Pub Date : 2024-07-04DOI: 10.1109/TCSS.2024.3415160
Yan Bai;Yanfeng Liu;Yongjun Li
Recently, fake news detection (FND) is an essential task in the field of social network analysis, and multimodal detection methods that combine text and image have been significantly explored in the last five years. However, the physical features of images that can be clearly shown in the frequency level are often ignored, and thus cross-modal feature extraction and interaction still remain a great challenge when the frequency domain is introduced for multimodal FND. To address this issue, we propose a frequency-aware cross-modal interaction network (FCINet) for multimodal FND in this article. First, a triple-branch encoder with robust feature extraction capacity is proposed to explore the representation of frequency, spatial, and text domains, separately. Then, we design a parallel cross-modal interaction strategy to fully exploit the interdependencies among them to facilitate multimodal FND. Finally, a combined loss function including deep auxiliary supervision and event classification is introduced to improve the generalization ability for multitask training. Extensive experiments and visual analysis on two public real-world multimodal fake news datasets show that the presented FCINet obtains excellent performance and exceeds numerous state-of-the-art methods.
{"title":"Learning Frequency-Aware Cross-Modal Interaction for Multimodal Fake News Detection","authors":"Yan Bai;Yanfeng Liu;Yongjun Li","doi":"10.1109/TCSS.2024.3415160","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3415160","url":null,"abstract":"Recently, fake news detection (FND) is an essential task in the field of social network analysis, and multimodal detection methods that combine text and image have been significantly explored in the last five years. However, the physical features of images that can be clearly shown in the frequency level are often ignored, and thus cross-modal feature extraction and interaction still remain a great challenge when the frequency domain is introduced for multimodal FND. To address this issue, we propose a frequency-aware cross-modal interaction network (FCINet) for multimodal FND in this article. First, a triple-branch encoder with robust feature extraction capacity is proposed to explore the representation of frequency, spatial, and text domains, separately. Then, we design a parallel cross-modal interaction strategy to fully exploit the interdependencies among them to facilitate multimodal FND. Finally, a combined loss function including deep auxiliary supervision and event classification is introduced to improve the generalization ability for multitask training. Extensive experiments and visual analysis on two public real-world multimodal fake news datasets show that the presented FCINet obtains excellent performance and exceeds numerous state-of-the-art methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6568-6579"},"PeriodicalIF":4.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368474","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 : 2024-07-04DOI: 10.1109/TCSS.2024.3412074
Cheng Cheng;Wenzhe Liu;Lin Feng;Ziyu Jia
Multimodal emotion recognition (MER) has attracted much attention since it can leverage consistency and complementary relationships across multiple modalities. However, previous studies mostly focused on the complementary information of multimodal signals, neglecting the consistency information of multimodal signals and the topological structure of each modality. To this end, we propose a dense graph convolution network (DGC) equipped with a joint cross attention (JCA), named DG-JCA, for MER. The main advantage of the DG-JCA model is that it simultaneously integrates the spatial topology, consistency, and complementarity of multimodal data into a unified network framework. Meanwhile, DG-JCA extends the graph convolution network (GCN) via a dense connection strategy and introduces cross attention to joint model well-learned features from multiple modalities. Specifically, we first build a topology graph for each modality and then extract neighborhood features of different modalities using DGC driven by dense connections with multiple layers. Next, JCA performs cross-attention fusion in intra- and intermodality based on each modality's characteristics while balancing the contributions of various modalities’ features. Finally, subject-dependent and subject-independent experiments on the DEAP and SEED-IV datasets are conducted to evaluate the proposed method. Abundant experimental results show that the proposed model can effectively extract and fuse multimodal features and achieve outstanding performance in comparison with some state-of-the-art approaches.
