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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

共 54229 条记录，以下是4241-4250 订阅

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A Review of Learning Early Warning Research Based on Learner Portraits 2

A Review of Learning Early Warning Research Based on Learner...

引用

2nd International conference on Artificial Intelligence and Computer information Technology, AICIT 2023

作者： Tian, Bin He, Kai Hong, Hanyu School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350302950

In the context of big data in education, how to use the massive data of colleges and universities to better help students finish their studies on time and avoid the risk of not being able to finish their studies properly due to factors such as delay, retake, and drop out? The previous learning warning system built according to the grade ranking can no longer meet the requirements of today's universities. With the gradual maturity of data mining technology in education, building a learning alert system based on learner portraits has become a trend. By collecting and analyzing students' data and using data mining algorithms to accurately analyze their learning behaviors, the learner portrait provides a theoretical basis for learning early warning. This paper first takes learner portraits as a pavement, introduces learning early warning content using online learning, focuses on the research direction of learning early warning, then studies the current status of domestic and international research to summarize the specific algorithms of learner portraits and learning early warning applications, and finally summarizes the problems and presents the outlook. The innovation of the whole paper is to show the learner portrait through the visualization interface, and based on constructing the learner portrait, secondary data extraction is conducted to build the learning alert model for students with learning difficulties, which provides technical support for educators to implement personalized and accurate teaching. © 2023 ieee.

关键词： Students

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Research on the Application of Virtual Teaching Systems in Experimental and Practical Teaching Based on CiteSpace Visual Analysis 13

Research on the Application of Virtual Teaching Systems in E...

引用

13th International conference on information Technology in Medicine and Education, ITME 2023

作者： Lu, Mengjiao Shao, Xiuting Song, Ziwen Chen, Hengbiao School of Information Science and Engineering Shandong Normal University Jinan China Hunan Automotive Engineering Vocational College Department of Economics and Management Zhuzhou China

ISBN: (纸本)9798350319156

This paper investigates the utilization of virtual teaching systems in experimental and practical education, specifically focusing on enhancing the design and application of virtual teaching platforms in China. The CNKI database was utilized to search for relevant literature while CiteSpace was employed to conduct a visual analysis of the selected literature. The analysis includes the visualization of annual publication volume, keyword co-occurrence analysis, keywords clustering analysis, timeline analysis, and burst analysis. The results reveals primary research focuses and prominent topics in this field, and indicates that research interest in the application of virtual teaching platforms has significantly increased over the years, and at the same time, driven by technological progress and policy implementation, advances in virtual reality and 3D modeling technology provide theoretical support and opportunities for constructing more sophisticated educational platforms. Overall, this study provides valuable insights into the application of virtual teaching systems in experimental and practical teaching, contributing to the development of innovative educational approaches. © 2023 ieee.

关键词： 3D modeling

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Using Counterfactual information for Breast Classification Diagnosis

Using Counterfactual Information for Breast Classification D...

引用

ieee Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Miguel Cardoso Carlos Santiago Jacinto C. Nascimento Institute for Systems and Robotics LARSyS Instituto Superior Técnico Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The last radiology report by the Royal College of Radiologists has identified the pressure that radiologists are suffering due to excessive workloads levels. This is due to the availability of a growing number of images and a short time to provide the report, making the diagnosis a difficult process. This suggests that the visualization of the radiologist should be accompanied, somehow, by an automatic "explainable" process. In this paper, we give emphasis to the breast cancer as it is one of the most common types of cancer in women. Specifically, we deal with mammography images because it is a primary step to be accomplished in an early radiological breast diagnosis. Although machine learning models are being used in medical imaging, these models still struggle to provide enough interpretability to provide reliability in the decision-making process of the radiologist. In this work, we explore solutions that improve an explainable model’s performance in mammography classification. We propose the use counterfactual information for improving the breast classification task. We compare multiple approaches to the integration of counterfactual information into the training process. The experimental evaluation testifies that incorporating such counterfactual information improves both balanced accuracy and interpretability for the breast classification task.

