检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Causal action empowerment for efficient reinforcement learning in embodied agents

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

GTE: learning code AST representation efficiently and effectively

引用

Science China(Information Sciences) 2025年第3期68卷 393-394页

作者： Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO College of Computer Science and Technology National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ALSTNet:Autoencoder fused long-and short-term time-series network for the prediction of tunnel structure

引用

Deep Underground Science and engineering 2025年第1期4卷 72-82页

作者： Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen State Key Laboratory of Software Development Environment Beihang UniversityBeijingChina State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanChina

It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex *** that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring ***,the proposed model was formalized on multiple time series of strain monitoring ***,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction *** the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of *** a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.

关键词： autoencoder deep learning structural health monitoring time-series prediction

来源：评论

学校读者我要写书评

暂无评论

Bayesian Network Reconstruction and Iterative Divergence Problem Solving Method Based on Norm Minimization

引用

Computer Modeling in engineering & Sciences 2025年第4期143卷 617-637页

作者： Kuo Li Aimin Wang Limin Wang Yuetan Zhao Xinyu Zhu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China College of Software Jilin UniversityChangchun130012China

A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing *** method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable *** the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 ***,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent *** the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 *** mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.

关键词： Bayesian norm minimization network reconstruction iterative divergence sparsity

来源：评论

学校读者我要写书评

暂无评论

A comparison on the effects of coal fines and sand fouling on the shear behaviors of railway ballast using large scale direct shear tests

引用

Journal of Traffic and Transportation engineering(English Edition) 2025年第1期12卷 52-67页

作者： Xu Zhang Shunying Ji Yang Feng Yongjun Li Chunfa Zhao School of Civil and Transportation Engineering Guangdong University of Technology State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of Technology

Railway ballast is easily contaminated by different fouling material and the fouled ballast always causes the deterioration of railway track. Many previous studies focused on the effect of a certain kind of fouling material on ballast performance, but few studies compared the influence of different fouling material. This paper presents a series of direct shear tests on the clean ballast and the fouled ballast mixed with coal fines or sand to understand the effects of different fouling material. A unified empirical model is proposed to well capture the shear stress-displacement responses of the clean and fouled *** equations are also obtained to characterize the variation of the nonlinear shear strength envelope of the fouled ballast with the fouling content. Based on these equations,a simple method is proposed to predict the shear strength of the fouled ballast using the shear test results of clean ballast. The measured results demonstrate significant differences between the effects of coal fines and sand on the shear behaviors of ballast, to which particular attention should be paid in order to take targeted maintenance measures for the fouled ballast. The mechanisms for the different effects of coal fines and sand are also analyzed to understand the roles that various fouling material plays in the fouled ballast.

关键词： Railway ballast Fouled ballast Shear strength Direct shear test Contamination

来源：评论

学校读者我要写书评

暂无评论

AsyCo: an asymmetric dual-task co-training model for partial-label learning

引用

Science China(Information Sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：