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

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EVRACE: Enhanced visual Retrieval and analysis for Large-Scale E-Bike Charging Curve exploration

EVRACE: Enhanced Visual Retrieval and Analysis for Large-Sca...

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2024 IEEE International conference on Big data, Bigdata 2024

作者： Hu, Saisai Zhang, Ji Li, Zhao Ding, Donghui Liu, Dong Pan, Zhijun Dong, Jiale Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nanjing China University of Southern Queensland Towoomba Australia Hangzhou Yugu Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9798350362480

This Paper introduces a novel Enhanced visual Retrieval and analysis for Charging Efficiency (EVRACE) system, which presents a novel two-stage framework for visual retrieval and advanced analytics for massive charging curves of electric vehicles. The EVRACE is becoming an important system for enhancing battery performance management, detecting and mitigating charging anomalies, and informing strategic decision-making for stakeholders in the EV ecosystem. EVRACE utilizes deep time-series clustering to categorize charging curves with distinct characteristics and retrieves the most relevant cluster and the top k curves for comprehensive data analysis and risk assessment. This innovative framework substantially enhances retrieval efficiency, mitigates computational complexity, and offers a robust solution for real-time processing of large-scale charging data. Validation using real-world datasets demonstrates that the EVRACE system significantly improves retrieval efficiency and achieves high accuracy in anomaly detection compared to traditional methods. © 2024 IEEE.

关键词： Charging stations

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Confides: A visual Analytics Solution for Automated Speech Recognition analysis and exploration

Confides: A Visual Analytics Solution for Automated Speech R...

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2024 IEEE visualization and visual Analytics conference, VIS 2024

作者： Ha, Sunwoo Lim, Chaehun Crouser, R. Jordan Ottley, Alvitta

ISBN: (纸本)9798350354850

Confidence scores of automatic speech recognition (ASR) outputs are often inadequately communicated, preventing its seamless integration into analytical workflows. In this paper, we introduce Confides, a visual analytic system developed in collaboration with intelligence analysts to address this issue. Confides aims to aid exploration and post-AI-transcription editing by visually representing the confidence associated with the transcription. We demonstrate how our tool can assist intelligence analysts who use ASR outputs in their analytical and exploratory tasks and how it can help mitigate misinterpretation of crucial information. We also discuss opportunities for improving textual data cleaning and model transparency for human-machine collaboration. © 2024 IEEE.

关键词： visual analytics

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data visualization in Transforming Raw data into Compelling visual Narratives 2

Data Visualization in Transforming Raw Data into Compelling ...

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2nd IEEE International conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sarkar, Prithu Priya, Yadavalli Devi Patel, Pravina B. Chatterjee Biswas, Prasanta Arigela, Sreenivasulu Sallaram, Sravanthi Amity University Amity School of Communication Kolkata India Computer Science and Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Vishakapatanam India Ganpat University Department of Computer Science Gujarat Mehsana India Cdoe Parul University Vadodara India Mohan Babu University Andhra Pradesh Tirupati India St.Martin's Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350384277

One of the main ways to evaluate information is through visual learning, which traditionally combines textual reading with visual aids like graphical representations. However, separating the visual method of learning to distinguish between text and visuals has been suggested in light of advancements in learning styles. The ability to swiftly create complex visual information and inexpensively distribute it digitally is another way that technology has improved visual presentation. The purpose of this research paper is to conduct a detailed investigation of the Virtualization of data, storytelling, and data visualization's Narrative, understand how Instruments and methods are used in visualization, and select the appropriate tool for presenting visual data stories and upcoming projects. To address this purpose, this research has reviewed a wide range of different journals and articles to gather information about data visualization, storytelling, and data narratives. Based on the analysis, this research paper finds an all-encompassing perspective on the visual data narrative process, incorporating the phases of discovering insights (data exploration), developing these insights into a tale (creating a story), and sharing this story with others (telling a story). Through the pursuit of these novel research directions, visualizations can facilitate more potent data storytelling. © 2024 IEEE.

关键词： Spatio-temporal data

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Classification of Open Government data Solutions' Help: A Novel Taxonomy and Cluster analysis 22nd

Classification of Open Government Data Solutions' Help: A No...

