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Interaction visualization for Analysing and Improving User Models 32

Interaction Visualization for Analysing and Improving User M...

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32nd ACM conference on User Modeling, Adaptation and Personalization (ACM UMAP)

作者： Lengauer, Stefan Shao, Lin Tytarenko, Mariia Klaffenboeck, Manfred Schreck, Tobias Graz Univ Technol Graz Austria TU Wien Vienna Austria

ISBN: (纸本)9798400704666

Many web-based systems such as online retail, information systems or search engines track the interactions users have with them. Tracked data can comprise high-level information like dwelling time, reviewed items, and clicked elements, but also fine-grained information in the form of mouse trajectories and keystrokes. While these data are often fed into user- or behavior models in recommender systems, there are few approaches for interactive visual exploration of multi-modal and complex interaction patterns. Yet, the thorough analysis could reveal important insights for the design and evaluation of said models. We propose a suitable visual analysis approach that allows to validate and correct models in an intuitive and interactive manner. Our tool provides insights into concrete user (inter)actions and also estimates more complex behavioral patterns. Level of detail views in our system outlines the certainty of detected behaviors and serve the explainability. Our approach can help engineers to understand user interactions and improve behavioral models.

关键词： User Modelling Model Validation and analysis Interaction visualization

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EmoVIT: Revolutionizing Emotion Insights with visual Instruction Tuning

EmoVIT: Revolutionizing Emotion Insights with Visual Instruc...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xie, Hongxia Peng, Chu-Jun Tseng, Yu-Wen Chen, Hung-Jen Hsu, Chan-Feng Shuai, Hong-Han Cheng, Wen-Huang Jilin Univ Changchun Peoples R China Natl Taiwan Univ Taipei Taiwan Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9798350353006

visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions. This paradigm shows promising zero-shot results in various natural language processing tasks but is still unexplored in vision emotion understanding. In this work, we focus on enhancing the model's proficiency in understanding and adhering to instructions related to emotional contexts. Initially, we identify key visual clues critical to visual emotion recognition. Subsequently, we introduce a novel GPT-assisted pipeline for generating emotion visual instruction data, effectively addressing the scarcity of annotated instruction data in this domain. Expanding on the groundwork established by InstructBLIP, our proposed EmoVIT architecture incorporates emotion-specific instruction data, leveraging the powerful capabilities of Large Language Models to enhance performance. Through extensive experiments, our model showcases its proficiency in emotion classification, adeptness in affective reasoning, and competence in comprehending humor. The comparative analysis provides a robust benchmark for Emotion visual Instruction Tuning in the era of LLMs, providing valuable insights and opening avenues for future exploration in this domain. Our code is available at https://***/aimmemotion/EmoVIT.

关键词： Emotion Recognition Large Language Model visual Instruction Tuning

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Applying visual analytics to firearms trafficking trails for the extraction of strategic intelligence

Applying visual analytics to firearms trafficking trails for...

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2024 IEEE International conference on Big data, Bigdata 2024

作者： Adamakis, E. Sykianaki, E. Margetis, G. Ntoa, S. Stephanidis, C. Foundation for Research and Technology Hellas Institute of Computer Science Heraklion Greece University of Crete Department of Computer Science Heraklion Greece

ISBN: (纸本)9798350362480

Big data analysis and insight extraction are critical in contemporary large-scale criminal investigations. Analyzing large sets of information related to criminal activities can assist in the identification of correlations between records which can be utilized as strategic intelligence information for the coordination of law enforcement efforts. A Law Enforcement Agency (LEA) officer tasked with this analysis requires tools that can enable Big data exploration while being intuitive and without requiring the need for prior knowledge. A common practice of data analysis software is the ability to create dashboards that can provide all the necessary visualizations and filters to support such an analysis. However, when it comes to dashboard-wide data exploration through drill-down or roll-up analysis;review and evaluation of correlations between data records;and creation of collections with information important to an investigation, current solutions fall short. In this paper, we present a system that addresses these limitations by facilitating the visualization and exploration of diverse data sources, allowing for individual or concurrent analysis via user-configured dashboards. Individual data records of interest to the analysis can be reviewed and their correlations evaluated by the user, allowing for the creation of collections of records and their relationships based on formalized types of correlations. © 2024 IEEE.

关键词： Crime

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Understanding visual Artists' Values and Attitudes towards Collaboration, Technology, and AI 24

Understanding Visual Artists' Values and Attitudes towards C...

