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

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The Concept of a New Neural Map for Clustering, data visualization and Prediction with Probability Distribution Approximation 19th

The Concept of a New Neural Map for Clustering, Data Visuali...

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19th European, Mediterranean, and Middle Eastern conference (EMCIS)

作者： Morajda, Janusz Krakow Univ Econ Krakow Poland

ISBN: (纸本)9783031306938;9783031306945

The paper submits a proposition of a new data analysis tool (named PDCM - Probability Distribution generating and Clustering Maps) constructed in the form of an extended neural map and dedicated to a variety of tasks, such as specific clustering, visualization, prediction (together with its possible visual analysis and justification) and generation of approximated Bayesian a posteriori probability density distribution for dependent variable. Basic theoretical aspects concerning the structure and training process of the proposed model have been presented. Also, research involving application of the PDCM method for real estate market data (Boston dataset) has been shown together with promising research results and conclusions.

关键词： neural maps neural networks SOM Self-Organizing Prediction Maps data analysis data exploration clustering prediction data visualization probability distribution management real estate

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ICCECE 2024 - International conference on Computer, Electrical and Communication Engineering

ICCECE 2024 - International Conference on Computer, Electric...

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2024 International conference on Computer, Electrical and Communication Engineering, ICCECE 2024

ISBN: (纸本)9798350386479

The proceedings contain 35 papers. The topics discussed include: automated texture feature based bone tumor segmentation and image analysis using supervised machine learning;analysis of barrel electroplating line with process mining and Petri-Net model;an effective EEG-based image classification methodology under low data and resource requirements using machine learning;identification of hub genes in interstitial lung disease and their association with lung cancer: an in-silico analysis;impact of basic communication modules and techniques in humanitarian crisis-a brief survey;a comparative analysis between deep learning and machine learning algorithms based on user review sentiment analysis from various OTT applications;FPGA implementation of serial peripheral interface transceiver module;and EmoEyes: a machine learning exploration of emotional states through eye movement tracking in visual content.

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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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visual analysis of Tobacco Leaf Recognition Research Based on CiteSpace 14

Visual Analysis of Tobacco Leaf Recognition Research Based o...

引用

14th International conference on Software Technology and Engineering, ICSTE 2024

作者： Dai, Yanting Zhou, Hua Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350378955

This study utilizes CiteSpace software to conduct a visual analysis of the literature in the field of tobacco leaf recognition technology in recent years, aiming to reveal the research hotspots, development trends, and contributions of key institutions and scholars in this domain. By analyzing the changes in the number of publications, keyword co-occurrence networks, author collaboration patterns, and keyword burst situations, this paper demonstrates the in-depth exploration and innovation in both basic research and practical applications of tobacco leaf recognition technology. Firstly, through the analysis of the annual publication volume, we found that research on tobacco leaf recognition has shown a significant growth trend over the past decade, indicating the increasing importance of this field in both academia and industrial applications. Secondly, the analysis of research institutions and authors reveals the dominant positions of institutions such as the Chinese Academy of Agricultural Sciences, the Chinese Academy of Sciences, and N anjing Agricultural University, as well as the outstanding contributions of scholars like Wang Ying, Wang Lu, and Guo Yongfeng in terms of publication volume and collaboration relationships. Furthermore, the keyword co-occurrence network and cluster analysis show that the research focus of tobacco leaf recognition technology covers multiple keyword groups, including gene expression, transcription factors, plant resistance, and biosynthesis, reflecting researchers' multifaceted efforts in theoretical exploration and practical applications. Finally, the keyword burst analysis reveals the rise and fall of keywords in different periods. For instance, the period from 2014 to 2017 was mainly concentrated on 'gene expression' and 'transgenic tobacco,' while from 2018 to 2020, emerging research hotspots such as 'functional analysis' and 'cell death' became prominent. This study provides profound insights and significant references for the

关键词： Agricultural Intelligence Deep Learning Tobacco Leaf Recognition

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SimGlim: Simplifying glimpse based active visual reconstruction 23

SimGlim: Simplifying glimpse based active visual reconstruct...

