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Digital, Volume 1, Issue 3 (September 2021) – 3 articles

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11 pages, 1175 KiB  
Article
Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence
by Nafissa Yusupova, Diana Bogdanova, Nadejda Komendantova and Hossein Hassani
Digital 2021, 1(3), 162-172; https://doi.org/10.3390/digital1030012 - 31 Aug 2021
Cited by 2 | Viewed by 3459
Abstract
The topic of affective computing has been growing rapidly in recent times. In the last five years, the volume of publications in this field has tripled. The question arises which research trends are most in demand today. This can only be judged by [...] Read more.
The topic of affective computing has been growing rapidly in recent times. In the last five years, the volume of publications in this field has tripled. The question arises which research trends are most in demand today. This can only be judged by analysing the publications that present the results of research. Since researchers have access to the entire global scientific publication space, the task of analysing big data arises. This leads to the problem of identifying the most significant results in the subject area of interest. This paper presents some results of the analysis of semi-structured information from scientific citation databases on the subject of “affective computing”. Full article
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17 pages, 317 KiB  
Systematic Review
Leveraging Vector Space Similarity for Learning Cross-Lingual Word Embeddings: A Systematic Review
by Kowshik Bhowmik and Anca Ralescu
Digital 2021, 1(3), 145-161; https://doi.org/10.3390/digital1030011 - 01 Jul 2021
Cited by 2 | Viewed by 4072
Abstract
This article presents a systematic literature review on quantifying the proximity between independently trained monolingual word embedding spaces. A search was carried out in the broader context of inducing bilingual lexicons from cross-lingual word embeddings, especially for low-resource languages. The returned articles were [...] Read more.
This article presents a systematic literature review on quantifying the proximity between independently trained monolingual word embedding spaces. A search was carried out in the broader context of inducing bilingual lexicons from cross-lingual word embeddings, especially for low-resource languages. The returned articles were then classified. Cross-lingual word embeddings have drawn the attention of researchers in the field of natural language processing (NLP). Although existing methods have yielded satisfactory results for resource-rich languages and languages related to them, some researchers have pointed out that the same is not true for low-resource and distant languages. In this paper, we report the research on methods proposed to provide better representation for low-resource and distant languages in the cross-lingual word embedding space. Full article
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15 pages, 3395 KiB  
Article
Aggregated Gaze Data Visualization Using Contiguous Irregular Cartograms
by Vassilios Krassanakis
Digital 2021, 1(3), 130-144; https://doi.org/10.3390/digital1030010 - 30 Jun 2021
Cited by 3 | Viewed by 3232
Abstract
Gaze data visualization constitutes one of the most critical processes during eye-tracking analysis. Considering that modern devices are able to collect gaze data in extremely high frequencies, the visualization of the collected aggregated gaze data is quite challenging. In the present study, contiguous [...] Read more.
Gaze data visualization constitutes one of the most critical processes during eye-tracking analysis. Considering that modern devices are able to collect gaze data in extremely high frequencies, the visualization of the collected aggregated gaze data is quite challenging. In the present study, contiguous irregular cartograms are used as a method to visualize eye-tracking data captured by several observers during the observation of a visual stimulus. The followed approach utilizes a statistical grayscale heatmap as the main input and, hence, it is independent of the total number of the recorded raw gaze data. Indicative examples, based on different parameters/conditions and heatmap grid sizes, are provided in order to highlight their influence on the final image of the produced visualization. Moreover, two analysis metrics, referred to as center displacement (CD) and area change (AC), are proposed and implemented in order to quantify the geometric changes (in both position and area) that accompany the topological transformation of the initial heatmap grids, as well as to deliver specific guidelines for the execution of the used algorithm. The provided visualizations are generated using open-source software in a geographic information system. Full article
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