496
, DOI: 10.13328/j.cnki.jos.005988
Abstract:
2020,31(5):1406-1434
, DOI: 10.13328/j.cnki.jos.005967
Abstract:
2020,31(5):1435-1453
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Abstract:
2020,31(5):1255-1275
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Abstract:
2020,31(5):1243-1254
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2020,31(4):1143-1161
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Abstract:
2020,31(4):1124-1142
, DOI: 10.13328/j.cnki.jos.005982
Abstract:
2020,31(4):912-931
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Abstract:
2020,31(3):893-908
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Abstract:
2020,31(3):600-619
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Abstract:
2020,31(3):806-830
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Abstract:
2020,31(3):866-892
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Abstract:
2020,31(2):544-563
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Abstract:
2020,31(2):302-320
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Abstract:
2020,31(1):47-66
, DOI: 10.13328/j.cnki.jos.005893
Abstract:
2020,31(1):1-19
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Abstract:
2020,31(1):20-46
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Abstract:
2020,31(1):162-182
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Abstract:
2020,31(1):67-81
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Abstract:
2020,31(1):183-207
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Abstract:
2020,31(1):228-245
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Abstract:
2020,31(1):137-161
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Abstract:
2020,31(1):113-136
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Abstract:
2020,31(1):82-112
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Abstract:
2020,31(1):208-227
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Abstract:
2019,30(10):3206-3226
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Abstract:
2019,30(10):3090-3114
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Abstract:
2019,30(9):2830-2856
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Abstract:
2019,30(9):2918-2924
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Abstract:
2019,30(9):2571-2592
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Abstract:
2019,30(8):2491-2516
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Abstract:
2019,30(8):2470-2490
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Abstract:
2019,30(8):2545-2568
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Abstract:
2019,30(8):2229-2267
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Abstract:
2019,30(7):2139-2174
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Abstract:
2019,30(7):2073-2090
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Abstract:
2019,30(7):2188-2207
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Abstract:
2019,30(7):2052-2072
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Abstract:
2019,30(6):1853-1874
, DOI: 10.13328/j.cnki.jos.005832
Abstract:
2019,30(6):1793-1818
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Abstract:
2019,30(6):1649-1660
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Abstract:
2019,30(6):1632-1648
, DOI: 10.13328/j.cnki.jos.005740
Abstract:
2019,30(6):1721-1741
, DOI: 10.13328/j.cnki.jos.005491
Abstract:
2019,30(5):1522-1531
, DOI: 10.13328/j.cnki.jos.005727
Abstract:
2019,30(5):1206-1223
, DOI: 10.13328/j.cnki.jos.005717
Abstract:
2019,30(5):1288-1307
, DOI: 10.13328/j.cnki.jos.005713
Abstract:
2019,30(4):1164-1190
, DOI: 10.13328/j.cnki.jos.005811
Abstract:
2019,30(4):962-980
, DOI: 10.13328/j.cnki.jos.005711
Abstract:
2019,30(3):799-821
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Abstract:
2019,30(3):845-864
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Abstract:
2019,30(2):302-322
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Abstract:
2019,30(2):266-281
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Abstract:
2019,30(2):440-468
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Abstract:
2019,30(2):244-265
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Abstract:
2019,30(2):416-439
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Abstract:
2019,30(2):323-345
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Abstract:
2019,30(1):33-61
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Abstract:
2019,30(1):80-109
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Abstract:
2019,30(1):3-21
, DOI: 10.13328/j.cnki.jos.005650
Abstract:
2019,30(1):142-163
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Abstract:
2019,30(1):22-32
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Abstract:
2019,30(1):164-193
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Abstract:
2019,30(1):62-79
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Abstract:
2019,30(1):127-141
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Abstract:
2019,30(1):110-126
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Abstract:
2018,29(11):3400-3411
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Abstract:
2018,29(10):3205-3222
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Abstract:
2018,29(10):3021-3050
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Abstract:
2018,29(10):3111-3133
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Abstract:
2018,29(10):3068-3090
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Abstract:
2018,29(9):2733-2752
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Abstract:
2018,29(9):2799-2820
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Abstract:
2018,29(9):2778-2798
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Abstract:
2018,29(9):2753-2777
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Abstract:
2018,29(8):2397-2426
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Abstract:
2018,29(8):2371-2396
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Abstract:
2018,29(8):2527-2544
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Abstract:
2018,29(8):2448-2469
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Abstract:
2018,29(7):2092-2115
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Abstract:
2018,29(7):2152-2176
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Abstract:
2018,29(7):2018-2045
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Abstract:
2018,29(7):2071-2091
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Abstract:
2018,29(7):2116-2132
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Abstract:
2018,29(7):2133-2151
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Abstract:
2018,29(7):2046-2070
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Abstract:
2018,29(6):1770-1791
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Abstract:
2018,29(6):1813-1825
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Abstract:
2018,29(6):1716-1738
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Abstract:
2018,29(5):1422-1450
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Abstract:
2018,29(5):1471-1514
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Abstract:
2018,29(4):1085-1093
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Abstract:
2018,29(2):320-339
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Abstract:
2018,29(2):251-266
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Abstract:
2018,29(2):225-250
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Abstract:
2018,29(2):506-523
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Abstract:
2018,29(2):340-362
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Abstract:
2018,29(2):417-441
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Abstract:
2018,29(2):299-319
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Abstract:
2018,29(2):483-505
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Abstract:
2018,29(1):150-159
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Abstract:
2018,29(1):131-149
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Abstract:
2018,29(1):69-88
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Abstract:
2018,29(1):109-130
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Abstract:
2018,29(1):1-22
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Abstract:
2018,29(1):196-224
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Abstract:
2018,29(1):23-41
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Abstract:
2018,29(1):42-68
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Abstract:
2018,29(1):89-108
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Abstract:
2018,29(1):176-195
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Abstract:
2018,29(1):160-175
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Abstract:
2017,28(9):2402-2430
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Abstract:
2017,28(9):2502-2523
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Abstract:
2017,28(9):2431-2449
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Abstract:
2017,28(8):2010-2025
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Abstract:
2017,28(8):2026-2045
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Abstract:
2017,28(6):1547-1564
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Abstract:
2017,28(6):1529-1546
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Abstract:
2017,28(6):1584-1605
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Abstract:
2017,28(6):1606-1628
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Abstract:
2017,28(6):1565-1583
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Abstract:
2017,28(1):17-34
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Abstract:
2017,28(1):59-83
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Abstract:
2017,28(1):35-58
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Abstract:
2017,28(1):1-16
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Abstract:
2017,28(1):160-183
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Abstract:
2017,28(1):135-159
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Abstract:
2017,28(1):105-134
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Abstract:
2017,28(1):84-104
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Abstract:
2016,27(9):2230-2247
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Abstract:
2016,27(8):2147-2167
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Abstract:
2016,27(8):2048-2067
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Abstract:
2016,27(8):2025-2047
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Abstract:
2016,27(8):1934-1947
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Abstract:
2016,27(4):993-1008
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Abstract:
2016,27(4):955-968
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Abstract:
2016,27(4):1026-1041
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Abstract:
2016,27(4):1009-1025
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Abstract:
2016,27(4):969-992
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Abstract:
2016,27(3):691-713
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Abstract:
2016,27(3):736-759
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Abstract:
2016,27(3):760-767
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Abstract:
2016,27(2):247-263
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Abstract:
2016,27(2):195-208
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Abstract:
2016,27(2):394-417
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Abstract:
2016,27(2):329-347
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Abstract:
2016,27(2):264-279
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Abstract:
2016,27(1):188-194
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Abstract:
2016,27(1):155-187
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Abstract:
2016,27(1):26-44
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Abstract:
2016,27(1):96-111
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Abstract:
