Pub Date : 2024-03-07DOI: 10.1109/TCSS.2024.3364544
Aditi Seetha;Satyendra Singh Chouhan;Emmanuel S. Pilli;Vaskar Raychoudhury;Snehanshu Saha
Detecting disruptive events (DEs), such as riots, protests, and natural calamities, from social media is essential for studying geopolitical dynamics. To automate the process, existing methods rely on classical machine learning (ML) models applied to static datasets, which is counterproductive. To detect DEs from dynamic data streams, this article introduces a novel DiEvD-SF framework, which uses continual machine learning (CML) with selective forgetting. Twitter (currently “X”) is used as a real-time and dynamic data source for validation. DiEvD-SF considers the temporal nature of DEs and “selectively forgets” outdated DEs through machine unlearning. To the best of our knowledge, this article is the first to apply CML with selective forgetting to discard outdated DEs and to continue learning about the new DEs. Extensive evaluation using a painstakingly collected Twitter dataset shows that the proposed framework continually identifies new DEs with an average incremental accuracy of 78.942% and successfully forgets old DEs with an average forgetting time of 118.498 seconds, which is better than the state-of-the-art. Additionally, computational analysis is performed to establish the effectiveness of the DiEvD-SF framework by applying various candidate selection strategies.
从社交媒体中检测骚乱、抗议和自然灾害等破坏性事件（DE）对于研究地缘政治动态至关重要。要实现这一过程的自动化，现有的方法依赖于应用于静态数据集的经典机器学习（ML）模型，这只会适得其反。为了从动态数据流中检测 DE，本文介绍了一种新颖的 DiEvD-SF 框架，该框架采用了带有选择性遗忘的持续机器学习 (CML)。Twitter（目前为 "X"）被用作验证的实时动态数据源。DiEvD-SF 考虑了 DE 的时间性，并通过机器非学习 "有选择地遗忘 "过时的 DE。据我们所知，这篇文章是第一篇应用选择性遗忘的 CML 来摒弃过时的 DE 并继续学习新 DE 的文章。使用精心收集的 Twitter 数据集进行的广泛评估表明，所提出的框架能持续识别新的 DE，平均增量准确率为 78.942%，并能成功遗忘旧的 DE，平均遗忘时间为 118.498 秒，优于最先进的技术。此外，我们还进行了计算分析，通过应用各种候选选择策略来确定 DiEvD-SF 框架的有效性。
{"title":"DiEvD-SF: Disruptive Event Detection Using Continual Machine Learning With Selective Forgetting","authors":"Aditi Seetha;Satyendra Singh Chouhan;Emmanuel S. Pilli;Vaskar Raychoudhury;Snehanshu Saha","doi":"10.1109/TCSS.2024.3364544","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3364544","url":null,"abstract":"Detecting disruptive events (DEs), such as riots, protests, and natural calamities, from social media is essential for studying geopolitical dynamics. To automate the process, existing methods rely on classical machine learning (ML) models applied to static datasets, which is counterproductive. To detect DEs from dynamic data streams, this article introduces a novel \u0000<italic>DiEvD-SF</i>\u0000 framework, which uses continual machine learning (CML) with selective forgetting. Twitter (currently “X”) is used as a real-time and dynamic data source for validation. \u0000<italic>DiEvD-SF</i>\u0000 considers the temporal nature of DEs and “selectively forgets” outdated DEs through machine unlearning. To the best of our knowledge, this article is the first to apply CML with selective forgetting to discard outdated DEs and to continue learning about the new DEs. Extensive evaluation using a painstakingly collected Twitter dataset shows that the proposed framework continually identifies new DEs with an average incremental accuracy of 78.942% and successfully forgets old DEs with an average forgetting time of 118.498 seconds, which is better than the state-of-the-art. Additionally, computational analysis is performed to establish the effectiveness of the \u0000<italic>DiEvD-SF</i>\u0000 framework by applying various candidate selection strategies.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319654","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 : 2024-03-07DOI: 10.1109/TCSS.2024.3364611
Gabriel Gentil;Amit Bhaya
This article generalizes two recently proposed opinion dynamics models with control. The generalized model consists of a standard model of agents interacting with each other, to which affine control inputs from players are added. The controls, influencing the opinions of agents, are exercised by entities called players, who specify targets, possibly conflicting, for agents. Three game-playing procedures are defined: sequential, parallel, and asynchronous. Each player has knowledge of the current state of all agents, but no other information about the other players. The player controls are designed using one step ahead optimization. This leads to several novel results: easily computable control policies for each player that depend only on the player's own information and conditions for convergence to the equilibrium as well as formulas for the latter. Comparisons showing advantages over prior Riccati equation-based methods for networks of different sizes are provided. The code to reproduce all examples and simulations is available on the GitHub site. Overall, the main contribution is the one step ahead optimal control (OSAOC) framework for influencing multiagent opinion dynamics in a decentralized game-theoretic setting.
