Abstract: The relevance of the study is dictated by the current state in the field of telephone fraud. According to research conducted by Kaspersky Lab, the share of users who encountered various unwanted spam calls in the spring of 2022 was at the level of 71%. The subject of the research is machine learning and deep learning technologies for determining emotions by the timbre of the voice. The authors consider in detail such aspects as: the creation of a marked-up dataset; the conversion of WAV audio format into a numerical form convenient for fast processing; machine learning methods for solving the problem of multiclass classification; the construction and optimization of neural network architecture to determine emotions in real time. A special contribution to the study of the topic is that the authors implemented a fast method of conversion sound formats into numerical coefficients, which significantly increased the speed of data processing, practically without sacrificing their informativeness. As a result, the models were trained by machine learning algorithms quickly and efficiently. It should be particularly noted that the architecture of a convolutional neural network was modeled, which allowed to obtain the quality of model training up to 98%. The model turned out to be lightweight and was taken as the basis for training the model to determine emotions in real time. The results of the real-time operation of the model were comparable with the results of the trained model. The developed algorithms can be implemented in the work of mobile operators or banks in the fight against telephone fraud. The article was prepared as part of the state assignment of the Government of the Russian Federation to the Financial University for 2022 on the topic "Models and methods of text recognition in anti-telephone fraud systems" (VTK-GZ-PI-30-2022).

Keywords: emotions, information security, mel-kepstral coefficients, convolutional neural networks, classification, neural network training, machine learning, artificial intelligence, phone fraud, fraud

Abstract: The Internet has emerged as a powerful infrastructure for worldwide communication and human interaction. Some unethical use of this technology spam, phishing, trolls, cyberbullying, viruses caused problems in the development of mechanisms that guarantee affordable and safe opportunities for its use. Currently, many studies are being conducted to detect spam and phishing. The detection of telephone fraud has become critically important, as it entails huge losses. Machine learning and natural language processing algorithms are used to analyze a huge amount of text data. Fraudsters are identified using text mining and can be implemented by analyzing the terms of a word or phrase. One of the difficult tasks is to divide this huge unstructured data into clusters. There are several thematic modeling models for these purposes. This article presents the application of these models, in particular LDA, LSI and NMF. A data set has been formed. A preliminary analysis of the data was carried out and signs were constructed for models in the task of recognizing the subject of the text. The approaches of keyword extraction in the tasks of text topic recognition are considered. The key concepts of these approaches are given. The disadvantages of these models are shown, and directions for improving text processing algorithms are proposed. The evaluation of the quality of the models was carried out. Improved models thanks to the selection of hyperparameters and changing the data preprocessing function.

Keywords: phishing, phone fraud, thematic modeling, NMF, LSI, LDA, text analysis, natural language processing, information security, machine learning

Abstract: In this paper, we consider the problem of developing compact tools that support programming in dynamic transactional memory, implying operational generation of transactional pages, for the Cilk++ language. It is argued that such an implementation requires weakened transaction isolation. The current state of the problem is analyzed. It is noted that the existing solutions are quite cumbersome, although they allow you to work with complex data structures such as lists and trees. It is argued that it is necessary to develop new solutions in the style of minimalism based on the use of specialized classes (generating transactional pages; implementing consistent transactional variables) in combination with a set of keywords characteristic of Cilk++.   Appropriate new solutions are proposed. New syntax elements are introduced, implemented using language extension tools specific to the Planning C platform. The semantics of new language elements is described. It is noted that, unlike analogues, the developed tools allow declaratively to "build" transactions into a network (network schedule of work), which determines the order of execution of transactions and the potential for parallelism that exists at the same time. The proposed approach was tested on the example of the task of constructing a histogram. It is also mentioned about the successful solution, using the developed tools, of the problem of training an artificial neural network by the method of error back propagation and the problem of integer linear programming by the method of branches and boundaries.

Keywords: Cilk++, software transactional memory, managing the order of transactions, language extension, syntactic constructions, class library, object-oriented programming, dynamic transactional memory, programming language, transaction network

Abstract: In the TV and radio industry, the efficiency of the hardware and software of broadcast production complexes is the basis of competitive advantage, and therefore, in the context of global digitalization, television industry companies face the task of constantly improving broadcast production complexes. Such improvement is largely provided by new technologies, some of which are aimed at automating broadcasting processes. In the article, the author analyzes the existing problems of automation of broadcast production complexes and concludes that such problems are caused by the desire of TV and radio companies to replace the traditional approach to the production of programs using a team of specialists with software.   However, the effectiveness of this approach is questionable, especially if the release of the program is associated with live work, where in an uncertain situation it is not possible to react promptly to any events using automated software. Hence, automation of production processes in the field of broadcasting becomes an effective and economical technology only with the correct configuration of the software and careful calculation of automation prospects. The real prospects for automation of broadcast production complexes are currently in almost unexplored areas for the television sphere - the areas of voice control, which is based on artificial intelligence based on various algorithms for training artificial neural networks, which requires additional study and development of an appropriate model adapted to a specific task.

Keywords: digitalization, radio broadcasting, television, TV industry, automation, production of programs, broadcasting and production complex, automation processes, neural networks, voice control

Abstract: One of the main tasks in the management of technological processes is to reduce emergencies and failures of existing equipment. The statistical data obtained during the operation of machines and mechanisms require appropriate mathematical processing to analyze the dynamics of technological processes and establish relationships between deviations, influencing factors and failures. Regression and cluster analyses are convenient tools for processing these data. The failures of cavitation systems are an essential, and at the same time poorly illuminated topic in scientific periodicals. Cavitators are relatively common technical devices that allow maintaining the technological parameters of fuel oil in tank farms at the required level (viscosity, water content, adhesive properties). The practice of using cavitators on fuel oil farms of thermal power plants in the Kaliningrad region shows that these technical devices can fail relatively often.   So, in case of disconnection or restriction of the supply of the required volumes of gas to the thermal power plant, reserves of fuel oil from the fuel park can be used. In turn, the failure of the cavitation system may lead to the impossibility of entering reserve fuel and, as a consequence, to the shutdown of power generation. Thus, the problem of ensuring energy security and the reliability of cavitation systems are closely interrelated. In this study, an array of accumulated statistical information on the parameters of the functioning of cavitators in fuel oil farms and the moments of failure is analyzed. Regression and cluster analyses were used to process the data array, which made it possible to determine the relationship between the types of failures and the influencing factors and to rank the weight of factors according to the degree of their impact on cavitation equipment. Based on the results of mathematical processing and data analysis, proposals have been developed to ensure greater technical reliability of cavitators, reorganize their maintenance system and reduce the number of failures.

Keywords: equipment reliability, equipment failures, analysis of statistical data, cavitation equipment, oil and gas equipment, k-clustering method, least squares method, cluster analysis, regression analysis, wear and tear