Pub Date : 2025-01-13DOI: 10.1109/TCSS.2024.3455415
Marco Furini;Luca Mariotti;Riccardo Martoglia;Manuela Montangero
This study explores the influence of social media on health-related discourse amid the COVID-19 pandemic, focusing on Italian-language tweets posted on Twitter from March 2020 to December 2021. Analyzing a dataset comprising 13 million tweets, the research addresses three key questions: who emerged as opinion leaders on Twitter during the pandemic in Italy?; did health institutions in Italy successfully establish themselves as opinion leaders?; and how did the content of COVID-19-related tweets in Italy evolve over time? Employing a custom-designed graph and the personalized PageRank algorithm, the study identifies opinion leaders on Twitter. Additionally, psycholinguistic analysis provides insights into the content, themes, and emotional undertones of the tweets. The findings of this research contribute to a deeper understanding of social media's influence on public opinion and behavior during the pandemic. Furthermore, they offer valuable insights for public health officials and policymakers seeking to address health-related issues on social media platforms.
{"title":"A Novel Graph-Based Approach to Identify Opinion Leaders in Twitter","authors":"Marco Furini;Luca Mariotti;Riccardo Martoglia;Manuela Montangero","doi":"10.1109/TCSS.2024.3455415","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3455415","url":null,"abstract":"This study explores the influence of social media on health-related discourse amid the COVID-19 pandemic, focusing on Italian-language tweets posted on Twitter from March 2020 to December 2021. Analyzing a dataset comprising 13 million tweets, the research addresses three key questions: who emerged as opinion leaders on Twitter during the pandemic in Italy?; did health institutions in Italy successfully establish themselves as opinion leaders?; and how did the content of COVID-19-related tweets in Italy evolve over time? Employing a custom-designed graph and the personalized PageRank algorithm, the study identifies opinion leaders on Twitter. Additionally, psycholinguistic analysis provides insights into the content, themes, and emotional undertones of the tweets. The findings of this research contribute to a deeper understanding of social media's influence on public opinion and behavior during the pandemic. Furthermore, they offer valuable insights for public health officials and policymakers seeking to address health-related issues on social media platforms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1268-1278"},"PeriodicalIF":4.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186007","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}
The ever-increasing entwinement of information and communication technology (ICT) infrastructure with the proliferation of electric vehicles (EVs) has resulted in a congruent coalescence of energy and transportation networks. However, the surfeit of data communication and processing capabilities inherent in these systems also poses a potential peril to cyber security. Hence, a bifurcated logistics operation and cyberattack defense strategy have been propounded for green integrated power-transportation networks (IPTN) with renewable penetration. This strategy leverages the potential of social participation from EVs to amplify the defense operation. The bifurcation comprises of a preclusive stage aimed at fortifying and preserving resource allocation within IPTN and a defensive stage aimed at mitigating the deleterious impacts of cyberattacks through rapid response measures. Conventional measures such as load shedding and operation adjustments are augmented by an innovative defense involvement incentive, designed to elicit additional support from EV users. A mean-risk distributionally robust optimization methodology predicated on Kullback–Leibler divergence is posited to address the limitations in data availability in simulating cyberattack consequences. Empirical investigations through case studies in an urbane IPTN are conducted to evaluate the adverse impacts of cyberattacks and examine countermeasures aimed at mitigating their effects to the greatest extent possible.
