Existing methods for detecting music plagiarism rely on diverse criteria but remain inherently subjective. Altered audio files (e.g., speed adjustments, pitch changes, etc.) are difficult to accurately determine infringement using traditional similarity measures. The ability to detect file modifications, alongside plagiarized content, is critical in protecting copyright in complex musical environments and holds considerable promise for real-world applications. This study utilizes Graph Attention Network (GAT) framework to improve the precision of similarity assessments between songs. Experimental evaluations demonstrate that the proposed method achieves a 7% improvement in detecting plagiarized and altered audio files compared to conventional non-network-based models. These findings underscore the efficacy of leveraging graph edge attention to enhance acoustic similarity analysis within the network.

This paper employs satellite imagery and machine learning approach to analyze regional disparities in human development (HDI) and multidimensional poverty (MPI). Concentrating on 213 subnational regions worldwide, this study strategically selects countries based on the Human Development Index (HDI), Gini coefficient, and the Theil-T index as measures of deprivation and inequality. The utilization of Google Static Map API and Convolutional Neural Networks (CNNs), facilitates the comprehensive analysis of these indicators. The findings challenge uniform development assumptions in nations, providing refined insights for targeted policy interventions. This quantitative approach, merging satellite technology and artificial intelligence, offers a significant contribution to understanding sustainable development.