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7th IEEE International conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Duvivier, Victor Da Silva, Matthieu Perreira Prié, Yannick Nantes Université École Centrale Nantes Cnrs LS2N Umr 6004 Nantes44300 France

ISBN: (纸本)9798331521578

By allowing to conduct experiments involving eco-logically valid tasks within controlled environments, Virtual Real-ity (VR) offers novel opportunities for studying human behavior. Several modalities can be leveraged, including event logs, motion trajectories, eye-tracking data, or physiological signals. However, analyzing such multimodal data presents considerable challenges due to their inherent complexity, the varied structures they imply, as well as the necessity to use exploratory approaches. There is therefore a need to design visual analytics tools for the exploration of immersive behavioral data without prior knowledge. Our idea is to integrate deep learned computational models -which leverage advanced techniques to extract valuable high-level features from unlabeled data- into exploratory visual Analytics tools. We introduce a conceptual framework for integrating deep learning models into the visual Analytics process of immersive behavioral data analysis, also focusing on the services that such systems should provide to the analysts. © 2025 IEEE.

关键词： visual analytics

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Analyzing the Shifts in Users data Focus in Exploratory visual analysis 25

Analyzing the Shifts in Users Data Focus in Exploratory Visu...

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30th International conference on Intelligent User Interfaces, IUI 2025

作者： Saha, Sanad Aryal, Nischal Battle, Leilani Termehchy, Arash Computer Science Oregon State University CorvallisOR United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States

ISBN: (纸本)9798400713064

Users often begin exploratory visual analysis (EVA) without clear analysis goals but iteratively refine them as they learn more about their data. As an essential step in data science, researchers want to aid EVA by developing responsive and personalized visualization tools. For this, accurate models of users' exploration behavior are becoming increasingly vital. However, many computational models assume that the human exploration behavior isstatic, which goes against thedynamic nature of EVA. In this benchmark study, we investigate how users dynamically shift their data focus in EVA and seek to find the best online learning methods for modeling users' data focus shifts. Through empirical analyses, we find reinforcement learning algorithms are better in this regard than existing approaches from visualization research. Furthermore, we discuss our findings and their impact on the future of user modeling for visualization system design. © 2025 Copyright held by the owner/author(s).

关键词： Human-centered Computing

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visualization Atlases: Explaining and Exploring Complex Topics through data, visualization, and Narration

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2025年第1期31卷 437-447页

作者： Wang, Jinrui Shu, Xinhuan Bach, Benjamin Hinrichs, Uta Univ Edinburgh Edinburgh Midlothian Scotland Newcastle Univ Newcastle NSW Australia INRIA Paris France

This paper defines, analyzes, and discusses the emerging genre of visualization atlases. We currently witness an increase in web-based, data-driven initiatives that call themselves "atlases" while explaining complex, contemporary issues through data and visualizations: climate change, sustainability, AI, or cultural discoveries. To understand this emerging genre and inform their design, study, and authoring support, we conducted a systematic analysis of 33 visualization atlases and semi-structured interviews with eight visualization atlas creators. Based on our results, we contribute (1) a definition of a visualization atlas as a compendium of (web) pages aimed at explaining and supporting exploration of data about a dedicated topic through data, visualizations and narration. (2) a set of design patterns of 8 design dimensions, (3) insights into the atlas creation from interviews and (4) the definition of 5 visualization atlas genres. We found that visualization atlases are unique in the way they combine i) exploratory visualization, ii) narrative elements from data-driven storytelling and iii) structured navigation mechanisms. They target a wide range of audiences with different levels of domain knowledge, acting as tools for study, communication, and discovery. We conclude with a discussion of current design practices and emerging questions around the ethics and potential real-world impact of visualization atlases, aimed to inform the design and study of visualization atlases.

关键词： visualization Atlases Information visualization data-driven Storytelling

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Automated data exploration and analysis 41st

Automated Data Exploration and Analysis

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41st Computer Graphics International conference, CGI 2024

作者： Wu, Tong Wang, Song Peng, Xin School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9783031820205

The paper presents a recommender algorithm for visual analysis based on data field Schema and Aggregation, and developed an automated data analysis solution recommendation system (AutoEDA) in conjunction with the Exploratory data analysis process (EDA). To address the issue of field type recognition, we designed a dynamic field recognition framework combining four classification models to automatically classify data field types. For the evaluation of analysis strategies, we proposed an analysis strategy evaluation method based on the Chart Attribute Length for designing custom search strategies and defining chart matching rules. Finally, through interactive visual backtracking and the functionality of modifying and tracing analysis solutions, we collaborated with data analysts of varying experience levels, and validated the effectiveness and feasibility of the proposed solution with structured data from publicly available websites. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data aggregation

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An Integrated System for Interacting with Multi-Page Scholarly Documents 21

An Integrated System for Interacting with Multi-Page Scholar...