{"title":"Dense Graph Convolutional With Joint Cross-Attention Network for Multimodal Emotion Recognition","authors":"Cheng Cheng;Wenzhe Liu;Lin Feng;Ziyu Jia","doi":"10.1109/TCSS.2024.3412074","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3412074","url":null,"abstract":"Multimodal emotion recognition (MER) has attracted much attention since it can leverage consistency and complementary relationships across multiple modalities. However, previous studies mostly focused on the complementary information of multimodal signals, neglecting the consistency information of multimodal signals and the topological structure of each modality. To this end, we propose a dense graph convolution network (DGC) equipped with a joint cross attention (JCA), named DG-JCA, for MER. The main advantage of the DG-JCA model is that it simultaneously integrates the spatial topology, consistency, and complementarity of multimodal data into a unified network framework. Meanwhile, DG-JCA extends the graph convolution network (GCN) via a dense connection strategy and introduces cross attention to joint model well-learned features from multiple modalities. Specifically, we first build a topology graph for each modality and then extract neighborhood features of different modalities using DGC driven by dense connections with multiple layers. Next, JCA performs cross-attention fusion in intra- and intermodality based on each modality's characteristics while balancing the contributions of various modalities’ features. Finally, subject-dependent and subject-independent experiments on the DEAP and SEED-IV datasets are conducted to evaluate the proposed method. Abundant experimental results show that the proposed model can effectively extract and fuse multimodal features and achieve outstanding performance in comparison with some state-of-the-art approaches.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6672-6683"},"PeriodicalIF":4.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368533","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 : 2024-07-03DOI: 10.1109/TCSS.2024.3368171
Huizhe Su;Xinzhi Wang;Jinpeng Li;Shaorong Xie;Xiangfeng Luo
In the field of social computing, the task of aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence. The absence of explicit opinion words in the implicit aspect sentiment expressions poses a greater challenge for capturing their sentiment features in the reviews from social media. Many recent efforts use dependency trees or attention mechanisms to model the association between the aspect and other contextual words. However, dependency tree-based methods are inefficient in constructing valuable associations for sentiment classification due to the lack of explicit opinion words. In addition, the use of attention mechanisms to obtain global semantic information easily leads to an undesired focus on irrelevant words that may have sentiments but are not directly related to the specific aspect. In this article, we propose a novel prototype-based demonstration (PD) model for the ABSA task, which contains prototype learning and PD stages. In the prototype learning stage, we employ mask-aware attention to capture the global sentiment feature of aspect and learn sentiment prototypes through contrastive learning. This allows us to acquire comprehensive central semantics of the sentiment polarity that contains the implicit sentiment features. In the PD stage, to provide explicit guidance for the latent knowledge within the T5 model, we utilize prototypes similar to the aspect sentiment as the neural demonstration. Our model outperforms others with a 1.68%/0.28% accuracy gain on the Laptop/Restaurant datasets, especially in the ISE slice, showing improvements of 1.17%/0.26%. These results confirm the superiority of our PD-ABSA in capturing implicit sentiment and improving classification performance. This provides a solution for implicit sentiment classification in social computing.
