IEEE Transactions on Computational Social Systems最新文献_第2页

IEEE Systems, Man, and Cybernetics Society Information 电气和电子工程师学会系统、人和控制论学会信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-02 DOI: 10.1109/TCSS.2024.3457713

引用次数: 0

IEEE Transactions on Computational Social Systems Publication Information 电气和电子工程师学会《计算社会系统期刊》出版信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-02 DOI: 10.1109/TCSS.2024.3457711

引用次数: 0

Platform-Driven Collaboration Patterns: Structural Evolution Over Time and Scale 平台驱动的协作模式：随时间和规模的结构演变

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-24 DOI: 10.1109/TCSS.2024.3452028

Negin Maddah;Babak Heydari

{"title":"Platform-Driven Collaboration Patterns: Structural Evolution Over Time and Scale","authors":"Negin Maddah;Babak Heydari","doi":"10.1109/TCSS.2024.3452028","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3452028","url":null,"abstract":"Within an increasingly digitalized organizational landscape, this research explores the dynamics of decentralized collaboration, contrasting it with traditional collaboration models. An effective capturing of high-level collaborations (beyond direct messages) is introduced as the network construction methodology including both temporal and content dimensions of user collaborations—an alternating timed interaction (ATI) metric as the first aspect, and a quantitative strategy of thematic similarity as the second aspect. This study validates three hypotheses that collectively underscore the complexities of digital team dynamics within sociotechnical systems. First, it establishes the significant influence of problem context on team structures in work environments. Second, the study reveals specific evolving patterns of team structures on digital platforms concerning team size and problem maturity. Last, it identifies substantial differences in team structure patterns between digital platforms and traditional organizational settings, underscoring the unexplored nature of digital collaboration dynamics. Focusing on Wikipedia's co-creation teams as a representative online platform, this study is instrumental for organizations navigating the digital era by identifying opportunities and challenges for managing information flow. The findings reveal significant collaborative potential and innovation in large online teams: the high speed of knowledge-sharing, numerous subcommunities, and highly decentralized leadership. This study paves the way for platform governors to design strategic interventions, tailored for different problem types, to optimize digital team dynamics and align them to broader organizational goals.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7814-7829"},"PeriodicalIF":4.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777650","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

Modeling the Contributions of Participator, Content, and Network to Topic Duration in Online Social Group 在线社交群体中参与者、内容和网络对话题持续时间的贡献建模

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-24 DOI: 10.1109/TCSS.2024.3414586

Guoshuai Zhang;Jiaji Wu;Gwanggil Jeon;Penghui Wang;Yuan Chen;Yuhui Wang;Mingzhou Tan

{"title":"Modeling the Contributions of Participator, Content, and Network to Topic Duration in Online Social Group","authors":"Guoshuai Zhang;Jiaji Wu;Gwanggil Jeon;Penghui Wang;Yuan Chen;Yuhui Wang;Mingzhou Tan","doi":"10.1109/TCSS.2024.3414586","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3414586","url":null,"abstract":"As a common phenomenon that often appears on social platforms, news sites, and community forums, topics have played an irreplaceable role in public opinion and social governance. Meanwhile, people's daily lives are increasingly dependent on the breeding, transformation, and attenuation of hot topics. This article aims to discuss the problem about topic duration, that is, what are the principle factors that affect topic duration? Why do some topics survive longer and even generate subtopics, while other topics disappear rapidly? To answer these questions, we innovatively use 104 121 alliance chat content in \u0000<italic>Nova Empire II</i>\u0000 from July 2023 to December 2023 as a case study. Dynamic topics trajectories are first obtained from a novel multilevel association model. Then, a potential factors system based on the dimensions of topic properties, topic users, and social network is established to quantitatively evaluate the influence for different factors. Experimental results from a robust statistical analysis framework demonstrate that higher topic discussion intensity, more content from opinion leader, faster information diffusion, and closer intertopic correlations will significantly improve the topic duration. Finally, a series of strategies are proposed to promote the design of social system applications from the perspectives of online social group.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7146-7158"},"PeriodicalIF":4.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789034","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

An Extensible Bounded Rationality-Based Task Recommendation Scheme for From-Scratch Mobile Crowdsensing 一种基于可扩展有限理性的从头开始的移动众测任务推荐方案

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-23 DOI: 10.1109/TCSS.2024.3452099

