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EDBT/ICDT-WS 2023 - Proceedings of the Workshops of the EDBT/ICDT 2023 Joint conference

EDBT/ICDT-WS 2023 - Proceedings of the Workshops of the EDBT...

引用

2023 Workshops of the EDBT/ICDT Joint conference, EDBT/ICDT-WS 2023

The proceedings contain 46 papers. The topics discussed include: optimizing analytical query processing on disaggregated hardware;HARVEST: a complete solution for smart agriculture monitoring;exposing geospatial cultural heritage content in map-based applications;clustering, universalities, and evolutionary schema design;natural language data interfaces: from keyword search to ChatGPT, are we there yet?;a tool for visual exploration and analysis of solar photovoltaic module data;graph peeling semantics;towards a multi-model approach to support user-driven extensibility in data warehouses: agro-ecology case study;easy-to-use interfaces for supporting the semantic annotation of web tables;HEALER: a data lake architecture for healthcare;and Toulouse: learning join order optimization policies for rule-based data engines.

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HoloInset: 3D Biomedical Image data exploration through Augmented Hologram Insets

HoloInset: 3D Biomedical Image Data Exploration through Augm...

引用

IEEE conference on Virtual Reality and 3D User Interfaces (IEEE VR)

作者： Choi, JunYoung Jeong, HaeJin Jeong, Won-Ki UNIST Ulsan South Korea Korea Univ Seoul South Korea

ISBN: (纸本)9781665484022

The extended reality (XR) provides realistic depth perception and huge visualization spaces, which can serve as a powerful workspace for 3D data exploration and analysis. However, a direct adaptation of XR to conventional 3D data exploration tasks is less feasible due to several hardware limitations, such as low screen resolution, dizziness, narrow field of view, etc. In this paper, we propose a novel mixed reality visualization scheme, HoloInset, which combines a conventional visual analytics system and a virtual environment to effectively explore 3D biomedical image data. We also demonstrate the usability of the proposed visualization through a real-world analysis case.

关键词： Human-centered computing Human computer interaction (HCI) Interaction paradigms Mixed / augmented reality

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Noisy Agents: Self-supervised exploration by Predicting Auditory Events

Noisy Agents: Self-supervised Exploration by Predicting Audi...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Gan, Chuang Chen, Xiaoyu Isola, Phillip Torralba, Antonio Tenenbaum, Joshua B. MIT Cambridge MA 02139 USA MIT IBM Watson AI Lab Cambridge MA 02142 USA Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Humans integrate multiple sensory modalities (e.g., visual and audio) to build a causal understanding of the physical world. In this work, we propose a novel type of intrinsic motivation for Reinforcement Learning (RL) that encourages the agent to understand the causal effect of its actions through auditory event prediction. First, we allow the agent to collect a small amount of acoustic data and use K-means to discover underlying auditory event clusters. We then train a neural network to predict the auditory events and use the prediction errors as intrinsic rewards to guide RL exploration. We first conduct proof-of-concept experiments using a set of Atari games for an in-depth analysis of our module. We then apply our model to embodied audio-visual exploration using the Habitat simulator and active exploration with a rolling robot using the ThreeDWorld (TDW) simulator. Experimental results demonstrate the advantages of using audio signals over vision-based models as intrinsic rewards to guide RL explorations.

关键词： visualization Neural networks Reinforcement learning Games Robot sensing systems Acoustics Noise measurement

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VerseMeet - A Virtual Meeting Application 10

VerseMeet - A Virtual Meeting Application

引用

10th International conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sarumathi, S. Gokul, M. Gokulavelan, M. Shimar Roshan, R.S. K.S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Virtual Reality (VR) has emerged as a potent technological tool for elevating remote collaboration and immersive communication experiences. This paper introduces a sophisticated VR meeting and collaboration application, harnessing the capabilities of VR headsets to craft dynamic and interactive virtual meeting environments. The application seamlessly integrates a diverse array of features, including a collaborative sketch board fostering visual interaction, an embedded web browser for streamlined online exploration, and the ability to import 3D models for real-time visualization. Additionally, strategic plans are in place to implement an avant-garde 3D visualization feature aimed at enhancing data analytics capabilities within the platform. The application offers users the functionality to record and review their sessions. Noteworthy is the adaptive nature of our VR meeting app, tailoring its environment based on the number of users, thereby promoting a distinctive collaborative experience. Developed utilizing VRTK, Unity 3D, and the Photon Engine, this application functions as a standalone system, obviating the necessity for a PC-to-headset intermediary. This paper delves comprehensively into the design, implementation, and development of our VR meeting application, shedding light on its unique capabilities and its potential to revolutionize remote collaboration and data analysis. © 2024 IEEE.

