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Issue 2 (208), article 5

DOI:https://doi.org/10.15407/kvt208.02.082

Cybernetics and Computer Engineering, 2022, 2(208)

PANAGIOTIS KATRAKAZAS, Ph.D., Research Area Manager
ORCID: 0000-0001-7433-786X
e-mail: p.katrakazas@zelus.gr

ILIAS SPAIS, Ph.D., Senior Project Manager
Researcher ID: https://www.semanticscholar.org/author/I.-Spais/1885927
e-mail: ilias.spais@zelus.gr

Zelus,
Tatoiou 92,
14452, Metamorfosi, Athens, GR

BLUEPRINTS ELICITATION FRAMEWORK FOR AN OPEN ACCESS
PAN-EUROPEAN NEURO-IMAGING ONLINE CENTRE

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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REFERENCES

1. Ravindranath V. et al., ‘Regional research priorities in brain and nervous system disorders’, Nature, vol. 527, no. 7578, Art. no. 7578, Nov. 2015, doi: 10.1038/nature16036.

2. GBD 2015 Neurological Disorders Collaborator Group, ‘Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015’, Lancet Neurol., vol. 16, no. 11, pp. 877–897, Nov. 2017, doi: 10.1016/S1474-4422(17)30299-5.

3. Vos T. et al., ‘Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013’, The Lancet, vol. 386, no. 9995, pp. 743–800, Aug. 2015, doi: 10.1016/S0140-6736(15)60692-4.

4. De Domenico M., Granell C., Porter M.A., and Arenas A., ‘The physics of spreading processes in multilayer networks’, Nat. Phys., vol. 12, no. 10, pp. 901–906, Oct. 2016, doi: 10.1038/nphys3865.

5. Battiston F., Nicosia V., and Latora V., ‘The new challenges of multiplex networks: Measures and models’, Eur. Phys. J. Spec. Top., vol. 226, no. 3, pp. 401–416, Feb. 2017, doi: 10.1140/epjst/e2016-60274-8.

6. Paraskevopoulos E., Chalas N., Anagnostopoulou A., and Bamidis P.D., ‘Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise’, Sci. Rep., vol. 12, no. 1, Art. no. 1, May 2022, doi: 10.1038/s41598-022-12158-9.

7. Mantzavinos V. and Alexiou A., ‘Biomarkers for Alzheimer’s Disease Diagnosis’, Curr. Alzheimer Res., vol. 14, no. 11, pp. 1149–1154, 2017, doi: 10.2174/1567205014666170203125942.

8. Larson-Prior L.J. et al., ‘Adding dynamics to the Human Connectome Project with MEG’, NeuroImage, vol. 80, pp. 190–201, Oct. 2013, doi: 10.1016/j.neuroimage.2013.05.056.

9. Dottori M. et al., ‘Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing’, Sci. Rep., vol. 7, no. 1, Art. no. 1,
Jun. 2017, doi: 10.1038/s41598-017-04204-8.

10. Iakovidou N.D., ‘Graph Theory at the Service of Electroencephalograms’, Brain Connect., vol. 7, no. 3, pp. 137–151, Apr. 2017, doi: 10.1089/brain.2016.0426.

11. Shabir M.Y., Iqbal A., Mahmood Z., and Ghafoor A., ‘Analysis of classical encryption techniques in cloud computing’, Tsinghua Sci. Technol., vol. 21, no. 1, pp. 102–113, Feb. 2016, doi: 10.1109/TST.2016.7399287.

12. Lingwei S., Fang Y., Ru Z., and Xinxin N., ‘Method of secure, scalable, and fine-grained data access control with efficient revocation in untrusted cloud’, J. China Univ. Posts Telecommun., vol. 22, no. 2, pp. 38–43, Apr. 2015, doi: 10.1016/S1005-8885(15)60637-9.

13. Lee K., Lee D.H., and Park J.H., ‘Efficient revocable identity-based encryption via subset difference methods’, Des. Codes Cryptogr., vol. 85, no. 1, pp. 39–76, Oct. 2017, doi: 10.1007/s10623-016-0287-3.

