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A Verifiable Transaction Selection Method for DAG Blockchain With VRF

IEEE Internet of Things Journal 2025年第13期12卷 24185-24198页

作者： Feng, Libo Yu, Bei He, Zhenli Yao, Shaowen Yunnan University Engineering Research Center of Cyberspace Yunnan Key Laboratory of Software Engineering School of Software Kunming650500 China

While DAG blockchain technologies improve scalability and throughput over traditional blockchains, they still face critical challenges, particularly in efficiently determining transaction order and ensuring verifiable transaction selection. These limitations significantly hinder their applicability in high-demand environments like IoT, where secure, high-throughput processing is essential. To address these issues, we propose a DAG-Partitioned Multi-chain Architecture (DPMA), which generates distinct chains for each user node, enabling parallel transaction processing. In addition, we introduce a Transaction Selection Algorithm with Verifiable Random Function (TSAV), which implements a verifiable two-tier transaction selection process, ensuring secure and transparent transaction ordering. To further enhance security, we propose a dynamic transaction confidence analysis method that adjusts VRF parameters in response to network conditions. Experimental results demonstrate that our approach effectively identifies malicious behavior and improves transaction credibility. Compared to existing methods, our solution offers enhanced scalability and security, making it well-suited for large-scale decentralized applications. © 2014 IEEE.

关键词： Blockchain

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E-PRedictor: an approach for early prediction of pull request acceptance

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Science China(Information Sciences) 2025年第5期68卷 380-395页

作者： Kexing CHEN Lingfeng BAO Xing HU Xin XIA Xiaohu YANG State Key Laboratory of Blockchain and Data Security Zhejiang University Software Engineering Application Technology Lab

A pull request(PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software development in the opensource community. Nevertheless, the massive number of PRs in an open-source software(OSS) project increases the workload of developers. To reduce the burden on developers, many previous studies have investigated factors that affect the chance of PRs getting accepted and built prediction models based on these factors. However, most prediction models are built on the data after PRs are submitted for a while(e.g., comments on PRs), making them not useful in practice. Because integrators still need to spend a large amount of effort on inspecting PRs. In this study, we propose an approach named E-PRedictor(earlier PR predictor) to predict whether a PR will be merged when it is created. E-PRedictor combines three dimensions of manual statistic features(i.e., contributor profile, specific pull request, and project profile) and deep semantic features generated by BERT models based on the description and code changes of PRs. To evaluate the performance of E-PRedictor, we collect475192 PRs from 49 popular open-source projects on GitHub. The experiment results show that our proposed approach can effectively predict whether a PR will be merged or not. E-PRedictor outperforms the baseline models(e.g., Random Forest and VDCNN) built on manual features significantly. In terms of F1@Merge, F1@Reject, and AUC(area under the receiver operating characteristic curve), the performance of E-PRedictor is 90.1%, 60.5%, and 85.4%, respectively.

关键词： pull request prediction model GitHub

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A buffer overflow detection and defense method based on RiSC-V instruction set extension

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Cybersecurity 2024年第2期7卷 28-43页

作者： Chang Liu Yan-Jun Wu Jing-Zheng Wu Chen Zhao Intelligent Software Research Center Institute of SoftwareChinese Academy of SciencesBejingChina University of Chinese Academy of Sciences BeijingChina State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijingChina

Buffer overflow poses a serious threat to the memory security of modern operating *** overwrites the con-tents of other memory areas by breaking through the buffer capacity limit,destroys the system execution environ-ment,and provides implementation space for various system attacks such as program control flow *** makes it a wide range of harms.A variety of security technologies have been proposed to deal with system security problems including buffer *** example,No eXecute(NX for short)is a memory management technology commonly used in Harvard *** can refuse the execution of code which residing in a specific memory,and can effectively suppress the abnormal impact of buffer overflow on control ***,in recent years,it has also been used in the field of system security,deriving a series of solutions based on NX technology,such as ExecShield,DEP,StackGuard,***,these security solutions often rely too much on the processor archi-tecture so that the protection coverage is insufficient and the accuracy is *** in the emerging system architecture field represented by RiSC-V,there is still a lack of effective solutions for buffer overflow *** the continuous rapid development of the system architecture,it is urgent to develop defense methods that are applicable to different system application environments and oriented to all executable memory spaces to meet the needs of system security ***,we propose BOP,A new system memory security design method based on RISC-V extended instructions,to build a RISC-V buffer overflow detection and defense system and deal with the buffer overflow threat in *** to this method,NX technology can be combined with program control flow analysis,and Nx bit mechanism can be used to manage the executability of memory space,so as to achieve a more granular detection and defense of buffer overflow attacks that may occur in RISC-V system

