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

作者： Wischnewski, Magdalena Research Center Trustworthy Data Science and Security Dortmund Germany

ISBN: (纸本)9798400713958

Rapidly evolving, non-embodied autonomous agents, oftentimes powered by AI, have been shown to trigger mental state attributions by users. However, unlike embodied agents, attributions to non-embodied autonomous agents are not caused by physical presence but by language or abstract interfaces. To better understand this fast-growing field, the present article presents a systematic literature review of mental state attribution to non-embodied autonomous agents. Based on a literature body of 400+ papers, 26 relevant publications were identified, providing an overview of the communities studying mental state attributions, the study’s design choices, and findings of these efforts. The findings indicate that (1) the HCI community is less involved in the study of mental state attributions, (2) a lacking validity of the measures limits the current measurement of mental state attributions, (3) there is a skew towards chat- and voice bots, and (4) mental state attributions increased vulnerabilities of users and shifts in accountability. © 2025 Copyright held by the owner/author(s).

关键词： agent artificial intelligence autonomous mental states mind perception non-embodied

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A measurement Study of Wechat Mini-Apps

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proceedings of the acm on measurement and analysis of computing systems 2021年第2期5卷 1–25页

作者： Zhang, Yue Turkistani, Bayan Yang, Allen Yuqing Zuo, Chaoshun Lin, Zhiqiang Ohio State Univ Columbus OH 43210 USA

A new mobile computing paradigm, dubbed mini-app, has been growing rapidly over the past few years since being introduced by WeChat in 2017. In this paradigm, a host app allows its end-users to install and run mini-apps inside itself, enabling the host app to build an ecosystem around (much like Google Play and Apple AppStore), enrich the host's functionalities, and offer mobile users elevated convenience without leaving the host app. It has been reported that there are over millions of mini-apps in WeChat. However, little information is known about these mini-apps at an aggregated level. In this paper, we present MiniCrawler, the first scalable and open source WeChat mini-app crawler that has indexed over 1,333,308 mini-apps. It leverages a number of reverse engineering techniques to uncover the interfaces and APIs in WeChat for crawling the mini-apps. With the crawled mini-apps, we then measure their resource consumption, API usage, library usage, obfuscation rate, app categorization, and app ratings at an aggregated level. The details of how we develop MiniCrawler and our measurement results are reported in this paper.

关键词： Mini-apps Crawler Wechat

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Crafting Human-AI Collaborative analysis for User Experience Evaluation

Crafting Human-AI Collaborative Analysis for User Experience...

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

作者： Kuang, Emily Rochester Institute of Technology RochesterNY United States

ISBN: (纸本)9781450394222

AI has been increasingly adopted in user experience (UX) analysis, in which UX evaluators review test recordings to identify usability problems. However, most AI-infused systems apply fully automatic approaches, leading to distrust from UX evaluators. In my dissertation work, we consider AI as assisting, not replacing human judgment. Through an international survey, we investigated the current practices and challenges of UX evaluators and identified an opportunity for AI assistance. We then studied nuanced cooperative work between UX evaluators and AI, by employing either non-interactive visualizations or interactive conversational assistants (CAs). The next steps include building upon our findings about the reactive Q&A dynamic with CAs, by exploring how a proactive approach or a combination of visualizations and CAs may better support UX evaluators. This research will identify interactions and representations that give rise to productive and trusting collaborations with AI. © 2023 Owner/Author.

关键词： Visualization

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Scaling the "memory Wall" for Multi-Dimensional Seismic Processing with Algebraic Compression on Cerebras CS-2 systems 23

Scaling the "memory Wall" for Multi-Dimensional Seismic Proc...

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2023 International Conference for High Performance computing, Networking, Storage and analysis, SC 2023

作者： Ltaief, Hatem Hong, Yuxi Wilson, Leighton Jacquelin, Mathias Ravasi, Matteo Keyes, David Elliot Extreme Computing Research Center Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Cerebras Systems Inc. SunnyvaleCA United States