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22nd IFIP WG 8.5 International conference on Electronic Government (EGOV) / International conference on E-Democracy and Open Government (CeDEM) / 15th IFIP WG 8.5 International conference on Electronic Participation (EPart)

作者： Crusoe, Jonathan Clarinval, Antoine Univ Gothenburg Dept Appl IT Swedish Ctr Digital Innovat Gothenburg Sweden Univ Boras Swedish Sch Lib & Informat Sci Boras Sweden Univ Namur Namur Digital Inst Namur Belgium

ISBN: (纸本)9783031411373;9783031411380

Open Government data (OGD) pose that public organisations should freely share data for anyone to reuse without restrictions. However, the rawness of this data proves to be a challenge for data or information seekers. OGD-based solutions, such as interactive maps and dashboards, could help seekers overcome this difficulty and use OGD to satisfy needs, helping them to work effectively, solve problems, or pursue hobbies. However, there are several challenges that need to be considered when designing solutions, such as seekers wanting to solve problems rather than consuming information and aiming for quick wins over quality. Previous research has classified OGD solutions, focusing on general concepts. The next step is to reveal helpful patterns in OGD solutions, helping seekers. This paper presents a taxonomy with 24 criteria to classify these patterns. It was tested on 40 OGD solutions, and the resulting classifications were grouped in a cluster analysis, identifying 16 key criteria and 6 clusters. The clusters are (1) simple-personalised, (2) proactive multi-visual, (3) lightly-facilitated exploration, (4) facilitated data-management, (5) facilitated information exploration, and (6) horizon solutions. One unexpected finding is that helpful patterns do not cluster following themes, types, or purposes of solutions. Another finding is that the importance of key criteria varies between the clusters.

关键词： Open Government data solution taxonomy classification cluster analysis information behaviour

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FCAvizIR: Exploring Relational data Set's Implications Using Metrics and Topics 1st

FCAvizIR: Exploring Relational Data Set's Implications Using...

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1st International Joint conference on Conceptual Knowledge Structures (CONCEPTS)

作者： Musslin, Lola Bazin, Alexandre Huchard, Marianne Martin, Pierre Poncelet, Pascal Raveneau, Vincent Sallaberry, Arnaud Univ Montpellier CNRS LIRMM Montpellier France UPR AIDA CIRAD F-34398 Montpellier France Univ Montpellier CIRAD AIDA Montpellier France Univ Paul Valery Montpellier 3 AMIS Montpellier France

ISBN: (纸本)9783031678677;9783031678684

Implication is a core notion of Formal Concept analysis and its extensions. It provides information about the regularities present in the data. When one considers a relational data set of real-size, implications are numerous and their formulation, which combines primitive and relational attributes computed using Relational Concept analysis framework, is complex. For an expert wishing to answer a question based on such a corpus of implications, having a smart exploration strategy is crucial. In this paper, we propose a visual approach, implemented in a web platform named FCAvizIR, for leveraging such corpus. Comprised of three interactive and coordinated views and a toolbox, FCAvizIR has been designed to explore corpora of implication rules following Schneiderman's famous mantra "overview first, zoom and filter, then details on demand". It enables metrics filtering, e.g. fixing a minimum and a maximum support value, and the multiple selection of relations and attributes in the premise and in the conclusion to identify the corresponding subset of implications presented as a list and Euler diagrams. An example of exploration is presented using an excerpt of Knomana to analyze plant-based extracts for controlling pests.

关键词： Formal Concept analysis Relational Concept analysis Information visualization visual Analytics Implication rules

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visual analysis of Voice in Crossover Singing 27

Visual Analysis of Voice in Crossover Singing

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27th International conference on Information visualisation (IV) / 19th International conference Computer Graphics, Imaging and visualization (CGiV)

作者： Hua, Jie Yi, Wei Shaoyang Univ Fac Informat Engn Shaoyang Hunan Peoples R China Macquarie Univ Dept MCCALL Sydney NSW Australia

ISBN: (纸本)9798350341614

The current research on acoustic properties in singing voice analysis has mainly focused on individual song segments, and analysed them using simple data tables and basic charts. However, there has been limited exploration of comparing data from multiple sources, and visual analysis in crossover singing has been either too simplistic or too complex to provide a comprehensive view. This study aims to address this gap by incorporating song segments from different musical styles and utilising an innovative graph drawing method to generate interactive graphs for comprehensive musical data analysis. The findings provide additional support for existing statements in the field of musical data analysis, and demonstrate the effectiveness of the proposed graph method for analysing multiple song segments. At this stage, the study's findings confirm that the formant frequency F-1 of singing across styles is less modified, but F-2-F-5 varies in styles singing in English. Additionally, the formant frequency in Mandarin and Cantonese singing may be associated with pitch. The study also identifies that visualised graphs can produce similar results as current vocal research and are convenient for reading multiple data simultaneously. The methodology has the potential to be extended to other areas of musical visualisation to uncover insights from complex music datasets.

