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Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural Architectures
The spread of the COVID-19 pandemic affected all areas of social life, especially education. Globally, many states have closed schools temporarily or imposed local curfews. According to UNESCO estimations, approximately 1.5 billion students have been affected by the closure of schools and the mandat...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117052/ https://www.ncbi.nlm.nih.gov/pubmed/35602636 http://dx.doi.org/10.1155/2022/6334802 |
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author | Yang, Yongzhao Xu, Shasha |
author_facet | Yang, Yongzhao Xu, Shasha |
author_sort | Yang, Yongzhao |
collection | PubMed |
description | The spread of the COVID-19 pandemic affected all areas of social life, especially education. Globally, many states have closed schools temporarily or imposed local curfews. According to UNESCO estimations, approximately 1.5 billion students have been affected by the closure of schools and the mandatory implementation of distance learning. Although rigorous policies are in place to ban harmful and dangerous content aimed at children, there are many cases where minors, mainly students, have been exposed relatively or unfairly to inappropriate, especially sexual content, during distance learning. Ensuring minors' emotional and mental health is a priority for any education system. This paper presents a severe attention neural architecture to tackle explicit material from online education video conference applications to deal with similar incidents. This is an advanced technique that, for the first time in the literature, proposes an intelligent mechanism that, although it uses attention mechanisms, does not have a square complexity of memory and time in terms of the size of the input. Specifically, we propose the implementation of a Generative Adversarial Network (GAN) with the help of a local, sparse attention mechanism, which can accurately detect obscene and mainly sexual content in streaming online video conferencing software for education. |
format | Online Article Text |
id | pubmed-9117052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91170522022-05-19 Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural Architectures Yang, Yongzhao Xu, Shasha Comput Intell Neurosci Research Article The spread of the COVID-19 pandemic affected all areas of social life, especially education. Globally, many states have closed schools temporarily or imposed local curfews. According to UNESCO estimations, approximately 1.5 billion students have been affected by the closure of schools and the mandatory implementation of distance learning. Although rigorous policies are in place to ban harmful and dangerous content aimed at children, there are many cases where minors, mainly students, have been exposed relatively or unfairly to inappropriate, especially sexual content, during distance learning. Ensuring minors' emotional and mental health is a priority for any education system. This paper presents a severe attention neural architecture to tackle explicit material from online education video conference applications to deal with similar incidents. This is an advanced technique that, for the first time in the literature, proposes an intelligent mechanism that, although it uses attention mechanisms, does not have a square complexity of memory and time in terms of the size of the input. Specifically, we propose the implementation of a Generative Adversarial Network (GAN) with the help of a local, sparse attention mechanism, which can accurately detect obscene and mainly sexual content in streaming online video conferencing software for education. Hindawi 2022-05-11 /pmc/articles/PMC9117052/ /pubmed/35602636 http://dx.doi.org/10.1155/2022/6334802 Text en Copyright © 2022 Yongzhao Yang and Shasha Xu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yang, Yongzhao Xu, Shasha Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural Architectures |
title | Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural Architectures |
title_full | Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural Architectures |
title_fullStr | Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural Architectures |
title_full_unstemmed | Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural Architectures |
title_short | Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural Architectures |
title_sort | tackling explicit material from online video conferencing software for education using deep attention neural architectures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117052/ https://www.ncbi.nlm.nih.gov/pubmed/35602636 http://dx.doi.org/10.1155/2022/6334802 |
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