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arXiv 2024年

作者： Pavanatto, Leonardo Biener, Verena Chandran, Jennifer Kalamkar, Snehanjali Lu, Feiyu Dudley, John J. Hu, Jinghui Nikki Ramirez-Saffy, G. Kristensson, Per Ola Giovannelli, Alexander Schlueter, Luke Müller, Jörg Grubert, Jens Bowman, Doug A. The Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States The Visualization Research Center University of Stuttgart Germany Coburg University of Applied Sciences and Arts Germany The University of Bayreuth Germany The Department of Engineering University of Cambridge United Kingdom

Although access to sufficient screen space is crucial to knowledge work, workers often find themselves with limited access to display infrastructure in remote or public settings. While virtual displays can be used to extend the available screen space through extended reality (XR) head-worn displays (HWD), we must better understand the implications of working with them in public settings from both users’ and bystanders’ viewpoints. To this end, we conducted two user studies. We first explored the usage of a hybrid AR display across real-world settings and tasks. We focused on how users take advantage of virtual displays and what social and environmental factors impact their usage of the system. A second study investigated the differences between working with a laptop, an AR system, or a VR system in public. We focused on a single location and participants performed a predefined task to enable direct comparisons between the conditions while also gathering data from bystanders. The combined results suggest a positive acceptance of XR technology in public settings and show that virtual displays can be used to accompany existing devices. We highlighted some environmental and social factors. We saw that previous XR experience and personality can influence how people perceive the use of XR in public. In addition, we confirmed that using XR in public still makes users stand out and that bystanders are curious about the devices, yet have no clear understanding of how they can be used. Copyright © 2024, The Authors. All rights reserved.

关键词： Virtual environments

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Prediction of Budd-Chiari syndrome based on attention mechanisms of high-risk factors in multi-hop graph learning

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science China(Information sciences) 2025年

作者： Lei WANG Shengli LI Xiaorui SU Zhengwei LI Mengmeng WEI Bowei ZHAO Maoheng ZU Qingqiao ZHANG Zhuhong YOU School of Computer Science and Technology China University of Mining and Technology Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Guangxi Academy of Sciences Clinical Research Institute The Affiliated Hospital of Xuzhou Medical University University of Chinese Academy of Sciences Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Department of Interventional Radiology The Affiliated Hospital of Xuzhou Medical University

Budd-Chiari syndrome (BCS) is a series of clinical syndromes caused by obstruction of the hepatic vein and/or hepatic segment inferior vena cava,which can seriously damage the health of the body and lead to patient death[1].Over recent years,with the improvement of diagnostic technology,especially the introduction of multidisciplinary intervention methods,more and more potential cases have been discovered and treated[2].However,conventional clinical diagnosis is usually based on traditional indicators[3],and diagnostic criteria are largely dependent on the results of statistical analyses,often ignoring the importance of the association between different factors[4],especially high-risk factors,and the disease[5].Effectively capturing the critical factors that contribute to disease occurrence is the challenge for improving the predictive performance of BCS.

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DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting 29

DP-Rewrite: Towards Reproducibility and Transparency in Diff...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Igamberdiev, Timour Arnold, Thomas Habernal, Ivan Trustworthy Human Language Technologies Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany

Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Transparency

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Communication Sequences Indicate Team Cohesion: A Mixed-Methods Study of Ad Hoc League of Legends Teams

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Proceedings of the ACM on human-computer interaction 2022年第CHIPLAY期6卷 1–27页

作者： Tan, Evelyn T S Rogers, Katja Nacke, Lennart E. Drachen, Anders Wade, Alex Department of Computer Science University of York United Kingdom Stratford School of Interaction Design and Business HCI Games Group Games Institute University of Waterloo Canada Stratford School of Interaction Design and Business HCI Games Group Games Institute University of Waterloo WaterlooON Canada Maersk McKinney Moeller Institute University of Southern Denmark Denmark Department of Psychology University of York United Kingdom

Team cohesion is a widely known predictor of performance and collaborative satisfaction. However, how it develops and can be assessed, especially in fast-paced ad hoc dynamic teams, remains unclear. An unobtrusive and objective behavioural measure of cohesion would help identify determinants of cohesion in these teams. We investigated team communication as a potential measure in a mixed-methods study with 48 teams (n=135) in the digital game League of Legends. We first established that cohesion shows similar performance and satisfaction in League of Legends. teams as in non-game teams and confirmed a positive relationship between communication word frequency and cohesion. Further, we conducted an in-depth exploratory qualitative analysis of the communication sequences in a high-cohesion and a low-cohesion team. High cohesion is associated with sequences of apology-→encouragement, suggestion-→agree/acknowledge, answer-→answer, and answer-→question, while low-cohesion is associated with sequences of opinion/analysis-→opinion/analysis, disagree-→disagree, command-→disagree, and frustration-→frustration. Our findings also show that cohesion is important to team satisfaction independently of the match outcomes. We highlight that communication sequences are more useful than frequencies to determine team cohesion via player interactions. © 2022 ACM.

