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

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Interactive visual exploration of Latent Spaces for Explainable AI: Bridging Concepts and Features

Interactive Visual Exploration of Latent Spaces for Explaina...

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Joint of the ACM Workshops at the International conference on Intelligent User Interfaces 2025, IUI-WS 2025

作者： Metta, Carlo Cappuccio, Eleonora Rinzivillo, Salvatore CNR-ISTI Italy Università di Pisa Pisa Italy Università degli studi di Bari Aldo Moro Bari Italy

Latent space exploration offers a powerful lens for interpreting and improving the explainability of black-box AI models. This paper introduces a visual interface based on β-Variational Autoencoders that enables users to navigate latent spaces interactively. By employing tools like visual latent sliders and transformation pathways, the interface demonstrates how latent dimensions can influence image representations, uncovering semantic structure and disentangled features. Using the MedMNIST medical image dataset, we illustrate the potential of this approach to bridge the gap between technical latent space analysis and intuitive understanding. Although the focus is on presenting the methodology, this work sets the stage for integrating user interaction and metrics, particularly in high-stakes domains such as medical imaging. © 2025 Copyright © 2025 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Healthcare Interactive Interfaces Latent Space XAI

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Context-Based Annotation visualisation in Virtual Reality: A Use Case in Archaeological data exploration 21st

Context-Based Annotation Visualisation in Virtual Reality: A...

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21st EuroXR International conference, EuroXR 2024

作者： De Bonis, Michele Nguyen, Huyen Bourdot, Patrick Université Paris-Saclay CNRS LISN Gif-sur-Yvette France

ISBN: (纸本)9783031785924

In this paper we present a Virtual Reality (VR) tool that facilitates the visualisation and exploration of context-based, multi-level annotations in archaeology. Indeed, thanks to photogrammetry and laser scanning techniques, archaeologists can capture and reconstruct almost faithfully ruins and excavations in 3D. Thus, they can maximise their work performance on the field within their limited excavation time and leave deep, but often slow, analysis tasks at later stages offsite. Using VR, we can enrich 3D captured and reconstructed environments by adding extra information for ex situ analysis of archaeological data. This approach can also help the archaeologists speed up their collaboration with other experts or easily share the information with the public. We aim to assist the exploration of numerous annotations and information at disposal that can be densely clustered in a 3D reconstructed world. We collaborated closely with archaeologists at AOROC in the process of designing annotations that convey information with increasing Levels of Detail (LoDs), reflecting the complexity of the contained data. We then developed different interaction techniques that allow users to explore the annotations by switching between LoDs, including active selection by clicking, proximity approach, and the combination of both. Additionally, we proposed and evaluated the effectiveness of annotation grouping mechanism to enhance visual clarity by aggregating annotations in close spatial proximity. The results of our user study with non-experts, aggregated with a qualitative study with the archaeologists, showed user preference for the combination of the interaction techniques, but evidenced some limitations for the grouping of annotations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Virtual reality

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Knowledge-based Graph exploration analysis on Customer's Preference of Insurance Services 10

Knowledge-based Graph Exploration Analysis on Customer's Pre...

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10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Chen, Yingfan Kongjit, Chalermpon Media and Technology Chiang Mai University College of Arts Chiang Mai Thailand

ISBN: (纸本)9798331543273

With the increasing number of Chinese students in Thailand, the demand for insurance services from these students has gradually shown diversity and personalized characteristics. However, the current research on the insurance demand of overseas students is relatively insufficient. Based on text analysis and knowledge graph technology, this study focuses on the needs and preferences of Chinese students in Thailand in terms of insurance choices, and presents the research results in a visual form. The researchers collected chat history content of some target groups, as well as public discussion data on social media platforms and forums frequently used by Chinese students in Thailand. The researchers used the text analysis method to extract high-frequency keywords related to international student insurance, and carried out sentiment analysis to obtain the main concerns of current international students in insurance selection. Then, a knowledge graph centered on insurance demand is constructed to visualize key nodes and relationships such as insurance categories, budget preferences, and demand characteristics. © 2025 IEEE.

关键词： Knowledge graph

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17th International conference on the Simulation of Adaptive Behavior, SAB 2024

17th International Conference on the Simulation of Adaptive ...

