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SSRN 2023年

作者： Zhu, Qian Yuan, Linping Xu, Zian Yang, Leni Xia, Meng Wang, Zhuo Liang, Hai-Ning Ma, Xiaojuan The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Carnegie Mellon University Human-Computer Interaction Institute PittsburghPA United States Xi’an Jiaotong Liverpool University Department of Computing Suzhou China

Informing people of health threats is crucial as they may lack situation awareness (SA) of risky situations when they do not have personal experiences or lessons learned from dangerous encounters. In this work, we explored the potential of using an interactive VR (Virtual Reality) story to raise people's SA of health risks. We first worked with seven participants to iteratively design the immersive story and summarized five design considerations. Then, we implemented the VR story with five design features referring to the design considerations. Using a between-subjects study (N=62), we evaluated the effects of the VR story on raising SA and investigated the role of each design feature. Our results show that the VR story can promote SA by enhancing people's connection to risky situations. Design features such as immersive visualizations, multiple perspectives, and embodied interactions contribute to this connection. Drawing on qualitative and quantitative findings, we discuss the implications of designing VR stories for promoting public health. © 2023, The Authors. All rights reserved.

关键词： Virtual reality

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The Impact of Item-Writing Flaws on Difficulty and Discrimination in Item Response Theory

arXiv

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arXiv 2025年

作者： Schmucker, Robin Moore, Steven Machine Learning Department Carnegie Mellon University PittsburghPA United States Human-Computer Interaction Carnegie Mellon University PittsburghPA United States

High-quality test items are essential for educational assessments, particularly within Item Response Theory (IRT). Traditional validation methods rely on resource-intensive pilot testing to estimate item difficulty and discrimination. More recently, Item-Writing Flaw (IWF) rubrics emerged as a domain-general approach for evaluating test items based on textual features. However, their relationship to IRT parameters remains underexplored. To address this gap, we conducted a study involving over 7,000 multiple-choice questions across various STEM subjects (e.g., math and biology). Using an automated approach, we annotated each question with a 19-criteria IWF rubric and studied relationships to data-driven IRT parameters. Our analysis revealed statistically significant links between the number of IWFs and IRT difficulty and discrimination parameters, particularly in life and physical science domains. We further observed how specific IWF criteria can impact item quality more and less severely (e.g., negative wording vs. implausible distractors). Overall, while IWFs are useful for predicting IRT parameters–particularly for screening low-difficulty MCQs–they cannot replace traditional data-driven validation methods. Our findings highlight the need for further research on domain-general evaluation rubrics and algorithms that understand domain-specific content for robust item validation. © 2025, CC BY.

关键词： Subjective testing

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Co-Creating Reassurance Journey Maps to Foster Engagement in Remote Patient Monitoring for Post-Operative Cancer Care 25

Co-Creating Reassurance Journey Maps to Foster Engagement in...

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

作者： Timinis, Constantinos Opie, Jeremy Watt, Simon Rogers, Yvonne Drobnjak, Ivana Department of Computer Science University College London London United Kingdom UCL Hawkes Institute University College London London United Kingdom UCL Interaction Centre University College London London United Kingdom

ISBN: (纸本)9798400713941

Remote patient monitoring can significantly enhance post-operative home recovery for cancer patients, yet its effectiveness is often hindered by low patient engagement. Reassurance has been identified as a key factor in improving engagement. Our study explored how cancer patients seek reassurance through a Patient Public Involvement workshop with former patients. This involved developing personas for participants to navigate reassurance scenarios and share their post-operative experiences. Based on this, we co-created a reassurance journey map to illustrate when reassurance is needed, the behaviours patients use to seek it, and how it can be effectively provided. Our findings highlight three key design principles: the limitations of digital technology in offering reassurance, the personalised nature of reassurance, and the need for holistic integration. These are intended to inform the design of reassurance-focused RPM systems that better support cancer patients during home recovery. Practical design recommendations are also provided for developers and clinicians. © 2025 Copyright held by the owner/author(s).