{"title":"Opinion Dynamic Games Under One Step Ahead Optimal Control","authors":"Gabriel Gentil;Amit Bhaya","doi":"10.1109/TCSS.2024.3364611","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3364611","url":null,"abstract":"This article generalizes two recently proposed opinion dynamics models with control. The generalized model consists of a standard model of agents interacting with each other, to which affine control inputs from players are added. The controls, influencing the opinions of agents, are exercised by entities called players, who specify targets, possibly conflicting, for agents. Three game-playing procedures are defined: sequential, parallel, and asynchronous. Each player has knowledge of the current state of all agents, but no other information about the other players. The player controls are designed using one step ahead optimization. This leads to several novel results: easily computable control policies for each player that depend only on the player's own information and conditions for convergence to the equilibrium as well as formulas for the latter. Comparisons showing advantages over prior Riccati equation-based methods for networks of different sizes are provided. The code to reproduce all examples and simulations is available on the GitHub site. Overall, the main contribution is the one step ahead optimal control (OSAOC) framework for influencing multiagent opinion dynamics in a decentralized game-theoretic setting.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319657","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 : 2024-03-06DOI: 10.1109/TCSS.2023.3306571
Jayoda Weerapperuma;Dasuni Nawinna;Narmada Gamage
This study investigates the underlying factors that contribute to the success of digital learning in higher education using a social capital perspective. It is important to address the issues faced in tertiary education as these students will soon be a part of the workforce. Although digital learning has advanced in developed countries, many developing nations, including Sri Lanka, are still in the early stages of adopting it. Previous research has not adequately explored the relationship between social capital and the challenges of digital learning in the Sri Lankan context. Thus, this study focuses on examining the structural, relational, and cognitive aspects of social capital in relation to the difficulties in digital education in tertiary institutions. The research uses a quantitative approach, and the data were collected through an online survey of students in nonstate universities in Sri Lanka. Structural equation modeling was used to analyze the data, and the results showed that the three dimensions of poor social capital have a negative impact on digital education in tertiary institutions. This study also used multigroup moderation analysis to examine the effect of gender and location. This article will provide new insights into the role of social capital in digital education and will help policy makers to improve the quality and accessibility of digital education for all.
{"title":"Digital Learning Challenges in Tertiary Education in Sri Lanka: A Social Capital Perspective","authors":"Jayoda Weerapperuma;Dasuni Nawinna;Narmada Gamage","doi":"10.1109/TCSS.2023.3306571","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3306571","url":null,"abstract":"This study investigates the underlying factors that contribute to the success of digital learning in higher education using a social capital perspective. It is important to address the issues faced in tertiary education as these students will soon be a part of the workforce. Although digital learning has advanced in developed countries, many developing nations, including Sri Lanka, are still in the early stages of adopting it. Previous research has not adequately explored the relationship between social capital and the challenges of digital learning in the Sri Lankan context. Thus, this study focuses on examining the structural, relational, and cognitive aspects of social capital in relation to the difficulties in digital education in tertiary institutions. The research uses a quantitative approach, and the data were collected through an online survey of students in nonstate universities in Sri Lanka. Structural equation modeling was used to analyze the data, and the results showed that the three dimensions of poor social capital have a negative impact on digital education in tertiary institutions. This study also used multigroup moderation analysis to examine the effect of gender and location. This article will provide new insights into the role of social capital in digital education and will help policy makers to improve the quality and accessibility of digital education for all.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10462093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326260","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 : 2024-02-27DOI: 10.1109/TCSS.2024.3362885
Vaishali Kansal;Pradumn Kumar Pandey
In this article, we have employed the SEI ${}_{3}$ R ${}_{2}$ D ${}_{2}$ V model for our analysis. We conducted stability analysis for infection-free equilibrium ( $X^{prime}$ ) and endemic equilibrium ( $X^{*}$ ). The obtained equilibrium points are globally asymptotically stable. Our findings reveal that the contact dynamics of the infected and uninfected populations primarily influence the dynamics of COVID-19. In managing COVID-19, it is crucial to ensure that the number of secondary infections ( $R_{t}$ ) remains below the threshold $left(boldsymbol{gamma}+(boldsymbol{1-gamma})/(boldsymbol{alpha_{t}})right)$ which determines the growth or decline of the disease. Additionally, we conducted a sensitivity analysis of $R_{t}$ to identify the key factors that significantly affect its value. It is observed that the recovery rate, transmission probability of the virus, contact rate of unreported infections, testing inaccuracy and hesitancy, vaccination rate, and its efficacy have the most substantial impact on the value of $R_{t}$ . The influential parameters are categorized into two sets based on their effective controllability, allowing for the prioritization of intervention strategies that require fewer resources and are easier to manage, thereby optimizing efforts to control disease transmission.