{"title":"Enhanced Implicit Sentiment Understanding With Prototype Learning and Demonstration for Aspect-Based Sentiment Analysis","authors":"Huizhe Su;Xinzhi Wang;Jinpeng Li;Shaorong Xie;Xiangfeng Luo","doi":"10.1109/TCSS.2024.3368171","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3368171","url":null,"abstract":"In the field of social computing, the task of aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence. The absence of explicit opinion words in the implicit aspect sentiment expressions poses a greater challenge for capturing their sentiment features in the reviews from social media. Many recent efforts use dependency trees or attention mechanisms to model the association between the aspect and other contextual words. However, dependency tree-based methods are inefficient in constructing valuable associations for sentiment classification due to the lack of explicit opinion words. In addition, the use of attention mechanisms to obtain global semantic information easily leads to an undesired focus on irrelevant words that may have sentiments but are not directly related to the specific aspect. In this article, we propose a novel prototype-based demonstration (PD) model for the ABSA task, which contains prototype learning and PD stages. In the prototype learning stage, we employ mask-aware attention to capture the global sentiment feature of aspect and learn sentiment prototypes through contrastive learning. This allows us to acquire comprehensive central semantics of the sentiment polarity that contains the implicit sentiment features. In the PD stage, to provide explicit guidance for the latent knowledge within the T5 model, we utilize prototypes similar to the aspect sentiment as the neural demonstration. Our model outperforms others with a 1.68%/0.28% accuracy gain on the Laptop/Restaurant datasets, especially in the ISE slice, showing improvements of 1.17%/0.26%. These results confirm the superiority of our PD-ABSA in capturing implicit sentiment and improving classification performance. This provides a solution for implicit sentiment classification in social computing.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5631-5646"},"PeriodicalIF":4.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368550","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}
As the demand for coal and other ore resources increases, the hauling capacity of heavy-haul railways is severely challenged. Virtual coupling technology has gained attention for its ability to improve operational efficiency in bottleneck sections and reduce the time it takes for trains operating on the line to resume normal operation during emergencies. In this article, virtual coupling-based timetable rescheduling method is proposed to reduce the delays under disruptions and improve the line capacity. A mixed-integer linear program (MILP) model that allows trains to be coupled either at departure or by sharing the same arrival and departure line is formulated to reduce the delay time and its propagation range. The strategies of retiming, rearranging tracks, and virtual coupling are adopted to collaboratively optimize the deviation in train schedules and track utilization under disruptions, aiming to enhance the occupancy capacity of arrival and departure lines while simultaneously reducing train delays. A heuristic algorithm utilizing simulated annealing (SA)-particle swarm optimization (PSO) algorithm is developed to generate optimal train coupling and stopping schemes. Numerical experiments are conducted to verify the effectiveness of the proposed model and heuristic algorithm on a real heavy-haul railway configuration. The results demonstrate that our method effectively reduces train delays and minimizes the impact of track utilization on adjacent stations, as well as the repercussions of train delays on subsequent stations.
{"title":"Virtual-Coupling-Based Timetable Rescheduling for Heavy-Haul Railways Under Disruptions","authors":"Xiaolan Ma;Min Zhou;Hongwei Wang;Weichen Song;Hairong Dong","doi":"10.1109/TCSS.2024.3404550","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3404550","url":null,"abstract":"As the demand for coal and other ore resources increases, the hauling capacity of heavy-haul railways is severely challenged. Virtual coupling technology has gained attention for its ability to improve operational efficiency in bottleneck sections and reduce the time it takes for trains operating on the line to resume normal operation during emergencies. In this article, virtual coupling-based timetable rescheduling method is proposed to reduce the delays under disruptions and improve the line capacity. A mixed-integer linear program (MILP) model that allows trains to be coupled either at departure or by sharing the same arrival and departure line is formulated to reduce the delay time and its propagation range. The strategies of retiming, rearranging tracks, and virtual coupling are adopted to collaboratively optimize the deviation in train schedules and track utilization under disruptions, aiming to enhance the occupancy capacity of arrival and departure lines while simultaneously reducing train delays. A heuristic algorithm utilizing simulated annealing (SA)-particle swarm optimization (PSO) algorithm is developed to generate optimal train coupling and stopping schemes. Numerical experiments are conducted to verify the effectiveness of the proposed model and heuristic algorithm on a real heavy-haul railway configuration. The results demonstrate that our method effectively reduces train delays and minimizes the impact of track utilization on adjacent stations, as well as the repercussions of train delays on subsequent stations.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"7045-7054"},"PeriodicalIF":4.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368423","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 recommendation of long-tail items has been a persistent issue in recommender system research. The primary reason for this problem is that the model cannot learn better item features due to the lack of interactive record data of tail items, which leads to a decline in the model's recommendation performance. Existing methods transfer the features of the head items to the tail items, thereby ignoring their differences and failing to produce a satisfactory recommendation effect. To address the issue, we propose a novel recommendation model called MetaGA based on metalearning. The MetaGA model obtains initial parameters from head items through metalearning and fine-tunes model parameters during the learning process of tail item features. Additionally, it employs a graph convolutional network and attention mechanism to enhance tail data and reduce the difference between head and tail data. Through the above two steps, the model utilizes the abundant data of the head items to address the problem of sparse data of the tail items, resulting in improved recommendation performance. We conducted extensive experiments on three real-world datasets, and the results demonstrate that our proposed MetaGA model significantly outperforms other state-of-the-art baselines for tail item recommendation.