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50th Graphics Interface conference (GI)

作者： Johnston, Hannah Thue, David Carleton Univ Ottawa ON Canada

ISBN: (纸本)9798400718281

Artificial Intelligence (AI) tools have recently gained widespread interest for image creation, but tool developers have largely focused on technical capabilities or specialized domain uses, rather than visual artists as users. We collected survey data from 89 practising visual artists and conducted follow-up interviews with 30 of them, to better understand their diverse needs and values. Through reflexive thematic analysis, we explored visual artists' attitudes towards collaboration in art creation both with human artists and with AI- and other technology-based support systems. Our results suggest that the focus of popular AI tools on high-quality, finished images does not meet the needs of visual artists. Instead, they wanted reference images, ideation support, and variant exploration. We identified similarities and differences between how visual artists view collaboration with other artists or with machine support, enabling designers of new tools to adopt a more user-centered approach.

关键词： visual Art Collaboration Artificial Intelligence Creativity Support Tools User Experience Generative Research

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Development and Application of data analysis and visualization Software in Oil Testing Process

Development and Application of Data Analysis and Visualizati...

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2024 International conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Wu, Liqiang Jiang, Wanxue Chen, Zhi Downhole Services Company Cnpc Bohai Drilling Engineering Company Limited Hebei Renqiu062550 China

ISBN: (纸本)9798350369212

This study is devoted to the development and application of data analysis and visualization software in the process of oil testing, aiming at improving the understanding of underground reservoirs and the accuracy of reservoir development decisions. Through in-depth investigation and analysis of the requirements of geological data analysis and visualization, a powerful Oilanalysis software platform is designed and implemented, which can effectively process and analyze various types of geological data and provide intuitive and interactive visual display. The software developed in this study has flexible data import and processing functions, and can handle various geological data types including logging data, seismic data and core analysis data. At the same time, the software also integrates various data analysis and visualization algorithms, including stratigraphic modeling, porosity analysis, seismic exploration profile display and other functions, providing users with comprehensive data analysis and display tools. Through the analysis of practical application cases, the practical utility of the software in the process of oil testing is verified, which proves that it has important application value and popularization prospect. © 2024 IEEE.

关键词： data accuracy

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The Role of visualization in Genomics data analysis Workflows: The Interviews

The Role of Visualization in Genomics Data Analysis Workflow...

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IEEE conference on visualization and visual Analytics (IEEE VIS)

作者： L'Yi, Sehi Wang, Qianwen Gehlenborg, Nils Harvard Med Sch Boston MA 02115 USA

ISBN: (纸本)9798350325577

The diversity of genome-mapped data and analysis tasks makes it challenging for a single visualization tool to fulfill all visualization needs. To design a visualization tool that supports various genomics workflows of users, it is critical to first gain insights into the diverse workflows and the limitations of existing genomics tools for supporting them. In this paper, we conducted semi-structured interviews (N=9) to understand the role of visualization in genomics data analysis workflows. Our main goals were to identify various genomics workflows, from data analysis to visual exploration and presentation, and to observe challenges that genomics analysts encounter in these workflows when using existing tools. Through the interviews, we found several unique characteristics of genomics workflows, such as the use of multiple visualization tools and many repetitive tasks, which can significantly affect the overall performance. Based on our findings, we discuss implications for designing effective visualization authoring tools that tightly support genomics workflows, such as supporting automation and reproducibility.

关键词： H.5.2 [Information Systems]: Information Interfaces and Presentation (e.g. HCI) User Interfaces

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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...

引用

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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Extracting Cognitive Workload Factors on Modelling Attention Resources using Ocular Information in Figure Search Task 28

Extracting Cognitive Workload Factors on Modelling Attention...

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28th International conference on Information visualisation (iv)

作者： Nakayama, Minoru Okano, Tomomi Tokyo Inst Technol Informat & Commun Engn Tokyo Japan

ISBN: (纸本)9798350380170;9798350380163

The possibility of measuring temporal change in cognitive workload during tasks is examined using microsaccade (MS) rates and pupillary change. The experimental task was designed as a search for a specific figure, where the task difficulty and reaction accuracy were controlled during the trials. Individual cognitive workload was measured after the experimental sessions were completed, using ratings for NASA-TLX scale. Since the source may be a common one, changes in latent attention resources required for the task were estimated using a designated state-space model, using the observation data in order to synthesise measurement of MS rates and pupillary change. The predicted levels of attention resources correspond to the activity during the performance of the experimental tasks during the trials, and reflected some of the rating scores for workload scales. Also, the ranges of confidence intervals for attention resources correlate significantly with the ratings for information processing at the stage where visual stimulus is presented during tasks.

关键词： cognitive load microsaccade rate pupil change modelling chronological analysis

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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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