Pub Date : 2025-01-07DOI: 10.1109/THMS.2024.3514742
Roland Oruche;Xiyao Cheng;Zian Zeng;Audrey Vazzana;MD Ashraful Goni;Bruce Wang Shibo;Sai Keerthana Goruganthu;Kerk Kee;Prasad Calyam
The advent of machine learning (ML) has led to the widespread adoption of developing task-oriented dialog systems for scientific applications (e.g., science gateways) where voluminous information sources are retrieved and curated for domain users. Yet, there still exists a challenge in designing chatbot dialog systems that achieve widespread diffusion among scientific communities. In this article, we propose a novel Vidura advisor design framework (VADF) to develop dialog system designs for information retrieval (IR) and question-answering (QA) tasks, while enabling the quantification of system utility based on human performance in diverse application environments. We adopt a socio-technical approach in our framework for designing dialog systems by utilizing domain expert feedback, which features a sparse retriever for enabling accurate responses in QA settings using linear interpolation smoothing. We apply our VADF for an exemplar science gateway, viz. KnowCOVID-19, to conduct experiments that demonstrate the utility of dialog systems based on IR and QA performance, application utility, and perceived adoption. Experimental results show our VADF approach significantly improves IR performance against retriever baselines (up to 5% increase) and QA performance against large language models (LLMs) such as ChatGPT (up to 43% increase) on scientific literature datasets. In addition, through a usability survey, we observe that measuring application utility and human performance when applying VADF to KnowCOVID-19 translates to an increase in perceived community adoption.
{"title":"Chatbot Dialog Design for Improved Human Performance in Domain Knowledge Discovery","authors":"Roland Oruche;Xiyao Cheng;Zian Zeng;Audrey Vazzana;MD Ashraful Goni;Bruce Wang Shibo;Sai Keerthana Goruganthu;Kerk Kee;Prasad Calyam","doi":"10.1109/THMS.2024.3514742","DOIUrl":"https://doi.org/10.1109/THMS.2024.3514742","url":null,"abstract":"The advent of machine learning (ML) has led to the widespread adoption of developing task-oriented dialog systems for scientific applications (e.g., science gateways) where voluminous information sources are retrieved and curated for domain users. Yet, there still exists a challenge in designing chatbot dialog systems that achieve widespread diffusion among scientific communities. In this article, we propose a novel Vidura advisor design framework (VADF) to develop dialog system designs for information retrieval (IR) and question-answering (QA) tasks, while enabling the quantification of system utility based on human performance in diverse application environments. We adopt a socio-technical approach in our framework for designing dialog systems by utilizing domain expert feedback, which features a sparse retriever for enabling accurate responses in QA settings using linear interpolation smoothing. We apply our VADF for an exemplar science gateway, viz. KnowCOVID-19, to conduct experiments that demonstrate the utility of dialog systems based on IR and QA performance, application utility, and perceived adoption. Experimental results show our VADF approach significantly improves IR performance against retriever baselines (up to 5% increase) and QA performance against large language models (LLMs) such as ChatGPT (up to 43% increase) on scientific literature datasets. In addition, through a usability survey, we observe that measuring application utility and human performance when applying VADF to KnowCOVID-19 translates to an increase in perceived community adoption.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"207-222"},"PeriodicalIF":3.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698213","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}
Pub Date : 2024-12-27DOI: 10.1109/THMS.2024.3515045
Wenfeng Li;Jinglong Zhou;Shaoyong Jiang;Chaoqun Wang;Anning Yang
The research on human-following robot is important for practical applications. It is a hot field of human–machine technology. This article proposes an adaptive recurrent proportional integral differential (PID) control algorithm with self-tuning filter based on vision to address the issue of insufficient recognition accuracy of specific following targets in the presence of occlusion, multiple people, or deformation. It also aims to further improve the control accuracy and immunity of a human-following robot. First, a depth camera-based red green blue (RGB) picture and a depth image are acquired. The person reidentification algorithm and the YOLOv8 algorithm are used to detect and track the targets. The spatial position information of the targets is calculated by the depth image. Additionally, the orientation proportional differential (PD) controller and the speed proportional integral (PI) controller are built. Its foundation is the discrepancy between the relative posture of the user and the robot. In order to minimize sensor data fluctuations and lessen the negative impacts of relative positional instability, a self-tuning filter is developed. To remember the relative postures between the robot and the user in the history window, an adaptive recurrent mechanism is suggested. The controller has the ability to output the control quantity in an adaptive manner based on the current system state. Finally, experiments are conducted to verify the reliability of the proposed method. The experimental findings demonstrate that the visual pedestrian tracking algorithm proposed in this article is highly adaptable. Compared to the traditional PID, fractional-order PID, and virtual spring model, our method demonstrates significant enhancements, reducing the average distance error by 64.29%, 57.14%, and 60.52% in steering scenarios, and by 42.86%, 40.00%, and 40.00% in straight-ahead scenarios, respectively.
