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

共 1754 条记录，以下是181-190 订阅

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How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior 22

How do you Converse with an Analytical Chatbot? Revisiting G...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Setlur, Vidya Tory, Melanie Tableau Res Palo Alto CA 94306 USA Northeastern Univ Roux Inst Portland ME USA

ISBN: (纸本)9781450391573

Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances. We subsequently conducted an exploratory analysis of three analytical chatbot systems (text + chart, voice + chart, voice-only) that implement these preferred design variants. Empirical evidence from a second 30-participant study informs implications specific to data-driven conversation such as interpreting intent, data orientation, and establishing trust through appropriate system responses.

关键词： chatbots intent visual analysis ambiguity repair refinement

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Interactive, visual-Learning based Tool for Hearing Impaired Children to Improve Language and Cognitive Skills 11

Interactive, Visual-Learning based Tool for Hearing Impaired...

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11th International conference on Information Technology, ICIT 2023

作者： Udbhasa, Saluka Lelkada, Parami Priyanka, Modeesha Senarathna, Esala Vidhanaarachchi, Samitha Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Colombo Sri Lanka Military Technological College Muscat Oman

ISBN: (纸本)9798350320060

Hearing impairment is a common condition that affects millions of people worldwide, with approximately 1 in 1000 newborns experiencing some degree of hearing loss. This condition can significantly impact a person's quality of life, causing communication difficulties and social isolation. Early childhood hearing impairment can present significant challenges to a child's cognitive development, making it difficult for them to learn in a traditional educational setting. Consequently, there is an urge for effective learning tools that can assist hearing-impaired children in learning their first language. This paper introduces visual-based and interactive learning tools as a promising approach to enhancing the learning experience and engagement of hearing-impaired children. The proposed system utilizes machine learning to assess a child's initial status and subsequently generates adaptive content while continuously monitoring progress. Moreover, the system incorporates an object exploration feature that enables children to learn from their surroundings. Additionally, it employs natural language learning processes to present contextually similar content, supported by audio and lip movement analysis features that guide correct pronunciation. In response to the ML models utilized, object detection was accomplished through YOLO5, while lip movements were analyzed using LipNet. Regression models were deployed for assessing a child's initial status and subsequent progress. The data collection process involved comprehensive sources that covered a wide array of visual, auditory, and linguistic elements, with the dataset being rigorously tested and verified. Testing implementations were conducted in various real-world settings, encompassing diverse environmental factors and learning conditions. The YOLO model demonstrated an accuracy of 90.3%, LipNet achieved 92.2% accuracy in lip movement analysis, and the multivariate regression models showed a prediction accuracy of 92.8% in ev

关键词： Object detection

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EuroVis 2022 - 24th Eurographics conference on visualization 2022, Short Papers

EuroVis 2022 - 24th Eurographics Conference on Visualization...

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24th Eurographics conference on visualization, EuroVis 2022

ISBN: (纸本)9783038681847

The proceedings contain 21 papers. The topics discussed include: evaluating countable texture elements to represent bathymetric uncertainty;how effective are uni- and multivariate typographic encodings? studying the usage of fontweight, oblique angle, and spacing;inferential tasks as an evaluation technique for visualization;face-based glyphs revisited;the effect of graph layout on the perception of graph density: an empirical study;metaphoric maps for dynamic vertex-weighted graphs;augmented intelligence with interactive Voronoi treemap for scalable grouping: a usage scenario with wearable data;blocks: creating rich tables with drag-and-drop interaction;towards multimodal exploratory data analysis: SoniScope as a prototypical implementation;data+Shift: supporting visual investigation of data distribution shifts by data scientists;explaining black box with visual exploration of latent space;DASH: visual analytics for debiasing image classification via user-driven synthetic data augmentation;and a design study of visualizing historical book movement.

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Through the Lens of Privacy: Exploring Privacy Protection in Vision-Language Model Interactions on Smart Glasses

Through the Lens of Privacy: Exploring Privacy Protection in...

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2025 CHI conference on Human Factors in Computing Systems, CHI EA 2025

作者： Zhang, Ziyang Bao, Chong Pan, Xiaokun Chang, Chia-Ming Igarashi, Takeo Zhang, Guofeng State Key Lab of CAD&CG Zhejiang University Hangzhou China The University of Tokyo Tokyo Japan

ISBN: (纸本)9798400713958

The integration of Vision-Language Models (VLMs) into smart glasses enables powerful interaction capabilities but also introduces significant privacy concerns. Through analysis of 376 real-life VLM interactions on smart glasses, we found frequent unintentional disclosure of sensitive information in the background, including people’s faces, location cues, and screen content. To address this, we introduce a hand-gesture-based auto-cropping mechanism that allows users to control visual input shared with VLM, effectively filtering out sensitive data. The exploration study results show that our approach reduces explicit privacy leakage by 70.6% and inferential disclosure by 47.6% while maintaining VLM response quality. A preliminary user study (N=7) demonstrated that our method is intuitive and effective at protecting user privacy. This work offers a starting point for designing privacy-preserving interactions for VLM-enabled smart glasses. © 2025 Copyright held by the owner/author(s).

