Pub Date : 2024-02-06DOI: 10.1109/TCSS.2023.3338370
Ying Zhao;Yingjie Wang;Peiyong Duan;Haijing Zhang;Zhaowei Liu;Xiangrong Tong;Zhipeng Cai
Mobile crowdsourcing (MCS) is a new paradigm that uses various mobile devices to collect sensed data. Mobile edge computing (MEC) can effectively utilize the device resources of mobile edge, greatly relieve the pressure of network bandwidth and improve the response speed. In this article, we construct a four-party evolutionary game model consisting of the platform, crowd workers, task requesters, and edge servers. The computing tasks are conducted on edge servers, which greatly reduce remote data transmission and network operating costs and improve service quality. Taking into account the collusion between the platform and workers, and that between the platform and requesters, we analyze the stability of the strategic equilibrium in MCS using replicator dynamics methods. The optimal payoff strategies of the participants in different initial states are obtained. To prevent cheating and false-reporting problems, reward and punishment strategies are provided. Finally, the stability of the equilibrium of the four-party evolutionary game system is verified by simulation experiments, and an incentive strategy is designed to motivate all parties to choose the trust strategies.
{"title":"Mobile Crowdsourcing Quality Control Method Based on Four-Party Evolutionary Game in Edge Cloud Environment","authors":"Ying Zhao;Yingjie Wang;Peiyong Duan;Haijing Zhang;Zhaowei Liu;Xiangrong Tong;Zhipeng Cai","doi":"10.1109/TCSS.2023.3338370","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3338370","url":null,"abstract":"Mobile crowdsourcing (MCS) is a new paradigm that uses various mobile devices to collect sensed data. Mobile edge computing (MEC) can effectively utilize the device resources of mobile edge, greatly relieve the pressure of network bandwidth and improve the response speed. In this article, we construct a four-party evolutionary game model consisting of the platform, crowd workers, task requesters, and edge servers. The computing tasks are conducted on edge servers, which greatly reduce remote data transmission and network operating costs and improve service quality. Taking into account the collusion between the platform and workers, and that between the platform and requesters, we analyze the stability of the strategic equilibrium in MCS using replicator dynamics methods. The optimal payoff strategies of the participants in different initial states are obtained. To prevent cheating and false-reporting problems, reward and punishment strategies are provided. Finally, the stability of the equilibrium of the four-party evolutionary game system is verified by simulation experiments, and an incentive strategy is designed to motivate all parties to choose the trust strategies.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326274","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}
To study the asymmetric jump behaviors of the stock markets, we propose a novel autoregressive conditional jump intensity (ARJI)—generalized autoregressive conditional heteroskedasticity (GARCH) model with a Markov chain. Compared with the existing models, it considers the asymmetric effects of the positive and negative shocks on jump volatilities. It is proposed to estimate the asymmetric jump volatilities of the stock markets in mainland China and Hong Kong under different volatility regimes. Multiple time-varying copula models are used to analyze the dynamic dependences of the jump risks between the two markets. Furthermore, we construct dynamic hedging portfolios for their spot and futures markets, estimate the minimum risk hedging ratios, and measure the hedging performance. Compared with other benchmark models, the results show that the proposed one has the best fitting effect for the Chinese stock markets. The correlations between the Chinese mainland and Hong Kong markets are always positive. When constructing hedging portfolios, the proposed model is superior to other models, which means that introducing asymmetric shocks on both normal and jump volatilities into a Markovian ARJI-GARCH model can effectively improve the performance of hedging portfolios. In addition, the results of the robustness test indicates that our proposed model performs well and is robust.
