Pub Date : 2024-08-16DOI: 10.1109/TCSS.2024.3426771
{"title":"IEEE Transactions on Computational Social Systems Publication Information","authors":"","doi":"10.1109/TCSS.2024.3426771","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3426771","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 4","pages":"C2-C2"},"PeriodicalIF":4.5,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10638072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993907","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 : 2024-08-16DOI: 10.1109/TCSS.2024.3427209
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TCSS.2024.3427209","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3427209","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 4","pages":"C3-C3"},"PeriodicalIF":4.5,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10638071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993927","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 : 2024-08-02DOI: 10.1109/TCSS.2024.3411486
Wenzhuo Liu;Jianli Lu;Junbin Liao;Yicheng Qiao;Guoying Zhang;Jiayin Zhu;Bozhang Xu;Zhiwei Li
Driving behavior classification is a critical component of social transportation systems and advanced driver assistance systems, and it has gained increasing attention in recent years. Accurate classification algorithms for driving behavior play a significant role in enhancing traffic safety, energy conservation, and related fields. In this article, we propose a novel driving behavior classification network named feature-attention-embedding-based multimodal-fusion driving-behavior-classification network (FMDNet). FMDNet incorporates eight types of data, including acceleration along the x-axis, y-axis, z-axis, roll angle, pitch angle, yaw angle, roadside image, and vehicle speed, to classify driving behavior. To effectively fuse features extracted from different modalities, taking into account their varying importance, we introduce the feature attention embedding-based fusion module (FAEF) as our fusion strategy. This fusion strategy enhances the network's capability to capture meaningful features by incorporating two feature attention embedding units that delve deeper into the interplay between different modes. Furthermore, we provide further validation of the effectiveness of our approach through extensive ablation experiments to investigate and analyze the impact of various modal data on the classification of driving behavior. Our proposed FMDNet achieves state-of-the-art performance on the public UAH-DriveSet dataset, demonstrating its effectiveness with an impressive F1-score of 99.0%. Additionally, the robustness of our model is confirmed on distracted dataset, achieving a remarkable F1-score of 99.7%. The model's outstanding performance on both the UAH-DriveSet dataset and the distracted-dataset highlights its capabilities and potential for real-world applications. https://github.com/Wenzhuo-Liu/FMDNet
驾驶行为分类是社会交通系统和先进驾驶辅助系统的重要组成部分，近年来受到越来越多的关注。准确的驾驶行为分类算法在提高交通安全、节约能源及相关领域发挥着重要作用。本文提出了一种新型驾驶行为分类网络，命名为基于特征-注意力-嵌入的多模态融合驾驶行为分类网络（FMDNet）。FMDNet 融合了八种类型的数据，包括沿 x 轴的加速度、沿 y 轴的加速度、沿 z 轴的加速度、滚动角、俯仰角、偏航角、路边图像和车速，对驾驶行为进行分类。为了有效融合从不同模态提取的特征，同时考虑到它们的不同重要性，我们引入了基于特征注意嵌入的融合模块（FAEF）作为融合策略。这种融合策略通过整合两个特征注意嵌入单元，更深入地研究不同模式之间的相互作用，从而增强了网络捕捉有意义特征的能力。此外，我们还通过广泛的消融实验进一步验证了我们方法的有效性，以研究和分析各种模式数据对驾驶行为分类的影响。我们提出的 FMDNet 在公共 UAH-DriveSet 数据集上实现了最先进的性能，以 99.0% 的惊人 F1 分数证明了其有效性。此外，我们的模型在分心数据集上的鲁棒性也得到了证实，F1 分数高达 99.7%。该模型在 UAH-DriveSet 数据集和分心数据集上的出色表现彰显了其在实际应用中的能力和潜力。https://github.com/Wenzhuo-Liu/FMDNet。
{"title":"FMDNet: Feature-Attention-Embedding-Based Multimodal-Fusion Driving-Behavior-Classification Network","authors":"Wenzhuo Liu;Jianli Lu;Junbin Liao;Yicheng Qiao;Guoying Zhang;Jiayin Zhu;Bozhang Xu;Zhiwei Li","doi":"10.1109/TCSS.2024.3411486","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3411486","url":null,"abstract":"Driving behavior classification is a critical component of social transportation systems and advanced driver assistance systems, and it has gained increasing attention in recent years. Accurate classification algorithms for driving behavior play a significant role in enhancing traffic safety, energy conservation, and related fields. In this article, we propose a novel driving behavior classification network named feature-attention-embedding-based multimodal-fusion driving-behavior-classification network (FMDNet). FMDNet incorporates eight types of data, including acceleration along the x-axis, y-axis, z-axis, roll angle, pitch angle, yaw angle, roadside image, and vehicle speed, to classify driving behavior. To effectively fuse features extracted from different modalities, taking into account their varying importance, we introduce the feature attention embedding-based fusion module (FAEF) as our fusion strategy. This fusion strategy enhances the network's capability to capture meaningful features by incorporating two feature attention embedding units that delve deeper into the interplay between different modes. Furthermore, we provide further validation of the effectiveness of our approach through extensive ablation experiments to investigate and analyze the impact of various modal data on the classification of driving behavior. Our proposed FMDNet achieves state-of-the-art performance on the public UAH-DriveSet dataset, demonstrating its effectiveness with an impressive F1-score of 99.0%. Additionally, the robustness of our model is confirmed on distracted dataset, achieving a remarkable F1-score of 99.7%. The model's outstanding performance on both the UAH-DriveSet dataset and the distracted-dataset highlights its capabilities and potential for real-world applications. \u0000<uri>https://github.com/Wenzhuo-Liu/FMDNet</uri>","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6745-6758"},"PeriodicalIF":4.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368413","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}
