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Extended Abstracts of the 2023 CHI Conference on Human Factors in computing Systems, CHI EA 2023

作者： Bhattacharjee, Ananya Kulzhabayeva, Dana Reza, Mohi Kumar, Harsh Seong, Eunchae Wu, Xuening Rifat, Mohammad Rashidujjaman Bowman, Robert Kornfield, Rachel Mariakakis, Alex Ahmed, Syed Ishtiaque De Choudhury, Munmun Doherty, Gavin Czerwinski, Mary P. Williams, Joseph Jay Department of Computer Science University of Toronto TorontoON Canada Department of Psychology University of Toronto TorontoON Canada School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Department of Preventive Medicine Northwestern University Evanston United States School of Interactive Computing Georgia Institute of Technology Atlanta United States Microsoft Research Redmond United States

ISBN: (纸本)9781450394222

Technology for promoting reflection can help people better understand their emotions and thought patterns, eventually motivating them to take action to adopt healthy or productive behaviors. However, existing work has often viewed users individualistically, addressing people's behaviors and emotions rather than recognizing the external factors that shape them (e.g., economic status, culture). We envision that the individualistic approaches can be extended and reimagined in ways that can consider such broader contexts. We believe such a shift in the design of interventions will help individuals reflect in a more holistic manner, supporting collaborative reflection processes that involve more than one person. With these aims in mind, we will discuss the two questions in our workshop. First, what individual and social contexts should HCI researchers consider while promoting reflection? Second, what role can various forms of technology (e.g., just-in-time adaptive interventions, peer-support platforms) play in supporting and augmenting reflective practices? Through our workshop, we hope to bring together a community of multidisciplinary researchers and practitioners who aim to design and develop reflection interventions that are situated within the fabric of users' individual and social contexts. © 2023 Owner/Author.

关键词： Reflection

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Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic

arXiv

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arXiv 2023年

作者： Chandra, Mohit Mattapalli, Anush de Choudhury, Munmun School of Interactive Computing Georgia Institute of Technology United States School of Computer Science Georgia Institute of Technology United States

The proliferation of online misinformation has emerged as one of the biggest threats to society. Considerable efforts have focused on building misinformation detection models, still the perils of misinformation remain abound. Mitigating online misinformation and its ramifications requires a holistic approach that encompasses not only an understanding of its intricate landscape in relation to the complex issue- and topic-rich information ecosystem online, but also the psychological drivers of individuals behind it. Adopting a time series analytic technique and robust causal inference-based design, we conduct a large-scale observational study analyzing over 32 million COVID-19 tweets and 16 million historical timeline tweets. We focus on understanding the behavior and psychology of users disseminating misinformation during COVID-19 and its relationship with the historical inclinations towards sharing misinformation on Non-COVID domains before the pandemic. Our analysis underscores the intricacies inherent to cross-domain misinformation, and highlights that users’ historical inclination toward sharing misinformation is positively associated with their present behavior pertaining to misinformation sharing on emergent topics and beyond. This work may serve as a valuable foundation for designing user-centric inoculation strategies and ecologically-grounded agile interventions for effectively tackling online misinformation. © 2023, CC BY.

关键词： COVID-19

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A Novel U-Net Based CNN Algorithm for High-Accuracy Weather Forecasting 16

A Novel U-Net Based CNN Algorithm for High-Accuracy Weather ...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Sivaraman, Arun Kumar Nithiyanantham, Janakiraman Ravindran, Priya Narayanan, S T Sarvesh University of Technology and Applied Sciences Department of Computing and Information Sciences Suhar Oman School of Cse Vellore Institute of Technology Chennai India Digital Engineering Solution Centre Photon Interactive Inc. ON Canada K.L.N. College of Engineering Department of ECE Tamilnadu India School of Computer Science Dalhousie University Halifax Canada

ISBN: (纸本)9798331505264

Accurate weather forecasting is essential for sectors like agriculture, aviation, and disaster management. However, deep learning algorithms face challenges in prediction accuracy due to issues like vanishing gradients, overfitting, and high computational demands. This research proposes a novel U-Net based architecture utilizing a Convolutional Neural Network (CNN) bottleneck layer to improve weather forecasting. Key features include a skip-connection mechanism, modified weight update rules, Gaussian-mutation operations, and the Adam optimizer for enhanced feature extraction and faster, more accurate predictions. The model was tested using precipitation data from Doppler Weather Radar (DWR) Chennai Radar and weather parameters from European Centre for Medium-Range Weather Forecasts (ECMWF). A dedicated GeoServer facilitates realtime data processing. Experimental results show the proposed algorithm achieves 97.5% accuracy, outperforming CNN and long short-term memory (LSTM) models by 5.84% and 2.41%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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GenieWizard: Multimodal App Feature Discovery with Large Language Models 25