2016,27(1):1-25
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Abstract:
2016,27(1):112-135
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Abstract:
2016,27(1):45-71
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Abstract:
2016,27(1):72-95
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Abstract:
2016,27(1):136-154
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Abstract:
2015,26(9):2356-2372
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Abstract:
2015,26(9):2373-2395
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Abstract:
2015,26(8):1853-1870
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Abstract:
2015,26(6):1356-1372
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Abstract:
2015,26(6):1285-1305
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Abstract:
2015,26(6):1265-1284
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Abstract:
2015,26(5):1213-1236
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Abstract:
2015,26(5):1151-1172
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Abstract:
2015,26(5):1129-1150
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Abstract:
2015,26(4):927-944
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Abstract:
2015,26(4):960-976
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Abstract:
2015,26(4):945-959
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Abstract:
2015,26(4):904-926
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Abstract:
2015,26(3):680-698
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Abstract:
2015,26(3):600-616
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Abstract:
2015,26(3):617-639
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Abstract:
2015,26(2):413-426
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Abstract:
2015,26(2):390-412
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Abstract:
2015,26(1):62-81
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Abstract:
2015,26(1):109-128
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Abstract:
2015,26(1):82-97
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Abstract:
2015,26(1):167-177
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Abstract:
2015,26(1):145-166
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Abstract:
2015,26(1):40-51
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Abstract:
2015,26(1):26-39
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Abstract:
2014,25(7):1527-1540
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Abstract:
2014,25(7):1505-1526
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Abstract:
2014,25(7):1459-1475
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Abstract:
2014,25(5):1085-1100
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Abstract:
2014,25(5):1061-1084
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Abstract:
2014,25(4):839-862
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Abstract:
2014,25(4):896-912
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Abstract:
2014,25(4):880-895
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Abstract:
2014,25(3):606-630
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Abstract:
2014,25(3):439-456
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Abstract:
2014,25(3):591-605
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Abstract:
2014,25(3):506-527
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Abstract:
2014,25(3):576-590
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Abstract:
2014,25(1):78-97
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Abstract:
2014,25(1):1-15
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Abstract:
2014,25(1):37-50
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Abstract:
2014,25(1):154-175
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Abstract:
2014,25(1):118-134
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Abstract:
2014,25(1):98-117
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Abstract:
2014,25(1):135-153
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Abstract:
2013,24(9):2042-2061
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Abstract:
2013,24(9):2001-2019
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Abstract:
2013,24(9):1981-2000
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Abstract:
2013,24(8):1804-1815
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Abstract:
2013,24(8):1695-1712
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Abstract:
2013,24(8):1786-1803
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Abstract:
2013,24(6):1346-1360
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Abstract:
2013,24(6):1390-1402
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Abstract:
2013,24(6):1222-1242
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Abstract:
2013,24(5):1078-1097
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Abstract:
2013,24(4):663-674
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Abstract:
2013,24(4):843-858
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Abstract:
2013,24(4):781-797
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Abstract:
2013,24(4):825-842
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Abstract:
2013,24(4):761-780
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Abstract:
2013,24(2):198-214
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Abstract:
2013,24(2):175-197
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Abstract:
2013,24(2):295-316
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Abstract:
2013,24(1):121-138
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Abstract:
2013,24(1):77-90
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Abstract:
2013,24(1):37-49
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Abstract:
2013,24(1):139-152
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Abstract:
2013,24(1):67-76
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Abstract:
2013,24(1):91-108
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Abstract:
2013,24(1):50-66
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Abstract:
2012,23(11):2871-2884
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Abstract:
2012,23(11):2885-2904
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Abstract:
2012,23(11):3009-3028
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Abstract:
2012,23(8):2058-2072
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Abstract:
2012,23(8):2173-2187
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Abstract:
2012,23(7):1824-1837
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Abstract:
2012,23(7):1729-1744
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Abstract:
2012,23(6):1542-1560
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Abstract:
2012,23(5):1148-1166
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Abstract:
2012,23(4):962-986
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Abstract:
2012,23(4):864-877
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Abstract:
2012,23(1):46-64
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Abstract:
2012,23(1):65-81
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Abstract:
2012,23(1):82-96
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Abstract:
2012,23(1):1-20
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Abstract:
2012,23(1):152-170
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Abstract:
2012,23(1):140-151
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Abstract:
2012,23(1):32-45
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Abstract:
2011,22(10):2317-2334
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Abstract:
2011,22(8):1699-1713
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Abstract:
2011,22(8):1838-1854
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Abstract:
2011,22(7):1503-1523
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Abstract:
2011,22(6):1316-1332
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Abstract:
2011,22(6):1281-1298
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Abstract:
2011,22(6):1299-1315
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Abstract:
2011,22(4):676-694
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Abstract:
2011,22(3):417-438
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Abstract:
2011,22(3):381-407
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Abstract:
2011,22(3):408-416
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Abstract:
2011,22(1):71-83
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Abstract:
2011,22(1):115-131
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Abstract:
2011,22(1):101-114
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Abstract:
2011,22(1):84-100
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Abstract:
2011,22(1):28-43
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Abstract:
2011,22(1):132-148
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2010,21(10):2454-2476
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2010,21(10):2524-2541
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2010,21(10):2542-2553
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2010,21(9):2262-2288
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2010,21(9):2089-2106
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2010,21(8):1834-1848
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2010,21(7):1635-1645
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2010,21(7):1620-1634
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2010,21(7):1605-1619
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2010,21(7):1589-1604
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2010,21(7):1576-1588
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2010,21(6):1326-1340
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2010,21(6):1308-1325
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2010,21(5):916-929
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2010,21(5):991-1006
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2010,21(5):1067-1082
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2010,21(5):899-915
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2010,21(4):732-749
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2010,21(1):133-146
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2010,21(1):119-132
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2009,20(11):2950-2964
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2009,20(11):3001-3009
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2009,20(11):2988-3000
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2009,20(10):2787-2798
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2009,20(9):2307-2319
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2009,20(9):2470-2482
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2009,20(8):2199-2213
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2009,20(8):2051-2061
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2009,20(8):2124-2137
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2009,20(8):2138-2152
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2009,20(7):1986-2004
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2009,20(7):1768-1784
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2009,20(6):1539-1552
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2009,20(6):1574-1590
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2009,20(6):1553-1573
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2009,20(6):1425-1443
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2009,20(6):1393-1405
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2009,20(5):1349-1361
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2009,20(5):1337-1348
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2009,20(5):1362-1376
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2009,20(5):1226-1240
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2009,20(3):567-582
