IEEE Transactions on Human-Machine Systems最新文献_第8页

Hybrid Workforce: Competences for Effective Collaboration Between Employees and Software Robots 混合劳动力：员工和软件机器人之间有效协作的能力

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-04-01 Epub Date: 2026-03-03 DOI: 10.1109/THMS.2026.3663559

Damian Kedziora;Marta Juszczyk;Agnieszka Leśniewska

引用次数: 0

A Lightweight Privacy-Preserving Face Authentication Scheme Based on Local Sensitive Hashing 一种基于局部敏感哈希的轻量级隐私保护人脸认证方案

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-04-01 Epub Date: 2026-02-18 DOI: 10.1109/THMS.2026.3655555

Hui Zhu;Ling Xiong;Dexing He

{"title":"A Lightweight Privacy-Preserving Face Authentication Scheme Based on Local Sensitive Hashing","authors":"Hui Zhu;Ling Xiong;Dexing He","doi":"10.1109/THMS.2026.3655555","DOIUrl":"https://doi.org/10.1109/THMS.2026.3655555","url":null,"abstract":"In the last few years, face recognition technology gains a huge development because of the popularity of intelligent devices and the development of computer vision. However, due to the fact that face images often contain important sensitive information, the leakage of sensitive information will have a serious impact on both data owners and users. Recently, a series of excellent privacy-preserving face recognition schemes has been proposed. However, existing face recognition schemes focus on the completion of recognition, but ignore the security issues during the recognition process, and the recognition results need to be transmitted through a secure additional channel. Although some schemes have addressed several security issues in the recognition process and protected face information through authentication, the additional identification or random numbers are needed to assist authentication. To address these issues, we propose a lightweight privacy-preserving face authentication scheme based on local sensitive hashing. First, in order to protect face information, the proposed scheme encrypts the face information and stores the encrypted face on the server. Second, the local sensitive hashing function can be used for fast retrieval of face images, which can help servers quickly match encrypted eigenface in the ciphertext domain. Third, the lightweight authentication scheme ensures that users only need to use face information to access the terminal without any additional authentication credentials. Meanwhile it can also achieve user anonymity, prevent internal server attacks, and so on. The comparison with related schemes indicates that the proposed one has fewer computation overhead and communication overhead.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 2","pages":"400-409"},"PeriodicalIF":4.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

CLEAR: Channel-Filtered Learning With Ensemble Attention and Robustness for Cognitive Domain Generalization 基于集成注意和鲁棒性的通道过滤学习在认知领域泛化中的应用

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-04-01 Epub Date: 2026-02-03 DOI: 10.1109/THMS.2026.3650903

Fengtao Xiang;Tuoxin Li;Junhai Chen;Wanpeng Zhang;Xin Xu

{"title":"CLEAR: Channel-Filtered Learning With Ensemble Attention and Robustness for Cognitive Domain Generalization","authors":"Fengtao Xiang;Tuoxin Li;Junhai Chen;Wanpeng Zhang;Xin Xu","doi":"10.1109/THMS.2026.3650903","DOIUrl":"https://doi.org/10.1109/THMS.2026.3650903","url":null,"abstract":"In dynamic and cognitively demanding human–machine environments, achieving reliable system performance across diverse domains is critical for ensuring ergonomic interaction and cognitive efficiency. This article introduces <italic>Channel-filtered Learning with Ensemble Attention and Robustness</i> (CLEAR), a domain generalization framework that simultaneously suppresses domain-specific feature interference and enhances domain-invariant representation learning. CLEAR integrates ensemble multiscale spatial attention with a progressive channel filtering mechanism to selectively eliminate features most indicative of domain identity. Furthermore, a momentum-based inference consistency loss is introduced to promote semantic stability by aligning class prototypes across domains over time. These components operate in concert to enable consistent task-relevant feature extraction and improved generalization under unseen distributional conditions. Extensive evaluations on multiple public benchmarks demonstrate that CLEAR achieves state-of-the-art performance in domain generalization while enhancing robustness and cognitive reliability in real-world human–machine systems. The framework contributes to the development of perceptually ergonomic AI models capable of maintaining accuracy across varying operational contexts.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 2","pages":"237-246"},"PeriodicalIF":4.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Predicting Situation Awareness From Physiological Signals 从生理信号预测态势感知