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17th International conference on the Simulation of Adaptive Behavior, SAB 2024

ISBN: (纸本)9783031715327

The proceedings contain 26 papers. The special focus in this conference is on Simulation of Adaptive Behavior. The topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Model of Foraging Nectarivorous Echolocating Bats;benefit of Varying Navigation Strategies in Robot Teams;no-brainer: Morphological Computation Driven Adaptive Behavior in Soft Robots;cuttleBot: Emulating Cuttlefish Behavior and Intelligence in a Novel Robot Design;the Emergence of a Complex Representation of Touch Through Interaction with a Robot;analyzing Multi-robot Leader-Follower Formations in Obstacle-Laden Environments;spatio-Temporal Dynamics of Social Contagion in Bio-inspired Interaction Networks;behavioural Contagion in Human and Artificial Multi-agent Systems: A Computational Modeling Approach;transient Milling Dynamics in Collective Motion with visual Occlusions;extended Swarming with Embodied Neural Computation for Human Control over Swarms;bio-Inspired Agent-Based Model for Collective Shepherding;DaNCES: A Framework for data-inspired Agent-Based Models of Collective Escape;the Role of Energy Constraints on the Evolution of Predictive Behavior;influence of the Costs of Acquisition of Private and Social Information on Animal Dispersal;integrated Information in Genetically Evolved Braitenberg Vehicles;neural Chaotic Dynamics for Adaptive exploration Control of an Autonomous Flying Robot;non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models;memory-Feedback Controllers for Lifelong Sensorimotor Learning in Humanoid Robots;extracting Principles of exploration Strategies with a Complex Ecological Task;the Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network;"Value" Emerges from Imperfect Memory;the Role of Theory of Mind in Finding Predator-Prey Nash Equilibria;nonverbal Immediacy analysis in Education: A Multimodal Computational Model.

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Modeling EEG data into Graphs for the Prognostic of Patients in Coma Using Graph Neural Networks 34th

Modeling EEG Data into Graphs for the Prognostic of Patients...

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34th Brazilian conference on Intelligent Systems

作者： Nascimento Junior, Odelmo O. Assis, Dhara L. C. Destro Filho, Joao B. Zhao, Liang Carneiro, Murillo G. Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil Univ Sao Paulo Dept Comp & Math Ribeirao Preto Brazil

ISBN: (纸本)9783031790379;9783031790386

In this work we present a modeling approach using Graph Neural Networks (GNNs) to support the prognosis of patients in coma (PPC) based on Electroencephalogram (EEG) exams. Coma is a critical medical condition characterized by an extended state of unconsciousness. EEG is an exam that captures the electrical activity of the brain through electrodes positioned on the patient's scalp. It provides valuable information about cerebral functions and can also be used for the monitoring and diagnosis of several diseases. In this study, we investigate four GNN architectures specifically designed for PPC analysis, leveraging the power of convolutional-based layers. In addition, we also develop a modeling strategy to represent EEG sequential data into a graph, named EEGraph, effectively harnessing the spatial positions of electrodes. The main contribution of representing the EEG exam in a graph structure is also to explore all the inherent spatial information the exam has. As it is a highly sensitive exam with generally noisy results on the microvolt scale, its spatial exploration may reveal the potential to improve the PPC task. Experimental results considering real data showed that our proposed architecture outperformed the LSTM model in terms of F1-score and accuracy. Statistical tests evidenced the enhanced predictive performance achieved by exploiting the spatial aspects of EEG data using GNNs. Moreover, the findings highlight the significant potential of GNNs to contribute to the PPC task from EEG data.

关键词： Graph Neural Networks Electroencephalogram Coma outcome Sequential data Graph-based learning EEGraph

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

A Conceptual Framework for Immersive Behavioral Data Explora...

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IEEE International conference on Artificial Intelligence and Virtual Reality (AivR)

作者： Victor Duvivier Matthieu Perreira Da Silva Yannick Prié École Centrale Nantes CNRS LS2N UMR 6004 Nantes Université Nantes France

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

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.

关键词： Deep learning Solid modeling Analytical models visual analytics Biological system modeling Computational modeling Virtual reality Feature extraction Transformers data models

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A Pipeline for Immersive data visualization in CAVE Systems

A Pipeline for Immersive Data Visualization in CAVE Systems

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Challa, Koundinya Jaiswal, Chandra Pendem, Abhinav Gokaraju, Balakrishna North Carolina A&T State University Dept. Computational Data Science and Engineering Greensboro United States

ISBN: (纸本)9798331504847

Virtual Reality (VR) has transformed data visualization by enabling immersive and interactive experiences. CAVE (Cave Automatic Virtual Environment) systems provide a unique platform for exploring complex datasets in an intuitive, spatially immersive manner. However, a standardized pipeline for developing VR scripts tailored to CAVE-based data visualization remains underexplored. This paper presents a structured pipeline for creating and deploying immersive data visualization in CAVE systems, specifically using the WorldViz Prism CAVE environment. Our approach facilitates seamless integration of multi-dimensional datasets, enabling real-time interaction with 27 visual graphs within a single VR scene. The system allows dynamic graph manipulation, interactive exploration, and enhanced user engagement. By streamlining VR script development, this pipeline improves accessibility for researchers and practitioners, fostering a more intuitive understanding of complex datasets. Experimental results demonstrate the effectiveness of this approach in enhancing data interpretability and decision-making. © 2025 IEEE.

