Pub Date : 2024-01-25DOI: 10.1109/TCSS.2024.3350956
{"title":"IEEE Transactions on Computational Social Systems Information for Authors","authors":"","doi":"10.1109/TCSS.2024.3350956","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3350956","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10414271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.1109/TCSS.2024.3350954
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TCSS.2024.3350954","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3350954","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10414225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.1109/TCSS.2024.3350952
{"title":"IEEE Transactions on Computational Social Systems Publication Information","authors":"","doi":"10.1109/TCSS.2024.3350952","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3350952","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10414223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.1109/TCSS.2024.3351191
Xiao Li;Weili Wu;Tiantian Chen
Blockchain technology, recognized for its decentralized and privacy-preserving capabilities, holds potential for enhancing privacy in contact tracing applications. Existing blockchain-based contact tracing frameworks often overlook one or more critical design details, such as the blockchain data structure, a decentralized and lightweight consensus mechanism with integrated tracing data verification, and an incentive mechanism to encourage voluntary participation in bearing blockchain costs. Moreover, the absence of framework simulations raises questions about the efficacy of these existing models. To solve above issues, this article introduces a fully third-party independent blockchain-driven contact tracing (BDCT) framework, detailed in its design. The BDCT framework features an Rivest-Shamir-Adleman (RSA) encryption-based transaction verification method (RSA-TVM), achieving over 96% accuracy in contact case recording, even with a 60% probability of individuals failing to verify contact information. Furthermore, we propose a lightweight reputation corrected delegated proof of stake (RC-DPoS) consensus mechanism, coupled with an incentive model, to ensure timely reporting of contact cases while maintaining blockchain decentralization. Additionally, a novel simulation environment for contact tracing is developed, accounting for three distinct contact scenarios with varied population density. Our results and discussions validate the effectiveness, robustness of the RSA-TVM and RC-DPoS, and the low storage demand of the BDCT framework.
{"title":"Blockchain-Driven Privacy-Preserving Contact-Tracing Framework in Pandemics","authors":"Xiao Li;Weili Wu;Tiantian Chen","doi":"10.1109/TCSS.2024.3351191","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3351191","url":null,"abstract":"Blockchain technology, recognized for its decentralized and privacy-preserving capabilities, holds potential for enhancing privacy in contact tracing applications. Existing blockchain-based contact tracing frameworks often overlook one or more critical design details, such as the blockchain data structure, a decentralized and lightweight consensus mechanism with integrated tracing data verification, and an incentive mechanism to encourage voluntary participation in bearing blockchain costs. Moreover, the absence of framework simulations raises questions about the efficacy of these existing models. To solve above issues, this article introduces a fully third-party independent blockchain-driven contact tracing (BDCT) framework, detailed in its design. The BDCT framework features an Rivest-Shamir-Adleman (RSA) encryption-based transaction verification method (RSA-TVM), achieving over 96% accuracy in contact case recording, even with a 60% probability of individuals failing to verify contact information. Furthermore, we propose a lightweight reputation corrected delegated proof of stake (RC-DPoS) consensus mechanism, coupled with an incentive model, to ensure timely reporting of contact cases while maintaining blockchain decentralization. Additionally, a novel simulation environment for contact tracing is developed, accounting for three distinct contact scenarios with varied population density. Our results and discussions validate the effectiveness, robustness of the RSA-TVM and RC-DPoS, and the low storage demand of the BDCT framework.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319571","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}
Graph neural networks (GNNs) enable many novel applications and achieve excellent performance. However, their performance may be significantly degraded by the graph adversarial attacks, which intentionally add small perturbations to the graph. Previous countermeasures usually handle such attacks by enhancing model robustness. However, robust models cannot identify the target nodes of the adversarial attacks, and thus we are unable to pinpoint the weak spots and analyze the causes or the targets of the attacks. In this article, we study the important research problem to detect the target nodes of graph adversarial attacks under the black-box detection scenario, which is particularly challenging because our detection models do not have any knowledge about the attacker, while the attackers usually employ unnoticeability strategies to minimize the chance of being detected. To our best knowledge, this is the first work that aims at detecting the target nodes of graph adversarial attacks under the black-box detector scenario. We propose two detection models, named Det-H and Det-RL , which employ different techniques that effectively detect the target nodes under the black-box detection scenario against various graph adversarial attacks. To enhance the generalization of the proposed detectors, we further propose two novel surrogate attackers that are able to generate effective attack examples and camouflage their attack traces for training robust detectors. In addition, we propose three strategies to effectively improve the training efficiency. Experimental results on multiple datasets show that our proposed detectors significantly outperform the other baselines against multiple state-of-the-art graph adversarial attackers with various attack strategies. The proposed Det-RL detector achieves an averaged area under curve (AUC) of $0.945$ against all the attackers, and our efficiency-improving strategies are able save up to $91$ % of the training time.
