Abstract:Related execution path generation, which generates the similar execution path according to the acquisition and analysis of the target execution path, is a key technique in the dynamic program analysis, and it is important to the domain of program characteristic analysis, compilation optimization and debugging. Current analysis mainly generates the similar execution path by altering the node list of the path, but lacks the guiding information of the key node, and thus a lot of redundant and infeasible paths are generated. A technique of k similar paths generation based on cooperative reverse analysis is proposed. Aiming at the post-condition of the target paths, the pre-condition of the basic block of the program is calculated by the reverse symbolic analysis, which can be used as the guidance information of the execution paths. Meanwhile, the similar paths that are k distance from the target execution path can be obtained. Experimental results show that the proposed method has an obvious advantage in the aspects of accuracy and efficiency.

Abstract:Software testing process normally expects to detect defects as many as possible with minimum cost. In order to reduce the modeling complexity, most works generally assume that all defects are independent of each other. However, in practical testing processes, defects are normally correlated. The software failure severity caused by different defects may also be distinctive. Making full usage of the relationships between correlated defects, it is argued, is beneficial to improve software testing efficiency. This paper proposes a new approach by making usage of the relationship between defects. Firstly, the defects correlation matrix is constructed, and the synthetic balancing weights are designed based on defect correlation coefficient, rebate and detecting rate. Next, the optimal testing problem is converted into a weighted routing problem and a composite optimization algorithm is provided to effectively construct a minimum spanning tree to find an optimal test strategy. Meanwhile, a new defect removing strategy is designed in accordance with the characteristic of the correlated defects to eliminate defects more efficiently. Simulation results show that the proposed approach has higher effectiveness in terms of defect identification rate and system rewards.

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.

Abstract:As a traditional form in NLP, the identification and comprehension of metaphor is a bottle-neck problem in NLP and machine translation. In this paper, based on the basic theory, current computational models for metaphor are reviewed. According to the methods, models are divided into metaphor identification and comprehension. The advantages of every model are also analyzed. At last, the paper introduces the current resources of metaphor processing.

Abstract:Real-Time tracking low-contrast target in the complex environment is a key problem in the visual area. The algorithm needs to not only copy with the high similar between target and background, revolution, scale variations and target occlusions but also satisfy the real-time tracking. This paper provides a method based on the likelihood similarity function to resolve the low-contrast tracking. In the model construction phase, the new method uses the single peak of the pyramid surface equation to enhance the target information. In the model matching phase, it innovates a new likelihood similarity function which provides more distinguishable measurements than the traditional one. Finally, the tracking process transforms to the maximum likelihood estimate. The algorithm is applied in the TMS320C6416 hardware system and successfully copes with the low contrast (LSCR=4.9) airplane in the cluster background. A series of experiments results show that the lowest limitation of tracking the target by the proposed method is about 3 (LSCR value).

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.

Abstract:The incorporation of inter-session network coding in wireless networks has the potential to remarkably improve the network performance. Unfortunately, the amount of existing coding opportunities in practical networks is limited, which hinders high performance gain by passively using coding opportunities. To overcome this issue, the network-coding-aware routing attempts to promote network coding by creating coding opportunities through constructing specific coding structures in the routing establishment phase. This paper systematically summarizes existing coding structures in inter-session network coding. From the perspective of coding structure, the state of the art of network-coding-aware routing is reported. At last, development trends of network-coding-aware routing are discussed.

Abstract:To address the issue of joint adaptation of rate, MIMO (multiple input multiple output) mode and channel width in IEEE 802.11n wireless networks, a joint adaptation algorithm based on non-stationary multi-armed bandit learning approach is proposed, and a novel reward function is also presented. To reduce the convergence time of the algorithm mentioned above, the prediction algorithms of MCS (modulation and coding scheme), MIMO mode and channel width based on classification and regression trees are developd to effectively utilize the statistical data collected by the wireless network interface driver to predict the reward values of different combination of MCS, MIMO mode and channel width, and shrink the search space of the joint adaptation algorithm. The proposed algorithm is easy to implement, approximately optimal, and has low computation complexity. The real experiment results show that the UDP throughput is improved significantly by the proposed algorithm under the interference-free environment and the environment with different interference conditions.

Abstract:This paper reviews previous research on the two basic searchable encryption problems, and introduces the classification of searchable encryption (SE), including its application scenarios and usage models. After discussing the resolution strategies, it divides SE into two groups, that is symmetric searchable encryption and asymmetric searchable encryption. Based on this classification, the research advance is surveyed on basic definition, typical construction and extended research. Finally, the need-to-be-solved problems and main research directions are discussed. This study aims at promoting further research of searchable encryption.

Abstract:To tackle the problems of security stream exchange in the large-scale complicated network, this paper applies proxy re-signature technology for the first time to solve the flow exchange security issues, and proposes a proxy re-signature scheme based on trapdoor Hash function for stream exchange. Firstly, aiming at the key exposure problem of trapdoor Hash function for stream exchange, a new trapdoor Hash functions without key exposure (EDL-MTH) is put forward and its security is analyzed. Then, a new proxy re-signature scheme based on EDL-MTH is constructed and is proved against the chosen-message attack in the random oracle model. Furthermore, the performance of the scheme is analyzed contrast to the existing proven security proxy signature scheme, and the result shows the efficiency becomes more prominent while the scale of stream exchange is increased. Finally, a case study is provided to demonstrate its availability and performance in security stream exchange.

Abstract:The explosive growth of the digital data brings great challenges to the relational database management systems in addressing issues in areas such as scalability and fault tolerance. The cloud computing techniques have been widely used in many applications and become the standard effective approach to manage large scale data because of their high scalability, high availability and fault tolerance. The existing cloud-based data management systems can't efficiently support complex queries such as multi-dimensional queries and join queries because of lacking of index or view techniques, limiting the application of cloud computing in many respects. This paper conducts an in-depth research on the index techniques for cloud data management to highlight their strengths and weaknesses. This paper also introduces its own preliminary work on the index for massive IOT data in cloud environment. Finally, it points out some challenges in the index techniques for big data in cloud environment.

Abstract:Graph aggregation and summarization is to obtain a concise supergraph covering the most information of the underlying input graph, and it is used to extract summarization, solve storage consumption and protect privacy in social networks. This paper investigates current graph aggregation and summarization techniques and further reviews and classifies their partitioning/grouping methods. Based on the consistency of grouping information, five grouping criteria are specified: The consistency of attribute information, the consistency of neighborhood group, the consistency of connection strength, the consistency of neighborhood vertex and reconstruction zero error. From the top level view, graph aggregation and summarization techniques can be classified into three types, namely, attribute similarity, structure cohesiveness and the hybrid of both. This paper comprehensively summarizes the state of art of current research works, and explores the research directions in the future.