14. Pasupuleti S.K. and Varma D., ‘Chapter 5 — Lightweight ciphertext-policy attribute-based encryption scheme for data privacy and security in cloud-assisted IoT’, in Real-Time Data Analytics for Large Scale Sensor Data, vol. 6, H. Das, N. Dey, and V. Emilia Balas, Eds. Academic Press, 2020, pp. 97–114. doi: 10.1016/B978-0-12-818014-3.00005-X.

15. Warsinske J. et al., The Official (ISC)2 Guide to the CISSP CBK Reference. John Wiley & Sons, 2019.

16. Zhou L., Fu A., Yu S., Su M., and Kuang B., ‘Data integrity verification of the outsourced big data in the cloud environment: A survey’, J. Netw. Comput. Appl., vol. 122, pp. 1–15, Nov. 2018, doi: 10.1016/j.jnca.2018.08.003.

17. Khraisat A., Gondal I., Vamplew P., and Kamruzzaman J., ‘Survey of intrusion detection systems: techniques, datasets and challenges’, Cybersecurity, vol. 2, no. 1, p. 20, Jul. 2019, doi: 10.1186/s42400-019-0038-7.

18. Nor’a M.N.A. and Ismail A.W., ‘Integrating Virtual Reality and Augmented Reality in a Collaborative User Interface’, Int. J. Innov. Comput., vol. 9, no. 2, Art. no. 2, Nov. 2019, doi: 10.11113/ijic.v9n2.242.

19. Sarasvuo S., Rindell A., and Kovalchuk M., ‘Toward a conceptual understanding of co-creation in branding’, J. Bus. Res., vol. 139, pp. 543–563, Feb. 2022, doi: 10.1016/j.jbusres.2021.09.051.

20. Saarijärvi H., ‘The mechanisms of value co-creation’, J. Strateg. Mark., vol. 20, no. 5, pp. 381–391, Aug. 2012, doi: 10.1080/0965254X.2012.671339.

21. Rizzo F., Deserti A., and Komatsu T., ‘Implementing social innovation in real contexts’, Int. J. Knowl.-Based Dev., vol. 11, no. 1, pp. 45–67, 2020.

22. Frow P., Nenonen S., Payne A., and Storbacka K., ‘Managing Co-creation Design:
A Strategic Approach to Innovation’, Br. J. Manag., vol. 26, no. 3, pp. 463–483, 2015, doi: 10.1111/1467-8551.12087.

23. Martínez-Cañas R., Ruiz-Palomino P., Linuesa-Langreo J., and Blázquez-Resino J.J., ‘Consumer Participation in Co-creation: An Enlightening Model of Causes and Effects Based on Ethical Values and Transcendent Motives’, Front. Psychol., vol. 7, 2016, Accessed: May 21, 2022. [Online]. Available: https://www.frontiersin.org/article/10.3389/fpsyg.2016.00793

24. Jansma S.R., Dijkstra A.M., and de Jong M.D.T., ‘Co-creation in support of responsible research and innovation: an analysis of three stakeholder workshops on nanotechnology for health’, J. Responsible Innov., vol. 9, no. 1, pp. 28–48, Jan. 2022, doi: 10.1080/23299460.2021.1994195.

Received 23.05.2022

Issue 2 (208), article 4

DOI:https://doi.org/10.15407/kvt208.02.060

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,
e-mail: aralova@ukr.net

BELOSHITSKIY P.V.², DSc (Medicine)
Professor of Biological Faculty
ORCID 0000-0002-6058-3602
e-mail: bilosh827@ukr.net

ZUBIETA-CALLEJA G.³ M.D. Professor
Director
ORCID: 0000-0002-4283-6514
e-mail: zubieta@altitudeclinic.com

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
email: aaaralova@gmail.com

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

AUTOMATED INFORMATION SYSTEM FOR THE EVALUATION OF CLIMBERS’ PERFORMANCE UNDER CONDITIONS OF EXTREMELY LOW pO₂ OF INHALED AIR

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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REFERENCES

1. Sirotinin N.N. Living on the heights and ailments on the heels. Kiev: Edition of the Academy of Sciences of the URS.1939, 222 p. (In Ukrainian)

