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1st International Symposium on Trustworthy Autonomous Systems, TAS 2023

作者： Parnell, Katie J. Merriman, Siobhan E. Getir Yaman, Sinem Plant, Katherine L. Calinescu, Radu Human Factors Engineering University of Southampton United Kingdom Department of Computer Science University of York United Kingdom

ISBN: (纸本)9798400707346

Developing resilient autonomous systems requires an interdisciplinary approach that can understand performance variability and respond to critical events when they occur. Resilience within autonomous systems must also account for social norms as well as broader ethical and legal considerations. Within this paper we outline the importance of embedding Social, Legal, Ethical, Empathetic and Cultural (SLEEC) constraints within the development of future autonomous systems. A novel methodological approach is presented that combines human Factors methods with computer science techniques to generate the environmental and situational requirements in combination with a computer rule-based requirements language. This approach also provides a possible structure for capturing contextual and situational information from key stakeholders in the development of autonomous systems. This structure will enable engagement with the stakeholders with respect to key elements identified from this interdisciplinary approach in a responsible way to ensure that future autonomous systems are user centred. The approach is domain independent, but it is applied here to the case of an autonomous assistive dressing robot that aids a user in a dressing task, with a specific critical event that requires a SLEEC resilient response © 2023 ACM.

关键词： Robots

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Diversity, Equity, and Inclusion in Computing science: Culture is the Key, Curriculum Contributes 2024

Diversity, Equity, and Inclusion in Computing Science: Cultu...

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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Toti, Giulia Lindner, Peggy Gao, Alice Baghban Karimi, Ouldooz Engineer, Rutwa Hur, Jinyoung Mcneill, Fiona Reckinger, Shanon Robinson, Rebecca Sollazzo, Anna Wicentowski, Richard Department of Computer Science University of British Columbia VancouverBC Canada Department of Information Science Technology University of Houston HoustonTX United States Department of Computer Science University of Toronto TorontoON Canada School of Computing Science Simon Fraser University SurreyBC Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga MississaugaON Canada Department of Computer Science University of Illinois Urbana-Champaign Urbana-ChampaignIL United States School of Informatics University of Edinburgh Edinburgh United Kingdom Department of Computer Science University of Illinois Chicago ChicagoIL United States Department of Human Centred Computing Monash University ClaytonVIC Australia School of Computing Science University of Glasgow United Kingdom Computer Science Department Swarthmore College SwarthmorePA United States

ISBN: (纸本)9798400712081

Undergraduate computer science programs worldwide struggle to attract and retain underrepresented students for many reasons. Culture, stereotype threats, uneven gender and racial representations, lack of role models, and uncertain career prospects for minority groups are among the many reasons behind this situation. Many computer science programs are trying to change course through strategies to foster equity, diversity, and inclusion (EDI), aimed at improving outreach, recruitment, admissions, and retention of underrepresented students. EDI approaches may also include modifications to the undergraduate computer science curriculum. However, if not properly planned, these modifications risk amplifying existing stereotypes rather than producing positive change [38]. In this study, through an extensive literature review, a rigorous curriculum analysis of 49 computer science programs across the globe, and qualitative and quantitative analysis of surveys and interviews bringing in the voices of 613 students and 30 educators participating from around the world, we explore equity, diversity, and inclusion in the computer science curriculum. We highlight the role of inclusive content and course design, discuss program flexibility, and the impact of inclusive courses and program design in attracting and retaining historically marginalized students. Finally, we provide concrete steps to make computing science undergraduate curricula more appealing to a diverse audience. © 2024 Copyright held by the owner/author(s).

关键词： Students

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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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Exploring UAV Interfaces: An Eye-Tracking Study on Novice and Experienced Users 24

Exploring UAV Interfaces: An Eye-Tracking Study on Novice an...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and Informatics

作者： Andreas Kousoulos Angelos Fotopoulos Dimosthenis Minas Michalis Xenos Department of Computer Engineering & Informatics / Software Quality and Human-Computer Interaction Laboratory University of Patras Patras Greece University of Patras Patras Greece

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computer vision-based hybrid efficient convolution for isolated dynamic sign language recognition

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Neural Computing and Applications 2024年第32期36卷 19951-19966页

