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

作者： Zihe Ran Xiyu Li Qing Xiao Xianzhe Fan Franklin Mingzhe Li Yanyun Wang Zhicong Lu Communication university of China Beijing Beijing China Communication University of China Beijing Beijing China Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Tsinghua University Beijing Beijing China College of Media Communication and Information University of Colorado Boulder Boulder Colorado USA Department of Computer Science George Mason University Fairfax Virginia USA

ISBN: (纸本)9798400713941

As blind and low-vision (BLV) players engage more deeply with games, accessibility features have become essential. While some research has explored tools and strategies to enhance game accessibility, the specific experiences of these players with mobile games remain underexamined. This study addresses this gap by investigating how BLV users experience mobile games with varying accessibility levels. Through interviews with 32 experienced BLV mobile players, we explore their perceptions, challenges, and strategies for engaging with mobile games. Our findings reveal that BLV players turn to mobile games to alleviate boredom, achieve a sense of accomplishment, and build social connections, but face barriers depending on the game's accessibility level. We also compare mobile games to other forms of gaming, highlighting the relative advantages of mobile games, such as the inherent accessibility of smartphones. This study contributes to understanding BLV mobile gaming experiences and provides insights for enhancing accessible mobile game design. © 2025 Copyright held by the owner/author(s).

关键词： Game Accessibility

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Augmenting Scientific Creativity with Retrieval across Knowledge Domains

arXiv

引用

arXiv 2022年

作者： Kang, Hyeonsu B. Mysore, Sheshera Huang, Kevin Chang, Haw-Shiuan Prein, Thorben McCallum, Andrew Kittur, Aniket Olivetti, Elsa Human-Computer Interaction Institute CMU PA United States Manning College of Information and Computer Sciences UMASS AmherstMA United States Department of Materials Science and Engineering MIT MA United States

Exposure to ideas in domains outside a scientist’s own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas outside such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can ‘zoom in’ to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration. © 2022, CC BY.

关键词： Search engines

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Communicative Grounding of Analogical Explanations in Dialogue: A Corpus Study of Conversational Management Acts and Statistical Sequence Models for Tutoring through Analogy

Communicative Grounding of Analogical Explanations in Dialog...

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2021 Conference on Reasoning and interaction, ReInAct 2021

作者： Del-Bosque-Trevino, Jorge Hough, Julian Purver, Matthew Education Lab. Computational Linguistics Lab. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Human Interaction Lab. Computational Linguistics Lab. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Jožef Stefan Institute Computational Linguistics Lab. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

ISBN: (纸本)9781955917070

We present a conversational management act (CMA) annotation schema for one-to-one tutorial dialogue sessions where a tutor uses an analogy to teach a student a concept. CMAs are more fine-grained sub-utterance acts compared to traditional dialogue act markup. The schema achieves an inter-annotator agreement (IAA) Cohen Kappa score of at least 0.66 across all 10 classes. We annotate a corpus of analogical episodes with the schema and develop statistical sequence models from the corpus which predict tutor content related decisions, in terms of the selection of the analogical component (AC) and tutor conversational management act (TCMA) to deploy at the current utterance, given the student’s behaviour. CRF sequence classifiers perform well on AC selection and robustly on TCMA selection, achieving respective accuracies of 61.9% and 56.3% on a cross-validation experiment over the corpus. © 2021 Association for Computational Linguistics.

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Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm

Towards Robotised Palpation for Cancer Detection through Onl...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Luca Beber Edoardo Lamon Giacomo Moretti Daniele Fontanelli Matteo Saveriano Luigi Palopoli Department of Information Engineering and Computer Science Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy Department of Industrial Engineering Università di Trento Trento Italy

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a new method for online estimating the penetration of the end-effector and the viscoelastic properties of a soft body, through palpation exams using a collaborative robotic arm. The estimator is based on the dimensionality reduction method that simplifies the nonlinear Hunt-Crossley model. In addition, in our algorithm, the model parameters can be found without a force sensor, leveraging only the robotic arm controller data. An extended Kalman filter is employed to achieve online estimation, which embeds the dynamic contact model. The algorithm is tested with various types of silicone, a material that resembles biological tissues, including samples with hard intrusions to simulate cancerous cells within a softer tissue. The results indicate that this technique can accurately determine the model parameters and estimate the penetration of the end-effector into the soft body. These promising preliminary results demonstrate robots’ potential to be an effective tool for early-stage cancer diagnostics.

关键词： Dimensionality reduction Biological system modeling Heuristic algorithms Biological tissues Estimation Collaboration Robot sensing systems Information filters End effectors Kalman filters

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A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

A Passivity-Based Variable Impedance Controller for Incremen...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Matteo Dalle Vedove Edoardo Lamon Daniele Fontanelli Luigi Palopoli Matteo Saveriano Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines 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)9798350358513

ISBN: (纸本)9798350358520

In intelligent manufacturing, robots are asked to dynamically adapt their behaviours without reducing productivity. human teaching, where an operator physically interacts with the robot to demonstrate a new task, is a promising strategy to quickly and intuitively reconfigure the production line. However, physical guidance during task execution poses challenges in terms of both operator safety and system usability. In this paper, we solve this issue by designing a variable impedance control strategy that regulates the interaction with the environment and the physical demonstrations, explicitly preventing at the same time passivity violations. We derive constraints to limit not only the exchanged energy with the environment but also the exchanged power, resulting in smoother interactions. By monitoring the energy flow between the robot and the environment, we are able to distinguish between disturbances (to be rejected) and physical guidance (to be accomplished), enabling smooth and controlled transitions from teaching to execution and vice versa. The effectiveness of the proposed approach is validated in wiping tasks with a real robotic manipulator.

