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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Liang, Yu Collaris, Dennis Willemsen, Martijn C. Van Wijk, Jarke J. Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Dept. Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Jheronimus Academy of Data Science Den Bosch Netherlands

ISBN: (纸本)9798400713064

Research in Explainable AI (XAI) has shown that explanations can improve users' understanding of AI models, improve user performance and potentially reduce overreliance on AI predictions. However, this is mostly evaluated by static rather than dynamic measures, and the role of XAI on learning over trials is rarely studied. In this study, we use a context-free sequence prediction task, in which 458 participants predict the next symbol in a fixed sequence (with some noise) over 80 trials. We compare performance with AI and XAI advice against no AI support, and subsequently we test for learning by taking away the AI support after 40 trials (i.e., a reversal study design). Our results show that users learn faster with XAI than with AI without explanations or no AI and are better able to recover in performance from the removal of AI. However, the benefits of XAI on learning are much smaller for more difficult tasks. This work demonstrates the benefits of repeated measures user studies and multilevel modeling to better understand learning processes in XAI. It also shows the potential of AI explanations to help users to learn and poses XAI design suggestions to support learning in human-AI collaboration. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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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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Social Simulation for Everyday Self-Care: Design Insights from Leveraging VR, AR, and LLMs for Practicing Stress Relief 25

Social Simulation for Everyday Self-Care: Design Insights fr...

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

作者： Fang, Anna Chhabria, Hriday Maram, Alekhya Zhu, Haiyi Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University PittsburghPA United States University of Michigan Ann ArborMI United States Barnard College New YorkNY United States Human Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400713941

Stress is an inevitable part of day-to-day life yet many find themselves unable to manage it themselves, particularly when professional or peer support are not always readily available. As self-care becomes increasingly vital for mental well-being, this paper explores the potential of social simulation as a safe, virtual environment for practicing in-the-moment stress relief for everyday social situations. Leveraging the immersive capabilities of VR, AR, and LLMs to create realistic interactions and environments, we developed eight interactive prototypes for various social stress related scenarios (e.g. public speaking, interpersonal conflict) across design dimensions of modality, interactivity, and mental health guidance in order to conduct prototype-driven semi-structured interviews with 19 participants. Our qualitative findings reveal that people currently lack effective means to support themselves through everyday stress and perceive social simulation - even at low immersion and interaction levels - to fill a gap for practical, controlled training of mental health practices. We outline key design needs for developing social simulation for self-care needs, and identify important considerations including risks of trauma from hyper-realism, distrust of LLM-recommended timing for mental health recommendations, and the value of accessibility for self-care interventions. © 2025 Copyright held by the owner/author(s).

关键词： Virtual environments

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Artificial Intelligence Driven Predictive Analysis of Acoustic and Linguistic Behaviors for ASD Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5709-5719页

作者： Ashwini, B. Deeptanshu Gulati, Sheffali Shukla, Jainendra Human-Machine Interaction Laboratory Delhi New Delhi110020 India All India Institute of Medical Sciences Department of Pediatrics Child Neurology Division Centre of Excellence and Advanced Research for Childhood Neurodevelopmental Disorders New Delhi110029 India Department of Computer Science and Engineering Delhi New Delhi110020 India

The identification of autism spectrum disorder (ASD) faces challenges due to the lack of reliable biomarkers and the subjectivity in diagnostic procedures, necessitating improved tools for objectivity and efficiency. Being a key characteristic of autism, language impairments are regarded as potential markers for identifying ASD. However, current research predominantly focuses on analyzing language characteristics in English, overlooking linguistic and contextual specificities in other resource-constrained languages. Motivated by these, we developed an artificial intelligence (AI)-based system to detect ASD, utilizing a range of acoustic and linguistic features extracted from dyadic conversations between a child and their communication partner. Validating our model on 76 English-speaking children [35 ASD and 41 typically developing (TD)] and 33 Hindi-speaking children (15 ASD and 18 TD), our extensive analysis of a diverse and comprehensive set of acoustic and linguistic speech attributes, including lexical, syntactic, semantic, and pragmatic elements revealed reliable speech attributes as predictors of ASD. This comprehensive analysis achieved a remarkable macro F1-score of approximately ∼91.30%. We further addressed the influence of linguistic diversity on speech-based ASD assessment by examining speech behaviors in both English and the low-resource language, Hindi. Specific features such as adverbs and distinct roots contributed significantly to ASD classification in English, while the proportion of unintelligible utterances and adposition use held greater importance in Hindi. This study underscores the reliability of speech-based biomarkers in ASD assessment, emphasizing their effectiveness across diverse linguistic backgrounds and highlighting the need for language-specific research in this domain. © 2024 IEEE.