关键词： Sensitive data

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A Network-Based visual Analytics Approach for Performance Evaluation of Swarms of Robots in the Surveillance Task 1

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11th Brazilian conference on Intelligent Systems (BRACIS)

作者： Linhares, Claudio D. G. Tinoco, Claudiney R. Ponciano, Jean R. Oliveira, Gina M. B. Travencolo, Bruno A. N. Univ Sao Paulo Inst Math & Comp Sci Sao Carlos Brazil Univ Fed Uberlandia Sch Comp Sci Uberlandia MG Brazil Getulio Vargas Fdn Sch Appl Math Rio De Janeiro Brazil

ISBN: (数字)9783031216862

ISBN: (纸本)9783031216855;9783031216862

Effectiveness in swarm robotics relies on aspects such as coordination and collective knowledge about the environment. By considering the evolution of intra-swarm communications over time as a temporal network, different strategies can be used in the data analysis. Information visualisation techniques are useful in this context because they can enhance the analysis of individual and global performances by including the user in the data exploration. This work proposes a visual analytics approach that considers a new matrix-based layout and other well-established ones to assess the swarm's efficiency. To analyse this approach, we also propose a temporal network dataset that models the evolution of the communications of a swarm of robots in the surveillance task, including eventual failures. We performed visual analyses in this network and demonstrated that the proposed approach allows easy identification of patterns, trends, and anomalies related to communication and task evolution. As a consequence, the decision-making process and eventual adjustments become faster and more reliable.

关键词： Swarm robotics Information visualisation Complex networks data analysis Surveillance task Evolutionary models

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visual Analytics exploration of PubMLST Meningitis Genomic data

Visual Analytics Exploration of PubMLST Meningitis Genomic D...

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2022 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Varga, Margaret J. Bratcher, Holly B. Jolley, Keith A. Harrison, Odile B. Bray, James E. Maiden, Martin C. J. University of Oxford Department of Biology Oxford United Kingdom

ISBN: (纸本)9781665468190

Bacterial meningitis is an infectious disease of the brain that occurs worldwide and is a major public health challenge. A leading cause of this often-fatal disease is the bacterium Neisseria meningitidis, also called meningococcus. Genomics is having a transformational impact on medicine. It enables advances in accurate diagnosis, analysis and prediction of anti-microbial resistance, development and assessment of vaccines etc. ***, developed and run by the authors, is a major open-access genomics reference database. It is an integrated collection of databases consisting of phenotype metadata linked to nucleotide sequence data including genome assemblies, for molecular typing of many bacterial species. This paper discusses visual analytics and visualization work conducted to explore and analyse Neisseria meningitidis data from PubMLST. © 2022 IEEE.

关键词： Genome

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18th International conference on Design Science Research in Information Systems and Technology, DESRIST 2023

18th International Conference on Design Science Research in ...

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18th International conference on Design Science Research in Information Systems and Technology, DESRIST 2023

ISBN: (纸本)9783031328077

The proceedings contain 29 papers. The special focus in this conference is on Design Science Research in Information Systems and Technology. The topics include: Scaling Sustainable Entrepreneurship for Impact: Design Knowledge for the Use of Digital Technologies;data Product Canvas: A visual Inquiry Tool Supporting data Product Design;design Principles for National Innovation Agencies in Social Market Economies;how to Invest in the Future of Food: An exploration Design Science Project;design Science Research: Progression, Schools of Thought and Research Themes;conducting Design Science Research in Society 5.0 – Proposal of an Explainable Artificial Intelligence Research Methodology;accessing the Design Science Knowledge Base - A Search Engine for the Accumulation of Knowledge Across Decentrally Organized Publications;Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-Based Textual data Quality Enrichment;Inquiry Frameworks for Research Question Scoping in DSR: A Realization for ChatGPT;enabling the Evaluation of Production Scheduling Algorithms in Complex Production Environments Using Individually Deployable Scheduling Services;design of an Augmented Reality App for Primary School Students Which visualizes Length Units to Promote the Conversion of Units;know the Knowledge of Your Students: A Flexible Analytics Tool for Student Exercises;designing Pedagogical Conversational Agents for Achieving Common Ground;computational Thinking for Design Science Researchers – A Modular Training Approach;SIREN: Designing Business Processes for Comprehensive Industrial IoT Security Management;what Does the Factory’s Suggestion Box Reveal?: An analysis of the Design Capabilities of a Train Maintenance Center from Its Idea Management System;co-designing a visual Inquiry Tool.

