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Propaganda is a rhetorical technique designed to serve a specific topic, which is often used purposefully in news article to achieve our intended purpose because of its specific psychological effect. Therefore, it is significant to be clear where and what propaganda techniques are used in the news for people to understand its theme efficiently during our daily lives. Recently, some relevant researches are proposed for propaganda detection but unsatisfactorily. As a result, detection of propaganda techniques in news articles is badly in need of research. In this paper, we are going to introduce our systems for detection of propaganda techniques in news articles, which is split into two tasks, Span Identification and Technique Classification. For these two tasks, we design a system based on the popular pretrained BERT model, respectively. Furthermore, we adopt the over-sampling and EDA strategies, propose a sentence-level feature concatenating method in our systems. Experiments on the dataset of about 550 news articles offered by SEMEVAL show that our systems perform state-of-the-art.

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The objective of this study is to investigate the efficient determination of C and γ for Support Vector Regression with RBF or mahalanobis kernel based on numerical and statistician considerations, which indicates the connection between C and kernels and demonstrates that the deviation of geometric distance of neighbour observation in mapped space effects the predict accuracy of -SVR. We determinate the arrange of γ & C and propose our method to choose their best values.