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2009,20(3):524-545
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2009,20(2):271-289
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2009,20(2):193-213
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2009,20(2):363-374
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2009,20(2):350-362
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2009,20(2):177-192
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2009,20(1):124-137
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2009,20(1):54-66
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2009,20(1):96-108
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2009,20(1):109-123
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2009,20(1):11-29
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2008,19(10):2706-2719
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2008,19(8):2018-2031
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2008,19(7):1794-1805
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2008,19(7):1716-1730
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2008,19(7):1565-1580
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2008,19(6):1481-1490
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2008,19(6):1452-1464
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2008,19(6):1439-1451
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2008,19(6):1386-1400
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2008,19(6):1328-1339
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2008,19(6):1317-1327
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2008,19(4):1051-1068
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2008,19(4):912-924
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2008,19(4):888-911
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2008,19(4):879-887
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2008,19(4):779-802
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2008,19(3):733-743
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2008,19(3):716-732
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2008,19(3):702-715
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2008,19(3):687-701
Abstract:
2008,19(3):565-583
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2008,19(2):419-431
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2008,19(2):404-418
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2008,19(2):389-403
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2008,19(2):291-300
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2008,19(1):90-102
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2008,19(1):48-61
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2008,19(1):27-38
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2008,19(1):17-26
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2007,18(11):2942-2954
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2007,18(11):2782-2799
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2007,18(11):2766-2781
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2007,18(11):2752-2765
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2007,18(11):2669-2682
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2007,18(7):1639-1651
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2007,18(6):1510-1521
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2007,18(6):1477-1490
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2007,18(6):1400-1418
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2007,18(6):1379-1399
Abstract:
2007,18(5):1218-1231
Abstract:
2007,18(4):943-954
Abstract:
2007,18(4):775-795
Abstract:
2007,18(3):625-635
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2007,18(3):477-489
Abstract:
2007,18(2):430-441
Abstract:
2007,18(2):246-258
Abstract:
2006,17(12):2403-2415
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2006,17(10):2131-2140
Abstract:
2006,17(10):2069-2086
Abstract:
2006,17(10):2029-2039
Abstract:
2006,17(9):2013-2028
Abstract:
2006,17(9):1933-1948
Abstract:
2006,17(9):1922-1932
Abstract:
2006,17(9):1848-1859
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2006,17(9):1837-1847
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2006,17(8):1717-1730
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2006,17(7):1588-1600
Abstract:
2006,17(5):1051-1067
Abstract:
2006,17(4):814-821
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2006,17(3):422-433
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2006,17(3):410-421
Abstract:
2006,17(2):209-215
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2005,16(12):2063-2079
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2005,16(10):1743-1756
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2005,16(8):1445-1455
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2005,16(3):434-444
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2005,16(2):239-250
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2004,15(12):1885-1893
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2004,15(12):1822-1836
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2004,15(12):1815-1821
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2004,15(12):1751-1763
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2004,15(11):1689-1699
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2004,15(8):1220-1226
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2004,15(8):1208-1219
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2004,15(8):1172-1181
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2004,15(8):1141-1149
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2004,15(3):428-442
Abstract:
2004,15(1):141-150
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2004,15(1):58-68
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2003,14(10):1768-1780
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2003,14(10):1753-1760
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2003,14(10):1740-1752
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2003,14(10):1728-1739
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2003,14(10):1717-1727
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2003,14(10):1661-1671
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2003,14(9):1635-1644
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2003,14(7):1324-1337
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2003,14(7):1300-1309
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2003,14(7):1282-1291
Abstract:
2003,14(5):1011-1022
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2003,14(4):804-810
Abstract:
2003,14(4):777-782
Abstract:
2003,14(2):268-277
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2003,14(2):253-267
Abstract:
2003,14(1):110-116
Abstract:
2002,13(12):2215-2224
Abstract:
2002,13(11):2076-2082
Abstract:
2002,13(11):2065-2075
Abstract:
2002,13(11):2057-2064
Abstract:
2002,13(10):1952-1961
Abstract:
2002,13(8):1552-1558
Abstract:
2002,13(8):1441-1449
Abstract:
2002,13(7):1228-1237
Abstract:
2002,13(3):354-363
Abstract:
2002,13(2):203-213
Abstract:
2002,13(1):42-50
Abstract:
2001,12(11):1614-1622
Abstract:
2001,12(6):822-829
Abstract:
2001,12(5):677-688
Abstract:
2000,11(12):1598-1606
Abstract:
2000,11(11):1460-1466
Abstract:
2000,11(9):1268-1275
Abstract:
2000,11(8):1087-1093
Abstract:
2000,11(7):899-907
Abstract:
2000,11(6):751-757
Abstract:
2000,11(5):606-615
Abstract:
2000,11(4):461-467
Abstract:
2000,11(2):179-186
Abstract:
2000,11(1):40-48
Abstract:
1997,8(4):241-251
Abstract:
1997,8(1):75-80
Abstract:
1993,4(6):7-15
Abstract:
1993,4(5):51-57
Abstract:
2025,36(8):1-18
, DOI: 10.13328/j.cnki.jos.007347
Abstract:
2025,36(3):939-940
, DOI: 10.13328/j.cnki.jos.007284
, CSTR: 32375.14.jos.007284
Abstract:
2025,36(3):1084-1106
, DOI: 10.13328/j.cnki.jos.007283
, CSTR: 32375.14.jos.007283
Abstract:
2025,36(3):1022-1039
, DOI: 10.13328/j.cnki.jos.007282
, CSTR: 32375.14.jos.007282
Abstract:
2025,36(3):995-1021
, DOI: 10.13328/j.cnki.jos.007281
, CSTR: 32375.14.jos.007281
Abstract:
2025,36(3):981-994
, DOI: 10.13328/j.cnki.jos.007280
, CSTR: 32375.14.jos.007280
Abstract:
2025,36(3):1065-1083
, DOI: 10.13328/j.cnki.jos.007279
, CSTR: 32375.14.jos.007279
Abstract:
2025,36(3):962-980
, DOI: 10.13328/j.cnki.jos.007278
, CSTR: 32375.14.jos.007278
Abstract:
2025,36(3):941-961
, DOI: 10.13328/j.cnki.jos.007277
, CSTR: 32375.14.jos.007277
Abstract:
2024,35(9):4011-4012
, DOI: 10.13328/j.cnki.jos.007140
Abstract:
2024,35(9):4310-4323
, DOI: 10.13328/j.cnki.jos.007139
Abstract:
2024,35(9):4287-4309
, DOI: 10.13328/j.cnki.jos.007137
Abstract:
2024,35(9):4265-4286
, DOI: 10.13328/j.cnki.jos.007136
Abstract:
2024,35(9):4242-4264
, DOI: 10.13328/j.cnki.jos.007135
Abstract:
2024,35(9):4218-4241
, DOI: 10.13328/j.cnki.jos.007134
Abstract:
2024,35(9):4193-4217
, DOI: 10.13328/j.cnki.jos.007133
Abstract:
2024,35(9):4179-4192
, DOI: 10.13328/j.cnki.jos.007132
Abstract:
2024,35(9):4160-4178
, DOI: 10.13328/j.cnki.jos.007131
Abstract:
2024,35(9):4141-4159
, DOI: 10.13328/j.cnki.jos.007130
Abstract:
2024,35(9):4123-4140
, DOI: 10.13328/j.cnki.jos.007129
Abstract:
2024,35(9):4100-4122
, DOI: 10.13328/j.cnki.jos.007128
Abstract:
2024,35(8):3551-3552
, DOI: 10.13328/j.cnki.jos.007125
Abstract:
2024,35(8):3698-3720
, DOI: 10.13328/j.cnki.jos.007124
Abstract:
2024,35(8):3684-3697
, DOI: 10.13328/j.cnki.jos.007122
Abstract:
2024,35(8):3668-3683
, DOI: 10.13328/j.cnki.jos.007121
Abstract:
2024,35(8):3647-3667
, DOI: 10.13328/j.cnki.jos.007120
Abstract:
2024,35(8):3626-3646
, DOI: 10.13328/j.cnki.jos.007119
Abstract:
2024,35(8):3610-3625
, DOI: 10.13328/j.cnki.jos.007118
Abstract:
2024,35(8):3591-3609
, DOI: 10.13328/j.cnki.jos.007117
Abstract:
2024,35(8):3577-3590
, DOI: 10.13328/j.cnki.jos.007116
Abstract:
2024,35(8):3553-3576
, DOI: 10.13328/j.cnki.jos.007115
Abstract:
2024,35(7):3069-3070
, DOI: 10.13328/j.cnki.jos.007114
Abstract:
2024,35(7):3093-3114
, DOI: 10.13328/j.cnki.jos.007113
Abstract:
2024,35(7):3245-3264
, DOI: 10.13328/j.cnki.jos.007111
Abstract:
2024,35(7):3071-3092
, DOI: 10.13328/j.cnki.jos.007110
Abstract:
2024,35(7):3227-3244
, DOI: 10.13328/j.cnki.jos.007109
Abstract:
2024,35(7):3204-3226
, DOI: 10.13328/j.cnki.jos.007108
Abstract:
2024,35(7):3180-3203
, DOI: 10.13328/j.cnki.jos.007107
Abstract:
2024,35(7):3162-3179
, DOI: 10.13328/j.cnki.jos.007106
Abstract:
2024,35(7):3141-3161
, DOI: 10.13328/j.cnki.jos.007105
Abstract:
2024,35(7):3115-3140
, DOI: 10.13328/j.cnki.jos.007104
Abstract:
2024,35(6):2583-2584
, DOI: 10.13328/j.cnki.jos.007103
Abstract:
2024,35(6):2668-2686
, DOI: 10.13328/j.cnki.jos.007102
Abstract:
2024,35(6):2724-2752
, DOI: 10.13328/j.cnki.jos.007101
Abstract:
2024,35(6):2608-2630
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Abstract:
2024,35(6):2648-2667
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Abstract:
2024,35(6):2631-2647
, DOI: 10.13328/j.cnki.jos.007097
Abstract:
2024,35(6):2585-2607
, DOI: 10.13328/j.cnki.jos.007096
Abstract:
2024,35(5):2099-2100
, DOI: 10.13328/j.cnki.jos.007030
Abstract:
2024,35(5):2176-2191
, DOI: 10.13328/j.cnki.jos.007029
Abstract:
2024,35(5):2192-2207
, DOI: 10.13328/j.cnki.jos.007028
Abstract:
2024,35(5):2165-2175
, DOI: 10.13328/j.cnki.jos.007027
Abstract:
2024,35(5):2208-2219
, DOI: 10.13328/j.cnki.jos.007026
Abstract:
2024,35(5):2133-2149
, DOI: 10.13328/j.cnki.jos.007025
Abstract:
2024,35(5):2150-2164
, DOI: 10.13328/j.cnki.jos.007024
Abstract:
2024,35(5):2220-2234
, DOI: 10.13328/j.cnki.jos.007023
Abstract:
2024,35(5):2101-2119
, DOI: 10.13328/j.cnki.jos.007022
Abstract:
2024,35(5):2120-2132
, DOI: 10.13328/j.cnki.jos.007021
Abstract:
2024,35(4):1585-1586
, DOI: 10.13328/j.cnki.jos.007020
Abstract:
2024,35(4):1774-1789
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Abstract:
2024,35(4):1751-1773
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Abstract:
2024,35(4):1732-1750
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Abstract:
2024,35(4):1601-1617
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Abstract:
2024,35(4):1703-1716