2. Biloshytsky P.V. Hypoxia. Encyclopedia of Successful Ukraine. Kyiv: Institute of Encyclopedic Research NAS of Ukraine. 2006, 5, pp. 625-626. (In Ukrainian)

3. Beloshitskiy P.V. Study of the problems of sports medicine at the Elbrus medical and biological station. Sports medicine. 2008, 1, pp. 83-94. (In Russian)

4. Kolchinskaya A.Z. On the classification of hypoxic conditions. Patol. physiology and experimental therapy. 1981, no. 4, pp. 3–10. (In Russian)

5. Beloshitskiy P.V. Chronicle of biomedical research in the Elbrus region (1929 – 2006). Kyiv. 2014, Ukrainian Academy of Sciences, 550 p. (In Russian)

6. Seredenko M.M., Nazarenko A.I, Rozova. K.V. The capital of hypoxia. Kyiv, 2002, Print line. 24 p (In Ukrainian)

7. First world congress of high altitude medicine and physiology.La Paz-Bolivia, 1994.

8. Second world congress of high altitude medicine and physiology. Acta Andina. 1996. Cusco-Peru.

9. Materials to the II Congress of Pathophysiologists of Ukraine, dedicated to the 100th anniversary of the day of the Birth Day of Ac. M.M. Syrotinin. Physiol. journal. v.42,no 3-4. (In Ukrainian)

10. The 3rd World Congress on Mountain Medicine and High Altitude Physiology and the 18th Japanese Symposium on Mountain Medicine, May 20th-24th. 1998, Matsumoto, Japan.

11. IV World Congress on Mountain Medicine and High Altitude Physiology. High Altitude medicine and biology. 2000, 1, N3, Arica, Chile.

12. V World Congress on Mountain Medicine and High Altitude Physiology. High Altitude medicine and biology. 2002, 3, N1. Barcelona, Spain.

13. Milledge JS. VI World Congress on Mountain Medicine & High Altitude Physiology, Xining, Qinghai, and Lhasa, Tibet, August 12-18, 2004. High Alt Med Biol. 2004 Winter;5(4):457-64. doi: 10.1089/ham.2004.5.457.

14. VI World Congress on Mountain Medicine and High Altitude Physiology, Xining, Qinghai, and Lhasa, Tibet, August 12-18, 2004. High Altitude medicine and biology, 2004, 5, N2.

15. Anholm JD. The VIIIth World Congress of High Altitude Medicine and Physiology, Arequipa, Peru, August 8-12, 2010, High Alt Med Biol. 2010 Winter;11(4):381-4. doi: 10.1089/ham.2010.1061.

16. West J. B. World Congresses of Mountain Medicine and High Altitude Physiology, 1994–2002 High Altitude Medicine & Biology. V. 3, No. 1, Published Online: 6 Jul 2004

17. 7th Chronic Hypoxia Symposium, Feb 23 – Mar 2. 2019 La Paz – Bolivia. Dedicated to the Late Danish Prof. PoulErik Paulev P. 24. https://zuniv.net/ symposium7/Abstracts7CHS.pdf

18. Catron T.F., Powell F.L., West J.B. A strategy for determining arterial blood gases on the summit of Mt. Everest. BMC Physiol. 2006 Mar 8;6:3. doi: 10.1186/1472-6793-6-3.

19. West J.B., Hackett P.H., Maret K.H. Milledge J.S., Peters R.M. Jr., Pizzo C.J., Winslow R.M. Pulmonary gas exchange on the summit of Mount Everest. J Appl Physiol Respir Environ Exerc Physiol. 1983 Sep;55(3):678-87.

20. Malconian M.K., Rock P.B., Reeves J.T., Cymerman A., Houston C.S. Operation Everest II: gas tensions in expired air and arterial blood at extreme altitude. Aviat Space Environ Med. 1993 Jan;64(1):37-42.

