2021 25th International Conference Information Visualisation (IV)最新文献_第3页

Visual exploration of the inner representation learned by a convolutional neural network 由卷积神经网络学习的内部表征的视觉探索

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00026

Barthélémy Serres, F. Bouali, C. Guinot, G. Venturini

引用次数: 0

Reconstruction and Visualization of Protein Structures by exploiting Bidirectional Neural Networks and Discrete Classes 利用双向神经网络和离散类的蛋白质结构重建和可视化

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00053

Alessia Auriemma Citarella, Lorenzo Porcelli, Luigi Di Biasi, M. Risi, G. Tortora

{"title":"Reconstruction and Visualization of Protein Structures by exploiting Bidirectional Neural Networks and Discrete Classes","authors":"Alessia Auriemma Citarella, Lorenzo Porcelli, Luigi Di Biasi, M. Risi, G. Tortora","doi":"10.1109/IV53921.2021.00053","DOIUrl":"https://doi.org/10.1109/IV53921.2021.00053","url":null,"abstract":"In recent years, Deep Learning techniques have achieved some success in bioinformatics tasks, including protein conformation prediction. In this work, we propose a Bidirectional Long Short-Term Memory (BLSTM) network system, called Human Proteins Angles Prediction (HPAP), in order to improve the prediction of dihedral angles of proteins. We have introduced a discrete subdivision in classes of 5° for protein torsion angles and four new features related to accessible surface area and volume. In total there are 73 classes (72 classes include the angles between -180° and 180°, a further class is used to code the free angles at the beginning of the sequence) with a maximum expected error of ±2.5°. We have tested three model variants in several parameter combinations. With our model, we have obtained a decrease of the mean absolute error of about 2° for the $psi$ angle. Although our dataset is reduced in size, the accuracy of $varphi$ and $psi$ angles is comparable to the existing methods. Predicting angles accurately is useful for accurately reconstructing the three-dimensional structure of a protein. In this context, the prediction is limited to the $varphi$ and $psi$ angles and we will visualize what happens locally when a prediction is correct. In case the prediction is far from true angles, even a small error can deconstruct the backbone.","PeriodicalId":380260,"journal":{"name":"2021 25th International Conference Information Visualisation (IV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125256761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

ARViz: Interactive Visualization of Association Rules for RDF Data Exploration 用于RDF数据探索的关联规则的交互式可视化

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00013

Aline Menin, L. Cadorel, A. Tettamanzi, A. Giboin, Fabien L. Gandon, M. Winckler

{"title":"ARViz: Interactive Visualization of Association Rules for RDF Data Exploration","authors":"Aline Menin, L. Cadorel, A. Tettamanzi, A. Giboin, Fabien L. Gandon, M. Winckler","doi":"10.1109/IV53921.2021.00013","DOIUrl":"https://doi.org/10.1109/IV53921.2021.00013","url":null,"abstract":"Association rule mining often leads the analyst into a rough rummaging process to identify rules that are relevant to understand specific problems. We propose a visualization interface to assist the rule selection process and evaluate it on an RDF knowledge graph derived from the COVID-19 Open Research Dataset. The user interface supports data exploration with focus on the overview of rules through a scatter plot, subsets of rules through a chord diagram chart, and itemsets through an association graph which is dynamically created by entering an item of interest (i.e. a named entity). Further, the analyst can interactively recover a list of publications containing the named entities involved in a particular rule. Among the original aspects of our approach, we highlight the representation of attributes describing measures of interest (i.e. confidence and interestingness), a visual indication of existence (or not) of symmetry in association rules, the exploration of subsets of rules according to clusters of publications and named entities, and an interactive prompting that aims at expanding the discovery of named entities within selected association rules. We assess our approach through a semi-structured interview involving experts in the domains of data mining and biomedicine, whose feedback could assist the refinement of the visual and interaction tools.","PeriodicalId":380260,"journal":{"name":"2021 25th International Conference Information Visualisation (IV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124912521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Analysis of Deep Neural Networks Correlations with Human Subjects on a Perception Task 感知任务中深度神经网络与人类受试者的相关性分析