{"title":"Socially Enhanced Defense in Energy-Transportation Systems","authors":"Alexis Pengfei Zhao;Shuangqi Li;Yunqi Wang;Mohannad Alhazmi","doi":"10.1109/TCSS.2024.3517140","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3517140","url":null,"abstract":"The ever-increasing entwinement of information and communication technology (ICT) infrastructure with the proliferation of electric vehicles (EVs) has resulted in a congruent coalescence of energy and transportation networks. However, the surfeit of data communication and processing capabilities inherent in these systems also poses a potential peril to cyber security. Hence, a bifurcated logistics operation and cyberattack defense strategy have been propounded for green integrated power-transportation networks (IPTN) with renewable penetration. This strategy leverages the potential of social participation from EVs to amplify the defense operation. The bifurcation comprises of a preclusive stage aimed at fortifying and preserving resource allocation within IPTN and a defensive stage aimed at mitigating the deleterious impacts of cyberattacks through rapid response measures. Conventional measures such as load shedding and operation adjustments are augmented by an innovative defense involvement incentive, designed to elicit additional support from EV users. A mean-risk distributionally robust optimization methodology predicated on Kullback–Leibler divergence is posited to address the limitations in data availability in simulating cyberattack consequences. Empirical investigations through case studies in an urbane IPTN are conducted to evaluate the adverse impacts of cyberattacks and examine countermeasures aimed at mitigating their effects to the greatest extent possible.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"563-572"},"PeriodicalIF":4.5,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783264","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}
Fake news detection on social media is crucial to purifying the online environment and protecting public safety. Many existing methods explore the news propagation structures through graph neural networks (GNNs) to determine the truthfulness of news. End-to-end supervised GNNs notoriously depend on large amounts of labels. Recently, self-supervised graph pretraining has been a promising solution to alleviate the dependence on labels. However, the application of graph pretraining in fake news detection still suffers from two challenges: 1) the missing and unreliable interactions intrinsic in the news propagation structures seriously damage the pretraining performance. 2) There is an inherent gap between pretraining and downstream fake news detection tasks due to inconsistency in optimization objectives, which hinders the efficient transfer of pretrained prior knowledge and causes suboptimal detection results. To address the above two challenges, we propose RGCP, a structure redefined graph pretraining with contrastive prompting for fake news detection. Specifically, we design a propagation structure refinement module that adds potential implicit interactions and removes noisy interactions according to the connection probabilities between posts estimated under the guidance of self-supervised contrastive learning. Thereby, the redefined structures provide reliable news propagation patterns to generate robust pretrained news representations. Moreover, we propose a novel prompt tuning based on the contrastive learning module that reformulates the downstream fake news detection task in a similar form as the graph contrastive pretraining, bridging the optimization objective gap. The extensive experiments on benchmark datasets demonstrate the superiority of RGCP, achieving an average improvement of 10.15% in few-shot classification.
{"title":"A Structure Redefined Graph Pretraining With Contrastive Prompting for Fake News Detection","authors":"Haosen Wang;Pan Tang;Linghong Zhou;Chenglong Shi;Can Xu;Pengfei Zheng;Surong Yan;Chunqi Wu","doi":"10.1109/TCSS.2024.3519657","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3519657","url":null,"abstract":"Fake news detection on social media is crucial to purifying the online environment and protecting public safety. Many existing methods explore the news propagation structures through graph neural networks (GNNs) to determine the truthfulness of news. End-to-end supervised GNNs notoriously depend on large amounts of labels. Recently, self-supervised graph pretraining has been a promising solution to alleviate the dependence on labels. However, the application of graph pretraining in fake news detection still suffers from two challenges: 1) the missing and unreliable interactions intrinsic in the news propagation structures seriously damage the pretraining performance. 2) There is an inherent gap between pretraining and downstream fake news detection tasks due to inconsistency in optimization objectives, which hinders the efficient transfer of pretrained prior knowledge and causes suboptimal detection results. To address the above two challenges, we propose RGCP, a structure redefined graph pretraining with contrastive prompting for fake news detection. Specifically, we design a propagation structure refinement module that adds potential implicit interactions and removes noisy interactions according to the connection probabilities between posts estimated under the guidance of self-supervised contrastive learning. Thereby, the redefined structures provide reliable news propagation patterns to generate robust pretrained news representations. Moreover, we propose a novel prompt tuning based on the contrastive learning module that reformulates the downstream fake news detection task in a similar form as the graph contrastive pretraining, bridging the optimization objective gap. The extensive experiments on benchmark datasets demonstrate the superiority of RGCP, achieving an average improvement of 10.15% in few-shot classification.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"2254-2266"},"PeriodicalIF":4.5,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315309","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}
Multimodal emotion recognition from electroencephalogram (EEG) and eye movement signals has shown to be a promising approach to provide more discriminative information about human emotional states. However, most current works rely on a subject-dependent approach, limiting their applicability to new users. Recently, some studies have explored multimodal domain adaptation to address the mentioned issue by transferring information from known subjects to new ones. Unfortunately, existing methods are still exposed to negative transfer as a suboptimal distribution alignment is performed between subjects, while irrelevant information is not discarded. In this article, we present a multimodal and multisource domain adaptation (MMDA) method, which adopts the following three strategies: 1) marginal and conditional distribution alignments must be performed between each known subject and a new one; 2) relevant distribution alignments must be prioritized to avoid a negative transfer; and 3) modality fusion results should be improved by extracting more discriminative features from EEG signals and selecting relevant features across modalities. Our proposed method was evaluated with leave-one-subject-out cross validation on four public datasets: SEED, SEED-GER, SEED-IV, and SEED-V. Experimental results show that our proposal outperforms state-of-the-art results for each dataset when subject data from different sessions are combined into a single dataset. Moreover, MMDA exceeds the state of the art in 8 out of 11 different sessions when each session is evaluated.
