Issue 2 (208), article 5


Cybernetics and Computer Engineering, 2022, 2(208)

PANAGIOTIS KATRAKAZAS, Ph.D., Research Area Manager
ORCID: 0000-0001-7433-786X

ILIAS SPAIS, Ph.D., Senior Project Manager
Researcher ID:

Tatoiou 92,
14452, Metamorfosi, Athens, GR


Introduction. Recent infrastructural endeavours in the field of neuroscience aimed at data integration and sharing and availability of research output. This approach recognized that opening experimental results produces significant gains for science advancement. Nonetheless, this leaves a large part of the grassroots neuroscience community underutilized: access to neuroimaging infrastructures remains locally restricted, obstructing data acquisition and the means to investigate novel hypotheses.

Purpose. Within our paper we seek to address this gap by providing the blueprints for a delocalized e-neuroscience centre, opening the access to functional neuroimaging acquisition systems at a pan-European level. This aim will be achieved by building operational interoperability, standardizing, and integrating the services of neuroscience centres across Europe and the development of a virtual environment allowing all European researchers to acquire state-of-the-art neuroimaging data, exploiting the principles of the European Charter for Access to Research Infrastructures

Results. The implementation of all necessary actions for the harmonization and interoperability of the experimental procedures of the labs entail standardization of protocols, procedures in the form of consensus-based guidelines, harmonization of hardware and software set-up and availability across laboratories, as well as adopting of common standards and formats for acquired data and metadata structures.

Conclusion. Consistent and streamlined mobility processes aim to become a blueprint for networking of the overall neuroscience community. The harmonized process framework presented in this paper can facilitate better use from current and future neuroscience projects. Data economies of scale and recruitment streamlining will put local EU and international funds to better use than the now dispersed efforts. This will lead to more successful projects and better pacing for EU neuroscientific communities in the international stage.

Keywords: multi-centre interoperability, operational harmonisation, neuroimaging, sharing infrastructures, open access framework.

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Received 23.05.2022

Issue 2 (208), article 4


Cybernetics and Computer Engineering, 2022, 2(208)

ARALOVA N.I.1, DSc (Engineering), Senior Researcher, 
Senior Researcher of Optimization of Controlled Processes Department,
ORCID 0000-0002-7246-2736,

BELOSHITSKIY P.V.2, DSc (Medicine)
Professor of Biological Faculty
ORCID 0000-0002-6058-3602

ORCID: 0000-0002-4283-6514

ARALOVA A.A.1 PhD (Mathematics)
Researcher of the Department of Methods for Discrete Optimization,
Mathematical Modelling and Analyses of Complex Systems
ORCID 0000-0001-7282-2036

1Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine,
40, Acad.Glushkov av., 03187, Kyiv, Ukraine

2Pavlo Tychyna Uman State Pedagogical University,
2, Sadova str, 20300, Uman, Chercassy distr., Ukraine

3High Altitude Pulmonary and Pathology Institute
HAPPI-IPPA La Paz, Bolivia


Introduction. Currently, as a result of ever-increasing intensity of human activity, unfavorable environment, the need to perform work in various extreme disturbances, significantly increase physical, mental and emotional stress on the human body, leading to pronounced changes in functional systems. Therefore, the task of studying the adaptation of the human body to work in extreme environments is urgent. The work of climbers is a fairly adequate model for studying the combined effects of hypobaric hypoxia and exercise hypoxia. The need to process large amounts of information necessitates the use of modern computer technology that allows the training process in the training of climbers, which would repeatedly, almost in real time to speed up the processing of survey data and accumulate for further use in determining current status and forecasting regulatory reactions of the body to external and internal disturbances

The purpose of the paper is to develop an automated information system of functional diagnostics using the model of regulation of oxygen regimes of the body and its practical application in the study of highly qualified climbers

Methods. Programming methods for creating an automated information system and methods of functional diagnostics.

Results. On the basis of the model of regulation of oxygen regimes of the organism the automated information system for functional diagnostics of the persons who are in the conditions of extreme disturbances is constructed. The results of approbation of the offered software for research of group of highly skilled climbers are resulted.