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00029

Loann Giovannangeli, R. Giot, D. Auber, J. Benois-Pineau, Romain Bourqui

{"title":"Analysis of Deep Neural Networks Correlations with Human Subjects on a Perception Task","authors":"Loann Giovannangeli, R. Giot, D. Auber, J. Benois-Pineau, Romain Bourqui","doi":"10.1109/IV53921.2021.00029","DOIUrl":"https://doi.org/10.1109/IV53921.2021.00029","url":null,"abstract":"In information visualization, it has become mandatory to assess visualization techniques efficiency either to write a survey, optimize a technique or even design a new one. To do so, the common way is to conduct user evaluations through which human subjects are asked to solve a task on different visualization techniques while their performances are measured to assess which technique is the most efficient. These evaluations can be complex to design and setup in order not to be biased and, in the end, their results can become contestable when the evaluation methods standards evolve. To overcome these flaws, new evaluation methods are emerging, mostly making use of modern and efficient computer vision techniques such as deep learning. These new methods rely on a strong assumption that has not been studied deeply enough yet: humans and deep learning models performances can be correlated. This paper explores the performances of both a state-of-the-art deep neural network and human subjects on an outlier detection task taken from a previous experiment of the literature. The objective is to study whether the machine and humans behaviors were different or if some correlations can be observed. Our study shows that their results are significantly correlated and a machine learning model efficiently learned to predict human performances using deep neural network metrics as input. Hence, this work presents a use case where using a deep neural network to assess human subjects performances is efficient.","PeriodicalId":380260,"journal":{"name":"2021 25th International Conference Information Visualisation (IV)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127709986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A brief review of dashboard visualizations employed to support management or business decisions 简要回顾用于支持管理或业务决策的仪表板可视化

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00025

Davi Augusto Galúcio Frazão, Thiago S. A. Costa, Tiago Araújo, B. Meiguins, Carlos G. R. Santos

引用次数: 2

A Web-based Interface for the Animation of Declarative Languages 基于web的声明性语言动画界面

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00060

Nada Hamdy, Nada Sharaf

引用次数: 0

A Visual Data Science Solution for Visualization and Visual Analytics of Big Sequential Data 面向大序列数据可视化和可视化分析的可视化数据科学解决方案

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/iv53921.2021.00044

C. Leung, Yan Wen, Chenru Zhao, Haolin Zheng, Fan Jiang, A. Cuzzocrea

{"title":"A Visual Data Science Solution for Visualization and Visual Analytics of Big Sequential Data","authors":"C. Leung, Yan Wen, Chenru Zhao, Haolin Zheng, Fan Jiang, A. Cuzzocrea","doi":"10.1109/iv53921.2021.00044","DOIUrl":"https://doi.org/10.1109/iv53921.2021.00044","url":null,"abstract":"In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the initiates of open data also led to the willingness of many government, researchers, and organizations to share their data and make them publicly accessible. An example of open big data is healthcare, disease and epidemiological data such as privacy-preserving statistics on patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Analyzing these open big data can be for social good. For instance, analyzing and mining the disease statistics helps people to get a better understanding of the disease, which may inspire them to take part in preventing, detecting, controlling and combating the disease. As “a picture is worth a thousand words”, having the pictorial representation further enhances people’s understanding of the data and the corresponding results for the analysis and mining. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. We illustrate the ideas through the visualization and visual analytics of sequences of real-life COVID-19 epidemiological data. Our solution enables people to visualize COVID-19 epidemiological data and their temporal trends. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. Evaluation of these real-life sequential COVID-19 epidemiological data demonstrates the effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data.","PeriodicalId":380260,"journal":{"name":"2021 25th International Conference Information Visualisation (IV)","volume":"36 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121014636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Visualization of trajectory-based queries in images database 图像数据库中基于轨迹查询的可视化

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00059

Roseval Donisete Malaquias, Renato Bueno

引用次数: 0

Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data 可视化分析和相似度搜索——科学数据中基于兴趣的相似度搜索

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00041

Midhad Blazevic, Lennart B. Sina, Dirk Burkhardt, Melanie Siegel, Kawa Nazemi

引用次数: 4

Pupil responses by level of valence sensitivity to emotion-evoking pictures 瞳孔对唤起情绪的图片的效价敏感水平的反应

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00031

Nijika Murokawa, M. Nakayama

引用次数: 0