IEEE Transactions on Computational Social Systems最新文献_第4页

Quantitative Estimation of Human Height and Weight Using Motion Data From Multiple Smart Devices 使用来自多个智能设备的运动数据定量估计人体身高和体重

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-19 DOI: 10.1109/TCSS.2024.3488694

Jianmin Dong;Zhongmin Cai

{"title":"Quantitative Estimation of Human Height and Weight Using Motion Data From Multiple Smart Devices","authors":"Jianmin Dong;Zhongmin Cai","doi":"10.1109/TCSS.2024.3488694","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3488694","url":null,"abstract":"This article proposes a methodological framework for quantitative estimations of height and weight using behavioral data collected from smart devices. We analyze the connections between height and weight information and behavioral data from three aspects: walking speed, stride length, and step frequency and then extract two kinds of motion features including basic kinematic features and advanced features which use statistical measurements summarizing the dynamics of walking behavior over time and relative intensity of walking speed change, energy cost of one step during walking, and walking frequency, respectively, to describe the motion behavior. After that, we qualitatively and quantitatively analyze the complementarity of different motion data sources and show that more useful information existed in multisource motion data than that of only one motion data source. Based on this, we propose a feature fusion approach named Serial+CC to dealing with the relationships between all motion features from multiple smart devices and user traits of height and weight and then a fused feature set with high discrimination and low complexity is constructed. Finally, five regression models of SVM, BP neural networks, Random Forest, LSTM, and BiLSTM are built with the fused feature set. Empirical evaluations were performed on a dataset collected from 56 subjects. The results demonstrate that motion data collected from smart devices can be used for height and weight quantitative estimation. The results also illustrate our method of using motion data collected from multiple smart devices can achieve better performance than those of only using one smart devices. The best performance is achieved with average errors of 0.95% (1.59 cm) and 4.75% (2.90 kg) for height and weight estimations, respectively, in the scenario of using multiple devices.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"708-724"},"PeriodicalIF":4.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783272","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}

引用次数: 0

How Social Attributes Affect the Movement Process of Subgroups When Facing a Static Obstacle 面对静态障碍时，社会属性如何影响子群体的运动过程

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-19 DOI: 10.1109/TCSS.2024.3493954

Wenhan Wu;Wenfeng Yi;Erhui Wang;Xiaolu Wang;Xiaoping Zheng

{"title":"How Social Attributes Affect the Movement Process of Subgroups When Facing a Static Obstacle","authors":"Wenhan Wu;Wenfeng Yi;Erhui Wang;Xiaolu Wang;Xiaoping Zheng","doi":"10.1109/TCSS.2024.3493954","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3493954","url":null,"abstract":"With the increasing number of studies on crowd behavior analysis, there has been a widespread interest in treating subgroups as an important topic. A previous experimental study has investigated the decision-making and motion behavior of subgroups when facing a static obstacle during movement. However, it is hard to quantify social attributes (e.g., interpersonal relationships and sense of identity) and little is known about how they affect the movement process of subgroups. Here, we propose a vision-driven model to solve this problem, in which two key model parameters are defined to control the spatial cohesion and attraction intensity, respectively. Numerical simulations demonstrate that the optimal regions of model parameters vary depending on different conditions of the three control variables (obstacle width, time pressure, and subgroup size). The spatial cohesion and attraction intensity barely change the movement process of subgroups in the maintaining state but significantly affect it in the splitting-merging state. This model can reproduce the herding effect of subgroup members in the merging process, which is affected to varying degrees by the modulation of model parameters. Overall, this work contributes to the simulation of subgroup behaviors from a sociopsychological perspective.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"658-670"},"PeriodicalIF":4.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783310","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}

引用次数: 0

Hybrid Learning Module-Based Transformer for Multitrack Music Generation With Music Theory 基于混合学习模块的多轨音乐生成转换器

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-19 DOI: 10.1109/TCSS.2024.3486604

