The Mode Exploration of Combining Chinese Traditional Culture Content with Computer Aided Instruction in Moral Education of Colleges and Universities

Deleted Journal, Journal Year: 2024, Volume and Issue: 20(2), P. 573 - 583

Published: April 8, 2024

According to the characteristics of learning style, this paper classifies traditional cultural classics according school. Audio, video, text, pictures and other forms are collected match students' styles. The system uses three-layer structure My Batis+ Spring MVC, Ajax+ Free marker+ JS technology, MySQL database, Apache Tomcat8 manager, Eclipse development platform, WebStorm network front-end tools. In addition, auxiliary tools such as Camtasia Studio PhotoshopCS5 used generate prototype systems for culture learning. Its data processing is fast.

Language: Английский

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Having a Smartphone and being enrolled in a higher education institution does not make students digitally literate!

Published: Aug. 22, 2024

Language: Английский

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Leveraging Synthetic Data as a Tool to Combat Bias in Artificial Intelligence (AI) Model Training

Journal of Engineering Research and Reports, Journal Year: 2024, Volume and Issue: 26(12), P. 24 - 46

Published: Nov. 27, 2024

This study investigates the efficacy of synthetic data in mitigating bias artificial intelligence (AI) model training, focusing on demographic inclusivity and fairness. Using Generative Adversarial Networks (GANs), datasets were generated from UCI Adult Dataset, COMPAS Recidivism MIMIC-III Clinical Database. Logistic regression models trained both original to evaluate fairness metrics predictive accuracy. Fairness was assessed through parity equality opportunity, which measure balanced prediction rates equitable outcomes across groups. Fidelity diversity evaluated using statistical tests such as Kolmogorov-Smirnov (KS) Kullback-Leibler (KL) divergence, along with Inception Score, quantifies data. The results revealed significant improvements for datasets. For dataset, increased 0.72 0.89, opportunity rose 0.65 0.83, without compromising accuracy (0.82 AUC-ROC compared 0.83 data). Based findings, this research recommends employing GANs generating bias-sensitive domains enhance ensure AI models. Furthermore, integrating human-in-the-loop (HITL) systems is critical monitor address residual biases during generation. Standardized validation frameworks, including fidelity tests, should be adopted transparency consistency applications. These practices can enable organizations leverage effectively while maintaining ethical standards development deployment.

Language: Английский

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Protecting Autonomous UAVs from GPS Spoofing and Jamming: A Comparative Analysis of Detection and Mitigation Techniques

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

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Artificial Intelligence and Global Security: Strengthening International Cooperation and Diplomatic Relations

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

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