Issue 2 (192), article 2

DOI:https://doi.org/10.15407/kvt192.02.023

Kibern. vyčisl. teh., 2018, Issue 2 (192), pp.

Grytsenko V.I.1,
Corresponding Member of NAS of Ukraine,
Director of 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
e-mail: vig@irtc.org.ua
Gladun A.Y.1,
PhD (Engineering), Senior Researcher of the Department of Complex Research
of Information Technologies and Systems
e-mail: glanat@yahoo.com
Rogushina Y.V.2,
PhD (Phyz&Math), Senior Researcher of the Department of Automated Information Systems
e-mail: ladanandraka2010@gmail.com
1International 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., 03187, Kiev, Ukraine
2Institute of Program Systems of the National Academy of Sciences of Ukraine,
40, Acad. Glushkov av., 03187, Kiev, Ukraine

MODELS AND METHODS OF THE SEMANTIC WIKI RESOURCES USE AS KNOWLEDGE SOURCES FOR RENEWAL OF FORMAL DOMEN ONTOLOGIES

Introduction. The construction and implementation of intelligent systems based on the formalization and reuse of knowledge is a promising direction for the practical application of artificial intelligence methods. The basis of such systems is formalized representations of knowledge about the subject area, for example, in the form of ontology. There remains an open question of the choice of the formal apparatus tools for the construction of ontology.
The purpose of the paper is to develop models of structured representation of knowledge in Wiki-resources on the basis of ontologies and methods of their application for improving and replenishing ontologies of the subject area. The offered approach will allow integrating the current information on changes in the subject area and creating actual ontologies for various applied information technologies using ontologies.
Results. The expediency of using ontologies for presentation of knowledge in systems of artificial intelligence oriented to functioning in the open environment of the Web is considered. The researches connected with the construction of formal ontologies of subject areas and the means of their formalization are analyzed. A formal model of ontology, which specifies the existing approaches, describing in more detail the properties and characteristics of the relations between the main elements of ontology is proposed. An example of using the proposed method in the task of transforming the natural text into a sign language in the system of information support of people with speech and hearing impairments is given.
Conclusions. The paper describes a method for renewal the ontology of a subject area based on the proposed model and the use of semantically-tagged Wiki-resources as a source of knowledge. This provides a dynamic replenishment of the knowledge base of applied intelligent systems. The proposed method of renewal formal ontologies of the subject domain from semantic Wiki-resources provides the expansion of the vocabulary and the construction of its specialized versions for various professional fields or subject areas using external databases. The automatic addition of new words from subject areas is particularly important for developing industries, especially for the IT sector, which has a large number of people with speech and hearing impairments. The proposed approach will improve the quality of life for many people, expanding the boundaries of their communication.

Keywords: formal ontology, ontological languages, formal model of ontology, interpretation of ontologies, semantic Wiki-resources, information system.

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REFERENCES

1 Gruber, T.R. A translation approach to portable ontology specifications. Knowledge Acquisition. 1993. Vol 5. P. 199–220. https://doi.org/10.1006/knac.1993.1008

2 Poli R. Decriptive, Formal and Formalized Ontology. In: Husserl’s Logical Investigations reconsidered, by Denis Fisette (ed.). Dordrecht: Kluwer Academic Publishers, 2003, pp. 183–210. 24p. https://www.ontology.co/essays/descriptive-ontologies.pdf

3 Degen W., Heller B., Herre H., Smith B. GOL: A General Ontological Language. 2002, 63 p. https://www.researchgate.net/publication/2498727_GOL_A_General_Ontological_Language

4 Genesereth V.R., Fikes R.E. Knowledge Interchange Format, Version 3.0, Reference Manual. Logic Group Report Logic-92-1, Computer Science Department, Stanford University. 1992. 68p. https://pdfs.semanticscholar.org/b20c/866025b85f165557235a68143c42d53fa70f.pdf

5 Guarino N. Formal Ontology and Information Systems. In: Formal Ontology in Information Systems. Proceedings of FOIS’98, by N. Guarino (ed.). Trento. Italy, Amsterdam, IOS-Press. 1998. P. 3–15. https://klevas.mif.vu.lt/~donatas/Vadovavimas/Temos/OntologiskaiTeisingasKoncepcinisModeliavi-mas/papildoma/Guarino98-Formal%20Ontology%20and%20Information%20Systems.pdf