Conclusions. The proposed software allows you to use a model of oxygen regimes of the body in real time, i.e. repeatedly accelerates the processing of data obtained during the survey of athletes, allows centralized collection of information for its pre-processing, storage and collective use, allows you to compare the basic parameters characterizing the functional respiratory system during natural sports activities and obtained during ergometric loading,

Key words: methods of functional diagnostics, highly qualified climbers, mathematical model of regulation of oxygen regimes of the organism, human adaptation to work in extreme environment, hypoxibritic hypoxia

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Received 19.01.2022

Issue 2 (208), article 3


Cybernetics and Computer Engineering, 2022, 2(208)

BONDAR S.O., PhD student,
Researcher of the Intelligent Control Department
ORCID: 0000-0003-4140-7985

SHEPETUKHA Yu.M., PhD (Engineering), Senior Researcher
Acting Head of the Intelligent Control Department
ORCID: 0000-0002-6256-5248

VOLOSHENYUK D.O., PhD (Engineering),
Senior Researcher of the Intelligent Control Department
ORCID: 0000-0003-3793-7801

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine.
40, Akad. Glushkov av., Kyiv, 03680, Ukraine.


Introduction. Original class of hybrid unmanned aerial vehicles is considered for multitask mission accomplishment at this article. Advantages of such vehicles usage for purposes that are always done by several different agents are considered. Perspective of the position precisioning for different tasks that could be done by unmanned aircrafts is analyzed.

The purpose of the paper is to universalize the process of surveillance, photo and video data collection and other missions that is provided by unmanned aerial vehicles today. The action of data precision during some periods of the misson accomplishment and increased specification for main targets of the mission could demonstrate brand new vector of the unmanned aerial vehicle usage and creation of the brand new domains for the unmanned aerial vehicles. Complex data gathering could help to avoid extra mediators and could simplify data processing on the next stages and also could do such data much more precise.

Results. The usable scenario of route for hybrid unmanned aerial vehicle and the model of it could be a proof of universal multitask unmanned aerial vehicle utilization. Such scenario unites several information missions of different scale and could provide data for several data centers that can use it for defferent problem solving just from one flight. Also it proves that utilization of such aircraft with an additional onboard precision block could be the next step at the mapping and digitalizing domains. Financial analysis of the market is provided for demonstration of the fact that such hybrid aircraft complex system would provide such scale as well as attention to the object details but be much cheaper then mapping and surveillance systems that are already existing.

Conclusion. A need for optimization of some problems that could be achieved by unmanned aerial vehicles leaded to the usage of hybrid vehicles that were represented at the article. Complex design of such an aircraft could be a collateral disadvantage but the whole influence of the hybrid UAV usage for different tasks would optimize a lot more processes, devices and unnecessary equipment that would be needed for a large list of tasks at each domain UAVs are using right now from surveillance to agricultural tasks. Model of different scale purpose universal hybrid unmanned aerial system is a proof of the possibility to use just one single aircraft for a complex mission that needs different set of capabilities, features and equipment. Also such aircraft could provide much more certain results of missions and do it at lower price. Further developments could provide information about the most effective hybrid UAV type for such type of missions and provide game changing rules to the digitalizing and surveillance processes because of the new information gathering way.

Keywords: unmanned aerial vehicle, hybrid vehicle, positioning, multipurpose flight.

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Received 06.04.2022

Issue 2 (208), article 2


Cybernetics and Computer Engineering, 2022, 2(208)

Savchenko-Synyakova Ye.A., PhD (Engineering),
Senior Researcher of the Department for Information
Technologies of Inductive Modeling

International Research and Training Center
for Information Technologies and Systems
of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine


Introduction. Nowadays, the application of machine learning methods and tools is developing very rapidly, given the overall automation and digitalization. The use of machine learning methods and tools for modeling complex processes makes it possible to solve problems that were previously difficult or impossible to solve.

However other methods of mathematical modeling also make it possible to solve the problem of constructing a model based on a sample of experimental data. The task was to compare various scientific areas of artificial intelligence, such as machine learning, mathematical modeling, statistics, data mining and inductive modeling in terms of building mathematical models, to find out what common and distinctive features they have.

