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IEEE Annual International Symposium Virtual Reality

作者： Leonardo Pavanatto Alexander Giovannelli Brian Giera Timo Bremer Haichao Miao Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding.

关键词： human computer interaction Location awareness Three-dimensional displays Navigation Virtual reality Inspection

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Progress of research in the application of ultrasound technology for the treatment of Alzheimer’s disease

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Neural Regeneration Research 2025年第10期20卷 2823-2837页

作者： Qiuquan Cai Lianghui Meng Meina Quan Ling Wang Jing Ren Chenguang Zheng Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjinChina Innovation Center for Neurological Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin UniversityTianjinChina Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin UniversityTianjinChina

Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.

关键词： Alzheimer’s disease blood-brain barrier drugs magnetic resonance imaging-guided focused ultrasound microbubbles scanning ultrasound ultrasound ultrasound stimulation

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Rethinking (human) Bodies in HRI: Embodied interactions Every Day 6

Rethinking (Human) Bodies in HRI: Embodied Interactions Ever...

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6th Social Robots with AI: Prospects, Risks, and Responsible Methods Robophilosophy, RP 2024

作者： Fantasia, Valentina Winkle, Katie Harrison, Katherine Ziemke, Tom Lisy, Dominika Dobrosovestnova, Anna Department of Philosophy and Cognitive Science Lund University Sweden Department of Information Technology Uppsala University Sweden Department of Thematic Studies Linköping University Sweden Department of Cognitive Science Linköping University Sweden Human Computer Interaction Group TU Wien Austria

ISBN: (纸本)9781643685670

Significant amounts of HRI research effort are spent on the design/evaluation of robot bodies. Critical discussion and debates concerning the way bodies are treated and considered within HRI have tended to focus on the implications of designing/interacting with highly anthropomorphic robots, yet drawing attention away from the human body in HRI, particularly in the context of everyday interactions with "unexpectedly" social robots, i.e. those non-humanoid robots that human users interact with in a somewhat "social" way. These kind of robots crystallise particular aspects of (and tensions between) the different ways in which bodies are understood across disciplines. At the same time, a fundamental, and largely neglected tool for HRI research is the researchers' own embodied experience of the intersubjective role. This workshop has invited speakers and audience to engage in cross-disciplinary conversations on embodiment in HRI, highlighting the different ways in which "the body" is or can be variously conceptualised and researched. © 2025 IOS Press. All rights reserved.

关键词： Social robots

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InterLink: Linking Text with Code and Output in Computational Notebooks 25

InterLink: Linking Text with Code and Output in Computationa...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Yanna Lin Leni Yang Haotian Li Huamin Qu Dominik Moritz Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China and Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Microsoft Research Asia Beijing China Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Folk Tales of IoT: Understanding the Impact of Stories on Users' Positive and Negative Perceptions of Smart Home IoT Devices 25

Folk Tales of IoT: Understanding the Impact of Stories on Us...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Leah Zhang-Kennedy Michaela Valiquette An Bella Chen Hilda Hadan Sangho Suh Stratford School of Interaction Design and Business University of Waterloo Waterloo Ontario Canada Human-Computer Interaction Carleton University Ottawa Ontario Canada Cheriton School of Computer Science University of Waterloo Waterloo Ontario Canada Department of Computer Science University of Toronto Toronto Ontario Canada

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Teacher-centric educational recommender systems in K12 practice: Usage and evaluation

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Heliyon 2025年第2期11卷 e42012页

作者： Bhatt, Sohum M. Verbert, Katrien Van Den Noortgate, Wim Faculty of Psychology and Educational Sciences and imec research group itec KU Leuven Belgium Human and Computer Interaction Group Department of Computer Science KU Leuven Belgium