关键词： Remote patient monitoring

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Effect of Duration and Delay on the Identifiability of VR Motion

Effect of Duration and Delay on the Identifiability of VR Mo...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Mark Roman Miller Vivek Nair Eugy Han Cyan DeVeaux Christian Rack Rui Wang Brandon Huang Marc Erich Latoschik James F. O’Brien Jeremy N. Bailenson Computer Science Illinois Institute of Technology Chicago USA Computer Science UC Berkeley Berkeley USA Communication Stanford University Stanford USA Human-Computer Interaction University of Würzburg Würzburg Germany Carnegie Mellon University Pittsburgh USA UC Berkeley Berkeley USA

ISBN: (数字)9798350394665

ISBN: (纸本)9798350394672

Social virtual reality is an emerging medium of communication. In this medium, a user’s avatar (virtual representation) is controlled by the tracked motion of the user’s headset and hand controllers. This tracked motion is a rich data stream that can leak characteristics of the user or can be effectively matched to previously-identified data to identify a user. To better understand the boundaries of motion data identifiability, we investigate how varying training data duration and train-test delay affects the accuracy at which a machine learning model can correctly classify user motion in a supervised learning task simulating re-identification. The dataset we use has a unique combination of a large number of participants, long duration per session, large number of sessions, and a long time span over which sessions were conducted. We find that training data duration and train-test delay affect identifiability; that minimal train-test delay leads to very high accuracy; and that train-test delay should be controlled in future experiments.

关键词： Headphones Wireless communication Privacy Solid modeling Accuracy Tracking Supervised learning

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How Shortening or Lengthening Design Processes Configure Decision Making 22

How Shortening or Lengthening Design Processes Configure Dec...

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12th Nordic Conference on human-computer interaction: Participative Computing for Sustainable Futures, NordiCHI 2022

作者： Falk, Jeanette Frauenberger, Christopher Kannabiran, Gopinaath Department of Artificial Intelligence and Human Interfaces University of Salzburg Salzburg Austria Department of Computer Science IT University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9781450396998

There have been repeated calls for developing time-sensitive discourses in HCI and design research, and for re-examining engagement with power. In response, we explore the relationship between time and decision making in design processes in order to better understand how this configures power structures. We analyse two design cases: a short-term hackathon and a long-term design process. We argue that the different temporalities of design activities configure decision making - and thereby power - and that both short- and long-term design processes differ in the ways of engaging people in designing technology: Decisions on values and concepts are prioritised in long-term design processes, decisions about implementing the vision are prioritised in short-term design processes, decisions requiring negotiations with the outside world are structurally limited in short-term design processes, non-decisions in short term design processes are pragmatic and habitual in long-term design processes. © 2022 Owner/Author.

关键词： Decision making

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Using Inductive Logic Programming to globally approximate Neural Networks for preference learning: challenges and preliminary results 1

Using Inductive Logic Programming to globally approximate Ne...

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1st Workshop on Bias, Ethical AI, Explainability and the Role of Logic and Logic Programming, BEWARE 2022

作者： Fossemò, Daniele Mignosi, Filippo Raggioli, Luca Spezialetti, Matteo D’Asaro, Fabio Aurelio Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy Department of Computer Science University of Manchester United Kingdom ICAR-CNR Palermo Italy Ethos Group Department of Human Sciences University of Verona Italy

In this paper we explore the use of Answer Set Programming (ASP), and in particular the state-of-the-art Inductive Logic Programming (ILP) system ILASP, as a method to explain black-box models, e.g. Neural Networks (NN), when they are used to learn user preferences. To this aim, we created a dataset of users preferences over a set of recipes, trained a set of NNs on these data, and performed preliminary experiments that investigate how ILASP can globally approximate these NNs. Since computational time required for training ILASP on high dimensional feature spaces is very high, we focused on the problem of making global approximation more scalable. In particular we experimented with the use of Principal Component Analysis (PCA) to reduce the dimensionality of the dataset while trying to keep our explanations transparent. © 2022 Copyright for this paper by its authors.

关键词： Principal component analysis

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LaCo: Large Language Model Pruning via Layer Collapse

LaCo: Large Language Model Pruning via Layer Collapse

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Yang, Yifei Cao, Zouying Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798891761681

Large language models (LLMs) based on transformer are witnessing a notable trend of size expansion, which brings considerable costs to both model training and inference. However, existing methods such as model quantization, knowledge distillation, and model pruning are constrained by various issues, including hardware support limitations, the need for extensive training, and alterations to the model internal structure. In this paper, we propose a concise layer-wise structured pruner called Layer Collapse (LaCo), in which rear model layers collapse into a prior layer, enabling a rapid reduction in model size while preserving the model structure. Comprehensive experiments show that our method maintains an average task performance of over 80% at pruning ratios of 25-30%, significantly outperforming existing state-of-the-art structured pruning methods. We also conduct post-training experiments to confirm that the LaCo effectively inherits the parameters of the original model. Additionally, we perform ablation studies on various settings of LaCo. Finally, we discuss our motivation from the perspective of layer-wise similarity and evaluate the performance of the pruned LLMs across various pruning ratios. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Exploring Trade-Offs for Online Mental Health Matching: Agent-Based Modeling Study