关键词： Video analysis

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Empowering Social Sciences with data visualization - An Insights and explorations into Behavioral Patterns and Urban Dynamics

Empowering Social Sciences with Data Visualization - An Insi...

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Pervasive Computational Technologies (ICPCT), International conference on

作者： Raihana Parveen Yeo Boon Chin K. Naveen B. Sonia B. Ramana G. LakshmiAlekhya Visual Media Science Research Laboratory QIS College of Engineering and Technology Ongole India Faculty of Engineering and Technology Multimedia University

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

visual data is an integral and essential aspect of any science, a powerful tool for description and exploration in social sciences005. Abstract: In this study, we apply new advanced ways of visualization to bring light on steps helping policy makers better understand multifaceted social phenomena using datasets publicly available. Through combining geospatial, temporal and network visualizations, the framework provides an intimate understanding of urban growth patterns of i-Social networks and behavior characteristics. Findings show that attractive visualizations make interpreting and revealing otherwise hidden patterns easier and provide insight into policy. Such an approach would finally close the gap between data complexity and actionable insights, offering social scientists a solid foundation for research with real impact.

关键词： visualization Fluctuations data analysis Correlation Geology Urban planning data visualization Solids Market research Geospatial analysis

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exploration and Practice of data visualization 24

Exploration and Practice of Data Visualization

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Proceedings of the 2024 5th International conference on Big data Economy and Information Management

作者： Gongzheng Yu Shaowu Yang College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9798400711862

Currently, data visualization is widely used in industries such as smart city display, rail transit monitoring and network traffic analysis, public opinion analysis. The data sources are relatively stable and the data formats are relatively regular. However, in some scenarios, the data is relatively scattered and of low quality, and there is limited introduction to the entire process of collecting, circulating, and aggregating this data into visual applications. Based on this, a comprehensive implementation method for metadata collection, raw data aggregation, and data visualization was explored and practiced, and a clear and scalable solution was proposed, from the backend to the frontend, the ideas and methods of data collection and aggregation, as well as the methods of building websites using the Django framework and implementing typical visualization applications using Echarts tools, were introduced.

关键词： data aggregation

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Preprocessing and visualisation of dataset for Neonatal Sepsis Prediction Using MIMIC-iv

Preprocessing and Visualisation of Dataset for Neonatal Seps...

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visual Analytics and data visualization (ICVADV), International conference on

作者： Madhura Devendra Ranade Dept. of Electronics Engineering SVKM's Shri Bhagubhai Mafatlal Polytechnic and College of Engineering Mumbai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This paper aims at exploring MIMIC-iv dataset for extraction for neonatal patients diagnosed with “Septicemia [sepsis] of newborn”. The term neonate is used for newborn babies less than four weeks old. Medical Information Mart for Intensive Care version 4 (MIMIC-iv) is a large, open access electronic health record database having more than 3 lakhs patients' data. The main objective of this paper is to access and filter this database using Google Big query. The Neonatal Sepsis is one of the reasons affecting neonatal mortality. Neonatal mortality reduction is one of the goal for sustainable development as mentioned by World Health Organization. This paper serves as the first step in identifying trends from the neonatal records from MIMIC dataset thereby paving away for creation of Sepsis prediction dataset and feature extraction. This paper deals with assessing the combined effect of various modules available in MIMIC database such as patient's demographic information, admission and diagnosis related details. It was found that there were total 96564 entries with sepsis diagnosis out of which 42.3 % were females and 57.7% were male neonates. The highest number of neonates diagnosed with Sepsis were of “White” ethnicity. The highest number of neonates were prescribed to perform “Blood Culture Neonate” test. The majority of neonates were found to have micro-organism named “Staph Aureus Coag+” in their body. This paper successfully concludes that the exploratory analysis of neonates with sepsis would be a foundation step in developing the neonatal sepsis prediction model using machine learning techniques.

关键词： Pediatrics Microorganisms databases MIMICs data visualization Mortality Predictive models Sepsis Market research Internet

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