Abstract:

Steel is one of the most important bulk materials these days. It is used almost everywhere - from medicine to industry. Detecting this material's defects is one of the most challenging problems for industries worldwide. This process is also manual and time-consuming. Through this study we tried to automate this process. A convolutional neural network model UNet was used for this task for more accurate segmentation with less training image data set for our model. The essence of this NN (neural network) is in step-by-step convolution of every image (encoding) and then stretching them to initial resolution, consequently getting a mask of an image with various classes on it. The foremost modification is changing an input image's size to 128x800 px resolution (original images in dataset are 256x1600 px) because of GPU memory size's limitation. Secondly, we used ResNet34 CNN (convolutional neural network) as encoder, which was pre-trained on ImageNet1000 dataset with modified output layer - it shows 4 layers instead of 34. After running tests of this model, we obtained 92.7% accuracy using images of hot-rolled steel sheets.

Abstract: Currently, there are a huge number of works dedicated to semantic role labeling of English texts [1–3]. However, semantic role labeling of Russian texts was an unexplored area for many years due to the lack of train and test corpora. Semantic role labeling of Russian Texts was widely disseminated after the appearance of the FrameBank corpus [4]. In this approach, we analyzed the influence of the word embedding models on the quality of semantic role labeling of Russian texts. Micro- and macro- F1 scores on word2vec [5], fastText [6], ELMo [7] embedding models were calculated. The set of experiments have shown that fastText models averaged slightly better than word2vec models as applied to Russian FrameBank corpus. The higher micro- and macro- F1 scores were obtained on deep tokenized word representation model ELMo in relation to classical shallow embedding models.

Abstract: The web graph is the most popular model of real Web fragments used in Web science. The study of communities in the web graph contributes to a better understanding of the organization of the fragment of the Web and the processes occurring in it. It is proposed to allocate a communication graph in a web graph containing only those vertices (and arcs between them) that have counter arcs, and in it to investigate the problem of splitting into communities. By analogy with social studies, connections realized through edges in a communication graph are proposed to be called "strong" and all others "weak". Thematic communities with meaningful interpretations are built on strong connections. At the same time, weak links facilitate communication between sites that do not have common features in the field of activity, geography, subordination, etc., and basically preserve the coherence of the fragments of the Web even in the absence of strong links. Experiments conducted for a fragment of the scientific and educational Web of Russia show the possibility of meaningful interpretation of the results and the prospects of such an approach.

Abstract: Methods of the semantic documents parsing in digital control system of scientific collections, including electronic journals, offered. The methods of processing documents containing mathematical formulas and methods for the conversion of documents from the OpenXML-format in ТеХ-format considered. The search algorithm for the mathematical formulas in the collections of documents stored in OpenXML-format designed. The algorithm is implemented as online-service on platform science.tatarstan.