关键词： Virtual environments

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Research on Key Technologies for Enhancing Image analysis Capability of Power Transmission and Transformation Equipment Based on Large visual Models 10

Research on Key Technologies for Enhancing Image Analysis Ca...

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IEEE 10th International conference on Edge Computing and Scalable Cloud (IEEE EdgeCom) / 11th IEEE International conference on Cyber Security and Cloud Computing (IEEE CSCloud)

作者： Zhang, Wangjun Chen, Yu Wu, Xiong Li, Fan Yang, Hongshan State Grid Shanghai Elect Power Co Shanghai Peoples R China ShanghaiCo Ltd Transwarp Technol Shanghai Peoples R China

ISBN: (纸本)9798350377149;9798350377132

This study significantly enhances the accuracy and efficiency of image analysis for power transmission and transformation equipment by introducing large model technology, bringing innovation to the field of intelligent inspection for power systems. Through an in-depth exploration of key aspects such as data preparation, model selection and training, and image analysis processing, this research employed a pre-trained large model based on the Transformer architecture. It was meticulously fine-tuned and trained according to the characteristics of power equipment images. By preprocessing and augmenting a vast collection of power transmission and distribution equipment images under various conditions, the model successfully extracted features, achieving high-precision defect identification and classification. The model was evaluated using cross-validation and an independent test set method, confirming its superiority over traditional image processing and existing intelligent inspection systems through metrics such as accuracy, recall, and F1 score. Experimental results have shown that this research method significantly outperforms traditional techniques and existing technologies on key performance indicators. Despite significant accomplishments, there remains room for enhancing the model's ability to generalize and adapt to intricate environments. Upcoming efforts will delve into streamlined model training techniques and refine the model's architecture to handle more intricate power image scenarios. Furthermore, the fusion of large model technology with other cutting-edge technologies will be explored to enable more astute, adaptable, and real-time scrutiny of power equipment. This study showcases the vast potential and practical worth of large model technology in power image analysis and intelligent inspection, poised to propel the intelligent evolution of the power sector.

关键词： Large Model Power Equipment Image analysis Transformer Architecture

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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： Nursing

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Beyond Generating Code: Evaluating GPT on a data visualization Course 1

Beyond Generating Code: Evaluating GPT on a Data Visualizati...

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1st IEEE VIS Workshop on visualization Education, Literacy, and Activities (EduVis) part of the IEEE VIS conference

作者： Chen, Zhutian Zhang, Chenyang Wang, Qianwen Troidl, Jakob Warchol, Simon Beyer, Johanna Gehlenborg, Nils Pfister, Hanspeter Harvard Univ Sch Engn & Appl Sci John A Paulson Sch Engn & Appl Sci Boston MA USA Harvard Boston MA USA Harvard Med Sch Biomed Informat Boston MA USA

ISBN: (纸本)9798350330304

This paper presents an empirical evaluation of the performance of the Generative Pre-trained Transformer (GPT) model in Harvard's CS171 data visualization course. While previous studies have focused on GPT's ability to generate code for visualizations, this study goes beyond code generation to evaluate GPT's abilities in various visualization tasks, such as data interpretation, visualization design, visual data exploration, and insight communication. The evaluation utilized GPT-3.5 and GPT-4 through the APIs of OpenAI to complete assignments of CS171, and included a quantitative assessment based on the established course rubrics, a qualitative analysis informed by the feedback of three experienced graders, and an exploratory study of GPT's capabilities in completing border visualization tasks. Findings show that GPT-4 scored 80% on quizzes and homework, and Teaching Fellows could distinguish between GPT- and human-generated homework with 70% accuracy. The study also demonstrates GPT's potential in completing various visualization tasks, such as data cleanup, interaction with visualizations, and insight communication. The paper concludes by discussing the strengths and limitations of GPT in data visualization, potential avenues for incorporating GPT in broader visualization tasks, and the need to redesign visualization education.