{"title":"MetaGA: Metalearning With Graph-Attention for Improved Long-Tail Item Recommendation","authors":"Bingjun Qin;Zhenhua Huang;Zhengyang Wu;Cheng Wang;Yunwen Chen","doi":"10.1109/TCSS.2024.3411043","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3411043","url":null,"abstract":"The recommendation of long-tail items has been a persistent issue in recommender system research. The primary reason for this problem is that the model cannot learn better item features due to the lack of interactive record data of tail items, which leads to a decline in the model's recommendation performance. Existing methods transfer the features of the head items to the tail items, thereby ignoring their differences and failing to produce a satisfactory recommendation effect. To address the issue, we propose a novel recommendation model called MetaGA based on metalearning. The MetaGA model obtains initial parameters from head items through metalearning and fine-tunes model parameters during the learning process of tail item features. Additionally, it employs a graph convolutional network and attention mechanism to enhance tail data and reduce the difference between head and tail data. Through the above two steps, the model utilizes the abundant data of the head items to address the problem of sparse data of the tail items, resulting in improved recommendation performance. We conducted extensive experiments on three real-world datasets, and the results demonstrate that our proposed MetaGA model significantly outperforms other state-of-the-art baselines for tail item recommendation.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6544-6556"},"PeriodicalIF":4.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368317","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 : 2024-06-28DOI: 10.1109/TCSS.2024.3409715
Shuzhen Li;Tong Zhang;C. L. Philip Chen
Multimodal emotion recognition (MER) integrates multiple modalities to identify the user's emotional state, which is the core technology of natural and friendly human–computer interaction systems. Currently, many researchers have explored comprehensive multimodal information for MER, but few consider that comprehensive multimodal features may contain noisy, useless, or redundant information, which interferes with emotional feature representation. To tackle this challenge, this article proposes a sparse interactive attention network (SIA-Net) for MER. In SIA-Net, the sparse interactive attention (SIA) module mainly consists of intramodal sparsity and intermodal sparsity. The intramodal sparsity provides sparse but effective unimodal features for multimodal fusion. The intermodal sparsity adaptively sparses intramodal and intermodal interactive relations and encodes them into sparse interactive attention. The sparse interactive attention with a small number of nonzero weights then act on multimodal features to highlight a few but important features and suppress numerous redundant features. Furthermore, the intramodal sparsity and intermodal sparsity are deep sparse representations that make unimodal features and multimodal interactions sparse without complicated optimization. The extensive experimental results show that SIA-Net achieves superior performance on three widely used datasets.
