The article describes the development of software aimed at processing images of murals of architectural monuments in order to identify defects. For flaw detection, a histogram approach was used – a comparison of the brightness characteristics of two images of frescoes. This method allows you to track the status of architectural monuments with low costs and minimal human involvement. The developed technology is used as part of the protection of the cultural heritage of the island-town of Sviyazhsk.

Model robustness to minor deviations in the distribution of input data is an important criterion in many tasks. Neural networks show high accuracy on training samples, but the quality on test samples can be dropped dramatically due to different data distributions, a situation that is exacerbated at the subgroup level within each category. In this article we show how the robustness of the model at the subgroup level can be significantly improved with the help of the domain adaptation approach to image embeddings. We have found that application of a competitive approach to embeddings limitation gives a significant increase of accuracy metrics in a complex subgroup in comparison with the previous models. The method was tested on two independent datasets, the accuracy in a complex subgroup on the Waterbirds dataset is 90.3 {y : waterbirds;a : landbackground}, on the CelebA dataset is 92.22 {y : blondhair;a : male}.

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.

Recently, such a direction of machine learning as reinforcement learning has been actively developing. As a consequence, attempts are being made to use reinforcement learning for solving computer vision problems, in particular for solving the problem of image classification. The tasks of computer vision are currently one of the most urgent tasks of artificial intelligence.

The article proposes a method for image classification in the form of a deep neural network using reinforcement learning. The idea of ​​the developed method comes down to solving the problem of a contextual multi-armed bandit using various strategies for achieving a compromise between exploitation and research and reinforcement learning algorithms. Strategies such as -greedy, -softmax, -decay-softmax, and the UCB1 method, and reinforcement learning algorithms such as DQN, REINFORCE, and A2C are considered. The analysis of the influence of various parameters on the efficiency of the method is carried out, and options for further development of the method are proposed.

Persistent homology is a topological data analysis tool which is reflecting changes in topological structure of data along its scale. Application of persistent homology to monitoring hydraulic fracturing which is allowing researchers to consider prior information in a natural way is given in the article

The article is devoted to our approaches to processing text in natural language to clarify the specific spatial relationship of objects and three-dimensional frame-by-frame visualization. The proposed approach allows us to show how the explicit constraints of the extracted spatial relationships affect and makes it possible to create possible layouts of objects on the scene. Natural language interpretations for spatial knowledge can generate three-dimensional scenes, which in turn are necessary to translate the scriptwriter's intent into the design of video games. The work also takes into account the rules of directing to create successful shots. Among them, accounting for the plan, camera rotation, as well as compositional nuances.

This article description the conception of History of Genius Discovery (History GD) software. The software has few similarities with GitHub software that have got wide famous at the professional developer’s community. The software appealed to solve two main science issues. History GD will save science and cultural heritage of Russian scientists and accumulate initial data for measuring tendencies of science theorems formation. The last will give the probability for appending The Structure of Scientific Revolutions by Thomas S. Kuhn by using numeric big data. Also, the software will minimise probability of losing scientific manuscripts by reason of scientists deaths. Software engineering, sociology, philosophy, law, and history are five scientific directions that are used as base for creating this software. The idea of creation have got at Kazan Federal University when we learned Big Data Science.

The materials presented at the Conference describe the results of recent years in the following areas: Open access to scientific data and knowledge in Earth Sciences; Data peculiarities in Earth Sciences: new concepts and methods, tools for their collection, integration and processing in different information systems, including systems with intensive use of data; Data mining and mathematical simulation of natural processes in Earth Sciences. Evolution of classical GIS-applications in Earth Sciences; Application to Critical Raw Materials (CRM); social aspects of mining (e.g., the Social Licence to Operate [SLO]); predictive mapping and applications to exploration, landuse and search for extensions of known deposits; Intelligent data analysis, elicitation of facts and knowledge from scientific publications. Thesauruses, ontologies and conceptual modeling. Semantic WEB, linked data. Services. Content semantic structuring. Applications for geosciences, e.g., Ontology-based Dynamic Decision Graphs for Expert systems and decision-aid tools; Application of methods and technologies of the remote sensing in Earth Sciences: from satellites to unmanned aerial vehicles; Information technologies for demonstration and popularization of scientific achievements in Earth Sciences; Applications: environmental risks including mining wastes, natural hazards, water resource management, etc.

Analysis of various presentations for context free grammars provided with parser generators. A new description format of context free grammars is proposed. Given a representation of context free grammar in JSON format. The concept of a new parser generator based on JSON data format of describing context free grammars is presented. Described a parser generation scheme based on that concept.

The article describes new approaches related to the collection of data from heterogeneous information systems of access to scientific publications using open international standards and protocols for the formation of systems of open access to scientific geological publications. Based on developed and adapted approaches and technological solutions, a set of programs of information and analytical system of access to scientific publications has been implemented, implementing functions of collection, search, cataloguing, filtering and management of scientific publications and their metadata.