{"title":"Dynamic Dependence and Hedging of Stock Markets: Evidence From Time-Varying Copula With Asymmetric Markovian Models","authors":"Jia Wang;MengChu Zhou;Xiwang Guo;Xu Wang;Yusuf Al-Turki","doi":"10.1109/TCSS.2023.3346439","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3346439","url":null,"abstract":"To study the asymmetric jump behaviors of the stock markets, we propose a novel autoregressive conditional jump intensity (ARJI)—generalized autoregressive conditional heteroskedasticity (GARCH) model with a Markov chain. Compared with the existing models, it considers the asymmetric effects of the positive and negative shocks on jump volatilities. It is proposed to estimate the asymmetric jump volatilities of the stock markets in mainland China and Hong Kong under different volatility regimes. Multiple time-varying copula models are used to analyze the dynamic dependences of the jump risks between the two markets. Furthermore, we construct dynamic hedging portfolios for their spot and futures markets, estimate the minimum risk hedging ratios, and measure the hedging performance. Compared with other benchmark models, the results show that the proposed one has the best fitting effect for the Chinese stock markets. The correlations between the Chinese mainland and Hong Kong markets are always positive. When constructing hedging portfolios, the proposed model is superior to other models, which means that introducing asymmetric shocks on both normal and jump volatilities into a Markovian ARJI-GARCH model can effectively improve the performance of hedging portfolios. In addition, the results of the robustness test indicates that our proposed model performs well and is robust.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326424","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 : 2024-02-02DOI: 10.1109/TCSS.2024.3351173
Jiaxing Chen;Chengyi Xia;Matjaž Perc
Simplicial complexes successfully resolve the limitation of social networks to describe the spread of infectious diseases in group interactions. However, the effects of quarantines in the context of group interactions remain largely unaddressed. In this article, we therefore propose a susceptible-infectious-quarantine-recovered-susceptible (SIQRS) model with quarantines and study its evolution on simplicial complexes. In the model, a fraction of infected individuals is subject to quarantine, but individuals leaving quarantine may still be contagious. Using mean-field (MF) methods, we derive the propagation threshold and the steady state infection densities as well as conditions for their stability. Numerical simulations moreover show that longer quarantine times and higher quarantine ratios tend to disrupt discontinuous phase transition and bistable phenomena that are commonly due to group interactions. Additionally, when epidemic outbreaks are recurrent, although quarantine measures can reduce the peak of the first wave and delay the onset of future waves, they may also lead to an increase in subsequent peak infected densities. This highlights the need to prepare sufficient resources to deal with periodic infections after the initial wave is over.
{"title":"The SIQRS Propagation Model With Quarantine on Simplicial Complexes","authors":"Jiaxing Chen;Chengyi Xia;Matjaž Perc","doi":"10.1109/TCSS.2024.3351173","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3351173","url":null,"abstract":"Simplicial complexes successfully resolve the limitation of social networks to describe the spread of infectious diseases in group interactions. However, the effects of quarantines in the context of group interactions remain largely unaddressed. In this article, we therefore propose a susceptible-infectious-quarantine-recovered-susceptible (SIQRS) model with quarantines and study its evolution on simplicial complexes. In the model, a fraction of infected individuals is subject to quarantine, but individuals leaving quarantine may still be contagious. Using mean-field (MF) methods, we derive the propagation threshold and the steady state infection densities as well as conditions for their stability. Numerical simulations moreover show that longer quarantine times and higher quarantine ratios tend to disrupt discontinuous phase transition and bistable phenomena that are commonly due to group interactions. Additionally, when epidemic outbreaks are recurrent, although quarantine measures can reduce the peak of the first wave and delay the onset of future waves, they may also lead to an increase in subsequent peak infected densities. This highlights the need to prepare sufficient resources to deal with periodic infections after the initial wave is over.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319655","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 : 2024-02-02DOI: 10.1109/TCSS.2024.3351886
Zhishuai Li;Gang Xiong;Yisheng Lv;Peijun Ye;Xiaoli Liu;Sasu Tarkoma;Fei-Yue Wang
The coronavirus disease 2019 (COVID-19) pandemic has changed the world deeply. Urban trajectory big data collected by wireless sensing devices provide great assistance for COVID-19 prevention. However, except for contact tracing, trajectory data are rarely employed in other preventative scenarios against the pandemic. In this article, we try to extend the application of trajectories auto-collected by wireless sensing devices and simulate the epidemic spread in a trajectory data-driven manner. After that, the effects of three nonpharmacological measures are quantified. In contrast to existing studies, additional requirements such as the complex topological networks are needless in our simulation, where the interactions between agents are derived by the intersections of their trajectories. Concretely, the dynamic of virus propagation among individuals is first modeled, and then an agent-based microsimulation environment is built as an artificial system to conduct the epidemic spread simulation. Finally, the trajectories are loaded into the agents as the reliance for their interactions, and the macroscopic changes under different interventions are revealed in a bottom–up way. As a case study, we conduct the simulation based on the trajectories in a real region, in which we find the following. 1) Among the three examined nonpharmacological interventions, community containment is more effective than keeping social distance, which can lower the deaths to nearly 1/9 compared to no action, while travel restrictions play limited roles. 2) There is a strong positive correlation between population densities and mortality. 3) The timing of community containment triggered by confirmed diagnoses is proportional to the number of deaths, thus early containment will significantly decrease mortality.