User alignment refers to linking a user's accounts across multiple social networks, which is important for studying community discovery, recommendation systems, and other related fields. However, existing methods primarily perform user alignment by correlating user features, neglecting the causal relationship between network topology and user alignment, which makes it challenging to achieve superior user alignment accuracy and generalization capabilities. Therefore, we propose a counterfactual inference-based social network user-alignment algorithm (CINUA). This improves user connection retention due to the non-Euclidean geometric characterization of hyperbolic spaces. The similarity of aligned users is augmented using a hyperbolic graph attention network. User-feature embedding and fusion facilitate user relevance mining. Furthermore, there are causal relationships between network topology structure and user linkages. In various communities, there are some highly similar user pairs, and based on counterfactual inference, the network topology is adjusted to enhance sample diversity. Multilevel factual and counterfactual networks are constructed through iterative diffusion based on user alignment and their linkages. By integrating the users’ causal features in multiple networks, the accuracy and generalization capabilities of the user alignment model are effectively improved. In this article, the experimental results indicate that CINUA achieves a user alignment accuracy improvement of 5.98% and 3.03%, on two datasets respectively compared to the baseline methods on average. CINUA can achieve favorable alignment results even when the training dataset is small. This demonstrates that our algorithm can ensure both user alignment accuracy and generalization capability.
{"title":"A Counterfactual Inference-Based Social Network User-Alignment Algorithm","authors":"Ling Xing;Yuanhao Huang;Qi Zhang;Honghai Wu;Huahong Ma;Xiaohui Zhang","doi":"10.1109/TCSS.2024.3405999","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3405999","url":null,"abstract":"User alignment refers to linking a user's accounts across multiple social networks, which is important for studying community discovery, recommendation systems, and other related fields. However, existing methods primarily perform user alignment by correlating user features, neglecting the causal relationship between network topology and user alignment, which makes it challenging to achieve superior user alignment accuracy and generalization capabilities. Therefore, we propose a counterfactual inference-based social network user-alignment algorithm (CINUA). This improves user connection retention due to the non-Euclidean geometric characterization of hyperbolic spaces. The similarity of aligned users is augmented using a hyperbolic graph attention network. User-feature embedding and fusion facilitate user relevance mining. Furthermore, there are causal relationships between network topology structure and user linkages. In various communities, there are some highly similar user pairs, and based on counterfactual inference, the network topology is adjusted to enhance sample diversity. Multilevel factual and counterfactual networks are constructed through iterative diffusion based on user alignment and their linkages. By integrating the users’ causal features in multiple networks, the accuracy and generalization capabilities of the user alignment model are effectively improved. In this article, the experimental results indicate that CINUA achieves a user alignment accuracy improvement of 5.98% and 3.03%, on two datasets respectively compared to the baseline methods on average. CINUA can achieve favorable alignment results even when the training dataset is small. This demonstrates that our algorithm can ensure both user alignment accuracy and generalization capability.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6939-6952"},"PeriodicalIF":4.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368224","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 : 2024-07-24DOI: 10.1109/TCSS.2024.3385493
Xiaojia Wang;Ziqing Luo;Ying Chen