{"title":"Human-Following Control Method Based on Adaptive Recurrent PID Controller With Self-Tuning Filter","authors":"Wenfeng Li;Jinglong Zhou;Shaoyong Jiang;Chaoqun Wang;Anning Yang","doi":"10.1109/THMS.2024.3515045","DOIUrl":"https://doi.org/10.1109/THMS.2024.3515045","url":null,"abstract":"The research on human-following robot is important for practical applications. It is a hot field of human–machine technology. This article proposes an adaptive recurrent proportional integral differential (PID) control algorithm with self-tuning filter based on vision to address the issue of insufficient recognition accuracy of specific following targets in the presence of occlusion, multiple people, or deformation. It also aims to further improve the control accuracy and immunity of a human-following robot. First, a depth camera-based red green blue (RGB) picture and a depth image are acquired. The person reidentification algorithm and the YOLOv8 algorithm are used to detect and track the targets. The spatial position information of the targets is calculated by the depth image. Additionally, the orientation proportional differential (PD) controller and the speed proportional integral (PI) controller are built. Its foundation is the discrepancy between the relative posture of the user and the robot. In order to minimize sensor data fluctuations and lessen the negative impacts of relative positional instability, a self-tuning filter is developed. To remember the relative postures between the robot and the user in the history window, an adaptive recurrent mechanism is suggested. The controller has the ability to output the control quantity in an adaptive manner based on the current system state. Finally, experiments are conducted to verify the reliability of the proposed method. The experimental findings demonstrate that the visual pedestrian tracking algorithm proposed in this article is highly adaptable. Compared to the traditional PID, fractional-order PID, and virtual spring model, our method demonstrates significant enhancements, reducing the average distance error by 64.29%, 57.14%, and 60.52% in steering scenarios, and by 42.86%, 40.00%, and 40.00% in straight-ahead scenarios, respectively.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 1","pages":"48-57"},"PeriodicalIF":3.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106847","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}
Pub Date : 2024-12-27DOI: 10.1109/THMS.2024.3509223
Wonse Jo;Ruiqi Wang;Baijian Yang;Daniel Foti;Mo Rastgaar;Byung-Cheol Min
The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multirobot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks, such as monitoring, exploration, and search and rescue operations. This article presents a deep reinforcement learning-based affective workload allocation controller specifically for multihuman multirobot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multirobot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we conduct an exploratory user experiment with various allocation strategies. The user experiment uses a multihuman multirobot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multihuman multirobot teams.
{"title":"Cognitive Load-Based Affective Workload Allocation for Multihuman Multirobot Teams","authors":"Wonse Jo;Ruiqi Wang;Baijian Yang;Daniel Foti;Mo Rastgaar;Byung-Cheol Min","doi":"10.1109/THMS.2024.3509223","DOIUrl":"https://doi.org/10.1109/THMS.2024.3509223","url":null,"abstract":"The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multirobot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks, such as monitoring, exploration, and search and rescue operations. This article presents a deep reinforcement learning-based affective workload allocation controller specifically for multihuman multirobot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multirobot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we conduct an exploratory user experiment with various allocation strategies. The user experiment uses a multihuman multirobot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multihuman multirobot teams.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 1","pages":"23-36"},"PeriodicalIF":3.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106845","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}
Pub Date : 2024-12-19DOI: 10.1109/THMS.2024.3502178
Shuting Xu;Wenqian Tan;Liguo Sun
This article proposes a shared control system model between the human pilot and autopilot for the special issue of carrier-based aircraft landing task. A key point of the shared and cooperative control is that the decision-sharing system depends on the longitudinal safety boundaries for manual/automatic landing. Two strategies of the human pilot are adopted, including capture strategy and tracking strategy. A hidden model tracking control method is utilized to model the autopilot. To address the issue of frequent switching between the human pilot and autopilot caused by relying solely on safety boundaries to allocate control authority, fuzzy control theory is introduced to reduce the workload of the human pilot. The time-domain simulation results show that considering the fuzzy control, the frequency of switching and the flight states have been improved compared with the results without fuzzy control. Nonlinear pilot-induced oscillation metric evaluation results show that the human-automation shared and cooperative control considering the fuzzy control can alleviate the workload of the human pilot. The shared and cooperative control system model has certain significance in ensuring the safety of carrier-based aircraft landing.