6 Vasyukov V.L. Formal ontology and artificial intelligence (monograph). Moscow: IF RAS, 2006. 140 p. (in Russian)

7 Rogushina Yu.V., Gladun A.Ya. Mereological aspects of the ontological analysis of intelligent Web-services. Proceedings of the VII International Conference “Intellectual Analysis of Information” IAI-2007, 12-14 Mai 2007, Kyiv. — P. 312–321. (in Russian)

8 Kotarbi!ǹ!sky T. Elements of the theory of cognition, formal logic and methodology of sciences. Lviv, 1929. 232p.

9 9. Wille R., Ganter B. Formale Begriffsanalyse. Berlin-Heidelberg: Springer-Verlag, 1996, 192 p.

10 Ganter B., Stumme G., Wille R., eds. Formal Concept Analysis: Foundation and Application. Lecture Notes in Artificial Intelligence. 2005, No. 3626. 349 p. https://link.springer.com/content/pdf/bfm%3A978-3-540-31881-1%2F1.pdf

11 Lammari N., Metais E. Building and maintaining ontologies: a set of algorithms. Data Knowledge Engineering, 2004, No. 48 (2), P 155–176.

12 Guarino N. Formal Ontology and Information Systems. In: Ontology in Information Systems. Proceedings of FOIS-08, Trento. Italy, by N. Guarino (ed.), Amsterdam, IOS-Press, 2009. 340 p. http://mba.eci.ufmg.br/downloads/recol/FormalOntologyinInforma-tionSystems2008.pdf

13 Rogushina Yu.V., Priyma S.M, Strokan O.V. Creating and using semantic Wiki-resources: tutorial. Melitopol, FOP Odnorog T.V., 2017. 169 p. (in Ukrainian).

14 Anisimov A.V., Lyman K.S., Marchenko A.A. Methods for computing measures of semantic proximity of natural language words. Artificial Intelligence, 2009, No 3. P. 612–617. (in Russian)

15 15. Lozynska O., Davydov M. Information technology for Ukrainian Sign Language translation based on ontologies. An International quarterly journal ECONTECHMOD, 2015, Vol. 04, No. 2, P. 13–18.

16 16. Gladun A.Ya. Khala K.O. Standard DSTU ISO/IEC 2382:2018 Information Technologies. Dictionary. Kyiv: UkrNDNC, 2018. 526 p. (in Ukrainian).

17 Anisimov A., Marchenko O., Taranukha V., Vozniuk T. Semantic and Syntactic Model of Natural Language Based on Non-negative Matrix and Tensor Factorization. Proceedings of the International Conference on Natural Language Processing, 2014, Springer, Cham. P. 177–184.

18 Gladun A.Ya., Rogushina Yu.V. Ontology Repository as a Tool for Reusing Knowledge for Recognizing Information Objects. Ontology of Design, No 1, 2013. P. 35–49. (in Russian).

19 Gladun A.Ya., Rogushina Yu.V. Semantic Technologies: Principles and Practices. Kyiv: Universarium, 2016. 387 p. (in Ukrainian).

20 Gladun A.Ya., Rogushina Yu.V. Bases of Methodology of Formation of Thesauruses with Use Ontologic and the Mereologic Analysis. Artificial Intelligence, 2008, No 5. P. 112–124. (in Ukrainian).

Received 02.04.2018

Issue 1 (191), article 3

DOI:https://doi.org/10.15407/kvt191.01.045

Kibern. vyčisl. teh., 2018, Issue 1 (191), pp.

Grytsenko V.I., Corresponding Member of NAS of Ukraine,
Director of 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
e-mail: vig@irtc.org.ua
Volkov О.Y., Senior Researcher,
Intellectual Control Department
e-mail: alexvolk@ukr.net
Komar M.M., Researcher,
Intellectual Control Department
e-mail: nickkomar08@gmail.com
Bogachuk Y.P., PhD (Engineering), Senior Researcher,
Intellectual Control Department
e-mail: dep185@irtc.org.ua
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., 03187, Kiev, Ukraine

