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

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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ISM:intra-class similarity mixing for time series augmentation

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Frontiers of Computer Science 2024年第6期18卷 273-275页

作者： Pin LIU Rui WANG Yongqiang HE Yuzhu WANG School of Information Engineering China University of GeosciencesBeijing 100083China State Key Lab of Software Development Environment Beihang UniversityBeijing 100191China

1 *** superior performance of deep models in classification tasks relies heavily on large-scale supervision data with rich features[1].Recent research has shown that improving the feature diversity while expanding the data scale can improve the classification performance[2,3].Time series augmentation possessing the dual strategy is essential in successfully applying deep models in time series classification.

关键词： classification series mixing

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of Computer Science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

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Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency Detection algorithms

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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

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Frontiers of Computer Science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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Dual Prompt-Based Few-Shot Learning for Automated Vulnerability Patch Localization 31

Dual Prompt-Based Few-Shot Learning for Automated Vulnerabil...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Zhang, Junwei Hu, Xing Bao, Lingfeng Xia, Xin Li, Shanping Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Software Engineering Application Technology Lab Huawei China

ISBN: (纸本)9798350330663

Vulnerabilities are disclosed with corresponding patches so that users can remediate them in time. However, there are instances where patches are not released with the disclosed vulnerabilities, causing hidden dangers, especially if dependent software remains uninformed about the affected code repository. Hence, it is crucial to automatically locate security patches for disclosed vulnerabilities among a multitude of commits. Despite the promising performance of existing learning-based localization approaches, they still suffer from the following limitations: (1) They cannot perform well in data scarcity scenarios. Most neural models require extensive datasets to capture the semantic correlations between the vulnerability description and code commits, while the number of disclosed vulnerabilities with patches is limited. (2) They struggle to capture the deep semantic correlations between the vulnerability description and code commits due to inherent differences in semantics and characters between code changes and commit messages. It is difficult to use one model to capture the semantic correlations between vulnerability descriptions and code commits. To mitigate these two limitations, in this paper, we propose a novel security patch localization approach named Prom VPat, which utilizes the dual prompt tuning channel to capture the semantic correlation between vulnerability descriptions and commits, especially in data scarcity (i.e., few-shot) scenarios. We first input the commit message and code changes with the vulnerability description into the prompt generator to generate two new inputs with prompt templates. Then, we adopt a pre-trained language model (i.e., PLM) as the encoder, utilize the prompt tuning method to fine-tune the encoder, and generate two correlation probabilities as the semantic features. In addition, we extract 26 handcrafted features from the vulnerability descriptions and the code commits. Finally, we utilize the attention mechanism to fuse the

关键词： Semantics

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of Computer Science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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A theoretical and experimental study of deformation mechanism dictated by disclination-dislocation coupling in Mg alloys at different temperatures

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材料科学技术（英文版） 2025年第5期208卷 176-188页

作者： Chunfeng Du Yipeng Gao Yizhen Li Quan Li Min Zha Cheng Wang Hailong Jia Hui-Yuan Wang Key Laboratory of Automobile Materials of Ministry of Education&School of Materials Science and Engineering Nanling CampusJilin UniversityChangchun 130025China Key Laboratory of Automobile Materials of Ministry of Education&School of Materials Science and Engineering Nanling CampusJilin UniversityChangchun 130025China International Center of Future Science Jilin UniversityChangchun 130012China International Center of Future Science Jilin UniversityChangchun 130012China State Key Lab of Superhard Materials and Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchun 130012China Key Laboratory of Automobile Materials of Ministry of Education&School of Materials Science and Engineering Nanling CampusJilin UniversityChangchun 130025China International Center of Future Science Jilin UniversityChangchun 130012China State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Material Science and EngineeringHebei University of TechnologyTianjin 300130China

Dislocations and disclinations are fundamental topological defects within crystals,which determine the mechanical properties of metals and *** their important roles in multiple physical mecha-nisms,e.g.,dynamic recovery and grain boundary mediated plasticity,the intrinsic coupling and corre-lation between disclinations and dislocations,and their impacts on the deformation behavior of metallic materials still remain obscure,partially due to the lack of a theoretical tool to capture the rotational na-ture of *** using a Lie-algebra-based theoretical framework,we obtain a general equation to quantify the intrinsic coupling of disclinations and *** quasi in-situ electron backscat-ter diffraction characterizations and disclination/dislocation density analyses in Mg alloys,the generation,coevolution and reactions of disclinations and dislocations during dynamic recovery and superplastic de-formation have been quantitatively *** has been demonstrated that the obtained governing equa-tion can capture multiple physical processes associated with mechanical deformation of metals,e.g.,grain rotation and grain boundary migration,at both room temperature and high *** establishing the disclination-dislocation coupling equation within a Lie algebra description,our work provides new insights for exploring the coevolution and reaction of disclinations/dislocations,with profound implica-tions for elucidating the microstructure-property relationship and underlying deformation mechanisms in metallic materials.

关键词： Magnesium alloys Dislocations Grain boundaries Plastic deformation Grain rotation Disclination-dislocation coupling

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Semantic-Aware Pseudo-labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing101408 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 314100 China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic-like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. First, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic-like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. © 1979-2012 IEEE.

关键词： Latent semantic analysis

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