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23rd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Jha, Abhishek Seifi, Soroush Tuytelaars, Tinne Katholieke Univ Leuven ESAT PSI Leuven Belgium

ISBN: (纸本)9781665493468

In active visual exploration, an agent with a limited field of view needs to sample the most informative local observations of an environment in order to model the global context. Current works train this selection strategy by defining a complex architecture built upon features learned through convolutional encoders. In this paper, we first discuss why vision transformers are better suited than CNNs for such an agent. Next, we propose a simple transformer-based active visual sampling model, called "SimGlim", which utilises transformer's inherent self-attention architecture to sequentially predict the best next location based on the current observable environment. We show the efficacy of our proposed method on the task of image reconstruction in the partial observable setting and compare our model against existing state-of-the-art active visual reconstruction methods. Finally, we provide ablations for the parameters of our design choice to understand their importance in the overall architecture.

关键词： visualization Computer vision Reconstruction algorithms Predictive models Transformers data models Task analysis

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GraphFederator: Federated visual analysis for Multi-party Graphs 17

GraphFederator: Federated Visual Analysis for Multi-party Gr...

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17th Pacific visualization conference (PacificVis)

作者： Han, Dongming Zhu, Haiyang Chen, Wei Pan, Rusheng Liu, Yijing Zhou, Jiehui Feng, Haozhe Zhang, Tianye Wang, Xumeng Zhu, Minfeng Tao, Jianrong Fan, Changjie Zhang, Xiaolong (Luke) Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Hithink RoyalFlush Informat Network Co Ltd Hangzhou Peoples R China Zhejiang Univ Minist Educ Lab Art & Archaeol Image Hangzhou Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China Zhejiang Univ Hangzhou Peoples R China Netease Fuxi AI Lab Hangzhou Peoples R China Penn State Univ University Pk PA 16802 USA Wuchan Zhongda Digital Technol Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798350393811;9798350393804

This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for federated modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a Federated Graph Representation Model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization tools for federated visualization, exploration, and analysis of multi-party graphs. Experimental results on two datasets demonstrate the effectiveness of our approach.

关键词： Federated learning Graph analysis Graph visual analysis Representation learning

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The TMMTA Model: Momentum Tracking and analysis of Tennis Matches 24

The TMMTA Model: Momentum Tracking and Analysis of Tennis Ma...

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2024 International conference on Smart City and Information System, ICSCIS 2024

作者： Chen, Feiyang Guo, Yang Wang, Tianqi School of Management Northeastern University at Qinhuangdao Qinhuangdao Hebei066099 China School of Mathematics and Statistics Northeastern University at Qinhuangdao Qinhuangdao Hebei066099 China

ISBN: (纸本)9798400710155

In sports, the generation and change of momentum are always accompanied, and tennis is no exception. It is of great significance for winning to analyze how the momentum generates, changes and its impact on the score in the tennis match. Most of the existing studies theoretically study the nature and influence of momentum in tennis matches or focus on the point results and whether winning or losing the match. However, the analysis of momentum changes in data series and the exploration of the relationship and influence degree of specific behavior and data features with momentum are missing. To this end, this study constructs a Tennis Matches Momentum Tracking and analysis Model (TMMTA) based on the time-series analysis to analyze the data of five games Carlos Alcaraz participated in and the model is applied to multiple players and multiple matches to verify the prediction effect and conclusion of the model. Experiments show that the visual analysis results are very good, which can effectively analyze the momentum changes and specific performance in players' matches and provide insight for player training and match strategy. The three game behaviors, double errors, untouchable winning shots and unforced errors, have the most weight on scoring and are closely related to strategic momentum. Strategic momentum has a higher impact on matches score than mental momentum, and the combination of the two is the best prediction. This has important implications for coaches to value and utilize the role of Momentum. © 2024 ACM.

关键词： Tennis

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Democratizing co-production of thematic co-explorations for Citizen Observatories 9

Democratizing co-production of thematic co-explorations for ...

