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Information Visualization Theory and Applications (IVAPP 2020)
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Information Visualization Theory and Applications (IVAPP 2020)

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (17 July 2020) | Viewed by 7991

Special Issue Editors

Prof. Dr. Andreas Kerren

E-Mail Website
Guest Editor
Department of Computer Science, Linnaeus University, Vaxjo, Sweden
Interests: information visualization; visualizations in bioinformatics; visualization of geographical data; visual analytics; software visualization; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christophe Hurter

E-Mail Website
Guest Editor
Interactive Data Visualization group, the French Civil Aviation University (ENAC), Toulouse, France
Interests: information visualization; human-computer interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will feature a selection of carefully revised and extended papers to be presented at the 11th International Conference on Information Visualization Theory and Applications (IVAPP 2020), to be held in Valletta, Malta, 27–29 February, 2020. IVAPP aims to become a major point of contact between researchers, engineers, and practitioners in information visualization. The conference covers a broad range of topics related to information visualization indicated by the topic list below. Papers describing advanced prototypes, systems, tools, and techniques as well as general survey papers indicating future directions are also encouraged.

Selected papers that were presented at the conference are invited to be submitted as extended versions to this Special Issue of the journal Information after the conference.

The conference paper should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter and include a statement on what has been changed compared to the original conference paper. Each submission to this journal issue should contain at least 50% new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Information and collected together on this Special Issue website.

Prof. Andreas Kerren
Prof. Christophe Hurter
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Area 1: abstract data visualization

  • Visual data analysis and knowledge discovery
  • Mathematical foundations of interactive visual analysis
  • Display and interaction technology
  • Databases and visualization, visual data mining
  • Graph visualization
  • Interface and interaction techniques for visualization
  • Internet, web, and security visualization
  • Software visualization
  • Information visualization
  • Visual analytical reasoning
  • Hardware-assisted visualization
  • High-dimensional data and dimensionality reduction
  • Text and document visualization
  • Visual representation and interaction
  • Data management and knowledge representation
  • Explainable machine learning by visualization

Area 2: general data visualization

  • Interactive visual interfaces for visualization
  • Perception and cognition in visualization
  • Visualization applications
  • Visualization taxonomies and models
  • Visualization algorithms and technologies
  • Visualization tools and systems for simulation and modeling
  • Augmented reality and data visualization
  • Mixed reality and data visualization
  • Immersive analytics
  • Time-dependent visualization
  • Usability studies and visualization
  • Glyph-based visualization
  • Human-centered aspects of visualization
  • Coordinated and multiple views
  • Interpretation and evaluation methods
  • Knowledge-assisted Visualization
  • Large data visualization
  • Integration of data analysis, interaction, and visualization
  • Data-driven storytelling

Area 3: spatial data visualization

  • Biomedical visualization and applications
  • Vector/tensor field visualization
  • Virtual environments and data visualization
  • Volume visualization
  • Scientific visualization
  • Flow visualization
  • GPU-based visualization
  • Image/Video summarization and visualization
  • Multi-field visualization
  • Parallel coordinate
  • Uncertainty visualization

Published Papers (2 papers)

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15 pages, 4805 KiB  
Article
Exploring Neural Network Hidden Layer Activity Using Vector Fields
by Gabriel D. Cantareira, Elham Etemad and Fernando V. Paulovich
Information 2020, 11(9), 426; https://doi.org/10.3390/info11090426 - 31 Aug 2020
Cited by 9 | Viewed by 4118
Abstract
Deep Neural Networks are known for impressive results in a wide range of applications, being responsible for many advances in technology over the past few years. However, debugging and understanding neural networks models’ inner workings is a complex task, as there are several [...] Read more.
Deep Neural Networks are known for impressive results in a wide range of applications, being responsible for many advances in technology over the past few years. However, debugging and understanding neural networks models’ inner workings is a complex task, as there are several parameters and variables involved in every decision. Multidimensional projection techniques have been successfully adopted to display neural network hidden layer outputs in an explainable manner, but comparing different outputs often means overlapping projections or observing them side-by-side, presenting hurdles for users in properly conveying data flow. In this paper, we introduce a novel approach for comparing projections obtained from multiple stages in a neural network model and visualizing differences in data perception. Changes among projections are transformed into trajectories that, in turn, generate vector fields used to represent the general flow of information. This representation can then be used to create layouts that highlight new information about abstract structures identified by neural networks. Full article
(This article belongs to the Special Issue Information Visualization Theory and Applications (IVAPP 2020))
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32 pages, 33195 KiB  
Article
Exploring Multiple and Coordinated Views for Multilayered Geospatial Data in Virtual Reality
by Maxim Spur, Vincent Tourre, Erwan David, Guillaume Moreau and Patrick Le Callet
Information 2020, 11(9), 425; https://doi.org/10.3390/info11090425 - 31 Aug 2020
Cited by 5 | Viewed by 3357
Abstract
Virtual reality (VR) headsets offer a large and immersive workspace for displaying visualizations with stereoscopic vision, as compared to traditional environments with monitors or printouts. The controllers for these devices further allow direct three-dimensional interaction with the virtual environment. In this paper, we [...] Read more.
Virtual reality (VR) headsets offer a large and immersive workspace for displaying visualizations with stereoscopic vision, as compared to traditional environments with monitors or printouts. The controllers for these devices further allow direct three-dimensional interaction with the virtual environment. In this paper, we make use of these advantages to implement a novel multiple and coordinated view (MCV) system in the form of a vertical stack, showing tilted layers of geospatial data. In a formal study based on a use-case from urbanism that requires cross-referencing four layers of geospatial urban data, we compared it against more conventional systems similarly implemented in VR: a simpler grid of layers, and one map that allows for switching between layers. Performance and oculometric analyses showed a slight advantage of the two spatial-multiplexing methods (the grid or the stack) over the temporal multiplexing in blitting. Subgrouping the participants based on their preferences, characteristics, and behavior allowed a more nuanced analysis, allowing us to establish links between e.g., saccadic information, experience with video games, and preferred system. In conclusion, we found that none of the three systems are optimal and a choice of different MCV systems should be provided in order to optimally engage users. Full article
(This article belongs to the Special Issue Information Visualization Theory and Applications (IVAPP 2020))
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