多模态情感识别（MER）整合了多种模态来识别用户的情感状态，是自然友好的人机交互系统的核心技术。目前，许多研究人员都在探索用于 MER 的综合多模态信息，但很少有人考虑到综合多模态特征可能包含噪声、无用或冗余信息，从而干扰情感特征表示。为了应对这一挑战，本文提出了一种用于 MER 的稀疏交互式注意力网络（SIA-Net）。在 SIA-Net 中，稀疏交互式注意力（SIA）模块主要包括模内稀疏性（intramodal sparsity）和模间稀疏性（intermodal sparsity）。模内稀疏性为多模态融合提供稀疏但有效的单模态特征。模式间稀疏性可以自适应地稀疏模式内和模式间的交互关系，并将其编码为稀疏交互式注意力。然后，带有少量非零权重的稀疏交互式注意力会作用于多模态特征，突出少数重要特征，抑制大量冗余特征。此外，模态内稀疏性和模态间稀疏性是一种深度稀疏表征，无需复杂的优化就能使单模态特征和多模态交互变得稀疏。大量实验结果表明，SIA-Net 在三个广泛使用的数据集上取得了优异的性能。
{"title":"SIA-Net: Sparse Interactive Attention Network for Multimodal Emotion Recognition","authors":"Shuzhen Li;Tong Zhang;C. L. Philip Chen","doi":"10.1109/TCSS.2024.3409715","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3409715","url":null,"abstract":"Multimodal emotion recognition (MER) integrates multiple modalities to identify the user's emotional state, which is the core technology of natural and friendly human–computer interaction systems. Currently, many researchers have explored comprehensive multimodal information for MER, but few consider that comprehensive multimodal features may contain noisy, useless, or redundant information, which interferes with emotional feature representation. To tackle this challenge, this article proposes a sparse interactive attention network (SIA-Net) for MER. In SIA-Net, the sparse interactive attention (SIA) module mainly consists of intramodal sparsity and intermodal sparsity. The intramodal sparsity provides sparse but effective unimodal features for multimodal fusion. The intermodal sparsity adaptively sparses intramodal and intermodal interactive relations and encodes them into sparse interactive attention. The sparse interactive attention with a small number of nonzero weights then act on multimodal features to highlight a few but important features and suppress numerous redundant features. Furthermore, the intramodal sparsity and intermodal sparsity are deep sparse representations that make unimodal features and multimodal interactions sparse without complicated optimization. The extensive experimental results show that SIA-Net achieves superior performance on three widely used datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6782-6794"},"PeriodicalIF":4.5,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368421","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}
Internet of Vehicles (IoV) improves traffic safety and efficiency by wireless communications among vehicles and infrastructures. To ensure secure communications in IoV, the problem of vehicle identity security must be solved before deployment. In this article, we propose a quick-response behavior-based vehicle identity verification method, called VLOG, for solving identity theft in IoV. This method is based on the idea of a vehicle usually having relatively stable traveling habit/behaivor. If we detect unusual behavior, the vehicle's identity may be stolen. VLOG captures vehicles’ latent behavior models from local and global two aspects, and further merges local and global models into a comprehensive behavior-based identity verification model. In the local part, we give a 2-D Gaussian model to fit the behavior data. In the global part, we learn vehicles’ traveling preferences under secure multiparty computation framework with considering the behavior volatility. The results of experiments based on a real-world vehicular trace dataset show the best performance of VLOG in terms of accuracy, F1 score, and cost. Meanwhile, VLOG also performs well in the area under the curve and precision-recall curve. Besides, since our model is preprepared, when a vehicle is required to be detected, the verification response time is short.
{"title":"VLOG: Vehicle Identity Verification Based on Local and Global Behavior Analysis","authors":"Zhong Li;Yubo Kong;Jie Luo;Yifei Meng;Changjun Jiang","doi":"10.1109/TCSS.2024.3414587","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3414587","url":null,"abstract":"Internet of Vehicles (IoV) improves traffic safety and efficiency by wireless communications among vehicles and infrastructures. To ensure secure communications in IoV, the problem of vehicle identity security must be solved before deployment. In this article, we propose a quick-response behavior-based vehicle identity verification method, called VLOG, for solving identity theft in IoV. This method is based on the idea of a vehicle usually having relatively stable traveling habit/behaivor. If we detect unusual behavior, the vehicle's identity may be stolen. VLOG captures vehicles’ latent behavior models from local and global two aspects, and further merges local and global models into a comprehensive behavior-based identity verification model. In the local part, we give a 2-D Gaussian model to fit the behavior data. In the global part, we learn vehicles’ traveling preferences under secure multiparty computation framework with considering the behavior volatility. The results of experiments based on a real-world vehicular trace dataset show the best performance of VLOG in terms of accuracy, F1 score, and cost. Meanwhile, VLOG also performs well in the area under the curve and precision-recall curve. Besides, since our model is preprepared, when a vehicle is required to be detected, the verification response time is short.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"7032-7044"},"PeriodicalIF":4.5,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368258","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 rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in human–computer interaction (HCI) by tailoring content based on individual preferences. Despite their importance, challenges persist in balancing recommendation accuracy with user satisfaction, addressing biases while preserving user privacy, and solving cold-start problems in cross-domain situations. This research argues that addressing these issues is not solely the recommender systems’ responsibility, and a human-centered approach is vital. We introduce the recommender system, assistant, and human (RAH) framework, an innovative solution with large language model (LLM)-based agents such as perceive, learn, act, critic, and reflect, emphasizing the alignment with user personalities. The framework utilizes the learn-act-critic loop and a reflection mechanism for improving user alignment. Using the real-world data, our experiments demonstrate the RAH framework's efficacy in various recommendation domains, from reducing human burden to mitigating biases and enhancing user control. Notably, our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.
