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Knowing When to Ask: Self-Gated Clarification for Hierarchical Language Agents Collaborative Human-Agent Protocol (CHAP) UXBench: Benchmarking User Experience in AI Assistants Impedance MPC for Physical Human-Robot Interaction: Predictive Disturbance Rejection with Joint-Limit Safety Formalizing all indexed mathematics as a benchmark for general reasoning, with the example of implementing dilatations of categories Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset What LLMs Must Forget to Teach Effectively: A DIY Approach to Premodern Japanese Language Pedagogy Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build Learning to Decide with AI Assistance under Human-Alignment Positive Alignment: Artificial Intelligence for Human Flourishing Sycophantic AI makes human interaction feel more effortful and less satisfying over time Exploring Interaction Paradigms for LLM Agents in Scientific Visualization The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers Participatory provenance as representational auditing for AI-mediated public consultation Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading Can LLMs Reason About Attention? 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VisMaker: a Question-Oriented Visualization Recommender System for Data Exploration
Raul de Araújo Lima, Simone Diniz Junqueira Barbosa · 2020-02-15 · via cs.HC updates on arXiv.org

The increasingly rapid growth of data production and the consequent need to explore data to obtain answers to the most varied questions have promoted the development of tools to facilitate the manipulation and construction of data visualizations. However, building useful data visualizations is not a trivial task: it may involve a large number of subtle decisions that require experience from their designer. In this paper, we present VisMaker, a visualization recommender tool that uses a set of rules to present visualization recommendations organized and described through questions, in order to facilitate the understanding of the recommendations and assisting the visual exploration process. We carried out two studies comparing our tool with Voyager 2 and analyzed some aspects of the use of tools. We collected feedback from participants to identify the advantages and disadvantages of our recommendation approach. As a result, we gathered comments to help improve the development of tools in this domain.