arXiv - CS - Human-Computer Interaction最新文献_第8页

Exploring Use and Perceptions of Generative AI Art Tools by Blind Artists 探索盲人艺术家对人工智能生成艺术工具的使用和看法

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-12 DOI: arxiv-2409.08226

Gayatri Raman, Erin Brady

引用次数: 0

OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering OmniQuery：从上下文角度增强捕捉到的多模态记忆，实现个人问题解答

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-12 DOI: arxiv-2409.08250

Jiahao Nick LiJerry, ZhuohaoJerry, Zhang, Jiaju Ma

{"title":"OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering","authors":"Jiahao Nick LiJerry, ZhuohaoJerry, Zhang, Jiaju Ma","doi":"arxiv-2409.08250","DOIUrl":"https://doi.org/arxiv-2409.08250","url":null,"abstract":"People often capture memories through photos, screenshots, and videos. While\u0000existing AI-based tools enable querying this data using natural language, they\u0000mostly only support retrieving individual pieces of information like certain\u0000objects in photos and struggle with answering more complex queries that involve\u0000interpreting interconnected memories like event sequences. We conducted a\u0000one-month diary study to collect realistic user queries and generated a\u0000taxonomy of necessary contextual information for integrating with captured\u0000memories. We then introduce OmniQuery, a novel system that is able to answer\u0000complex personal memory-related questions that require extracting and inferring\u0000contextual information. OmniQuery augments single captured memories through\u0000integrating scattered contextual information from multiple interconnected\u0000memories, retrieves relevant memories, and uses a large language model (LLM) to\u0000comprehensive answers. In human evaluations, we show the effectiveness of\u0000OmniQuery with an accuracy of 71.5%, and it outperformed a conventional RAG\u0000system, winning or tying in 74.5% of the time.","PeriodicalId":501541,"journal":{"name":"arXiv - CS - Human-Computer Interaction","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Situated Visualization in Motion for Swimming 游泳运动中的情景可视化

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-12 DOI: arxiv-2409.07695

Lijie Yao, Anastasia Bezerianos, Romain Vuillemot, Petra Isenberg

引用次数: 0

Testing the Test: Observations When Assessing Visualization Literacy of Domain Experts 测试测试：评估领域专家可视化素养时的观察结果

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-12 DOI: arxiv-2409.08101

Seyda Öney, Moataz Abdelaal, Kuno Kurzhals, Paul Betz, Cordula Kropp, Daniel Weiskopf

引用次数: 0

Visual Compositional Data Analytics for Spatial Transcriptomics 空间转录组学的可视化合成数据分析

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-11 DOI: arxiv-2409.07306

David Hägele, Yuxuan Tang, Daniel Weiskopf

引用次数: 0

Trust Dynamics in Human-Autonomy Interaction: Uncover Associations between Trust Dynamics and Personal Characteristics 人与自主性互动中的信任动态：发现信任动态与个人特征之间的关联

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-11 DOI: arxiv-2409.07406

Hyesun Chung, X. Jessie Yang

{"title":"Trust Dynamics in Human-Autonomy Interaction: Uncover Associations between Trust Dynamics and Personal Characteristics","authors":"Hyesun Chung, X. Jessie Yang","doi":"arxiv-2409.07406","DOIUrl":"https://doi.org/arxiv-2409.07406","url":null,"abstract":"While personal characteristics influence people's snapshot trust towards\u0000autonomous systems, their relationships with trust dynamics remain poorly\u0000understood. We conducted a human-subject experiment with 130 participants\u0000performing a simulated surveillance task aided by an automated threat detector.\u0000A comprehensive pre-experimental survey collected data on participants'\u0000personal characteristics across 12 constructs and 28 dimensions. Based on data\u0000collected in the experiment, we clustered participants' trust dynamics into\u0000three types and assessed differences among the three clusters in terms of\u0000personal characteristics, behaviors, performance, and post-experiment ratings.\u0000Participants were clustered into three groups, namely Bayesian decision makers,\u0000disbelievers, and oscillators. Results showed that the clusters differ\u0000significantly in seven personal characteristics: masculinity, positive affect,\u0000extraversion, neuroticism, intellect, performance expectancy, and high\u0000expectations. The disbelievers tend to have high neuroticism and low\u0000performance expectancy. The oscillators tend to have higher scores in\u0000masculinity, positive affect, extraversion and intellect. We also found\u0000significant differences in the behaviors and post-experiment ratings among the\u0000three groups. The disbelievers are the least likely to blindly follow the\u0000recommendations made by the automated threat detector. Based on the significant\u0000personal characteristics, we developed a decision tree model to predict cluster\u0000types with an accuracy of 70%.","PeriodicalId":501541,"journal":{"name":"arXiv - CS - Human-Computer Interaction","volume":"157 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Situated Visualization in Motion 运动中的情景可视化

