Computer Supported Cooperative Work-The Journal of Collaborative Computing最新文献_第7页

A New Targeted Online Password Guessing Algorithm Based on Old Password 一种新的基于旧密码的针对性在线猜密码算法

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/CSCWD57460.2023.10152712

Xizhe Zhang, Xiong Zhang, Jiahao Hu, Yuesheng Zhu

引用次数: 0

HeteFed: Heterogeneous Federated Learning with Privacy-Preserving Binary Low-Rank Matrix Decomposition Method 基于隐私保护的二值低秩矩阵分解方法的异构联邦学习

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/CSCWD57460.2023.10152714

Jiaqi Liu, Kaiyu Huang, Lunchen Xie

{"title":"HeteFed: Heterogeneous Federated Learning with Privacy-Preserving Binary Low-Rank Matrix Decomposition Method","authors":"Jiaqi Liu, Kaiyu Huang, Lunchen Xie","doi":"10.1109/CSCWD57460.2023.10152714","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152714","url":null,"abstract":"Federated learning is a machine learning paradigm where many clients collaboratively train a machine learning model while ensuring the nondisclosure of local data sets. Existing federated learning methods conduct optimization over the same model structure, which ensures the convenience of parameter updates. However, the same structure among clients and the server may pose risks of privacy leakage as parameters from one’s model can fit in others’ models. In this paper, we propose a heterogeneous federated learning method to preserve privacy. Each client utilizes neural architecture search to determine distinct models via local data and update the server model via a federated learning framework with knowledge distillation. Besides, we develop a privacy-preserving binary low-rank matrix decomposition method (Blow), i.e., decomposing the output matrix into two low-rank binary matrices, to further ensure the secrecy of distilled information. A simple but efficient alternating optimization method is proposed to address a key subproblem arising from the binary low-rank matrix decomposition, which falls into the category of the Np-hard bipartite boolean quadratic programming. Based on extensive experiments over the image classification task, we show our algorithm provides satisfactory accuracy and outperforms baseline algorithms in both privacy protection and communication efficiency.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"11 1","pages":"1238-1244"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88722861","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

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/CSCWD57460.2023.10152566

Weihong Yang, Hua Dai, Haozhe Wu, Geng Yang, Meng Lu, Guineng Zheng

{"title":"A Human Pose Similarity Calculation Method Based on Partition Weighted OKS Model","authors":"Weihong Yang, Hua Dai, Haozhe Wu, Geng Yang, Meng Lu, Guineng Zheng","doi":"10.1109/CSCWD57460.2023.10152566","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152566","url":null,"abstract":"Image-based calculation of human pose similarity is one of the computer vision research fields. Most existing research uses the human skeleton joint to calculate the human pose similarity, but usually does not consider the influence of inaccurate recognition of skeleton joint on similarity calculation caused by the complex environment (such as the occlusion of body parts, etc.). We propose a human pose similarity calculation method based on partition weighted OKS model. Due to the influence of external factors such as occlusion, the skeleton joint extracted by the human pose estimation algorithm is inaccurate, which leads to the decrease of the accuracy of the human pose similarity calculation. We propose the partition rule of human skeleton joints and the dynamic strategy adjustment of partition weight. The partition weighted OKS model and a human pose similarity calculation method based on the partition weighted OKS model are given. The experimental results on datasets show that the proposed method for human pose similarity calculation is superior to the traditional one.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"222 1","pages":"1760-1765"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91164461","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

Dirty page prediction by machine learning methods based on temporal and spatial locality 基于时空局部性的机器学习脏页面预测方法

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/CSCWD57460.2023.10152768

Yahui Lu, Yuping Jiang

引用次数: 0

Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition 基于骨架的动作识别的长时间记忆图卷积网络

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/CSCWD57460.2023.10152568

Yanpeng Qi, Chen Pang, Yiliang Liu, Hong Liu, Lei Lyu

{"title":"Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition","authors":"Yanpeng Qi, Chen Pang, Yiliang Liu, Hong Liu, Lei Lyu","doi":"10.1109/CSCWD57460.2023.10152568","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152568","url":null,"abstract":"Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"57 1","pages":"843-848"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77407709","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

A Reinforcement Learning-driven Iterated Greedy Algorithm for Traveling Salesman Problem 旅行商问题的强化学习驱动迭代贪心算法

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/CSCWD57460.2023.10152696

Xi Song, Mingyang Li, Weidong Xie, Yuanyuan Mao

引用次数: 0

Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk 通过众包调查作者研究相关性:MTurk的复制研究

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/CSCWD57460.2023.10152707

António Correia, Dennis Paulino, H. Paredes, D. Guimaraes, D. Schneider, Benjamim Fonseca

{"title":"Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk","authors":"António Correia, Dennis Paulino, H. Paredes, D. Guimaraes, D. Schneider, Benjamim Fonseca","doi":"10.1109/CSCWD57460.2023.10152707","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152707","url":null,"abstract":"Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk’s microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"26 3","pages":"77-82"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72394065","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

Unsupervised Graph Neural Network with Self-Expressive Attention for Community Detection 基于自表达注意的无监督图神经网络社区检测

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/CSCWD57460.2023.10152715

Xu Sun, Weiyu Zhang, Xinchao Guo, Wenpeng Lu

引用次数: 0

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