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-04-01 Epub Date: 2026-02-26 DOI: 10.1109/THMS.2025.3647796

Kieran J. Smith;Tristan C. Endsley;Torin K. Clark

{"title":"Predicting Situation Awareness From Physiological Signals","authors":"Kieran J. Smith;Tristan C. Endsley;Torin K. Clark","doi":"10.1109/THMS.2025.3647796","DOIUrl":"https://doi.org/10.1109/THMS.2025.3647796","url":null,"abstract":"Situation awareness (SA)—comprising the ability to 1) perceive critical elements in the environment, 2) comprehend their meanings, and 3) project their future states—is critical for human operator performance. Due to the disruptive nature of gold-standard SA measures, researchers have sought physiological indicators to provide real-time information about SA. We extend prior work by using a multimodal suite of neurophysiological, psychophysiological, and behavioral signals, predicting all three levels of SA along a continuum, and predicting a comprehensive measure of SA in a complex multitasking simulation. We present a lab study in which 31 participants controlled an aircraft simulator task battery while wearing physiological sensors and responding to SA “freeze-probe” assessments. We demonstrate the validity of task and assessment for measuring SA. Multimodal physiological models predict SA with greater predictive performance (<italic>Q</i><sup>2</sup> for levels 1–3 and total, respectively: 0.14, 0.00, 0.26, and 0.36) than models built with shuffled labels, demonstrating that multimodal physiological signals provide useful, albeit imperfect, information in predicting all SA levels. Level 3 SA (projection) was best predicted, and level 2 SA (comprehension) was the most challenging to predict. Ablation analysis and single-sensor models found EEG and eye-tracking signals to be particularly useful to predictions of level 3 and total SA. A reduced sensor fusion model showed that predictive performance can be maintained with a subset of sensors. This rigorous cross-validation assessment of predictive performance demonstrates the utility of multimodal physiological signals for inferring complex, holistic, objective measures of SA at all levels, nondisruptively, and along a continuum.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 2","pages":"213-223"},"PeriodicalIF":4.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Novel Hybrid Feature Selection Technique for Epileptic Seizure Classification 一种新的混合特征选择技术用于癫痫发作分类

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-04-01 Epub Date: 2026-02-18 DOI: 10.1109/THMS.2026.3655407

Sunday Timothy Aboyeji;Xin Wang;Yan Chen;Ijaz Ahmad;Lin Li;Zhenzhen Liu;Chen Yao;Guanglin Li;Yu Zhang;Guoru Zhao;Shixiong Chen