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-11 DOI: arxiv-2409.07005

Lijie Yao, Anastasia Bezerianos, Petra Isenberg

引用次数: 0

"My Grade is Wrong!": A Contestable AI Framework for Interactive Feedback in Evaluating Student Essays "我的分数错了！"：评价学生作文的交互式反馈的可竞争人工智能框架

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-11 DOI: arxiv-2409.07453

Shengxin Hong, Chang Cai, Sixuan Du, Haiyue Feng, Siyuan Liu, Xiuyi Fan

{"title":"\"My Grade is Wrong!\": A Contestable AI Framework for Interactive Feedback in Evaluating Student Essays","authors":"Shengxin Hong, Chang Cai, Sixuan Du, Haiyue Feng, Siyuan Liu, Xiuyi Fan","doi":"arxiv-2409.07453","DOIUrl":"https://doi.org/arxiv-2409.07453","url":null,"abstract":"Interactive feedback, where feedback flows in both directions between teacher\u0000and student, is more effective than traditional one-way feedback. However, it\u0000is often too time-consuming for widespread use in educational practice. While\u0000Large Language Models (LLMs) have potential for automating feedback, they\u0000struggle with reasoning and interaction in an interactive setting. This paper\u0000introduces CAELF, a Contestable AI Empowered LLM Framework for automating\u0000interactive feedback. CAELF allows students to query, challenge, and clarify\u0000their feedback by integrating a multi-agent system with computational\u0000argumentation. Essays are first assessed by multiple Teaching-Assistant Agents\u0000(TA Agents), and then a Teacher Agent aggregates the evaluations through formal\u0000reasoning to generate feedback and grades. Students can further engage with the\u0000feedback to refine their understanding. A case study on 500 critical thinking\u0000essays with user studies demonstrates that CAELF significantly improves\u0000interactive feedback, enhancing the reasoning and interaction capabilities of\u0000LLMs. This approach offers a promising solution to overcoming the time and\u0000resource barriers that have limited the adoption of interactive feedback in\u0000educational settings.","PeriodicalId":501541,"journal":{"name":"arXiv - CS - Human-Computer Interaction","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridging Quantitative and Qualitative Methods for Visualization Research: A Data/Semantics Perspective in Light of Advanced AI 在可视化研究中衔接定量和定性方法：从数据/符号学角度看先进的人工智能

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-11 DOI: arxiv-2409.07250

Daniel Weiskopf

引用次数: 0

Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination 唤醒幻灯片：通过语言模型协调实现无调谐和知识调控的人工智能辅导系统

arXiv - CS - Human-Computer Interaction Pub Date : 2024-09-11 DOI: arxiv-2409.07372

Daniel Zhang-Li, Zheyuan Zhang, Jifan Yu, Joy Lim Jia Yin, Shangqing Tu, Linlu Gong, Haohua Wang, Zhiyuan Liu, Huiqin Liu, Lei Hou, Juanzi Li

{"title":"Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination","authors":"Daniel Zhang-Li, Zheyuan Zhang, Jifan Yu, Joy Lim Jia Yin, Shangqing Tu, Linlu Gong, Haohua Wang, Zhiyuan Liu, Huiqin Liu, Lei Hou, Juanzi Li","doi":"arxiv-2409.07372","DOIUrl":"https://doi.org/arxiv-2409.07372","url":null,"abstract":"The vast pre-existing slides serve as rich and important materials to carry\u0000lecture knowledge. However, effectively leveraging lecture slides to serve\u0000students is difficult due to the multi-modal nature of slide content and the\u0000heterogeneous teaching actions. We study the problem of discovering effective\u0000designs that convert a slide into an interactive lecture. We develop\u0000Slide2Lecture, a tuning-free and knowledge-regulated intelligent tutoring\u0000system that can (1) effectively convert an input lecture slide into a\u0000structured teaching agenda consisting of a set of heterogeneous teaching\u0000actions; (2) create and manage an interactive lecture that generates responsive\u0000interactions catering to student learning demands while regulating the\u0000interactions to follow teaching actions. Slide2Lecture contains a complete\u0000pipeline for learners to obtain an interactive classroom experience to learn\u0000the slide. For teachers and developers, Slide2Lecture enables customization to\u0000cater to personalized demands. The evaluation rated by annotators and students\u0000shows that Slide2Lecture is effective in outperforming the remaining\u0000implementation. Slide2Lecture's online deployment has made more than 200K\u0000interaction with students in the 3K lecture sessions. We open source\u0000Slide2Lecture's implementation in\u0000https://anonymous.4open.science/r/slide2lecture-4210/.","PeriodicalId":501541,"journal":{"name":"arXiv - CS - Human-Computer Interaction","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0