{"title":"An Urban Trajectory Data-Driven Approach for COVID-19 Simulation","authors":"Zhishuai Li;Gang Xiong;Yisheng Lv;Peijun Ye;Xiaoli Liu;Sasu Tarkoma;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3351886","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3351886","url":null,"abstract":"The coronavirus disease 2019 (COVID-19) pandemic has changed the world deeply. Urban trajectory big data collected by wireless sensing devices provide great assistance for COVID-19 prevention. However, except for contact tracing, trajectory data are rarely employed in other preventative scenarios against the pandemic. In this article, we try to extend the application of trajectories auto-collected by wireless sensing devices and simulate the epidemic spread in a trajectory data-driven manner. After that, the effects of three nonpharmacological measures are quantified. In contrast to existing studies, additional requirements such as the complex topological networks are needless in our simulation, where the interactions between agents are derived by the intersections of their trajectories. Concretely, the dynamic of virus propagation among individuals is first modeled, and then an agent-based microsimulation environment is built as an artificial system to conduct the epidemic spread simulation. Finally, the trajectories are loaded into the agents as the reliance for their interactions, and the macroscopic changes under different interventions are revealed in a bottom–up way. As a case study, we conduct the simulation based on the trajectories in a real region, in which we find the following. 1) Among the three examined nonpharmacological interventions, community containment is more effective than keeping social distance, which can lower the deaths to nearly 1/9 compared to no action, while travel restrictions play limited roles. 2) There is a strong positive correlation between population densities and mortality. 3) The timing of community containment triggered by confirmed diagnoses is proportional to the number of deaths, thus early containment will significantly decrease mortality.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319602","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 : 2024-02-02DOI: 10.1109/TCSS.2024.3353467
Yihao Zhang;Kaibei Li;Junlin Zhu;Meng Yuan;Yonghao Huang;Xiaokang Li
Knowledge graph (KG) is of growing significance in enabling explainable recommendations. Recent research works involve constructing propagation-based recommendation models. Nevertheless, most of the current propagation-based recommendation methods cannot explicitly handle the diverse relations of items, resulting in the inability to model the underlying hierarchies and diverse relations, and it is difficult to capture the high-order collaborative information of items to learn premium representation. To address these issues, we leverage hyperbolic dynamic neural networks for knowledge-aware recommendation (KHDNN). Technically speaking, we embed users and items (forming user–item bipartite graphs), along with entities and relations (constituting KGs), into hyperbolic space, followed by encoding these embeddings using an encoder. The encoded embedding is passed through a hyperbolic dynamic filter to explicitly handle relations and model different relational structures. Furthermore, we design a fresh aggregation strategy based on relations to propagate and capture higher-order collaborative signals as well as knowledge associations. Meanwhile, we extract semantic information via a bilateral memory network to fuse item collaborative signals and knowledge associations. Empirical results from four datasets show that KHDNN surpasses cutting-edge baseline methods. Additionally, we demonstrate that the KHDNN can perform knowledge-aware recommendations with complex relations.