关键词： computer programming

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Sustainable Foodtures: Exploring Roles of Future Technology in Sustainable Food Shopping 22

Sustainable Foodtures: Exploring Roles of Future Technology ...

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

作者： Lindrup, Martin Valdemar Anker Cheon, EunJeong Skov, Mikael B. Raptis, Dimitrios Comber, Rob Department of Computer Science Aalborg University Denmark School of Information Studies Syracuse University United States Department of Media Technology and Interaction Design Kth Royal Institute of Technology Sweden

ISBN: (纸本)9781450396998

Several efforts have been made to use technology to encourage people to buy more environmentally sustainable food. However, only a small amount of research has attempted to understand the roles of technology in food shopping by incorporating perspectives from various stakeholders. We aimed to explore how the design space for future technology in sustainable food shopping is negotiated among sustainability-aware individuals, sustainability professionals, and designers through co-design workshops and speculative enactments. We identify three roles of technology in sustainable food shopping: technology as guidance, technology as mediator, and technology for reflection. Additional insights are provided regarding how parts of these roles for future technology are rejected, adopted, or adjusted when enacted in everyday food shopping. We discuss design implications for the roles of sustainability technology in terms of data accuracy and transparency, as well as embodied data experiences. Furthermore, we provide methodological considerations for speculative enactments in SHCI. © 2022 ACM.

关键词： Sustainable development

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Machine Learning-Based Automated Detection of ADHD Using Heart Rate Variability Data 16th

Machine Learning-Based Automated Detection of ADHD Using Hea...

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16th International Conference on Bioinformatics and Computational Biology, BICOB 2024

作者： Ji, Yanqing Zhang-Lea, Janet Tran, John Department of Electrical and Computer Engineering Gonzaga University SpokaneWA United States Department of Human Physiology Gonzaga University SpokaneWA United States Department of Psychiatry and Behavioral Science University of California San Francisco Fresno FresnoCA United States

This study addresses the pressing need for effective methods in detecting Attention-Deficit/Hyperactivity Disorder (ADHD), a neurodevelopmental condition significantly impacting individuals' attention, impulse control, and activity regulation. Leveraging advancements in machine learning and wearable technology, the research explores the potential of Heart Rate Variability (HRV) data as a novel source for ADHD detection. Six machine learning algorithms, including Logistic Regression, Random Forest, XGBoost, LightGBM, Neural Network, and Support Vector Machine, were rigorously investigated using an HRV dataset, marking a pioneering effort in utilizing HRV data for ADHD identification. The results demonstrate promising performance, with Logistic Regression exhibiting the highest F1 score (0.71), and Support Vector Machine achieving the highest Matthews Correlation Coefficient (0.44). This study showcases the capacity of machine learning utilizing HRV data for identifying ADHD, contributing to the evolving landscape of machine learning applications in mental health diagnostics. © 2024, EasyChair. All rights reserved.

关键词： Adversarial machine learning

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Deep Learning-Based computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images

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SN computer science 2023年第5期4卷 1-10页

作者： Kumar, Sumit Bhupati Bhambu, Pawan Pachar, Sunita Cotrina-Aliaga, Juan Carlos Arias-Gonzáles, José Luis Department of Computer Science and Engineering College of Engineering Roorkee India Department of IoT K L Deemed to be University Vaddeswaram Guntur 522302 India Department of Computer Science Engineering VGU University Jaipur Jaipur India IBM Department GLA University Mathura India Faculty of Human Medicine Universidad Privada San Juan Bautista Chincha Peru Pontificia Universidad Católica del Perú San Miguel Peru

