Automated Program Repair (APR) is increasingly vital for managing the complexity of modern software systems. However, current APR techniques suffer from inefficiently selecting repair components, resulting in suboptimal patches. To address these limitations, we propose PCRepair, a context-aware template-based methodology for automated software fault repair. This approach integrates predefined repair templates with context-aware analysis to improve repair accuracy and efficiency. PCRepair first localizes suspicious statements via the Ochiai technique, then matches their contextual patterns with relevant templates. This strategy narrows the search space and generates semantically relevant candidate patches. We prioritize these patches using a weighted fusion similarity metric and sequentially validate them against existing test cases. Evaluations on the Defects4J benchmark show that PCRepair successfully repaired 38 defects, demonstrating competitive performance compared to existing methods, particularly in terms of repair efficiency, with a 9.62% success rate and an average repair time of fewer than 30 min per defect.

With the increasing scale and complexity of software recently, automated software bug repair has grown in importance. However, the current automated software bug repair process suffers from issues such as coarse-grained repair granularity and poor patch quality. To address these problems, we propose a graph mining template-based automatic software repair (GMRepair) to improve the performance of automated software bug repair. First, this approach adopts the Ochiai fault localization technique to locate and generate a list of suspicious defect statements. We utilize the GumTree tool to parse the bug and repair program files, generating edit scripts. These edit scripts are then transformed into a graphical representation. Second, we utilize a frequent graph miner to obtain graph mining templates by matching the context of the suspicious statements with the context of the graph mining templates, generating an initial population for them. The buggy program is evolved using genetic programming through mutation and crossover operations, generating new individuals. Finally, we sequentially pass the candidate patches (CPs) through corresponding test cases and prioritize the test cases using priority sorting techniques. Patches that fail to pass the test cases are filtered out, and the patches that pass the test cases are output. We conducted the experiments using two datasets, QuixBugs and Defects4J. In Defects4J, the GMRepair successfully repaired 41 defects, while in QuixBugs, it successfully repaired 15 defects. Compared to the existing methods, GMRepair offers a higher success rate and efficiency in defect repair.

The rapid evolution of the Industrial Internet of Things (IIoT) has driven unprecedented growth in industrial data volumes. To enhance cost efficiency and data-sharing capabilities, massive amounts of this data are stored in the cloud. Public Key Encryption with Keyword Search (PEKS) technology enables efficient encrypted data retrieval without key management and distribution issues and has been extensively studied for this purpose. However, due to inherent IIoT characteristics—such as heterogeneous data formats, resource-constrained devices, and heightened vulnerability to attacks, existing PEKS schemes face significant limitations: 1) typically restricted to single-keyword searches; 2) prohibitive computational overhead for resource-limited IIoT devices; 3) heightened risks of exploitation by attackers. To address these issues, we propose a lightweight certificateless authenticated encryption with multi-keyword search scheme, named CLAEMKS. It enables efficient multi-keyword search while substantially enhancing computational efficiency by eliminating the intensive bilinear pairing operations. Meanwhile, by leveraging certificateless cryptography, CLAEMKS solves certificate management problems while avoiding key escrow issues. Furthermore, formal security proofs and efficiency analyses are conducted to validate the effectiveness of our proposed scheme. The results demonstrate that CLAEMKS delivers substantial performance and security improvements.

Secure communication between mobile users and edge servers in Mobile Edge Computing (MEC) environments needs to address challenges in data integrity, privacy, and authentication. Existing authentication and key agreement (AKA) schemes suffer from vulnerabilities such as offline key guessing and Known Session Specific Temporary Information (KSSTI) attacks, and are inefficient, putting pressure on resource-limited devices. This research proposes a novel pairing-free, multi-factor AKA scheme for mobile infrastructures that eliminates these attack surfaces while ensuring, among other things, perfect forward security, user anonymity, and untraceability. The scheme is formally validated against threats such as replay, man-in-the-middle, and KSSTI under the random oracle model based on the elliptic curve discrete logarithmic problem and the Computational Diffie-Hellman (CDH) assumption. By avoiding bilinear pairing, the scheme significantly reduces 51.3% of server-side computation overhead, 35.3% of device-side computation overhead, 54.6% of energy consumption, 41.3% of communication overhead, and can enhance resilience to resource-constrained mobile devices, as compared to existing schemes.

