Computer Supported Cooperative Work-The Journal of Collaborative Computing最新文献_第8页

Simplified-Xception: A New Way to Speed Up Malicious Code Classification 简化异常:一种加速恶意代码分类的新方法

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152755

Xinshuai Zhu, Songheng He, Xuren Wang, Chang Gao, Yushi Wang, Peian Yang, Yuxia Fu

{"title":"Simplified-Xception: A New Way to Speed Up Malicious Code Classification","authors":"Xinshuai Zhu, Songheng He, Xuren Wang, Chang Gao, Yushi Wang, Peian Yang, Yuxia Fu","doi":"10.1109/CSCWD57460.2023.10152755","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152755","url":null,"abstract":"Traditional malicious code detection methods require a lot of manpower and resources, which makes the research of malicious code very difficult. The selection of malicious code features mainly relies on the subjective analysis and selection of experts, which has a large impact on the detection effect of the model. In this paper, malicious codes are converted into greyscale images as model inputs, and features are automatically extracted using a deep-learning model. An improved convolutional neural network model based on Xception (Simplified Xception) is proposed for malicious code family classification. The model reduces the number of modules in the original model and adds a depth-separable convolutional layer with a step size of 2 to enhance the generated grey-scale images. The model is compared with CNN models, ResNet50, and improved models related to Inception. The experimental results show that the accuracy of SimplifiedXception is 98%, which is better than other related models. Compared to the Xception model, the accuracy of the Simplified-Xception model was improved by 1.3% and the number of parameters was reduced by half.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"39 1","pages":"582-587"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85627107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Group Signature Authentication Scheme with Credit Evaluation Mechanism in VANET VANET中具有信用评估机制的组签名认证方案

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152641

Yanfei Lu, Suzhen Cao, Qizhi He, Zixuan Fang, Junjian Yan, Yi Guo

{"title":"Group Signature Authentication Scheme with Credit Evaluation Mechanism in VANET","authors":"Yanfei Lu, Suzhen Cao, Qizhi He, Zixuan Fang, Junjian Yan, Yi Guo","doi":"10.1109/CSCWD57460.2023.10152641","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152641","url":null,"abstract":"Aiming at the problems of privacy disclosure and false information provided by malicious vehicles in vehicular ad-hoc network (VANET) communication, this paper proposes a group signature authentication scheme with credit evaluation mechanism combining certificateless public key cryptography and group signature technology. In this scheme, the problem of certificate management and key escrow were solved by using certificateless public key cryptography; secondly, used group signature technology, any member of the group could sign the message anonymously on behalf of the group to protect the private information of the vehicle; finally, a single factor weight evaluation mechanism and a reward and punishment mechanism were introduced to evaluate the reliability of shared information and encourage users to share real information. Based on the computational Diffie-Hellman difficult problem, the scheme is proved to satisfy the security of signature unforgeability under the random oracle model. Compared with the existing schemes, the experimental results show that the scheme reduces the time of vehicle signature by 10.82%~45.95%, and the time of group administrator verification by 4.87%~30.09%, which proves that the scheme is more effective.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"34 1","pages":"1703-1709"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85082052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 1

A Constraint-based Recommender System via RDF Knowledge Graphs 基于RDF知识图的约束推荐系统

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152701

Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou

引用次数: 0

Applying Robust Gradient Difference Compression to Federated Learning 鲁棒梯度差分压缩在联邦学习中的应用

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152826

Yueyao Chen, Beilun Wang, Tianyi Ma, Cheng Chen

引用次数: 0

Practical privacy-preserving mixing protocol for Bitcoin 实用的比特币隐私保护混合协议

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152733

Qianqian Chang, Lin Xu, L. Zhang

引用次数: 0

New Employee Training Scheduling Using the E-CARGO Model 基于E-CARGO模型的新员工培训计划

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152637

Tianshuo Yang, Haibin Zhu

引用次数: 0

A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 一种基于图序列生成器和多头自关注机制的知识图推理体系结构

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152706

Yuejia Wu, Jian-tao Zhou

{"title":"A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture","authors":"Yuejia Wu, Jian-tao Zhou","doi":"10.1109/CSCWD57460.2023.10152706","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152706","url":null,"abstract":"Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"11 1","pages":"1520-1525"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75260639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Computation of Mobile Phone Collaborative Embedded Devices for Object Detection Task 手机协同嵌入式设备对目标检测任务的计算

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152744

Yin Xie, Yigui Luo, Haihong She, Zhaohong Xiang

引用次数: 0

An Answer Summarization Scheme Based on Multilayer Attention Model 一种基于多层注意力模型的答案汇总方案

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152597

Xiaolong Xu, Yihao Dong, Jian Song

引用次数: 0

ATIPM: A Blockchain-Based Anonymous and Traceable Intellectual Property Management Scheme ATIPM:基于区块链的匿名和可追溯知识产权管理方案

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152748

Han Zhang, Lubin Lin, Guipeng Zhang, Zhenguo Yang, Wenyin Liu

{"title":"ATIPM: A Blockchain-Based Anonymous and Traceable Intellectual Property Management Scheme","authors":"Han Zhang, Lubin Lin, Guipeng Zhang, Zhenguo Yang, Wenyin Liu","doi":"10.1109/CSCWD57460.2023.10152748","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152748","url":null,"abstract":"With the spread of information on the Internet and the explosive growth of intellectual property information, the traditional intellectual property management model relying on third-party institutions cannot meet the demand for intellectual property protection, which has a cumbersome process, low efficiency, and insufficient evidence of rights protection. To address the issues of information falsification and leakage, we present an anonymous and traceable intellectual property management system based on blockchain, namely ATIPM, which employs the non-interactive zero knowledge proof to realize user unlinkability and anonymous transactions to protect the users’ intellectual property information. To avoid a single point of accountability, the ATIPM introduces a threshold ramp secret sharing scheme to achieve the traceability of intellectual property for all users and greatly improve the users’ privacy security and autonomy by preventing information leakage from malicious third-party institutions. Furthermore, the ATIPM can improve the management efficiency of intellectual property by utilizing smart contracts to realize efficient retrieval and verification of intellectual property. The evaluation results demonstrate the effectiveness of our proposed system.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1080-1085"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77622604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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