ISBN: (纸本)9798400701092

We exploit the high memory bandwidth of AI-customized Cerebras CS-2 systems for seismic processing. By leveraging low-rank matrix approximation, we fit memory-hungry seismic applications onto memory-austere SRAM wafer-scale hardware, thus addressing a challenge arising in many wave-equation-based algorithms that rely on Multi-Dimensional Convolution (MDC) operators. Exploiting sparsity inherent in seismic data in the frequency domain, we implement embarrassingly parallel tile low-rank matrix-vector multiplications (TLR-MVM), which account for most of the elapsed time in MDC operations, to successfully solve the Multi-Dimensional Deconvolution (MDD) inverse problem. By reducing memory footprint along with arithmetic complexity, we fit a standard seismic benchmark dataset into the small local memories of Cerebras processing elements. Deploying TLR-MVM execution onto 48 CS-2 systems in support of MDD gives a sustained memory bandwidth of 92.58PB/s on 35, 784, 000 processing elements, a significant milestone that highlights the capabilities of AI-customized architectures to enable a new generation of seismic algorithms that will empower multiple technologies of our low-carbon future. © 2023 Owner/Author(s).

关键词： Energy efficiency

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Code Code Evolution: Understanding How People Change Data Science Notebooks Over Time 23

Code Code Evolution: Understanding How People Change Data Sc...

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CHI conference on Human Factors in computing systems (CHI)

作者： Raghunandan, Deepthi Roy, Aayushi Shi, Shenzhi Elmqvist, Niklas Battle, Leilani Univ Maryland College Pk MD 20742 USA Univ Washington Seattle WA USA

ISBN: (纸本)9781450394215

Sensemaking is the iterative process of identifying, extracting, and explaining insights from data, where each iteration is referred to as the "sensemaking loop." However, little is known about how sensemaking behavior evolves from exploration and explanation during this process. This gap limits our ability to understand the full scope of sensemaking, which in turn inhibits the design of tools that support the process. We contribute the first mixed-method to characterize how sensemaking evolves within computational notebooks. We study 2,574 Jupyter notebooks mined from GitHub by identifying data science notebooks that have undergone significant iterations, presenting a regression model that automatically characterizes sensemaking activity, and using this regression model to calculate and analyze shifts in activity across GitHub versions. Our results show that notebook authors participate in various sensemaking tasks over time, such as annotation, branching analysis, and documentation. We use our insights to recommend extensions to current notebook environments.

关键词： Computational notebooks machine learning sensemaking data science data exploration analysis

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Attached to "The Algorithm": Making Sense of Algorithmic Precarity on Instagram 23

Attached to "The Algorithm": Making Sense of Algorithmic Pre...

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CHI conference on Human Factors in computing systems (CHI)

作者： Register, Yim Qin, Lucy Baughan, Amanda Spiro, Emma S. Univ Washington Seattle WA 98195 USA Brown Univ Providence RI USA Univ Washington CSE Seattle WA USA

ISBN: (纸本)9781450394215

This work explores how users navigate the opaque and ever-changing algorithmic processes that dictate visibility on Instagram through the lens of Attachment Theory. We conducted thematic analysis on 1,100 posts and comments on r/Instagram to understand how users engage in collective sensemaking with regards to Instagram's algorithms, user-perceived punishments, and strategies to counteract algorithmic precarity. We found that the unpredictability in how Instagram rewards or punishes a user can lead to distress, hypervigilance, and a need to appease "the algorithm". We therefore frame these findings through Attachment Theory, drawing upon the metaphor of Instagram as an unreliable paternalistic figure that inconsistently rewards users [74]. User experiences are then contextualized through the lens of anxious, avoidant, disorganized, and secure attachment. We conclude by making suggestions for fostering secure attachment towards the Instagram algorithm, by suggesting potential strategies to help users successfully cope with uncertainty.

关键词： Instagram social media algorithms algorithmic transparency thematic analysis social media and mental health Attachment Theory algorithmic precarity content moderation folk theorization

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Speech-Augmented Cone-of-Vision for Exploratory Data analysis 23

Speech-Augmented Cone-of-Vision for Exploratory Data Analysi...

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CHI conference on Human Factors in computing systems (CHI)

作者： Bovo, Riccardo Giunchi, Daniele Sidenmark, Ludwig Newn, Joshua Gellersen, Hans Costanza, Enrico Heinis, Thomas Imperial Coll London London England UCL London England Univ Lancaster Lancaster England

ISBN: (纸本)9781450394215

Mutual awareness of visual attention is crucial for successful collaboration. Previous research has explored various ways to represent visual attention, such as field-of-view visualizations and cursor visualizations based on eye-tracking, but these methods have limitations. Verbal communication is often utilized as a complementary strategy to overcome such disadvantages. This paper proposes a novel method that combines verbal communication with the Cone of Vision to improve gaze inference and mutual awareness in VR. We conducted a within-group study with pairs of participants who performed a collaborative analysis of data visualizations in VR. We found that our proposed method provides a better approximation of eye gaze than the approximation provided by head direction. Furthermore, we release the first collaborative head, eyes, and verbal behaviour dataset. The results of this study provide a foundation for investigating the potential of verbal communication as a tool for enhancing visual cues for joint attention.

