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Virtual Reality (ICVR), IEEE International Conference on

作者： Chiwoong Hwang Tiare Feuchtner Kaj Grønbæk Department of Computer Science Aarhus University Denmark Human-Computer Interaction Group Konstanz University Germany

ISBN: (数字)9798350364231

ISBN: (纸本)9798350364248

Training in virtual reality (VR) offers advantages such as a safe learning environment, good scalability, and cost saving. However, state-of-the-art simulations are limited in faithfully conveying real-world physical object properties such as weight without complex technologies and instrumentation of the user or environment, which may negatively impact learning. A potential solution is the application of pseudo-haptics: illusory haptic sensations induced by a mismatch between real and virtual hand positions. Researchers have proposed various pseudo-haptic techniques to alter the perception of weight in virtual environments, but their effectiveness with hand tracking compared to VR controllers is underexplored. Hence, we investigate impacts of the interaction method for two different types of pseudo-haptic feedback based on Tracking Delay and Vertical Offset. Our results indicate that pseudo-haptics can effectively facilitate the sensation of weight both with controllers and bare hands, whereby each has benefits: hand tracking can enhance body ownership, while controllers may improve perceived responsiveness of virtual environments. Also, pseudo-haptics with Tracking Delay may be preferable for repetitive lifting tasks, as Vertical Offset can be prone to higher physical demand. The current study provides insights into the design of pseudo-haptic feedback and interaction technologies for weight perception in VR.

关键词： Hands Training Manufacturing industries Solid modeling Scalability Instruments Virtual environments User experience Delays Haptic interfaces

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Towards Intelligent Augmented Reality (iAR): A Taxonomy of Context, an Architecture for iAR, and an Empirical Study

arXiv

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arXiv 2024年

作者： Davari, Shakiba Stover, Daniel Giovannelli, Alexander Ilo, Cory Bowman, Doug A. Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States

Recent advancements in Augmented Reality (AR) research have highlighted the critical role of context awareness in enhancing interface effectiveness and user experience. This underscores the need for intelligent AR (iAR) interfaces that dynamically adapt across various contexts to provide optimal experiences. In this paper, we (a) propose a comprehensive framework for contextaware inference and adaptation in iAR, (b) introduce a taxonomy that describes context through quantifiable input data, and (c) present an architecture that outlines the implementation of our proposed framework and taxonomy within iAR. Additionally, we present an empirical AR experiment to observe user behavior and record user performance, context, and user-specified adaptations to the AR interfaces within a context-switching scenario. We (d) explore the nuanced relationships between context and user adaptations in this scenario and discuss the significance of our framework in identifying these patterns. This experiment emphasizes the significance of context-awareness in iAR and provides a preliminary training dataset for this specific Scenario. © 2024, CC BY-NC-ND.

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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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An Energy-Efficient Task Scheduling Strategy Based on Improved Fireworks Algorithm in Heterogeneous Cloud 23

An Energy-Efficient Task Scheduling Strategy Based on Improv...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhao, Hui Meng, Fanxin Feng, Nanzhi Wang, Quan Wan, Bo Wang, Jing School of Computer Science and Technology Xidian University Xi'an China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi'an China

ISBN: (纸本)9781665494571

With the development of cloud computing, energy consumption is getting bigger and bigger, and energy optimization has become one of the research focuses. Task scheduling is an efficient way to reduce the energy consumption of cloud. Most previous works ignored the heterogeneity of physical machines (PMs) configured with different CPUs and GPUs, which may result in high energy consumption in heterogeneous cloud. In this paper, an energy-efficient Task Scheduling Strategy based on Improved Fireworks Algorithm (TSS-IFWA) is proposed to reduce the energy consumption of the CPU/GPU heterogeneous cloud. First, a task scheduling model is constructed, in which the energy consumption of CPUs and GPUs are separately consid-ered. Then we obtain the task scheduling optimization problem of minimizing the energy consumption of heterogeneous cloud. Second, as traditional fireworks algorithm consumes massive computing resource and has a poor convergence performance, we improve a new calculation method of explosion amplitude of the fireworks algorithm. We also optimize the mutation strategy. Third, we utilize the improved fireworks algorithm to solve the task scheduling optimization problem. Specifically, the corre-sponding coding and decoding schemes of the fireworks algorithm are designed to initialize the fireworks individuals. We then obtain task scheduling results. Finally, TSS- IFWA is compared with traditional fireworks algorithm (FWA), round-robin algorithm (RR), and genetic algorithm (GA). The experimental results show that TSS- IFWA has a good performance in reducing energy consumption of heterogeneous cloud. © 2021 IEEE.

关键词： Cloud computing Fireworks algorithm Energy consumption Processor scheduling Scheduling Energy efficiency Explosions

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Layered interactions: Exploring Non-Intrusive Digital Craftsmanship Design Through Lacquer Art Interfaces 25

Layered Interactions: Exploring Non-Intrusive Digital Crafts...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Dong, Yan Yu, Hanjie Chen, Yanran Zhang, Zipeng Qiong, Wu Department of Information Art & Design Academy of Arts & Design Tsinghua University Beijing China Pervasive Human Computer Interaction Laboratory Tsinghua University Beijing China

ISBN: (纸本)9798400713941

Integrating technology with the distinctive characteristics of craftsmanship has become a key issue in the field of digital craftsmanship. This paper introduces Layered interactions, a design approach that seamlessly merges human-computer interaction (HCI) technologies with traditional lacquerware craftsmanship. By leveraging the multi-layer structure and material properties of lacquerware, we embed interactive circuits and integrate programmable hardware within the layers, creating tangible interface that support diverse interactions. This method enhances the adaptability and practicality of traditional crafts in modern digital contexts. Through the development of a lacquerware toolkit, along with user experiments and semi-structured interviews, we demonstrate that this approach not only makes technology more accessible to traditional artisans but also enhances the materiality and emotional qualities of interactive interfaces. Additionally, it fosters mutual learning and collaboration between artisans and technologists. Our research introduces a cross-disciplinary perspective to the HCI community, broadening the material and design possibilities for interactive interfaces. © 2025 Copyright held by the owner/author(s).