{"title":"MMDA: A Multimodal and Multisource Domain Adaptation Method for Cross-Subject Emotion Recognition From EEG and Eye Movement Signals","authors":"Magdiel Jiménez-Guarneros;Gibran Fuentes-Pineda;Jonas Grande-Barreto","doi":"10.1109/TCSS.2024.3519300","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3519300","url":null,"abstract":"Multimodal emotion recognition from electroencephalogram (EEG) and eye movement signals has shown to be a promising approach to provide more discriminative information about human emotional states. However, most current works rely on a subject-dependent approach, limiting their applicability to new users. Recently, some studies have explored multimodal domain adaptation to address the mentioned issue by transferring information from known subjects to new ones. Unfortunately, existing methods are still exposed to negative transfer as a suboptimal distribution alignment is performed between subjects, while irrelevant information is not discarded. In this article, we present a multimodal and multisource domain adaptation (MMDA) method, which adopts the following three strategies: 1) marginal and conditional distribution alignments must be performed between each known subject and a new one; 2) relevant distribution alignments must be prioritized to avoid a negative transfer; and 3) modality fusion results should be improved by extracting more discriminative features from EEG signals and selecting relevant features across modalities. Our proposed method was evaluated with leave-one-subject-out cross validation on four public datasets: SEED, SEED-GER, SEED-IV, and SEED-V. Experimental results show that our proposal outperforms state-of-the-art results for each dataset when subject data from different sessions are combined into a single dataset. Moreover, MMDA exceeds the state of the art in 8 out of 11 different sessions when each session is evaluated.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"2214-2227"},"PeriodicalIF":4.5,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315431","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}
Ethereum, as one of the most active cryptocurrency trading platforms, has garnered significant academic interest due to its transparent and accessible transaction data. In recent years, phishing scams have emerged as a serious criminal activity on Ethereum. Although most studies model Ethereum account transactions as networks and analyze them using traditional machine learning or network representation learning techniques, these approaches often rely solely on the latest static transaction records or use manually designed features while neglecting transaction histories, thus failing to fully capture the dynamic interactions and potential trading patterns between accounts. This article introduces an innovative multiperspective cascaded dynamic graph neural network model named DMPCG, which extracts phishing transaction data from authoritative databases like blockchain explorers to construct transaction network graphs. The model elevates the analysis from the microscopic features of nodes to the macroscopic dynamics of the entire network, integrating the attributes of static snapshot graphs with the evolution of dynamic trading networks, significantly enhancing the accuracy of phishing detection. Experimental results demonstrate that the DMPCG method achieves an impressive precision of 92.6% and an F1-score of 90.9%, outperforming existing baseline models and traditional subgraph sampling techniques.