关键词： Field of View multi-modal visual attention cues VR collaborative analytics eye-tracking

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Older Adults Using Technology for Meaningful Activities During COVID-19: An analysis Through the Lens of Self-Determination Theory 23

Older Adults Using Technology for Meaningful Activities Duri...

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CHI conference on Human Factors in computing systems (CHI)

作者： Zhao, Wei Kelly, Ryan M. Rogerson, Melissa J. Waycott, Jenny Univ Melbourne Melbourne Australia

ISBN: (纸本)9781450394215

Restrictions during the COVID-19 pandemic significantly affected people's opportunities to engage in activities that are meaningful to their lives. In response to these constraints, many people, including older adults, turned to digital technologies as alternative ways to pursue meaningful activities. These technology-mediated activities, however, presented new challenges for older adults' everyday use of technology. In this paper, we investigate how older adults used digital technologies for meaningful activities during COVID-19 restrictions. We conducted in-depth interviews with 40 older adults and analyzed the interview data through the lens of self-determination theory (SDT). Our analysis shows that using digital technologies for meaningful activities can both support and undermine older people's three basic psychological needs for autonomy, competence, and relatedness. We argue that future technologies should be designed to empower older adults' content creation, engagement in personal interests, exploration of technology, effortful communication, and participation in beneficent activities.

关键词： older adult older people technology meaningful activity self-determination theory COVID-19

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Family Negotiation in Joint Media Engagement with Creative computing 23

Family Negotiation in Joint Media Engagement with Creative C...

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CHI conference on Human Factors in computing systems (CHI)

作者： Yu, Junnan Widman, Sari Roque, Ricarose Hong Kong Polytech Univ Sch Design Hong Kong Peoples R China Univ Colorado Boulder Sch Educ Boulder CO USA Univ Colorado Boulder Dept Informat Sci Boulder CO USA

ISBN: (纸本)9781450394215

HCI researchers have been investigating family dynamics with new and emerging technologies during joint media engagement (JME) experiences. However, most studies describe family dynamics from parents' perspectives, such as their roles and mediation practices, while the roles and agency of other family members are less understood. In this paper, we examine family dynamics through the lens of negotiation between family members. Our study is located within an informal learning program called Family Creative Learning, where families from non-dominant groups were invited to participate in a series of workshops to create with a programming app called ScratchJr. Through analysis of data that included process, artifact, and reflective data, we identify negotiation practices of family members as they advocate for device and creative control. We further discuss how the lens of negotiation expands the meaning of productive JME in family contexts and highlight design considerations to facilitate engaging joint family experiences with educational technologies.

关键词： negotiation family joint media engagement creative making

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Understanding Oversubscribed Memory Management for Deep Learning Training 5

Understanding Oversubscribed Memory Management for Deep Lear...

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5th Workshop on Machine Learning and systems-EUROMLSYS-Annual

作者： Lin, Mao Jeon, Hyeran Univ Calif Merced Merced CA 95343 USA

ISBN: (纸本)9798400715389

This paper analyzes the performance impact of unified virtual memory (UVM) while running large deep learning (DL) workloads on a GPU with limited memory capacity. Due to the page fault handling overhead of UVM, DL frameworks have not officially supported UVM. However, given the rising GPU price, the global semiconductor shortages, and the ever-increasing DL model size, we cannot optimistically rely on adding more GPUs for DL processing. Various other solutions such as compression and quantization reduce memory footprint at the cost of performance and accuracy. Using UVM with memory oversubscription could tackle such concerns. However, UVM has not been actively leveraged in DL computing due to its performance overhead. In this study, we investigate the performance impact of UVM for DL computing to better understand the benefits and limitations. Our results show that while UVM enables training large models beyond GPU capacity, its effectiveness depends on the interplay between oversubscription factors and memory management strategies. We find that PCA significantly mitigates page fault overhead, improving performance at moderate oversubscription levels, but also increases migration traffic. These findings highlight the potential of integrating UVM with advanced memory management strategies to optimize DL workloads on limited-memory GPUs.

关键词： GPU Unified Virtual Memory Performance analysis DNN LLM

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