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Tiles to Move: Investigating Tile-Based Locomotion for Virtual Reality

Proceedings of the ACM on human-computer interaction 2023年第CHI PLAY期7卷 1044–1067页

作者： Funke, Jana Franceska Schikorr, Anja Karaosmanoglu, Sukran Hirzle, Teresa Steinicke, Frank Rukzio, Enrico Institute of Media Informatics Ulm University James-Franck Ring Ulm Ulm89081 Germany Human-Computer Interaction Universität Hamburg Hamburg Vogt-Kölln-Straße 30 Hamburg22527 Germany Department of Computer Science University of Copenhagen Copenhagen Universitetsparken 1 Copenhagen2100 Denmark Institute of Media Informatics Ulm University Ulm Germany

Tile-based locomotion (TBL) is a popular locomotion technique for computer, console, and board games. However, despite its simplicity and unconventional movement, the transfer of TBL to virtual reality (VR) as a game platform remains unexplored. To fill this gap, we introduce TBL for VR on the example of two techniques: a controller and a feet-based one. In a first user study, we evaluated the usability and acceptance of the techniques compared to teleportation and touchpad locomotion. In a second exploratory user study, we evaluated the user experience of both TBL techniques in a maze and a museum scenario. The findings show that both techniques provide enjoyment and acceptable usability by creating either a relaxing (controller-based) or a physically active (feet-based) solution. Finally, our results highlight that TBL techniques work particularly well for small, constrained spaces that allow users to focus on exploring details in the nearby environment (important for games) in contrast to large open spaces that require faster locomotion, like teleportation. © 2023 Owner/Author.

关键词： Virtual reality

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Multivariate Time Series Anomaly Detection based on Pre-trained Models with Dual-Attention Mechanism 35

Multivariate Time Series Anomaly Detection based on Pre-trai...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Sun, Yongqian Guo, Yang Liang, Minghan Wen, Xidao Kuang, Junhua Zhang, Shenglin Li, Hongbo Xia, Kaixu Pei, Dan Nankai University China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China BizSeer Haihe Laboratory of Information Technology Application Innovation China Tencent Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

In major tech companies, monitoring server performance data with anomaly detection algorithms is crucial for assessing operational status. Existing models often require separate training or fine-tuning for each server due to generalization limitations, leading to increased storage, memory, and training costs. As the number of servers grows, this approach becomes impractical. To address this, we propose using pretrained language models for time series anomaly detection, leveraging their strong generalization capabilities. Specifically, we employ two pre-trained GPT-2 models as backbones and implement a two-stage fine-tuning strategy to retain learned knowledge while adapting to specific business data characteristics. Our experiments on multiple anomaly detection datasets demonstrate that our method achieves the best average F1-Score, outperforming the leading baseline by 7%. © 2024 IEEE.

关键词： Time series

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GPT4ESG: Streamlining Environment, Society, and Governance Analysis with Custom AI Models 4

GPT4ESG: Streamlining Environment, Society, and Governance A...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Lin, Lydia Hsiao-Mei Ting, Fang-Kai Chang, Ting-Jui Wu, Jun-Wei Tsai, Richard Tzong-Han National Taipei University of Technology Department of Interaction Design Taipei City Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City Taiwan Center for Gis Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan

ISBN: (纸本)9798350360721

Many companies now prioritize Environment, Society, and Governance (ESG) and financial performance due to concerns about climate change. We created GPT4ESG, a quick method to analyze a company's ESG investment and influence. GPT4ESG is a model system architecture based on BERT and GPT with five parts:(1) Data cleaning: The data is cleaned and preprocessed to ensure it is in a suitable format for the model. (2) Tokenizer adaptation: A customized GPT assistant is used to assist in scoring, and results are given after expert review. (3) Model adjustment: A classification layer is added to the output. (4) Fine-tuning: The pre-trained model is fine-tuned according to the ESG field know-how. (5) Evaluation and Testing: The model was tested on a validation set during training and then on a testing set. We collected ESG reports of well-known listed companies in the technology industries in the United States as a data set. The experimental results showed that the customized GPT4ESG model was more effective than ESG-BERT in classifying ESG data. This model simplifies ESG reporting for stakeholders to make responsible decisions. © 2024 IEEE.

关键词： Climate change

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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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What Helps to Help? Exploring Influencing human-, Technology-, and Context-Related Factors on the Adoption Process of Telemedical Applications in Nursing Homes 7th

What Helps to Help? Exploring Influencing Human-, Technology...

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7th and 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2021 and ICT4AWE 2022

作者： Offermann, Julia Wilkowska, Wiktoria Schaar, Anne Kathrin Brokmann, Jörg Christian Ziefle, Martina Human-Computer Interaction Center RWTH Aachen University Campus-Boulevard 57 Aachen52074 Germany Emergency Department University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany

ISBN: (纸本)9783031374951

Currently, the shortage of personnel and the increasing proportions of older individuals in need of care pose enormous challenges to care facilities. Some of the resulting hospitalizations in nursing home are not medically necessary for geriatric patients and can even cause a deterioration of their health. Using telemedical consultations in nursing homes represents one approach aiming at the reduction of unnecessary hospitalizations of geriatric patients and support of care personnel in acute and uncertain situations. Hence, the patients’ as well as the care personnel’s perspectives and their acceptance are essential for a successful implementation of these telemedical consultations. This paper presents qualitative and quantitative results of acceptance patterns in using telemedical consultations from a social communication science perspective within the Optimal@NRW project. An initial qualitative interview study with residents and care personnel of different nursing homes (N = 28) identified attitudes, perceived advantages, and concerns as well as requirements regarding the usage of telemedical consultations. These results provided the basis for further quantifications and for the comparison of acceptance parameters of using telemedical consultations in nursing homes across the entire duration of the project. In a second step, we present the results of a first quantitative study (N = 145) that quantified and validated the previously identified qualitative parameters. Insights of the current empirical approach should be considered within the conceptualization and implementation of telemedical services in nursing homes to fulfill the needs of the respective users, in particular geriatric patients and their care personnel. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nursing

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Usability Assessment of OpenEMR: Enhancing Healthcare interactions 68th

Usability Assessment of OpenEMR: Enhancing Healthcare Intera...