Yun Tie;Xin Guo;Donghui Zhang;Jiessie Tie;Lin Qi;Yuhang Lu

{"title":"Hybrid Learning Module-Based Transformer for Multitrack Music Generation With Music Theory","authors":"Yun Tie;Xin Guo;Donghui Zhang;Jiessie Tie;Lin Qi;Yuhang Lu","doi":"10.1109/TCSS.2024.3486604","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3486604","url":null,"abstract":"In recent years, multitrack music generation has garnered significant attention in both academic and industrial spheres for its versatile utilization of various instruments in collaborative settings. The primary challenge lies in achieving a harmonious balance within individual tracks and fostering effective collaboration across multiple tracks. To address this issue, this article introduces a pioneering hybrid learning encoder architecture. Each music track's encoder is implemented as an independent transformer architecture, preserving self-attention mechanisms within a single track and interattention mechanisms between different tracks. The resulting features are then seamlessly integrated into the decoder through concatenation. Of particular significance, previous multitrack music generation efforts have predominantly operated under unconditional settings, yielding music that lacks practical value due to noncompliance with established music theory principles. Recognizing this limitation, the article proposes a novel approach to multitrack music generation guided by music theory rules. Employing reinforcement learning techniques, the decoder-generated music serves as the initial state. Positive feedback is provided when the generated music adheres to music theory rules; conversely, negative feedback is applied to compel the multitrack music to align with widely accepted music theory principles. Finally, comprehensive simulation validation is conducted on both the publicly available LMD dataset and the self-constructed MUT dataset. The plethora of experimental results overwhelmingly corroborates the efficacy of the proposed methodology.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"862-872"},"PeriodicalIF":4.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769381","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}

引用次数: 0

Privacy Utility Tradeoff Between PETs: Differential Privacy and Synthetic Data pet之间的隐私效用权衡：差异隐私和合成数据

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-14 DOI: 10.1109/TCSS.2024.3479317

Qaiser Razi;Sujoya Datta;Vikas Hassija;GSS Chalapathi;Biplab Sikdar

{"title":"Privacy Utility Tradeoff Between PETs: Differential Privacy and Synthetic Data","authors":"Qaiser Razi;Sujoya Datta;Vikas Hassija;GSS Chalapathi;Biplab Sikdar","doi":"10.1109/TCSS.2024.3479317","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3479317","url":null,"abstract":"Data privacy is a critical concern in the digital age. This problem has compounded with the evolution and increased adoption of machine learning (ML), which has necessitated balancing the security of sensitive information with model utility. Traditional data privacy techniques, such as differential privacy and anonymization, focus on protecting data at rest and in transit but often fail to maintain high utility for machine learning models due to their impact on data accuracy. In this article, we explore the use of synthetic data as a privacy-preserving method that can effectively balance data privacy and utility. Synthetic data is generated to replicate the statistical properties of the original dataset while obscuring identifying details, offering enhanced privacy guarantees. We evaluate the performance of synthetic data against differentially private and anonymized data in terms of prediction accuracy across various settings—different learning rates, network architectures, and datasets from various domains. Our findings demonstrate that synthetic data maintains higher utility (prediction accuracy) than differentially private and anonymized data. The study underscores the potential of synthetic data as a robust privacy-enhancing technology (PET) capable of preserving both privacy and data utility in machine learning environments.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"473-484"},"PeriodicalIF":4.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783232","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}

引用次数: 0

Multiknowledge and LLM-Inspired Heterogeneous Graph Neural Network for Fake News Detection 多知识和法学硕士启发的异构图神经网络假新闻检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-13 DOI: 10.1109/TCSS.2024.3488191

Bingbing Xie;Xiaoxiao Ma;Xue Shan;Amin Beheshti;Jian Yang;Hao Fan;Jia Wu

{"title":"Multiknowledge and LLM-Inspired Heterogeneous Graph Neural Network for Fake News Detection","authors":"Bingbing Xie;Xiaoxiao Ma;Xue Shan;Amin Beheshti;Jian Yang;Hao Fan;Jia Wu","doi":"10.1109/TCSS.2024.3488191","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3488191","url":null,"abstract":"The widespread diffusion of fake news has become a critical problem on dynamic social media worldwide, which requires effective strategies for fake news detection to alleviate its hazardous consequences for society. However, most recent efforts only focus on the features of news content and social context without realizing the benefits of large language models (LLMs) and multiple knowledge graphs (KGs), thus failing to improve detection capabilities further. To tackle this issue, we present a multiknowledge and LLM-inspired heterogeneous graph neural network for fake news detection (MiLk-FD), by combining KGs, LLMs, and graph neural networks (GNNs). Specifically, we first model news content as a heterogeneous graph (HG) containing news, entity, and topic nodes and then fuse the knowledge from three KGs to augment the factual basis of news articles. Meanwhile, we leverage TransE to initialize the knowledge features and employ LLaMa2-7B to obtain the initial feature vectors of news articles. After that, we utilize the devised HG transformer to learn news embeddings with specific feature distribution in high-dimensional spaces by aggregating neighborhood information according to metapaths. Finally, a classifier based on multilayer perceptron (MLP) is trained to predict each news article as fake or true. Through experiments, we demonstrate that our proposed framework surpasses ten baselines according to accuracy, precision, F1-score, recall, and ROC in four public real-world benchmarks (i.e., COVID-19, FakeNewsNet, PAN2020, Liar).","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"682-694"},"PeriodicalIF":4.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783320","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}