{"title":"Unraveling the Deception of Web3 Phishing Scams: Dynamic Multiperspective Cascade Graph Approach for Ethereum Phishing Detection","authors":"Lejun Zhang;Xucan Zhang;Siyi Xiao;Zexin Li;Shen Su;Jing Qiu;Zhihong Tian","doi":"10.1109/TCSS.2024.3516144","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3516144","url":null,"abstract":"Ethereum, as one of the most active cryptocurrency trading platforms, has garnered significant academic interest due to its transparent and accessible transaction data. In recent years, phishing scams have emerged as a serious criminal activity on Ethereum. Although most studies model Ethereum account transactions as networks and analyze them using traditional machine learning or network representation learning techniques, these approaches often rely solely on the latest static transaction records or use manually designed features while neglecting transaction histories, thus failing to fully capture the dynamic interactions and potential trading patterns between accounts. This article introduces an innovative multiperspective cascaded dynamic graph neural network model named DMPCG, which extracts phishing transaction data from authoritative databases like blockchain explorers to construct transaction network graphs. The model elevates the analysis from the microscopic features of nodes to the macroscopic dynamics of the entire network, integrating the attributes of static snapshot graphs with the evolution of dynamic trading networks, significantly enhancing the accuracy of phishing detection. Experimental results demonstrate that the DMPCG method achieves an impressive precision of 92.6% and an F1-score of 90.9%, outperforming existing baseline models and traditional subgraph sampling techniques.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"498-510"},"PeriodicalIF":4.5,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783261","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 : 2024-12-27DOI: 10.1109/TCSS.2024.3507927
Jun Wang;Fangyu Zhang;Wei Zhang
In this article, the classic portfolio selection problem is reformulated as nine convex optimization problems to maximize nine risk-adjusted performance indexes based on nine different risk measures in Markowitz's return-risk framework. The exact convex reformulations facilitate a decision maker to optimize portfolios efficiently by maximizing one of the nine risk-adjusted performance criteria using widely available convex optimization problem solvers, without compromising the portfolio optimality. The superior performances of the proposed approaches to the state-of-the-art methods, in terms of out-of-sample risk-adjusted returns, annualized returns, and portfolio sparsity, are demonstrated through extensive experimentation on 13 datasets from major world stock markets.
{"title":"Portfolio Selection by Maximizing Various Risk-Adjusted Return Ratios via Convex Reformulations","authors":"Jun Wang;Fangyu Zhang;Wei Zhang","doi":"10.1109/TCSS.2024.3507927","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3507927","url":null,"abstract":"In this article, the classic portfolio selection problem is reformulated as nine convex optimization problems to maximize nine risk-adjusted performance indexes based on nine different risk measures in Markowitz's return-risk framework. The exact convex reformulations facilitate a decision maker to optimize portfolios efficiently by maximizing one of the nine risk-adjusted performance criteria using widely available convex optimization problem solvers, without compromising the portfolio optimality. The superior performances of the proposed approaches to the state-of-the-art methods, in terms of out-of-sample risk-adjusted returns, annualized returns, and portfolio sparsity, are demonstrated through extensive experimentation on 13 datasets from major world stock markets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1202-1217"},"PeriodicalIF":4.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179156","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}
Next point-of-interest (POI) recommendation is to explore the historical check-in sequence information in location-based social networks (LBSNs) to recommend the next location that he/she might be interested in. However, most previous methods used only limited information of unimodal data (i.e., check-in sequences), while some recent methods have attempted to explore multimodal data (e.g., textual content) but lacked sufficient interactions between geographic behavior patterns and content behavior patterns. In this work, we argue that users usually consider geographical trajectories and textual content interdependently to determine the next location to visit. To this end, we propose a novel cross-modal aggregation attention network (CMAAN), which interactively learns multiview representations from POI sequence and content sequence for predicting the next POI. Our approach models inter-modal interaction correlations, intra-modal sequence correlations, and intra-modal semantic correlations simultaneously to fully discover contextual potential relations along the trajectories. Specifically, the intra-modal semantic correlations are able to capture the variable location functionalities under different contextual relationships of cross-modal interaction information. Moreover, we apply the aggregation attention to adaptively aggregate multiview representations which represent the comprehensive hidden state of the next POI. Extensive experiments on two large-scale datasets clearly demonstrate that our CMAAN achieves state-of-the-art performance.