{"title":"Stability and Parameter Sensitivity Analyses of SEI${}_{3}$R${}_{2}$D${}_{2}$V Model to Control COVID-19 Pandemic","authors":"Vaishali Kansal;Pradumn Kumar Pandey","doi":"10.1109/TCSS.2024.3362885","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3362885","url":null,"abstract":"In this article, we have employed the SEI\u0000<inline-formula><tex-math>${}_{3}$</tex-math></inline-formula>\u0000R\u0000<inline-formula><tex-math>${}_{2}$</tex-math></inline-formula>\u0000D\u0000<inline-formula><tex-math>${}_{2}$</tex-math></inline-formula>\u0000V model for our analysis. We conducted stability analysis for infection-free equilibrium (\u0000<inline-formula><tex-math>$X^{prime}$</tex-math></inline-formula>\u0000) and endemic equilibrium (\u0000<inline-formula><tex-math>$X^{*}$</tex-math></inline-formula>\u0000). The obtained equilibrium points are globally asymptotically stable. Our findings reveal that the contact dynamics of the infected and uninfected populations primarily influence the dynamics of COVID-19. In managing COVID-19, it is crucial to ensure that the number of secondary infections (\u0000<inline-formula><tex-math>$R_{t}$</tex-math></inline-formula>\u0000) remains below the threshold \u0000<inline-formula><tex-math>$left(boldsymbol{gamma}+(boldsymbol{1-gamma})/(boldsymbol{alpha_{t}})right)$</tex-math></inline-formula>\u0000 which determines the growth or decline of the disease. Additionally, we conducted a sensitivity analysis of \u0000<inline-formula><tex-math>$R_{t}$</tex-math></inline-formula>\u0000 to identify the key factors that significantly affect its value. It is observed that the recovery rate, transmission probability of the virus, contact rate of unreported infections, testing inaccuracy and hesitancy, vaccination rate, and its efficacy have the most substantial impact on the value of \u0000<inline-formula><tex-math>$R_{t}$</tex-math></inline-formula>\u0000. The influential parameters are categorized into two sets based on their effective controllability, allowing for the prioritization of intervention strategies that require fewer resources and are easier to manage, thereby optimizing efforts to control disease transmission.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319702","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 : 2024-02-27DOI: 10.1109/TCSS.2024.3356713
Wei Qian;Dan Guo;Kun Li;Xiaowei Zhang;Xilan Tian;Xun Yang;Meng Wang
Remote photoplethysmography (rPPG)-based physiological measurement is an emerging yet crucial vision task, whose challenge lies in exploring accurate rPPG prediction from facial videos accompanied by noises of illumination variations, facial occlusions, head movements, etc., in a noncontact manner. Existing mainstream convolutional neural network (CNN)-based models make efforts to detect physiological signals by capturing subtle color changes in facial regions of interest (ROI) caused by heartbeats. However, such models are constrained by the limited local spatial or temporal receptive fields in the neural units. Unlike them, a native transformer-based framework called dual-path TokenLearner (dual-TL) is proposed in this article, which utilizes the concept of learnable tokens to integrate both spatial and temporal informative contexts from the global perspective of the video. Specifically, the proposed dual-TL uses a spatial TokenLearner (S-TL) to explore associations in different facial ROIs, which promises the rPPG prediction far away from noisy ROI disturbances. Complementarily, a temporal TokenLearner (T-TL) is designed to infer the quasi-periodic pattern of heartbeats, which eliminates temporal disturbances such as head movements. The two TokenLearners, S-TL and T-TL, are executed in a dual-path mode. This enables the model to reduce noise disturbances for final rPPG signal prediction. Extensive experiments on four physiological measurement benchmark datasets are conducted. The dual-TL achieves state-of-the-art performances in both intra and cross-dataset testings, demonstrating its immense potential as a basic backbone for rPPG measurement.
