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检索条件"任意字段=2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025"

共 614 条记录，以下是291-300 订阅

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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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Inspection Screening and Damage visualization by Differential Analysis of Three-Dimensional Point Cloud data of Bridges 20th

Inspection Screening and Damage Visualization by Differentia...

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20th international conference on Computing in Civil and Building Engineering, ICCCBE 2024

作者： Kubota, Satoshi Sano, Yuito Seki, Kazuhiko Faculty of Environmental and Urban Engineering Kansai University Osaka Suita Japan EYESAY Co. Ltd. Tokyo Japan

ISBN: (纸本)9783031842078

In the periodic inspection of bridges, the condition is determined based on visual inspection, and the classification of countermeasures and diagnosis of soundness are conducted to serve. Condition data on the external shape and degree of damage are obtained for reference when utilizing past inspection results and considering maintenance and management plans. Furthermore, periodic inspections must be conducted appropriately while sharing information with other maintenance providers under the division of roles, and it is important to transfer information such as inspection results and repairs. In this study, we proposed a method of extracting damage from three-dimensional point cloud data measured using terrestrial laser scanner (TLS) during a field inspection prior to a periodic inspection in order to perform primary screening to identify damage that is subject to detailed inspection of bridges. We measured a bridge using a TLS and a camera, analyzed the difference between the acquired point cloud data and a reference plane, extracted damage, and verified a simplified method for aligning the point cloud data and the reference plane. The manually created reference plane is aligned with the point cloud data of the bridge, and a difference analysis is performed. The point cloud data were analyzed by subtracting them from the reference plane, and the damage was extracted. The damage extracted from the point cloud data was quantitatively evaluated by differential analysis and compared with the actual size of the damage. In the primary screening of bridge inspections, damage is evaluated in 10 mm in the depth direction and in 50 mm in the width direction. It was verified that the results of the difference analysis satisfied this requirement. These results suggest that the required accuracy can be met, and that the damage extracted using point cloud data can be evaluated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Laser applications

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Big data analytics in Industry 4.0: Automating Production Metrics With an Edge-Based Architecture

Big Data Analytics in Industry 4.0: Automating Production Me...

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IEEE international conference on Industrial Technology (ICIT)

作者： Arthur Kretzer Denise Costa Gabriel Pardini Jardel Wolkers Renato Luz Frank Siqueira Cooperated Production Center - CPC CERTI Foundation / UFSC Florianópolis Brazil Research and Development - R&D Lenovo Indaiatuba Brazil Department of Informatics and Statistics - INE Federal University of Santa Catarina - UFSC Florianópolis Brazil

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents the development and evaluation of a Big data analytics (BDA) platform designed for Industry 4.0 applications, focusing on the integration, processing, and visualization of data at the edge. The platform integrates tools such as Apache Spark, Airflow, MinIO, Airbyte, and Streamlit, to facilitate automated data collection and report generation. Deployed in a real manufacturing environment in São Paulo, Brazil, the solution aims to minimize data latency by avoiding cloud transmission and leveraging edge computing. Automated daily reports have been generated since May 2024, improving decision-making and reducing human error associated with manual data integration. This work provides a practical contribution to industrial data integration, highlighting the benefits of edge-based data processing, automation, and visualization to optimize production efficiency and decision-making processes.

关键词： Measurement Decision making data visualization data integration Production Manuals Big data Real-time systems Fourth Industrial Revolution Edge computing

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6th international conference on Knowledge Graphs and Semantic Web, KGSWC 2024

6th International Conference on Knowledge Graphs and Semanti...

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6th international conference on Knowledge Graphs and Semantic Web, KGSWC 2024