{"title":"CMAAN: Cross-Modal Aggregation Attention Network for Next POI Recommendation","authors":"Zhuang Zhuang;Lingbo Liu;Heng Qi;Yanming Shen;Baocai Yin","doi":"10.1109/TCSS.2024.3513947","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3513947","url":null,"abstract":"Next point-of-interest (POI) recommendation is to explore the historical check-in sequence information in location-based social networks (LBSNs) to recommend the next location that he/she might be interested in. However, most previous methods used only limited information of unimodal data (i.e., check-in sequences), while some recent methods have attempted to explore multimodal data (e.g., textual content) but lacked sufficient interactions between geographic behavior patterns and content behavior patterns. In this work, we argue that users usually consider geographical trajectories and textual content interdependently to determine the next location to visit. To this end, we propose a novel cross-modal aggregation attention network (CMAAN), which interactively learns multiview representations from POI sequence and content sequence for predicting the next POI. Our approach models inter-modal interaction correlations, intra-modal sequence correlations, and intra-modal semantic correlations simultaneously to fully discover contextual potential relations along the trajectories. Specifically, the intra-modal semantic correlations are able to capture the variable location functionalities under different contextual relationships of cross-modal interaction information. Moreover, we apply the aggregation attention to adaptively aggregate multiview representations which represent the comprehensive hidden state of the next POI. Extensive experiments on two large-scale datasets clearly demonstrate that our CMAAN achieves state-of-the-art performance.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1025-1037"},"PeriodicalIF":4.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178873","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 : 2024-12-24DOI: 10.1109/TCSS.2024.3459929
Xin Nie;Laurence T. Yang;Zhe Li;Xianjun Deng;Fulan Fan;Zecan Yang
Multimodal sentiment analysis (MSA) integrates multiple sources of sentiment information for processing and has demonstrated superior performance compared to single-modal sentiment analysis, making it widely applicable in domains such as human–computer interaction and public opinion supervision. However, current MSA models heavily rely on black-box deep learning (DL) methods, which lack interpretability. Additionally, effectively integrating multimodal data, reducing noise and redundancy, as well as bridging the semantic gap between heterogeneous data remain challenging issues in multimodal DL. To address these challenges, we propose an interpretable multimodal Tucker fusion model with information filtering (IMTFMIF). We are the first to utilize the multimodal Tucker fusion model for MSA tasks. This approach maps multimodal data into a unified tensor space for fusion, effectively reducing modal heterogeneity and eliminating redundant information while maintaining interpretability. Furthermore, mutual information is employed to filter out task-irrelevant information and explain the association between input and output from an information flow perspective. We propose a novel approach to enhance the comprehension of multimodal data and optimize model performance in MSA tasks. Finally, extensive experiments conducted on three public multimodal datasets demonstrate that our proposed IMTFMIF achieves competitive performance compared to state-of-the-art methods.
{"title":"Interpretable Multimodal Tucker Fusion Model With Information Filtering for Multimodal Sentiment Analysis","authors":"Xin Nie;Laurence T. Yang;Zhe Li;Xianjun Deng;Fulan Fan;Zecan Yang","doi":"10.1109/TCSS.2024.3459929","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3459929","url":null,"abstract":"Multimodal sentiment analysis (MSA) integrates multiple sources of sentiment information for processing and has demonstrated superior performance compared to single-modal sentiment analysis, making it widely applicable in domains such as human–computer interaction and public opinion supervision. However, current MSA models heavily rely on black-box deep learning (DL) methods, which lack interpretability. Additionally, effectively integrating multimodal data, reducing noise and redundancy, as well as bridging the semantic gap between heterogeneous data remain challenging issues in multimodal DL. To address these challenges, we propose an interpretable multimodal Tucker fusion model with information filtering (IMTFMIF). We are the first to utilize the multimodal Tucker fusion model for MSA tasks. This approach maps multimodal data into a unified tensor space for fusion, effectively reducing modal heterogeneity and eliminating redundant information while maintaining interpretability. Furthermore, mutual information is employed to filter out task-irrelevant information and explain the association between input and output from an information flow perspective. We propose a novel approach to enhance the comprehension of multimodal data and optimize model performance in MSA tasks. Finally, extensive experiments conducted on three public multimodal datasets demonstrate that our proposed IMTFMIF achieves competitive performance compared to state-of-the-art methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1351-1364"},"PeriodicalIF":4.5,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184369","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 : 2024-12-24DOI: 10.1109/TCSS.2024.3508803
Arnaud Z. Dragicevic
This study examines the dynamics of bargaining in a social system that incorporates risk sharing through exchange network models and stochastic matching between agents. The analysis explores three scenarios: convergent expectations, divergent expectations, and social preferences among model players. The study introduces stochastic shocks through a Poisson process, which can disrupt coordination within the decentralized exchange mechanism. Despite these shocks, agents can employ a risk-sharing protocol utilizing Pareto weights to mitigate their effects. The model outcomes do not align with the generalized Nash bargaining solutions across all scenarios. However, over a sufficiently long time frame, the dynamics consistently converge to a fixed point that slightly deviates from the balanced outcome or Nash equilibrium. This minor deviation represents the risk premium necessary for hedging against mutual risk. The risk premium is at its minimum in the scenario with convergent expectations and remains unchanged in the case involving social preferences.
