DOI: 10.7256/2454-0714.2023.4.68680

EDN: TCQVHG

Abstract: The subject of the research in this article are the features and regularities of the functioning of systems based on ChatGPT technologies, the knowledge of which makes it possible to formulate appropriate modifications of the Turing test, as well as the features and regularities of the formulation and use of the Turing test for systems based on ChatGPT technologies. The purpose of the study is to identify the features and patterns of functioning of systems based on the technologies of ChatGPT, as well as the features and patterns of formulation and use of the Turing test for systems based on the technologies of Chat GPT. As research methods, the method of social experiment was used, when during the study of a system based on Chat GPT technologies, certain questions were asked, answers were received, the analysis of which allowed us to conclude about the features of the "thinking" of systems based on ChatGPT technologies. In the course of the study, the following was found. Unlike human thinking, which is based on certain facts, the "thinking" of systems based on ChatGPT technologies, in some cases is not based on facts that take place in reality, often the user is given deliberately false information about facts and circumstances that take place in reality. In contrast to human thinking, which is usually systemic in nature, the "thinking" of systems based on ChatGPT technologies is disorderly and fragmentary. Systems based on ChatGPT technologies cannot admit their mistakes, and attempts to force systems based on ChatGPT technologies to critically comprehend their answers lead to a malfunction of these systems. The article also provides a Turing test developed by the author for ChatGPT, which made it possible to identify the features of the "thinking" of systems based on ChatGPT technologies.

DOI: 10.7256/2454-0714.2023.4.68973

EDN: BLOZQY

Abstract: The subject of this study are investment design management tools that allow evaluating the effectiveness of investment project options. The object of the study is digital products (software solutions) designed for automated efficiency assessment and selection of attractive projects for investment. The author has determined that the choice of the most profitable project for investment is the key task of the pre-investment stage of the investment process, while the presence of a large amount of information, the influence of external and internal weakly controlled factors, the state of uncertainty accompanying the investment process actualize the use of software products. Particular attention is paid to identifying and formalizing requirements for software products for risk analysis and evaluation of the effectiveness of investment projects, which will save time and financial resources and eliminate the influence of the human factor on the choice of a project for investment.   The research methodology includes the use of a systematic approach to identifying tools and indicators for evaluating the effectiveness of investment decisions. The author conducted a comparative analysis of software products that act as tools for assessing the attractiveness of investment projects when choosing the most acceptable for the development of commercial activities of industrial enterprises. The results of the comparative analysis of domestic and foreign investment project management software solutions presented on the technology market made it possible to systematize programs, identify their strengths and weaknesses and formulate requirements for an optimal software package for analyzing and evaluating the effectiveness of investment projects of an industrial enterprise. The digital solution developed by the author for risk analysis and evaluation of the effectiveness of investment projects should have the following characteristics: functionality, reliability and stability, interface and usability, compatibility, price and licensing conditions, technical support. Based on the results of the study, the scope of application of the results of the comparative analysis of software products is determined – the further development of digital solutions for evaluating the effectiveness of investment projects, ensuring the effectiveness of the management process of investment design of enterprises.

DOI: 10.7256/2454-0714.2023.4.69011

EDN: FEIPTC

Abstract: The subject of this study is to identify a set of common structural properties inherent in recurrent neural networks and stochastic automata, the feature of which is purposeful behavior in dynamic environments. At the same time, the necessary commonality of properties is revealed both in the process of their functioning and in the process of their training (tuning). The author considers in detail such topics as: formalization of purposeful behavior, consideration of the design of automata, as well as a comparative analysis of the considered designs of automata. From the revealed commonality of functioning and the established one-to-one correspondence of neurons of a fully connected recurrent neural network and states of a probabilistic automaton with a variable structure, it follows that the structure of a tuned stochastic automaton can be considered as a reference for a set of connections of a recurrent neural network. This leads, even at the setup stage, to the removal of redundant states (neurons) and connections between them, based on the parameters of the corresponding automaton. The methodology of the conducted research is the construction of a one-to-one correspondence between the neurons of a fully connected recurrent neural network and the internal states of an automaton with a variable structure and the probabilities of transitions between them that are relevant after the tuning process. With a one-to-one correspondence, the probabilities of transitions of the automaton correspond to the weights of connections between neurons of the optimal configuration. The main conclusions of the study: 1. Comparing the structures of recurrent neural networks and automata with a variable structure allows one to take advantage of an automaton with a variable structure to solve the problem of appropriate behavior in dynamic environments and build a recurrent neural network based on it; 2. The correspondence of the internal structure of a recurrent neural network and an automaton with a variable structure allows already at the training stage to release the trained recurrent neural network from redundant neurons and redundant connections in its structure; 3. Due to the fact that an automaton with a variable structure approaches the optimal automaton with linear tactics for these conditions with nonlinear values of the learning rate, this allows a logical analysis of the structure of the final recurrent neural network.