{"title":"Leveraging Hyperbolic Dynamic Neural Networks for Knowledge-Aware Recommendation","authors":"Yihao Zhang;Kaibei Li;Junlin Zhu;Meng Yuan;Yonghao Huang;Xiaokang Li","doi":"10.1109/TCSS.2024.3353467","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3353467","url":null,"abstract":"Knowledge graph (KG) is of growing significance in enabling explainable recommendations. Recent research works involve constructing propagation-based recommendation models. Nevertheless, most of the current propagation-based recommendation methods cannot explicitly handle the diverse relations of items, resulting in the inability to model the underlying hierarchies and diverse relations, and it is difficult to capture the high-order collaborative information of items to learn premium representation. To address these issues, we leverage hyperbolic dynamic neural networks for knowledge-aware recommendation (KHDNN). Technically speaking, we embed users and items (forming user–item bipartite graphs), along with entities and relations (constituting KGs), into hyperbolic space, followed by encoding these embeddings using an encoder. The encoded embedding is passed through a hyperbolic dynamic filter to explicitly handle relations and model different relational structures. Furthermore, we design a fresh aggregation strategy based on relations to propagate and capture higher-order collaborative signals as well as knowledge associations. Meanwhile, we extract semantic information via a bilateral memory network to fuse item collaborative signals and knowledge associations. Empirical results from four datasets show that KHDNN surpasses cutting-edge baseline methods. Additionally, we demonstrate that the KHDNN can perform knowledge-aware recommendations with complex relations.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319649","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}
Previous recommendation models build interest embeddings heavily relying on the observed interactions and optimize the embeddings with a contrast between the interactions and randomly sampled negative instances. To our knowledge, the negative interest signals remain unexplored in interest encoding, which merely serves losses for backpropagation. Besides, the sparse undifferentiated interactions inherently bring implicit bias in revealing users’ interests, leading to suboptimal interest prediction. The negative interest signals would be a piece of promising evidence to support detailed interest modeling. In this work, we propose a perturbed graph contrastive learning with negative propagation (PCNP) for recommendation, which introduces negative interest to assist interest modeling in a contrastive learning (CL) architecture. An auxiliary channel of negative interest learning generates a contrastive graph by negative sampling and propagates complementary embeddings of users and items to encode negative signals. The proposed PCNP contrasts positive and negative embeddings to promote interest modeling for recommendation. Extensive experiments demonstrate the capability of PCNP using two-level CL to alleviate interaction sparsity and bias issues for recommendation.
{"title":"Graph Contrastive Learning With Negative Propagation for Recommendation","authors":"Meishan Liu;Meng Jian;Yulong Bai;Jiancan Wu;Lifang Wu","doi":"10.1109/TCSS.2024.3356071","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3356071","url":null,"abstract":"Previous recommendation models build interest embeddings heavily relying on the observed interactions and optimize the embeddings with a contrast between the interactions and randomly sampled negative instances. To our knowledge, the negative interest signals remain unexplored in interest encoding, which merely serves losses for backpropagation. Besides, the sparse undifferentiated interactions inherently bring implicit bias in revealing users’ interests, leading to suboptimal interest prediction. The negative interest signals would be a piece of promising evidence to support detailed interest modeling. In this work, we propose a perturbed graph contrastive learning with negative propagation (PCNP) for recommendation, which introduces negative interest to assist interest modeling in a contrastive learning (CL) architecture. An auxiliary channel of negative interest learning generates a contrastive graph by negative sampling and propagates complementary embeddings of users and items to encode negative signals. The proposed PCNP contrasts positive and negative embeddings to promote interest modeling for recommendation. Extensive experiments demonstrate the capability of PCNP using two-level CL to alleviate interaction sparsity and bias issues for recommendation.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319645","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 : 2024-02-02DOI: 10.1109/TCSS.2024.3350199
Runmei Li;Xiaoting Shu;Chen Li
Accurate behavior prediction of surrounding vehicles can greatly improve the operating safety of autonomous vehicles. However, in real traffic scence, the complexity and uncertainties of traffic flow bring great challenges to driving behavior prediction. This article proposes a driving behavior prediction model using a wide-deep framework that combines gradient boosting decision tree (GBDT), convolutional neural network (CNN), and long short-term memory network (LSTM) algorithm to fully mine driving behavior characteristics while improve interpretability of the CNN-LSTM model. The GBDT algorithm can quantitatively describe the interaction between the autonomous vehicle and its surrounding vehicles during the driving process, obtaining a series of driving behavior rules, and integrating the driving behavior rule features into the CNN-LSTM neural network. The CNN-LSTM neural network model is constructed to find the spatial features in driving trajectory by CNNs and the temporal features by LSTM networks. The accuracy of the driving behavior prediction model is further improved. Simulation experiments show the rationality and validity of the model and algorithm.
