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Liu Xinya,Qin Fei,Zhou Xuehan,Hu Xuan,Zhang Yu 서울대학교 교육연구소 2020 Asia Pacific Education Review Vol.21 No.3
While education equality is considered crucial for broader social equality, policies that aim to equalize educational resources are sometimes suspected of discriminating against high achievers. Such potential discrimination should be examined empirically to provide robust evidence for policymakers and the broader public. Using a quasi-experimental design and longitudinal dataset, this paper reports on research which has investigated potential discrimination arising from China’s high school quota admission policy, which is considered a successful initiative for distributing high achievers across middle schools in ways that equalize achievement, and hence improves overall quality. The results presented in this paper indicate there is basically no such discrimination after controlling for self-selection bias. The paper also reveals the broader value of evaluating potential discrimination as part of similar forms of education development.
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Musical Genre Classification Based on Deep Residual Auto-Encoder and Support Vector Machine
( Xue Han ),( Wenzhuo Chen ),( Changjian Zhou ) 한국정보처리학회 2024 Journal of information processing systems Vol.20 No.1
Music brings pleasure and relaxation to people. Therefore, it is necessary to classify musical genres based on scenes. Identifying favorite musical genres from massive music data is a time-consuming and laborious task. Recent studies have suggested that machine learning algorithms are effective in distinguishing between various musical genres. However, meeting the actual requirements in terms of accuracy or timeliness is challenging. In this study, a hybrid machine learning model that combines a deep residual auto-encoder (DRAE) and support vector machine (SVM) for musical genre recognition was proposed. Eight manually extracted features from the Mel-frequency cepstral coefficients (MFCC) were employed in the preprocessing stage as the hybrid music data source. During the training stage, DRAE was employed to extract feature maps, which were then used as input for the SVM classifier. The experimental results indicated that this method achieved a 91.54% F1-score and 91.58% top-1 accuracy, outperforming existing approaches. This novel approach leverages deep architecture and conventional machine learning algorithms and provides a new horizon for musical genre classification tasks.
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