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2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020

作者： Lisle, Lee Chen, Xiaoyu Edward Gitre, J.K. North, Chris Bowman, Doug A. Center for Human-Computer Interaction Department of Computer Science Virginia Tech United States Virginia Tech Grado Department of Industrial and Systems Engineering United States Center for Human-Computer Interaction Department of History Virginia Tech United States

ISBN: (纸本)9781728165325

Sensemaking with large multimedia dataset is a cognitively intensive task that requires analysts to understand the underlying stories that the dataset tells. Often, analysts use tools in order to offload cognition as well as convey their new understanding of the dataset;however, existing tools are limited by their underlying technologies. We have proposed a novel virtual reality tool to support sensemaking called the Immersive Space to Think (IST). IST can aid the process of analyzing multimedia data, but it remains unproven whether IST improves sensemaking performance over a traditional desktop setting. In a study performed over six weeks, one participant used both IST and traditional methods of sensemaking with a dataset of 100 text documents of transcribed survey responses from World War 2 soldiers to perform historical analysis. The participant was asked guided questions that produced three essays with their understanding of the data. After conducting a blind evaluation of the participant's interpretation of the data, a team of three experts in historical analysis concluded that the essays written with IST displayed a better understanding of the dataset. Furthermore, the participant gave positive feedback on IST, and also suggested possible improvements. © 2020 IEEE.

关键词： Surveys

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A Dual-Prompting for Interpretable Mental Health Language Models

arXiv

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arXiv 2024年

作者： Jeon, Hyolim Yoo, Dongje Lee, Daeun Son, Sejung Kim, Seungbae Han, Jinyoung Department of Applied Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Human-AI Interaction Sungkyunkwan University Seoul Korea Republic of Department of Computer Engineering Chung-Ang University Seoul Korea Republic of Computer Science & Engineering Department University of South Florida TampaFL United States

Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability. The CLPsych 2024 Shared Task1 aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content. We propose a dual-prompting approach: (i) Knowledge-aware evidence extraction by leveraging the expert identity and a suicide dictionary with a mental health-specific LLM;and (ii) Evidence summarization by employing an LLM-based consistency evaluator. Comprehensive experiments demonstrate the effectiveness of combining domain-specific information, revealing performance improvements and the approach’s potential to aid clinicians in assessing mental state progression. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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

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

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

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

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

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%.

关键词： Training Adaptation models Time series analysis Production Reliability engineering Data models Software reliability Servers Anomaly detection Monitoring

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Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing

Auto-PIP: Real-time Identification of Critical Performance I...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Shenglin Zhang Xiao Xiong Mengyao Li Yongqian Sun Yongxin Zhao Xia Chen Bowen Deng Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area" (SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research.

关键词： Accuracy Manuals Software systems Market research Real-time systems Software reliability Stress Anomaly detection Optimization Testing

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Bioimaging of Dissolvable Microneedle Arrays: Challenges and Opportunities

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Research 2022年第4期2022卷 633-655页

作者： Yanni Wang Gehua Ma Guangzhi Gao Ji Tao Wenzhao Cao Haohao Sun Fengsen Ma Yilong Zhang Yen Wei Mei Tian Laboratory of Biologics and Biomaterials Collge of PharmacyZhejiang University of TechnologyHangzhou 310014China Cllge of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Human Phenome Institute Fudan UniversityShanghai 201203China College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China Life Science Research Center Frontier Crossing InstituteZhejiang University of TechnologyHangzhou 310023China Engineering Research Center of Intelligent Sensing and System Ministry of EducationHangzhou 310023China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China Department of Chemistry and the Tsinghua Center for Frontier Polymer Research Tsinghua UniversityBeijing 100084China

The emergence of microneedle arrays(MNAs)as a novel,simple,and minimally invasive administration approach largely addresses the challenges of traditional drug *** particular,the dissolvable MNAs act as a promising,multifarious,and well-controlled platform for micro-nanotransport in medical research and cosmetic formulation *** effective delivery mostly depends on the behavior of the MNAs penetrated into the body,and accurate assessment is urgently *** imaging technologies offer high sensitivity and resolution visualization of cross-scale,multidimensional,and multiparameter information,which can be used as an important aid for the evaluation and development of new *** combination of MNA technology and imaging can generate considerable new knowledge in a cost-effective manner with regards to the pharmacokinetics and bioavailability of active substances for the treatment of various *** addition,noninvasive imaging techniques allow rapid,receptive assessment of transdermal penetration and drug deposition in various tissues,which could greatly facilitate the translation of experimental MNAs into clinical *** on the recent promising development of bioimaging,this review is aimed at summarizing the current status,challenges,and future perspective on in vivo assessment of MNA drug delivery by various imaging technologies.

关键词： needle mostly formulation

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A Passive Variable Impedance Control Strategy with Viscoelastic Parameters Estimation of Soft Tissues for Safe Ultrasonography

A Passive Variable Impedance Control Strategy with Viscoelas...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Luca Beber Edoardo Lamon Davide Nardi Daniele Fontanelli Matteo Saveriano Luigi Palopoli Department of Industrial Engineering Università di Trento Trento Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the context of telehealth, robotic approaches have proven a valuable solution to in-person visits in remote areas, with decreased costs for patients and infection risks. In particular, in ultrasonography, robots have the potential to reproduce the skills required to acquire high-quality images while reducing the sonographer’s physical efforts. In this paper, we address the control of the interaction of the probe with the patient’s body, a critical aspect of ensuring safe and effective ultrasonography. We introduce a novel approach based on variable impedance control, allowing the real-time optimisation of compliant controller parameters during ultrasound procedures. This optimisation is formulated as a quadratic programming problem and incorporates physical constraints derived from viscoelastic parameter estimations. Safety and passivity constraints, including an energy tank, are also integrated to minimise potential risks during human-robot interaction. The proposed method’s efficacy is demonstrated through experiments on a patient’s dummy torso, highlighting its potential for achieving safe behaviour and accurate force control during ultrasound procedures, even in cases of contact loss.