{"title":"Dual-Path TokenLearner for Remote Photoplethysmography-Based Physiological Measurement With Facial Videos","authors":"Wei Qian;Dan Guo;Kun Li;Xiaowei Zhang;Xilan Tian;Xun Yang;Meng Wang","doi":"10.1109/TCSS.2024.3356713","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3356713","url":null,"abstract":"Remote photoplethysmography (rPPG)-based physiological measurement is an emerging yet crucial vision task, whose challenge lies in exploring accurate rPPG prediction from facial videos accompanied by noises of illumination variations, facial occlusions, head movements, etc., in a noncontact manner. Existing mainstream convolutional neural network (CNN)-based models make efforts to detect physiological signals by capturing subtle color changes in facial regions of interest (ROI) caused by heartbeats. However, such models are constrained by the limited local spatial or temporal receptive fields in the neural units. Unlike them, a native transformer-based framework called dual-path TokenLearner (dual-TL) is proposed in this article, which utilizes the concept of learnable tokens to integrate both spatial and temporal informative contexts from the global perspective of the video. Specifically, the proposed dual-TL uses a spatial TokenLearner (S-TL) to explore associations in different facial ROIs, which promises the rPPG prediction far away from noisy ROI disturbances. Complementarily, a temporal TokenLearner (T-TL) is designed to infer the quasi-periodic pattern of heartbeats, which eliminates temporal disturbances such as head movements. The two TokenLearners, S-TL and T-TL, are executed in a dual-path mode. This enables the model to reduce noise disturbances for final rPPG signal prediction. Extensive experiments on four physiological measurement benchmark datasets are conducted. The dual-TL achieves state-of-the-art performances in both intra and cross-dataset testings, demonstrating its immense potential as a basic backbone for rPPG measurement.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319564","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 : 2024-02-22DOI: 10.1109/TCSS.2024.3360435
Lijuan Xu;Ziyu Han;Zhen Wang;Dawei Zhao
While the interpretability of deep learning (DL)-based models has been extensively explored in academia, applying existing interpretation methods to anomaly detection in industrial control systems (ICSs) poses challenges for two primary reasons. First, security experts in ICS have distinct interpretive priorities, emphasizing the need for stability and readability. Second, there are various types of device components in ICS, and the potential interactions between sensors and actuators are yet to be explored. To tackle the above challenges, we propose DeepINT, an interpreter for anomaly detection in ICS. In DeepINT, we adopt a search optimization algorithm to find the reference and capture feature importance by the backpropagation gradient to improve interpretation performance and reliability. In addition, we construct a finite difference-based interaction detection, which tests the interaction of different device components, in order to address the problem that actuators in ICS are not easily interpreted, meanwhile improving the comprehensiveness and accuracy of the interpretation results. In comprehensive experiments on two real water treatment datasets [secure water treatment (SWaT) and water distribution (WADI)], DeepINT shows excellent interpretation performance compared to the six state-of-the-art baseline methods, especially on the SWaT dataset, with a 60% improvement in interpretation accuracy. In addition, our method significantly improves the efficiency of interaction detection, which balances interpretation performance and time efficiency.