The disclosure of critical audit matters (CAMs) plays an important part in audit report reform and financial risk warnings. Current CAMs include matters that need to be focused on from the audit after a comprehensive evaluation of the internal control and other enterprise information, combined with the experience of the project manager, which is closely related to subjective factors, such as auditor professionalism and independence. An increase in subjective judgment becomes a breeding ground for audit failure. First, since long short-term memory (LSTM) is often used to process temporal data, MacBERT is often used as a text encoding, so LSTM is used to encode financial information to overcome the influence of subjective factors, and MacBert is used to encode nonfinancial information. The two modes are then separately encoded to form a dual-stream structure that simulates the process of auditors reviewing documents. Second, a transformer is used to perform multimodal interactions on the dual-stream encoding results to simulate the process of auditors integrating important information. Finally, the multimodal interaction results are fed into the fully connected layers and the SoftMax function to achieve cross-modal fusion, which simulates the process of auditors obtaining CAMs. Simulating single-modal coding, multimodal interaction, and cross-modal fusion helps to realize the automatic generation of CAMs. This ensemble model is called the CAMs automatic generation model and is based on LSTM–MacBert dual-stream transfer learning. The experimental results show that the features of financial statements and public disclosure text extracted by the model can effectively screen CAMs and realize the automatic generation of high-level CAMs.
{"title":"Automatic Generation of Critical Audit Matters (CAMs) Using LSTM–MacBert-Based Dual-Stream Transfer Learning","authors":"Xiaojia Wang;Ziqing Luo;Ying Chen","doi":"10.1109/TCSS.2024.3385493","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3385493","url":null,"abstract":"The disclosure of critical audit matters (CAMs) plays an important part in audit report reform and financial risk warnings. Current CAMs include matters that need to be focused on from the audit after a comprehensive evaluation of the internal control and other enterprise information, combined with the experience of the project manager, which is closely related to subjective factors, such as auditor professionalism and independence. An increase in subjective judgment becomes a breeding ground for audit failure. First, since long short-term memory (LSTM) is often used to process temporal data, MacBERT is often used as a text encoding, so LSTM is used to encode financial information to overcome the influence of subjective factors, and MacBert is used to encode nonfinancial information. The two modes are then separately encoded to form a dual-stream structure that simulates the process of auditors reviewing documents. Second, a transformer is used to perform multimodal interactions on the dual-stream encoding results to simulate the process of auditors integrating important information. Finally, the multimodal interaction results are fed into the fully connected layers and the SoftMax function to achieve cross-modal fusion, which simulates the process of auditors obtaining CAMs. Simulating single-modal coding, multimodal interaction, and cross-modal fusion helps to realize the automatic generation of CAMs. This ensemble model is called the CAMs automatic generation model and is based on LSTM–MacBert dual-stream transfer learning. The experimental results show that the features of financial statements and public disclosure text extracted by the model can effectively screen CAMs and realize the automatic generation of high-level CAMs.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6435-6452"},"PeriodicalIF":4.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368552","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 : 2024-07-23DOI: 10.1109/TCSS.2024.3416029
Min Hu;Lei Liu;Xiaohua Wang;Yiming Tang;Jiaoyun Yang;Ning An
Depression is a prevalent and severe mental illness that significantly impacts patients’ physical health and daily life. Recent studies have focused on multimodal depression assessment, aiming to objectively and conveniently evaluate depression using multimodal data. However, existing methods based on audio–visual modalities struggle to capture the dynamic variations in depression clues and cannot fully explore multimodal data over a long time. In addition, they rely heavily on insufficient single-stage multimodal fusion, which limits the accuracy of depression assessment. To address these limitations, we propose a novel parallel multiscale bridge fusion network (PMBFN) for audio–visual depression assessment. PMBFN comprehensively captures subtle multilevel dynamic changes in depression expression through parallel multiscale dynamic convolutions and long short-term memories (LSTMs) and effectively solves the problem of long-term audio–visual sequence information loss by using spatiotemporal attention pooling modules. Furthermore, the multimodal bridge fusion module is proposed in PMBFN to achieve multistage interactive recursive multimodal fusion, enhancing the expressive capacity of multimodal depression-related features to improve the accuracy of assessment. Extensive experiments on the DAIC-WOZ and E-DAIC datasets demonstrate that our method outperforms current state-of-the-art methods and clearly shows our method's effectiveness eventually.