DOI: 10.7256/2454-0714.2023.4.44113

EDN: HMHCXC

Abstract: The subject of the research is the development of a software package for intelligent forecasting of the execution of government contracts using machine learning methods and analysis of unstructured information. The object of the study is the process of control and decision-making in the field of public procurement, including the selection of contractors, the execution of contracts and the assessment of the timing and cost of their implementation. Special attention in the study is paid to the development and application of interpreted machine learning methods to solve the problems of assessing the risks of choosing an unscrupulous contractor, the risks of non-fulfillment of the contract on time and forecasting the likely timing and cost of contract implementation. The authors consider in detail such aspects as a unique set of data that was collected from various information systems. They have also developed automated data collection and update systems that can be installed on customers' servers. The methods of machine learning, analysis of unstructured information and interpreted methods were used in the work. Interpreted machine learning models were built to assess the risk of choosing an unscrupulous contractor, assess the risk of non-fulfillment of the contract on time, as well as assess the likely timing and cost of contract implementation. A unique set of data was collected in the work, including more than 83 thousand data on more than 190 features from various systems, such as the Unified Information System (UIS) Public Procurement Register, the Register of Unscrupulous Suppliers (RNP) EIS and SPARK Information System. Automated data collection and updating systems have been developed that can be deployed on customer servers. In the course of the study, software packages were developed for intelligent forecasting of the execution of government contracts, which provide an opportunity to conduct a more accurate risk analysis using unstructured information analysis methods, machine learning models and interpreted methods. This makes it possible to increase the effectiveness of monitoring the implementation of government contracts and reduce the likelihood of corruption and violations. The study demonstrates the importance and applicability of machine learning methods and models in the field of public contracts and provides new opportunities for improving control and decision-making processes in the field of public procurement.

DOI: 10.7256/2454-0714.2023.4.68876

EDN: KIUUOL

Abstract: The subject of the research is a logical approach to data analysis and the development of software tools capable of identifying hidden patterns, even with a limited amount of data. The input data consists of indicators of the diagnosis of patients, their diagnoses and the experience of doctors obtained in the course of medical practice. The research method is the development of software tools based on systems of multivalued predicate logic for the analysis of patient data. This approach considers the source data as a set of general rules, among which it is possible to distinguish those rules that are sufficient to explain all the observed data. These rules, in turn, are generative for the area under consideration and help to better understand the nature of the objects under study. The novelty of the study lies in the use of multivalued logic to analyze a limited amount of medical data of patients in order to determine the most likely diagnosis with a given accuracy. The proposed approach makes it possible to detect hidden patterns in the symptoms and results of patient examinations, classify them and identify unique signs of various forms of gastritis. Unlike neural networks, logical analysis is transparent and does not require training on large amounts of data. The conclusions of the study show the possibility of such an approach for diagnosis with a lack of information, as well as the offer of alternatives if the required accuracy of diagnosis is not achieved.