{"title":"Exploring Risk Sharing in Stochastic Exchange Networks","authors":"Arnaud Z. Dragicevic","doi":"10.1109/TCSS.2024.3508803","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3508803","url":null,"abstract":"This study examines the dynamics of bargaining in a social system that incorporates risk sharing through exchange network models and stochastic matching between agents. The analysis explores three scenarios: convergent expectations, divergent expectations, and social preferences among model players. The study introduces stochastic shocks through a Poisson process, which can disrupt coordination within the decentralized exchange mechanism. Despite these shocks, agents can employ a risk-sharing protocol utilizing Pareto weights to mitigate their effects. The model outcomes do not align with the generalized Nash bargaining solutions across all scenarios. However, over a sufficiently long time frame, the dynamics consistently converge to a fixed point that slightly deviates from the balanced outcome or Nash equilibrium. This minor deviation represents the risk premium necessary for hedging against mutual risk. The risk premium is at its minimum in the scenario with convergent expectations and remains unchanged in the case involving social preferences.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1181-1192"},"PeriodicalIF":4.5,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178876","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 : 2024-12-24DOI: 10.1109/TCSS.2024.3517656
Xiaobo Chen;Yuwen Liang;Junyu Wang;Qiaolin Ye;Yingfeng Cai
Accurately predicting the future trajectories of traffic agents is paramount for autonomous unmanned systems, such as self-driving cars and mobile robotics. Extracting abundant temporal and social features from trajectory data and integrating the resulting features effectively pose great challenges for predictive models. To address these issues, this article proposes a novel multibranch attentive transformer (MBAT) trajectory prediction network for traffic agents. Specifically, to explore and reveal diverse correlations of agents, we propose a decoupled temporal and spatial feature learning module with multibranch to extract temporal, spatial, as well as spatiotemporal features. Such design ensures each branch can be specifically tailored for different types of correlations, thus enhancing the flexibility and representation ability of features. Besides, we put forward an attentive transformer architecture that simultaneously models the complex correlations possibly occurring in historical and future timesteps. Moreover, the temporal, spatial, and spatiotemporal features can be effectively integrated based on different types of attention mechanisms. Empirical results demonstrate that our model achieves outstanding performance on public ETH, UCY, SDD, and INTERACTION datasets. Detailed ablation studies are conducted to verify the effectiveness of the model components.
{"title":"Multibranch Attentive Transformer With Joint Temporal and Social Correlations for Traffic Agents Trajectory Prediction","authors":"Xiaobo Chen;Yuwen Liang;Junyu Wang;Qiaolin Ye;Yingfeng Cai","doi":"10.1109/TCSS.2024.3517656","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3517656","url":null,"abstract":"Accurately predicting the future trajectories of traffic agents is paramount for autonomous unmanned systems, such as self-driving cars and mobile robotics. Extracting abundant temporal and social features from trajectory data and integrating the resulting features effectively pose great challenges for predictive models. To address these issues, this article proposes a novel multibranch attentive transformer (MBAT) trajectory prediction network for traffic agents. Specifically, to explore and reveal diverse correlations of agents, we propose a decoupled temporal and spatial feature learning module with multibranch to extract temporal, spatial, as well as spatiotemporal features. Such design ensures each branch can be specifically tailored for different types of correlations, thus enhancing the flexibility and representation ability of features. Besides, we put forward an attentive transformer architecture that simultaneously models the complex correlations possibly occurring in historical and future timesteps. Moreover, the temporal, spatial, and spatiotemporal features can be effectively integrated based on different types of attention mechanisms. Empirical results demonstrate that our model achieves outstanding performance on public ETH, UCY, SDD, and INTERACTION datasets. Detailed ablation studies are conducted to verify the effectiveness of the model components.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"525-538"},"PeriodicalIF":4.5,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783268","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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