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A novel interaction for competence assessment using micro-behaviors: Extending CACHET to graphs and charts 23

A novel interaction for competence assessment using micro-be...

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CHI conference on Human Factors in computing systems (CHI)

作者： Fiorenzo, Colarusso Cheng, Peter C. H. Grecia, Garcia Garcia Aaron, Stockdill Daniel, Raggi Mateja, Jamnik Univ Sussex Dept Informat Brighton England

ISBN: (纸本)9781450394215

Competence Assessment by Chunk Hierarchy Evaluation with Transcription-tasks (CACHET) was proposed by Cheng [14]. It analyses micro-behaviors captured during cycles of stimulus viewing and copying in order to probe chunk structures in memory. This study extends CACHET by applying it to the domain of graphs and charts. Since drawing strategies are diverse, a new interactive stimulus presentation method is introduced: Transcription with Incremental Presentation of the Stimulus (TIPS). TIPS aims to reduce strategy variations that mask the chunking signal by giving users manual element-by-element control over the display of the stimulus. The potential of TIPS, is shown by the analysis of six participants transcriptions of stimuli of different levels of familiarity and complexity that reveal clear signals of chunking. To understand how the chunk size and individual differences drive TIPS measurements, a CPM-GOMS model was constructed to formalize the cognitive process involved in stimulus comprehension and chunk creation.

关键词： Competence assessment Chunking Task analysis GOMS Individual Differences

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Leveraging Large Language Models for Personalized Public Messaging

Leveraging Large Language Models for Personalized Public Mes...

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

作者： Das, Amit Kumar Bearfield, Cindy Xiong Mueller, Klaus Stony Brook University Stony Brook NY United States Georgia Tech Atlanta GA United States

ISBN: (纸本)9798400713958

We present a novel methodology for crafting effective public messages by combining large language models (LLMs) and conjoint analysis. Our approach personalizes messages for diverse personas – context-specific archetypes representing distinct attitudes and behaviors – while reducing the costs and time associated with traditional surveys. We tested this method in public health contexts (e.g., COVID-19 mandates) and civic engagement initiatives (e.g., voting). A total of 153 distinct messages were generated, each composed of components with varying levels, and evaluated across five personas tailored to each context. Conjoint analysis identified the most effective message components for each persona, validated through a study with 2,040 human participants. This research highlights LLMs’ potential to enhance public communication, providing a scalable, cost-effective alternative to surveys, and offers new directions for HCI, particularly for the design of adaptive, user-centered, persona-driven interfaces and systems. © 2025 Copyright held by the owner/author(s).

关键词： Conjoint analysis human-AI collaboration Large Language Models (LLM) Message Personalization Persona-based Messaging Public Communication

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Reviewing and Reflecting on Smart Home Research from the Human-Centered Perspective 23

Reviewing and Reflecting on Smart Home Research from the Hum...

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CHI conference on Human Factors in computing systems (CHI)

作者： Yao, Yuan Li, Huang He, Yi Ma, Zhijun Xu, Xuhai Mi, Haipeng Beijing Jiaotong Univ Sch Architecture & Design Beijing Peoples R China Tsinghua Univ Future Lab Beijing Peoples R China Tsinghua Univ Future Lab Acad Arts & Design Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Digital Media & Design Arts Beijing Peoples R China Southeast Univ Sch Architecture Nanjing Peoples R China Univ Washington Informat Sch Seattle WA USA

ISBN: (纸本)9781450394215

While there has been rapid growth in smart home research from a technical perspective - focusing on home automation, devices, software, and protocols - few review papers examine the human-centered perspective. A human-centered focus is crucial for achieving the goals of providing natural, convenient, comfortable, friendly, and safe user experiences in the smart home. To understand key innovations in human-centered smart home research, we analyzed keyword changes over time via 19,091 papers from 2000 to 2022, then selected 55 papers from high-impact venues in the last five years, and summarized them through a combination of qualitative and quantitative methods. Our analysis revealed five research trends with unique characteristics and interdependence. Drawing on this review, we elaborate on the future of smart home design research with respect to multidisciplinary development, stakeholder involvement, and the shift of design implications.

关键词： smart home human-centered design literature analysis research trends

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SYnergy: Fine-grained Energy-Efficient Heterogeneous computing for Scalable Energy Saving 23

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computi...

