Abstract:Semi-supervised semantic segmentation methods typically employ various data augmentation schemes to ensure differentiation in the input of network branches, enabling mutual self-supervision. While successful, this approach faces several issues: 1) insufficient diversity in feature extraction leads to feature signal assimilation during inference; 2) inadequate diversity in supervision signals results in the assimilation of loss learning. These issues cause network branches to converge on similar solutions, degrading the functionality of multi-branch networks. To address these issues, a cross semi-supervised semantic segmentation method based on differential feature extraction is proposed. First, a differential feature extraction strategy is employed, ensuring that branches focus on distinct information, such as texture, semantics, and shapes, thus reducing reliance on data augmentation. Second, a cross-fusion pseudo-labeling method is introduced, where branches alternately generate neighboring pixel fusion pseudo-labels, enhancing the diversity of supervision signals and guiding branches toward different solutions. Experimental results demonstrate this method achieves excellent performance on the Pascal VOC 2012 and Cityscapes validation datasets, with scores of 80.2% and 76.8%, outperforming the latest methods by 0.3% and 1.3%, respectively.

Abstract:GUI testing is one of the most important measures to ensure mobile application (App) quality. With the continuous development of the mobile ecosystem, especially the strong rise of the domestic mobile ecosystem, e.g., HarmonyOS, GUI test script recording and replay has become one of the prominent challenges in GUI testing. GUI test scripts must be migrated from traditional mobile platforms to emerging mobile platforms to ensure the reliability of App quality and consistency in user experience across diverse platforms. However, differences in underlying implementations across platforms have created substantial obstacles to the cross-platform migration of mobile App test scripts. This challenge is particularly pronounced in the testing migration for emerging domestic mobile ecosystem platforms. Cross-platform test script recording and replay is essential for maintaining consistency and a high-quality user experience across different platforms and devices. Current state-of-the-art approaches only address the “one-to-one” test event matching situations. However, due to inconsistencies in development practices across platforms, the replay of test events does not always map “one-to-one”; instead, “multiple-to-multiple” mapping situations are common. This means that some test events need to be mapped to a different number of test events to fulfill the same business logic. To address these issues and challenges, this study proposes a cross-platform mobile App test script recording and replay method based on large language model semantic matching (LLMRR). The LLMRR method integrates image matching, text matching, and large language model semantic matching technologies. During the recording phase, user operation information is captured using image segmentation algorithms and saved as recorded test scripts. During the replay phase, corresponding widgets on the replay App page are located using image matching and text matching modules to execute operations. When matching fails, the large language model semantic matching module is invoked for semantic matching, ensuring efficient operation across different platforms. This study presents the first exploration of testing for domestic HarmonyOS Apps, using 20 Apps and a total of 100 test scripts for migration testing across iOS, Android, and HarmonyOS platforms. The effectiveness of the LLMRR method is compared with the current state-of-the-art cross-platform test script recording and replay approaches, LIRAT and MAPIT. The results demonstrate that the LLMRR method exhibits significant advantages in test script recording and replay.

Abstract:Blockchain, as a distributed ledger technology, ensures data security, transparency, and immutability through encryption and consensus mechanisms, offering transformative solutions across various industries. In China, blockchain-based software has attracted widespread attention and application, demonstrating considerable potential in fields such as cross-border payments, supply chain finance, and government services. These applications not only enhance the efficiency and transparency of business processes but also reduce trust costs and offer new approaches for the digital transformation of traditional industries. This study investigates the development trends and core technologies of Chinese blockchain software, focusing on key technological breakthroughs, promoting integration and innovation, and providing a foundation for the formulation of technical standards. The aim is to enhance the competitiveness of Chinese blockchain technologies, broaden application scenarios, and support the standardized development of the industry. Three core research questions are addressed: (1) What are the development trends of Chinese blockchain software? (2) What are the core technologies involved? (3) What are the differences in core technologies between Chinese and foreign blockchain software? To address these questions, 1268 blockchain software entries have been collected through three channels. Based on information regarding affiliated companies and chief technology officers (CTOs), 103 Chinese blockchain software entries are identified. A statistical analysis of basic software attributes is conducted, examining development trends from three perspectives: software development history, distribution, and interrelationships. Given the importance of technical and development documentation, 39 high-quality blockchain software entries containing detailed technical information are further selected. Subsequently, a statistical and analytical evaluation of the core technologies of these 39 software systems is conducted across six technical layers of blockchain architecture. Based on this analysis, differences in core technologies between Chinese and foreign blockchain software are compared. In total, 28 phenomena and 13 insights are identified. These findings provide researchers, developers, and practitioners with a comprehensive understanding of the current state of Chinese blockchain development and offer valuable references for future adoption and improvement of Chinese blockchain software.