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9th International conference on Smart and Sustainable Technologies (SpliTech)

作者： Lopez-de-Ipina, Diego Sanchez-Corcuera, Ruben Eduardo Vergara, Felipe Emaldi, Mikel Wolosiuk, Dawid Casado-Mansilla, Diego Feliciotti, Alessandra Peters-Anders, Jan Univ Deusto Fac Engn Bilbao Spain Univ Deusto DeustoTech Bilbao Spain VRVis Forsch GmbH Visual Analyt Grp Vienna Austria MindEarth Biel Switzerland AIT Austrian Inst Technol GmbH Vienna Austria

ISBN: (纸本)9798350390797;9789532901351

Citizen Observatories are a promising instrument to drive societal behaviour change towards greener more sustainable practices. However, assembling Citizen Observatories is not easy, since apart from the continuous engagement of their participants, there is the need to have some specialized domain and technical knowledge. data quality, continuous engagement and retention and factual impact into decision making are three usual roadblocks which impend a wider adoption of this practice. This paper explains how GREENGAGE project aims to democratize the co-production of thematic co-explorations and overcome those barriers.

关键词： Citizen Observatory thematic co-exploration co-production data analysis Green Deal

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Composition of Geospatial visualizations for Scale-aware Views of Multiple Outcome Variables in Population Surveys 26

Composition of Geospatial Visualizations for Scale-aware Vie...

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26th International conference Information visualisation (iv)

作者： Ravindra, Harshitha Sreevalsan-Nair, Jaya IIIT Bangalore Graph Visualizat Comp Lab Bangalore India

ISBN: (纸本)9781665490078

Population survey data is important for understanding the status of well-being along any dimensions, i.e., social, economic, political, health, etc. This data generates spatial point patterns which can be explored and analyzed using visualization. Given the spatial aspect of the data, there is a requirement of using cartographic maps, which are mostly limited to visualizing a single variable in most cases. Here, it is also important that the choice of visualizations also enable scale-aware analysis when zooming in and out of the maps, since the data is from the smaller political units and can be aggregated to larger political units. Thus, we explore the different visual compositions which use mathematical operators and the composite layouts for visualizing multiple outcome variables in survey data. The mathematical operators allow the use of univariate and bivariate data modeling and representation, and composite layouts of interest are juxtaposition and superimposed views. We demonstrate the inferences from visualizations using a case study on malnutrition in children under five in India. Our work shows that a visual composition of binary relationships represented in a visualization and a juxtaposed layout of such pairwise variables is effective in making inferences from the multivariate spatial point patterns in population data.

关键词： Population survey data spatial point patterns geospatial visualization cartographic maps choropleth heatmap hotspot visual composition mathematical operator composite visualization views comparative visualization multivariate data spatial scale scale-aware analysis

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Interpreting Autonomous Driving Corner Cases: A visual Analytics Approach 17

Interpreting Autonomous Driving Corner Cases: A Visual Analy...

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17th Pacific visualization conference (PacificVis)

作者： Sun, Yi Shao, Zekai Qiu, Xingyu Li, Yun Liu, Ting Xiang, Linbing Sun, Dong Chen, Siming Fudan Univ Shanghai Peoples R China NIO Shanghai Peoples R China

ISBN: (纸本)9798350393811;9798350393804

With the progression of artificial intelligence, there has been substantial advancement in autonomous driving technology. However, even the most advanced systems may confront failures in certain corner cases, necessitating enhanced analytical approaches. Traditional approaches focused on the numerical analysis of isolated sensor data, are often insufficient for deriving meaningful insights in such situations. To address this inadequacy, we propose a visual analytics approach, crafted to aid domain experts in performing analyses and extracting system improvements from cases with unexpected behaviors. This approach intricately integrates extensive driving scenarios and low-level module behaviors into the autonomous driving decision-making process, utilizing rich visualizations and an interface for interactive exploration and systematic synthesis of findings. Uniquely, our system opens the "black box" of modules in the decision-making pipeline during corner cases, taking into account both the overall decision-making pipeline and the fine-grained behaviors of the modules in the pipeline, setting our approach apart from previous works. To validate our system's effectiveness, we perform two case studies, inviting domain experts for evaluation, and the results confirm our system's efficacy in allowing experts to obtain crucial insights into autonomous driving systems.

关键词： visual Analytics-data visualization-Interactive visualization Autonomous Driving-AD Simulations-Corner Cases

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