DOI: 10.7256/2454-0714.2023.4.69306

EDN: MZLZMK

Abstract: The scientific research is focused on conducting a study of current artificial neural network architectures in order to highlight the advantages and disadvantages of current approaches. The relevance of the research relies on the growing interest in machine learning technologies and regular improvement of computer vision algorithms.Within the scope of this paper, an analytical study of the advantages and disadvantages of existing solutions has been conducted and advanced SOTA architectures have been reviewed. The most effective approaches to improve the accuracy of basic models have been studied. The number of parameters used, the size of the training sample, the accuracy of the model, its size, adaptability, complexity and the required computational resources for training a single architecture were determined.Prospects for further research in the field of hybridization of convolutional neural networks and visual transformers are revealed, and a new solution for building a complex neural network architecture is proposed.In the framework of the present research work, a detailed analysis of the internal structure of the most effective neural network architectures.Plots of the accuracy dependence on the number of parameters used in the model and the size of the training sample are plotted. The conducted comparative analysis of the efficiency of the considered solutions allowed to single out the most effective methods and technologies for designing artificial neural network architectures. A novel method focused on creating a complex adaptive model architecture that can be dynamically tuned depending on an input set of parameters is proposed, representing a potentially significant contribution to the field of adaptive neural network design.

DOI: 10.7256/2454-0714.2023.4.69171

EDN: NHGRVH

Abstract: The object of the study is information transmission systems. Improving the quality of information on the receiving side is considered as the subject of the study. The authors consider in detail such aspects of the topic as the formation of a procedure that significantly reduces the influence of intersymbol interference caused by the interaction of the signal itself and the sampling window. This source of errors, as a rule, is dominant at information transfer rates close to the bandwidth of the communication channel. Studies on reducing the influence of intersymbol distortions are important and relevant, and in recent years a significant number of works have been devoted to them. A signal model simulating the transmission of a sequence of non-modulated pulses of the meander type was considered. It was assumed that the processing of the incoming pulse stream is carried out in real time based on the procedures of periodic evaluation of each element. The methodological basis of the research was the methods of mathematical modeling of information transmission systems and linearization methods using the generalized superposition principle. The main result of the conducted research is the proposed method of forming a homomorphic filter for processing the incoming pulse stream in real time based on the procedures of periodic evaluation of each element. The algorithm of its functioning ensures the transformation of the multiplicative interaction of the signal and the sampling windows into an additive one and ensures the separation of the multiplicatively interacting transmitted information signal and the sampling window in the communication channel. The resulting procedure, which reduces the effect of intersymbol interference on the receiving side, is an implementation of an optimal filter based on a homomorphic transformation. An estimate of the magnitude of intersymbol interference is obtained when using the proposed processing method. The effectiveness of the method in signal streaming processing is demonstrated. The expressions are obtained in the most general form and can be detailed within the framework of the described information transfer model, which is the subject of further research.

DOI: 10.7256/2454-0714.2023.4.69055

EDN: NJAYIY

Abstract: The article focuses on the process of teaching students technical aspects of video surveillance systems in the course "Technical Means of Security". The main attention is paid to the methods of developing professional competencies related to installation and configuration of equipment, mastering video surveillance software and mastering the application of facial recognition technologies. The article describes laboratory work in detail, starting from the theoretical basis laid at the beginning of the course to practical skills such as connecting cameras, configuring programs and creating databases for identifying individuals. The learning process includes preparation and analysis of theoretical material, performance of laboratory works, as well as testing and evaluation of the obtained results. The result is to provide learners with a comprehensive understanding of video surveillance systems and practical skills relevant for their future careers in security and use in everyday life. The research methodology in the article combines theoretical learning and practical laboratory work. It includes the steps of connecting video monitoring cameras, configuring software and facial recognition algorithms. Students gained experience with real equipment and programs, which contributed to deep learning of the material and development of practical skills. The scientific novelty of this article lies in the integrated approach to teaching students how to use video surveillance systems, including technical aspects of connecting the equipment, configuring software and face recognition algorithms. This approach provides not only theoretical training, but also practical mastery of skills, which is innovative in the context of security technology education programs. The findings of the article emphasize the importance of practical training in student learning. It is shown that real-life experience with equipment and programs significantly improves the quality of education and readiness of students for future professional activities. The article emphasizes that modern education in the field of security systems requires the integration of theoretical knowledge and practical skills, thus providing comprehensive training of specialists in this important and relevant field.