关键词： Training visualization Accuracy Refining Machine learning Radiology Entropy

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Interactive Dashboard on Online Assessment Tool to Test Competency Levels of Trainees 7

Interactive Dashboard on Online Assessment Tool to Test Comp...

引用

7th International conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Daivakeshwar, P. Akshita, A. Anwen, S.N. Grace Ananya, V. Gampala, Veerraju Ande, Pavan Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350397284

Analytical dashboard is a place, where all the analysis of the data is displayed using various visualizing techniques. Visualizing is a process of presenting all the hidden patterns and trends in dataset. This analytical dashboard is used by data analysts to check all the variations of a particular variable that occurred over a period of time. The visualizations include different methodologies, where likely and unlikely characters of the dataset are compared with each other in order to find the result and the future impacts of these characters. The prediction theory intends to obtain useful insights from graphical representations is important when it comes to stock market analysis, weather forecasting etc. The proposed dashboard acts as a monitor to analyze the performance effectively. The main purpose of this study is to successfully develop a dashboard to analyze the student data. © 2023 ieee.

关键词： visualization

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Understanding Failures of Deep Networks via Robust Feature Extraction

Understanding Failures of Deep Networks via Robust Feature E...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Singla, Sahil Nushi, Besmira Shah, Shital Kamar, Ece Horvitz, Eric Univ Maryland College Pk MD 20742 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665445092

Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances. We introduce and study a method aimed at characterizing and explaining failures by identifying visual attributes whose presence or absence results in poor performance. In distinction to previous work that relies upon crowdsourced labels for visual attributes, we leverage the representation of a separate robust model to extract interpretable features and then harness these features to identify failure modes. We further propose a visualization method aimed at enabling humans to understand the meaning encoded in such features and we test the comprehensibility of the features. An evaluation of the methods on the ImageNet dataset demonstrates that: (i) the proposed workflow is effective for discovering important failure modes, (ii) the visualization techniques help humans to understand the extracted features, and (iii) the extracted insights can assist engineers with error analysis and debugging.

关键词： Measurement visualization Computer vision Error analysis Aggregates Debugging Feature extraction

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Map4OLAP: A web-based tool for interactive map visualization of OLAP queries

<i>Map4OLAP</i>: A web-based tool for interactive map visual...

引用

9th ieee International conference on Big Data (ieee BigData)

作者： Bimonte, Sandro Edoh-Alove, Elodie Coulibaly, Fagnine Alassane Univ Clermont Auvergne INRAE TSCF Aubiere France Geosyst France Montigny Le Bretonneux France

ISBN: (纸本)9781665439022

Spatial OLAP (SOLAP) systems allow to explore and analyze huge volume of georeferenced data by means of pivot tables, graphical displays and maps. In the last years, SOLAP and Spatial Data Warehouses has received a lot of attention from the academic and industrial communities, who mainly focus on logical and physical data models to improve SOLAP queries performance. Although, geovisualization is essential for effective SOLAP analysis, few works propose advanced methods for the visualization of SOLAP queries results using cartographic displays. In this work, we present some geovisualization methods to reach an effective visualization of pivot table data over maps. We implement these proposals in a web-based SOLAP system, and we use a real case study concerning the analysis of the agro-biodiversity data for the demo scenario.

关键词： Spatial OLAP Geovisualization Agro-biodiversity

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OncoPrint++: An Enhanced OncoPrint Beyond Data visualization for Clinical Bioinformatics and Genomics

OncoPrint++: An Enhanced OncoPrint Beyond Data Visualization...