关键词： Acoustic analysis crossover singing formant interaction multi-dimensional visualisation vocal visualisation

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Advanced visualization of adaptive optics telemetry data 9

Advanced visualization of adaptive optics telemetry data

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conference on Adaptive Optics Systems IX

作者： Silva, Beatriz Gomes, Tiago Correia, Carlos M. Garcia, Paulo J., V Univ Porto Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal Inst Super Tecn Ctr Astrophys & Gravitat Ave Rovisco Pais 1 P-1049001 Lisbon Portugal

ISBN: (纸本)9781510675186;9781510675179

The Adaptive Optics Telemetry (AOT) format has recently been proposed to standardize the telemetry data generated by adaptive optics systems. Yet its usability remains limited by the user's programming expertise and familiarity with the accompanying Python package. There is an opportunity for substantial improvement in data accessibility by offering users an alternative tool for conducting exploratory data analysis in a visual and intuitive manner. We aim to design and develop an open-source Python visualization tool for exploring AOT data. This tool should support researchers and users by offering a broad set of interactive features for the analysis and exploration of the data. We designed a prototype dashboard and performed user testing to validate its usability. We compared the prototype with existing data visualization and exploration tools to ensure we provided the necessary functionality. We made publicly available a user-friendly dashboard for analyzing and exploring AOT data.

关键词： Adaptive Optics Telemetry visualization Dashboard

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Taken By Surprise? Evaluating how Bayesian Surprise & Suppression Influences Peoples' Takeaways in Map visualizations

Taken By Surprise? Evaluating how Bayesian Surprise & Suppre...

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IEEE conference on visualization and visual Analytics (IEEE VIS)

作者： Ndlovu, Akim Shrestha, Hilson Harrison, Lane T. Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9798350325577

Choropleth maps have been studied and extended in many ways to counteract the many biases that can occur when using them. Two recent techniques, Surprise metrics and Value Suppressing Uncertainty Palettes (VSUPs), offer promising solutions but have yet to be tested empirically with users of visualizations. In this paper, we explore how well people can make use of these techniques in map exploration tasks. We report a crowdsourced experiment where n = 300 participants are assigned to one of Choropleth, Surprise (only), and VSUP conditions (depicting rates and Surprise in a suppressed palette). Results show clear differences in map analysis outcomes, e.g. with Surprise maps leading people to significantly higher areas of population, or VSUPs performing similar or better than Choropleths for rate selection. Qualitative analysis suggests that many participants may only consider a subset of the metrics presented to them during exploration and decision-making. We discuss how these results generally support the use of Surprise and VSUP techniques in practice, and opportunities for further technique development. The material for the study (data, study results and code) is publicly available on https://***/exb95/.

关键词： data visualization Choropleth Maps Bayesian Surprise

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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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EAID: An Eye-Tracking Based Advertising Image dataset with Personalized Affective Tags 40th

EAID: An Eye-Tracking Based Advertising Image Dataset with P...

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40th Computer Graphics International conference (CGI)

作者： Liang, Song Liu, Ruihang Qian, Jiansheng China Univ Min & Technol Sch Mines Xuzhou Peoples R China China Univ Min & Technol Xuhai Coll Xuzhou Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou Peoples R China

ISBN: (纸本)9783031500688;9783031500695

Contrary to natural images with randomized content, advertisements contain abundant emotion-eliciting manufactured scenes and multi-modal visual elements with highly related semantics. However, little research has evaluated the interrelationships of advertising vision and affective perception. The absence of advertising data sets with affective labels and visual attention benchmarks is one of the most pressing issues that have to be addressed. Meanwhile, growing evidence indicates that eye movements can reveal the internal states of human minds. Inspired by these, we use a high-precision eye tracker to record the eye-moving data of 57 subjects when they observe 1000 advertising images. 7-score opinion ratings for the five advertising attributes (i.e., ad liking, emotional, aesthetic, functional, and brand liking) are then collected. We further make a preliminary analysis of the correlation among advertising attributes, subjects' visual attention, eye movement characteristics, and personality traits, obtaining a series of enlightening conclusions. To our best knowledge, the proposed dataset is the largest advertising image dataset based on eye tracking and with multiple personalized affective tags. It provides a new exploration space and data foundation for multimedia visual analysis and affection computing community. The data are available at: https://***/lscumt/EAID.

关键词： Advertising Affective computing Eye movement analysis

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