关键词： visualization

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Supporting Serendipitous Discovery and Balanced analysis of Online Product Reviews with Interaction-Driven Metrics and Bias-Mitigating Suggestions 22

Supporting Serendipitous Discovery and Balanced Analysis of ...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Jasim, Mahmood Collins, Christopher Sarvghad, Ali Mahyar, Narges Univ Massachusetts Amherst MA 01003 USA Ontario Tech Univ Oshawa ON Canada

ISBN: (纸本)9781450391573

In this study, we investigate how supporting serendipitous discovery and analysis of online product reviews can encourage readers to explore reviews more comprehensively prior to making purchase decisions. We propose two interventions - exploration Metrics that can help readers understand and track their exploration patterns through visual indicators and a Bias Mitigation Model that intends to maximize knowledge discovery by suggesting sentiment and semantically diverse reviews. We designed, developed, and evaluated a text analytics system called Serendyze, where we integrated these interventions. We asked 100 crowd workers to use Serendyze to make purchase decisions based on product reviews. Our evaluation suggests that exploration metrics enabled readers to efficiently cover more reviews in a balanced way, and suggestions from the bias mitigation model influenced readers to make confident data-driven decisions. We discuss the role of user agency and trust in text-level analysis systems and their applicability in domains beyond review exploration.

关键词： serendipity product review exploration bias mitigation model

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A SPATIO-TEMPORAL visual ANALYTICS TOOL FOR SIMULATED TRANSPORT ORIENTED-LAND USE URBAN PLANNING data 7

A SPATIO-TEMPORAL VISUAL ANALYTICS TOOL FOR SIMULATED TRANSP...

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7th International conference on Smart data and Smart Cities, SDSC 2022

作者： Merchan Zorro, L. Vivas Meza, A.A. Bautista Acosta, O.D. Castro Barrera, H.E. Peña, J.R. Guzman, L.A. Hernandez, J.T. Imagine Research Group Sur Research Group University de Los Andes Colombia

This article presents a proposal framed in urban visual analytics, which aims to facilitate the exploration and analysis of zonal spatial indicators over time for a case of a large city-region. The context in which this exploration is carried out is the evaluation of the impact of interventions in land use and transportation, based on data produced by an integrated simulation system. The large amount of data to evaluate a scenario makes urgent the effort to design new interactive analysis environments. The resulting prototype (VIZ-BUS: visual Analytics for Bogota Urban Simulator) has, for its design, an analyst-centric approach to the analysis task. Our first web deployment has been very well received by urban planning specialists in order to evaluate scenarios for a sustainable and smart city-region. © 2022 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： data visualization

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visual presentation and exploration of musical corpora: Case Study: Oskar Kolberg's Opera Omnia 23

Visual presentation and exploration of musical corpora: Case...

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10th International conference on Digital Libraries for Musicology, DLfM 2023

作者： Matuszewska, Anna Maria The Institute of Literary Research of the Polish Academy of Sciences Polish Academy of Sciences Poland

ISBN: (纸本)9798400708336

Extensive musical collections are growing with increasing momentum, and there are progressively more digital tools for analysing musical corpora. These tools visualize statistical information in diagrams, simplifying the analysis. This kind of data presentation is effective both for analysing single pieces of music and looking for trends for an entire collection. Increasingly, authors of digital collections, are also developing interfaces that allow viewing individual works in user-friendly analytical interfaces. However, interfaces that allow intuitive browsing of musical collections and comparison of its subsets are still rare. Accordingly, this paper proposes a methodology for working with musical data that is inspired by diagrammatic reasoning elaborated by Charles Sanders Peirce. The goal of the project is to develop a method that would enable researchers, especially those not associated with the creation of the selected corpus, to explore its characteristics quickly and without requiring theoretical preparation, classify its subsets, and detail the characteristics of individual compositions. The proposed interactive diagrammatic method for viewing and analysing musical data has been implemented on analytical dashboards in Tableau Public. Each dashboard contains a collection of diagrams presenting the results of computer-based music analysis of rhythm or melody of tunes from different perspectives. © 2023 ACM.

关键词： Music

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