作者： Chowdhury, Prothoma Khan Oyshe, Kabiratun Ummi Rahaman, Muhammad Aminur Debnath, Tanoy Rahman, Anichur Kumar, Neeraj Department of Computer Science and Engineering Green University of Bangladesh 220/D Begum Rokeya Sarani Purbachal American City Kanchon Dhaka1207 Bangladesh Constituent Institute of the University of Dhaka Kohinoor gate Dhaka-Aricha Hwy Nayarhat Savar Dhaka1350 Bangladesh Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India University of Economics and Human Sciences WARSAW Poland

Isolated dynamic sign language recognition (IDSLR) has the potential to change accessibility and inclusion by enabling speech and/or hearing-impaired people to engage more completely in a variety of spheres of life, including social interactions, work, and more. IDSLR is a challenging task due to considering a sequence of image frame analysis with multiple linguistic features for a single gesture in cluttered backgrounds and an illumination variation environment. We have proposed a Hybrid Efficient Convolution (HEC) model that ensembles EfficientNet-B3 and a few modified layers as an alternative to traditional machine learning techniques with improved performances in cluttered backgrounds with illumination variation environments. The architecture of the HCE integrates pre-trained layers of EfficientNet-B3 loaded with customized weights and a new custom dense layer featuring 256 units, followed by batch normalization, dropout, and the final output layer. To enhance the robustness of the system, we employed the augmentation technique during pre-processing. Then, the system executes channel-wise feature transformation through point-wise convolution that reduces the computational complexity and increases the accuracy. The updated dense layer with 256 units processes the output from the standard EfficientNet-B3, shaping the model into a hybrid form to achieve better performance. We have created our own gesture dataset, called "BdSL_OPA_23_GESTURES," which consists of 6000 video clips of 100 isolated dynamic Bangla Sign Language words, with 60 videos for each word from 20 different people in the cluttered background with illumination variation environments to train and evaluate the performances of the proposed model. We have considered 80% of the total dataset for training purpose, while the remaining 20% is dedicated to testing and validation. In a small number of epochs, our proposed HEC model achieves a superior accuracy of 93.17% on our created "BdSL_OPA_23_GESTURES"

关键词： Convolution

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How context and design shape human-robot trust and attributions

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Paladyn 2021年第1期12卷 74-86页

作者： Biermann, Hannah Brauner, Philipp Ziefle, Martina Chair for Communication Science and Human-Computer Interaction Center RWTH Aachen University Campus-Boulevard 57 Aachen52074 Germany

In increasingly digitized working and living environments, human-robot collaboration is growing fast with human trust toward robotic collaboration as a key factor for the innovative teamwork tosucceed. This article explores the impact of design factors of the robotic interface (anthropomorphic vs functional) and usage context (production vs care) on human-robot trust and attributions. Theresults of a scenario-based survey with N = 228 N=228 participants showed a higher willingness to collaborate with production robots compared to care. Context and design influenced the trust attributed to the robots: robots with a technical appearance in production were trusted more than anthropomorphic robots or robots in the care context. The evaluation of attributions by means of a semanticdifferential showed that differences in robot design were less pronounced for the production context in comparison to the care context. In the latter, anthropomorphic robots were associated with positive attributes. The results contribute to a better understanding of the complex nature of trust in automation and can be used to identify and shape use case-specific risk perceptions as well as perceived opportunities to interacting with collaborative robots. Findings of this study are pertinent to research (e.g., experts in human-robot interaction) and industry, with special regard given tothe technical development and design. © 2021 Hannah Biermann et al., published by De Gruyter.

关键词： Risk perception

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[DC] Context-Aware Inference and Adaptation in Augmented Reality

[DC] Context-Aware Inference and Adaptation in Augmented Rea...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Shakiba Davari Center for Human-Computer Interaction and Department of Computer Science Virginia TechUSA

ISBN: (纸本)9781665484039

Augmented Reality(AR) offers the potential for easy and efficient information access, reinforcing the wide belief that AR Glasses are the next-generation of personal computing devices. However, to realize this all-day AR vision, the AR interface must be able to address the challenges that constant and pervasive presence of virtual content can cause for the user. The optimal interface, that is the most efficient yet least intrusive, in one context may be the worst interface for another context. Throughout the day, as the user switches context, an optimal all-day interface must adapts its virtual content display and interactions as well. This work aims to propose a research agenda to design and validate different adaptation techniques and context-aware AR interfaces and introduce a framework for the design of such intelligent interfaces.