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JMIR Formative Research 2024年 8卷 e58241页

作者： Liu, Yuhan Fang, Anna Moriarty, Glen Firman, Cristopher Kraut, Robert E. Zhu, Haiyi Department of Computer Science Princeton University Princeton NJ United States Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States 7 Cups Wilmington DE United States

Background: Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped or highly naive. Objective: In this study, we collaborated with one of the world’s largest OMHCs;our contribution is to show the application of agent-based modeling for the design of online community matching algorithms. We developed an agent-based simulation framework and showcased how it can uncover trade-offs in different matching algorithms between people seeking support and volunteer counselors. Methods: We used a comprehensive data set spanning January 2020 to April 2022 to create a simulation framework based on agent-based modeling that replicates the current matching mechanisms of our research site. After validating the accuracy of this simulated replication, we used this simulation framework as a “sandbox” to test different matching algorithms based on the deferred acceptance algorithm. We compared trade-offs among these different matching algorithms based on various metrics of interest, such as chat ratings and matching success rates. Results: Our study suggests that various tensions emerge through different algorithmic choices for these communities. For example, our simulation uncovered that increased waiting time for support seekers was an inherent consequence on these sites when intelligent matching was used to find more suitable matches. Our simulation also verified some intuitive effects, such as that the greatest number of support seeker–counselor matches occurred using a “first come, first served” protocol, whereas relatively fewer matches occurred using a “last come, first served” protocol. We also discuss practical findings regarding matching for vulnerable versus overall populations. Results by demographic group reveal

关键词： agent-based modeling algorithmic matching mental health online communities social computing

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Beyond the User: Mapping Subject Positions for Robots in Public Spaces 20

Beyond the User: Mapping Subject Positions for Robots in Pub...

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20th Annual ACM/IEEE International Conference on human-Robot interaction, HRI 2025

作者： Dobrosovestnova, Anna Babel, Franziska Pelikan, Hannah Human-Computer Interaction group Tu Wien Vienna Austria Linköping University Dept. of Computer and Information Science Linköping Sweden Linköping University Dept. of Culture and Society Linköping Sweden

ISBN: (纸本)9798350378931

The concept of the 'user' is central to research in human- Robot interaction (HRI). Drawing on the field studies of functional service robots in public spaces, the paper contributes a conceptual framework for describing positions and roles humans can take beyond being robot users. We rely on the concept of 'subject position' from human-computer interaction (HCI) to map out people beyond users into three ecologies: Service, Street Life, and Public Discourse. Through the lens of these ecologies, we highlight how people encounter the robot as (i) an actor in service provision, (ii) a (social and material) actor moving with other members of the street, and (iii) a matter of public concern. The paper contributes a vocabulary for describing the diverse roles of humans and offers methodological suggestions for more holistic research and design of HRI. Further, we outline disciplinary and ethical considerations relating to designing with ecologies in mind. © 2025 IEEE.

关键词： framework public discourse service stakeholders street

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SiamCircle: Trajectory Representation Learning in Free Settings 23rd

SiamCircle: Trajectory Representation Learning in Free Sett...

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23rd International Symposium on Intelligent Data Analysis, IDA 2025

作者： Nasri, Maedeh Baratchi, Mitra Koutamanis, Alexander Rieffe, Carolien Department of Developmental Psychology Leiden University Leiden Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Faculty of Architecture and the Built Environment Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of Psychology and Human Development Institute of Education UCL London United Kingdom

ISBN: (纸本)9783031913976

Trajectory representation learning (TRL) is an intermediate step in handling trajectory data to realize various downstream machine-learning tasks. While most previous TRL research focuses on modeling structured movements in large-scale urban spaces (e.g., cars or pedestrians on streets), this paper focuses on a more challenging scenario of modeling free movement in small-scale social spaces (e.g., children playing in a schoolyard). We present a TRL model, SiamCircle, to process raw trajectories without additional feature extraction to prevent information loss. SiamCircle adopts a Siamese network with Circle Loss to learn trajectory embeddings. Furthermore, SiamCircle employs a data augmentation process to enable self-supervised learning and enrich the input data to address the limited access to high-quality data and ground truth. We evaluate the performance of SiamCircle in downstream tasks using trajectory ranking and clustering performance via seven evaluation metrics collectively. Using an ablation study, we explored the impact of different loss functions on the model’s performance. Accordingly, we selected a 2-D convolutional design with Circle Loss as the best-performing model. In a comparative study, we compared our model against three other baselines. We observed up to 19% improvements in trajectory ranking tasks and achieved the highest average rank in supervised clustering tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sorting

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