Abstract:Accurate workload forecasting is essential for effective cloud resource management. However, existing models typically employ fixed architectures to extract sequential features from different perspectives, which limits the flexibility of combining various model structures to further improve forecasting performance. To address this limitation, a novel ensemble framework SAC-MWF is proposed based on the soft actor-critic (SAC) algorithm for multi-view workload forecasting. A set of feature sequence construction methods is developed to generate multi-view feature sequences at low computational cost from historical windows, enabling the model to focus on workload patterns from different perspectives. Subsequently, a base prediction model and several feature prediction models are trained on historical windows and their corresponding feature sequences, respectively, to capture workload dynamics from different views. Finally, the SAC algorithm is employed to integrate these models to generate the final forecast. Experimental results on three datasets demonstrate that SAC-MWF performs excellently in terms of effectiveness and computational efficiency.

Abstract:In recent years, pre-trained models that take code as input have achieved significant performance gains in various critical code-based tasks. However, these models remain susceptible to adversarial attacks implemented through semantic-preserving code transformations, which can severely compromise model robustness and pose serious security issues. Although adversarial training, leveraging adversarial examples as augmented data, has been employed to enhance robustness, its effectiveness and efficiency often fall short when facing unseen attacks with varying granularities and strategies. To address these limitations, a novel adversarial defense technique based on code normalization, named CoDefense, is proposed. This method integrates a multi-granularity code normalization approach as a preprocessing module, which normalizes both the original training data during training and the inputcode during inference. By doing so, the proposed method mitigates the impact of potential adversarial examples and effectively defends against attacks of diverse types and granularities. To evaluate the effectiveness and efficiency of CoDefense, a comprehensive experimental study is constructed, encompassing 27 scenarios across three representative adversarial attack methods, three widely-used pre-trained code models, and three code-based classification and generation tasks. Experimental results demonstrate that CoDefense significantly outperforms state-of-the-art adversarial training methods in both robustness and efficiency. Specifically, it achieves an average defense success rate of 95.33% against adversarial attacks and improves time efficiency by an average of 85.86%.

Abstract:Smart contracts, as automatically executed computer transaction protocols, are widely applied in blockchain networks to implement various types of business logic. However, the strict immutability of blockchain poses significant challenges for smart contract maintenance, making upgradeability a prominent research topic. This study focuses on upgradeable smart contracts, systematically reviewing their development status both domestically and internationally, and introducing seven mainstream upgradeable contract models. The research is summarized from four key perspectives: upgradeable smart contracts, application requirements, upgrade frameworks, and security oversight. It covers multiple stages, including design, implementation, testing, deployment, and maintenance. The goal is to provide insights and references for the further development of blockchain applications.

Abstract:This study investigates meet-in-the-middle attacks on three types of unbalanced generalized Feistel structures and conducts quantum meet-in-the-middle attacks in Q1 model. First, for the 3-branch Type-III generalized Feistel structure, a 4-round meet-in-the-middle distinguisher is constructed using multiset and differential enumeration techniques. By expanding one round forward and one round backward, a 6-round meet-in-the-middle attack is conducted. With the help of Grover’s algorithm and the quantum claw finding algorithm, a 6-round quantum key recovery attack is performed, requiring O(2^3?/2·?) quantum queries, where ? is the branch length of the generalized Feistel structure. Then, for the 3-branch Type-I structure, a 9-round distinguisher is similarly extended by one round in both directions to conduct an 11-round meet-in-the-middle attack and a quantum key recovery attack with time complexities of O(2^2?) 11-round encryptions and O(2^3?/2·?) quantum queries. Finally, taking the 3-cell generalized Feistel structure as a representative case, this study explores a quantum meet-in-the-middle attack on an n-cell structure. A 2n-round meet-in-the-middle distinguisher is constructed, enabling a 2(n+1)-round meet-in-the-middle attack and quantum key recovery attack. The associated time complexities are O(2^2?) 2(n+1)-round encryptions and O(2^3?/2·?) quantum queries. The results demonstrate that the time complexity in Q1 model is significantly reduced compared with classical scenarios.