21. West J.B. Lactate during exercise at extreme altitude. Fed Proc, 1986 Dec;45(13):2953-7.

22. West J.B. Tolerance to severe hypoxia: lessons from Mt. Everest. Acta Anaesthesiol Scand Suppl. 1990;94:18-23.

23. West J.B. Acclimatization and tolerance to extreme altitude. J Wilderness Med. 1993 Feb;4(1):17-26. doi: 10.1580/0953-9859-4.1.17.

24. West J.B. Limiting factors for exercise at extreme altitudes. Clin Physiol. 1990 May;10(3):265-72. doi: 10.1111/j.1475-097x.1990.tb00095.x.

25. West J.B. Climbing Mt. Everest without oxygen: an analysis of maximal exercise during extreme hypoxia. Respir Physiol. 1983, Jun;52(3):265-79. doi: 10.1016/0034-5687(83)90085-3.

26. Wagner P.D., Wagner H.E., Groves B.M., Cymerman A., Houston C.S. Hemoglobin P(50) during a simulated ascent of Mt. Everest, Operation Everest II. High Alt Med Biol. 2007 Spring;8(1):32-42. doi: 10.1089/ham.2006.1049.

27. Wagner P.D. Operation Everest II. High Alt Med Biol. 2010 Summer;11(2):111-9. doi: 10.1089/ham.2009.1084.

28. Samaja M., Mariani C., Prestini A., Cerretelli P. Acid-base balance and O2 transport at high altitude. Acta Physiol Scand. 1997 Mar;159(3):249-56. doi: 10.1046/j.1365-201X.1997.574342000.x.

29. Hoiland R.L., Howe C.A., Coombs G.B., Ainslie P.N. Ventilatory and cerebrovascular regulation and integration at high-altitude. Clin Auton Res. 2018 Aug;28(4):423-435. doi: 10.1007/s10286-018-0522-2.

30. Bärtsch P., Saltin B. General introduction to altitude adaptation and mountain sickness. Scand J Med Sci Sports. 2008 Aug;18 Suppl 1:1-10. doi: 10.1111/j.1600-0838.2008.00827.x.

31. Grocott M.P., Martin D.S., Levett D.Z., McMorrow R., Windsor J., Montgomery H.E.; Caudwell Xtreme Everest Research Group. Arterial blood gases and oxygen content in climbers on Mount Everest. N Engl J Med. 2009, Jan 8;360(2):140-9. doi: 10.1056/NEJMoa0801581.

32. Zubieta-Calleja G.R., Paulev P.E., Zubieta-Calleja L., Zubieta-Castillo G. Altitude adaptation through hematocrit changes. J Physiol Pharmacol. 2007, Nov;58 Suppl 5(Pt 2):811-8. PMID: 18204195.

33. Zubieta-Calleja G.R., Ardaya G., Zubieta N., Paulev P.E., Zubieta-Castillo G. Tolerance to Hypoxia [Internet]. Vol. 59, J Fisiol. 2013. p. 65–71. URL: https://zuniv.net/pub/TolerancetoHypoxiaFiziol.pdf

34. Paulev P.E., Zubieta-Calleja G.R. Essentials in the diagnosis of acid-base disorders and their high altitude application. J Physiol Pharmacol. 2005 Sep;56 Suppl 4:155-70. PMID: 16204789.

35. Zubieta-Calleja G., Zubieta-DeUrioste N. The Oxygen Transport Triad in High-Altitude Pulmonary Edema: A Perspective from the High Andes. Int J Environ Res Public Health, 2021 Jul 17;18(14):7619. doi: 10.3390/ijerph18147619. PMID: 34300070; PMCID: PMC8305285.

36. Zubieta-Castillo G., Zubieta-Calleja G.R., Zubieta-Calleja L., Zubieta-Calleja Nancy. Adaptation to life at the altitude of the summit of Everest. Physiol. Journal, 1994.

37. Schoene R.B. Limits of human lung function at high altitude. J Exp Biol. 2001 Sep;204(Pt 18):3121-7.

38. Schoene R.B. Limits of respiration at high altitude. Clin Chest Med. 2005, Sep;26(3):405-14, vi. doi: 10.1016/j.ccm.2005.06.015.