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A Systematic Approach to Tuning Cover Parameters in Mapper for Improved TDA Representation 3

A Systematic Approach to Tuning Cover Parameters in Mapper f...

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3rd IEEE International conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Vannoni, Stefano Tassi, Emma Sampaio, Inês Won Bianchi, Anna M. Maggioni, Eleonora Politecnico di Milano Dept. of Electronics Information and Bioengineering Milan Italy Fondazione Irccs Ca' Granda Osp. Maggiore Policlinico Dept. of Neurosciences and Mental Health Milan Italy

ISBN: (纸本)9798350378009

Topological data analysis (TDA) offers a powerful framework for analyzing complex datasets by leveraging algebraic topology to unveil underlying structures. Within TDA, the Mapper algorithm plays a pivotal role in transforming data into a simplified graph representation, known as a simplicial complex. However, selecting appropriate parameters within the Mapper algorithm remains a challenge due to the absence of a standardized approach. In this study, we propose a systematic methodology for choosing Cover parameters by integrating quantitative assessment, using the Netsimile metric and graph topological features, with qualitative visual analysis. We demonstrate the efficacy of our approach using brain features from the Human Connectome Project dataset. Through a comprehensive evaluation, we identify parameter configurations that enhance graph properties stability producing informative graph structures. Our findings highlight the importance of integrating quantitative metrics into the parameter selection process, paving the way for enhanced data exploration and understanding. The open-source nature of our methodology promotes broader adoption and application across diverse datasets and analytical workflows. © 2024 IEEE.

关键词： data Analytics

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Bio Inspired Approaches for Indoor Path Navigation and Spatial Map Formation by Analysing Depth data

Bio Inspired Approaches for Indoor Path Navigation and Spati...

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2022 IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Chaudhuri, Rapti Deb, Suman Shubham, Shubham National Institute of Technology Cse Department Agartala India

ISBN: (纸本)9781665483162

Indoor Navigation presents a significant domain for carrying out with research issues. Literature review depicts inspite of having various existing graph theoretic optimization approaches for safe path exploration, the mobile robot faces difficulties in achieving robust and efficient computation. The cause of difficulties include limitation in sensor availability, limitation in reachability, having dynamic on path obstacles, and space-time complexity. The paper presents a working model for mobile robot navigation by detecting objects analysing depth data in the concerned environment followed by optimized path exploration using bio inspired path planning techniques. This is accompanied by spatial map formation incorporating with Robot Operating System (ROS) environment for getting an idea of visual inference of the taken indoor environment. analysis of working principle and respective methodology of the algorithms have been done and experimental results are noted. Comparative ease in computation and optimization are obtained and presented neatly in subsequent sections. The analysis would prove to be remarkable citation for future research in the field of Optimized Collision-free Indoor Navigation (OCIN). © 2022 IEEE.

关键词： Motion planning

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ExTraVis: exploration of Traffic Incidents Using a visual Interactive System 25

ExTraVis: Exploration of Traffic Incidents Using a Visual In...

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25th International conference Information visualisation (IV) - AI and visual Analytics and data Science

作者： Zerafa, Joshua Islam, Md Rafiqul Kabir, Muhammad Ashad Xu, Guandong Univ Technol Sydney Adv Analyt Inst AAi Sydney NSW Australia Charles Sturt Univ Sch Comp & Math Bathurst NSW Australia

ISBN: (纸本)9781665438278

The impact of road traffic incidents (e.g., road accidents, vehicle breakdowns) have become progressively worse over the years, being a major cause of many adverse issues such as serious injury, economic loss, and lifelong disabilities. Thus, it is essential to acknowledge these issues and proactively construct appropriate solutions to mitigate the impact of these issues in the future. This study outlines the history of traffic incident research and covers several solutions such as machine learning, mathematical modeling, and visualization system to traffic incident analysis. In this paper, we design a unique visualization system, ExTraVis, for incident data exploration and analysis that can be used to help traffic management controllers, aid to make decisions, and help them to understand how past incidents affected and where incidents may occur. The key features of this system are visual exploration and analysis to overcome the problems linked with road traffic incidents and to encourage future work and improvements. Additionally, we gather various custom queries for free text search feature. We find that people ask questions and our system provide 90% correct visual insights. Finally, we demonstrate the effectiveness and robustness of ExTraVis by comparing with three different incident visualization dashboards and a user study.

关键词： Traffic incidents Interactive system visualization Information system

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