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21st conference on Information and Research Science Connecting to Digital and Library Science, IRCDL 2025

作者： Massai, Lorenzo Marinai, Simone DINFO - University of Florence via S. Marta 3 Firenze Italy

In this work we present a preliminary version of a comprehensive interface for supporting users to interact with scholarly documents, enabling multi-layered exploration and offering deeper insights by integrating diverse features and contextual information. By bridging diverse information our work pursues the identification, characterization, and linking of visual elements to semantic and context data, leveraging large language models for interoperability. Recent advances in retrieval augmented generation are also exploited to address some language models limitations, allowing them to access latent information from document representations such as graph and vector embeddings. The system under development performs an analysis of input documents and enables the extraction of visual and semantic features, making them accessible in a comprehensive framework. The association of structural information to visual data allows formal analysis of documents and is exploited in our model to enhance visual extraction, performing a novel ontology-based constraint violation detection. The information extracted through this framework is semantically explorable, providing access to the document structure, which can be exploited in many applications like question answering and document understanding. © 2025 Copyright for this paper by its authors.

关键词： Latent semantic analysis

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10th International Workshop on Big data Management and Service, BDMS 2024, 9th International Workshop on Big data Quality Management, BDQM 2024, DASFAA 2024 Workshop on Emerging Results in data Science and Engineering, ERDSE 2024 and 8th International Workshop on Graph data Management and analysis, GDMA 2024 held in conjunction with 29th International conference on database Systems for Advanced Applications, DASFAA 2024

10th International Workshop on Big Data Management and Servi...

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10th International Workshop on Big data Management and Service, BDMS 2024, 9th International Workshop on Big data Quality Management, BDQM 2024, DASFAA 2024 Workshop on Emerging Results in data Science and Engineering, ERDSE 2024 and 8th International Workshop on Graph data Management and analysis, GDMA 2024 held in conjunction with 29th International conference on database Systems for Advanced Applications, DASFAA 2024

ISBN: (纸本)9789819609130

The proceedings contain 26 papers. The special focus in this conference is on Big data Management and Service. The topics include: FAITH: A Fast, Accurate, and Lightweight database-Agnostic Learned Cost Model;fast Approximate Temporal Butterfly Counting on Bipartite Graphs via Edge Sampling;Financial-ICS: Identifying Peer Firms via LongBERT from 10K Reports;establishing a Decentralized Diamond Quality Management System: Advancing Towards Global Standardization;co-estimation of data Types and Their Positional Distribution;Enhancing Load Forecasting with VAE-GAN-Based data Cleaning for Electric Vehicle Charging Loads;audio-Guided visual Knowledge Representation;boundary Point Detection Combining Gravity and Outlier Detection Methods;A Meta-learning Approach for Category-Aware Sequential Recommendation on POIs;automatic Post-editing of Speech Recognition System Output Using Large Language Models;comparative analysis with Multiple Large-Scale Language Models for Automatic Generation of Funny Dialogues;effectiveness of the Programmed visual Contents Comparison Method for Two Phase Collaborative Learning in Computer Programming Education: A Case Study;generating Achievement Relationship Graph Between Actions for Alternative Solution Recommendation;generating News Headline Containing Specific Person Name;Investigating Evidence in Sentence Similarity Using MASK in BERT;acceleration of Synopsis Construction for Bounded Approximate Query Processing;Query Expansion in Food Review Search with Synonymous Phrase Generation by LLM;Question Answer Summary Generation from Unstructured Texts by Using LLMs;Real Estate Information exploration in VR with LoD Control by Physical Distance;voices of Asynchronous Learning Students: Revealing Learning Characteristics Through Vocabulary analysis of Notes Tagged in Videos;review Search Interface Based on Search Result Summarization Using Large Language Model;yes-No Flowchart Generation for Interactive exploration of Personalized Health Improve

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Transactional panorama: a conceptual framework for user perception in analytical visual interfaces (extended version)

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VLDB Journal 2025年第4期34卷 1-23页

作者： Tang, Dixin Fekete, Alan Gupta, Indranil Parameswaran, Aditya G. University of Texas at Austin Austin United States University of Sydney Sydney Australia University of Illinois Urbana-Champaign Champaign United States University of California Berkeley United States

Many tools empower analysts and data scientists to consume analysis results in a visual interface. When the underlying data changes, these results need to be updated, but this update can take a long time—all while the user continues to explore the results. Tools can either (i) hide away results that haven’t been updated, hindering exploration;(ii) make the updated results immediately available to the user (on the same screen as old results), leading to confusion and incorrect insights;or (iii) present old—and therefore stale—results to the user during the update. To help users reason about these options and others, and make appropriate trade-offs, we introduce Transactional Panorama, a formal framework that adopts transaction s to jointly model the system refreshing the analysis results and the user interacting with them. We introduce three key properties that are important for user perception in this context: visibility (allowing users to continuously explore results), consistency (ensuring that results presented are from the same version of the data), and monotonicity (making sure that results don’t "go back in time"). Within transactional panorama, we characterize all feasible property combinations, design new mechanisms (that we call lenses) for presenting analysis results to the user while preserving a given property combination, formally prove their relative orderings for various performance criteria, and discuss their use cases. We propose novel algorithms to preserve each property combination and efficiently present fresh analysis results. We implement our framework into a popular, open-source BI tool, illustrate the relative performance implications of different lens es, and demonstrate the benefits of the novel lens es and our optimizations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Open systems

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DM-Vis: A Graph-Based data Reconnaissance System for Multi-domain Urban data 41st

DM-Vis: A Graph-Based Data Reconnaissance System for Multi...