INTELLECTUALIZATION OF MODERN SYSTEMS OF AUTOMATIC CONTROL OF UNMANNED AERIAL VEHICLES

Introduction. The article discusses the actual questions of the need of creation of modern systems of automatic control of unmanned aerial vehicle (UAV) and describes new methods of its intellectualization. Today’s development of information technology requires accelerated development of the theory of intellectual control and the theory of systemic information technology. New technologies of intellectual control are extremely important for solving the problems of modern unmanned aviation.
The purpose of the article is to solve the issues of the development of the control system of UAV and to provide a number of measures aimed to ensuring its intellectualization. The approach considered in the article is based on the theory of high-precision remote control of dynamic objects and on the complex interaction of methods of theory of invariance, adaptive control and intellectualization of processes of UAV control.
Results. The development and implementation of control algorithms using functional program modules written in modern high-level programming languages in the computer environment based on microprocessors with a computing power sufficient to implement these algorithms in the form of a unified hardware and software complex of the integrated avionics.
The expansion of the range of functional capabilities of UAV control system that is offered to supplement the developed channels and algorithms of autopilot by the methods of intellectualization.
Conclusions. It is shown that combining the developed control laws for UAV autopilot into a unified hardware and software complex of integrated avionics and supplementing them with the proposed components of intellectualization will create a synergy effect and ensure the effectiveness and sustainability of the process of controlling the motion of the UAV.

Keywords: unmanned aerial vehicle, control system, invariance, intellectualization,

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REFERENCES

  1. Krasil’shchikovM.N., SerebryakovG.G.Modern information technologies in the tasks of navigation and guidance of unmanned maneuverable aircrafts. Moscow: FIZMATLIT, 2009. 556 p. (in Russian).
  2. Kharchenko V.P., Chepizhenko V.I., Tunik A.А., Pavlova S.V. Avionic-sofunmannedaerialvehicles. Kyiv: Abris-Print, 2012. 464 p. (in Ukrainian)
  3. Fedosov E.A., Bobronnikov V.T., Kukhtenko V.I. Dynamic design of control systems for automatic maneuverable aircrafts. Moscow: Mashinostroyeniye, 1997. 336 p. (In Russian).
  4. Pavlova S., Komar M. The Invariant Adaptation of the Aircraft Control System in Emergency Situation During the Flight. ProceedingoftheNationalAviationUniversity. 2016. № 4(69). P. 28–33.
  5. Fahlstrom P., Gleason T. Introduction to UAV systems. Hoboken: Wiley, 2012. 4th ed. 308 p.
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  7. Austin R. Unmannedaircraftsystems. UAVsdesign, developmentanddeployment. JohnWiley&Sons, 2010. 372 p.
  8. Randal W. Beard, Timothy W. McLaine Small unmanned aerial vehicles: theory and practice. Moscow: TEKHNOSFERA, 2015. 312 c.
  9. AlyoshinB.S.Orientationandnavigationofmobileobjects: moderninformationtechnologies. Moscow: FIZMATLIT, 2006. 424 p. (In Russian).
  10. Volkov A.E., Pavlova S.V. Modelingoftheinvariantmethodforresolvingthedynamic-conflictsofaircraft. Cyberneticsandsystemsanalysis. 2017. № 53 (4). P. 105–112 (In Russian).
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Received 27.12.2017

Issue 4 (190), article 1

DOI:https://doi.org/10.15407/kvt190.04.005

Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.

Grytsenko V.I., Corresponding Member of NASU of Ukraine,
Director of International research and training
center for Information technologies and systems
of the NASU and MESU
e-mail: vig@irtc.org.ua
Onyshchenko I.M., PhD (Economics),
Senior Researcher of the Department of Economic and Social
Systems and Information Technologies
e-mail: standardscoring@gmail.com
International research and training center for Information
technologies and systems of the NASU and MESU
,
40, Ave Glushkov, 03680, Kiev, Ukraine

DETERMINING THE INFORMATIVITY OF PARAMETERS IN A PROGNOSTIC MODEL FOR EVALUATING THE PROBABILITY OF PRODUCT SELECTION IN THE CONDITIONS OF “BIG DATA”