39. Hawkins M.N. Raven P.B., Snell P.G., Stray-Gundersen J., Levine B.D. Maximal oxygen uptake as a parametric measure of cardiorespiratory capacity. Med Sci Sports Exerc. 2007 Jan;39(1):103-7. doi: 10.1249/01.mss.0000241641.75101.64.

40. Noakes T.D. Maximal oxygen uptake as a parametric measure of cardiorespiratory capacity: comment. Med Sci Sports Exerc. 2008 Mar;40(3):585; author reply 586. doi: 10.1249/MSS.0b013e3181617350.

41. Bassett D.R. Jr., Howley E.T. Limiting factors for maximum oxygen uptake and determinants of endurance performance. Med Sci Sports Exerc. 2000 Jan;32(1):70-84. doi: 10.1097/00005768-200001000-00012

42. Feitosa W. G., Barbosa T. M., Correia R. De A., Castro F. A. De S. Maximal oxygen uptake, total metabolic energy expenditure, and energy cost in swimmers with physical disabilities. International Journal of Performance Analysis in Sport . 2019, Volume 19, Issue 4, pp. 503-516.

43. Kang M.-Y., Sapoval B. Prediction of maximal oxygen uptake at high altitude. European Respiratory Journal 2016 48: PA1583; DOI: 10.1183/13993003.congress-2016.PA1583

44. Bernardi L., Schneider A., Pomidori. L., Paolucci E., Cogo A. Hypoxic ventilatory response in successful extreme altitude climbers. European Respiratory Journal 2006 27: 165-171; DOI: 10.1183/09031936.06.00015805

45. Biloshitsky P.V., Onopchyk Yu.M., Marchenko D.I., Aralova N.I. Mathematic methods for investigation of reliability problem of organism functioning in extreme high mountain conditions Physiol. Journal. 2003, pp.139-143 (In Ukrainian)

46. Aralova A.A., Aralova N.I., Beloshitsky P.V., Onopchuk Yu.N. Automated Information System for Functional Diagnostics of Mountaineers. Sports Medicine. 2008. 1: 163-169.

47. Kolchinskaya A.Z., Monogarov V.D., Radzievsky P.A., Molchanova N.I. Complex control in mountaineering and its role in the management of the training process. Сontrol of the process of adaptation of the organism of sports-shifts of high qualification: Collection of scientific works. works / Ed. board: D.A. Polishchuk and others. Kyiv, KSIPT 1992, p.122-132 (in Russian)

48. Kolchinskaya A.Z., Monogarov V.D., Radzievsky P.A. Scientific and methodological support for the preparation of Soviet climbers for the traverse of the Kanchenjunga massif. Theory and practice of physical culture. 1991,4 pp.10-14. (in Russian)

49. Kolchinskaya A.Z., Beloshitsky P.V., Monogarov V.D., Pivnutel R.V., Radzievsky P.A., Krasyuk A.N., Ivashkevich A.A., Borisov A.N. Physiological performance of climbers in conditions of extremely low in inhaled air. Physiological journal. 1989, 35, 2, pp. 68-74. (in Russian)

50. Lauer N.V., Kolchinskaya A.Z. About the oxygen organism regime and its regulation. K.: Nauk.Dumka, 1966, pp. 157–200 (In Russian)

51. Klyuchko O.M., Aralova N.I., Aralova A.A. Electronic automated work places for biological investigations. Biotechnologia Acta, K, 2019, V.12, №2, pp.5-26. https://doi.org/10.15407 /biotech12/02/005

52. Aralova A.A., Aralova N.I., Kovalchuk-Khimyuk L.A., Onopchuk Yu.N., Automated information system for athletes functional diagnostics. Control systems and machines. 2008 3: 73–78. (in Russian).

53. Aralova N.I. Mathematical model of the mechanism short- and medium-functional adaptation of breath of persons work in extreme conditions high. Kibernetika i vyčislitelnaâ tehnika. 2015,182, pp. 15-25. (in Russian).