引用次数: 0

Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data 跨数据粒度链接：拟合多元Hawkes过程到部分间隔截尾数据

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-12 DOI: 10.1109/TCSS.2024.3486117

Pio Calderon;Alexander Soen;Marian-Andrei Rizoiu

{"title":"Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data","authors":"Pio Calderon;Alexander Soen;Marian-Andrei Rizoiu","doi":"10.1109/TCSS.2024.3486117","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3486117","url":null,"abstract":"The multivariate Hawkes process (MHP) is widely used for analyzing data streams that interact with each other, where events generate new events within their own dimension (via self-excitation) or across different dimensions (via cross excitation). However, in certain applications, the timestamps of individual events in some dimensions are unobservable, and only event counts within intervals are known, referred to as partially interval-censored data. The MHP is unsuitable for handling such data since its estimation requires event timestamps. In this study, we introduce the partially censored multivariate Hawkes process (PCMHP), a novel point process that shares parameter equivalence with the MHP and can effectively model both timestamped and interval-censored data. We demonstrate the capabilities of the PCMHP using synthetic and real-world datasets. First, we illustrate that the PCMHP can approximate MHP parameters and recover the spectral radius using synthetic event histories. Next, we assess the performance of the PCMHP in predicting YouTube popularity and find that the PCMHP outperforms the popularity estimation algorithm Hawkes intensity process (HIP) <xref>[1]</xref>. Comparing with the fully interval-censored HIP, we show that the PCMHP improves prediction performance by accounting for point process dimensions, particularly when there exist significant cross-dimension interactions. Last, we leverage the PCMHP to gain qualitative insights from a dataset comprising daily COVID-19 case counts from multiple countries and COVID-19-related news articles. By clustering the PCMHP-modeled countries, we unveil hidden interaction patterns between occurrences of COVID-19 cases and news reporting.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"25-37"},"PeriodicalIF":4.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361488","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}

引用次数: 0

On Evolutionary Analysis of Customer Purchasing Behavior by the Supervision of E-Commerce Platforms 基于电子商务平台监管的顾客购买行为演化分析

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3485959

Xuwang Liu;Biying Zhou;Rong Du;Wei Qi;Zhiwu Li;Junwei Wang

{"title":"On Evolutionary Analysis of Customer Purchasing Behavior by the Supervision of E-Commerce Platforms","authors":"Xuwang Liu;Biying Zhou;Rong Du;Wei Qi;Zhiwu Li;Junwei Wang","doi":"10.1109/TCSS.2024.3485959","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3485959","url":null,"abstract":"As Internet technology undergoes rapid development and widespread adoption, e-commerce emerges as a pivotal component of the platform economy, permeating various facets of daily life. However, due to the influence of time, space, and other factors, the problem of integrity becomes severe in the real trading environment. As the platforms, sellers, and consumers are the main participants and their decision-making is restricted by historical experiences and contextual conditions, they exhibit constrained rationality. Utilizing evolutionary game theory, the study constructs a tripartite game model that analyses the influence of relevant parameters on the behavior of the participants. To deal with the behaviors of the participants, we built a simulation system on MATLAB to demonstrate the effects of beginning circumstances and associated parameter adjustments on the evolution outcomes for participants. Through theoretical analysis and numerical simulation analysis, we identify that the e-commerce platforms should standardize the good faith behavior of sellers by increasing the punishment, which can reduce the malicious return behavior of consumers. Sellers can mitigate the probability of fraud by improving production technology. Consumers can improve their learning to avoid returning products. This research provides a theoretical framework and decision support for e-commerce platforms, and it also promotes the long-term growth of online transactions.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"38-51"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361489","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}

引用次数: 0

Review of Advancements in Depression Detection Using Social Media Data 基于社交媒体数据的抑郁症检测进展综述

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3448624

Sumit Dalal;Sarika Jain;Mayank Dave

{"title":"Review of Advancements in Depression Detection Using Social Media Data","authors":"Sumit Dalal;Sarika Jain;Mayank Dave","doi":"10.1109/TCSS.2024.3448624","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3448624","url":null,"abstract":"A large population embraced social media to share thoughts, emotions, and daily experiences through text, images, audio, or video posts. This user-generated content (UGC) serves various purposes, including user profiling, sentiment analysis, and disease detection or tracking. Notably, researchers recognized the potential of UGC for assessing mental health due to its unobtrusive and real-time monitoring capabilities. Recent reviews on depression identification from textual UGC using AI models covered tools and techniques but overlooked critical components such as datasets, lexicons, features, and subtasks, which are essential for understanding the progress and tasks undertaken. This survey adopts a systematic approach and formulates five research questions to examine the relevant literature concerning these elements. Additionally, it organizes machine learning and deep learning (ML/DL) training features from textual UGC in a hierarchical manner and maps the literature on depression detection into various subtasks. The review highlights that despite the prevalence studies, datasets are limited in both quantity and size, with many relying on less reliable ground truth collection methods such as self-reported diagnosis statements (SRDS). Furthermore, the review identifies an overemphasis on certain textual features, such as n-grams and affective elements, while others, such as life events, egocentric graphs, and intervention/coping style, remain largely unexplored. It is crucial for practical AI depression detection systems to develop expertise in tasks such as severity, symptom detection, and explainable/interpretable depression analysis to instill confidence and trust among users.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"77-100"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361493","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}

