The development and improvement of effective tools for predicting human behavior in real life through the features of its virtual activity opens up broad prospects for psychological support of the individual. The presence of such tools can be used by psychologists in educational, professional and other areas in the formation of trajectories of harmonious person's development.Currently, active research is underway to determine psychological characteristics based on publicly available data. Such studies develop the direction of "Psychology of social networks". As markers for determining the psychological characteristics of people, various parameters obtained from their personal pages in social networks are used (texts of posts and reposts, the number of different elements on the page, statistical information about audio and video recordings, information about groups, and others). There is a difficulty in obtaining and analyzing a data set this big, as there are non-linear and hidden relationships between individual data elements. As a result, the classic methods of information processing become inefficient. Therefore, in our work to develop a comprehensive model of success based on the analysis of qualitative and quantitative data, we use an approach based on artificial neural networks. The labels of the input records are used to divide the subjects of the study into five clusters using clustering methods (kmeans). In the course of our work, we gradually expand the set of input parameters to include metrics of users' personal pages, and compare the results to determine the impact of qualitative parameters on the accuracy of the artificial neural network. The results reflect the solution of one of the tasks of the research carried out within the framework of the project of the Russian Science Foundation and serve as material for an information and analytical system for automatic forecasting of human life activity based on the metrics of his personal profile in the social network VKontakte.

This article provides an overview of existing solutions for semantic analysis of mathematical documents, and also presents a method for automatic semantic analysis of documents in PDF format. This method searches for local variables in the text of the article, extracts their definitions and connects concepts with formulas. The advantage of the method over the existing ones is independence from the markup of the original PDF document, which expands the scope of the method. We provide estimates of recall, precision and F-measure for algorithms for finding variables and linking local variables with formulas. The resulting semantic markup of the document will be used to create a collection of documents suitable for the semantic formula search service, which is part of the set of services of the Lobachevskii-DML digital publishing system.

The article provides an overview of existing digital publishing systems, existing ways to expand the functionality of such systems, and also proposes a project for a set of services to expand the functionality of the Open Journal Systems publishing system on the platform of the Lobachevskii-DML digital mathematical library. The proposed set of services includes services aimed at the authors of articles and intended for the editorial staff of the journal. The existing developments in individual parts of the project are described, and the main ideas for the development of all services are proposed.

This paper discusses the technical details of obtaining and processing data to determine a set of characteristics of texts from social networks, genre preferences in movies and music genres for students of Kazan Federal University who have different academic performance (successful, average, not-successful). The selection of such characteristics is carried out using machine learning methods (Word2Vec, tSNE). The data obtained is used in the development of a functional psychometric model of cognitive behavioral predictors of an individual’s activity within the framework of their educational activities. We also developed a web application for visualizing the obtained data using the Flask engine.

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