{"title":"Finding Component Relationships: A Deep-Learning-Based Anomaly Detection Interpreter","authors":"Lijuan Xu;Ziyu Han;Zhen Wang;Dawei Zhao","doi":"10.1109/TCSS.2024.3360435","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3360435","url":null,"abstract":"While the interpretability of deep learning (DL)-based models has been extensively explored in academia, applying existing interpretation methods to anomaly detection in industrial control systems (ICSs) poses challenges for two primary reasons. First, security experts in ICS have distinct interpretive priorities, emphasizing the need for stability and readability. Second, there are various types of device components in ICS, and the potential interactions between sensors and actuators are yet to be explored. To tackle the above challenges, we propose DeepINT, an interpreter for anomaly detection in ICS. In DeepINT, we adopt a search optimization algorithm to find the reference and capture feature importance by the backpropagation gradient to improve interpretation performance and reliability. In addition, we construct a finite difference-based interaction detection, which tests the interaction of different device components, in order to address the problem that actuators in ICS are not easily interpreted, meanwhile improving the comprehensiveness and accuracy of the interpretation results. In comprehensive experiments on two real water treatment datasets [secure water treatment (SWaT) and water distribution (WADI)], DeepINT shows excellent interpretation performance compared to the six state-of-the-art baseline methods, especially on the SWaT dataset, with a 60% improvement in interpretation accuracy. In addition, our method significantly improves the efficiency of interaction detection, which balances interpretation performance and time efficiency.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319651","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 : 2024-02-22DOI: 10.1109/TCSS.2024.3361027
Junyuan Fang;Haixian Wen;Jiajing Wu;Qi Xuan;Zibin Zheng;Chi K. Tse
Graph neural networks (GNNs) have found successful applications in various graph-related tasks. However, recent studies have shown that many GNNs are vulnerable to adversarial attacks. In a vast majority of existing studies, adversarial attacks on GNNs are launched via direct modification of the original graph such as adding/removing links, which may not be applicable in practice. In this article, we focus on a realistic attack operation via injecting fake nodes. The proposed global attack strategy via node injection (GANI) is designed under the comprehensive consideration of an unnoticeable perturbation setting from both structure and feature domains. Specifically, to make the node injections as imperceptible and effective as possible, we propose a sampling operation to determine the degree of the newly injected nodes, and then generate features and select neighbors for these injected nodes based on the statistical information of features and evolutionary perturbations obtained from a genetic algorithm, respectively. In particular, the proposed feature generation mechanism is suitable for both binary and continuous node features. Extensive experimental results on benchmark datasets against both general and defended GNNs show strong attack performance of GANI. Moreover, the imperceptibility analyses also demonstrate that GANI achieves a relatively unnoticeable injection on benchmark datasets.
{"title":"GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections","authors":"Junyuan Fang;Haixian Wen;Jiajing Wu;Qi Xuan;Zibin Zheng;Chi K. Tse","doi":"10.1109/TCSS.2024.3361027","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3361027","url":null,"abstract":"Graph neural networks (GNNs) have found successful applications in various graph-related tasks. However, recent studies have shown that many GNNs are vulnerable to adversarial attacks. In a vast majority of existing studies, adversarial attacks on GNNs are launched via direct modification of the original graph such as adding/removing links, which may not be applicable in practice. In this article, we focus on a realistic attack operation via injecting fake nodes. The proposed global attack strategy via node injection (GANI) is designed under the comprehensive consideration of an unnoticeable perturbation setting from both structure and feature domains. Specifically, to make the node injections as imperceptible and effective as possible, we propose a sampling operation to determine the degree of the newly injected nodes, and then generate features and select neighbors for these injected nodes based on the statistical information of features and evolutionary perturbations obtained from a genetic algorithm, respectively. In particular, the proposed feature generation mechanism is suitable for both binary and continuous node features. Extensive experimental results on benchmark datasets against both general and defended GNNs show strong attack performance of GANI. Moreover, the imperceptibility analyses also demonstrate that GANI achieves a relatively unnoticeable injection on benchmark datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994016","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 : 2024-02-22DOI: 10.1109/TCSS.2024.3354315
Yi-Cheng Chen;Yen-Liang Chen;Chia-Hsiang Hsu
Recently, due to the surge in e-commerce, growing attention has been paid to how to recommend a customer's next purchase based on sequential or session-based data. However, most prior studies have generally focused on what items may be interesting for users, but have neglected the consideration of when the next items are likely to be purchased. Clearly, the timing information is an essential factor for companies to adopt proper selling strategies at the “right” time. In this study, a novel recommendation system, G-TransRec, is proposed to predict customers’ next items of interest with the potential purchase time by exploiting a user temporal interaction sequence. Moreover, by integrating the graph embedding technique, we include the global user information to explore more collaborative knowledge for effective recommendations. Several experiments were conducted on two real datasets to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on several evaluation metrics. We also use a case study to show the practicability of the proposed G-TransRec for users to recommend what they want at what time from a massive amount of merchandise.