{"title":"Modeling Shared Control System Between Human Pilot and Autopilot for a Carrier-Based Aircraft Landing Task","authors":"Shuting Xu;Wenqian Tan;Liguo Sun","doi":"10.1109/THMS.2024.3502178","DOIUrl":"https://doi.org/10.1109/THMS.2024.3502178","url":null,"abstract":"This article proposes a shared control system model between the human pilot and autopilot for the special issue of carrier-based aircraft landing task. A key point of the shared and cooperative control is that the decision-sharing system depends on the longitudinal safety boundaries for manual/automatic landing. Two strategies of the human pilot are adopted, including capture strategy and tracking strategy. A hidden model tracking control method is utilized to model the autopilot. To address the issue of frequent switching between the human pilot and autopilot caused by relying solely on safety boundaries to allocate control authority, fuzzy control theory is introduced to reduce the workload of the human pilot. The time-domain simulation results show that considering the fuzzy control, the frequency of switching and the flight states have been improved compared with the results without fuzzy control. Nonlinear pilot-induced oscillation metric evaluation results show that the human-automation shared and cooperative control considering the fuzzy control can alleviate the workload of the human pilot. The shared and cooperative control system model has certain significance in ensuring the safety of carrier-based aircraft landing.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 1","pages":"102-111"},"PeriodicalIF":3.5,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106815","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}
Pub Date : 2024-12-11DOI: 10.1109/THMS.2024.3503473
Lukas Bergmann;Lea Hansmann;Philip von Platen;Steffen Leonhardt;Chuong Ngo
Acknowledging the vital importance of fatigue management for improving rehabilitation results, customizing treatment, safeguarding patient well-being, and enhancing the quality of life of hemiplegic patients, this study presents the development of a tailored fatigue model and a corresponding human-in-the-loop (HiL) control system for exoskeleton-assisted walking. For this, the selected three-compartment controller fatigue model including a resting recovery parameter was adapted to a dynamic walking task scenario, incorporating a torque–velocity–angle dependency to quantify muscle activity. The model parameters were experimentally verified in a study with six healthy subjects, demonstrating accurate prediction of maximum voluntary contraction (MVC) decline with an average mean absolute error of 4.9%MVC. Subsequently, an HiL control mechanism was developed, utilizing ratings of perceived fatigue and state of fatigue values as reference metrics. The presented control approach effectively regulates fatigue levels within a 0%MVC–6%MVC steady-state error range during simulations. Experimental validation confirmed this performance, however, with partly higher steady-state errors mainly due to the restrictions of the exoskeleton's assistance. This preliminary study provides a promising foundation for future research, demonstrating the potential to manage fatigue effectively in exoskeleton users, offering an improved, personalized experience.
{"title":"Fatigue Assessment and Control With Lower Limb Exoskeletons","authors":"Lukas Bergmann;Lea Hansmann;Philip von Platen;Steffen Leonhardt;Chuong Ngo","doi":"10.1109/THMS.2024.3503473","DOIUrl":"https://doi.org/10.1109/THMS.2024.3503473","url":null,"abstract":"Acknowledging the vital importance of fatigue management for improving rehabilitation results, customizing treatment, safeguarding patient well-being, and enhancing the quality of life of hemiplegic patients, this study presents the development of a tailored fatigue model and a corresponding human-in-the-loop (HiL) control system for exoskeleton-assisted walking. For this, the selected three-compartment controller fatigue model including a resting recovery parameter was adapted to a dynamic walking task scenario, incorporating a torque–velocity–angle dependency to quantify muscle activity. The model parameters were experimentally verified in a study with six healthy subjects, demonstrating accurate prediction of maximum voluntary contraction (MVC) decline with an average mean absolute error of 4.9%MVC. Subsequently, an HiL control mechanism was developed, utilizing ratings of perceived fatigue and state of fatigue values as reference metrics. The presented control approach effectively regulates fatigue levels within a 0%MVC–6%MVC steady-state error range during simulations. Experimental validation confirmed this performance, however, with partly higher steady-state errors mainly due to the restrictions of the exoskeleton's assistance. This preliminary study provides a promising foundation for future research, demonstrating the potential to manage fatigue effectively in exoskeleton users, offering an improved, personalized experience.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 1","pages":"10-22"},"PeriodicalIF":3.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106915","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}
Pub Date : 2024-12-04DOI: 10.1109/THMS.2024.3488559
Zahra Zahedi;Sailik Sengupta;Subbarao Kambhampati
In scenarios involving robots generating and executing plans, conflicts can arise between cost-effective robot execution and meeting human expectations for safe behavior. When humans supervise robots, their accountability increases, especially when robot behavior deviates from expectations. To address this, robots may choose a highly constrained plan when monitored and a more optimal one when unobserved. While this behavior is not driven by human-like motives, it stems from robots accommodating diverse supervisors. To optimize monitoring costs while ensuring safety, we model this interaction in a trust-based game-theoretic framework. However, pure-strategy Nash equilibrium often fails to exist in this model. To address this, we introduce the concept of a trust boundary within the mixed strategy space, aiding in the discovery of optimal monitoring strategies. Human studies demonstrate the necessity of optimal strategies and the benefits of our suggested approaches.