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

作者： Fan, Kaijie D'Antonio, Marco Carpentieri, Lorenzo Cosenza, Biagio Ficarelli, Federico Cesarini, Daniele Technical University Berlin Berlin Germany University of Salerno Salerno Italy CINECA Bologna Italy

ISBN: (纸本)9798400701092

Energy-efficient computing uses power management techniques such as frequency scaling to save energy. Implementing energy-efficient techniques on large-scale computing systems is challenging for several reasons. While most modern architectures, including GPUs, are capable of frequency scaling, these features are often not available on large systems. In addition, achieving higher energy savings requires precise energy tuning because not only applications but also different kernels can have different energy characteristics. We propose SYnergy, a novel energy-efficient approach that spans languages, compilers, runtimes, and job schedulers to achieve unprecedented fine-grained energy savings on large-scale heterogeneous clusters. SYnergy defines an extension to the SYCL programming model that allows programmers to define a specific energy goal for each kernel. For example, a kernel can aim to minimize well-known energy metrics such as EDP and ED2P or to achieve predefined energy-performance tradeoffs, such as the best performance with 25% energy savings. Through compiler integration and a machine learning model, each kernel is statically optimized for the specific target. On large computing systems, a SLURM plug-in allows SYnergy to run on all available devices in the cluster, providing scalable energy savings. The methodology is inherently portable and has been evaluated on both NVIDIA and AMD GPUs. Experimental results show unprecedented improvements in energy and energy-related metrics on real-world applications, as well as scalable energy savings on a 64-GPU cluster. © 2023 Owner/Author(s).

关键词： Energy efficiency

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System-Auditing, Data analysis and Characteristics of Cyber Attacks for Big Data systems 22

System-Auditing, Data Analysis and Characteristics of Cyber ...

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31st acm International Conference on Information and Knowledge Management (CIKM)

作者： Huang, Liangyi Hall, Sophia Shao, Fei Nihar, Arafath Chaudhary, Vipin Wu, Yinghui French, Roger Xiao, Xusheng Arizona State Univ Tempe AZ 85281 USA Case Western Reserve Univ Cleveland OH USA

ISBN: (纸本)9781450392365

Using big data, distributed computing systems such as Apache Hadoop requires processing massive amount of data to support business and research applications. Thus, it is critical to ensure the cyber security of such systems. To better defend from advanced cyber attacks that pose threats to even well-protected enterprises, system-auditing based techniques have been adopted for monitoring system activities and assisting attack investigation. In this demo, we are building a system that collects system auditing logs from a big data system and performs data analysis to understand how system auditing can be used more effectively to assist attack investigation on big systems. We also built a demo application that detects unexpected file deletion and presents root causes for the deletion.

关键词： system auditing big data systems cyber attack investigation

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A Systematic Literature Review to Characterize Asymmetric Interaction in Collaborative systems 25

A Systematic Literature Review to Characterize Asymmetric In...

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

作者： Bréhault, Victor Dubois, Emmanuel Prouzeau, Arnaud Serrano, Marcos IRIT University of Toulouse Toulouse France Université Paris-Saclay Inria CNRS Paris France

ISBN: (纸本)9798400713941

Computer-mediated collaboration often relies on symmetrical interactions between users, where all the collaborators use identical devices. However, in some cases, either due to constraints (e.g. users in different environments) or by choice (e.g. using devices with different properties), users engage in asymmetrical interactions. Addressing such asymmetries in heterogeneous systems can be difficult as there has been no systematic analysis of how to define them, or their impact on collaboration. In this paper, we characterize the asymmetries that can arise between users' interactions within collaborative heterogeneous systems. To this end, we conduct a systematic literature review of asymmetric collaborative systems, coding their properties, including the interaction spaces, their input and output modalities, and shared feedback. We then define the dimensions of asymmetry that emerge from this review. We discuss their impact on collaboration and outline a set of challenges and opportunities for future research. © 2025 Copyright held by the owner/author(s).

关键词： asymmetric interaction Remote collaboration systematic literature review

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VALUEEXPERT: Exploring Value Patterns in GPU-Accelerated Applications 27

VALUEEXPERT: Exploring Value Patterns in GPU-Accelerated App...

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27th acm International Conference on Architectural Support for Programming Languages and Operating systems (ASPLOS)

作者： Zhou, Keren Hao, Yueming Mellor-Crummey, John Meng, Xiaozhu Liu, Xu Rice Univ Houston TX 77005 USA North Carolina State Univ Raleigh NC USA