关键词： Arts Digital Craft DIY Tangible interaction Toolkit

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LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices 35

LabelEase: A Semi-Automatic Tool for Efficient and Accurate ...

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35th IEEE International Symposium on Software Reliability Engineering, ISSRE 2024

作者： Zhang, Shenglin Che, Zeyu Pan, Zhongjie Nie, Xiaohui Sun, Yongqian Pan, Lemeng Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Chinese Academy of Sciences Computer Network Information Center China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei China Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350353884

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose LabelEase, a novel semi-Automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, LabelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, LabelEase aggregates the labeled anomalous traces and identifies the service's failures for operators to label. Our systematic experiments on two large-scale datasets show that LabelEase achieves over 0.98 F1-score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, LabelEase can reduce operators' labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-Automatic trace labeling tool capable of achieving efficient and accurate trace labeling. © 2024 IEEE.

关键词： Labeled data

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A Dual-Prompting for Interpretable Mental Health Language Models 9

A Dual-Prompting for Interpretable Mental Health Language Mo...

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9th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2024

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

ISBN: (纸本)9798891760806

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. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Predicting the Noticeability of Dynamic Virtual Elements in Virtual Reality 24

Predicting the Noticeability of Dynamic Virtual Elements in ...

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Proceedings of the CHI Conference on human Factors in Computing Systems

作者： Zhipeng Li Yi Fei Cheng Yukang Yan David Lindlbauer Human-Computer Interaction Institute Carnegie Mellon University United States and Department of Computer Science and Technology Tsinghua University China Human-Computer Interaction Institute Carnegie Mellon University United States

ISBN: (纸本)9798400703300

While Virtual Reality (VR) systems can present virtual elements such as notifications anywhere, designing them so they are not missed by or distracting to users is highly challenging for content creators. To address this challenge, we introduce a novel approach to predict the noticeability of virtual elements. It computes the visual saliency distribution of what users see, and analyzes the temporal changes of the distribution with respect to the dynamic virtual elements that are animated. The computed features serve as input for a long short-term memory (LSTM) model that predicts whether a virtual element will be noticed. Our approach is based on data collected from 24 users in different VR environments performing tasks such as watching a video or typing. We evaluate our approach (n = 12), and show that it can predict the timing of when users notice a change to a virtual element within 2.56 sec compared to a ground truth, and demonstrate the versatility of our approach with a set of applications. We believe that our predictive approach opens the path for computational design tools that assist VR content creators in creating interfaces that automatically adapt virtual elements based on noticeability.

关键词： Computational interaction Mixed Reality Virtual Reality

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A Mapping-based Dynamic Semi-Online Task Scheduling Method for Minimizing Energy in Edge Computing 23

A Mapping-based Dynamic Semi-Online Task Scheduling Method f...

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作者： Zhao, Hui Feng, Nanzhi Meng, Fanxin Wang, Quan Wan, Bo Wang, Jing School of Computer Science and Technology Xidian University Xi'an China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi'an China

ISBN: (纸本)9781665494571

Semi-online task scheduling in the edge computing platform refers to a scheduling scenario where there exists unknown performance computing nodes in the system. Existing online and offline task scheduling methods may lead to long makespan or transmission time due to the influence of unknown nodes, and existing semi-online methods are not fully applicable to the scenario discussed in this paper, which aggravates the problem of high energy consumption in edge computing plat-form. To solve this problem, this paper proposes a Mapping-based Dynamic Semi-online Scheduling(MDSS) strategy for edge computing platform. Firstly, we consider three main factors that affect the energy consumption in edge computing platform, that is, the energy consumption of task execution, task transmission and idle state. Then an energy consumption optimization problem is established from the perspectives of the processing speed, routing delay and queuing delay of the edge nodes, which correspond to those three factors. Secondly, for the unknown node, this paper introduces a mapping mechanism, which maps the performance of unknown nodes to a certain given node. Then the mapping relations are dynamically adjusted by continuously checking the task queue lengths of the mapped two-sides. Therefore, the prior knowledge of known nodes can be fully used. Thirdly, an im-proved Power-of- D algorithm based on the mapping mechanism is proposed to solving the formulated problem. It utilizes the prior knowledge of known node performance, which reduces task processing time and transmission time, so that the energy consumption can be reduced. Finally, some experiments are conducted to evaluate MDSS. Results show that the dynamic mapping mechanism is effective in the semi-online scenario and MDSS can greatly reduce the energy consumption in the edge computing platform. © 2021 IEEE.

关键词： Energy consumption Processor scheduling Heuristic algorithms Dynamic scheduling Routing Delays Task analysis

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Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes 10

Summit Vitals: Multi-Camera and Multi-Signal Biosensing at H...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Ke Tang, Jiankai Jiang, Zhang Wang, Yuntao Liu, Xiaojing Li, Dong Shi, Yuanchun Qinghai University Department of Computer Technology and Applications Qinghai810016 China National Key Laboratory of Human Factors Engineering Beijing100094 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

ISBN: (纸本)9798331520861

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO 2 predictions by 7. 6 % and 1 0. 6 % compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2. 5 % for SpO 2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO 2 estimation by 1 7. 8 %. © 2024 IEEE.

关键词： Photoplethysmography

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