ISBN: (纸本)9783031812200

The proceedings contain 23 papers. The special focus in this conference is on Knowledge Graphs and Semantic Web. The topics include: Towards Complex Ontology Alignment Using Large Language Models;enhancing Knowledge Graph Construction: Evaluating with Emphasis on Hallucination, Omission, and Graph Similarity Metrics;views, Semantic data Catalog and Role-Based Access Control for Ontologies and Knowledge Graphs;accelerating Medical Knowledge Discovery Through Automated Knowledge Graph Generation and Enrichment;ontoSeer - A Recommendation System to Improve the Quality of Ontologies;Adaptive Planning on the Web: Using LLMs and Affordances for Web Agents;Enriching RDF data with LLM Based Named Entity Recognition and Linking on Embedded Natural Language Annotations;TEDME-KG Metrics Framework: A Metrics Framework for TEmporal data Modelling Evaluation in Knowledge Graphs;SFARDE: A Knowledge-Centric Semantic Strategic Framework for Heritage Artifact Recommendation Integrating Generative AI and Differential Enrichment of Ontologies;leveraging Graph Models for Comprehensive visual analytics of Equine Heritage;OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment;Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF and Smart Energy Appliances;YOKO ONtO: You only KNIT One Ontology;CoKGLM: Detecting Hallucinations Generated by Large Language Models via Knowledge Graph Verification;autOnto: Towards A Semi-Automated Ontology Engineering Methodology;Enhancing Question Answering Systems with Generative AI: A Study of LLM Performance and Error Analysis;disjointness Violations in Wikidata;enhancing WebProtégé with Version Control Systems;visual Presentation and Summarization of Linked data Schemas;a Proposed Ontology Evaluation Tool to Assist Ontology Engineers in Selecting Ontologies During the Reuse Phase;manufacturing Commonsense Knowledge.

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Self-Optimizing 6G RAN: A Unified Approach with Image and Video analytics

Self-Optimizing 6G RAN: A Unified Approach with Image and Vi...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Rakshesh P Bhatt Divya G Nair Sivaramakrishnan Swaminathan Andrianov Anatoly Nokia Bangalore India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Nokia Schaumburg USA

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The advent of 6G wireless technology heralds a new era of network optimization driven by AI/ML algorithms across its entirety. As outlined in 3GPP TS 28.104 (Release 19), Management data analytics (MDA) presents diverse use cases and requirements. However, the variety of data formats and AI/ML application methods introduces complexity and limits generic implementation. Compounded by the exponential deployment of cells and a range of new devices like XR devices, UAVs, etcs., the resulting data deluge poses a formidable big-data challenge for 5G advanced, 6G, and beyond. Anticipating the unique demands of 6G, characterized by metaverse-enabled networking and AR/VR applications, there arises a need for AI/ML-driven visual data processing services. This paper presents a novel approach: representing wireless network data in the form of images and videos. Such a representation not only optimizes storage and processing requirements but also facilitates the reuse of AI/ML models across various use cases and data formats. Experimental validation is provided through two key avenues: Firstly, text-based logs are transformed into images, enabling the identification of recurrent issues through visual pattern recognition. Secondly, Comma Seperated Variables (CSV) data is converted into a series of images, effectively simulating video frames, thus extending the realm of video analytics to encompass all data types pertinent to self-optimizing networks in 6G RAN. The advantages of this image and video-based data representation paradigm are meticulously elucidated, offering a compelling proposition for the future landscape of wireless network optimization in 6G and beyond.

关键词： 6G mobile communication data analysis visual analytics Wireless networks Key performance indicator data visualization data models Pattern recognition Optimization Videos

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Big data analytics in Industry 4.0: Automating Production Metrics With an Edge-Based Architecture 26

Big Data Analytics in Industry 4.0: Automating Production Me...

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26th international conference on Industrial Technology, ICIT 2025

作者： Kretzer, Arthur Costa, Denise Pardini, Gabriel Wolkers, Jardel Luz, Renato Siqueira, Frank Cooperated Production Center - Cpc Certi Foundation Ufsc Florianópolis Brazil Research and Development - R&d Lenovo Indaiatuba Brazil Federal University of Santa Catarina - Ufsc Department of Informatics and Statistics - Ine Florianópolis Brazil

ISBN: (纸本)9798331521950

关键词： data integration

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A Real-Time analytics Approach for Computer Vision Syndrome Detection 4

A Real-Time Analytics Approach for Computer Vision Syndrome ...

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4th international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Alvaro, Gabriel Murwantara, I. Made Universitas Pelita Harapan Graduate Informatics Departments Jakarta Indonesia

ISBN: (纸本)9798331506490

The widespread use of digital devices has increased the prevalence of Computer Vision Syndrome (CVS), characterized by symptoms such as low blink rates, eye strain, and irritation. Existing CVS prevention systems often focus solely on blink rate, limiting the range of symptoms detected. This paper presents a real-time CVS identification system that integrates multiple visual metrics, including pupil dilation, red-eye detection, and blink rate, for a more accurate and holistic CVS detection. A method based on eye and iris landmark detection relies on a pre-trained convolutional neural network model to track the eye region. The proposed method was demonstrated by training data from five racial groups using fifty equally distributed images between male and female subjects. The fastest detection was performed for Caucasian males, and the slowest was observed on East Asian male subjects, with the detection time consistently under one second. In detecting pupil dilation, the accuracy of the proposed method is 78% based on videos of normal and dilated pupils. Designed for efficiency with training data across diverse demographics, the system achieved an overall performance score of 84%, achieving strong detection precision and wide coverage of symptoms. © 2024 IEEE.