Received 19.01.2022

Issue 2 (208), article 3

DOI:https://doi.org/10.15407/kvt208.02.044

Cybernetics and Computer Engineering, 2022, 2(208)

BONDAR S.O., PhD student,
Researcher of the Intelligent Control Department
ORCID: 0000-0003-4140-7985
e-mail: orangearrows@bigmir.net

SHEPETUKHA Yu.M., PhD (Engineering), Senior Researcher
Acting Head of the Intelligent Control Department
ORCID: 0000-0002-6256-5248
e-mail: shepetukha@irtc.org.ua

VOLOSHENYUK D.O., PhD (Engineering),
Senior Researcher of the Intelligent Control Department
ORCID: 0000-0003-3793-7801
e-mail: p-h-o-e-n-i-x@ukr.net

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.

USING OF HIGH-QUALITY POSITIONING TOOLS FOR HYBRID UNMANNED AERIAL VEHICLES AUTOMATIC CORRECTION UNDER THE LIMITED SPACE CONDITION

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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REFERENCES

1. Flores Reyes A., Flores Colunga G.R. Design And Development Of An Uav With Hybrid Flight Capabilities. Leon, Guanajuato, Mexico, 2018. URL: HYPERLINK “https://cio.repositorioinstitucional.mx/jspui/bitstream/1002/800/1/17444.pdf” https://cio.repositorioinstitucional.mx/jspui/bitstream/1002/800/1/17444.pdf

2. Adnan S. Saeed, Ahmad BaniYounes, Chenxiao Cai, Guowei Cai. A survey of hybrid Unmanned Aerial Vehicles. HYPERLINK “https://www.sciencedirect.com/journal/progress-in-aerospace-sciences” \n Go to Progress in Aerospace Sciences on ScienceDirectProgress in Aerospace Sciences Journal. Oxford, United Kingdom, 2018, Vol.98, pp. 98–105. URL: HYPERLINK “https://doi.org/10.1016/j.paerosci.2018.03.007” https://doi.org/10.1016/j.paerosci.2018.03.007

3. delftAcopter: innovative single-propeller hybrid drone URL: HYPERLINK “https://tudelftrobotics-institute.nl/robots/delftacopter” https://tudelftrobotics-institute.nl/robots/delftacopter

4. Barskiy R. Gyrotrak: brand new hybrid UAV concept. Science and technic. 2020. URL:
”https://naukatehnika.com/gyrotrak-novaya-gibridnaya-koncepcii-bespilotnikov.html” https://naukatehnika.com/gyrotrak-novaya-gibridnaya-koncepcii-bespilotnikov.html

5. Makeflyeasy Freeman 2300 Tilt VTOL Aerial Survey Carrier Span Fpv Rc Fix-wing Model drone Wing 2300mm UAV mapping Long range pryce. URL: HYPERLINK “https://www.uavmodel.com/products/makeflyeasy-freeman-2300-tilt-vtol-aerial-survey-carrier-span-fpv-rc-fix-wing-model-drone-wing-2300mm-uav-mapping-long-range” https://www.uavmodel.com/products/makeflyeasy-freeman-2300-tilt-vtol-aerial-survey-carrier-span-fpv-rc-fix-wing-model-drone-wing-2300mm-uav-mapping-long-range

6. Grytsenko V.I., Volkov O.Ye., Komar.M.M. et al. Modern unmanned aerial vehicle automatic control systems intellectualization. Cybernetics and Computer Engineering Journal. 2018, № 1, pp. 45–59. URL: http://nbuv.gov.ua/UJRN/Kivt_2018_1_4. (in Ukrainian)

7. Volkov O.Ye., Grytsenko V.I., Komar M.M. Integral Adaptive Autopilot for an Unmanned Aerial Vehicle. AVIATION: Scientific journal: scientific article. Vilnius, Lithuania, 2018, Vol. No 22, pp. 129–195.