Introduction. Fast growth of collected and stored data due to IT bumming caused a problem called “Big Data Problem”. Most of the new data are unstructured and this is the core reason why traditional relational data warehouse are so inefficient to deal with “Big Data”. Predicting and modeling based on “Big Data” also can be problematic because of high volume and velocity. To avoid some problems online learning algorithms can be successful for high-load systems.
The purpose of the article is to develop an approach to feature selection and modeling in case of “Big Data” with using online learning algorithm.
Method. Online learning algorithm for FTRL (Follow-The-Regularized-Leader) model with L1 and L2 regularization to select only important features was used.
Results. The approaches of modeling in cases of using batch and online learning algorithms are described on the example of online auction system. The online learning algorithm has very strong preferences in case of high load and high velocity. Mathematical background for modification of linear discriminator of FTL (Follow-The-Leader) model with adding regularization was described. L1 and L2 regularization allows us to select important features in real time. If the feature becomes useless, the regularization will set the corresponding coefficient equal to 0. But it does not remove the feature from training process and the coefficient can be restored with some value in case of its importance for model. The full process is prepared as a program in Python and can be used in practice.
The results may be applied for modeling and predicting in projects with high volume or velocity of data for example — social networks, online auctions, online gaming, recommendation systems and others.
The results may be applied for modeling and forcasting in projects with high volume or velocity of data, for example — social networks, online auctions, online gaming, recommendation systems and others .
Conclusions. FTRL model to work as online learning algorithm that allows to predict binary outcomes in high load “Big Data” systems was modified.
Getting into account that number of predictors can be enormous it takes much computing resources, time and make the process difficult. This feature selection problem was solved with using L1 regularization. The selection procedure was added to modified online learning FTRL model. L1 regularization to score the importance of predictors in real time was used.
A program that runs described mathematical algorithm was developed. Note that the algorithm effectively works with sparse matrices by analyzing incoming data and updating weights only for predictors that are presented. The algorithm has L1 and L2 regularization features that may be used for feature selection and avoid overfitting.
Keywords: information technologies in economics, economical and mathematical modeling, online learning algorithms, regularization, Big Data.

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REFERENCES

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6 H Brendan McMahan. Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization. International Conference on Artificial Intelligence and Statistics, pages 525–533, 2011.

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14 Hrytsenko V.I. Zastosuvannia instrumentiv Big Data dlia pidvyshchennia efektyvnosti onlain reklamy. Ekonomiko-matematychne modeliuvannia sotsialno-ekonomichnykh system. Vypusk 21. — Kyiv, 2016. P 5–21 (in Ukrainian).

15 Big Data — Wikipedia. [Elektronnyi resurs] — Rezhym dostupu: https://en.wikipedia.org/wiki/Big_data

16 Chto takoe Real-Time Bidding. [Elektronnyi resurs] — rezhym dostupu: http://konverta.ru/how (in Russian).

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18 Riedman J. H. Regularization paths for generalized linear models via coordinate descent / Riedman J. H., Hastie T., Tibshirani R. / Journal of Statistical Software. 2010. Vol. 33, no. 1. pp. 1–22

19 L1- y L2-rehuliaryzatsyia v mashynnom obuchenyy. [Elektronnyi resurs] — rezhym dostupu: https://msdn.microsoft.com/uk-ua/magazine/dn904675.aspx (in Russian).

20 L1-rehuliaryzatsyia lyneinoi rehressyy. Rehressyia naymenshykh uhlov (alhorytm LARS). [Elektronnyi resurs] — rezhym dostupu: chrome-extension: //ecnphlgnajanjnkcmbpancdjoidceilk/content/web/viewer.html?source=extension_pdfhandler &file=http%3A%2F%2Fwww.machinelearning.ru%2Fwiki%2Fimages%2F7%2F7e%2F VetrovSem11_LARS.pdf (in Russian).

Received 28.09.2017

Issue 2 (188), article 6

Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.

80TH ANNIVERSARY OF CORRESPONDING MEMBER OF NAS OF UKRAINE VLADIMIR ILYICH GRITSENKO

May 23, 2017 the 80th anniversary of Vladimir Ilyich Gritsenko, known scientist in computer science, information technologies and its applications in economics, industrial and technological field, biological and medical cybernetics, computer technology training, director of the International Scientific and Training Center for Information Technologies and Systems. He is an initiator of development of a new class of high technologies — intelligent information technologies. Gritsenko V.I. is a member of a number of leading international and state councils of Ukraine on informatics, Permanent Representative of Ukraine to the Council of UNESCO Intergovernmental Programme on the information and communications, heads the UNESCO Chair “New Information Technologies in Education for All”, the chief editor of the scientific journals “Control Systems and Machines” and “Cybernetics”.

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Issue 2 (188), article 1

DOI:https://doi.org/10.15407/kvt188.02.005

Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.

