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

共 2791 条记录，以下是211-220 订阅

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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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A Hierarchical Approach to Anomalous Subgroup Discovery 39

A Hierarchical Approach to Anomalous Subgroup Discovery

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39th IEEE International conference on data Engineering, ICDE 2023

作者： Pastor, Eliana Baralis, Elena De Alfaro, Luca Politecnico di Torino Torino Italy Uc Santa Cruz Santa Cruz United States

ISBN: (纸本)9798350322279

Understanding peculiar and anomalous behavior of machine learning models for specific data subgroups is a fundamental building block of model performance and fairness evaluation. The analysis of these data subgroups can provide useful insights into model inner working and highlight its potentially discriminatory behavior. Current approaches to subgroup exploration ignore the presence of hierarchies in the data, and can only be applied to discretized attributes. The discretization process required for continuous attributes may significantly affect the identification of relevant *** propose a hierarchical subgroup exploration technique to identify anomalous subgroup behavior at multiple granularity levels, along with a technique for the hierarchical discretization of data attributes. The hierarchical discretization produces, for each continuous attribute, a hierarchy of intervals. The subsequent hierarchical exploration can exploit data hierarchies, selecting for each attribute the optimal granularity to identify subgroups that are both anomalous, and with enough elements to be statistically and practically significant. Compared to non- hierarchical approaches, we show that our hierarchical approach is more powerful in identifying anomalous subgroups and more stable with respect to discretization and exploration parameters. © 2023 IEEE.

关键词： data slicing discretization fairness generalized itemsets hierarchies model evaluation subgroup discovery

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An Analytical Model for Sustainable Product Ideation Based on Main Parameter Values and Social Network data 24th

An Analytical Model for Sustainable Product Ideation Based o...

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24th International TRIZ Future conference

作者： Ghane, Mostafa Cavallucci, Denis Univ Kebangsaan Malaysia Bangi Malaysia M5 Solutions AI Selangor Malaysia INSA Strasbourg 24 Blvd Victoire F-67084 Strasbourg France

ISBN: (纸本)9783031759253;9783031759239;9783031759222

The Main Parameter of Value (MPV) is a vital factor in TRIZ' Trend of Engineering System Evolution (TESE), helping to understand what customers like in specific products. The Trend of S-Curve Evolution in TESE shows how the MPV changes over time, guiding decisions for innovating products. In the current industrial landscape, the surge in industrial revolution is reshaping consumer choices towards sustainable products. Manufacturers vie for market dominance, continually seeking insights into customers' preferences regarding MPVs for design forecasts and sustainable innovations. However, existing MPV analyses are primarily manual, lacking a computational model for predicting future MPVs. This study addresses this gap by proposing an AI-driven computational model for identifying potential MPVs. Utilizing the Python programming, the methodology incorporates Natural Language Processing (NLP) for feature extraction and Machine Learning algorithms, including Naive Bayes and Linear Regression Classifier to achieve accurate means of predicting and understanding MPVs. The anticipated outcome is an automated S-Curve analysis derived from AI-analyzed MPV data. This visual representation of evolving MPVs over time is expected to empower manufacturers with insights for strategic decision-making and innovation in sustainable product development, fostering a competitive edge in the market.

关键词： TRIZ Artificial Intelligence Main Parameter of Value

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Analysing visual Representations of Adult Online Learning Across Formats 16th

Analysing Visual Representations of Adult Online Learning Ac...

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16th International conference on UniversalAccess inHuman-Computer Interaction (UAHCI) Held as Part of the 24th International conference on Human-Computer Interaction (HCii)

作者： Bergdahl, Nina Torkildsen, Lisbeth Gyllander Halmstad Univ Akad Iarande Humaniora & Samhalle Box 823 S-30118 Halmstad Sweden Malmo Univ Nordenskioldsgatan 1 S-21119 Malmo Sweden

ISBN: (纸本)9783031050398;9783031050381

The past years have triggered the established blended learning format to develop into other kinds of online teaching formats, with, for example, combinations of a/synchronous learning with more flexible (co-)location requirements for teachers and students. This has led to a renewed exploration of ways to ensure accessible and life-long learning through developed educational practices. Comparing online education formats can be challenging but is necessary. Building on case study methodology, the objective of this study was to explore online learning situations (n = 21): Asynchronous Distance Education (ADE) (n = 15) and (synchronous) Emergency Remote Teaching (ERT) (n = 6), to develop a method to systematically analyse and evaluate different formats of online education using engagement theory. To do so, a schema was developed through which visual representations of learning situations were analysed. Results show that visual representations of learning situations enable nuanced comparisons across different formats of online education. analysis reveals that the format of education affects the conditions under which the teacher more readily facilitates student engagement and that asynchronous and synchronous formats supported different nuances of engagement.