Abstract In wireless networks, the Contention Window (CW) is a crucial parameter for wireless channel sharing among numerous stations, directly influencing overall network performance. With the rapid growth and increasing diversity of traffic in wireless networks, differentiating and serving various types of traffic to enhance network performance has become a pressing issue that needs to be addressed. Deep Reinforcement Learning (DRL) optimizes decision-making through the interaction between an agent and its environment, enabling it to handle complex state spaces and high-dimensional data, making it particularly suitable for dynamically adjusting the contention window and adapting to environmental changes in real time. Access Category (AC) is used to differentiate various types of traffic to support different quality of service (QoS) requirements. Therefore, this paper proposes the integration of DRL techniques into the EDCA mechanism, utilizing AC to differentiate network traffic, while the DRL technology adjusts the contention window to adequately ensure QoS for different types of traffic and enhance overall network performance. This paper proposes a CW back off scheme based on DRL for differentiating ACs. The scheme uses DRL technology to observe the channel collision rate and sense the current network conditions, thereby adaptively adjusting the CW size for ACs. In addition, it dynamically adjusts the back off strategy according to the perceived current network conditions to optimize the data transmission process. In the range of 20–120 stations, the scheme was tested in both single AC and multiple AC traffic scenarios, demonstrating excellent performance. The collision rate consistently remained below 18%, while the normalized throughput was maintained above 76%. Additionally, there is a significant improvement compared to existing deep reinforcement learning-based optimization schemes. Experimental results show that this scheme effectively discriminates between different ACs, resulting in lower latency and higher throughput for high-priority traffic. Furthermore, adaptively adjusting the CW size and improving the back off strategy maintains a low collision rate and stable throughput even under heavy load conditions, significantly improving the overall network performance.

n IEEE 802.11 networks, the Contention Window (CW) is a crucial parameter for wireless channel sharing among numerous stations, directly influencing overall network performance. In order to mitigate the performance degradation caused by the increasing number of stations in the network, we propose a novel adaptive CW backoff scheme, termed the ACWB-DQN algorithm. This algorithm leverages the Deep Q-Leaning Network (DQN) to explore a CW threshold, which is utilized as a boundary to differentiate the network load circumstances and learn the best configurations for different network conditions. When stations transmit data frames, different CW optimization strategies are employed based on station transmission status and the CW threshold. This approach aims to enhance network performance by adjusting the CW to increase transmission efficiency when there are fewer competing stations, and lower collision probabilities when there are more competing stations. Simulation results indicate that this approach can optimize station CW, reduce network collision rates, maintain constant throughput and significantly enhance the performance of Wi-Fi networks by means of adjusting the CW threshold according to real-time network conditions.

Abstract Surface defect detection is an essential task for ensuring the quality of products. Many excellent object detectors have been employed to detect surface defects in resent years, which has achieved outstanding success. To further improve the detection performance, a defect detector based on state‐of‐the‐art YOLOv8, named improved YOLOv8 by neck, head and data (NHD‐YOLO), is proposed. Specifically, YOLOv8 from three crucial aspects including neck, head and data is improved. First, a shortcut feature pyramid network is designed to effectively fuse features from backbone by improving the information transmission. Then, an adaptive decoupled head is proposed to alleviate the feature spatial misalignment between the classification and regression tasks. Finally, to enhance the training on small objects, a data augmentation method named selective small object copy and paste is proposed. Extensive experiments are conducted on three real‐world datasets: detection dataset from Northeastern University (NEU‐DET), printed circuit boards from Peking University (PKU‐Market‐PCB) and common objects in context (COCO). According to the results, NHD‐YOLO achieves the highest detection accuracy and exhibits outstanding inference speed and generalisation performance.