IEEE Transactions on Computational Social Systems最新文献

{"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}

{"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}

{"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}

{"title":"Fair Link Prediction With Overlapping Groups","authors":"Manjish Pal;Sandipan Sikdar;Niloy Ganguly","doi":"10.1109/TCSS.2024.3479702","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3479702","url":null,"abstract":"In this article, we introduce FairLPG, a framework for ensuring fairness for the task of link prediction in graphs with <italic>multiple</i> sensitive attributes. In the context of link prediction in graphs, the fairness notions of demographic parity and equalized odds try to ensure equal <italic>average linking probability</i> and <italic>true positive rates</i> across different demographic groups consisting of various node pairs. Existing methods for achieving fairness in link prediction only consider a single sensitive attribute, which makes them unsuited for applications where multiple sensitive attributes need to be accounted for. Additionally, considering multiple sensitive attributes in the context of link prediction leads to <italic>overlapping</i> and <italic>intersectional</i> groups, which further complicates designing such a framework. The proposed framework FairLPG assumes that the link prediction model generates a prediction score for each node pair to form an edge, and formulates a convex optimization problem that minimizes the squared Euclidean distance between the original prediction scores and transformed scores, subject to the fairness constraints. The transformed scores are then utilized for fair link prediction. To the best of our knowledge, this work is the first to handle the case of intersectional sensitive groups in the graph setting. To demonstrate its effectiveness, we deploy FairLPG on several real-world datasets and graph neural network based link prediction models. It either outperforms or performs competitively with existing methods both in terms of fairness and prediction accuracy across all the datasets and link prediction models at the same time being computationally more efficient.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"998-1012"},"PeriodicalIF":4.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178919","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}

{"title":"Hyper-Relational Knowledge Enhanced Network for Hypertension Medication Recommendation","authors":"Ke Zhang;Zhichang Zhang;Wei Wang;Yali Liang;Xia Wang","doi":"10.1109/TCSS.2024.3489973","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3489973","url":null,"abstract":"Hypertension is a prevalent cardiovascular disease that requires timely and precise medication management. However, previous medication recommendation studies have largely relied on analyzing electronic health records (EHR), overlooking the specialized knowledge required for hypertension treatment. Moreover, the hypertension-related knowledge contained in existing general medical knowledge graphs is overly simplistic, and the binary relation representations they employ fail to accurately represent the complex treatment logic, thus falling short of meeting medication recommendation needs. To tackle these concerns, we present a novel hyper-relational knowledge-enhanced hypertension medication recommendation model (HKRec). HKRec incorporates both professional treatment knowledge and individual characteristics of patients to provide personalized medication treatment plans. Specifically, a hyper-relational knowledge graph designed for hypertension medication treatment is first constructed. Next, we design a knowledge-driven encoder to capture the representations of hyper-relational knowledge within the graph, and develop an EHR-driven encoder to extract patient-specific features from the EHRs. By integrating medical knowledge entities and patient information, a recurrent mechanism is introduced to model the development process of patients’ hypertension conditions, thereby enabling more effective medication recommendations. Results from experiments on real-world MIMIC-III and MIMIC-IV datasets demonstrate that the HKRec model outperforms several competitive baseline methods. The approach enables physicians to create more accurate and personalized medication plans, leading to better management of hypertension and improved patient outcomes. Our code is publicly accessible at <uri>https://github.com/zk0814/HKRec</uri>.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"984-997"},"PeriodicalIF":4.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178882","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}

{"title":"CDHFL-HA: Collaborative Dynamic Hierarchical Federated Learning With Hypernetwork Aggregation for Sentimental Analysis","authors":"Zhiguo Qu;Jian Ding;Bo Liu;Le Sun;Shahid Mumtaz","doi":"10.1109/TCSS.2024.3487613","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3487613","url":null,"abstract":"In recent years, more and more scholars have begun to focus on sentiment analysis on social media. Current sentiment analysis collects all relevant data, including public thoughts, opinions, and feelings, from a variety of open sources. In addition, it automatically predicts different aspects of outcomes or trends based on information collected globally in real time. This research area explores how to extract sentiment information from different modalities (e.g., text, images, and audio). However, the currently existing techniques face several challenges. It is difficult to achieve effective interaction with completely heterogeneous data, and these techniques cannot adequately guarantee data security during data interaction, which is particularly important when dealing with sensitive information. Therefore, this article introduces existing methods for protecting data privacy. Based on this foundation, we propose a novel algorithm called collaborative dynamic hierarchical federated learning with hypernetwork aggregation (CDHFL-HA), which is suitable for sentimental analysis. CDHFL-HA ensures that the data remain local to each participant while leveraging the data similarity between participants on the server and processing interference data on the participant to enhance the accuracy of the current sentimental analysis. In addition, an essential aspect considered in the proposed algorithm is explainability. Understanding the decisions and predictions made by sentiment analysis models is crucial for gaining trust and acceptance in real-world applications. CDHFL-HA incorporates explainability features, providing insights into the decision-making process, thus enhancing the interpretability of sentiment analysis results. Numerous experimental results show that the algorithm outperforms existing algorithms in complex scenarios, with a minimum accuracy of 0.6007 and a maximum of 0.9962. In addition, it can be seen from the experimental results in this article, that the communication parameters in the experiments are similar to those of other federated learning, while the number of training rounds is improved by up to 50% (i.e., 20 rounds faster) relative to other algorithms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1339-1350"},"PeriodicalIF":4.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186008","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}

{"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}

{"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}

{"title":"REDGCN: Rating-Oriented Explicit Disentangling Graph Convolution Network for Review-Aware Recommendation","authors":"Sheng Sang;Feng Xue;Hao Guo;Kang Liu;Shuaiyang Li;Richang Hong","doi":"10.1109/TCSS.2024.3486935","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3486935","url":null,"abstract":"Rating prediction is a challenging task in review-aware recommendation. Although current methods effectively combine collaborative signals with review data, they fail to differentiate user preferences across various ratings and overlook the independence between these ratings. In this article, we emphasize the importance of independence modeling among representations for different rating levels. To this end, we propose a rating-oriented explicit disentangling graph convolution network for review-aware recommendation, short for REDGCN. Specifically, we introduce a rating-oriented disentangled representation learning that segments representations and rating graph based on ratings. It also employs an explicit graph learning approach to ensure the independence of disentangled representations during information propagation, which mitigates noise from review features. Furthermore, we define and model one kind of cross-rating correlation, based on the characteristics of user rating behavior. By leveraging this approach, we introduce a cross-rating constraint as an additional task to further enhance the independence among disentangled representations and improve the stability of model training. We conduct extensive experiments on six public datasets to prove the effectiveness of REDGCN. The complete data and codes of REDGCN are available at <uri>https://github.com/hfutmars/REDGCN</uri>.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1243-1255"},"PeriodicalIF":4.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186006","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}

{"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}