8. Makeflyeasy Freeman 2300 Specification & Options URL: https://aliexpress.com/ item/10000223137957.html

Received 06.04.2022

Issue 2 (208), article 2

DOI:https://doi.org/10.15407/kvt208.02.030

Cybernetics and Computer Engineering, 2022, 2(208)

Savchenko-Synyakova Ye.A., PhD (Engineering),
Senior Researcher of the Department for Information
Technologies of Inductive Modeling
https://orcid.org/0000-0003-4851-9664
e-mail: savchenko_e@meta.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., Kyiv, 03187, Ukraine

COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS AND OTHER DIRECTIONS OF ARTIFICIAL INTELLIGENCE

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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REFERENCES

1. Matematychni modeli: oznachennya, kharakterystyky, etapy pobudovy. [Online]. Available at: <https://web.posibnyky.vntu.edu.ua/firen/3zlepko_osnovy_biomedychnogo_radioelektronnogo_aparatobuduvannya/6.html> [Accessed 27 Apr. 2022] (In Ukrainian).

2. Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat Methods. 15, pp. 233–234 (2018). https://doi.org/10.1038/nmeth.4642.

3. Alber, M., Buganza Tepole, A., Cannon, W.R. et al. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. npj Digit. Med. 2, 115 (2019). HYPERLINK “https://doi.org/10.1038/s41746-019-0193-y” https://doi.org/10.1038/s41746-019-0193-y.

4. Statistical Modeling VS Machine Learning. [Online]. Available at: < HYPERLINK “https://blog.arkieva.com/statistical-modeling-vs-machine-learning/” https://blog.arkieva.com/statistical-modeling-vs-machine-learning/> [Accessed 2 Dec. 2021].

5. Statistical Modelling vs Machine Learning. [Online]. Available at: <https://www.kdnuggets.com/2019/08/statistical-modelling-vs-machine-learning.html> [Accessed 2 Apr. 2022].

6. Traditional Modeling vs. Machine Learning. [Online]. Available at: <https://medium.com/thal%C4%93s/thales-traditional-modeling-vs-machine-learning-b7996342ea93> [Accessed 12 Apr. 2022].

7. Srivastava, T. Difference between Machine Learning & Statistical Modeling. [Online]. Available at: <https://www.analyticsvidhya.com/blog/2015/07/difference-machine-learning-statistical-modeling/> [Accessed 25 Apr. 2022].

8. Difference Between Algorithm and Model in Machine Learning. [Online]. Available at: <https://machinelearningmastery.com/difference-between-algorithm-and-model-in-machine-learning/> [Accessed 2 Apr. 2022].

9. Honchar, A. Obucheniye bez uchitelya — ubiytsa matematicheskogo modelirovaniya? [Online]. Available at: <https://dou.ua/lenta/articles/ml-without-teacher/> [Accessed 2 Apr. 2022].

10. The actual difference between statistics and machine learning. [Online]. Available at: < https://novator.io/kejsy/fakticheskaya-raznitsa-mezhdu-statistikoj-i-mashinnym-obucheniem> [Accessed 5 May 2022].

Received 22.04.2022

Issue 2 (208), article 1

DOI:https://doi.org/10.15407/kvt208.02.005

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,
https://orcid.org/0000-0002-3414-5334
e-mail: dar@infrm.kiev.ua

GRITSENKO V.I.¹, Corresponding Member of NAS of Ukraine,
 Directorate Adviser,
ORCID ID 0000-0002-6250-3987
e-mail: vig@irtc.org.ua

VOLKOV O.E.¹, PhD (Engineering), 
Director,
https://orcid.org/0000-0002-5418-6723
e-mail: alexvolk@ukr.net

GOLTSEV A.D.¹, PhD (Engineering), Senior Researcher,
Acting Head of the Dept. of Neural Information Processing Technologies,
https://orcid.org/0000-0002-2961-0908
e-mail: root@adg.kiev.ua

REVUNOVA E.G.¹, DSc (Engineering),
Senior Researcher, Dept. of Neural Information Processing Technologies,
https://orcid.org/0000-0002-3053-7090
e-mail: egrevunova@gmail.com

KLEYKO D.³, PhD (Computer Science), Researcher,
https://orcid.org/0000-0002-6032-6155
e-mail: denis.kleyko@ri.se

LUKOVICH V.V.¹ Researcher of the Dept. of Neural Information Processing Technologies
ORCID ID 0000-0002-3848-4712
e-mail: vvl97@ukr.net

OSIPOV E.², PhD (Computer Science), Professor,
https://orcid.org/0000-0003-0069-640X
e-mail: evgeny.osipov@ltu.se