Qiqi Shen;Miao Ma;Mengge Li

{"title":"An Extensible Bounded Rationality-Based Task Recommendation Scheme for From-Scratch Mobile Crowdsensing","authors":"Qiqi Shen;Miao Ma;Mengge Li","doi":"10.1109/TCSS.2024.3452099","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3452099","url":null,"abstract":"Mobile crowdsensing (MCS) has recently shown good performance in solving large-scale sensing tasks. As an essential topic in MCS, recommending tasks to participants has received extensive attention from researchers. Most studies assume that participants are absolutely rational, which is unrealistic because it is difficult for participants to know all the information about the transaction. Furthermore, most of them do not consider how to learn the preferences of new participants. In addition, their works are difficult to extend to different MCS scenarios. Considering the above problems, we propose an extensible bounded rationality-based task recommendation scheme (EBRTR), which contains a task recommendation framework and a bounded rationality decision-making model. First, a task recommendation framework that can be easily extended to various MCS scenarios is designed. Second, in our bounded rationality decision-making model, for participants with historical task information, according to the implicit information in their historical tasks, the human thinking mode with bounded rationality is simulated, and the improved classification and regression tree (ICART) algorithm is designed to construct the decision tree. For participants who newly join the platform, social information is introduced to construct an initial decision tree. Finally, extensive experimental evaluations demonstrate the effectiveness of the proposed scheme.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7871-7880"},"PeriodicalIF":4.5,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777954","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

Incentivizing Socio-Ethical Integrity in Decentralized Machine Learning Ecosystems for Collaborative Knowledge Sharing 在分散的机器学习生态系统中激励社会伦理诚信以促进协作知识共享

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-17 DOI: 10.1109/TCSS.2024.3450494

Yuanfang Chi;Qiyue Zhang;Jiaxiang Sun;Wei Cai;Z. Jane Wang;Victor C. M. Leung

{"title":"Incentivizing Socio-Ethical Integrity in Decentralized Machine Learning Ecosystems for Collaborative Knowledge Sharing","authors":"Yuanfang Chi;Qiyue Zhang;Jiaxiang Sun;Wei Cai;Z. Jane Wang;Victor C. M. Leung","doi":"10.1109/TCSS.2024.3450494","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3450494","url":null,"abstract":"To broaden domain knowledge and enable advanced analytics, machine learning (ML) algorithms increasingly utilize comprehensive datasets across diverse sectors. However, these disparate datasets held by various stakeholders raise concerns over data heterogeneity, privacy, and security. Decentralized ML research aims to protect data privacy and integrate knowledge bases, especially knowledge graphs, to address data heterogeneity challenges. Yet, the question of how to foster trustworthy collaborations in decentralized ML ecosystems remains underexplored. This study pioneers two innovative socio-economic mechanisms designed to ensure dependable collaborations with socio-ethical integrity within a decentralized knowledge inference framework, enabling participants to share knowledge while maintaining data privacy and ethical standards. We employ an evolutionary game theory model to analyze the dynamic interactions between requestors and workers, focusing on achieving a stable equilibrium through theoretical and numerical evaluations. Furthermore, we explore how various critical factors, such as incentive schemes and the accuracy of identifying malicious workers, influence the system's equilibrium, providing insights into optimizing collaborative efforts in decentralized ML ecosystems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7857-7870"},"PeriodicalIF":4.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777741","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

ReOP: Generating Transferable Fake Users for Recommendation Systems via Reverse Optimization ReOP：通过反向优化为推荐系统生成可转移的假用户

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-17 DOI: 10.1109/TCSS.2024.3451452

Fulan Qian;Yan Cui;Hai Chen;Wenbin Chen;Yuanting Yan;Shu Zhao

{"title":"ReOP: Generating Transferable Fake Users for Recommendation Systems via Reverse Optimization","authors":"Fulan Qian;Yan Cui;Hai Chen;Wenbin Chen;Yuanting Yan;Shu Zhao","doi":"10.1109/TCSS.2024.3451452","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3451452","url":null,"abstract":"Recent research has demonstrated that recommendation systems exhibit vulnerability under data poisoning attacks. The primary process of data poisoning attacks involves generating malicious data (i.e., fake users) through surrogate models and injecting the malicious data into the target models’ datasets, thereby manipulating the output results of the target models. However, current methods generating fake users based on gradient descent may cause them to fall into undesired local minimum in the loss landscape and overfitting to the surrogate model, thus limiting the performance of attacking other recommendation models. To address this problem, we propose the reverse optimization algorithm (ReOP), which utilizes the reverse direction of optimization to update fake users, enabling them to steer clear of sharp local minimum in loss landscape and navigate towards the flat local minimum. ReOP makes fake users less sensitive to model changes, alleviates their overfitting to the surrogate model, and thus significantly improves the transferability of fake users. Experimental results demonstrate that ReOP surpasses the state-of-the-art baseline methods, effectively generating fake users with significant attack effects on various target models.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7830-7845"},"PeriodicalIF":4.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777953","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

iCyberGuard: A FlipIt Game for Enhanced Cybersecurity in IIoT iCyberGuard：工业物联网中增强网络安全的FlipIt游戏

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-16 DOI: 10.1109/TCSS.2024.3443174

Xiaoguang Chen;Wenyuan Cao;Lili Chen;Jinpeng Han;Manzhi Yang;Zhen Wang;Fei-Yue Wang