{"title":"RAH! RecSys–Assistant–Human: A Human-Centered Recommendation Framework With LLM Agents","authors":"Yubo Shu;Haonan Zhang;Hansu Gu;Peng Zhang;Tun Lu;Dongsheng Li;Ning Gu","doi":"10.1109/TCSS.2024.3404039","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3404039","url":null,"abstract":"The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in human–computer interaction (HCI) by tailoring content based on individual preferences. Despite their importance, challenges persist in balancing recommendation accuracy with user satisfaction, addressing biases while preserving user privacy, and solving cold-start problems in cross-domain situations. This research argues that addressing these issues is not solely the recommender systems’ responsibility, and a human-centered approach is vital. We introduce the recommender system, assistant, and human (RAH) framework, an innovative solution with large language model (LLM)-based agents such as perceive, learn, act, critic, and reflect, emphasizing the alignment with user personalities. The framework utilizes the learn-act-critic loop and a reflection mechanism for improving user alignment. Using the real-world data, our experiments demonstrate the RAH framework's efficacy in various recommendation domains, from reducing human burden to mitigating biases and enhancing user control. Notably, our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6759-6770"},"PeriodicalIF":4.5,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368278","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}
Autism spectrum disorder (ASD) is accompanied by impaired social cognition and behavior. The expense of supporting patients with ASD turns into a significant problem for society. Parsing neurobiological subtypes is a crucial way for delineating the heterogeneity in autistic brains, with significant implications for improving ASD diagnosis and promoting the development of personalized intervention models. Nevertheless, a comprehensive understanding of the heterogeneity in cortical morphology of ASD is still lacking, and the question of whether neuroanatomical subtypes remain stable during cortical development remains unclear. Here, we used T1-weighted images of 515 male patients with ASD, including 216 autistic children (6–11 years), 187 adolescents (12–17 years), and 112 young adults (18–29 years), along with 595 age and gender-matched typically developing (TD) individuals. Cortical thickness (CT), surface area (SA), and volumes of cortical (CV) and subcortical (SV) regions were extracted. A single network layer was established by calculating the covariance of each feature across brain regions between participants, thereby constructing a multilayer intersubject covariance network. Applying a community detection algorithm to multilayer networks derived from different feature combinations, we observed that the network comprising CT and CV layers exhibited the most prominent modular organization, resulting in three subtypes of ASD for each of the three age groups. Subtypes within the corresponding age group significantly differed in terms of brain morphology and clinical scales. Furthermore, the subtypes of children with ASD underwent reorganization with development, transitioning from childhood to adolescence and adulthood, rather than consistently persist. Additionally, subtype categorization largely improved the diagnostic accuracy of ASD compared to diagnosing the entire ASD cohort. These findings demonstrated distinct neuroanatomical manifestations of ASD subtypes across various developmental periods, highlighting the significance of age-related subtyping in facilitating the etiology and diagnosis of ASD.