{"title":"Driving Behavior Prediction Based on Combined Neural Network Model","authors":"Runmei Li;Xiaoting Shu;Chen Li","doi":"10.1109/TCSS.2024.3350199","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3350199","url":null,"abstract":"Accurate behavior prediction of surrounding vehicles can greatly improve the operating safety of autonomous vehicles. However, in real traffic scence, the complexity and uncertainties of traffic flow bring great challenges to driving behavior prediction. This article proposes a driving behavior prediction model using a wide-deep framework that combines gradient boosting decision tree (GBDT), convolutional neural network (CNN), and long short-term memory network (LSTM) algorithm to fully mine driving behavior characteristics while improve interpretability of the CNN-LSTM model. The GBDT algorithm can quantitatively describe the interaction between the autonomous vehicle and its surrounding vehicles during the driving process, obtaining a series of driving behavior rules, and integrating the driving behavior rule features into the CNN-LSTM neural network. The CNN-LSTM neural network model is constructed to find the spatial features in driving trajectory by CNNs and the temporal features by LSTM networks. The accuracy of the driving behavior prediction model is further improved. Simulation experiments show the rationality and validity of the model and algorithm.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319640","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 : 2024-02-01DOI: 10.1109/TCSS.2024.3355113
Run Wang;Peng Song;Wenming Zheng
The precondition that most of the existing facial expression recognition (FER) algorithms have succeeded lies in that the training (source) and test (target) samples are independent of each other and identically distributed. However, it is too strict to satisfy this precondition in the real-world. To this end, we propose a novel graph-diffusion-based domain-invariant representation learning (GDRL) model for the cross-domain FER scenario where there exist distribution shifts between various domains. Specifically, a low-dimensional space mapping strategy is first adopted to diminish the domain mismatch. Then, by skillfully combining the local graph embedding and affinity graph diffusion, the local geometric structures can be effectively modeled and the deeper higher-order relationships of samples from various domains can be captured. In addition, in order to better guide the transfer process and learn a more discriminative and invariant representation, we take into account the label consistency. Experimental results on four laboratory-controlled databases and two in-the-wild databases demonstrate that our proposed model can yield better recognition performance compared with state-of-the-art domain adaptation methods.
大多数现有面部表情识别（FER）算法取得成功的前提条件是训练样本（源样本）和测试样本（目标样本）相互独立且分布相同。然而，在现实世界中满足这一前提条件过于严格。为此，我们针对不同领域间存在分布偏移的跨领域 FER 场景，提出了一种基于图扩散的新型领域不变表示学习（GDRL）模型。具体来说，首先采用低维空间映射策略来减少领域不匹配。然后，通过巧妙地结合局部图嵌入和亲和图扩散，可以有效地对局部几何结构进行建模，并捕捉到来自不同域的样本之间更深层次的高阶关系。此外，为了更好地指导转移过程并学习更具区分性和不变性的表示，我们还考虑了标签一致性。在四个实验室控制数据库和两个野生数据库上的实验结果表明，与最先进的领域适应方法相比，我们提出的模型能产生更好的识别性能。
{"title":"Graph-Diffusion-Based Domain-Invariant Representation Learning for Cross-Domain Facial Expression Recognition","authors":"Run Wang;Peng Song;Wenming Zheng","doi":"10.1109/TCSS.2024.3355113","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3355113","url":null,"abstract":"The precondition that most of the existing facial expression recognition (FER) algorithms have succeeded lies in that the training (source) and test (target) samples are independent of each other and identically distributed. However, it is too strict to satisfy this precondition in the real-world. To this end, we propose a novel graph-diffusion-based domain-invariant representation learning (GDRL) model for the cross-domain FER scenario where there exist distribution shifts between various domains. Specifically, a low-dimensional space mapping strategy is first adopted to diminish the domain mismatch. Then, by skillfully combining the local graph embedding and affinity graph diffusion, the local geometric structures can be effectively modeled and the deeper higher-order relationships of samples from various domains can be captured. In addition, in order to better guide the transfer process and learn a more discriminative and invariant representation, we take into account the label consistency. Experimental results on four laboratory-controlled databases and two in-the-wild databases demonstrate that our proposed model can yield better recognition performance compared with state-of-the-art domain adaptation methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319648","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 : 2024-01-31DOI: 10.1109/TCSS.2024.3350087
Al Muktadir Munam;Ahammad Hossain