{"title":"A Game-Theoretic Model of Trust in Human–Robot Teaming: Guiding Human Observation Strategy for Monitoring Robot Behavior","authors":"Zahra Zahedi;Sailik Sengupta;Subbarao Kambhampati","doi":"10.1109/THMS.2024.3488559","DOIUrl":"https://doi.org/10.1109/THMS.2024.3488559","url":null,"abstract":"In scenarios involving robots generating and executing plans, conflicts can arise between cost-effective robot execution and meeting human expectations for safe behavior. When humans supervise robots, their accountability increases, especially when robot behavior deviates from expectations. To address this, robots may choose a highly constrained plan when monitored and a more optimal one when unobserved. While this behavior is not driven by human-like motives, it stems from robots accommodating diverse supervisors. To optimize monitoring costs while ensuring safety, we model this interaction in a trust-based game-theoretic framework. However, pure-strategy Nash equilibrium often fails to exist in this model. To address this, we introduce the concept of a trust boundary within the mixed strategy space, aiding in the discovery of optimal monitoring strategies. Human studies demonstrate the necessity of optimal strategies and the benefits of our suggested approaches.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 1","pages":"37-47"},"PeriodicalIF":3.5,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106846","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}
Pub Date : 2024-12-03DOI: 10.1109/THMS.2024.3509052
{"title":"2024 Index IEEE Transactions on Human-Machine Systems Vol. 54","authors":"","doi":"10.1109/THMS.2024.3509052","DOIUrl":"https://doi.org/10.1109/THMS.2024.3509052","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 6","pages":"819-835"},"PeriodicalIF":3.5,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10774072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-11-28DOI: 10.1109/THMS.2024.3489795
Luis A. Oliveira Rodríguez;Roberto García Fernández;David Melendi Palacio
It is essential to monitor and follow up with athletes, both from the point of view of physical and emotional well-being. This allows optimizing the strategy to be followed to achieve full individual and collective development, thus resulting in an improvement in performance, which helps in the prevention of injuries, and better collective work. This is especially important in the early stages of an athlete's career. The present study is based on a follow-up survey consisting of 117 female football players ranging from 10 and 20 years old, making it one of the first studies amongst this age group. A low-cost electronic performance and tracking system was developed to gather data on the players. During the training sessions, objective data (position, distances, etc.) and subjective parameters were collected using forms based on the rate of perceived exertion. This article deals with the evolution of the player's performance from both a physical and mental point of view. An emotional evaluation, based on well-being forms, is carried out and its possible influence on training. Finally, analysis is conducted on the level of health risk. It was found that the performance of female footballers improves with age and in competition-like situations. It has also been concluded that sporting activity leads to healthy lifestyle habits, which translates into a lower risk to their health.
{"title":"Individual Performance in Women's Grassroots Football: A Physical and Emotional Perspective","authors":"Luis A. Oliveira Rodríguez;Roberto García Fernández;David Melendi Palacio","doi":"10.1109/THMS.2024.3489795","DOIUrl":"https://doi.org/10.1109/THMS.2024.3489795","url":null,"abstract":"It is essential to monitor and follow up with athletes, both from the point of view of physical and emotional well-being. This allows optimizing the strategy to be followed to achieve full individual and collective development, thus resulting in an improvement in performance, which helps in the prevention of injuries, and better collective work. This is especially important in the early stages of an athlete's career. The present study is based on a follow-up survey consisting of 117 female football players ranging from 10 and 20 years old, making it one of the first studies amongst this age group. A low-cost electronic performance and tracking system was developed to gather data on the players. During the training sessions, objective data (position, distances, etc.) and subjective parameters were collected using forms based on the rate of perceived exertion. This article deals with the evolution of the player's performance from both a physical and mental point of view. An emotional evaluation, based on well-being forms, is carried out and its possible influence on training. Finally, analysis is conducted on the level of health risk. It was found that the performance of female footballers improves with age and in competition-like situations. It has also been concluded that sporting activity leads to healthy lifestyle habits, which translates into a lower risk to their health.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 1","pages":"83-92"},"PeriodicalIF":3.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-11-22DOI: 10.1109/THMS.2024.3497077
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2024.3497077","DOIUrl":"https://doi.org/10.1109/THMS.2024.3497077","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 6","pages":"C3-C3"},"PeriodicalIF":3.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766344","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-11-22DOI: 10.1109/THMS.2024.3497079
{"title":"IEEE Transactions on Human-Machine Systems Information for Authors","authors":"","doi":"10.1109/THMS.2024.3497079","DOIUrl":"https://doi.org/10.1109/THMS.2024.3497079","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 6","pages":"C4-C4"},"PeriodicalIF":3.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
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