ISBN: (纸本)9781450392051

General-purpose GPUs have become common in modern computing systems to accelerate applications in many domains, including machine learning, high-performance computing, and autonomous driving. However, inefficiencies abound in GPU-accelerated applications, which prevent them from obtaining bare-metal performance. Performance tools play an important role in understanding performance inefficiencies in complex code bases. Many GPU performance tools pinpoint time-consuming code and provide high-level performance insights but overlook one important performance issue-value-related inefficiencies, which exist in many GPU code bases. In this paper, we present VALUEEXPERT, a novel tool to pinpoint value-related inefficiencies in GPU applications. VALUEEXPERT monitors application execution to capture values produced and used by each load and store operation in GPU kernels, recognizes multiple value patterns, and provides intuitive optimization guidance. We address systemic challenges in collecting, maintaining, and analyzing voluminous performance data from many GPU threads to make VALUEEXPERT applicable to complex applications. We evaluate VALUEEXPERT on a wide range of well-tuned benchmarks and applications, including PyTorch, Darknet, LAMMPS, Castro, and many others. VALUEEXPERT is able to identify previously unknown performance issues and provide suggestions for nontrivial performance improvements with typically less than five lines of code changes. We verify our optimizations with application developers and upstream fixes to their repositories.

关键词： GPUs GPU profilers Profiling Tools Value analysis Value Patterns

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Divisi: Interactive Search and Visualization for Scalable Exploratory Subgroup analysis 25

Divisi: Interactive Search and Visualization for Scalable Ex...

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

作者： Sivaraman, Venkatesh Li, Zexuan Perer, Adam Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States School of Information University of Michigan Ann Arbor MI United States

ISBN: (纸本)9798400713941

Analyzing data subgroups is a common data science task to build intuition about a dataset and identify areas to improve model performance. However, subgroup analysis is prohibitively difficult in datasets with many features, and existing tools limit unexpected discoveries by relying on user-defined or static subgroups. We propose exploratory subgroup analysis as a set of tasks in which practitioners discover, evaluate, and curate interesting subgroups to build understanding about datasets and models. To support these tasks we introduce Divisi, an interactive notebook-based tool underpinned by a fast approximate subgroup discovery algorithm. Divisi's interface allows data scientists to interactively re-rank and refine subgroups and to visualize their overlap and coverage in the novel Subgroup Map. Through a think-aloud study with 13 practitioners, we find that Divisi can help uncover surprising patterns in data features and their interactions, and that it encourages more thorough exploration of subtypes in complex data. © 2025 Copyright held by the owner/author(s).

关键词： Exploratory Data analysis Model Evaluation Slice Discovery Subgroup analysis

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A Case Study on Scaffolding Exploratory Data analysis for AI Pair Programmers

A Case Study on Scaffolding Exploratory Data Analysis for AI...

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

作者： Zhou, Haoquan Li, Jingbo Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781450394222

Recent advances in automatic code generation have made tools like GitHub Copilot attractive for programmers, as they allow for the creation of code blocks by simply providing descriptive prompts to the AI. While researchers have studied the performance of these AI-based tools in general-purpose programming, their effectiveness in data analysis is understudied. Unlike general-purpose programming which focuses more on algorithm-driven tasks like building novel software, data analysis requires a data-driven approach to actually gain insights. It remains unclear how these tools could be utilized to help data scientists analyze real-world problems. In this paper, we conducted a qualitative user study with 5 participants to understand the use of GitHub Copilot in solving problems by scaffolding prompts at different levels of specificity among data scientists. We discovered that effective prompts require carefully selected terminology, properly arranged word order, and sufficiently established interaction between humans and GitHub Copilot. We also spot some potential flaws in GitHub Copilot that hinder data scientists from efficiently scaffolding prompts. Our work points out some improvement directions for both data scientists and GitHub Copilot in the future. © 2023 Owner/Author.

关键词： Scaffolds

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DisMASTD: An Efficient Distributed Multi-Aspect Streaming Tensor Decomposition (Poster Abstract) 23

DisMASTD: An Efficient Distributed Multi-Aspect Streaming Te...

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2023 acm Turing Award Celebration Conference, CHINA 2023

作者： Yang, Keyu Gao, Yunjun Shen, Yifeng Zheng, Baihua Chen, Lu College of Computer Science Zhejiang University Hangzhou China Alibaba-Zhejiang University Joint Institute of Frontier Technologies Hangzhou China School of Information Systems Singapore Management University Singapore

ISBN: (纸本)9798400702334

Tensor decomposition is a fundamental multi-dimensional data analysis tool for many data-driven applications. However, the rapidly increasing data requires an efficient distributed dynamic tensor decomposition without re-computing the whole tensor from scratch. In this paper, we propose DisMASTD, an efficient distributed multi-aspect streaming tensor decomposition. First, we prove the optimal tensor partitioning problem is NP-hard. Second, we present two heuristic tensor partitioning approaches to ensure the load balancing. Third, we develop a distributed multi-aspect streaming tensor decomposition computation method, which avoids repetitive computation and reduces the communication. Last but not least, extensive experiments on both real and synthetic datasets are conducted to verify the efficiency and scalability of DisMASTD. © 2023 Owner/Author.

关键词： Tensors

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