引用

ieee International conference on Big Data

作者： Shiquan Wu Kwangbom Choi Paul Jung Jifeng Qian Paul Hiser Melih Acar Edith Szafer Glusman Leo Wang-Kit Cheung Bioinformatics & Computational Oncology Genomics Research Center AbbVie Company South San Franciso CA USA Bioinformatics Data Engineering Genomics Research Center AbbVie Company South San Franciso CA USA Information Research Genomics Research Center AbbVie Company South San Franciso CA USA Precision Medicine Oncology AbbVie Company South San Franciso CA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

It is presented in this article a web-based tool named OncoPrint++. OncoPrint++ is a tool integrated with traditional OncoPrint/heatmap and enhanced functions and features for processing, visualizing and analyzing clinicogenomics data in oncology. OncoPrint++ functions as an all-in-one tool primarily designed for oncology studies, but it could be extended for general clinical bioinformatics and genomics. It will be available to use for free at GitHub/AWS.

关键词： Data visualization Genomics Big Data Oncology Bioinformatics

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Modeling and Simulation of Manned and Unmanned Cooperative Reconnaissance and Surveillance Scenario Based on DoDAF

Modeling and Simulation of Manned and Unmanned Cooperative R...

引用

ieee conference on Industrial Electronics and Applications (ICIEA)

作者： Jin Xiao Fengnan Chen Siyuan Cao Xiaoguang Hu Xinwei Cao Wenming Ren State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing China Interoperability Laboratory AVIC China Aero-Polytechnology Establishment Beijing China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Due to the advantages of improving system operational effectiveness, the manned and unmanned cooperative operational methods are increasingly being applied in modern battlefields. However, in complex scenarios, the information interaction between manned aircraft and unmanned aerial vehicles can lead to issues such as poor timeliness and low efficiency in resource scheduling. Constructing an architecture framework for manned and unmanned system based on DoDAF to design the top-level structure of the system can solve the problem of poor system interoperability. In this paper, We propose a method of constructing a DoDAF model for the scenario and verifying the logical consistency of the system task architecture. We design an information exchange process for manned and unmanned cooperative reconnaissance scenario and verify the logical self-consistency of the process to improve system interoperability. In addition, We apply the STK tool to construct a scenario visualization model, which more intuitively reflects the information interaction relationship of the system in multiple domains, to further jointly verify the logical correctness of information interaction.

关键词： Industrial electronics visualization Job shop scheduling Autonomous systems Surveillance Reconnaissance Autonomous aerial vehicles

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Electronic Nose with Augmented Reality visualization For Odor Classification

Electronic Nose with Augmented Reality Visualization For Odo...

引用

2023 International conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Shou-Hsuan Chen, Hsing-Yi Li, Ru-Ying Wang, Pei-Xuan Yan, Min-Hsuan Soochow University Department of Data Science Taipei Taiwan

ISBN: (纸本)9798350324174

The electronic nose imitates human smell mechanism and has been used in many diverse fields for odor detection. Our approach involves utilizing multiple gas sensors integrated with the Arduino platform and constructing a multivariate analysis pipeline using time series data from these sensors. Additionally, we incorporate augmented reality technology to overlay virtual objects representing predicted results onto real-time captured images. By conducting experiments using our collected odor sensor data, our approach demonstrates a performance measurement of 86% F1. These results indicate that our analysis techniques, encompassing feature engineering, dimensional reduction, and machine learning methods, possess strong discriminative capabilities for odor classification. © 2023 ieee.

关键词： Augmented reality

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Feature Activation Regression for Image-to-Image Feature Generation

Feature Activation Regression for Image-to-Image Feature Gen...

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ieee National conference on Aerospace and Electronics (NAECON)

作者： Patrick Hafner Safwan Altwargi Anna Carollo Mohammed Alshehri Theus H. Aspiras Vijayan K. Asari Electrical and Computer Engineering University of Dayton Dayton United States

ISBN: (数字)9798350367621

ISBN: (纸本)9798350367638

With the need for explainable AI, several visualization methods have been developed to explore neural networks. Our proposed work seeks to isolate and generate image features of input data using any neural network. We employed a modified gradient ascent algorithm with a smoothed gradient loss function for image-to-image feature generation.

关键词： Analytical models Explainable AI Neural networks Pipelines Data visualization Clustering algorithms Predictive models

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