{"title":"G-TransRec: A Transformer-Based Next-Item Recommendation With Time Prediction","authors":"Yi-Cheng Chen;Yen-Liang Chen;Chia-Hsiang Hsu","doi":"10.1109/TCSS.2024.3354315","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3354315","url":null,"abstract":"Recently, due to the surge in e-commerce, growing attention has been paid to how to recommend a customer's next purchase based on sequential or session-based data. However, most prior studies have generally focused on what items may be interesting for users, but have neglected the consideration of when the next items are likely to be purchased. Clearly, the timing information is an essential factor for companies to adopt proper selling strategies at the “right” time. In this study, a novel recommendation system, G-TransRec, is proposed to predict customers’ next items of interest with the potential purchase time by exploiting a user temporal interaction sequence. Moreover, by integrating the graph embedding technique, we include the global user information to explore more collaborative knowledge for effective recommendations. Several experiments were conducted on two real datasets to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on several evaluation metrics. We also use a case study to show the practicability of the proposed G-TransRec for users to recommend what they want at what time from a massive amount of merchandise.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319643","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 : 2024-02-21DOI: 10.1109/TCSS.2024.3360518
Amar Khelloufi;Huansheng Ning;Abdenacer Naouri;Abdelkarim Ben Sada;Attia Qammar;Abdelkader Khalil;Lingfeng Mao;Sahraoui Dhelim
The Social Internet of Things (SIoT) is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item interactions using contextual information, devices’ SIoT relationships, and correlation social groups but these schemes do not account for latent semantic item–item structures underlying the sparse multimodal contents in SIoT environment. In this article, we propose a latent-based SIoT recommendation system that learns item–item structures and aggregates multiple modalities to obtain latent item graphs which are then used in graph convolutions to inject high-order affinities into item representations. Experiments showed that the proposed recommendation system outperformed state-of-the-art SIoT recommendation methods and validated its efficacy at mining latent relationships from multimodal features.
{"title":"A Multimodal Latent-Features-Based Service Recommendation System for the Social Internet of Things","authors":"Amar Khelloufi;Huansheng Ning;Abdenacer Naouri;Abdelkarim Ben Sada;Attia Qammar;Abdelkader Khalil;Lingfeng Mao;Sahraoui Dhelim","doi":"10.1109/TCSS.2024.3360518","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3360518","url":null,"abstract":"The Social Internet of Things (SIoT) is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item interactions using contextual information, devices’ SIoT relationships, and correlation social groups but these schemes do not account for latent semantic item–item structures underlying the sparse multimodal contents in SIoT environment. In this article, we propose a latent-based SIoT recommendation system that learns item–item structures and aggregates multiple modalities to obtain latent item graphs which are then used in graph convolutions to inject high-order affinities into item representations. Experiments showed that the proposed recommendation system outperformed state-of-the-art SIoT recommendation methods and validated its efficacy at mining latent relationships from multimodal features.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10440644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993974","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 : 2024-02-21DOI: 10.1109/TCSS.2024.3359905
Yixin Chen;Jinghui Zhong;Wei-Li Liu;Linbo Luo;Wentong Cai
Guidance signage placement is a fundamental operation for crowd control in public places. The current methods mainly rely on manual design or mathematical models, which are not flexible and effective enough for crowd control in large public places. To address this issue, this article proposes a multiobjective evolutionary framework that can search for high-quality guidance signage placement strategies automatically. In the proposed method, an agent-based crowd simulation model is proposed to simulate the wayfinding behaviors of pedestrians in public places. Furthermore, a new safety metric is proposed to quantitatively evaluate the quality of guidance signage placement strategies. On this basis, an indicator-based multiobjective evolutionary algorithm (IBEA) is utilized to search for optimal guidance signage placement strategies that have tradeoffs between crowd safety and pedestrians’ travel time. Simulation experiments on both synthetic and real-world scenes were conducted to evaluate the proposed method, and the simulation results show that the proposed framework can generate very promising guidance signage placement strategies in comparison with several existing methods.
{"title":"Automatic Guidance Signage Placement Through Multiobjective Evolutionary Algorithm","authors":"Yixin Chen;Jinghui Zhong;Wei-Li Liu;Linbo Luo;Wentong Cai","doi":"10.1109/TCSS.2024.3359905","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3359905","url":null,"abstract":"Guidance signage placement is a fundamental operation for crowd control in public places. The current methods mainly rely on manual design or mathematical models, which are not flexible and effective enough for crowd control in large public places. To address this issue, this article proposes a multiobjective evolutionary framework that can search for high-quality guidance signage placement strategies automatically. In the proposed method, an agent-based crowd simulation model is proposed to simulate the wayfinding behaviors of pedestrians in public places. Furthermore, a new safety metric is proposed to quantitatively evaluate the quality of guidance signage placement strategies. On this basis, an indicator-based multiobjective evolutionary algorithm (IBEA) is utilized to search for optimal guidance signage placement strategies that have tradeoffs between crowd safety and pedestrians’ travel time. Simulation experiments on both synthetic and real-world scenes were conducted to evaluate the proposed method, and the simulation results show that the proposed framework can generate very promising guidance signage placement strategies in comparison with several existing methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319562","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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