关键词： visual representation visual display Method Engagement Learning design

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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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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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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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Correlation of OD Matrix and Traffic Volume with Functional data analysis: A Simulation-Based Case Study in the City of Portici 24

Correlation of OD Matrix and Traffic Volume with Functional ...

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24th EEEIC International conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Tascione, Alessandra Marisa Di Costanzo, Luca Forcina, Davide Pariota, Luigi Capezza, Christian Simonelli, Fulvio University of Naples 'Federico II' Department of Civil Architectural and Environmental Engineering Naples Italy

ISBN: (纸本)9798350355185

This paper addresses the dynamic fluctuations within a single day of origin-destination (OD) flows and traffic volumes within urban networks, crucial for effective transportation planning and management. In an urban network, travel patterns are represented by two main variables: origin-destination (OD) flows and traffic volumes. The former (input of the assignment problem) are defined as the number of movements that occur between two points within the study area using transportation infrastructure, while the latter (output of the assignment problem) are defined as the number of vehicles crossing the transportation infrastructure within a given time interval. Traditional approaches encounter challenges due to the unobservability of OD flows and limitations in traffic data coverage and quality. Motivated by these limitations, we use a methodology based on functional data analysis with linear regression (FDA-1r), which enables the exploration of functional relationships between predictor (OD flow) and response (link volume). We present a case study in Portici, Italy, utilizing the FDA-1r methodology to analyze the correlation between OD flows and link flows. Results demonstrate the efficacy of the method in modeling the relationship between OD flows and link flows, providing valuable insights for urban traffic management. This paper contributes to advancing Intelligent Transportation Systems (ITS) services by addressing real-time OD matrix correction challenges. © 2024 IEEE.

关键词： Urban transportation

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Cloud Forensics analysis Framework for Secure and Efficient data Retrieval

Cloud Forensics Analysis Framework for Secure and Efficient ...

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2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Revathi, V. Aishwarya, B.K. Tripathi, Manju Singh, Navdeep Alabdely, Haider Kumar, Ashwani New Horizon College of Engineering Department of Applied Sciences Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal India Ies College of Technology Department of Computer Science & Engineering M. P. Bhopal India Lovely Professional University Phagwara India The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9798350373783

This study's Cloud Forensics analysis Framework seeks secure and effective data recovery methods. The framework's five strategies make digital evidence retrieval methodical and versatile. These include adaptive evidence collecting, secure data retrieval, machine learning-based anomaly detection, intelligent automation, and context-aware metadata analysis. Each approach targets a distinct forensic process step. This makes cloud forensics investigations seem difficult. The customizable Evidence gathering Algorithm adapts to cloud characteristics to collect evidence. This allows contextually aware and flexible digital evidence retrieval. The secure data Retrieval Techniques Algorithm protects data privacy and chain of custody with advanced encryption and validation. The Machine Learning-Based Anomaly Detection Algorithm checks protected data for anomalies before sending it, making the system safer. Investigators may focus on more complex cases by simplifying and sorting basic forensic activities with the Intelligent Automation Algorithm. The Context-Aware Metadata study Algorithm weights and prioritizes cloud metadata. This completes metadata analysis. Compared to other approaches, the framework has greater Precision, Recall, F1 Score, Processing Time, Resource Utilization, and Compatibility Score. Charts convey system operation in a simple manner. This powerful and adaptable Cloud Forensics analysis Framework improves cloud forensics by providing a thorough and effective solution to recover and safeguard data. The Cloud Forensics, This paper discusses context-aware metadata analysis, data retrieval, intelligent automation, machine learning-based anomaly detection, performance metrics, secure data retrieval techniques, support vector machines, and visual representation. © 2024 IEEE.

关键词： Cloud security

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