GenieWizard: Multimodal App Feature Discovery with Large Lan...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Yang, Jackie Shi, Yingtian Gu, Chris Zheng, Zhang Jain, Anisha Li, Tianshi Lam, Monica S. Landay, James A. Computer Science Department Stanford University StanfordCA United States Skywalk Inc. Palo AltoCA United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States Independent Researcher StanfordCA United States Khoury College of Computer Sciences Northeastern University BostonMA United States

ISBN: (纸本)9798400713941

Multimodal interactions are more flexible, efficient, and adaptable than graphical interactions, allowing users to execute commands beyond simply tapping GUI buttons. However, the flexibility of multimodal commands makes it hard for designers to prototype and provide design specifications for developers. It is also hard for developers to anticipate what actions users may want. We present GenieWizard, a tool to aid developers in discovering potential features to implement in multimodal interfaces. GenieWizard supports user-desired command discovery early in the implementation process, streamlining the development process. GenieWizard uses an LLM to generate potential user interactions and parse these interactions into a form that can be used to discover the missing features for developers. Our evaluations showed that GenieWizard can reliably simulate user interactions and identify missing features. Also, in a study (N = 12), we demonstrated that developers using GenieWizard can identify and implement 42% of the missing features of multimodal apps compared to only 10% without GenieWizard. © 2025 Copyright held by the owner/author(s).

关键词： Graphical user interfaces

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Human-Centered Evaluation and Auditing of Language Models

Human-Centered Evaluation and Auditing of Language Models

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Liu, Yu Lu Deng, Wesley Hanwen Lam, Michelle S. Eslami, Motahhare Kim, Juho Liao, Q. Vera Xu, Wei Novikova, Jekaterina Xiao, Ziang Johns Hopkins University BaltimoreMD United States Human-Computer Interaction Institution Carnegie Mellon University PittsburghPA United States Dept. of Computer Science Stanford University StanfordCA United States School of Computer Science Carnegie Mellon University PittsburghPA United States School of Computing KAIST Daejeon Korea Republic of Microsoft Research MontrealQC Canada School of Interactive Computing Georgia Institute of Technology AtlantaGA United States AI Risk and Vulnerability Alliance SeattleWA United States

ISBN: (纸本)9798400713958

The recent advancements in Large Language Models (LLMs) have significantly impacted numerous, and will impact more, real-world applications. However, these models also pose significant risks to individuals and society. To mitigate these issues and guide future model development, responsible evaluation and auditing of LLMs are essential. This workshop aims to address the current "evaluation crisis" in LLM research and practice by bringing together HCI and AI researchers and practitioners to rethink LLM evaluation and auditing from a human-centered perspective. The workshop will explore topics around understanding stakeholders’ needs and goals with evaluation and auditing LLMs, establishing human-centered evaluation and auditing methods, developing tools and resources to support these methods, building community and fostering collaboration. By soliciting papers, organizing invited keynote and panel, and facilitating group discussions, this workshop aims to develop a future research agenda for addressing the challenges in LLM evaluation and auditing. Following a successful first iteration of this workshop at CHI 2024, we introduce the theme of "mind the context" for this second iteration, where participants will be encouraged to tackle the challenges and nuances of LLM evaluation and auditing in specific contexts. © 2025 Copyright held by the owner/author(s).

关键词： Audit Evaluation Generative AI Large Language Models

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On Training Strategies for LSTMs in Sensor-Based Human Activity Recognition

On Training Strategies for LSTMs in Sensor-Based Human Activ...

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IEEE Annual Conference on Pervasive computing and Communications Workshops (PerCom)

作者： Shuai Shao Yu Guan Xin Guan Paolo Missier Thomas Plötz Department of Computer Science University of Warwick Coventry UK School of Computing Newcastle University Newcastle upon Tyne UK School of Interactive Computing Georgia Institute of Technology Atlanta USA

Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing periodic/static activities (e.g, walking, lying, cycling, etc.) has become a well-studied problem. What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non-periodic, and occur less frequently when compared with the often large amount of irrelevant “background” activities. Recent works suggested that sequential DL models (such as LSTMs) have great potential for modeling non-periodic behaviours, and in this paper we studied some LSTM training strategies for SAR. Specifically, we proposed two simple yet effective LSTM variants, i.e., a delay model and an inverse model. The delay model can effectively exploit pre-defined delay intervals as additional contextual information, while the inverse model can learn patterns from the time-series in an inverse manner, which can be complementary to the forward model (i.e., LSTM). These two LSTM variants are very practical, and they can be deemed as training strategies without alteration of the LSTM fundamentals. We also studied some additional LSTM training strategies, which can further improve the accuracy. We evaluated our models on two public datasets, and the promising results suggested the effectiveness of our approaches in recognizing sporadic activities.