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68th International Annual Meeting of the human Factors and Ergonomics Society, HFES 2024

作者： Mahmoud, Abdelrahman Saleem, Jason J. University of Louisville LouisvilleKY United States Department of Computer Science and Engineering Alamein International University Matrouh New Alamein City Egypt Department of Computer Science and Engineering University of Louisville KY United States Center for Human Systems Engineering University of Louisville KY United States

Electronic medical record (EMR) systems are pivotal in modern healthcare for digitizing patient records and improving clinical workflows. However, persistent usability challenges hinder their effective adoption and user satisfaction. This study employs a comprehensive user-centered design approach to identify and address usability issues in one such EHR, an open-source system named openEMR. Our main objective was to assess openEMR’s usability through several usability metrics. In addition to a summative usability test with 10 participants, we employed usability inspection (i.e., a heuristic evaluation) to generate additional findings using multiple methods. The results from the summative usability test provide a comprehensive perspective on openEMR’s performance. In addition to task-specific observations, participants offered insights into openEMR’s overall usability through a debrief interview. Through quantitative surveys, usability testing, qualitative interviews, and a heuristic evaluation, this study identified critical usability issues, proposes targeted solutions, and discusses implications for enhancing EMR systems in healthcare settings. © 2024 human Factors and Ergonomics Society.

关键词： Electronic health record

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Vision Paper: Leveraging Industrial Big Data - Past, Present, and Future of the World Wide Lab

Vision Paper: Leveraging Industrial Big Data - Past, Present...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Behery, Mohamed Glawe, Felix Koren, Istvan Ziefle, Martina Lakemeyer, Gerhard Brauner, Philipp Rwth Aachen University Knowledge-Based Systems Group Aachen Germany Rwth Aachen University Human Computer Interaction Center Aachen Germany Rwth Aachen University Chair of Process and Data Science Aachen Germany

ISBN: (纸本)9798350324457

Many initiatives aim at answering the challenges of the fourth industrial revolution (Industry 4.0). A key aspect of these initiatives is the connectivity of the different industrial entities. Communication and information exchange between entities usually flows in two dimensions;a vertical one, that spans the different levels of abstraction within the company, and a horizontal one connecting the different entities in the supply chain. In contrast, the Internet of Production (IoP) presents the World Wide Lab (WWL) allowing an extra dimension of communication, which we call the cross-company dimension. Hence, the WWL integrates the different entities within and across potentially competing labs and companies. The WWL leverages and aggregates accessible production data fostering an exchange of information, that prevents redundant experimentation and increases quality of data-driven models by including data from diverse manufacturing settings. This paper discusses work done towards this vision and technical, economic, social, legal, and ethical challenges the WWL faces and provides a roadmap towards infrastructuring the WWL. © 2023 IEEE.

关键词： Digital Shadows Industrial Data Space Industrial Internet of Things Internet of Production World Wide Lab

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Bioderived Hygromorphic Twisted Actuator for Untethered Sustainable Systems 12th

Bioderived Hygromorphic Twisted Actuator for Untethered Sust...

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Proceedings of the 12th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2023

作者： Whatmore, Reece Keely, Emelia Lee, Zoe Minori, Adriane Yao, Lining Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA15218 United States Industrial Design Department Rhode Island School of Design ProvidenceRI02903 United States Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University PittsburghPA15218 United States

ISBN: (纸本)9783031388569

Environmentally friendly and hygromorphic actuators have gained increasing attention for energy harvesting, field robotics, seeding and biodegradable active structures and sensors. While recent works have used hygromorphic seeds as sources of bio-inspiration, it is challenging to engineer synthetic hygromorphic actuators with comparable stiffness, energy density, and reaction speed, and the processing of synthetic materials or need for external power sources inevitably increases fossil fuel consumption and waste production. In this paper, we harness an alternative bioderived design approach, utilizing the natural twisted body of the seed, Hesperostipa Spartea, itself as a sustainable and biodegradable hygromorphic twisting actuator that converts atmospheric water concentration into mechanical energy. Our self-powered twisted actuator is capable of providing twisting actuation with a load over 11,000 times its body mass. We further demonstrate the potential of modular biohybrid design to create untethered self-locking and crawling systems, powered solely by environmental stimuli. The ability to create biodegradable self-actuating systems with modular bioderived design presents opportunities to create biocompatible systems, improving sustainability in the field of soft robotics and beyond. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Robotics

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Recommendations in Visual Analytics - An Analytical Approach for Elaboration in science

Recommendations in Visual Analytics - An Analytical Approach...

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International Conference on Information Visualisation (IV)

作者： Midhad Blazevic Lennart B. Sina Cristian A. Secco Kawa Nazemi Department of Media & Department of Computer Science Human-Computer Interaction and Visual Analytics Darmstadt University of Applied Sciences Darmstadt Germany

The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel Visual Analytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a Visual Analytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model.

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