Science China Information Sciences最新文献_第10页

What can we learn from quality assurance badges in open-source software? 我们能从开源软件的质量保证徽章中学到什么？

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-03-26 DOI: 10.1007/s11432-022-3611-3

Feng Li, Yiling Lou, Xin Tan, Zhenpeng Chen, Jinhao Dong, Yang Li, Xuanzhi Wang, Dan Hao, Lu Zhang

{"title":"What can we learn from quality assurance badges in open-source software?","authors":"Feng Li, Yiling Lou, Xin Tan, Zhenpeng Chen, Jinhao Dong, Yang Li, Xuanzhi Wang, Dan Hao, Lu Zhang","doi":"10.1007/s11432-022-3611-3","DOIUrl":"https://doi.org/10.1007/s11432-022-3611-3","url":null,"abstract":"In the development of open-source software (OSS), many developers use badges to give an overview of the software and share some key features/metrics conveniently. Among various badges, quality assurance (QA) badges make up a large proportion and are the most prevalent because QA is of vital importance in software development, and ineffective QA may lead to anomalies or defects. In this paper, we focus on QA badges in open-source projects, which present quality assurance information directly and instantly, and aim to produce some interesting findings and provide practical implications. We collect and analyze 100000 projects written in popular programming languages from GitHub and conduct a comprehensive empirical study both inside and outside QA badges. Inside QA badges, we build a category classification for all QA badges based on the properties they focus on, which shows the types of QA badges developers use. Then, we analyze the frequency of the properties that QA badges focus on, and property combinations, too, which present their use status. We find that QA badges focus on various properties while developers give different preferences to different properties. The use status also differs between different programming languages. For example, projects written in C focus on Security to a great extent. Our findings also provide implications for developers and badge providers. Outside QA badges, we conduct a correlation analysis between QA badges and some software metrics that have potential relationships with code quality, contribution quality, and popularity. We find that QA badges have statistically significant correlations with various software metrics.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":"1 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retrieval-and-alignment based large-scale indoor point cloud semantic segmentation 基于检索和对齐的大规模室内点云语义分割

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-03-25 DOI: 10.1007/s11432-022-3928-x

Zongyi Xu, Xiaoshui Huang, Bo Yuan, Yangfu Wang, Qianni Zhang, Weisheng Li, Xinbo Gao

{"title":"Retrieval-and-alignment based large-scale indoor point cloud semantic segmentation","authors":"Zongyi Xu, Xiaoshui Huang, Bo Yuan, Yangfu Wang, Qianni Zhang, Weisheng Li, Xinbo Gao","doi":"10.1007/s11432-022-3928-x","DOIUrl":"https://doi.org/10.1007/s11432-022-3928-x","url":null,"abstract":"Current methods for point cloud semantic segmentation depend on the extraction of descriptive features. However, unlike images, point clouds are irregular and often lack texture information, making it demanding to extract discriminative features. In addition, noise, outliers, and uneven point distribution are commonly present in point clouds, which further complicates the segmentation task. To address these problems, a novel architecture is proposed for direct and accurate large-scale point cloud segmentation based on point cloud retrieval and alignment. The proposed approach involves using a feature-based point cloud retrieval method for searching for reference point clouds with annotations from a dataset. In the following segmentation stage, an overlap-based point cloud registration method has been developed to align the target and reference point clouds. For accurate and robust alignment, an overlap region estimation module is trained to locate the optimal overlap region between two pieces of point clouds in a coarse-to-fine manner. In the detected overlap region, the global and local features of the points are extracted and combined for feature-metric registration to obtain accurate transformation parameters between the target and reference point clouds. After alignment, the annotated segmentation of the reference is transferred to the target point clouds to obtain accurate segmentation results. Extensive experiments are conducted to show that the developed method outperforms the state-of-the-art approaches in terms of both accuracy and robustness against noise and outliers.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":"441 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust cooperative multi-agent reinforcement learning via multi-view message certification 通过多视角信息认证进行稳健的多代理合作强化学习

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-03-22 DOI: 10.1007/s11432-023-3853-y

Lei Yuan, Tao Jiang, Lihe Li, Feng Chen, Zongzhang Zhang, Yang Yu

{"title":"Robust cooperative multi-agent reinforcement learning via multi-view message certification","authors":"Lei Yuan, Tao Jiang, Lihe Li, Feng Chen, Zongzhang Zhang, Yang Yu","doi":"10.1007/s11432-023-3853-y","DOIUrl":"https://doi.org/10.1007/s11432-023-3853-y","url":null,"abstract":"Many multi-agent scenarios require message sharing among agents to promote coordination, hastening the robustness of multi-agent communication when policies are deployed in a message perturbation environment. Major relevant studies tackle this issue under specific assumptions, like a limited number of message channels would sustain perturbations, limiting the efficiency in complex scenarios. In this paper, we take a further step in addressing this issue by learning a robust cooperative multi-agent reinforcement learning via multi-view message certification, dubbed CroMAC. Agents trained under CroMAC can obtain guaranteed lower bounds on state-action values to identify and choose the optimal action under a worst-case deviation when the received messages are perturbed. Concretely, we first model multi-agent communication as a multi-view problem, where every message stands for a view of the state. Then we extract a certificated joint message representation by a multi-view variational autoencoder (MVAE) that uses a product-of-experts inference network. For the optimization phase, we do perturbations in the latent space of the state for a certificate guarantee. Then the learned joint message representation is used to approximate the certificated state representation during training. Extensive experiments in several cooperative multi-agent benchmarks validate the effectiveness of the proposed CroMAC.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":"30 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linear shallow neural network to accelerate transmitter dispersion eye closure quaternary (TDECQ) assessment 线性浅层神经网络加速发射机色散眼闭合四次方（TDECQ）评估