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Abstract:
2024,35(4):1587-1600
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Abstract:
2024,35(4):1717-1731
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Abstract:
2024,35(4):1651-1666
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Abstract:
2024,35(4):1667-1681
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Abstract:
2024,35(3):1049-1050
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Abstract:
2024,35(3):1140-1153
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Abstract:
2024,35(3):1173-1193
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Abstract:
2024,35(3):1074-1089
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Abstract:
2024,35(3):1194-1206
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Abstract:
2024,35(3):1125-1139
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Abstract:
2024,35(3):1090-1106
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Abstract:
2024,35(3):1051-1073
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Abstract:
2024,35(3):1154-1172
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Abstract:
2024,35(3):1107-1124
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Abstract:
2023,34(10):4437-4438
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Abstract:
2023,34(10):4565-4583
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Abstract:
2023,34(10):4533-4547
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Abstract:
2023,34(10):4501-4517
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Abstract:
2023,34(10):4548-4564
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Abstract:
2023,34(10):4518-4532
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Abstract:
2023,34(10):4477-4500
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Abstract:
2023,34(10):4584-4600
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Abstract:
2023,34(10):4463-4476
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Abstract:
2023,34(9):3939-3940
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Abstract:
2023,34(9):4018-4036
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Abstract:
2023,34(9):4003-4017
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Abstract:
2023,34(9):3981-4002
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Abstract:
2023,34(9):4069-4095
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Abstract:
2023,34(9):3941-3965
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Abstract:
2023,34(9):3966-3980
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Abstract:
2023,34(9):4056-4068
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Abstract:
2023,34(9):4037-4055
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Abstract:
2023,34(8):3465-3466
, DOI: 10.13328/j.cnki.jos.006871
Abstract:
2023,34(8):3574-3586
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Abstract:
2023,34(8):3527-3548
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Abstract:
2023,34(8):3549-3573
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Abstract:
2023,34(8):3467-3484
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Abstract:
2023,34(8):3507-3526
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Abstract:
2023,34(8):3485-3506
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Abstract:
2023,34(7):2979-2980
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Abstract:
2023,34(7):3043-3063
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Abstract:
2023,34(7):3080-3098
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Abstract:
2023,34(7):3116-3133
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Abstract:
2023,34(7):3099-3115
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Abstract:
2023,34(7):3022-3042
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Abstract:
2023,34(7):2981-3001
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Abstract:
2023,34(7):3064-3079
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Abstract:
2023,34(7):3002-3021
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Abstract:
2023,34(6):2507-2508
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Abstract:
2023,34(6):2562-2585
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Abstract:
2023,34(6):2628-2640
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Abstract:
2023,34(6):2586-2605
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Abstract:
2023,34(6):2543-2561
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Abstract:
2023,34(6):2526-2542
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Abstract:
2023,34(6):2641-2668
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Abstract:
2023,34(6):2509-2525
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Abstract:
2023,34(5):1997-1999
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Abstract:
2023,34(5):2083-2100
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Abstract:
2023,34(5):2152-2169
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Abstract:
2023,34(5):2134-2151
, DOI: 10.13328/j.cnki.jos.006772
Abstract:
2023,34(5):2068-2082
, DOI: 10.13328/j.cnki.jos.006771
Abstract:
2023,34(5):2024-2034
, DOI: 10.13328/j.cnki.jos.006770
Abstract:
2023,34(5):2116-2133
, DOI: 10.13328/j.cnki.jos.006769
Abstract:
2023,34(5):2051-2067
, DOI: 10.13328/j.cnki.jos.006768
Abstract:
2023,34(5):2035-2050
, DOI: 10.13328/j.cnki.jos.006766
Abstract:
2023,34(3):1007-1009
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Abstract:
2023,34(3):1109-1125
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Abstract:
2023,34(3):1087-1108
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Abstract:
2023,34(3):1065-1086
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Abstract:
2023,34(3):1277-1291
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Abstract:
2023,34(3):1049-1064
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Abstract:
2023,34(3):1292-1309
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Abstract:
2023,34(3):1027-1048
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Abstract:
2023,34(3):1259-1276
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Abstract:
2023,34(3):1010-1026
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Abstract:
2023,34(3):1148-1167
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Abstract:
2023,34(3):1213-1235
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Abstract:
2023,34(3):1193-1212
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Abstract:
2023,34(3):1126-1147
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Abstract:
2022,33(10):3529-3530
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Abstract:
2022,33(10):3619-3634
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Abstract:
2022,33(10):3565-3581
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Abstract:
2022,33(10):3656-3672
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Abstract:
2022,33(10):3531-3545
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Abstract:
2022,33(10):3546-3564
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Abstract:
2022,33(9):3137-3138
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Abstract:
2022,33(9):3165-3179
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Abstract:
2022,33(9):3210-3222
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Abstract:
2022,33(9):3139-3151
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Abstract:
2022,33(9):3195-3209
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Abstract:
2022,33(9):3152-3164
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Abstract:
2022,33(9):3180-3194
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Abstract:
2022,33(8):2753-2754
, DOI: 10.13328/j.cnki.jos.006611
Abstract:
2022,33(8):2995-3014
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Abstract:
2022,33(8):2980-2994
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Abstract:
2022,33(8):2964-2979
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Abstract:
2022,33(8):2947-2963
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Abstract:
2022,33(8):2930-2946
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Abstract:
2022,33(8):2918-2929
, DOI: 10.13328/j.cnki.jos.006605
Abstract:
2022,33(8):2896-2917
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Abstract:
2022,33(8):2875-2895
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Abstract:
2022,33(8):2839-2850
, DOI: 10.13328/j.cnki.jos.006601
Abstract:
2022,33(8):2815-2838
, DOI: 10.13328/j.cnki.jos.006600
Abstract:
2022,33(8):2797-2814
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Abstract:
2022,33(8):2782-2796
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Abstract:
2022,33(8):2769-2781
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Abstract:
2022,33(8):2755-2768
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Abstract:
2022,33(7):2365-2366
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Abstract:
2022,33(7):2525-2537
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Abstract:
2022,33(7):2538-2561
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Abstract:
2022,33(7):2447-2463
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Abstract:
2022,33(7):2464-2481
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Abstract:
2022,33(7):2482-2498
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Abstract:
2022,33(7):2499-2524
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Abstract:
2022,33(6):2113-2114
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Abstract:
2022,33(6):2264-2287
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Abstract:
2022,33(6):2246-2263
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Abstract:
2022,33(6):2224-2245
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Abstract:
2022,33(6):2208-2223
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Abstract:
2022,33(6):2189-2207
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Abstract:
2022,33(6):2172-2188
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Abstract:
2022,33(6):2150-2171
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Abstract:
2022,33(6):2127-2149
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Abstract:
2022,33(6):2115-2126
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Abstract:
2022,33(6):1959-1960
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Abstract:
2022,33(6):2097-2112
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Abstract:
2022,33(6):2082-2096
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Abstract:
2022,33(6):2061-2081
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Abstract:
2022,33(6):2047-2060
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Abstract:
2022,33(6):2030-2046
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Abstract:
2022,33(6):2012-2029
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Abstract:
2022,33(6):1996-2011
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Abstract:
2022,33(6):1978-1995
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Abstract:
2022,33(6):1961-1977
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Abstract:
2022,33(5):1527-1528
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Abstract:
2022,33(5):1774-1799
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Abstract:
2022,33(5):1758-1773
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Abstract:
2022,33(5):1736-1757
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Abstract:
2022,33(5):1711-1735
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Abstract:
2022,33(5):1699-1710
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Abstract:
2022,33(5):1674-1698
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Abstract:
2022,33(5):1652-1673
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Abstract:
2022,33(5):1635-1651
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Abstract:
2022,33(5):1587-1611
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Abstract:
2022,33(5):1569-1586
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Abstract:
2022,33(5):1551-1568
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Abstract:
2022,33(5):1529-1550
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Abstract:
2022,33(4):1153-1155
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Abstract:
2022,33(4):1439-1450
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Abstract:
2022,33(4):1516-1526
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Abstract:
2022,33(4):1315-1325
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Abstract:
2022,33(4):1274-1286
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Abstract:
2022,33(4):1170-1182
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Abstract:
2022,33(4):1218-1230
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Abstract:
2022,33(4):1267-1273
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Abstract:
2022,33(4):1373-1389
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Abstract:
2022,33(4):1430-1438
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Abstract:
2022,33(4):1477-1500
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Abstract:
2022,33(4):1354-1372
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Abstract:
2022,33(4):1412-1429
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Abstract:
2022,33(4):1501-1515
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Abstract:
2022,33(4):1156-1169
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Abstract:
2022,33(4):1183-1199
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Abstract:
2022,33(4):1338-1353
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Abstract:
2022,33(4):1326-1337
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Abstract:
2022,33(4):1231-1243
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Abstract:
2022,33(4):1200-1217
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Abstract:
2022,33(4):1390-1411
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Abstract:
2022,33(4):1451-1476
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Abstract:
2022,33(3):771-773
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Abstract:
2022,33(3):1111-1127
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Abstract:
2022,33(3):1043-1056
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Abstract:
2022,33(3):832-848
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Abstract:
2022,33(3):931-949
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Abstract:
2022,33(3):867-890
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Abstract:
2022,33(3):968-984
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Abstract:
2022,33(3):814-831
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Abstract:
2022,33(3):1005-1017
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Abstract:
2022,33(3):1093-1110
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Abstract:
2022,33(3):1018-1042
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Abstract:
2022,33(3):797-813
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Abstract:
2022,33(3):985-1004
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Abstract:
2022,33(3):950-967
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Abstract:
2022,33(3):849-866
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Abstract:
2022,33(3):891-908
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Abstract:
2022,33(3):909-930
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Abstract:
2022,33(3):774-796
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Abstract:
2022,33(3):1141-1152
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Abstract:
2022,33(3):1128-1140
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Abstract:
2021,32(8):2377-2378
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Abstract:
2021,32(8):2457-2468
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Abstract:
2021,32(8):2439-2456
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Abstract:
2021,32(8):2425-2438
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Abstract:
2021,32(8):2408-2424
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Abstract:
2021,32(8):2391-2407
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Abstract:
2021,32(8):2379-2390
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Abstract:
2021,32(8):2287-2288
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Abstract:
2021,32(8):2365-2376
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Abstract:
2021,32(8):2352-2364
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Abstract:
2021,32(8):2341-2351
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Abstract:
2021,32(8):2329-2340
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Abstract:
2021,32(8):2319-2328
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Abstract:
2021,32(8):2307-2318
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Abstract:
2021,32(8):2289-2306
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Abstract:
2021,32(7):1923-1925
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Abstract:
2021,32(7):1999-2015
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Abstract:
2021,32(7):2242-2259
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Abstract:
2021,32(7):2142-2165
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Abstract:
2021,32(7):1978-1998
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Abstract:
2021,32(7):2103-2117
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Abstract:
2021,32(7):2183-2203
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Abstract:
2021,32(7):2166-2182
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Abstract:
2021,32(7):2219-2241
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Abstract:
2021,32(7):2039-2055
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Abstract:
2021,32(7):1957-1977
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Abstract:
2021,32(7):2204-2218
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Abstract:
2021,32(6):1579-1580
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Abstract:
2021,32(6):1882-1909
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Abstract:
2021,32(6):1867-1881
, DOI: 10.13328/j.cnki.jos.006254
Abstract:
2021,32(6):1849-1866
, DOI: 10.13328/j.cnki.jos.006253
Abstract:
2021,32(6):1830-1848
, DOI: 10.13328/j.cnki.jos.006252
Abstract:
2021,32(6):1818-1829
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Abstract:
2021,32(6):1799-1817
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Abstract:
2021,32(6):1779-1798
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Abstract:
2021,32(6):1748-1778
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Abstract:
2021,32(6):1733-1747
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Abstract:
2021,32(6):1717-1732
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Abstract:
2021,32(6):1701-1716
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Abstract:
2021,32(6):1682-1700
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Abstract:
2021,32(6):1663-1681
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Abstract:
2021,32(6):1647-1662
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Abstract:
2021,32(6):1631-1646
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Abstract:
2021,32(6):1612-1630
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Abstract:
2021,32(6):1597-1611
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Abstract:
2021,32(6):1581-1596
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Abstract:
2021,32(5):1229-1230
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Abstract:
2021,32(5):1341-1359
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Abstract:
2021,32(5):1322-1340
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Abstract:
2021,32(5):1302-1321
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Abstract:
2021,32(5):1284-1301
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Abstract:
2021,32(5):1256-1283
, DOI: 10.13328/j.cnki.jos.006232
Abstract:
2021,32(5):1231-1255
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Abstract:
2021,32(4):887-888
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Abstract:
2021,32(4):953-972
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Abstract:
2021,32(4):1023-1038
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Abstract:
2021,32(4):1006-1022
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Abstract:
2021,32(4):973-987
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Abstract:
2021,32(4):988-1005
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Abstract:
2021,32(4):934-952
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Abstract:
2021,32(4):904-933
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Abstract:
2021,32(4):889-903
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Abstract:
2021,32(4):1051-1066
, DOI: 10.13328/j.cnki.jos.006221
Abstract:
2021,32(4):1039-1050
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Abstract:
2021,32(3):601-603
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Abstract:
2021,32(3):636-649
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Abstract:
2021,32(3):818-830
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Abstract:
2021,32(3):753-762
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Abstract:
2021,32(3):831-844
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Abstract:
2021,32(3):781-804
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Abstract:
2021,32(3):763-780
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Abstract:
2021,32(3):622-635
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Abstract:
2021,32(3):742-752
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Abstract:
2021,32(3):845-858
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Abstract:
2021,32(3):689-711
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Abstract:
2021,32(3):669-688
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Abstract:
2021,32(3):650-668
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Abstract:
2021,32(3):712-725
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Abstract:
2021,32(3):805-817
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Abstract:
2021,32(3):726-741
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Abstract:
2020,31(10):2981-2982
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Abstract:
2020,31(10):3074-3086
, DOI: 10.13328/j.cnki.jos.006071
Abstract:
2020,31(10):3056-3073
, DOI: 10.13328/j.cnki.jos.006070
Abstract:
2020,31(10):3038-3055
, DOI: 10.13328/j.cnki.jos.006069
Abstract:
2020,31(10):3019-3037
, DOI: 10.13328/j.cnki.jos.006068
Abstract:
2020,31(10):3004-3018
, DOI: 10.13328/j.cnki.jos.006064
Abstract:
2020,31(10):2983-3003
, DOI: 10.13328/j.cnki.jos.006063
Abstract:
2020,31(9):2785-2801
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Abstract:
2020,31(9):2770-2784
, DOI: 10.13328/j.cnki.jos.005944
Abstract:
2020,31(9):2756-2769
, DOI: 10.13328/j.cnki.jos.005943
Abstract:
2020,31(9):2723-2740
, DOI: 10.13328/j.cnki.jos.005941
Abstract:
2020,31(9):2709-2722
, DOI: 10.13328/j.cnki.jos.005940
Abstract:
2020,31(9):2741-2755
, DOI: 10.13328/j.cnki.jos.005939
Abstract:
2020,31(9):2691-2708
, DOI: 10.13328/j.cnki.jos.005938
Abstract:
2020,31(9):2678-2690
, DOI: 10.13328/j.cnki.jos.005937
Abstract:
2020,31(8):2336-2361
, DOI: 10.13328/j.cnki.jos.005964
Abstract:
2020,31(8):2309-2335
, DOI: 10.13328/j.cnki.jos.005963
Abstract:
2020,31(8):2285-2308
, DOI: 10.13328/j.cnki.jos.005962
Abstract:
2020,31(8):2375-2387
, DOI: 10.13328/j.cnki.jos.005961
Abstract:
2020,31(8):2362-2374
, DOI: 10.13328/j.cnki.jos.005960
Abstract:
2020,31(8):2388-2403
, DOI: 10.13328/j.cnki.jos.005959
Abstract:
2020,31(7):1969-1979
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Abstract:
2020,31(7):1933-1942
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Abstract:
2020,31(7):1959-1968
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Abstract:
2020,31(7):1943-1958
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Abstract:
2020,31(6):1672-1680
, DOI: 10.13328/j.cnki.jos.006000
Abstract:
2020,31(6):1654-1671
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Abstract:
2020,31(6):1638-1653
, DOI: 10.13328/j.cnki.jos.005997
Abstract:
2020,31(6):1619-1637
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Abstract:
2020,31(6):1600-1618
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Abstract:
2020,31(6):1587-1599
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Abstract:
2020,31(5):1241-1242
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Abstract:
2020,31(5):1353-1373
, DOI: 10.13328/j.cnki.jos.005957
Abstract:
2020,31(5):1332-1352
, DOI: 10.13328/j.cnki.jos.005956
Abstract:
2020,31(5):1314-1331
, DOI: 10.13328/j.cnki.jos.005955
Abstract:
2020,31(5):1294-1313
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Abstract:
2020,31(5):1392-1405
, DOI: 10.13328/j.cnki.jos.005953
Abstract:
2020,31(5):1374-1391
, DOI: 10.13328/j.cnki.jos.005952
Abstract:
2020,31(5):1276-1293
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Abstract:
2020,31(3):597-599
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Abstract:
2020,31(3):680-694
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Abstract:
2020,31(3):778-793
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Abstract:
2020,31(3):763-777
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Abstract:
2020,31(3):620-633
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Abstract:
2020,31(3):726-747
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Abstract:
2020,31(3):634-647
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Abstract:
2020,31(3):710-725
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Abstract:
2020,31(3):748-762
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Abstract:
2020,31(3):663-679
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Abstract:
2020,31(3):648-662
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Abstract:
2020,31(3):695-709
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Abstract:
2020,31(3):831-844
, DOI: 10.13328/j.cnki.jos.005899
Abstract:
2020,31(3):845-865
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Abstract:
2020,31(3):794-805
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Abstract:
2019,30(10):3054-3055
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Abstract:
2019,30(10):3227-3241
, DOI: 10.13328/j.cnki.jos.005797
Abstract:
2019,30(10):3186-3205
, DOI: 10.13328/j.cnki.jos.005795
Abstract:
2019,30(10):3168-3185
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Abstract:
2019,30(10):3148-3167
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Abstract:
2019,30(10):3127-3147
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Abstract:
2019,30(10):3115-3126
, DOI: 10.13328/j.cnki.jos.005791
Abstract:
2019,30(10):3071-3089
, DOI: 10.13328/j.cnki.jos.005789
Abstract:
2019,30(10):3056-3070
, DOI: 10.13328/j.cnki.jos.005788
Abstract:
2019,30(10):2925-2926
, DOI: 10.13328/j.cnki.jos.005787
Abstract:
2019,30(10):2964-2985
, DOI: 10.13328/j.cnki.jos.005786
Abstract:
2019,30(10):2986-3004
, DOI: 10.13328/j.cnki.jos.005785
Abstract:
2019,30(10):3037-3053
, DOI: 10.13328/j.cnki.jos.005784
Abstract:
2019,30(10):3026-3036
, DOI: 10.13328/j.cnki.jos.005783
Abstract:
2019,30(10):3005-3016
, DOI: 10.13328/j.cnki.jos.005782
Abstract:
2019,30(10):2942-2953
, DOI: 10.13328/j.cnki.jos.005781
Abstract:
2019,30(10):3017-3025
, DOI: 10.13328/j.cnki.jos.005780
Abstract:
2019,30(10):2954-2963
, DOI: 10.13328/j.cnki.jos.005779
Abstract:
2019,30(10):2927-2941
, DOI: 10.13328/j.cnki.jos.005778
Abstract:
2019,30(9):2569-2570
, DOI: 10.13328/j.cnki.jos.005777
Abstract:
2019,30(9):2671-2685
, DOI: 10.13328/j.cnki.jos.005776
Abstract:
2019,30(9):2655-2670
, DOI: 10.13328/j.cnki.jos.005774
Abstract:
2019,30(9):2608-2619
, DOI: 10.13328/j.cnki.jos.005773
Abstract:
2019,30(9):2593-2607
, DOI: 10.13328/j.cnki.jos.005772
Abstract:
2019,30(9):2636-2654
, DOI: 10.13328/j.cnki.jos.005771
Abstract:
2019,30(9):2620-2635
, DOI: 10.13328/j.cnki.jos.005770
Abstract:
2019,30(8):2268-2286
, DOI: 10.13328/j.cnki.jos.005768
Abstract:
2019,30(8):2375-2391
, DOI: 10.13328/j.cnki.jos.005766
Abstract:
2019,30(8):2415-2427
, DOI: 10.13328/j.cnki.jos.005765
Abstract:
2019,30(8):2314-2336
, DOI: 10.13328/j.cnki.jos.005764
Abstract:
2019,30(8):2362-2374
, DOI: 10.13328/j.cnki.jos.005763
Abstract:
2019,30(8):2349-2361
, DOI: 10.13328/j.cnki.jos.005762
Abstract:
2019,30(8):2287-2313
, DOI: 10.13328/j.cnki.jos.005761
Abstract:
2019,30(8):2392-2414
, DOI: 10.13328/j.cnki.jos.005760
Abstract:
2019,30(8):2337-2348
, DOI: 10.13328/j.cnki.jos.005759
Abstract:
2019,30(7):2033-2051
, DOI: 10.13328/j.cnki.jos.005757
Abstract:
2019,30(7):2018-2032
, DOI: 10.13328/j.cnki.jos.005756
Abstract:
2019,30(7):2003-2017
, DOI: 10.13328/j.cnki.jos.005755
Abstract:
2019,30(7):1980-2002
, DOI: 10.13328/j.cnki.jos.005754
Abstract:
2019,30(7):1966-1979
, DOI: 10.13328/j.cnki.jos.005753
Abstract:
2019,30(7):1953-1965
, DOI: 10.13328/j.cnki.jos.005752
Abstract:
2019,30(7):1939-1952
, DOI: 10.13328/j.cnki.jos.005750
Abstract:
2019,30(7):1916-1938
, DOI: 10.13328/j.cnki.jos.005749
Abstract:
2019,30(7):1903-1915
, DOI: 10.13328/j.cnki.jos.005748
Abstract:
2019,30(6):1575-1576
, DOI: 10.13328/j.cnki.jos.005747
Abstract:
2019,30(6):1705-1720
, DOI: 10.13328/j.cnki.jos.005746
Abstract:
2019,30(6):1692-1704
, DOI: 10.13328/j.cnki.jos.005745
Abstract:
2019,30(6):1681-1691
, DOI: 10.13328/j.cnki.jos.005744
Abstract:
2019,30(6):1670-1680
, DOI: 10.13328/j.cnki.jos.005743
Abstract:
2019,30(6):1661-1669
, DOI: 10.13328/j.cnki.jos.005742
Abstract:
2019,30(6):1614-1631
, DOI: 10.13328/j.cnki.jos.005739
Abstract:
2019,30(6):1594-1613
, DOI: 10.13328/j.cnki.jos.005738
Abstract:
2019,30(6):1577-1593
, DOI: 10.13328/j.cnki.jos.005737
Abstract:
2019,30(5):1203-1205
, DOI: 10.13328/j.cnki.jos.005736
Abstract:
2019,30(5):1422-1437
, DOI: 10.13328/j.cnki.jos.005735
Abstract:
2019,30(5):1269-1287
, DOI: 10.13328/j.cnki.jos.005734
Abstract:
2019,30(5):1561-1574
, DOI: 10.13328/j.cnki.jos.005733
Abstract:
2019,30(5):1407-1421
, DOI: 10.13328/j.cnki.jos.005732
Abstract:
2019,30(5):1547-1560
, DOI: 10.13328/j.cnki.jos.005731
Abstract:
2019,30(5):1375-1385
, DOI: 10.13328/j.cnki.jos.005730
Abstract:
2019,30(5):1243-1255
, DOI: 10.13328/j.cnki.jos.005729
Abstract:
2019,30(5):1532-1546
, DOI: 10.13328/j.cnki.jos.005728
Abstract:
2019,30(5):1510-1521
, DOI: 10.13328/j.cnki.jos.005726
Abstract:
2019,30(5):1386-1406
, DOI: 10.13328/j.cnki.jos.005725
Abstract:
2019,30(5):1359-1374
, DOI: 10.13328/j.cnki.jos.005724
Abstract:
2019,30(5):1464-1480
, DOI: 10.13328/j.cnki.jos.005723
Abstract:
2019,30(5):1342-1358
, DOI: 10.13328/j.cnki.jos.005722
Abstract:
2019,30(5):1481-1497
, DOI: 10.13328/j.cnki.jos.005721
Abstract:
2019,30(5):1450-1463
, DOI: 10.13328/j.cnki.jos.005720
Abstract:
2019,30(5):1224-1242
, DOI: 10.13328/j.cnki.jos.005719
Abstract:
2019,30(5):1498-1509
, DOI: 10.13328/j.cnki.jos.005718
Abstract:
2019,30(5):1256-1268
, DOI: 10.13328/j.cnki.jos.005716
Abstract:
2019,30(5):1330-1341
, DOI: 10.13328/j.cnki.jos.005715
Abstract:
2019,30(5):1438-1449
, DOI: 10.13328/j.cnki.jos.005714
Abstract:
2019,30(5):1308-1329
, DOI: 10.13328/j.cnki.jos.005712
Abstract:
2019,30(4):865-866
, DOI: 10.13328/j.cnki.jos.005668
Abstract:
2019,30(4):954-961
, DOI: 10.13328/j.cnki.jos.005667
Abstract:
2019,30(4):896-913
, DOI: 10.13328/j.cnki.jos.005666
Abstract:
2019,30(4):867-883
, DOI: 10.13328/j.cnki.jos.005665
Abstract:
2019,30(4):884-895
, DOI: 10.13328/j.cnki.jos.005664
Abstract:
2019,30(4):941-953
, DOI: 10.13328/j.cnki.jos.005663
Abstract:
2019,30(4):927-940
, DOI: 10.13328/j.cnki.jos.005662
Abstract:
2019,30(4):914-926
, DOI: 10.13328/j.cnki.jos.005661
Abstract:
2019,30(3):495-497
, DOI: 10.13328/j.cnki.jos.005701
Abstract:
2019,30(3):620-647
, DOI: 10.13328/j.cnki.jos.005700
Abstract:
2019,30(3):552-572
, DOI: 10.13328/j.cnki.jos.005699
Abstract:
2019,30(3):759-769
, DOI: 10.13328/j.cnki.jos.005697
Abstract:
2019,30(3):498-514
, DOI: 10.13328/j.cnki.jos.005696
Abstract:
2019,30(3):738-758
, DOI: 10.13328/j.cnki.jos.005695
Abstract:
2019,30(3):684-699
, DOI: 10.13328/j.cnki.jos.005694
Abstract:
2019,30(3):667-683
, DOI: 10.13328/j.cnki.jos.005693
Abstract:
2019,30(3):515-536
, DOI: 10.13328/j.cnki.jos.005692
Abstract:
2019,30(3):604-619
, DOI: 10.13328/j.cnki.jos.005691
Abstract:
2019,30(3):784-798
, DOI: 10.13328/j.cnki.jos.005690
Abstract:
2019,30(3):589-603
, DOI: 10.13328/j.cnki.jos.005688
Abstract:
2019,30(3):700-717
, DOI: 10.13328/j.cnki.jos.005687
Abstract:
2019,30(3):648-666
, DOI: 10.13328/j.cnki.jos.005686
Abstract:
2019,30(3):718-737
, DOI: 10.13328/j.cnki.jos.005685
Abstract:
2019,30(3):537-551
, DOI: 10.13328/j.cnki.jos.005684
Abstract:
2019,30(3):770-783
, DOI: 10.13328/j.cnki.jos.005683
Abstract:
2019,30(3):573-588
, DOI: 10.13328/j.cnki.jos.005682
Abstract:
2019,30(3):822-844
, DOI: 10.13328/j.cnki.jos.005681
Abstract:
2018,29(11):3239-3240
, DOI: 10.13328/j.cnki.jos.005484
Abstract:
2018,29(11):3340-3354
, DOI: 10.13328/j.cnki.jos.005483
Abstract:
2018,29(11):3374-3387
, DOI: 10.13328/j.cnki.jos.005482
Abstract:
2018,29(11):3295-3305
, DOI: 10.13328/j.cnki.jos.005481
Abstract:
2018,29(11):3241-3259
, DOI: 10.13328/j.cnki.jos.005480
Abstract:
2018,29(11):3306-3325
, DOI: 10.13328/j.cnki.jos.005479
Abstract:
2018,29(11):3278-3294
, DOI: 10.13328/j.cnki.jos.005478
Abstract:
2018,29(11):3326-3339
, DOI: 10.13328/j.cnki.jos.005477
Abstract:
2018,29(11):3260-3277
, DOI: 10.13328/j.cnki.jos.005476
Abstract:
2018,29(11):3355-3373
, DOI: 10.13328/j.cnki.jos.005475
Abstract:
2018,29(11):3388-3399
, DOI: 10.13328/j.cnki.jos.005474
Abstract:
2018,29(10):2897-2898
, DOI: 10.13328/j.cnki.jos.005554
Abstract:
2018,29(10):2995-3008
, DOI: 10.13328/j.cnki.jos.005553
Abstract:
2018,29(10):2931-2947
, DOI: 10.13328/j.cnki.jos.005552
Abstract:
2018,29(10):2966-2994
, DOI: 10.13328/j.cnki.jos.005551
Abstract:
2018,29(10):2948-2965
, DOI: 10.13328/j.cnki.jos.005550
Abstract:
2018,29(10):2915-2930
, DOI: 10.13328/j.cnki.jos.005549
Abstract:
2018,29(10):2899-2914
, DOI: 10.13328/j.cnki.jos.005548
Abstract:
2018,29(9):2545-2546
, DOI: 10.13328/j.cnki.jos.005401
Abstract:
2018,29(9):2580-2594
, DOI: 10.13328/j.cnki.jos.005400
Abstract:
2018,29(9):2632-2648
, DOI: 10.13328/j.cnki.jos.005399
Abstract:
2018,29(9):2595-2605
, DOI: 10.13328/j.cnki.jos.005398
Abstract:
2018,29(9):2606-2615
, DOI: 10.13328/j.cnki.jos.005397
Abstract:
2018,29(9):2616-2631
, DOI: 10.13328/j.cnki.jos.005396
Abstract:
2018,29(9):2547-2558
, DOI: 10.13328/j.cnki.jos.005395
Abstract:
2018,29(9):2559-2579
, DOI: 10.13328/j.cnki.jos.005394
Abstract:
2018,29(8):2485-2500
, DOI: 10.13328/j.cnki.jos.005543
Abstract:
2018,29(8):2177-2179
, DOI: 10.13328/j.cnki.jos.005534
Abstract:
2018,29(8):2210-2225
, DOI: 10.13328/j.cnki.jos.005533
Abstract:
2018,29(8):2322-2335
, DOI: 10.13328/j.cnki.jos.005532
Abstract:
2018,29(8):2243-2257
, DOI: 10.13328/j.cnki.jos.005531
Abstract:
2018,29(8):2350-2370
, DOI: 10.13328/j.cnki.jos.005530
Abstract:
2018,29(8):2180-2197
, DOI: 10.13328/j.cnki.jos.005529
Abstract:
2018,29(8):2226-2242
, DOI: 10.13328/j.cnki.jos.005528
Abstract:
2018,29(8):2198-2209
, DOI: 10.13328/j.cnki.jos.005527
Abstract:
2018,29(8):2336-2349
, DOI: 10.13328/j.cnki.jos.005526
Abstract:
2018,29(8):2306-2321
, DOI: 10.13328/j.cnki.jos.005525
Abstract:
2018,29(8):2258-2271
, DOI: 10.13328/j.cnki.jos.005524
Abstract:
2018,29(8):2294-2305
, DOI: 10.13328/j.cnki.jos.005523
Abstract:
2018,29(8):2283-2293
, DOI: 10.13328/j.cnki.jos.005522
Abstract:
2018,29(8):2272-2282
, DOI: 10.13328/j.cnki.jos.005521
Abstract:
2018,29(7):1827-1829
, DOI: 10.13328/j.cnki.jos.005366
Abstract:
2018,29(7):2006-2017
, DOI: 10.13328/j.cnki.jos.005365
Abstract:
2018,29(7):1981-2005
, DOI: 10.13328/j.cnki.jos.005364
Abstract:
2018,29(7):1963-1980
, DOI: 10.13328/j.cnki.jos.005363
Abstract:
2018,29(7):1953-1962
, DOI: 10.13328/j.cnki.jos.005362
Abstract:
2018,29(7):1937-1952
, DOI: 10.13328/j.cnki.jos.005361
Abstract:
2018,29(7):1922-1936
, DOI: 10.13328/j.cnki.jos.005360
Abstract:
2018,29(7):1909-1921
, DOI: 10.13328/j.cnki.jos.005359
Abstract:
2018,29(7):1893-1908
, DOI: 10.13328/j.cnki.jos.005358
Abstract:
2018,29(7):1880-1892
, DOI: 10.13328/j.cnki.jos.005357
Abstract:
2018,29(7):1863-1879
, DOI: 10.13328/j.cnki.jos.005356
Abstract:
2018,29(7):1852-1862
, DOI: 10.13328/j.cnki.jos.005355
Abstract:
2018,29(7):1830-1851
, DOI: 10.13328/j.cnki.jos.005354
Abstract:
2018,29(6):1515-1516
, DOI: 10.13328/j.cnki.jos.005473
Abstract:
2018,29(6):1670-1680
, DOI: 10.13328/j.cnki.jos.005472
Abstract:
2018,29(6):1607-1621
, DOI: 10.13328/j.cnki.jos.005471
Abstract:
2018,29(6):1681-1698
, DOI: 10.13328/j.cnki.jos.005470
Abstract:
2018,29(6):1595-1606
, DOI: 10.13328/j.cnki.jos.005469
Abstract:
2018,29(6):1699-1715
, DOI: 10.13328/j.cnki.jos.005468
Abstract:
2018,29(6):1527-1543
, DOI: 10.13328/j.cnki.jos.005467
Abstract:
2018,29(6):1622-1634
, DOI: 10.13328/j.cnki.jos.005466
Abstract:
2018,29(6):1566-1581
, DOI: 10.13328/j.cnki.jos.005465
Abstract:
2018,29(6):1544-1565
, DOI: 10.13328/j.cnki.jos.005463
Abstract:
2018,29(6):1582-1594
, DOI: 10.13328/j.cnki.jos.005462
Abstract:
2018,29(6):1517-1526
, DOI: 10.13328/j.cnki.jos.005461
Abstract:
2018,29(6):1647-1669
, DOI: 10.13328/j.cnki.jos.005460
Abstract:
2018,29(6):1635-1646
, DOI: 10.13328/j.cnki.jos.005459
Abstract:
2018,29(5):1177-1178
, DOI: 10.13328/j.cnki.jos.005509
Abstract:
2018,29(5):1360-1378
, DOI: 10.13328/j.cnki.jos.005508
Abstract:
2018,29(5):1213-1229
, DOI: 10.13328/j.cnki.jos.005507
Abstract:
2018,29(5):1244-1257
, DOI: 10.13328/j.cnki.jos.005506
Abstract:
2018,29(5):1199-1212
, DOI: 10.13328/j.cnki.jos.005505
Abstract:
2018,29(5):1179-1198
, DOI: 10.13328/j.cnki.jos.005504
Abstract:
2018,29(5):1230-1243
, DOI: 10.13328/j.cnki.jos.005503
Abstract:
2018,29(5):1318-1332
, DOI: 10.13328/j.cnki.jos.005502
Abstract:
2018,29(5):1275-1287
, DOI: 10.13328/j.cnki.jos.005501
Abstract:
2018,29(5):1348-1359
, DOI: 10.13328/j.cnki.jos.005500
Abstract:
2018,29(5):1258-1274
, DOI: 10.13328/j.cnki.jos.005499
Abstract:
2018,29(5):1303-1317
, DOI: 10.13328/j.cnki.jos.005498
Abstract:
2018,29(5):1379-1391
, DOI: 10.13328/j.cnki.jos.005497
Abstract:
2018,29(5):1333-1347
, DOI: 10.13328/j.cnki.jos.005496
Abstract:
2018,29(5):1410-1421
, DOI: 10.13328/j.cnki.jos.005495
Abstract:
2018,29(5):1392-1409
, DOI: 10.13328/j.cnki.jos.005494
Abstract:
2018,29(5):1288-1302
, DOI: 10.13328/j.cnki.jos.005493
Abstract:
2018,29(4):897-899
, DOI: 10.13328/j.cnki.jos.005417
Abstract:
2018,29(4):945-956
, DOI: 10.13328/j.cnki.jos.005416
Abstract:
2018,29(4):935-944
, DOI: 10.13328/j.cnki.jos.005415
Abstract:
2018,29(4):1071-1084
, DOI: 10.13328/j.cnki.jos.005414
Abstract:
2018,29(4):1002-1016
, DOI: 10.13328/j.cnki.jos.005413
Abstract:
2018,29(4):1060-1070
, DOI: 10.13328/j.cnki.jos.005412
Abstract:
2018,29(4):987-1001
, DOI: 10.13328/j.cnki.jos.005411
Abstract:
2018,29(4):973-986
, DOI: 10.13328/j.cnki.jos.005410
Abstract:
2018,29(4):1049-1059
, DOI: 10.13328/j.cnki.jos.005409
Abstract:
2018,29(4):1039-1048
, DOI: 10.13328/j.cnki.jos.005408
Abstract:
2018,29(4):926-934
, DOI: 10.13328/j.cnki.jos.005407
Abstract:
2018,29(4):957-972
, DOI: 10.13328/j.cnki.jos.005406
Abstract:
2018,29(4):914-925
, DOI: 10.13328/j.cnki.jos.005405
Abstract:
2018,29(4):1029-1038
, DOI: 10.13328/j.cnki.jos.005404
Abstract:
2018,29(4):900-913
, DOI: 10.13328/j.cnki.jos.005403
Abstract:
2018,29(4):1017-1028
, DOI: 10.13328/j.cnki.jos.005402
Abstract:
2018,29(3):525-527
, DOI: 10.13328/j.cnki.jos.005458
Abstract:
2018,29(3):853-868
, DOI: 10.13328/j.cnki.jos.005457
Abstract:
2018,29(3):627-641
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Abstract:
2018,29(3):599-613
, DOI: 10.13328/j.cnki.jos.005455
Abstract:
2018,29(3):839-852
, DOI: 10.13328/j.cnki.jos.005454
Abstract:
2018,29(3):824-838
, DOI: 10.13328/j.cnki.jos.005453
Abstract:
2018,29(3):587-598
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Abstract:
2018,29(3):703-720
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Abstract:
2018,29(3):569-586
, DOI: 10.13328/j.cnki.jos.005450
Abstract:
2018,29(3):689-702
, DOI: 10.13328/j.cnki.jos.005449
Abstract:
2018,29(3):811-823
, DOI: 10.13328/j.cnki.jos.005448
Abstract:
2018,29(3):614-626
, DOI: 10.13328/j.cnki.jos.005447
Abstract:
2018,29(3):883-895
, DOI: 10.13328/j.cnki.jos.005446
Abstract:
2018,29(3):721-733
, DOI: 10.13328/j.cnki.jos.005445
Abstract:
2018,29(3):663-688
, DOI: 10.13328/j.cnki.jos.005444
Abstract:
2018,29(3):545-568
, DOI: 10.13328/j.cnki.jos.005443
Abstract:
2018,29(3):642-662
, DOI: 10.13328/j.cnki.jos.005442
Abstract:
2018,29(3):528-544
, DOI: 10.13328/j.cnki.jos.005441
Abstract:
2018,29(3):869-882
, DOI: 10.13328/j.cnki.jos.005440
Abstract:
2018,29(3):799-810
, DOI: 10.13328/j.cnki.jos.005439
Abstract:
2018,29(3):734-755
, DOI: 10.13328/j.cnki.jos.005438
Abstract:
2018,29(3):786-798
, DOI: 10.13328/j.cnki.jos.005437
Abstract:
2018,29(3):772-785
, DOI: 10.13328/j.cnki.jos.005436
Abstract:
2018,29(3):756-771
, DOI: 10.13328/j.cnki.jos.005435
Abstract:
2017,28(9):2227-2228
, DOI: 10.13328/j.cnki.jos.005188
Abstract:
2017,28(9):2281-2292
, DOI: 10.13328/j.cnki.jos.005187
Abstract:
2017,28(9):2370-2387
, DOI: 10.13328/j.cnki.jos.005186
Abstract:
2017,28(9):2229-2247
, DOI: 10.13328/j.cnki.jos.005185
Abstract:
2017,28(9):2309-2322
, DOI: 10.13328/j.cnki.jos.005184
Abstract:
2017,28(9):2293-2308
, DOI: 10.13328/j.cnki.jos.005183
Abstract:
2017,28(9):2334-2353
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Abstract:
2017,28(9):2264-2280
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Abstract:
2017,28(9):2354-2369
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Abstract:
2017,28(9):2388-2401
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Abstract:
2017,28(9):2323-2333
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Abstract:
2017,28(9):2248-2263
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Abstract:
2017,28(8):1927-1928
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Abstract:
2017,28(8):1940-1951
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Abstract:
2017,28(8):1999-2009
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Abstract:
2017,28(8):1968-1981
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Abstract:
2017,28(8):1982-1998
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Abstract:
2017,28(8):1929-1939
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Abstract:
2017,28(8):1952-1967
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Abstract:
2017,28(6):1327-1329
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Abstract:
2017,28(6):1474-1487
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Abstract:
2017,28(6):1516-1528
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Abstract:
2017,28(6):1357-1372
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Abstract:
2017,28(6):1488-1497
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Abstract:
2017,28(6):1455-1473
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Abstract:
2017,28(6):1343-1356
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Abstract:
2017,28(6):1405-1417
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Abstract:
2017,28(6):1418-1434
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Abstract:
2017,28(6):1389-1404
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Abstract:
2017,28(6):1435-1454
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Abstract:
2017,28(6):1330-1342
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Abstract:
2017,28(6):1373-1388
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Abstract:
2017,28(6):1498-1515
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Abstract:
2017,28(5):1049-1050
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Abstract:
2017,28(5):1204-1220
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Abstract:
2017,28(5):1183-1203
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Abstract:
2017,28(5):1128-1143
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Abstract:
2017,28(5):1167-1182
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Abstract:
2017,28(5):1144-1166
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Abstract:
2017,28(5):1233-1246
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Abstract:
2017,28(5):1091-1106
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Abstract:
2017,28(5):1051-1069
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Abstract:
2017,28(5):1107-1117
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Abstract:
2017,28(5):1080-1090
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Abstract:
2017,28(5):1070-1079
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Abstract:
2017,28(5):1118-1127
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Abstract:
2017,28(5):1221-1232
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Abstract:
2017,28(4):745-746
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Abstract:
2017,28(4):764-785
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Abstract:
2017,28(4):819-826
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Abstract:
2017,28(4):786-803
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Abstract:
2017,28(4):845-859
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Abstract:
2017,28(4):747-763
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Abstract:
2017,28(4):883-897
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Abstract:
2017,28(4):804-818
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Abstract:
2017,28(4):860-882
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Abstract:
2017,28(4):827-844
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Abstract:
2016,27(10):2459-2461
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Abstract:
2016,27(10):2462-2472
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Abstract:
2016,27(10):2519-2529
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Abstract:
2016,27(10):2612-2621
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Abstract:
2016,27(10):2499-2508
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Abstract:
2016,27(10):2473-2487
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Abstract:
2016,27(10):2587-2599
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Abstract:
2016,27(10):2622-2631
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Abstract:
2016,27(10):2661-2675
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Abstract:
2016,27(10):2654-2660
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Abstract:
2016,27(10):2600-2611
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Abstract:
2016,27(10):2676-2690
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Abstract:
2016,27(10):2642-2653
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Abstract:
2016,27(10):2632-2641
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Abstract:
2016,27(10):2488-2498
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Abstract:
2016,27(10):2530-2541
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Abstract:
2016,27(10):2509-2518
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Abstract:
2016,27(10):2557-2573
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Abstract:
2016,27(10):2574-2586
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Abstract:
2016,27(10):2542-2556
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Abstract:
2016,27(8):1889-1890
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Abstract:
2016,27(8):1891-1903
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Abstract:
2016,27(8):1922-1933
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Abstract:
2016,27(8):1904-1921
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Abstract:
2016,27(7):1685-1699
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Abstract:
2016,27(7):1626-1644
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Abstract:
2016,27(7):1729-1740
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Abstract:
2016,27(7):1605-1625
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Abstract:
2016,27(7):1671-1684
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Abstract:
2016,27(7):1645-1654
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Abstract:
2016,27(7):1700-1714
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Abstract:
2016,27(7):1715-1728
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Abstract:
2016,27(7):1655-1670
