IEEE Transactions on Computational Social Systems最新文献

2024 Index IEEE Transactions on Computational Social Systems Vol. 11 计算社会系统学报第11卷

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-05 DOI: 10.1109/TCSS.2024.3512113

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IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-02 DOI: 10.1109/TCSS.2024.3493357

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IEEE Transactions on Computational Social Systems Information for Authors IEEE计算社会系统信息汇刊

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-02 DOI: 10.1109/TCSS.2024.3493359

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Metacracy: A New Governance Paradigm Beyond Bounded Intelligence 元统治：超越有限智能的新治理范式

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-02 DOI: 10.1109/TCSS.2024.3493372

Fei-Yue Wang;Rui Qin;Juanjuan Li;Levente Kováacs;Bin Hu

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IEEE Transactions on Computational Social Systems Publication Information IEEE计算社会系统汇刊信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-02 DOI: 10.1109/TCSS.2024.3493355

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Guest Editorial: Special Issue on Social Manufacturing After ChatGPT 嘉宾评论：ChatGPT后社会制造特刊

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-02 DOI: 10.1109/TCSS.2024.3496032

Fei-Yue Wang;Pingyu Jiang;Gang Xiong;MengChu Zhou;Bernd Kuhlenkötter;Petri Helo;Zhen Shen

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Generating-Based Attacks to Online Social Networks 基于生成的在线社交网络攻击

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-24 DOI: 10.1109/TCSS.2024.3461710

Tianchong Gao;Yucheng Bian;Feng Li;Agnideven Palanisamy Sundar

{"title":"Generating-Based Attacks to Online Social Networks","authors":"Tianchong Gao;Yucheng Bian;Feng Li;Agnideven Palanisamy Sundar","doi":"10.1109/TCSS.2024.3461710","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3461710","url":null,"abstract":"Online social network (OSN) privacy leakage problem addresses more and more users’ concerns. Studying the problem from attackers’ view could tell us how to prevent further data leakage. Currently, attackers mainly focus on mapping identities between their background knowledge and the published data to collect useful information. However, it becomes difficult to find the global optimal mapping strategy because of the complexity of the OSN data. This article proposes a novel generating-based attack on OSN data, no longer restricted to mapping-based information collection. Generally, the proposed scheme learns OSN properties from the attackers’ background knowledge and employs the knowledge to fill the unknown area in the published data. The proposed scheme employs a generative adversarial network to ensure the similarity between the generated graph and the published data. The conditional information is also added in the generation process such that the generated graph is restricted to the conditions under attackers’ background knowledge. Experimental results show that the proposed scheme successfully infer private information with real-world OSN datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7881-7891"},"PeriodicalIF":4.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777637","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}

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Conversational Crowdsensing in the Age of Industry 5.0: A Parallel Intelligence and Large Models Powered Novel Sensing Approach 工业5.0时代的会话式群体感知：一种并行智能和大模型驱动的新型感知方法

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-15 DOI: 10.1109/TCSS.2024.3451649

Zhengqiu Zhu;Yong Zhao;Sihang Qiu;Kai Xu;Quanjun Yin;Jincai Huang;Zhong Liu;Fei-Yue Wang

{"title":"Conversational Crowdsensing in the Age of Industry 5.0: A Parallel Intelligence and Large Models Powered Novel Sensing Approach","authors":"Zhengqiu Zhu;Yong Zhao;Sihang Qiu;Kai Xu;Quanjun Yin;Jincai Huang;Zhong Liu;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3451649","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3451649","url":null,"abstract":"The transition from cyber-physical-system-based (CPS-based) Industry 4.0 to cyber-physical-social-system-based (CPSS-based) Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in large language models (LLMs) and retrieval augmented generation (RAG). Therefore, the advancement of parallel intelligence powered crowdsensing intelligence (CSI) is witnessed, which is currently advancing toward linguistic intelligence. In this article, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0 (especially for social manufacturing). It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., interhuman, human–AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multiagent systems, scenarios engineering and conversational human–AI cooperation. Finally, we present potential applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"8046-8063"},"PeriodicalIF":4.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789123","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

IEEE Transactions on Computational Social Systems Information for Authors 电气和电子工程师学会计算社会系统论文集作者信息

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.2024.3457715

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Computational Aids on Mental Health: Revolutionizing Care in the Digital Age 心理健康计算辅助工具：数字时代的护理革命

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.2024.3458128

Shiqiu Meng;Jie Shi;Bin Hu

引用次数: 0