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-03-21 DOI: 10.1007/s11432-023-3947-8

Junjiang Xiang, Zejun Chen, Yijun Cheng, Hailin Yang, Xuancheng Huo, Meng Xiang, Gai Zhou, Yuwen Qin, Songnian Fu

引用次数: 0

A model reduction approach for discrete-time linear time-variant systems with delayed inputs 具有延迟输入的离散时线性时变系统的模型还原方法

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-03-08 DOI: 10.1007/s11432-022-3766-3

Ai-Guo Wu, Guang-Ren Duan, Yu Wang, Jie Zhang

引用次数: 0

Output feedback stabilization of stochastic high-order planar nonlinear systems with stochastic inverse dynamics and output-constraint 具有随机逆动态和输出约束的随机高阶平面非线性系统的输出反馈稳定化

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-02-22 DOI: 10.1007/s11432-023-3875-5

Ruiming Xie, Shengyuan Xu

引用次数: 0

When debugging encounters artificial intelligence: state of the art and open challenges 当调试遇到人工智能：技术现状与挑战

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-02-21 DOI: 10.1007/s11432-022-3803-9

Yi Song, Xiaoyuan Xie, Baowen Xu

{"title":"When debugging encounters artificial intelligence: state of the art and open challenges","authors":"Yi Song, Xiaoyuan Xie, Baowen Xu","doi":"10.1007/s11432-022-3803-9","DOIUrl":"https://doi.org/10.1007/s11432-022-3803-9","url":null,"abstract":"Both software debugging and artificial intelligence techniques are hot topics in the current field of software engineering. Debugging techniques, which comprise fault localization and program repair, are an important part of the software development lifecycle for ensuring the quality of software systems. As the scale and complexity of software systems grow, developers intend to improve the effectiveness and efficiency of software debugging via artificial intelligence (artificial intelligence for software debugging, AI4SD). On the other hand, many artificial intelligence models are being integrated into safety-critical areas such as autonomous driving, image recognition, and audio processing, where software debugging is highly necessary and urgent (software debugging for artificial intelligence, SD4AI). An AI-enhanced debugging technique could assist in debugging AI systems more effectively, and a more robust and reliable AI approach could further guarantee and support debugging techniques. Therefore, it is important to take AI4SD and SD4AI into consideration comprehensively. In this paper, we want to show readers the path, the trend, and the potential that these two directions interact with each other. We select and review a total of 165 papers in AI4SD and SD4AI for answering three research questions, and further analyze opportunities and challenges as well as suggest future directions of this cross-cutting area.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":"44 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140005387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A FAS approach for stabilization of generalized chained forms: part 2. Continuous control laws 广义链式稳定的 FAS 方法：第 2 部分。连续控制法

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-02-21 DOI: 10.1007/s11432-023-3920-8

Guang-Ren Duan

引用次数: 0

Density peak clustering using tensor network 利用张量网络进行密度峰聚类

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-02-20 DOI: 10.1007/s11432-023-3869-3

Xiao Shi, Yun Shang

引用次数: 0

Joint UAV trajectory and communication design with heterogeneous multi-agent reinforcement learning 利用异构多代理强化学习进行无人机轨迹和通信联合设计

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-02-20 DOI: 10.1007/s11432-023-3906-3

Xuanhan Zhou, Jun Xiong, Haitao Zhao, Xiaoran Liu, Baoquan Ren, Xiaochen Zhang, Jibo Wei, Hao Yin

{"title":"Joint UAV trajectory and communication design with heterogeneous multi-agent reinforcement learning","authors":"Xuanhan Zhou, Jun Xiong, Haitao Zhao, Xiaoran Liu, Baoquan Ren, Xiaochen Zhang, Jibo Wei, Hao Yin","doi":"10.1007/s11432-023-3906-3","DOIUrl":"https://doi.org/10.1007/s11432-023-3906-3","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are recognized as effective means for delivering emergency communication services when terrestrial infrastructures are unavailable. This paper investigates a multi-UAV-assisted communication system, where we jointly optimize UAVs’ trajectories, user association, and ground users (GUs)’ transmit power to maximize a defined fairness-weighted throughput metric. Owing to the dynamic nature of UAVs, this problem has to be solved in real time. However, the problem’s non-convex and combinatorial attributes pose challenges for conventional optimization-based algorithms, particularly in scenarios without central controllers. To address this issue, we propose a multi-agent deep reinforcement learning (MADRL) approach to provide distributed and online solutions. In contrast to previous MADRL-based methods considering only UAV agents, we model UAVs and GUs as heterogeneous agents sharing a common objective. Specifically, UAVs are tasked with optimizing their trajectories, while GUs are responsible for selecting a UAV for association and determining a transmit power level. To learn policies for these heterogeneous agents, we design a heterogeneous coordinated QMIX (HC-QMIX) algorithm to train local Q-networks in a centralized manner. With these well-trained local Q-networks, UAVs and GUs can make individual decisions based on their local observations. Extensive simulation results demonstrate that the proposed algorithm outperforms state-of-the-art benchmarks in terms of total throughput and system fairness.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":"142 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140055999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0