Sentence ordering (SO) is a key component of verbal ability. It is also crucial for automatic text generation. While numerous researchers developed various methods to automatically evaluate the informativeness of the produced contents, the evaluation of readability is usually performed manually. In contrast to that, we present a selfsu cient metric for SO assessment based on text topic-comment structure. We show that this metric has high accuracy.

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The information spread through the Web influences politics, stock markets, public health, people's reputation and brands. For these reasons, it is crucial to filter out false information. In this paper, we compare different automatic approaches for fake news detection based on statistical text analysis on the vaccination fake news dataset provided by the Storyzy company. Our CNN works better for discrimination of the larger classes (fake vs trusted) while the gradient boosting decision tree with feature stacking approach obtained better results for satire detection. We contribute by showing that efficient satire detection can be achieved using merged embeddings and a specific model, at the cost of larger classes. We also contribute by merging redundant information on purpose in order to better predict satire news from fake news and trusted news.

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Abstract. In this paper is proposed a method to predict deviations of accuracy of a basic shape of holes due to a thermal deformation (TD) at the design stage of the technological process. Proposed method of control of technological process consists of two stages: the first stage (auxiliary) is based on the finite element method (FEM), the second (main) -on the modeling of artificial neural network (Ann). In this paper is developed an algorithm of calculation of input and output parameters of the network using LS-DYNA. A structure of the Ann to predict and to adjust a trajectory of a movement of a tool at the preparation stage of the technological process for work pieces included in group process is invented in this article.