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Intro -- Editorial -- Outline placeholder -- Smart Approaches in Information Technology, Computer Science, and Mathematical Modelling -- Innovations and Smart Technologies in Geosciences -- Innovations and Smart Technologies in Biology -- Innovations and Smart Technologies in Linguistics -- Innovations and Smart Technologies in Philosophy and Social Studies -- Innovations and Smart Technologies in Historical Studies -- Innovations and Smart Technologies in Economics -- Contents -- Smart Approaches in Information Technology and Computer Science -- Modelling the Structure of Protest Movement Advocacy in Social Media Using Graph and Neural Network Analysis -- 1 Introduction -- 2 Roles of Users -- 2.1 Role Identification Method -- 3 Bridges -- 3.1 Method of Identifying Bridges -- 4 The Most Influential Users -- 5 Structure Analysis -- 6 Conclusion -- References -- Automatic Building of Electrical Circuits of Biomorphic Neuroprocessor Units and Visualization of Their Numerical Simulation -- 1 Introduction -- 2 Automatic Building of the Electric Circuit of the Input Unit -- 3 Automatic Building of the Electrical Circuit of the Output Unit -- 4 Specialized Program MDC-SPICE for Simulating Large Electrical Circuits Containing Memristor-Diode Crossbars -- 5 MDC-SPICE Simulation Results Visualization -- 6 Conclusion -- References -- Diagnostics by Statistical Characteristics of Different Deep Levels -- 1 Introduction -- 2 Diagnostics Based on Statistical Characteristics of Different Deep Levels of a Random Variable -- 2.1 Calculation of Statistical Characteristics of Different Deep Levels -- 2.2 Selection of Significant Statistical Characteristics -- 2.3 Building a Multidimensional Range that Reflects the Condition of the Object -- 3 Discussion and Conclusion -- References -- Robotic Process Automation: Application of Technology -- 1 Introduction.

This work is performed as part of the cognitive-discursive paradigm of modern linguistics. The object of the investigation is the concept "computer virus" and its linguistic representation in a computer virology discourse. The research is based on the texts from the Corpus of Contemporary American English. The first stage of the linguistic and conceptual investigation of "computer virus" is based on Metaphor Investigation Process (MIPVU) developed by the Metaphor Lab at VU University Amsterdam. The identification procedure includes the following main steps: reading the text/discourse, acquiring a general understanding of its meaning, selecting lexical units from the text/discourse, and establishing their contextual and contemporary meanings. If the contextual meaning contrasts with the basic meaning but can be understood in comparison with it, the lexical unit is marked as metaphorical (Pragglejaz Group 2007). The second stage of the investigation is based on the method of metaphorical modeling, which implies taxonomic categorization, developed by S. Mishlanova (Mishlanova 2002). On the basis of corpus analysis a metaphorical model "computer virus" was constructed. The model represents a taxonomic structure, which includes the basic taxons "man as a social subject," "man as a biological creature," and "animal." The most representative areas of source domains included, "military operation" (represented by such linguistic metaphors as attack, strike, defeat ), "diseases" ( crippling, succumb, infected ), and "interpersonal relations" ( be angry at, enter into, want ). At the current stage of computer virology development, metaphor plays a very important role as a universal tool for conceptualization and categorization of new knowledge that relies on the preceding experience of the person participating in the cognitive process. The structure of the concept "computer virus" is a hierarchical system, fixing the unscientific notion of a computer virus, the peculiarities of its functioning, and the ways to combat this malicious program.

Up-to-date information plays a crucial role in modern linguistic research. For this reason,computational linguistic methods, including those aided with analytical and machine-learning tools, are attracting growing attention. Some of their applications in cognitive-discursive linguistics are keyword extraction, topic modelling, and content analysis. Text-mining tools facilitate time-consuming linguistic work andadd to the results' reliability and greater statistical precision by processing a significantly larger data volume.Most studies, however, have overlooked interference of socially significant but context-irrelevant (e.g. political) information into a specialized discourse by focusing mainly on one data format. The current study,aimed at topic modelling, has been carried out on the computer security discourse. We have implemented theproject on the KNIME analytical platform. The model enables comparison between topics extracted frompublished articles and date-specific RSS news feeds. The study provides important insights into infodemiology and political incidental news exposure occurring in computer-security-oriented RSS feeds on theKaspersky website but untraceable in the papers published on the same website in a PDF format. The resultsreported here provide further evidence for the need to consider the hypercontext of professional communication and employ real-time data in solving similar problems within cognitive-discursive linguistics.Our contribution to the development of cognitive-discursive linguistics is the method for comparingtopics within one discourse, taking into account near-real-time data. For computational linguistics, the significance of our work lies in describing a new application of the topic extraction workflow freely available onthe KNIME hub.

In the era of modern information society, the study of effective ways of transmitting, receiving, processing, and storing knowledge becomes relevant. Special knowledge, formed in the process of professional activity, stored in the form of ontology in the mind of an individual and represented in the form of the language of professional communication, terminologies, and term systems is of particular value. At the same time, the role of interdisciplinary communication and the communication of special knowledge increases, and the problem of the misrepresentation of special knowledge, caused by the mismatch of communicants' ontologies, arises in the course of this communication. The solution to this problem could be the mediation of special knowledge during its transfer, which allows transforming the outgoing knowledge with minimal information losses and a high degree of assimilation.This article presents the results of a project aimed at solving this problem of the misrepresentation of special knowledge in the process of interdisciplinary communication. In the framework of the project, the methodology of knowledge mediation was developed, and new software was created to automate the process of term system generation (TSBuilder) and identifying potentially close subject areas for the selection of effective linguistic means of cognitive mediation (TSGraph).The novelty of the research lies in its interdisciplinary approach to the problem solution and the use of data mining tools for the implementation of ontological and metaphorical modelling. The developed methodology of special knowledge mediation can be used to optimize professional communication in various subject areas, especially between experts and nonexperts, as well as in the implementation of students' professional learning. The software provided is applicable for both linguistic research and other areas involved in professional communication.