{"title":"Detecting Targets of Graph Adversarial Attacks With Edge and Feature Perturbations","authors":"Boyi Lee;Jhao-Yin Jhang;Lo-Yao Yeh;Ming-Yi Chang;Chia-Mei Chen;Chih-Ya Shen","doi":"10.1109/TCSS.2023.3344642","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3344642","url":null,"abstract":"Graph neural networks (GNNs) enable many novel applications and achieve excellent performance. However, their performance may be significantly degraded by the graph adversarial attacks, which intentionally add small perturbations to the graph. Previous countermeasures usually handle such attacks by enhancing model robustness. However, robust models cannot identify the \u0000<italic>target nodes</i>\u0000 of the adversarial attacks, and thus we are unable to pinpoint the weak spots and analyze the causes or the targets of the attacks. In this article, we study the important research problem to detect the \u0000<italic>target nodes</i>\u0000 of graph adversarial attacks under the \u0000<italic>black-box detection</i>\u0000 scenario, which is particularly challenging because our detection models do not have any knowledge about the attacker, while the attackers usually employ unnoticeability strategies to minimize the chance of being detected. To our best knowledge, this is the first work that aims at detecting the \u0000<italic>target nodes</i>\u0000 of graph adversarial attacks under the \u0000<italic>black-box detector</i>\u0000 scenario. We propose two detection models, named \u0000<italic>Det-H</i>\u0000 and \u0000<italic>Det-RL</i>\u0000, which employ different techniques that effectively detect the target nodes under the black-box detection scenario against various graph adversarial attacks. To enhance the generalization of the proposed detectors, we further propose two novel surrogate attackers that are able to generate effective attack examples and camouflage their attack traces for training robust detectors. In addition, we propose three strategies to effectively improve the training efficiency. Experimental results on multiple datasets show that our proposed detectors significantly outperform the other baselines against multiple state-of-the-art graph adversarial attackers with various attack strategies. The proposed \u0000<italic>Det-RL</i>\u0000 detector achieves an averaged area under curve (AUC) of \u0000<inline-formula><tex-math>$0.945$</tex-math></inline-formula>\u0000 against all the attackers, and our efficiency-improving strategies are able save up to \u0000<inline-formula><tex-math>$91$</tex-math></inline-formula>\u0000% of the training time.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319653","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}
Pub Date : 2024-01-25DOI: 10.1109/TCSS.2024.3352611
Shan Liu;Xiaoqing Wu
Collective behavior frequently emerges in this dynamic and interconnected world, making in-depth research into its societal impacts both highly significant and challenging. The emergence and development of collective behavior in social systems are intricately linked to the dissemination of event information. A rational dissemination model needs to be constructed as a foundation for analysis, exploring factors influencing collective behavior, and predicting the onset of such behaviors. During the information dissemination progress, individual interactions are exceedingly intricate and subject to dynamic changes, some of which may even involve nonlinear mutations. Therefore, this article dissects interpersonal interactions into psychological and behavioral dimensions, and establish an emergency dissemination model, considering the coevolution of dissemination strategies, and dissemination behaviors. Additionally, the influence of the initial probability distribution on strategies, dissemination cost, and time decay coefficients on the propagation trends was examined through simulation analysis. Building upon this model, a methodology is proposed for predicting the emergence timepoint from four indicators. Empirical validation and analysis substantiate the proposed approach, affirming its rationality, advancement, and efficacy in addressing the research objectives.
