Computer Supported Cooperative Work-The Journal of Collaborative Computing最新文献_第4页

Multi-feature content popularity prediction algorithm based on GRU-Attention in V-NDN 基于GRU-Attention的V-NDN多特征内容流行度预测算法

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/CSCWD57460.2023.10152582

Min Feng, Meiju Yu, Ru Li

引用次数: 0

A Reputation System based on Blockchain and Deep Learning in Social Networks 基于区块链和深度学习的社交网络声誉系统

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/CSCWD57460.2023.10152658

Haikun Yu, Dacheng Jiang, Guipeng Zhang, Zhenguo Yang, Wenyin Liu

{"title":"A Reputation System based on Blockchain and Deep Learning in Social Networks","authors":"Haikun Yu, Dacheng Jiang, Guipeng Zhang, Zhenguo Yang, Wenyin Liu","doi":"10.1109/CSCWD57460.2023.10152658","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152658","url":null,"abstract":"Existing social networks, such as Twitter and Facebook, are rife with inaccurate and damaging information that is bad for society. Most existing solutions usually use deep learning models for disinformation detection in addition to artificial recognition. However, the result is easily tampered with by people. At the same time, if we strictly manage public opinions, freedom of speech will also cause controversy. In order to solve the above problems and maintain a good social network environment, we propose a new reputation mechanism based on blockchain and deep learning. To assess the reputation of message senders, our proposed mechanism utilizes smart contracts that automate programs without human intervention. Our approach avoids unduly restricting users’ freedom of expression and instead employs deep learning models for rumor detection and sentiment analysis to identify and label messages. By controlling the dissemination of messages based on labels of messages and the sender’s reputation, we aim to balance freedom of speech with social stability. Finally, we analyze the usability and performance of our proposed system.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"35 1","pages":"630-635"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89339254","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

An Epidemic Model Based on Intra- and Inter-group Interactions 基于群体内和群体间相互作用的流行病模型

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/CSCWD57460.2023.10152787

Wencong Geng, Guijuan Zhang, Dianjie Lu

{"title":"An Epidemic Model Based on Intra- and Inter-group Interactions","authors":"Wencong Geng, Guijuan Zhang, Dianjie Lu","doi":"10.1109/CSCWD57460.2023.10152787","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152787","url":null,"abstract":"The global spread of COVID-19 causes great losses to human society. Accurate calculation of the scale of epidemic spread is of great significance for the implementation of corresponding epidemic prevention measures. However, the existing method ignores the group formed by social relations of the population, which reduces the accuracy of the epidemic spread number calculation. In this paper, we propose an epidemic model based on intra- and inter-group interactions. Firstly, we construct a dual network model of epidemic spread based on intra- and inter-group interactions. The network describes how epidemics spread intra- and inter-group. To capture the intergroup influences, we construct a model for social mobility to calculate the inter-group spread rate. Secondly, we propose a computational model for the epidemic spread. We calculate the infection probability of groups in the upper layer network by using a continuous-time Markov chain (CTMC). We describe a dynamic evolution of the intra-group infection in the underlying network based on the mean field equation. And the number of infections in the population is calculated by integrating intra- and inter-group effects. Finally, we implement an epidemic spread simulation system to visualize the spread process. The experimental results show that the model can analyze the epidemic spread process more accurately.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"486-491"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89846841","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

Creativity Support in AI Co-creative Tools: Current Research, Challenges and Opportunities 人工智能协同创造工具中的创造力支持:当前研究、挑战和机遇

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/CSCWD57460.2023.10152832

Bin Ning, Fang Liu, Zhixiong Liu

引用次数: 1

Privileged Label Enhancement with Adaptive Graph 基于自适应图的特权标签增强

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/CSCWD57460.2023.10152848

Qin Qin, Chao Tan, Chong Li, G. Ji

{"title":"Privileged Label Enhancement with Adaptive Graph","authors":"Qin Qin, Chao Tan, Chong Li, G. Ji","doi":"10.1109/CSCWD57460.2023.10152848","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152848","url":null,"abstract":"Label distribution learning has gained an increasing amount of attention in comparison to single-label and multi-label learning due to its more universal capacity to communicate label ambiguity. Unfortunately, label distribution learning cannot be used directly in many real tasks, because it is very difficult to obtain the label distribution datasets, and many training sets only contain simple logical labels. To resolve this problem and recover the label distributions from the logical labels, label enhancement is proposed. This paper proposes a novel label enhancement algorithm called Privileged Label Enhancement with Adaptive Graph(PLEAG). PLEAG first apply adaptive graph to capture the hidden information between instances and treat it as privileged information. As a result, the similarity matrix of instances is not only influenced by the feature space, but is also adaptively modified in accordance with the degree of similarity between instances in the label space. Then, we adopt RSVM+ model in the paradigm of LUPI (learning with privileged information) to handle the new dataset with privileged information in order to gain better learning effect. Our comparison experiments on 12 datasets show that our proposed algorithm PLEAG , is more accurate than prior label enhancement algorithms for recovering label distribution from logical labels.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"39 4 1","pages":"1867-1872"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75418088","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

An Overview of Blockchain Scalability for Storage 区块链存储可扩展性概述

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/CSCWD57460.2023.10152720

Fanshu Gong, Lanju Kong, Yuxuan Lu, Jin Qian, Xinping Min

引用次数: 0

A Signal Control Algorithm of Urban Intersections based on Traffic Flow Prediction 基于交通流预测的城市交叉口信号控制算法

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/CSCWD57460.2023.10152556

Xiao-Min Hu, G. Wang, Min Li, Zi-Liang Chen

{"title":"A Signal Control Algorithm of Urban Intersections based on Traffic Flow Prediction","authors":"Xiao-Min Hu, G. Wang, Min Li, Zi-Liang Chen","doi":"10.1109/CSCWD57460.2023.10152556","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152556","url":null,"abstract":"Traffic signals play an important role in traffic management, and traffic dynamics on the road can be adjusted by changing signal timing. Signal timing optimization and traffic flow prediction are traditionally separate. To improve the effect of signal control, a traffic signal control algorithm for urban intersections based on traffic flow prediction is proposed by combining these two technologies. The goal is to minimize the average delay time of the total vehicles at all signalized intersections in the road network. First, a new Prediction-based Signal Control (PSC) model is proposed, which includes a traffic flow prediction module and a signal timing optimization module. Secondly, a traffic flow prediction strategy and a quantum particle swarm optimization algorithm based on phase angle coding is designed to form the signal control algorithm proposed in this paper. Finally, the PSC algorithm is verified with real traffic data. The results show that the proposed algorithm is better than the fixed signal control and traditional adaptive control algorithms, and the reduction of total queue length and average delay time is significantly improved.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"72 1","pages":"1372-1377"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91240593","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

T-Sorokin: A General Mobility Model in Opportunistic Networks T-Sorokin:机会主义网络中的一般流动性模型

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/CSCWD57460.2023.10152854

Jinbin Tu, Qing Li, Yun Wang

引用次数: 1

Multi-Source Domain Transfer Learning on Epilepsy Diagnosis 多源领域迁移学习在癫痫诊断中的应用

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/CSCWD57460.2023.10152684

Aimei Dong, Zhiyun Qi, Yi Zhai, Guohua Lv

引用次数: 0

Privacy Protection Based on Packet Filtering for Home Internet-of-Things 基于包过滤的家庭物联网隐私保护