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Neural semi-Markov CRF for monolingual word alignment 59

Neural semi-Markov CRF for monolingual word alignment

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Lan, Wuwei Jiang, Chao Xu, Wei Department of Computer Science and Engineering Ohio State University United States School of Interactive Computing Georgia Institute of Technology United States

ISBN: (纸本)9781954085527

Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc. In this paper, we present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans. We also create a new benchmark with human annotations that cover four different text genres to evaluate monolingual word alignment models in more realistic settings. Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment as well as a competitive QA-based baseline, which was previously only applied to bilingual data. Our model demonstrates good generalizability to three out-of-domain datasets and shows great utility in two downstream applications: automatic text simplification and sentence pair classification tasks. © 2021 Association for Computational Linguistics

关键词： Alignment

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The Collaborative Sensemaking Play of Jubensha Games: A Deconstruction, Taxonomy, and Analysis

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Games: Research and Practice 2025年第1期3卷 1-34页

作者： Shano Liang Max Chen Phoebe O. Toups Dugas Gillian Smith Rose Bohrer Interactive Media and Game Development Worcester Polytechnic Institute Worcester United States Human-Centred Computing / Faculty of Information Technology / Exertion Games Lab Monash University Melbourne Australia Computer Science Worcester Polytechnic Institute Worcester United States

Jubensha games, popular in China, combine storytelling, social deduction, and script-centered group play, sparking widespread interest among gamers and researchers worldwide. However, enthusiasts and researchers have struggled to accurately describe Jubensha, often defaulting to comparisons with genres like murder mystery and live-action role-playing games. This reliance on comparisons hinders efforts to generalize Jubensha or to deconstruct and adapt its unique design components, dynamics of player interaction, and playing experience into other games. This research provides a taxonomy and analysis of Jubensha games, based on a thematic analysis of over 80 Jubensha games accessed through mobile applications and physical copies. The analysis combines the authors’ positionalities as native Chinese and English speakers and lenses from close reading of the games, discourse analysis, and distributed cognition. We provide summative case studies to exemplify our taxonomy and discuss design implications for Jubensha games for future projects. Our work provides a descriptive tool and vocabulary for researchers and designers to facilitate communication and theorize the evolving Jubensha gaming phenomenon highlighting how gameplay centers collaborative sensemaking. In addition, we argue that the design of game narratives in Jubensha games, including structures in scripts and the evolving performance of players, can be generalized and transferred to the design of a wider range of analog and video games.

关键词： Jubensha murder mystery games taxonomy game analysis game design cross-cultural context

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Enhancing Smartphone Eye Tracking with Cursor-Based interactive Implicit Calibration 25

Enhancing Smartphone Eye Tracking with Cursor-Based Interact...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Liu, Chang Wang, Xiangyang Yu, Chun Shi, Yingtian Wang, Chongyang Liu, Ziqi Liang, Chen Shi, Yuanchun Department of Computer Science and Technology Tsinghua University BeiJing China School of Interactive Computing Georgia Institute of Technology Atlanta United States West China Hospital Sichuan University Chengdu China Tsinghua University Beijing Beijing China Guangdong Guangzhou China Qinghai University Xining China

ISBN: (纸本)9798400713941

The limited accuracy of eye-tracking on smartphones restricts its use. Existing RGB-camera-based eye-tracking relies on extensive datasets, which could be enhanced by continuous fine-tuning using calibration data implicitly collected from the interaction. In this context, we propose COMETIC (Cursor Operation Mediated Eye-Tracking Implicit Calibration), which introduces a cursor-based interaction and utilizes the inherent correlation between cursor and eye movement. By filtering valid cursor coordinates as proxies for the ground truth of gaze and fine-tuning the eye-tracking model with corresponding images, COMETIC enhances accuracy during the interaction. Both filtering and fine-tuning use pre-trained models and could be facilitated using personalized, dynamically updated data. Results show COMETIC achieves an average eye-tracking error of 278.3 px (1.60 cm, 2.29°), representing a 27.2% improvement compared to that without fine-tuning. We found that filtering cursor points whose actual distance to gaze is 150.0 px (0.86 cm) yields the best eye-tracking results. © 2025 Copyright held by the owner/author(s).

关键词： Eye Tracking

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AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability

AI Literacy: Finding Common Threads between Education, Desig...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in computing Systems, CHI EA 2023

作者： Long, Duri Roberts, Jessica Magerko, Brian Holstein, Kenneth Dipaola, Daniella Martin, Fred Department of Communication Studies Northwestern University United States School of Interactive Computing Georgia Tech United States School of Literature Media and Communication Georgia Tech United States Human-Computer Interaction Institute Carnegie Mellon University United States Personal Robots Group Mit Media Lab United States Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781450394222

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy. © 2023 Owner/Author.

关键词： Public policy

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