Special Issue "Perception and Communication"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editor

Prof. Dr. Birgitta Dresp-Langley
E-Mail Website
Guest Editor
Centre National de la Recherche Scientifique (CNRS), ICube Lab UMR 7357 CNRS, Université de Strasbourg, F-67081 Strasbourg, France
Interests: cognitive neuroscience; neuroscience; memory cognitive neuropsychology; neurophysiology; behavioral neuroscience brain; cognitive psychology
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Special Issue Information

Dear Colleagues,

The conditions in which visual communication may facilitate or hinder action, or behavioural responses to facts and events depends on the effectiveness with which critical aspects of information are encoded, perceived, and processed. Visually or diagrammatically communicated information content in images may reveal complex data and interactions more effectively than verbal discourse or text messages, depending on how the information is highlighted in an image. Specific perceptual cues such as contrast, colour, or shape may help a human operator to focus attention on relevant information and thereby facilitate the execution of a specific task significantly. "Visual" signals in image representations may also be perceived by intelligent machines, and can be exploited to design effective command structures for system tasks at a minimal computational cost in comparison with other, more complex approaches. This Special Issue on 'perception and communication' welcomes research articles, theoretical/conceptual papers and opinion pieces on visually (image) mediated communication and the many ways in which data and contents represented visually or diagrammatically may be perceived and processed by humans or machines.

Prof. Dr. Birgitta Dresp-Langley
Guest Editor

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 papers will be 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1500 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

  • visual signals
  • signal interpretation
  • visual communication
  • human operators
  • machines
  • task structure
  • performance

Published Papers (1 paper)

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Review

Open AccessReview
Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets
Appl. Sci. 2019, 9(15), 3065; https://doi.org/10.3390/app9153065 - 29 Jul 2019
Abstract
Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards [...] Read more.
Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Full article
(This article belongs to the Special Issue Perception and Communication)
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