关键词： Surface impedance Incremental learning Tracking Education End effectors Safety Impedance Usability Robots Oscillators

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Machine learning-based prediction of low-value care for hospitalized patients

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Intelligence-Based Medicine 2023年 8卷 100115-100115页

作者： King, Andrew J. Tang, Lu Davis, Billie S. Preum, Sarah M. Bukowski, Leigh A. Zimmerman, John Kahn, Jeremy M. Department of Critical Care Medicine University of Pittsburgh School of Medicine Pittsburgh PA United States Department of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA United States Department of Computer Science Dartmouth College Hanover NH United States Human-Computer Interaction Institute Carnegie Mellon University School of Computer Science Pittsburgh PA United States

Objective: Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods: We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period;and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results: We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion: Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system. © 2023 The Authors

关键词： Clinical decision support Evidence-based medicine Low-value care Machine learning Resuscitation

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HCIlab at Memotion 2.0 2022: Analysis of Sentiment, Emotion and Intensity of Emotion Classes from Meme Images using Single and Multi Modalities 1

HCILab at Memotion 2.0 2022: Analysis of Sentiment, Emotion ...

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1st Workshop on Multimodal Fact-Checking and Hate Speech Detection, DE-FACTIFY 2022

作者： Nguyen, Thanh Tin Pham, Nhat Truong Nguyen, Ngoc Duy Nguyen, Hai Nguyen, Long H. Kim, Yong-Guk Human Computer Interaction Lab Department of Computer Engineering Sejong University Seoul Korea Republic of Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Institute for Intelligent Systems Research and Innovation Deakin University Victoria Australia Khoury College of Computer Sciences Northeastern University Boston United States Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

Nowadays, memes found on internet are overwhelming. Although they are innocuous and sometimes entertaining, there exist memes that contain sarcasm, offensive, or motivational feelings. In this study, several approaches are proposed to solve the multiple modality problem in analysing the given meme dataset. The imbalance issue has been addressed by using a new Auto Augmentation method and the uncorrelation issue has been mitigated by adopting deep Canonical Correlation Analysis to find the most correlated projections of visual and textual feature embedding. In addition, both stacked attention and multi-hop attention network are employed to efficiently generate aggregated features. As a result, our team, i.e. HCIlab, achieved a weighted F1 score of 0.4995 for sentiment analysis, 0.7414 for emotion classification, and 0.5301 for scale/intensity of emotion classes on the leaderboard. This results are obtained by using concatenation between image and text model and our code can be found at https://***/JMRa8. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Sentiment analysis

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Mediating The Marginal: A Quantitative Analysis of Curated LGBTQ+ Content on Instagram 25

Mediating The Marginal: A Quantitative Analysis of Curated L...

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

作者： Garrett Souza Nina Lutz Katlyn M Turner Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Human Centered Design and Engineering University of Washington Seattle Washington USA Media Lab Massachusetts Institute of Technology Cambridge Massachusetts USA

ISBN: (纸本)9798400713941

Control and curation of dominant visual culture - rendering who and what is visible - is central to identity formation, particularly for LGBTQ+ communities relying on digital spaces for safe self-expression. In this work, we analyze Instagram as a site of algorithmic visual curation, performing a quantitative analysis of algorithmically mediated image feeds delivered to a gay-coded user. Our persona account exclusively followed #gay and #instagay feeds, and engaged in content within these discursive spaces to seed algorithmic content promotion to a normative gay user. We present an analysis of skin tone presentations, emoji usage, and engagement metrics alongside analysis of generative outputs of dominant visual trends within the #gay search and Explore feeds. We observe content depicting darker-skinned individuals has higher engagement yet less algorithmic promotion relative to lighter skin tones, while hypermasculine and homonormative content is heavily promoted. These results suggest that, while marginalized positionalities have certainly been rendered more visible through social media platforms, this visibility is increasingly contingent on assimilation to normative ideals through algorithmically determined modes that are not necessarily consistent with user choices, preferences, or realities. © 2025 Copyright held by the owner/author(s).

关键词： Social Networks

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A 'plug and play' approach to testing virtual environment interaction techniques 6

A 'plug and play' approach to testing virtual environment in...

引用

6th Eurographics Workshop on Virtual Environments, EGVE 2000

作者： Willans, James S. Harrison, Michael D. Human-Computer Interaction Group Department of Computer Science University of York Heslington YorkYO10 5DD United Kingdom

ISBN: (纸本)3211835164

It is generally agreed that the usability of virtual environment interaction techniques is very poor. One reason for this is because toolkits used by virtual environment developers supply a small number of predefined techniques which are expected to be used regardless of context. In addition, there is no software to facilitate the design and testing of interaction techniques akin to that found for the appearance of the environment. We have developed the Marigold toolset to aid in the systematic design, testing and refining of virtual environment interaction techniques. The toolset uses a visual hybrid specification as a starting point. In this paper we demonstrate how Marigold can be used to aid in determining the suitability of interaction techniques by the rapid testing of alternatives in a `plug and play' style. © 2000 Proceedings of the 6th Eurographics Workshop on Virtual Environments, EGVE 2000. All rights reserved.

关键词： Virtual reality

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Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media

arXiv

引用

arXiv 2021年

作者： Wu, Julia Sivaraman, Venkatesh Kumar, Dheekshita Banda, Juan M. Sontag, David Dept of Electrical Engineering and Computer Science Massachusetts Institute of Technology Human-Computer Interaction Institute Carnegie Mellon University Department of Computer Science Georgia State University

The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research. © 2021, CC BY.

关键词： Data mining

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