关键词： RISc-V Operating system security Buffer overflow Control flow hijacking Nx bit Xibop

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Robust load-balanced backbone-based multicast routing in mobile opportunistic networks

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Frontiers of Computer Science 2023年第4期17卷 109-125页

作者： Di ZHANG Dong ZHAO uadong MA Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile opportunistic network(MON)is an efficient way of communication when there is no persistent connection between *** in MONs can be used to efficiently deliver messages to multiple destination ***,because multiple destination nodes are involved,multicast routing is more complex than unicast and brings a higher communication ***-based routing can effectively reduce the network overhead and the complexity of routing ***,the load of backbone nodes is larger than that of regular *** the backbone node’s buffer is exhausted,it will have a significant impact on the performance of the routing *** balancing can improve the ability of backbone to deal with the change of network load,and backbone maintenance algorithm can provide backbone *** this paper,we propose a robust load-balanced backbone-based multicast routing scheme in *** the backbone construction algorithm,we transform the problem of backbone construction into a multi-objective optimization problem,and propose a multi-objective evolutionary algorithmbased backbone construction algorithm,namely LBMBCMOEA *** addition,in order to increase the robustness of the backbone-based routing scheme,we propose a localized multicast backbone maintenance algorithm(MBMA)to deal with the buffer exhaustion of backbone *** a backbone node’s residual buffer is insufficient,MBMA algorithm selects other nodes to replace the backbone *** results on extensive simulations show that when considering the node buffer size constraints,compared with previous backbone-based multicast routing schemes,our proposed algorithm has better performance,and when the node’s residual buffer is insufficient,MBMA algorithm can significantly improve the performance of the backbone-based multicast routing scheme.

关键词： mobile opportunistic network multicast multiobjective optimization backbone construction backbone maintenance

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An intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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Causal action empowerment for efficient reinforcement learning in embodied agents

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Science China(Information Sciences) 2025年第5期68卷 5-19页

作者： Hongye CAO Fan FENG Jing HUO Yang GAO National Key Laboratory for Novel Software Technology Nanjing University Department of Electrical Engineering City University of Hong Kong

Reinforcement learning(RL) has been widely adopted for intelligent decision-making in embodied agents due to its effective trial-and-error learning capabilities. However, most RL methods overlook the causal relationships among states and actions during policy exploration and lack the human-like ability to distinguish signal from noise and reason with important abstractions, resulting in poor sample efficiency. To address this issue, we propose a novel method named causal action empowerment(CAE) for efficient RL, designed to improve sample efficiency in policy learning for embodied agents. CAE identifies and leverages causal relationships among states, actions, and rewards to extract controllable state variables and reweight actions for prioritizing high-impact behaviors. Moreover, by integrating a causality-aware empowerment term, CAE significantly enhances an embodied agent's execution of causally-aware behavior for more efficient exploration via boosting controllability in complex embodied environments. Benefiting from these two improvements, CAE bridges the gap between local causal discovery and global causal empowerment. To comprehensively evaluate the effectiveness of CAE, we conduct extensive experiments across 25 tasks in 5 diverse embodied environments, encompassing both locomotion and manipulation skill learning with dense and sparse reward settings. Experimental results demonstrate that CAE consistently outperforms existing methods across this wide range of scenarios, offering a promising avenue for improving sample efficiency in RL.

关键词： reinforcement learning causal empowerment embodied agents sample efficiency

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intelligent Inspection of Electronic Devices in Specific Environments via a Novel Cascade Network of Combining Mixed Sampling and Nonstrided Convolution

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3287-3299页

作者： Liu, Bo Guo, Jing Wang, Yaowei Yang, Chengrong Nan, Fengtao Yang, Yun Yunnan University School of Software Yunnan Provincial Key Laboratory of Software Engineering Kunming650504 China Northwest A&F University College of Information Engineering Xianyang712100 China