Grytsenko V.I., Corresponding Member of NAS of Ukraine, Director
e-mail: vig@irtc.org.ua
International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of Ministry of Education and Science of Ukraine,
av. Acad. Glushkova, 40, Kiev, 03680, Ukraine

Rachkovskij D.A., Doctor of Engineering, Leading Researcher,
Dept. of Neural Information Processing Technologies,
e-mail: dar@infrm.kiev.ua
International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of Ministry of Education and Science of Ukraine,
av. Acad. Glushkova, 40, Kiev, 03680, Ukraine

Frolov A.A., Doctor of Biology, Professor,
Faculty of Electrical Engineering and Computer Science FEI,
e-mail: docfact@gmail.com
Technical University of Ostrava, 17 listopadu 15, 708 33 Ostrava-Poruba, Czech Republic

Gayler R., PhD,
Independent Researcher,
r.gayler@gmail.com
Melbourne, VIC, Australia

Kleyko D., PhD post graduated,
Department of Computer Science, Electrical and Space Engineering,
denis.kleyko@ltu.se
Lulea University of Technology, 971 87 Lulea, Sweden

Osipov E., PhD, Professor,
Department of Computer Science, Electrical and Space Engineering,
evgeny.osipov@ltu.se
Lulea University of Technology, 971 87 Lulea, Sweden

NEURAL DISTRIBUTED AUTOASSOCIATIVE MEMORIES: A SURVEY.

Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension.

The purpose of the paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons).

Scope. The survey is focused mainly on the networks of Hopfield, Willshaw, and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections, and networks with a bipartite graph structure for non-binary data with linear constraints.

Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.

Keywords: distributed associative memory, sparse binary vector, Hopfield network, Willshaw memory, Potts model, nearest neighbor, similarity search

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Recieved 15.04.2017

Issue 1 (187), article 1

DOI: https://doi.org/10.15407/kvt187.01.005

Kibern. vyčisl. teh., 2017, Issue 1 (187), pp.5-11

Grytsenko V.I., Corresponding Member of NAS of Ukraine, Director of International
Research and Training Center for Information Technologies and Systems of National
Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine

e-mail: vig@irtc.org.ua

20 YEARS OF THE INTERNATIONAL RESEARCH AND TRAINING CENTER FOR INFORMATION TECHNOLOGIES AND SYSTEMS

May 5, 1997 the International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine was established by National Academy of Sciences of Ukraine.

During 20 years new scientific direction — Intelligent Information Technology (IIT), was formed. This methodology, the software and hardware became the basis for the deve-lopment of IIT of imaginative thinking, neural network technology, IIT for digital medicine, the E-education and intelligent control technologies.

The basic directions of fundamental and applied scientific research in the International Center are: creation of intelligent information technologies based on methods and means of imaginative thinking, comprehensive research of problems of intelligent management, intelligent robotics, digital medicine, e-learning, digital information space and technologies for the development of a secure information society.

By the main directions of the International Center, scientific schools in the field of information technologies and systems, technical cybernetics, biological and medical cybernetics, and mathematical analysis of comprehensive economic systems have been formed. An important contribution to the development of these scientific schools was made by outstanding Ukrainian scientists — academicians V.I. Skurikhin, A.G. Ivakhnenko,
N.M. Amosov and A.A. Bakaev. Their students and followers successfully develop these scientific directions in our country and abroad.

The International Center is the initiator of research and development of the concept of a new class of information technologies — intelligent information technologies. These are special, knowledge-intensive information technologies that differ from the known IT in the new quality — operating images of information objects. At the same time, an understanding of human speech, recognition of real and artificially created objects, active interaction with the environment, revealing the essence of the phenomenon, operating knowledge and the choice of strategy and tactics for achieving the set goal are achieved through the contours of intellectual IT.

Technical Committee for Standardization of information technologies, scientific journals “Control Systems and Computers” and “Cibernatics and Computer Engineering”, presentations of our scientists at prestigious international conferences, symposia and exhibitions make an important contribution for increasing the authority of the International Center.

The International Center has formed a program of work for the nearest years and defined the mechanisms for its implementation in the context of the rapid development of intellectualization of information technologies in all spheres of our society. As the comprehensive analysis showed, this program fully corresponds to global trends that the term “digital transformation” characterizes and covers the research priorities in information technology for a period of 5–10 years.

Keywords: intelligent information technology, imaginative thinking, intelligent management, digital medicine, e-learning, robotics, information society.

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