{"title":"Parallel Multiscale Bridge Fusion Network for Audio–Visual Automatic Depression Assessment","authors":"Min Hu;Lei Liu;Xiaohua Wang;Yiming Tang;Jiaoyun Yang;Ning An","doi":"10.1109/TCSS.2024.3416029","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3416029","url":null,"abstract":"Depression is a prevalent and severe mental illness that significantly impacts patients’ physical health and daily life. Recent studies have focused on multimodal depression assessment, aiming to objectively and conveniently evaluate depression using multimodal data. However, existing methods based on audio–visual modalities struggle to capture the dynamic variations in depression clues and cannot fully explore multimodal data over a long time. In addition, they rely heavily on insufficient single-stage multimodal fusion, which limits the accuracy of depression assessment. To address these limitations, we propose a novel parallel multiscale bridge fusion network (PMBFN) for audio–visual depression assessment. PMBFN comprehensively captures subtle multilevel dynamic changes in depression expression through parallel multiscale dynamic convolutions and long short-term memories (LSTMs) and effectively solves the problem of long-term audio–visual sequence information loss by using spatiotemporal attention pooling modules. Furthermore, the multimodal bridge fusion module is proposed in PMBFN to achieve multistage interactive recursive multimodal fusion, enhancing the expressive capacity of multimodal depression-related features to improve the accuracy of assessment. Extensive experiments on the DAIC-WOZ and E-DAIC datasets demonstrate that our method outperforms current state-of-the-art methods and clearly shows our method's effectiveness eventually.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6830-6842"},"PeriodicalIF":4.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368331","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 : 2024-07-23DOI: 10.1109/TCSS.2024.3417275
Shan Liu;Kun Huang;Hao Wen
The arrival of the era of media convergence has promoted the expansion of the film industry and the user market. At present, the data of movies and users show complex attribute characteristics, and the reasonable division of massive data is still an urgent problem to be solved in this field. Motivated by this observation, based on the classical complex network model, this article proposes the definition of object distance and the evolution rules of association network, which can be used to analyze the feature attributes of movies and users. Second, a new clustering model, in which clustering units have different interactive behavior patterns, is designed to realize dynamic clustering in association networks. Finally, we measure the market influence of different types of movies and design a prediction model of potential market user popularity of combined-types according to the related network architecture. Compared with the actual data on Douban, the rationality and accuracy of the model for market prediction of different types of combinations are verified. These findings shed new light on the practical application value of providing guidance for better film marketing production.