关键词： Semantics

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Advanced Markov Modeling and Simulation for Safety Analysis of Autonomous Driving Functions up to SAE 5 for Development, Approval and Main Inspection 32nd

Advanced Markov Modeling and Simulation for Safety Analysis ...

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32nd European Safety and Reliability Conference, ESREL 2022

作者： Häring, Ivo Satsrisakul, Yupak Finger, Jörg Vogelbacher, Georg Köpke, Corinna Höflinger, Fabian Gelhausen, Patrick Fraunhofer EMI Freiburg Germany University of Hagen Human-Computer Interaction Faculty of Mathematics and Computer Science Hagen Germany

ISBN: (纸本)9789811851834

The development, approval and recurring testing of driving assistance, partial and conditional automation (SAE levels 1 to 3) and, above all, high and full automation (4 to 5) requires an ever-increasing effort. At the same time, there is a lack of recognized sufficiently scalable general quantitative approaches in this area, in particular simulation methods including models, software and hardware up to vehicle-in-the-loop simulations, e.g. in the context of main inspection. In this context, the paper explores the potential of non-classical Markov modeling. First, it exemplifies how vehicles, drivers and other road users can be modeled for different driving scenarios using the Systems Modeling Language (SysML). Based on this model an abstract Markov diagram is presented. The two Markov simulation methods used operate on a discrete finite state space and allow for time-dependent state transitions. When compared to the matrix solver-based simulation, the Monte Carlo based simulation method is in principle extensible to state history-dependent as well as rule-based state transitions. Also, it allows to include subsystem simulation models. The extended Markov model allows to evaluate states with respect to functionality and safety, e.g. to determine whether it is sufficiently likely to reach fail operational states. It can also be used to determine dominant critical transitions and insufficient system resolution. In addition, numerous standardized safety and reliability measures are accessible. It will be shown how such a reference model can be quantified using simple failure models, as well as how it could be fed with further transition and failure models at different levels of abstraction using simulations as well as software tests data up to field test data. Finally, the paper hints at the potential of such an extended Markov modeling, especially with respect to an understandable and efficient safety verification in different product life cycle phases including after

关键词： Markov processes

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From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 6073-6087页

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong University of York Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Sciences United Kingdom Linkoping University Computer Vision Laboratory Sweden Nanyang Technological University College of Computing & Data Science Singapore China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi'an China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 1979-2012 IEEE.

关键词： computer aided design

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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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To Share or Not to Share: What Risks Would Laypeople Accept to Give Sensitive Data to Differentially-Private NLP Systems? 30

To Share or Not to Share: What Risks Would Laypeople Accept ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Weiss, Christopher Kreuter, Frauke Habernal, Ivan Trustworthy Human Language Technologies Department of Computer Science Technical University of Darmstadt Germany Department of Statistics Ludwig-Maximilians-Universität München Germany Trustworthy Human Language Technologies Department of Computer Science Paderborn University Germany

ISBN: (纸本)9782493814104

Although the NLP community has adopted central differential privacy as a go-to framework for privacy-preserving model training or data sharing, the choice and interpretation of the key parameter, privacy budget Ε that governs the strength of privacy protection, remains largely arbitrary. We argue that determining the Ε value should not be solely in the hands of researchers or system developers, but must also take into account the actual people who share their potentially sensitive data. In other words: Would you share your instant messages for Ε of 10? We address this research gap by designing, implementing, and conducting a behavioral experiment (311 lay participants) to study the behavior of people in uncertain decision-making situations with respect to privacy-threatening situations. Framing the risk perception in terms of two realistic NLP scenarios and using a vignette behavioral study help us determine what Ε thresholds would lead lay people to be willing to share sensitive textual data - to our knowledge, the first study of its kind. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sensitive data

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Systematic Evaluation Approach to Support Effective Web interaction among Visually Impaired Users

Systematic Evaluation Approach to Support Effective Web Inte...

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2022 Applied Informatics International Conference, AiIC 2022

作者： Yazid, Mariam Azwa Jantan, Azrul Hazri Universiti Putra Malaysia Faculty of Computer Science and Information Technology Human-Computer Interaction Research Group Serdang Malaysia

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

Today, Internet has become one of the most useful tools for visually impaired community. They use Internet to help with their daily activities such as communication, information retrieval, online shopping, transportation, and many more using interactive websites. However, with the complexity of current website design, it somehow gives them a hard time. Therefore, it is critical to evaluate the interaction experience perceived by the visually impaired users when interacting with website. Based on analysis of previous research, the evaluation can be measured through three major aspects which are user experience, web accessibility and technology acceptance. Those three aspects are expected to influence the overall judgement of web application itself. This paper is meant to provide some insight on the methodological approach for evaluation of interactive website which emphasis on web accessibility need by the visually impaired users. © 2022 IEEE.

关键词： Web Design

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