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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

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Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural

Yongzhao Yang¹, Shasha Xu¹

¹Zhengzhou Preschool Education College, Zhengzhou, Henan 450000, China.

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|May 23, 2022

Summary

The COVID-19 pandemic led to widespread distance learning, exposing students to inappropriate content. This study introduces a novel neural network to detect explicit material in online education video conferences, safeguarding minors.

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Area of Science:

Computer Science
Artificial Intelligence
Education Technology

Background:

The COVID-19 pandemic necessitated a global shift to distance learning, impacting approximately 1.5 billion students.
Online learning environments have inadvertently exposed minors to inappropriate, particularly sexual, content, posing risks to their emotional and mental well-being.
Existing policies to protect children online have proven insufficient in addressing the specific challenges of distance education platforms.

Purpose of the Study:

To present an advanced attention neural architecture designed to detect explicit material within online education video conference applications.
To introduce a novel, computationally efficient intelligent mechanism for identifying inappropriate content during live educational sessions.
To enhance the safety and mental health of students participating in remote learning environments.

Main Methods:

Implementation of a Generative Adversarial Network (GAN) model.
Integration of a local, sparse attention mechanism to overcome the computational limitations of traditional attention models.
Development of a system capable of accurately detecting obscene and sexual content in real-time video streams from educational platforms.

Main Results:

The proposed architecture effectively detects explicit and sexual content in online video conferencing for education.
The novel attention mechanism achieves high accuracy without the quadratic time and memory complexity associated with standard attention methods.
This represents a significant advancement in content moderation for educational technology.

Conclusions:

The developed neural network offers a robust solution for identifying and mitigating exposure to harmful content in distance learning.
This technology is crucial for ensuring a safer online educational experience for minors.
The study provides a scalable and efficient method for content moderation in educational video conferencing.