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Abstract:
2016,27(6):1325-1327
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Abstract:
2016,27(6):1402-1416
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Abstract:
2016,27(6):1463-1474
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Abstract:
2016,27(6):1592-1601
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Abstract:
2016,27(6):1384-1401
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Abstract:
2016,27(6):1577-1591
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Abstract:
2016,27(6):1328-1348
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Abstract:
2016,27(6):1432-1450
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Abstract:
2016,27(6):1475-1486
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Abstract:
2016,27(6):1511-1522
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Abstract:
2016,27(6):1366-1383
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Abstract:
2016,27(6):1451-1462
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Abstract:
2016,27(6):1566-1576
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Abstract:
2016,27(6):1538-1548
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Abstract:
2016,27(6):1549-1565
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Abstract:
2016,27(6):1417-1431
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Abstract:
2016,27(6):1349-1365
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Abstract:
2016,27(6):1498-1510
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Abstract:
2016,27(6):1523-1537
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Abstract:
2016,27(6):1487-1497
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Abstract:
2016,27(5):1059-1060
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Abstract:
2016,27(5):1163-1173
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Abstract:
2016,27(5):1061-1073
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Abstract:
2016,27(5):1188-1198
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Abstract:
2016,27(5):1199-1211
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Abstract:
2016,27(5):1174-1187
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Abstract:
2016,27(5):1074-1090
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Abstract:
2016,27(5):1091-1102
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Abstract:
2016,27(5):1114-1126
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Abstract:
2016,27(5):1140-1150
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Abstract:
2016,27(5):1151-1162
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Abstract:
2016,27(5):1103-1113
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Abstract:
2016,27(5):1127-1139
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Abstract:
2016,27(4):769-770
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Abstract:
2016,27(4):839-854
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Abstract:
2016,27(4):855-878
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Abstract:
2016,27(4):828-838
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Abstract:
2016,27(4):771-784
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Abstract:
2016,27(4):814-827
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Abstract:
2016,27(4):879-900
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Abstract:
2016,27(4):901-915
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Abstract:
2016,27(4):943-954
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Abstract:
2016,27(4):802-813
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Abstract:
2016,27(4):785-801
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Abstract:
2016,27(4):916-932
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Abstract:
2016,27(4):933-942
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Abstract:
2016,27(3):495-496
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Abstract:
2016,27(3):682-690
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Abstract:
2016,27(3):670-681
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Abstract:
2016,27(3):655-669
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Abstract:
2016,27(3):645-654
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Abstract:
2016,27(3):633-644
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Abstract:
2016,27(3):611-632
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Abstract:
2016,27(3):593-610
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Abstract:
2016,27(3):580-592
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Abstract:
2016,27(3):562-579
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Abstract:
2016,27(3):547-561
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Abstract:
2016,27(3):527-546
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Abstract:
2016,27(3):517-526
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Abstract:
2016,27(3):497-516
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Abstract:
2015,26(11):2749-2751
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Abstract:
2015,26(11):2811-2819
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Abstract:
2015,26(11):2951-2963
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Abstract:
2015,26(11):2994-3009
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Abstract:
2015,26(11):2856-2868
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Abstract:
2015,26(11):2981-2993
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Abstract:
2015,26(11):2820-2835
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Abstract:
2015,26(11):2781-2794
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Abstract:
2015,26(11):3010-3025
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Abstract:
2015,26(11):2939-2950
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Abstract:
2015,26(11):2930-2938
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Abstract:
2015,26(11):2795-2810
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Abstract:
2015,26(11):2912-2929
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Abstract:
2015,26(11):2847-2855
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Abstract:
2015,26(11):2897-2911
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Abstract:
2015,26(11):2884-2896
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Abstract:
2015,26(11):2762-2780
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Abstract:
2015,26(11):2964-2980
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Abstract:
2015,26(11):2752-2761
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Abstract:
2015,26(11):2869-2883
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Abstract:
2015,26(11):2836-2846
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Abstract:
2015,26(11):3026-3042
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Abstract:
2015,26(4):711-712
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Abstract:
2015,26(4):886-903
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Abstract:
2015,26(4):835-848
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Abstract:
2015,26(4):778-789
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Abstract:
2015,26(4):730-746
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Abstract:
2015,26(4):760-777
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Abstract:
2015,26(4):867-885
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Abstract:
2015,26(4):713-729
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Abstract:
2015,26(4):802-818
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Abstract:
2015,26(4):790-801
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Abstract:
2015,26(4):819-834
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Abstract:
2015,26(4):747-759
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Abstract:
2015,26(4):849-866
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Abstract:
2015,26(3):447-448
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Abstract:
2015,26(3):474-490
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Abstract:
2015,26(3):584-599
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Abstract:
2015,26(3):509-532
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Abstract:
2015,26(3):550-561
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Abstract:
2015,26(3):533-549
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Abstract:
2015,26(3):460-473
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Abstract:
2015,26(3):574-583
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Abstract:
2015,26(3):562-573
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Abstract:
2015,26(3):449-459
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Abstract:
2015,26(3):491-508
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Abstract:
2015,26(2):179-180
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Abstract:
2015,26(2):380-389
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Abstract:
2015,26(2):364-379
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Abstract:
2015,26(2):348-363
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Abstract:
2015,26(2):332-347
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Abstract:
2015,26(2):321-331
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Abstract:
2015,26(2):305-320
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Abstract:
2015,26(2):297-304
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Abstract:
2015,26(2):279-296
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Abstract:
2015,26(2):269-278
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Abstract:
2015,26(2):254-268
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Abstract:
2015,26(2):239-253
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Abstract:
2015,26(2):223-238
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Abstract:
2015,26(2):202-222
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Abstract:
2015,26(2):181-201
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Abstract:
2014,25(12):2731-2732
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Abstract:
2014,25(12):2893-2904
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Abstract:
2014,25(12):2767-2776
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Abstract:
2014,25(12):2777-2789
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Abstract:
2014,25(12):2790-2807
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Abstract:
2014,25(12):2877-2892
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Abstract:
2014,25(12):2865-2876
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Abstract:
2014,25(12):2852-2864
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Abstract:
2014,25(12):2733-2752
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Abstract:
2014,25(12):2837-2851
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Abstract:
2014,25(12):2753-2766
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Abstract:
2014,25(12):2824-2836
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Abstract:
2014,25(12):2808-2823
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Abstract:
2014,25(10):2187-2188
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Abstract:
2014,25(10):2251-2265
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Abstract:
2014,25(10):2206-2219
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Abstract:
2014,25(10):2189-2205
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Abstract:
2014,25(10):2220-2234
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Abstract:
2014,25(10):2235-2250
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Abstract:
2014,25(9):1889-1908
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Abstract:
2014,25(9):1887-1888
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Abstract:
2014,25(9):2102-2118
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Abstract:
2014,25(9):2149-2159
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Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.
