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检索条件"任意字段=Conference on Visual Data Exploration and Analysis IV"

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A Conceptual Framework for Immersive Behavioral data exploration Assisted by a Deep Learned Representation of data 7

A Conceptual Framework for Immersive Behavioral Data Explora...

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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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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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A Real-Time Sentiment Feedback System: Binary Categorization and Context Understanding Based on Product Reviews 16th

A Real-Time Sentiment Feedback System: Binary Categorization...

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16th International conference on Social Networks analysis and Mining

作者： Buttar, Arshpreet S. Fan, Jiawei Fatoki, Olukoye O. Geleta, Roba Leung, Carson K. Univ Manitoba Winnipeg MB Canada

ISBN: (纸本)9783031785535;9783031785542

In the digital age, user reviews are crucial for decision-making and product development. With the growing volume of Amazon product reviews, traditional analysis methods fall short, highlighting the need for smarter, quicker, and more scalable solutions. In this paper, we present a sentiment analysis system tailored for Amazon reviews, utilizing n-grams for better context comprehension and enabling exploration of the machine learning model via real-time application. Evaluation results show model accuracy by concentrating on positive and negative reviews and fine-tuning hyperparameters.

关键词： social network analysis social network mining big data analytics sentiment analysis Amazon reviews n-grams context understanding machine learning hyperparameter optimization text preprocessing dataset balance

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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...

引用

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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Making Social Platforms Accessible: Emotion-Aware Speech Generation with Integrated Text analysis 16th

Making Social Platforms Accessible: Emotion-Aware Speech Gen...

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16th International conference on Social Networks analysis and Mining

作者： De, Suparna Bostan, Ionut Sastry, Nishanth Univ Surrey Sch Comp Sci & Elect Engn Guildford Surrey England Nquiringminds Univ Pkwy Southampton SO16 7PT Hants England

ISBN: (纸本)9783031785535;9783031785542

Recent studies have outlined the accessibility challenges faced by blind or visually impaired, and less-literate people, in interacting with social networks, in-spite of facilitating technologies such as monotone text-to-speech (TTS) screen readers and audio narration of visual elements such as emojis. Emotional speech generation traditionally relies on human input of the expected emotion together with the text to synthesise, with additional challenges around data simplification (causing information loss) and duration inaccuracy, leading to lack of expressive emotional rendering. In real-life communications, the duration of phonemes can vary since the same sentence might be spoken in a variety of ways depending on the speakers' emotional states or accents (referred to as the one-to-many problem of text to speech generation). As a result, an advanced voice synthesis system is required to account for this unpredictability. We propose an end-to-end context-aware Text-to-Speech (TTS) synthesis system that derives the conveyed emotion from text input and synthesises audio that focuses on emotions and speaker features for natural and expressive speech, integrating advanced natural language processing (NLP) and speech synthesis techniques for real-time applications. Our system also showcases competitive inference time performance when benchmarked against the state-of-the-art TTS models, making it suitable for real-time accessibility applications.

关键词： Text-to-Speech Emotion embeddings Sentiment detection

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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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Guidance Source Matters: How Guidance from AI, Expert, or a Group of Analysts Impacts visual data Preparation and analysis 25

Guidance Source Matters: How Guidance from AI, Expert, or a ...

引用

30th International conference on Intelligent User Interfaces, IUI 2025

作者： Narechania, Arpit Endert, Alex Sinha, Atanu R Georgia Institute of Technology AtlantaGA United States Adobe Research Bangalore India

ISBN: (纸本)9798400713064

The progress in generative Artificial Intelligence (AI) has fueled AI-powered tools like co-pilots and assistants to provision better guidance, particularly during data analysis. However, research on guidance has not yet examined the perceived efficacy of the source from which guidance is offered and the impact of this source on the user's perception and usage of guidance. We ask whether users perceive all guidance sources as equal, with particular interest in three sources: (i) "AI,"(ii) "human expert,"and (iii) "a group of human analysts."As a benchmark, we consider a fourth source, (iv) "unattributed guidance,"where guidance is provided without attribution to any source, enabling isolation of and comparison with the effects of source-specific guidance. We design a five-condition between-subjects study, with one condition for each of the four guidance sources and an additional (v) "no-guidance"condition, which serves as a baseline to evaluate the influence of any kind of guidance. We situate our study in a custom data preparation and analysis tool wherein we task users to select relevant attributes from an unfamiliar dataset to inform a business report. Depending on the assigned condition, users can request guidance, which the system then provides in the form of attribute suggestions. To ensure internal validity, we control for the quality of guidance across source-conditions. Through several metrics of usage and perception, we statistically test five preregistered hypotheses and report on additional analysis. We find that the source of guidance matters to users, but not in a manner that matches received wisdom. For instance, users utilize guidance differently at various stages of analysis, including expressing varying levels of regret, despite receiving guidance of similar quality. Notably, users in the AI condition reported both higher post-task benefit and regret. These findings strongly indicate the need to further understand how different guidance sources imp

关键词： visual analytics

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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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