{"title":"Decomposing Neuroanatomical Heterogeneity of Autism Spectrum Disorder Across Different Developmental Stages Using Morphological Multiplex Network Model","authors":"Xiang Fu;Ying Wang;Jialong Li;Hongmin Cai;Xinyan Zhang;Zhijun Yao;Minqiang Yang;Weihao Zheng","doi":"10.1109/TCSS.2024.3411113","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3411113","url":null,"abstract":"Autism spectrum disorder (ASD) is accompanied by impaired social cognition and behavior. The expense of supporting patients with ASD turns into a significant problem for society. Parsing neurobiological subtypes is a crucial way for delineating the heterogeneity in autistic brains, with significant implications for improving ASD diagnosis and promoting the development of personalized intervention models. Nevertheless, a comprehensive understanding of the heterogeneity in cortical morphology of ASD is still lacking, and the question of whether neuroanatomical subtypes remain stable during cortical development remains unclear. Here, we used T1-weighted images of 515 male patients with ASD, including 216 autistic children (6–11 years), 187 adolescents (12–17 years), and 112 young adults (18–29 years), along with 595 age and gender-matched typically developing (TD) individuals. Cortical thickness (CT), surface area (SA), and volumes of cortical (CV) and subcortical (SV) regions were extracted. A single network layer was established by calculating the covariance of each feature across brain regions between participants, thereby constructing a multilayer intersubject covariance network. Applying a community detection algorithm to multilayer networks derived from different feature combinations, we observed that the network comprising CT and CV layers exhibited the most prominent modular organization, resulting in three subtypes of ASD for each of the three age groups. Subtypes within the corresponding age group significantly differed in terms of brain morphology and clinical scales. Furthermore, the subtypes of children with ASD underwent reorganization with development, transitioning from childhood to adolescence and adulthood, rather than consistently persist. Additionally, subtype categorization largely improved the diagnostic accuracy of ASD compared to diagnosing the entire ASD cohort. These findings demonstrated distinct neuroanatomical manifestations of ASD subtypes across various developmental periods, highlighting the significance of age-related subtyping in facilitating the etiology and diagnosis of ASD.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6557-6567"},"PeriodicalIF":4.5,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368442","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 : 2024-06-25DOI: 10.1109/TCSS.2024.3412911
Xing Li;Zhiyu Zhou;Chengjun Zhang;Yilin Wu;Yu Liu
Localization and pose estimation algorithms play an important role in intelligent transportation systems (ITSs), as ITS need to accurately sense and understand the traffic environment to support autonomous navigation, traffic flow management, and autonomous material handling. This article proposes a pose estimation method in the front end of lidar odometry with geometric constraints. The proposed method can accurately capture the geometric information in the environment and ensure the effectiveness of the point cloud participating in the registration to improve the accuracy of registration. In the back end, an enhanced pose estimation strategy combining rough registration and fine registration is adopted to further improve localization accuracy. Comprehensive experimental results show that the proposed method achieves higher localization accuracy against other baselines, which also demonstrates that the proposed method can cope with challenging scenes such as complex road conditions and dynamic objects.
{"title":"Geometric Constraints and Rough-Fine Registration-Based Localization Method for Social Intelligent Transportation Systems","authors":"Xing Li;Zhiyu Zhou;Chengjun Zhang;Yilin Wu;Yu Liu","doi":"10.1109/TCSS.2024.3412911","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3412911","url":null,"abstract":"Localization and pose estimation algorithms play an important role in intelligent transportation systems (ITSs), as ITS need to accurately sense and understand the traffic environment to support autonomous navigation, traffic flow management, and autonomous material handling. This article proposes a pose estimation method in the front end of lidar odometry with geometric constraints. The proposed method can accurately capture the geometric information in the environment and ensure the effectiveness of the point cloud participating in the registration to improve the accuracy of registration. In the back end, an enhanced pose estimation strategy combining rough registration and fine registration is adopted to further improve localization accuracy. Comprehensive experimental results show that the proposed method achieves higher localization accuracy against other baselines, which also demonstrates that the proposed method can cope with challenging scenes such as complex road conditions and dynamic objects.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6771-6781"},"PeriodicalIF":4.5,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368396","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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