Abstract:Query optimization is a critical component in database systems, where execution costs are minimized by identifying the most efficient query execution plan. Traditional query optimizers typically rely on fixed rules or simple heuristic algorithms to refine or select candidate plans. However, with the growing complexity of relational schemas and queries in real-world applications, such optimizers struggle to meet the demands of modern applications. Learned query optimization algorithms integrate machine learning techniques into the optimization process. They capture features of query plans and complex schemas to assist traditional optimizers. These algorithms offer innovative and effective solutions in areas such as cost modeling, join optimization, plan generation, and query rewriting. This study reviews recent achievements and developments in four main categories of learned query optimization algorithms. Future research directions are also discussed, aiming to provide a comprehensive understanding of the current state of research and to support further investigation in this field.

Abstract:The evolution of RFID-based passive Internet of Things (IoT) systems comprises three stages: traditional UHF RFID (also referred to as standalone or Passive 1.0), local area network-based coverage (networked or Passive 2.0), and wide-area cellular coverage (cellular or Passive 3.0). Wireless sensing in passive IoT is characterized by zero power consumption, low cost, and ease of deployment, enabling object tagging and close-proximity sensing. With the emergence of cellular passive IoT, passive IoT wireless sensing is playing an increasingly important role in enabling ubiquitous sensing within IoT systems. This study first introduces the concept and development path of passive IoT. Based on fundamental sensing principles, recent research advancements are reviewed across four representative objectives: localization and tracking, object status detection, human behavior recognition, and vital sign monitoring. Given that most existing research relies on commercial UHF RFID devices to extract signal features for data processing, the development direction of passive IoT wireless sensing technology is further examined from the perspectives of new architecture, new air interface, and new capabilities. Moreover, this study offers reflections on the integration of communication and sensing in the design of next-generation air interfaces from a sensing-oriented perspective, aiming to provide new insights into the advancements in passive IoT wireless sensing technologies.

Abstract:As concerns over data privacy continue to grow, secure multi-party computation (MPC) has gained considerable research attention due to its ability to protect sensitive information. However, the communication and memory demands of MPC protocols limit their performance in privacy-preserving machine learning (PPML). Reducing interaction rounds and memory overhead in secure computation protocols remains both essential and challenging, particularly in GPU-accelerated environments. This study focuses on the design and implementation of GPU-friendly protocols for linear and nonlinear computations. To eliminate overhead associated with integer operations, 64-bit integer matrix multiplication, and convolution are implemented using CUDA extensions in PyTorch. A most significant bit (MSB) extraction protocol with low communication rounds is proposed, based on 0-1 encoding. In addition, a low-communication-complexity hybrid multiplication protocol is introduced to reduce the communication overhead of secure comparison, enabling efficient computation of ReLU activation layers. Finally, Antelope, a GPU-based 3-party framework, is proposed to support efficient privacy-preserving machine learning. This framework significantly reduces the performance gap between secure and plaintext computation and supports end-to-end training of deep neural networks. Experimental results demonstrate that the proposed framework achieves 29×–101× speedup in training and 1.6×–35× in inference compared to the widely used CPU-based FALCON (PoPETs 2020). When compared with GPU-based approaches, training performance reaches 2.5×–3× that of CryptGPU (S&P 2021) and 1.2×–1.6× that of Piranha (USENIX Security 2022), while inference is accelerated by factors of 11× and 2.8×, respectively. Notably, the proposed secure comparison protocol exhibits significant advantages when processing small input sizes.