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41st Computer Graphics International conference, CGI 2024

作者： Wang, Hesong Wang, Song Wang, Yanru Long, Hao School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9783031818059

As urbanization accelerates, data on the diverse aspects of urban life, including the environment, finance, and transportation, are increasing exponentially. Single-domain data analysis falls short for complex tasks, necessitating multi-source data integration to offer decision-makers a holistic view of interconnections and trends. However, challenges arise in effectively representing data associations and swiftly identifying relevant datasets. This paper proposes techniques to integrate multi-domain urban data relationships and facilitate the acquisition of target datasets. We introduce a method to transform data entities into association graphs and a data extraction method to aid users in understanding data relationships and acquiring target datasets. Using these methods, we developed DM-Vis, a data reconnaissance visual analysis system, to facilitate data integration and exploration from multiple sources. Real-data case studies and positive feedback from users and experts validate the utility and reliability of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data extract data integration data reconnaissance multi-domain data query embedding

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The Influence of Curiosity Traits and On-Demand Explanations in AI-Assisted Decision-Making 25

The Influence of Curiosity Traits and On-Demand Explanations...

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30th International conference on Intelligent User Interfaces, IUI 2025

作者： Cau, Federico Maria Spano, Lucio Davide Department of Mathematics and Computer Science University of Cagliari Cagliari Italy

ISBN: (纸本)9798400713064

Previous research on eXplainable Artificial Intelligence (XAI) in AI-assisted decision-making has shown mixed results in increasing users' accuracy while mitigating overreliance on AI. A promising yet underexplored strategy consists of providing AI assistance on-demand through explicit interaction. Preliminary results show that users with high Need for Cognition (NFC) benefit more from such a paradigm, though the effects predicted by similar cognitive measures require further investigation. In addition, hybrid approaches consisting of descriptive statistics on the training data (global data-centric) with model-centric explanations have shown the potential to mitigate overreliance while improving accuracy for experts and lay users in the health domain. However, the impact of this approach in other fields is still *** paper investigates the effects of four on-demand explanation types - local model-centric, global data-centric, local/global model-centric, and hybrid - on users' accuracy and overreliance. We also assess how variations in Need for Cognition (NFC), Epistemic Curiosity (EC), and Curiosity and exploration Inventory-ii (CEI-ii) impact these metrics and explore correlations among these *** findings indicate no significant differences among on-demand explanations to improve accuracy or mitigate overreliance. The same holds for low and high NFC, EC, and CEI-ii individuals, although we found moderate positive correlations among these psychometrics. Post-hoc analysis revealed that personality traits and the on-demand intervention influenced other decision-making behaviors more than the type of explanation provided. Users who requested on-demand assistance exhibited lower confidence, suggesting that seeking data or AI support may undermine self-confidence. Interestingly, individuals with higher NFC and CEI-ii scores showed greater confidence, and those scoring higher on CEI-ii requested AI assistance less *** contribute to expanding the k

关键词： Job applicants data

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Fine-Tuning Large Language Models for Automatic Font Skeleton Generation: exploration and analysis 17th

Fine-Tuning Large Language Models for Automatic Font Skelet...

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17th Asian conference on Computer Vision, ACCV 2024

作者： Liu, Yuxuan Fujii, Yasuhisa Zhu, Xinru Nohara, Kayoko School of Environment and Society Institute of Science Tokyo Tokyo Japan Google DeepMind Tokyo Japan

ISBN: (纸本)9789819609079

Despite the pivotal role that font skeletons could play in typeface research and font design, the availability of font skeleton data is sparse and limited. This research explores the possibility of using Large Language Models (LLMs) to generate font skeleton data based on font outline data. Our method represents font skeletons and font outlines as sequences of text tokens derived from SVG commands, and formulates the font skeleton task as a language modeling task predicting the token sequence for a font skeleton given the token sequence of a font outline. As a first attempt, we fine-tuned GPT-3.5 on a dataset of 8,213 Japanese font outlines and corresponding skeletons. Both quantitative and qualitative evaluations show the effectiveness of the approach in terms of rasterized pixel distance, Chamfer distance, and visual analysis. Our proposed method achieved average results of 14.678 for rasterized pixel distance and 1.713 for Chamfer distance, both better than the baseline method (PolyVectorization). In visual analysis, we found better generation results for complex shapes which logograms such as Chinese characters tend to have, than for the simple shapes of syllabograms such as Japanese kana, phonograms such as Latin alphabets, and symbols. Although our fine-tuned model has limitations in generating the skeletons of other font styles, this research establishes a foundation for the automatic generation of font skeletons using LLMs, setting the stage for future work on automatic skeleton generation and the wider application of font skeletons in typography. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musculoskeletal system

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