关键词： human computer interaction Three-dimensional displays Conferences Glass Next generation networking Augmented reality

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First or Third-Person Hearing? A Controlled Evaluation of Auditory Perspective on Embodiment and Sound Localization Performance

First or Third-Person Hearing? A Controlled Evaluation of Au...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Yi Fei Cheng Laurie M. Heller Stacey Cho David Lindlbauer Human-Computer Interaction Institute Carnegie Mellon University Department of Psychology Carnegie Mellon University

ISBN: (数字)9798331516475

ISBN: (纸本)9798331516482

Virtual Reality (VR) allows users to flexibly choose the perspective through which they interact with a synthetic environment. Users can either adopt a first-person perspective, in which they see through the eyes of their virtual avatar, or a third-person perspective, in which their viewpoint is detached from the virtual avatar. Prior research has shown that the visual perspective affects different interactions and influences core experiential factors, such as the user’s sense of embodiment. However, there is limited understanding of how auditory perspective mediates user experience in immersive virtual environments. In this paper, we conducted a controlled experiment $(N=24)$ on the effect of the user’s auditory perspective on their performance in a sound localization task and their sense of embodiment. Our results showed that when viewing a virtual avatar from a third-person visual perspective, adopting the auditory perspective of the avatar may increase agency and self-avatar merging, even when controlling for variations in task difficulty caused by shifts in auditory perspective. Additionally, our findings suggest that differences in auditory perspective generally have a smaller effect than differences in visual perspective. We discuss the implications of our empirical investigation of audio perspective for designing embodied auditory experiences in VR.

关键词： Location awareness Visualization Avatars Merging Virtual environments Auditory system User experience Augmented reality

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Gaze Behaviour in Adolescents with Obsessive-Compulsive Disorder During Exposure Within Cognitive-Behavioural Therapy 17th

Gaze Behaviour in Adolescents with Obsessive-Compulsive Di...

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17th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2023

作者： Thierfelder, Annika Severitt, Björn Klein, Carolin S. Alt, Annika K. Hollmann, Karsten Bulling, Andreas Ilg, Winfried Hertie Institute for Clinical Brain Research Section for Computational Sensomotorics University Hospital Tübingen Tübingen Germany Department of Computer Science Human-Computer Interaction University of Tübingen Tübingen Germany Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Tübingen Tübingen Germany Institute for Visualisation and Interactive Systems University Stuttgart Stuttgart Germany

ISBN: (纸本)9783031597169

Digital health interventions that involve monitoring patient behaviour increasingly benefit from improvements in sensor technology. Eye tracking in particular can provide useful information for psychotherapy but an effective method to extract this information is currently missing. We propose a method to analyse natural gaze behaviour during exposure exercises for obsessive-compulsive disorder (OCD). At the core of our method is a neural network to detect fixations based on gaze patch similarities. Detected fixations are clustered into exposure-relevant, therapist, and other locations and corresponding eye movement metrics are correlated with subjective stress reported during exposure. We evaluate our method on gaze and stress data recorded during video-based psychotherapy of four adolescents with OCD. We found that fixation duration onto exposure-relevant locations consistently increases with the perceived stress level as opposed to fixations onto other locations. Fixation behaviour towards the therapist varied largely between patients. Taken together, our results not only demonstrate the effectiveness of our method for analysing natural gaze behaviour during exposure sessions. The fixation analysis shows that patients allocate more attention towards exposure-related objects under higher stress levels, suggesting higher mental load. As such, providing feedback on fixation behaviour holds significant promise to support therapists in monitoring intensity of exposure exercises. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Location

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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

Emotion Recognition from Eye Movements Using Multi-way Autor...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Tian-Fang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (纸本)9798350386226

The application of physiological signals in emotion recognition is a popular research topic in human-computer interactions. Eye movement, as an important physiological signal, plays an essential role in medicine, psychology, cognitive science, and other scientific research fields. Previous studies have successfully identified human emotions by combining various eye-related measurements, selecting features, and utilizing machine learning techniques. However, the exploration of eye movement signals in emotion recognition remains insufficient. In this study, we utilize eye tracking heatmap and eye movement trajectory data for emotion recognition for the first time. Based on the two types of eye movement data, we develop a multi-way autoregressive model capable of processing multi-view eye movement data. Compared to traditional deep learning baseline models, our model better adapts to the structure of eye movement data and significantly improves the classification performance. Furthermore, we integrate heatmap and trajectory data with commonly used eye-related measurement features, which further enhance the performance of emotion recognition beyond previous methods. © 2024 IEEE.

关键词： Emotion Recognition

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