Cancer of the breast is an illness that has the potential to be fatal for females all over the world. Even with the advancements that have been made in treatment, breast cancer cannot be prevented or cured;however, with early identification, one's life expectancy can be increased. A woman's overall health can be improved, which can add years to her life expectancy, if breast cancer is detected at an earlier stage. Radiological screening is a well-known method that is utilised for cancer prevention and detection in significant amounts. Mammograms have the ability to detect breast cancer as well as tumours that may be present in the breast. Recent study has demonstrated that DL-based CAD models can assist radiologists in establishing automated diagnosis of breast cancer in patients. The DL-based CAD model helps radiologists diagnose breast cancer automatically, according to recent research. DL techniques utilising convolutional neural network have gained interest because to their effectiveness in automating data feature representation and maximising accuracy by merging classification and feature representations. It successfully diagnoses clinical pictures. The research aims to build DL-based breast cancer diagnosis models and to review state-of-the-art ML and DL models for breast cancer diagnosis and classification. The research also examines the performance of the proposed models on the benchmark dataset. Sensitivity, specificity, accuracy, and F-measure measure performance. The experimental results showed that the proposed models are effective compared to modern methods. The proposed models are effective for breast cancer diagnosis and categorization. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Breast cancer Classification computer-aided diagnosis Deep learning Machine learning Mammography

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A Dual-Prompting for Interpretable Mental Health Language Models

arXiv

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arXiv 2024年

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

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Towards Understanding Interactive Sonic Gastronomy with Chefs and Diners 25

Towards Understanding Interactive Sonic Gastronomy with Chef...

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

作者： Wang, Hongyue Deng, Jialin He, Linjia Overdevest, Nathalie Wee, Ryan Wang, Yan Toups Dugas, Phoebe O. Elvitigala, Don Samitha Mueller, Florian Floyd Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia Diverse-ability Interaction Lab School of Computer Science Bristol United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Mineral Resources Commonwealth Scientific and Industrial Research Organisation Melbourne Australia

ISBN: (纸本)9798400713941

With advancements in interactive technologies, research in human-food interaction (HFI) has begun to employ interactive sound to enrich the dining experience. However, chefs' creative use of this sonic interactivity as a new "ingredient"in their culinary practices remains underexplored. In response, we conducted an empirical study with six pairs of chefs and diners utilizing SoniCream, an ice cream cone that plays digital sounds while consuming. Through exploration, creation, collaboration, and reflection, we identified four themes concerning culinary creativity, dining experience, interactive sonic gastronomy deployment, and chef-diner interplay. Building on the discussions at the intersection of these themes, we derived four design implications for creating interactive systems that could support chefs' culinary creativity, thereby enriching dining experiences. Ultimately, our work aims to help interaction designers fully incorporate chefs' perspectives into HFI research. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： human-food interaction

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Enhancing the De-identification of Personally Identifiable Information in Educational Data

arXiv

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arXiv 2025年

作者： Shen, Yuntian Ji, Zilyu Lin, Jionghao Koedinger, Kenneth R. Carnegie Mellon University PittsburghPA15217 United States Faculty of Education The University of Hong Kong Hong Kong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Faculty of Information Technology Monash University ClaytonVIC3800 Australia Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University PittsburghPA15217 United States

Protecting Personally Identifiable Information (PII), such as names, is a critical requirement in learning technologies to safeguard student and teacher privacy and maintain trust. Accurate PII detection is an essential step toward anonymizing sensitive information while preserving the utility of educational data. Motivated by recent advancements in artificial intelligence, our study investigates the GPT-4o-mini model as a cost-effective and efficient solution for PII detection tasks. We explore both prompting and fine-tuning approaches and compare GPT-4omini’s performance against established frameworks, including Microsoft Presidio and Azure AI Language. Our evaluation on two public datasets, CRAPII and TSCC, demonstrates that the fine-tuned GPT-4o-mini model achieves superior performance, with a recall of 0.9589 on CRAPII. Additionally, fine-tuned GPT-4o-mini significantly improves precision scores (a threefold increase) while reducing computational costs to nearly one-tenth of those associated with Azure AI Language. Furthermore, our bias analysis reveals that the fine-tuned GPT-4o-mini model consistently delivers accurate results across diverse cultural backgrounds and genders. The generalizability analysis using the TSCC dataset further highlights its robustness, achieving a recall of 0.9895 with minimal additional training data from TSCC. These results emphasize the potential of fine-tuned GPT-4o-mini as an accurate and cost-effective tool for PII detection in educational data. It offers robust privacy protection while preserving the data’s utility for research and pedagogical analysis. Our code is available on GitHub: https://***/AnonJD/PrivacyAI © 2025, CC BY.

关键词： Differential privacy

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