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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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In the Moment of Glitch: Engaging with Misalignments in Ethical Practice 25

In the Moment of Glitch: Engaging with Misalignments in Ethi...

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

作者： Garrett, Rachael Hawkins, Kat Brundell, Patrick Castle-Green, Simon Tennent, Paul Zhou, Feng Lampinen, Airi Höök, Kristina Benford, Steve Media Technology and Interaction Design Kth Royal Institute of Technology Stockholm Sweden Centre for Dance Research Coventry University Coventry United Kingdom Mixed Reality Lab School of Computer Science University of Nottingham Nottingham United Kingdom Computer Science University of Nottingham Nottingham United Kingdom Computer Science / Mixed Reality Lab The University of Nottingham Nottingham United Kingdom Department of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (纸本)9798400713941

Glitches - moments when technologies do not work as desired - will become increasingly common as industrially-designed robots move into complex contexts. Taking glitches to be potential sites of critical ethical reflection, we examine a glitch that occurred in the context of a collaborative research project where professional dancers with different disabilities improvised with a robotic arm. Through a first-person account, we analyse how the dancer, the robot, and the rest of the research team enacted ethics in the moment of glitch. Through this analysis, we discovered a deep and implicit ethical misalignment wherein our enactments of ethics in response to the glitch did not align with the values of the project. This prompted a critical re-engagement with our research process through which we forged a dialogue between different ethical perspectives that acted as an invitation to bring us back into ethical alignment with the project's values. © 2025 Copyright held by the owner/author(s).

关键词： artist-led research crip feminism dance disability ethics felt ethics glitches misalignment research ethics robots somabotics

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Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

Self-Prompting Large Language Models for Zero-Shot Open-Doma...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: human Language Technologies, NAACL 2024

作者： Li, Junlong Wang, Jinyuan Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China SJTU-Paris Elite Institute of Technology Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9798891761148

Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. This task becomes notably challenging in a zero-shot setting where no data is available to train tailored retrieval-reader models. While recent Large Language Models (LLMs) like GPT-3 have demonstrated their effectiveness in zero-shot ODQA using direct prompting methods, these methods still fall short of fully harnessing the potential of LLMs when implicitly invoked. In this paper, we propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of LLMs and their strong instruction understanding abilities. Concretely, we prompt LLMs step by step to generate multiple pseudo QA pairs with background passages and explanations entirely from scratch. These generated elements are then utilized for in-context learning. Experimental results show that our method significantly surpasses previous state-of-the-art zero-shot methods on three widely-used ODQA datasets and even achieves comparable performance with various customized fine-tuned models on full training data. ©2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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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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LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds

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IEEE Access 2025年 13卷 85507-85525页

作者： Kočić, Veljka Lukač, Niko Rožajac, Dž Schweng, Stefan Gollob, Christoph Nothdurft, Arne Stampfer, Karl Ser, Javier Del Holzinger, Andreas University of Natural Resources and Life Sciences Vienna Human-Centered AI Lab Institute of Forest Engineering Department of Ecosystem Management Climate and Biodiversity Vienna1190 Austria TU Graz Institute of Human-Centered Computing Graz8010 Austria University of Maribor Faculty of Electrical Engineering and Computer Science Maribor2000 Slovenia Institute of Forest Growth Department of Ecosystem Management Climate and Biodiversity University of Natural Resources and Life Sciences Vienna Vienna1190 Austria TECNALIA Derio48160 Spain Department of Mathematics Leioa48940 Spain

Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to real-world applications where explainability and robustness are paramount. © 2013 IEEE.

关键词： k-nearest neighbors

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On Single Valued Neutrosophic Regularity Spaces

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computer Modeling in Engineering & sciences 2022年第3期130卷 1625-1648页

作者： Yaser Saber Fahad Alsharari Florentin Smarandache Mohammed Abdel-Sattar Department of Mathematics College of Science and Human StudiesHotat SudairMajmaah UniversityMajmaah11952Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt Department of Mathematics University of New MexicoGallupNM87301USA Department of Mathematics College of Science and Arts King Khaled UniversityMhayal Asier61913Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceBeni-Suef UniversityBeni-Suef62511Egypt Department of Mathematics College of Science and ArtsJouf UniversityGurayat77455Saudi Arabia

This article aims to present new terms of single-valued neutrosophic notions in theˇSostak sense,known as singlevalued neutrosophic regularity *** such as r-single-valued neutrosophic semi￡-open,r-single-valued neutrosophic pre-￡-open,r-single valued neutrosophic regular-￡-open and r-single valued neutrosophicα￡-open are defined and their properties are studied as well as the relationship between ***,we introduce the concept of r-single valued neutrosophicθ￡-cluster point and r-single-valued neutrosophicγ￡-cluster point,r-θ￡-closed,andθ￡-closure operators and study some of their ***,we present and investigate the notions of r-single-valued neutrosophicθ￡-connectedness and r-single valued neutrosophicδ￡-connectedness and investigate relationship with single-valued neutrosophic almost￡-*** compare all these forms of connectedness and investigate their properties in single-valued neutrosophic semiregular and single-valued neutrosophic almost regular in neutrosophic ideal topological spaces inˇSostak *** usefulness of these concepts are incorporated to multiple attribute groups of comparison within the connectedness and separateness ofθ￡andδ￡.

关键词： Single valued neutrosophicθ￡-closed single valued neutrosophicθ￡-separated single valued neutrosophicδ￡-separated single-valued neutrosophicδ￡-connected single valued neutrosophicδ￡-connected single valued neutrosophic almost￡-egular

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Friction in Processual Ethics: Reconfiguring Ethical Relations in Interdisciplinary Research 25

Friction in Processual Ethics: Reconfiguring Ethical Relatio...