{"title":"A Novel Hybrid Feature Selection Technique for Epileptic Seizure Classification","authors":"Sunday Timothy Aboyeji;Xin Wang;Yan Chen;Ijaz Ahmad;Lin Li;Zhenzhen Liu;Chen Yao;Guanglin Li;Yu Zhang;Guoru Zhao;Shixiong Chen","doi":"10.1109/THMS.2026.3655407","DOIUrl":"https://doi.org/10.1109/THMS.2026.3655407","url":null,"abstract":"Patients with epilepsy experience significant neurological impairments due to abnormal electrical activities in their brains, impacting daily activities. Computer-aided diagnosis systems can assist neurologists in managing patients with this disease. Moreover, feature extraction has been proposed to be an integral part of the classification process. However, training machine learning (ML) models with multiple features from multichannel electroencephalogram (EEG) signals is computationally demanding. Feature selection (FS) minimizes the system’s computational cost by identifying deterministic features. However, individual FS techniques often show unstable performance across EEG datasets. Therefore, this study proposed a hybrid FS technique with a Random Forest Bayesian optimization classifier to dynamically select relevant features that improve ML performance across EEG datasets. Initially, the Bonn, CHB-MIT, and TUH EEG datasets were segmented into 1-s epochs with an overlap of 0.75. Relevant features are obtained through a hybrid of ANOVA, correlation, and graph-based FS techniques. Mean decrease impurity and meta-model were used to evaluate feature importance and ranking. RF-BO was used to predict the outcome of each FS technique for relevant cases of the Bonn, CHB-MIT, and TUH datasets after subject-level splitting with holdout. An average accuracy of 98.93% and 95.91% was obtained for the Bonn dataset’s binary and ternary classification cases, respectively. In addition, accuracies of 96.51% and 91.71%, respectively, obtained for the binary case of CHB-MIT and the multiclass case of TUH datasets with 60 features are better than those in previous studies, making the proposed model effective for seizure classifications.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 2","pages":"331-342"},"PeriodicalIF":4.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

GMCDA: Graph-Based Multisource Conditional Distribution Domain Adaptation Network for EEG Recognition of Emotions 基于图的多源条件分布域自适应网络的情绪脑电识别

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-04-01 Epub Date: 2026-01-30 DOI: 10.1109/THMS.2026.3654245

Qingshan She;Senda Gao;Chenqi Zhang;Feng Fang;Jian Wang;Anton Zhilenkov;Yingchun Zhang

{"title":"GMCDA: Graph-Based Multisource Conditional Distribution Domain Adaptation Network for EEG Recognition of Emotions","authors":"Qingshan She;Senda Gao;Chenqi Zhang;Feng Fang;Jian Wang;Anton Zhilenkov;Yingchun Zhang","doi":"10.1109/THMS.2026.3654245","DOIUrl":"https://doi.org/10.1109/THMS.2026.3654245","url":null,"abstract":"Electroencephalography (EEG) emotion recognition is critical in human-computer interface applications. EEG is a prevalent tool for discerning various emotional states. However, EEG is confronted with the challenges, including nonstationarity, small amplitude, low signal-to-noise ratio, and significant inter-subject variability. Furthermore, existing methods primarily focus on frequency domain features of EEG signal, overlooking the interchannel relationships. To solve these problems, we introduce a novel approach for emotion recognition using a graph-based conditional distribution-aligned domain adaptation (DA) network. This approach harmonizes both domain-invariant and domain-specific features. With the premise that diverse EEG data share consistent underlying features, we employ dynamic graph convolution to extract the domain-invariant features. Subsequently, we build separate branches for distinct source domains to harness domain-specific features, execute conditional distribution alignment, and eventually determine the scores based on distribution distance. Specifically, the contribution of each source domain is weighted according to its distribution similarity with the target domain, thereby assigning greater importance to more relevant sources. The final outcome is obtained via these weighted scores. The final outcome is ascertained via the associated weighted scores. Based on the leave-one-out cross-validation, we tested our model on the open-source SEED and SEED-IV datasets. The mean accuracy rates were 90.33% and 62.83% in the cross-subject situation, as well as 93.26% and 67.06% in the cross-session situation. Compared with multiple state-of-the-art DA algorithms, our proposed algorithm garners better classification results.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 2","pages":"356-365"},"PeriodicalIF":4.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Call for Papers: IEEE Transactions on Human-Machine Systems 论文征集：IEEE人机系统汇刊

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-01-26 DOI: 10.1109/THMS.2026.3656624

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-01-26 DOI: 10.1109/THMS.2026.3651175

引用次数: 0

IEEE Transactions on Human-Machine Systems Information for Authors IEEE人机系统信息汇刊

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-01-26 DOI: 10.1109/THMS.2026.3651173

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2026-01-26 DOI: 10.1109/THMS.2026.3651171

引用次数: 0