关键词： Torso Parameter estimation Tracking Telemedicine Ultrasonography Safety Impedance

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Drone-based digital twins for water quality monitoring:A systematic review

Digital Twins and Applications

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Digital Twins and Applications 2024年第2期1卷 131-160页

作者： Abdulmughni Hamzah Faisal Aqlan Sabur Baidya Center for Human Systems Engineering University of LouisvilleLouisvilleKentuckyUSA Department of Industrial Engineering University of LouisvilleLouisvilleKentuckyUSA Department of Computer Science and Engineering University of LouisvilleLouisvilleKentuckyUSA

The rapid advancement of drone technology and digital twin systems has significantly transformed environmental monitoring,particularly in the field of water quality *** paper systematically reviews the current state of research on the application of drones,digital twins,and their integration for water quality monitoring and *** highlights key themes,insights,research trends,commonly used methodologies,and future directions from existing studies,aiming to provide a foundational reference for further research to harness the promising potential of these technologies for effective,scalable solutions in water resource management,addressing both immediate and long-term environmental *** systematic review followed PRISMA guidelines,rigorously analysing hundreds of relevant *** findings emphasise the effective-ness of drones in capturing real-time,high-resolution spatial and temporal data,as well as the value of digital twins for predictive and simulation-based *** importantly,the review demonstrates the potential of integrating these technologies to enhance sus-tainable water management ***,it also identifies a significant research gap in fully integrating drones with digital twins for comprehensive water quality *** response,the review outlines future research directions,including improvements in data integration techniques,predictive models,and interdisciplinary collaboration.

关键词： digital twin drones environmental management water monitoring water quality

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Virtual Reality for Health and Wellbeing 22

Virtual Reality for Health and Wellbeing

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21st International Conference on Mobile and Ubiquitous Multimedia, MUM 2022

作者： Ferreira-Brito, Filipa Gjoreski, Hristijan Mayora, Oscar Luštrek, Mitja Kizhevska, Emilija Guerreiro, João Gerling, Kathrin Bermúdez I Badia, Sergi Guerreiro, Tiago LASIGE Faculdade de Ciências da Universidade de Lisboa Portugal Ss. Cyril and Methodius University in Skopje Skopje Macedonia Center for Health and Wellbeing Fondazione Bruno Kessler Trento Italy Department of Intelligent Systems Jožef Stefan Institute Slovenia Slovenia Ss. Cyril and Methodius University in Skopje Macedonia KU Leuven Department of Computer Science Human-Computer Interaction Belgium Departamento de Engenharia Informatica and NOVA Laboratory for Computer Science and Informatics Universidade da Madeira Portugal and ARDITI Portugal

ISBN: (纸本)9781450398213

Virtual Reality (VR) has become a mainstream opportunity to develop immersive environments where people are exposed to relevant stimuli in a systematic but safe way. Besides the controlled exposure to stimuli, VR systems enable a more efficient and unbiased gathering of information with practical applications in diagnosis, intervention, and health monitoring processes. Nonetheless, scientific literature has demonstrated that interacting with VR technology and its peripherals can be challenging, especially in populations less familiar with technology (e.g., older adults) or with clinical conditions that are not adequately accommodated by the technology, such as cognitive (e.g., dementia) or physical impairments (e.g., blindness). This workshop will focus on systems, technological approaches, and theoretical frameworks to develop user/patient-centred VR tools for diagnosis, intervention, and monitoring of health and wellbeing outcomes. Moreover, we welcome work focused on accessibility issues, aiming to promote equal access and opportunity to use VR tools by people with diverse abilities. © 2022 ACM.

关键词： Virtual reality

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Learning Adaptation and Generalization from human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis 6

Learning Adaptation and Generalization from Human-Inspired M...

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6th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023

作者： Ho, Joshua Wang, Chien-Min King, Chung-Ta You, Yi-Hsin Feng, Chi-Wei Chen, Yen-Min Kuo, Bo-Yi Institute of Information Science Academia Sinica Taipei11529 Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Taiwan University Taipei10631 Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei10022 Taiwan

ISBN: (纸本)9798350331288

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a humaninspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future. © 2023 IEEE.

关键词： Reinforcement learning

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Raise Your Eyebrows Higher: Facilitating Emotional Communication in Social Virtual Reality Through Region-Specific Facial Expression Exaggeration 25

Raise Your Eyebrows Higher: Facilitating Emotional Communica...

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

作者： Xueyang Wang Sheng Zhao Yihe Wang Howard Ziyu Han Xinge Liu Xin Yi Xin Tong Hewu Li Institute for Network Sciences and Cyberspace Tsinghua University Beijing China and Zhongguancun Laboratory Beijing China Weiyang College Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University pittsburgh Pennsylvania USA Department of Computer Science and Technology Tsinghua University Beijing China and Beijing National Research Center for Information Science and Technology Beijing China Thrust of Computational Media and Arts Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

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