The purpose of the paper is a comparative analysis of the areas of mathematical modeling, statistics and machine learning. And also compare the methods of inductive modeling and inductive generation of models.

Results. A comparative analysis of machine learning and other approaches to solving artificial intelligence problems was carried out.

Conclusion. The conducted analysis showed that the machine learning tasks of mathematical (statistical) modeling are close, but not the same, and it is difficult to draw a hard line between them. They can be distinguished by the purpose, the ability to check or interpret the obtained results.

Keywords: machine learning, mathematical modeling, statistics, inductive approach, inductive generation of models.

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Received 22.04.2022

Issue 2 (208), article 1


Cybernetics and Computer Engineering, 2022, 2(208)

RACHKOVSKIJ D.A.1,2, DSc (Engineering),
Chief Researcher, Dept. of Neural Information Processing Technologies,
Visiting Professor, Dept. of Computer Science, Electrical and Space Engineering,

GRITSENKO V.I.1, Corresponding Member of NAS of Ukraine,
 Directorate Adviser,
ORCID ID 0000-0002-6250-3987

VOLKOV O.E.1, PhD (Engineering), 

GOLTSEV A.D.1, PhD (Engineering), Senior Researcher,
Acting Head of the Dept. of Neural Information Processing Technologies,

REVUNOVA E.G.1, DSc (Engineering),
Senior Researcher, Dept. of Neural Information Processing Technologies,

KLEYKO D.3, PhD (Computer Science), Researcher,

LUKOVICH V.V.1 Researcher of the Dept. of Neural Information Processing Technologies
ORCID ID 0000-0002-3848-4712

OSIPOV E.2, PhD (Computer Science), Professor,

1 International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of the MES of Ukraine, 40, Acad. Glushkova av., Kyiv, 03680, Ukraine

2 Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 971 87 Lulea, Sweden

3 RISE Research Institutes of Sweden AB, 164 40 Kista, Sweden


Introduction.Current progress in the field of specialized Artificial Intelligence is associated with the use of Deep Neural Networks. However, they have a number of disadvantages: the need for huge data sets for learning, the complexity of learning procedures, excessive specialization for the training set, instability to adversarial attacks, lack of integration with knowledge of the world, problems of operating with structures known as binding or composition problem. Overcoming these shortcomings is a necessary condition for advancing from specialized Artificial Intelligence to general one, which requires the development of alternative approaches.

The purpose of the paper is to present an overview of research in this direction, which has been carried out at the International Center for 25 years. The approach being developed stems from the ideas of N. M. Amosov and his scientific school. Connections to the Hyperdimensional Computing (HDC) and Vector Symbolic Architectures (VSA) field as well as to current brain research are also provided.

Results. The concept of distributed data representation is outlined, including HDC/VSA that are capable of representing various data structures. The developed paradigm of Associative-Projective Neural Networks is considered: codevector representation of data, superposition and binding operations, general architecture, transformation of data of various types into codevectors, methods for solving problems and applications.

Conclusion. An adequate representation of data is one of the key issues within the Artificial Intelligence. The main area of research reviewed in this article is the problem of representing heterogeneous data in Artificial Intelligence systems in a unified format based on modeling the neural organization of the brain and the mechanisms of thinking. The approach under development is based on the hypothesis of distributed representation of information in the brain and allows representing various types of data, from numeric values to graphs, as vectors of large but fixed dimensionality.

The most important advantages of the developed approach are the possibility of natural integration and efficient processing of various types of data and knowledge, a high degree of parallel computing, reliability and resistance to noise, the possibility of hardware implementation with high performance and energy efficiency, data processing based on associative similarity search — similar to how human memory works. This allows one to unify the methods, algorithms, and software and hardware for Artificial Intelligence systems, increase their scalability in terms of speed and memory with an increase in data volume and complexity.

The research creates the basis for overcoming the shortcomings of current approaches to the specialized Artificial Intelligence based on Deep Neural Networks and paves the way for the creation of Artificial General Intelligence.

Keywords: distributed data representation, associative-projective neural networks, codevectors, hyperdimensional computing, vector symbolic architectures, artificial intelligence.

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Received 17.03.2022