{"title":"Research on the Association Network and Combined-Type Prediction of Films and Users Based on Complex Networks","authors":"Shan Liu;Kun Huang;Hao Wen","doi":"10.1109/TCSS.2024.3417275","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3417275","url":null,"abstract":"The arrival of the era of media convergence has promoted the expansion of the film industry and the user market. At present, the data of movies and users show complex attribute characteristics, and the reasonable division of massive data is still an urgent problem to be solved in this field. Motivated by this observation, based on the classical complex network model, this article proposes the definition of object distance and the evolution rules of association network, which can be used to analyze the feature attributes of movies and users. Second, a new clustering model, in which clustering units have different interactive behavior patterns, is designed to realize dynamic clustering in association networks. Finally, we measure the market influence of different types of movies and design a prediction model of potential market user popularity of combined-types according to the related network architecture. Compared with the actual data on Douban, the rationality and accuracy of the model for market prediction of different types of combinations are verified. These findings shed new light on the practical application value of providing guidance for better film marketing production.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6897-6910"},"PeriodicalIF":4.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368416","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 : 2024-07-19DOI: 10.1109/TCSS.2024.3397680
Ge Jiao;Jian Zhang;Zhilin Zhang;Jinglong Wu;Junru Zhu;Qunxi Dong;Aihua Wang;Shengyuan Yu
Many headache patients often report cognitive disturbances, but tactile cognitive data are limited. Applying computing-aided strategies to reveal the association between migraine without aura (MOA) or tension-type headache (TTH) and tactile cognition is one of the research highlights. The aim of this study was to investigate whether MOA or TTH patients had a decline in tactile discrimination by utilizing a tactile angle discrimination tester. A cross-sectional study was performed between 1 January 2021, and 1 January 2022. A total of 301 participants were enrolled, with 107 in control, 90 in MOA, and 104 in TTH groups. A tactile cognition tester was used to objectively examine tactile discrimination in all participants. Tactile angle discrimination thresholds were measured to compare tactile cognitive functions among three groups. There were no statistically significant differences in their demographic characteristics. Compared to the normal control group, the MOA and TTH groups exhibited significantly higher tactile angle discrimination thresholds (showing decline in tactile discrimination), whereas no significant differences were found between the MOA and TTH groups. Differences in tactile angle discrimination thresholds were observed between young (≤ 44 years old) and middle-aged/elderly (≥ 45 years old) participants in the normal control group but not in the MOA and TTH groups. Moreover, the tactile deficits shown in the MOA or TTH groups were evident only in young participants. This study first demonstrated that patients with MOA or TTH, especially those patients younger than 44 years old, had decreased tactile angle discrimination ability, suggesting decline of tactile cognition.
{"title":"Declined Tactile Angle Discrimination in Young Patients With Migraine Without Aura or Tension-Type Headache","authors":"Ge Jiao;Jian Zhang;Zhilin Zhang;Jinglong Wu;Junru Zhu;Qunxi Dong;Aihua Wang;Shengyuan Yu","doi":"10.1109/TCSS.2024.3397680","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3397680","url":null,"abstract":"Many headache patients often report cognitive disturbances, but tactile cognitive data are limited. Applying computing-aided strategies to reveal the association between migraine without aura (MOA) or tension-type headache (TTH) and tactile cognition is one of the research highlights. The aim of this study was to investigate whether MOA or TTH patients had a decline in tactile discrimination by utilizing a tactile angle discrimination tester. A cross-sectional study was performed between 1 January 2021, and 1 January 2022. A total of 301 participants were enrolled, with 107 in control, 90 in MOA, and 104 in TTH groups. A tactile cognition tester was used to objectively examine tactile discrimination in all participants. Tactile angle discrimination thresholds were measured to compare tactile cognitive functions among three groups. There were no statistically significant differences in their demographic characteristics. Compared to the normal control group, the MOA and TTH groups exhibited significantly higher tactile angle discrimination thresholds (showing decline in tactile discrimination), whereas no significant differences were found between the MOA and TTH groups. Differences in tactile angle discrimination thresholds were observed between young (≤ 44 years old) and middle-aged/elderly (≥ 45 years old) participants in the normal control group but not in the MOA and TTH groups. Moreover, the tactile deficits shown in the MOA or TTH groups were evident only in young participants. This study first demonstrated that patients with MOA or TTH, especially those patients younger than 44 years old, had decreased tactile angle discrimination ability, suggesting decline of tactile cognition.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6684-6689"},"PeriodicalIF":4.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368358","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 : 2024-07-18DOI: 10.1109/TCSS.2024.3362393
Manzhi Yang;Jian Zhang;Liyuan Lin;Jinpeng Han;Xiaoguang Chen;Zhen Wang;Fei-Yue Wang
As a challenge of practical significance, fraud detection has great potential for telecom fraud prevention, economic crime prevention, and personal property preservation. Fraudulent activities are always buried in massive regular transactions, making it hard to find them. Traditional rule-based approaches need multiple domain-specific rules and multistep verification, which limits their transferability and efficiency. Machine learning-based methods might ignore the intricate interactions or the temporal relations among accounts. Meanwhile, the lack of sufficient manual labels restricts their performance. To overcome the above limitations, we present a multipattern integrated network (MPIN) in this article to identify fraudulent accounts in transaction networks. Specifically, MPIN considers the interactions among nodes from three perspectives: inflows, outflows, and their mutual influences. To learn the behavior pattern of each node, MPIN first applies an attention mechanism to integrate the short-term information and then learns the long-term patterns by aggregating multiple short-term patterns. Behavior patterns from different perspectives together with long short-term modeling enable the model to precisely distinguish fraudulent accounts from the normal ones. Moreover, contrastive pretraining with temporal consistency and local tightness guarantee is adopted to alleviate the label sparsity issue and provide the model with low-variance performance. We conducted experiments on two real-world transaction networks, and the results showed the effectiveness of MPIN compared with five state-of-the-art baselines.