{"title":"Research on Emergence of Collective Behavior for Information Dissemination in Complex Networks","authors":"Shan Liu;Xiaoqing Wu","doi":"10.1109/TCSS.2024.3352611","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3352611","url":null,"abstract":"Collective behavior frequently emerges in this dynamic and interconnected world, making in-depth research into its societal impacts both highly significant and challenging. The emergence and development of collective behavior in social systems are intricately linked to the dissemination of event information. A rational dissemination model needs to be constructed as a foundation for analysis, exploring factors influencing collective behavior, and predicting the onset of such behaviors. During the information dissemination progress, individual interactions are exceedingly intricate and subject to dynamic changes, some of which may even involve nonlinear mutations. Therefore, this article dissects interpersonal interactions into psychological and behavioral dimensions, and establish an emergency dissemination model, considering the coevolution of dissemination strategies, and dissemination behaviors. Additionally, the influence of the initial probability distribution on strategies, dissemination cost, and time decay coefficients on the propagation trends was examined through simulation analysis. Building upon this model, a methodology is proposed for predicting the emergence timepoint from four indicators. Empirical validation and analysis substantiate the proposed approach, affirming its rationality, advancement, and efficacy in addressing the research objectives.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319624","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}
Pub Date : 2024-01-25DOI: 10.1109/TCSS.2024.3350958
Bin Hu;Lixian Zhu;Qunxi Dong;Kun Qian;Hanshu Cai;Fuze Tian
With great pride and enthusiasm, we present the inaugural edition of IEEE Transactions on Computational Social Systems (TCSS) for 2024. Reflecting on the year gone by, 2023 stands as a hallmark of academic excellence and prolific output, wherein our journal has successfully disseminated a substantial volume of scholarly work—301 articles encompassing approximately 3600 pages, distributed across six distinct issues.
{"title":"Physiological Electrosignal Asynchronous Acquisition Technology: Insight and Perspectives","authors":"Bin Hu;Lixian Zhu;Qunxi Dong;Kun Qian;Hanshu Cai;Fuze Tian","doi":"10.1109/TCSS.2024.3350958","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3350958","url":null,"abstract":"With great pride and enthusiasm, we present the inaugural edition of IEEE Transactions on Computational Social Systems (TCSS) for 2024. Reflecting on the year gone by, 2023 stands as a hallmark of academic excellence and prolific output, wherein our journal has successfully disseminated a substantial volume of scholarly work—301 articles encompassing approximately 3600 pages, distributed across six distinct issues.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10414277","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-24DOI: 10.1109/TCSS.2023.3349269
Debasmita Manna;Somanath Tripathy
Due to its admirable accuracy and performance across a wide range of classification and identification tasks, deep learning algorithms have gained popularity in several applications. However, the models’ security has become a serious concern, as antagonists could use them to promote their malicious goals. This work proposes a triggerless targeted model poisoning attack (TriMPA) against deep neural network without requiring any change in input to trigger the backdoor. TriMPA identifies active neurons that highly contribute to the prediction of the victim output label and replaces those neurons with that corresponding to the target output label. The performance of the proposed mechanism is evaluated through experiments as well as analyzed theoretically. It is shown that TriMPA achieves a higher attack success rate.
{"title":"TriMPA: Triggerless Targeted Model Poisoning Attack in DNN","authors":"Debasmita Manna;Somanath Tripathy","doi":"10.1109/TCSS.2023.3349269","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3349269","url":null,"abstract":"Due to its admirable accuracy and performance across a wide range of classification and identification tasks, deep learning algorithms have gained popularity in several applications. However, the models’ security has become a serious concern, as antagonists could use them to promote their malicious goals. This work proposes a triggerless targeted model poisoning attack (TriMPA) against deep neural network without requiring any change in input to trigger the backdoor. TriMPA identifies active neurons that highly contribute to the prediction of the victim output label and replaces those neurons with that corresponding to the target output label. The performance of the proposed mechanism is evaluated through experiments as well as analyzed theoretically. It is shown that TriMPA achieves a higher attack success rate.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993956","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}
Pub Date : 2024-01-24DOI: 10.1109/TCSS.2023.3344173
Zhaowei Liu;Zongxing Zhao
Mobile crowd sensing (MCS) is an emerging approach to collect data using smart devices. In MCS, task assignment is described as assigning existing tasks to known workers outside the constraints of task demand attributes and worker attributes, and maximizing the profit of the platform. However, workers and tasks often exist in different environments and heterogeneous features such as workers with attributes are not considered, leading to nondeterministic polynomial (NP)-hard task assignment problems. To optimize such problems, this article proposes a multiattribute environments-classes, agents, roles, groups, and objects (E-CARGO) task assignment model based on adaptive heterogeneous residual networks (AHRNets). The AHRNet is integrated into deep reinforcement learning (DRL) to optimize the NP-hard problem, dynamically adjust task assignment decisions and learn the relationship between workers with different attributes and task requirements. Multiattribute E-CARGO uses group task assignment policy to obtain the ideal worker-task assignment relationship. Compared with traditional heuristic algorithms for solving NP-hard, this method has the flexibility and applicability of adaptive networks, enabling the solver to interact with and adapt to new environments and generalize its experience to different situations. Under various experimental conditions, a large number of numerical results show that this method can achieve better results than the reference scheme.