关键词： Convolutional neural networks

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Cytosplore EvoViewer: visual analytics of Conserved Evolutionary Patterns in multi-species single-cell sequencing data

Cytosplore EvoViewer: Visual Analytics of Conserved Evolutio...

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Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Soumyadeep Basu Morgan Wirthlin Jeroen Eggermont Thomas Kroes Boudewijn Lelieveldt Ed Lein Trygve Bakken Thomas Höllt Leiden University Medical Center Delft University of Technology Allen Institute for Brain Science

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Single-cell transcriptomics has enhanced our understanding of the brain’s cellular composition. Biologists now analyze complex datasets to explore how marker genes influence biological processes, genetic variations, and phenotypic traits. A challenge is comparing these datasets across species to detect subtle differences or similarities to evolutionary development. Here, we present Cytosplore EvoViewer to facilitate examining relationships between transcriptomic datasets across species, simplifying the analysis of marker gene regulation and its impact on biological functions and integrating these findings with prior evolutionary knowledge or species-specific traits. We conducted a design study, including domain analysis, implementation of the results into Cytosplore EvoViewer, and an expert evaluation. Cytosplore EvoViewer offers valuable insights into genetic variations and evolutionary dynamics, helping to understand the diversity and the unity within diversity across species and their evolutionary *** Cytosplore EvoViewer installer application can be downloaded from the Cytosplore Viewer website 1 , and its source code is available on the ManiVault Studio GitHub 2 . 1 https://*** 2 https://***/ManiVaultStudio/CytosploreEvoViewer

关键词： Sequential analysis High dimensional data visual analytics Source coding Transcriptomics data visualization Collaboration Genetics Regulation Biological information theory

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visualization Method for visual Analysis of Security data Using Neural Networks

Visualization Method for Visual Analysis of Security Data Us...

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international Russian Smart Industry conference (SmartIndustryCon)

作者： Ksenia Zhernova International Digital Forensics Center St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Saint-Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The aim of this study is to develop a method for creating effective visualization models that can generate graphs from security data for later visual analysis using artificial intelligence methods. This study systematically analyzes the areas of information security where neural networks are applied, and presents the most common neural network architectures used for image analysis. To develop the method, conceptual visualization models are proposed that take into account the technical features of convolutional neural networks and the differences in perception between human operators and neural networks. The method is based on these conceptual models and a set of visual design principles. This approach will allow one to not only design visualization models suitable for neural network analysis but also automate the work of human operators. The proposed solution can play an important role in developing information security applications. The study also suggests an architecture for a software prototype based on the proposed method. The findings of this study may be useful for developers of decision support systems and researchers working in the field of neural network technology.

关键词： visualization Neural networks data visualization Information security Prototypes Computer architecture Software data models Security Convolutional neural networks

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Advance Infratructure Maintenance Using Parallel data Processing with visual analytics and IoT

Advance Infratructure Maintenance Using Parallel Data Proces...

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2025 international conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Muneshwara, M.S. Swetha, M.S. Prasad, Meghana Preethi, K.R. Pruthvi Shetty, S. Reddy, R. Nithish Kumar Bms Institute of Technology & Management Department of Computer Science and Engineering Bengaluru India Bms Institute of Technology & Management Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

A very important strategy for strengthening the security, durability, and efficiency of civil infrastructure is predictive maintenance. This research explores how to assess critical assets such as highways, bridges, and dams by integrating sensor networks, machine learning algorithms, and structural health monitoring techniques. Apache Kafka's realtime data streaming allows for efficient handling of large data volumes, and a Flutter-Dart interface provides easy access to predictive insights. Using predictive analytics enables stakeholders to predict their need for maintenance, improves their schedules, and lowers the probability of costly breakdowns. This study underscores the potential for predictive maintenance in making infrastructure management resilient against downtime and maximizing returns. © 2025 IEEE.

关键词： Predictive maintenance

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