{"title":"iCyberGuard: A FlipIt Game for Enhanced Cybersecurity in IIoT","authors":"Xiaoguang Chen;Wenyuan Cao;Lili Chen;Jinpeng Han;Manzhi Yang;Zhen Wang;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3443174","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3443174","url":null,"abstract":"Social manufacturing has significantly advanced the industrial Internet of Things (IIoT), integrating information technology and operation technology to enhance production efficiency and quality, and to foster new business models. This integration, however, introduces novel risks, including advanced persistent threats, which demand robust security measures to safeguard IIoT systems. This article proposes an iCyberGuard game model, tailored for IIoT environments, designed to imitate the cyber and physical attacks for information and operation technologies. Then, we used a reinforcement learning algorithm to compute the optimal strategy. We conducted comprehensive simulation experiments, which demonstrate that our model the strategic interactions between attackers and defenders. Participants are enabled to learn adaptively, discerning optimal strategies based on the intelligence of their adversaries. Finally, we explain the practical significance of the best strategy of defenders or attackers, and how users can rely on these best strategies to strengthen the security performance of the network.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"8005-8014"},"PeriodicalIF":4.5,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777595","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 Review on Machine Theory of Mind 机器心智理论研究综述

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-13 DOI: 10.1109/TCSS.2024.3416707

Yuanyuan Mao;Shuang Liu;Qin Ni;Xin Lin;Liang He

{"title":"A Review on Machine Theory of Mind","authors":"Yuanyuan Mao;Shuang Liu;Qin Ni;Xin Lin;Liang He","doi":"10.1109/TCSS.2024.3416707","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3416707","url":null,"abstract":"Theory of Mind (ToM) is the ability to attribute mental states to others, an important component of human cognition. At present, there has been growing interest in the artificial intelligence (AI) with cognitive abilities, for example in healthcare and the motoring industry. Research indicates that infants exhibit early signs in cognitive and social understanding, including some basic abilities related to beliefs, desires, and intentions (BDIs). Thus, the ability to attribute BDIs to others is also crucial for the development of machine ToM. In this article, we review recent progress in machine ToM on BDIs. And we shall introduce the experiments, datasets, and methods of machine ToM on these three aspects, summarize the development of different tasks and datasets in recent years, and compare well-behaved models in aspects of advantages, limitations, and applicable conditions, hoping that this study can guide researchers to quickly keep up with latest trend in this field. Unlike other domains with a specific task and resolution framework, machine ToM lacks a unified instruction and a series of standard evaluation tasks, which make it difficult to formally compare the proposed models. And the existing models still cannot exhibit the same ToM reasoning ability as real humans, lack of transferability, interpretability, few-shot learning, etc. We argue that, one method to address this difficulty is now to present a standard assessment criteria and dataset, better a large-scale dataset covered multiple aspects of ToM. Besides, for developing an AI of ToM, it requires the cooperation of experts from various domains.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7114-7132"},"PeriodicalIF":4.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789003","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

Real-Time Driver and Traffic Data Integration for Enhanced Road Safety 实时驾驶员和交通数据集成提高道路安全

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-11 DOI: 10.1109/TCSS.2024.3448400

Yufei Huang;Shan Jiang;Mohsen Jafari;Peter J. Jin

{"title":"Real-Time Driver and Traffic Data Integration for Enhanced Road Safety","authors":"Yufei Huang;Shan Jiang;Mohsen Jafari;Peter J. Jin","doi":"10.1109/TCSS.2024.3448400","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3448400","url":null,"abstract":"Traditional roadway safety assessment heavily relies on historical crash data, overlooking real-time factors such as driver behaviors and current traffic conditions and lacking forward-looking analysis for predicting future trends. This study introduces an enhanced innovative data fusion method based on the safe route mapping (SRM) methodology with combined use of historical crash data and real-time data, leveraging a custom-built Android app to amalgamate road and vehicle data effectively, showcasing notable advancements in real-time risk assessment. The enhanced safe route mapping (ESRM) framework monitors driver actions and road conditions meticulously. Data collected from drivers is analyzed on a central server using facial recognition algorithm to detect signs of fatigue and distractions, assessing overall driving competence. Simultaneously, roadside cameras capture live traffic data, analyzed using a specialized video analytics method to track vehicle speed and paths. The fusion of these data streams enables the introduction of a predictive model, Light gradient boosting machine (GBM), forecasting potential immediate issues for drivers. Predicted risk scores are integrated with historical crash data using a Fuzzy logic model, delineating risk levels for different road sections. The performance of ESRM model is tested using real-world data and a driving simulation, demonstrating remarkable accuracy, especially in accounting for real-time fusion of driver behavior and traffic conditions. The resultant visual risk heatmap aids authorities in identifying safer routes, proactive law enforcement deployment, and informed trip planning based on real-time risk levels. This study not only underscores the importance of real-time data in roadway safety but also paves the way for data-driven, dynamic risk assessment models, potentially reducing road accidents and fostering a safer driving environment.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7711-7722"},"PeriodicalIF":4.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761537","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