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

作者： Garrett, Rachael Brundell, Patrick Castle-Green, Simon Hawkins, Kat Tennent, Paul Zhou, Feng Lampinen, Airi Höök, Kristina Benford, Steve Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Mixed Reality Lab School of Computer Science University of Nottingham Nottingham United Kingdom Computer Science University of Nottingham Nottingham United Kingdom Centre for Dance Research Coventry University Coventry United Kingdom Computer Science Mixed Reality Lab University of Nottingham Nottingham United Kingdom Department of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (纸本)9798400713941

Friction - disagreement and breakdown - is an omnipresent aspect of conducting interdisciplinary research yet is rarely presented in formal research reporting. We analyse a performance-led research process where professional dancers with different disabilities explored how to improvise with an industrial robot, with the support of an interdisciplinary team of human-computer and human-robot interaction researchers. We focus on one site of friction in our research process;how to dance - safely - with robots? By presenting our research process, we exemplify the different ways in which we encountered this friction and how we reconfigured the research process around it. We contribute five ways in which we arrived at a generative ethical outcome, which may be helpful in productively engaging with friction in interdisciplinary collaboration. © 2025 Copyright held by the owner/author(s).

关键词： artist-led research crip feminism dance disability ethics felt ethics friction misalignment processual ethics research ethics robots somabotics

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Sound Tagging in Infant-centric Home Soundscapes

Sound Tagging in Infant-centric Home Soundscapes

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IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Mohammad Nur Hossain Khan Jialu Li Nancy L. McElwain Mark Hasegawa–Johnson Bashima Islam Department of Electrical and Computer Engineering Worcester Polytechnic Institute Department of Electrical and Computer Engineering University of Illinois Department of Human Development and Family Studies University of Illinois Beckman Institute for Advanced Science and Technology University of Illinois

ISBN: (数字)9798350345018

ISBN: (纸本)9798350345025

Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic environment is an active research area, previous studies focused on data collected from a non-stationary microphone placed in the environment or from the perspective of adults. Further, many of these works ignore infants or young children in the environment or have data collected from only a single family where noise from the fixed sound source can be moderate at the infant's position or vice versa. Thus, despite the recent success of large pre-trained models for noise event detection, the performance of these models on infant-centric noise soundscapes in the home is yet to be explored. To bridge this gap, we have collected and labeled noises in home soundscapes from 22 families in an unobtrusive manner, where the data are collected through an infant-worn recording device. In this paper, we explore the performance of a large pre-trained model (Audio Spectrogram Transformer [AST]) on our noise-conditioned infant-centric environmental data as well as publicly available home environmental datasets. Utilizing different training strategies such as resampling, utilizing public datasets, mixing public and infant-centric training sets, and data augmentation using noise and masking, we evaluate the performance of a large pre-trained model on sparse and imbalanced infant-centric data. Our results show that fine-tuning the large pre-trained model by combining our collected dataset with public datasets increases the F1-score from 0.11 (public datasets) and 0.76 (collected datasets) to 0.84 (combined datasets) and Cohen's Kappa from 0.013 (public datasets) and 0.77 (collected datasets) to 0.83 (combined datasets) compared to only training with public or collected datasets, respectively.

关键词： Training Performance evaluation Pediatrics Working environment noise Noise Tagging Transformers

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Boundary Conditions for human Gaze Estimation on A Social Robot using State-of-the-Art Models

Boundary Conditions for Human Gaze Estimation on A Social Ro...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Linlin Cheng Artem V. Belopolsky Koen V. Hindriks Faculty of Science Computer Science Vrije Universiteit Amsterdam Department of Human Movement Sciences Vrije Universiteit Amsterdam

Appearance-based methods are a promising solution for gaze estimation, as they eliminate the need for additional devices and calibration. This makes them particularly well-suited for human-robot interaction (HRI) research. However, until recently their performance was under par compared to traditional eye-trackers. Recent breakthroughs have been made with the release of two large-scale datasets with a wide range of gaze directions (Gaze360 and ETH-XGaze) and the accompanying state-of-the-art deep neural networks (L2CS and ETH). In this paper, we systematically evaluate the performance of these two appearance-based models on a social robot. In our setup, we vary the distance from the robot (1-3m) and camera resolution (640 * 480 and 3840 * 2160) and analyze the performance in terms of accuracy and precision. We find that the L2CS model trained on the Gaze360 dataset combined with a 4K camera achieves the best performance on the 2 m and 3 m distances. We show that a simple offset correction on pitch and yaw can further increase the accuracy and precision by 18.6% and 9.6% respectively. We conclude that for a range up to 3 m appearance-based gaze estimation models provide a promising approach for application in HRI research.

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Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions

arXiv

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arXiv 2024年

作者： Xiao, Qing Zheng, Yuhang Fan, Xianzhe Zhang, Bingbing Lu, Zhicong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States University of Amsterdam Amsterdam Netherlands Tsinghua University Beijing China School of Journalism and Mass Communication University of Iowa Iowa CityIA United States Department of Computer Science George Mason University FairfaxVA United States

Previous research pays attention to how users strategically understand and consciously interact with algorithms but mainly focuses on an individual level, making it difficult to explore how users within communities could develop a collective understanding of algorithms and organize collective algorithmic actions. Through a two-year ethnography of online fan activities, this study investigates 43 core fans who always organize large-scale fans collective actions and their corresponding general fan groups. This study aims to reveal how these core fans mobilize millions of general fans through collective algorithmic actions. These core fans reported the rhetorical strategies used to persuade general fans, the steps taken to build a collective understanding of algorithms, and the collaborative processes that adapt collective actions across platforms and cultures. Our findings highlight the key factors that enable computer-supported collective algorithmic actions and extend collective action research into the large-scale domain targeting algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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