{"title":"Multiattribute E-CARGO Task Assignment Model Based on Adaptive Heterogeneous Residual Networks","authors":"Zhaowei Liu;Zongxing Zhao","doi":"10.1109/TCSS.2023.3344173","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3344173","url":null,"abstract":"Mobile crowd sensing (MCS) is an emerging approach to collect data using smart devices. In MCS, task assignment is described as assigning existing tasks to known workers outside the constraints of task demand attributes and worker attributes, and maximizing the profit of the platform. However, workers and tasks often exist in different environments and heterogeneous features such as workers with attributes are not considered, leading to nondeterministic polynomial (NP)-hard task assignment problems. To optimize such problems, this article proposes a multiattribute environments-classes, agents, roles, groups, and objects (E-CARGO) task assignment model based on adaptive heterogeneous residual networks (AHRNets). The AHRNet is integrated into deep reinforcement learning (DRL) to optimize the NP-hard problem, dynamically adjust task assignment decisions and learn the relationship between workers with different attributes and task requirements. Multiattribute E-CARGO uses group task assignment policy to obtain the ideal worker-task assignment relationship. Compared with traditional heuristic algorithms for solving NP-hard, this method has the flexibility and applicability of adaptive networks, enabling the solver to interact with and adapt to new environments and generalize its experience to different situations. Under various experimental conditions, a large number of numerical results show that this method can achieve better results than the reference scheme.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326334","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}
Pub Date : 2024-01-22DOI: 10.1109/TCSS.2024.3350736
Hossein B. Jond
A social network population engages in collective actions as a direct result of forming a particular opinion. The strategic interactions among the individuals acting independently and selfishly naturally portray a noncooperative game. Nash equilibrium allows for self-enforcing strategic interactions between selfish and self-interested individuals. This article presents a differential game approach to the opinion formation problem in social networks to investigate the evolution of opinions as a result of a Nash equilibrium. The opinion of each individual is described by a differential equation, which is the continuous-time Hegselmann–Krause model for opinion dynamics with a time delay in input. The objective of each individual is to seek optimal strategies for its own opinion evolution by minimizing an individual cost function. Two differential game problems emerge, one for a population that is not stubborn and another for a population that is stubborn. The open-loop Nash equilibrium actions and their associated opinion trajectories are derived for both differential games using Pontryagin's principle. Additionally, the receding horizon control scheme is used to practice feedback strategies where the information flow is restricted by fixed and complete social graphs, as well as the second neighborhood concept. The game strategies were executed on the well-known Zachary's Karate Club social network and a representative family opinion network. The resulting opinion trajectories associated with the game strategies showed consensus, polarization, and disagreement in final opinions.
{"title":"Differential Game Strategies for Social Networks With Self-Interested Individuals","authors":"Hossein B. Jond","doi":"10.1109/TCSS.2024.3350736","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3350736","url":null,"abstract":"A social network population engages in collective actions as a direct result of forming a particular opinion. The strategic interactions among the individuals acting independently and selfishly naturally portray a noncooperative game. Nash equilibrium allows for self-enforcing strategic interactions between selfish and self-interested individuals. This article presents a differential game approach to the opinion formation problem in social networks to investigate the evolution of opinions as a result of a Nash equilibrium. The opinion of each individual is described by a differential equation, which is the continuous-time Hegselmann–Krause model for opinion dynamics with a time delay in input. The objective of each individual is to seek optimal strategies for its own opinion evolution by minimizing an individual cost function. Two differential game problems emerge, one for a population that is not stubborn and another for a population that is stubborn. The open-loop Nash equilibrium actions and their associated opinion trajectories are derived for both differential games using Pontryagin's principle. Additionally, the receding horizon control scheme is used to practice feedback strategies where the information flow is restricted by fixed and complete social graphs, as well as the second neighborhood concept. The game strategies were executed on the well-known Zachary's Karate Club social network and a representative family opinion network. The resulting opinion trajectories associated with the game strategies showed consensus, polarization, and disagreement in final opinions.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319634","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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