In environments where intelligent video surveillance systems (IVSSs) are deployed, particularly in review room, the detection of electronic devices constitutes a crucial task. Nevertheless, this task presents significant challenges attributed to the high rates of false positives and false negatives in electronic device detection (EDD), compounded by the low resolution of objects when viewed from multiple *** address these challenges, we propose a deep learning-based cascaded detection framework. Specifically, we design a mixed region sampling (MRS) method to enhance the foreground perception with background information and image details. We design a nonstrided downsampling method (ASDP) to map the attention spatial features to depth and improve the detection of low-resolution objects with fine-grained features. We enhance the model’s robustness to different viewing angles by feature perturbation during training. Moreover, we use a cascaded strategy to reduce false positives. To evaluate our method, we construct a real review room dataset (EDD) with 28,000 images from multiple angles. Our method improves the multiview generalization performance by 4.48% mAP and 5.62% mAR. On the public datasets Pascal VOC-2007 and visDrone-2019, our method is also superior to other suboptimal methods. We propose a framework for review environment detection, which is accurate, fast, and generalizable to other scenarios. © 2013 IEEE.

关键词： Feature extraction Reviews Detectors Training Object detection Video surveillance Convolution Cameras Visualization Inspection

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A nonlinear hysteresis compensation control of hydro-viscous drive in air transport operations

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Chinese Journal of Aeronautics 2025年第2期38卷 161-174页

作者： Liang WANG Jianjian LIANG Lin ZHANG Shoukun WANG Junzheng WANG School of Automation and Software Shanxi UniversityTaiyuan 030031China Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China

The Hydro-Viscous Drive(HVD)speed regulating system finds extensive application in air transport transmission systems to regulate the stepless speed or conduct overload ***,its intrinsic hysteretic behaviors,such as the asymmetric hysteretic and dead zone,could introduce inaccuracy and delay in control applications,posing challenges to system *** paper investigates a Nonlinear Hysteresis Compensation Control(NHCC)that consists of two parts to control the HVD output speed by operating the valve under different engine operating *** the first part,the Inverse Hysteresis Compensator(IHC)based on major loop data is introduced for the asymmetric hysteresis characterization and compensation of the HVD speed control system of the power generation and distribution,which aims to reduce the hysteresis and dead zone effect and expand the effective input *** the second part,the Active Disturbance Rejection Controller(ADRC)is employed to mitigate the hysteresis effects of the compensated system and remove the steady-state error,which allows real-time compensation of the estimated perturbations as state feedback to achieve the required *** experimental laboratory station has been fabricated to evaluate the proposed *** test results show that the NHCC method can regulate the fan speed to the desired value(45 r/min at steady state)and broaden the effective input range to the full range under different engine ***,the proposed control method can reduce the non-linearity of the input and output curves(from 18%to 4%)and compensate for the asymmetric hysteresis(from 38%to 5%).

关键词： Hydro-viscous drive Nonlinear hysteresis compensation Inverse hysteresis compensation Speed regulating system ADRC

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IPFA-Net: Important Points Feature Aggregating Net for Point Cloud Classification and Segmentation

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Chinese Journal of Electronics 2025年第1期34卷 322-337页

作者： Jingya Wang Yu Zhang Bin Zhang Jinxiang Xia Weidong Wang School of Information and Software Engineering University of Electronic Science and Technology of China National Key Laboratory of Automotive Chassis Integration and Bionics Jilin University

This paper focuses on the problems of point cloud deep neural networks in classification and segmentation tasks, including losing important information during down-sampling, ignoring relationships among points when extracting features, and network performance being susceptible to the sparsity of point cloud. To begin with, this paper proposes a farthest point sampling-important points sampling method for down-sampling, which can preserve important information of point clouds and maintain the geometry of input data. Then, the local feature relation aggregating method is proposed for feature extraction, improving the network's ability to learn contextual information and extract rich local region features. Based on these methods, the important points feature aggregating net(IPFA-Net) is designed for point cloud classification and segmentation tasks. Furthermore, this paper proposes the multi-scale multi-density feature connecting method to reduce the negative impact of point cloud data sparsity on network performance. Finally, the effectiveness of IPFA-Net is demonstrated through experiments on ModelNet40, ShapeNet part, and ScanNet v2 datasets. IPFA-Net is robust to reducing the number of point clouds, with only a 3.3% decrease in accuracy under a 16-fold reduction of point number. In the part segmentation experiments, our method achieves the best segmentation performance for five objects.

关键词： Point cloud compression Representation learning Geometry Fuses Semantics Artificial neural networks Feature extraction Sampling methods Robustness Data mining

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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