The Coronavirus Disease of 2019 (COVID-19) pandemic has threatened the global economy, livelihoods, and physical and mental health since it began in 2019. This study aimed to examine how the COVID-19 pandemic affected the mental health of a representative sample of Bangladeshi youth and to identify the influencing factors. Through social media, 390 people were asked to participate in an online survey using the cross-sectional methodology. The chi-square test was used to examine the associations between the status of mental health and other variables. It was found that because of the lockdown, 59.3% and 21% of the participants were severely and moderately affected in terms of mental health, respectively. Poor mental health outcomes are strongly associated with family status, profession, marital status, avoiding shaking hands, cleaning and disinfecting objects and surfaces which are frequently used, knowledgeable community, impact on livelihood, food availability, routine behavior, impact on education, and impact on mental health. A multinomial logistic regression (MLR) model with 95% confidence interval (CI) with a p-value < 0.05 was used to determine the effect of explanatory variables on the adjusted odds ratio (AOR) of mental health status. The results of MLR showed that age, marital status, the risk of participants of their family members getting sick from COVID-19, impact on wages, physical and mental abuse, closed schools, etc., significantly predicted mental health outcomes. This study facilitated a deeper understanding of mental health during the COVID-19 outbreak.
{"title":"Factors Influencing Mental Health Among Youth During the COVID-19 Lockdown: A Cross-Sectional Study in Bangladesh","authors":"Al Muktadir Munam;Ahammad Hossain","doi":"10.1109/TCSS.2024.3350087","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3350087","url":null,"abstract":"The Coronavirus Disease of 2019 (COVID-19) pandemic has threatened the global economy, livelihoods, and physical and mental health since it began in 2019. This study aimed to examine how the COVID-19 pandemic affected the mental health of a representative sample of Bangladeshi youth and to identify the influencing factors. Through social media, 390 people were asked to participate in an online survey using the cross-sectional methodology. The chi-square test was used to examine the associations between the status of mental health and other variables. It was found that because of the lockdown, 59.3% and 21% of the participants were severely and moderately affected in terms of mental health, respectively. Poor mental health outcomes are strongly associated with family status, profession, marital status, avoiding shaking hands, cleaning and disinfecting objects and surfaces which are frequently used, knowledgeable community, impact on livelihood, food availability, routine behavior, impact on education, and impact on mental health. A multinomial logistic regression (MLR) model with 95% confidence interval (CI) with a p-value < 0.05 was used to determine the effect of explanatory variables on the adjusted odds ratio (AOR) of mental health status. The results of MLR showed that age, marital status, the risk of participants of their family members getting sick from COVID-19, impact on wages, physical and mental abuse, closed schools, etc., significantly predicted mental health outcomes. This study facilitated a deeper understanding of mental health during the COVID-19 outbreak.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319639","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 : 2024-01-30DOI: 10.1109/TCSS.2024.3351681
Hongrun Wu;Qiurong Wu;Zhenglong Xiang;Xiang Zhang;Lei Zhang;Yingpin Chen;Hui Wang;Jianhua Song
As language is intrinsic to the expression of culture, the rise and fall of a language directly affect the culture associated with it. Therefore, constructing a computational model to study the mechanisms of language competition and explore policies of language preservation is very important. We address the language system’s macroscopic aspects, such as the prestige of languages, the difficulty level of learning languages, and natives’ tolerance toward nonnative languages, as well as individual interactions at the microscopic level, and then propose an agent network computation-based evolutionary game model (ANC-EGM), including two major components—the definition of language attractiveness and the language competition game, to model a more realistic dynamic evolving language system. The replicator equation is adopted to solve the evolutionary equilibrium, and the stability of the equilibrium points is analyzed by the local stability analysis of the Jacobian matrix. The theoretical analysis and simulations illustrate that the ANC-EGM can comprehensively model the competition between two languages and estimate how individual interactions lead to the demise or coexistence of languages. We further validate the conclusions of the ANC-EGM on the empirical data of the Minnan dialect and Mandarin, which show that the ANC-EGM can provide an experimental computing platform for the in-depth study of language policy regulation and language evolution rules.
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