{"title":"Multipattern Integrated Networks With Contrastive Pretraining for Graph Anomaly Detection","authors":"Manzhi Yang;Jian Zhang;Liyuan Lin;Jinpeng Han;Xiaoguang Chen;Zhen Wang;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3362393","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3362393","url":null,"abstract":"As a challenge of practical significance, fraud detection has great potential for telecom fraud prevention, economic crime prevention, and personal property preservation. Fraudulent activities are always buried in massive regular transactions, making it hard to find them. Traditional rule-based approaches need multiple domain-specific rules and multistep verification, which limits their transferability and efficiency. Machine learning-based methods might ignore the intricate interactions or the temporal relations among accounts. Meanwhile, the lack of sufficient manual labels restricts their performance. To overcome the above limitations, we present a multipattern integrated network (MPIN) in this article to identify fraudulent accounts in transaction networks. Specifically, MPIN considers the interactions among nodes from three perspectives: inflows, outflows, and their mutual influences. To learn the behavior pattern of each node, MPIN first applies an attention mechanism to integrate the short-term information and then learns the long-term patterns by aggregating multiple short-term patterns. Behavior patterns from different perspectives together with long short-term modeling enable the model to precisely distinguish fraudulent accounts from the normal ones. Moreover, contrastive pretraining with temporal consistency and local tightness guarantee is adopted to alleviate the label sparsity issue and provide the model with low-variance performance. We conducted experiments on two real-world transaction networks, and the results showed the effectiveness of MPIN compared with five state-of-the-art baselines.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5619-5630"},"PeriodicalIF":4.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368520","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 : 2024-07-18DOI: 10.1109/TCSS.2024.3385671
Poly Z.H. Sun;Hongwei Jiang;Chengjun Wang;Xinfeng Ru;Xinguo Ming
As an emerging topic in industrial digital transformation, digital business development in the context of platformization has received widespread attention. A large number of industrial companies have established new platform-based systems for digital business development by integrating their original information systems. The unified platform development mode promotes the integration of previously decentralized knowledge. However, the massive expansion of the knowledge system under platformization causes it to be no easier for developers to master or understand the core knowledge (context, concepts, and elements) of the business to be developed. According to the above dilemmas we have observed in the industry, in this article, a domain knowledge network modeling method for the knowledge system under platformization and a GP-based rule generation method for recognizing core business knowledge in the domain knowledge network are proposed for the first time. Our experiment and practical case study verify that our method can recognize a set of core business knowledge from a large knowledge network efficiently, which could help developers understand the business to be developed with a lower cognitive load. We hope the idea of platform-based business development and core business knowledge recognition can provide a reference for those companies that need efficient digital business development.
作为工业数字化转型的新兴课题，平台化背景下的数字化业务发展受到了广泛关注。大量工业企业通过整合原有信息系统，建立了新的平台化系统，以实现数字化业务发展。统一的平台化发展模式促进了原有分散知识的整合。然而，平台化下知识体系的大量扩充导致开发人员难以掌握或理解待开发业务的核心知识（背景、概念和要素）。根据我们在业界观察到的上述困境，本文首次提出了平台化下知识体系的领域知识网络建模方法和基于 GP 的规则生成方法，用于识别领域知识网络中的核心业务知识。我们的实验和实际案例研究验证了我们的方法可以从一个庞大的知识网络中高效地识别出一组核心业务知识，从而帮助开发人员以较低的认知负荷理解待开发的业务。我们希望基于平台的业务开发和核心业务知识识别的理念能为那些需要高效数字业务开发的公司提供参考。
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