Personalized learning is hard to apply in classroom practice due to the workload of teachers. Recommender systems can help teachers make choices and hence personalize the learning environment. However, few studies have investigated how teachers use recommender systems in their practice. Educational recommender systems also require more investigation into how teachers assess recommendations to provide better systems in the future. Therefore, this research aimed to provide a better insight into the practical application of a teacher-centric educational recommender system, conduct a user-centric evaluation of the recommender system, and present teacher beliefs on the use of educational recommender systems for personalized learning. Our results indicated that teachers similarly used the recommender system by creating new lessons rather than adapting suggested lessons. Their evaluations of the recommender system highlighted the importance of accuracy and utility. Diversity and serendipity were considered to be less important. Teachers perceived recommender systems as a first step to personalized learning and as a tool for simplifying such learning. However, the current system did not affect the quality of personalized learning. Based on these results, we propose to focus more on accuracy and utility as opposed to diversity and serendipity for teacher-centric recommender system evaluation. For personalized learning, we conclude that, according to teachers, recommender systems can lead to more effective personalization by allowing teachers to focus on student needs. © 2025 The Authors

关键词： Data science applications in education Elementary education Improving classroom teaching Secondary education

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Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task

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Neuromodulation 2025年第4期28卷 652-662页

作者： Chen, Jingxin Ke, Yufeng Ni, Guangjian Liu, Shuang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China Department of Biomedical Engineering College of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin China

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV). Objective: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task. Materials and Methods: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks. Results: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV. Conclusions: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions. © 2025 International Neuromodulation Society

关键词： Heart rate variability (HRV) LC-NA pupil response pupillometry taVNS

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ArsenicSkinNet: A Deep Learning Approach for Arsenicosis Skin Lesion Classification 17th

ArsenicSkinNet: A Deep Learning Approach for Arsenicosis Sk...

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Aakash, Abhinav O’Halloran, Tony Obaido, George Mienye, Ibomoiye Domor Mienye, Ebikella Ogunruku, Oyindamola Omolara Aliyu, Mikail Department of Computer Science KIIT University Bhubaneswar India Department of Computer Science National University of Ireland Galway Ireland Center for Human-Compatible Artificial Intelligence Berkeley Institute for Data Science University of California BerkeleyCA United States Institute of Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Decision Sciences Western Illinois University MacombIL61455 United States School of Public Health University of California BerkeleyCA United States

ISBN: (纸本)9789819606917

In many low-resource settings, the debilitating effects of arsenicosis remain a critical public health concern. This condition leads to serious health problems, including skin lesions, skin cancer, cardiovascular disease, and neurological impairment. Visual inspection is the standard practice for diagnosing arsenicosis;however, it is often subjective and can lead to variability in diagnosis. To address the urgent need for efficient arsenicosis skin detection, we propose ArsenicSkinNet, adapted from the EfficientNet architecture for the automatic classification of arsenicosis skin disease. ArsenicSkinNet is a state-of-the-art technique based on the EfficientNet neural network architecture and transfer learning, known for achieving high performance with fewer computational resources compared to traditional models. Our results show that ArsenicSkinNet achieved a classification accuracy of 99.61% on the arsenicosis skin lesion dataset. Our study demonstrates that deep learning could significantly improve the capacity for early detection and management of arsenicosis, thereby mitigating its impact on affected populations in low-resource settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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"I am a Technology Creator": Black Girls as Technosocial Change Agents in a Culturally-Responsive Robotics Camp 25

"I am a Technology Creator": Black Girls as Technosocial Cha...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Chun Li Jaemarie Solyst Safiyyah Scott Gabriella Howse Tara Nkrumah Erin Walker Amy Ogan Angela E.B. Stewart Department of Informatics and Networked System University of Pittsburgh Pittsburgh Pennsylvania USA Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA School of Computer Science Carnegie Mellon University Pittsburgh Pennsylvania USA Center for Gender Equity in Science and Technology Arizona State University Tempe Arizona USA Department of Computer Science University of Pittsburgh Pittsburgh Pennsylvania USA

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