引用次数: 0

A Multigranularity Learning Path Recommendation Framework Based on Knowledge Graph and Improved Ant Colony Optimization Algorithm for E-Learning 基于知识图和改进蚁群优化算法的多粒度学习路径推荐框架

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3488373

Yaqian Zheng;Deliang Wang;Yaping Xu;Yanyan Li

{"title":"A Multigranularity Learning Path Recommendation Framework Based on Knowledge Graph and Improved Ant Colony Optimization Algorithm for E-Learning","authors":"Yaqian Zheng;Deliang Wang;Yaping Xu;Yanyan Li","doi":"10.1109/TCSS.2024.3488373","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3488373","url":null,"abstract":"In e-learning, extracting suitable learning objects (LOs) from a vast resource pool and organizing them into high-quality learning paths is crucial for helping e-learners achieve their goals. Numerous approaches have been proposed to recommend optimal learning paths for e-learners. However, it is essential to emphasize that e-learning systems typically consist of a wide range of LOs with varying levels of granularity, ranging from fine-grained to coarse-grained. Unfortunately, current research has not adequately considered the underlying granularity structure of LOs when optimizing learning paths. Existing methods primarily focus on organizing LOs at a single granularity level, limiting their applicability in real-world e-learning systems. To address the limitations, we propose a multigranularity learning path recommendation (MGLPR) framework that aims to flexibly and effectively integrate the diverse granularity levels of LOs into high-quality learning paths. In this framework, a two-layer [knowledge point (KP) and LO layers] model is developed to formulate the MGLPR problem as a constrained optimization problem and an improved ant colony optimization algorithm (IACO) is introduced to solve it to identify optimal learning paths for e-learners. To evaluate the effectiveness of the proposed IACO, we conducted extensive computational experiments using 30 simulation datasets with varying problem sizes and complexities. The results demonstrate that our proposed IACO achieves superior performance and robustness compared with other competitors. Additionally, an empirical study was conducted to investigate the efficacy of the proposed approach in an authentic learning context, with results indicating that the proposed method outperforms the traditional self-organized ones.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"586-607"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783381","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}

引用次数: 0

Ugan: Uncertainty-Guided Graph Augmentation Network for EEG Emotion Recognition 乌干达：用于脑电图情绪识别的不确定性引导图增强网络

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3488201

Bianna Chen;C. L. Philip Chen;Tong Zhang

{"title":"Ugan: Uncertainty-Guided Graph Augmentation Network for EEG Emotion Recognition","authors":"Bianna Chen;C. L. Philip Chen;Tong Zhang","doi":"10.1109/TCSS.2024.3488201","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3488201","url":null,"abstract":"The underlying time-variant and subject-specific brain dynamics lead to statistical uncertainty in electroencephalogram (EEG) representations and connectivities under diverse individual biases. Current works primarily augment statisticallike EEG data based on deterministic modes without comprehensively considering uncertain statistical discrepancies in representations and connectivities. This results in insufficient domain diversity to cover more domain variations for a generalized model independent of individuals. This article proposes an uncertainty-guided graph augmentation network (Ugan) to generalize EEG emotion recognition across subjects by comprehensively mimicking and constraining the uncertain statistical shifts across individuals. Specifically, an uncertainty-guided graph augmentation module is employed to augment both connectivities and features of EEG graph by manipulating domain statistical characteristics. With the original and augmented EEG graph covering diverse domain variations, the model can mimic the uncertain domain shifts to achieve better generalizability against potential subject variability. To extract discriminative characteristics and preserve emotional semantics after augmentation, a graph coteaching learning module is designed to facilitate coteaching knowledge learning between the original and augmented views. Moreover, a coteaching regularization module is developed to constrain semantic domain invariance and consistency, thereby rendering the model invariant to uncertain statistical shifts. Extensive experiments on three public EEG emotion datasets, i.e., Shanghai Jiao Tong University emotion EEG dataset (SEED), SEED-IV, and SEED-V, validate the superior generalizability of Ugan compared to the state-of-the-art methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"695-707"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783311","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}

引用次数: 0