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

NEURAL DISTRIBUTED REPRESENTATIONS FOR ARTIFICIAL INTELLIGENCE AND MODELING OF THINKING

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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REFERENCES

1. Amosov N.M. Modelling of thinking and the mind. New York: Spartan Books, 1967, 192 p.
2. Amosov N.M., Kasatkin A.M., Kasatkina L.M., Kussul E.M., Talaev S.A. Intelligent behaviour systems based on semantic networks. Kybernetes. 1973, Vol. 2, N 4, pp. 211–216.
3. Amosov N.M., Kussul E.M., Fomenko V.D. Transport robot with a neural network control system. Advance papers of 4th Intern. Joint Conf. on Artif. Intelligence. 1975, Vol 9, pp. 1–10.
4. Amosov N.M. Algorithms of the Mind. Kiev: Naukova Dumka, 1979, 223 p. (in Russian)
5. Amosov N.M., Baidyk T.N., Goltsev A.D., Kasatkin A.M., Kasatkina L.M., Rachkovskij D.A. Neurocomputers and intelligent robots. Kiev: Naukova Dumka. 1991, 269 p. (in Russian)
6. Kussul E.M. Associative neuron-like structures. Kiev: Naukova Dumka. 1992, 144 p.
   (in Russian)
7. Kleyko D., Rachkovskij D.A., Osipov E., Rahimi A. A survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and data transformations. ACM Computing Surveys. 2022. https://doi.org/10.1145/3538531
8. Kleyko D., Rachkovskij D.A., Osipov E., Rahimi A. A survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges. Accepted, ACM Computing Surveys. 2022. Available online: arXiv:2112.15424.
9. Thorpe S. Localized versus distributed representations The Handbook of Brain Theory and Neural Networks. Edited by M.A. Arbib.Cambridge, MA: The MIT Press. 2003, pp. 643–646.
10. Rachkovskij D.A., Kussul E.M. Binding and normalization of binary sparse distributed representations by context-dependent thinning. Neural Computation. 2001, Vol. 13, N 2, pp. 411–452.
11. Plate T. Holographic Reduced Representation: Distributed Representation for Cognitive Structures. Stanford: CSLI Publications, 2003, 300 p.
12. Kanerva P. Hyperdimensional computing: an introduction to computing in distributed representation with high-dimensional random vectors. Cognitive Computation. 2009, Vol. 1, N2, pp. 139–159.
13. Gayler R.W. Multiplicative binding, representation operators, and analogy. Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences. Sofia, Bulgaria: New Bulgarian University, 1998, p. 405.
14. Kussul E.M., Rachkovskij D.A., Baidyk T.N. Associative-Projective Neural Networks: Architecture, Implementation, Applications. Proc. Neuro-Nimes’91. 1991, pp. 463-476.
15. Kussul E.M., Rachkovskij D.A., Baidyk T.N. On image texture recognition by associative-projective neurocomputer. Proc. ANNIE’91 Conference “Intelligent engineering systems through artificial neural networks”. St. Louis, MO: ASME Press, 1991, pp. 453–458.
16. Kussul E.M., Rachkovskij D.A. Multilevel assembly neural architecture and processing of sequences. In Neurocomputers and Attention: Connectionism and Neurocomputers, vol. 2. Manchester and New York: Manchester University Press, 1991, pp. 577–590.
17. Gritsenko V.I., Rachkovskij D.A., Goltsev A.D., Lukovych V.V., Misuno I.S., Revunova E.G., Slipchenko S.V., Sokolov A.M., Talayev S.A. Neural distributed representation for intelligent information technologies and modeling of thinking. Cybernetics and Computer Engineering. 2013, Vol. 173, pp. 7–24. (in Russian).
18. Rachkovskij D.A., Kussul E.M., Baidyk T.N. Building a world model with structure-sensitive sparse binary distributed representations. Biologically Inspired Cognitive Architectures. 2013, Vol. 3, pp. 64–86.
19. Gritsenko V.I., Rachkovskij D.A., Frolov A.A., Gayler R., Kleyko D., Osipov E. Neural distributed autoassociative memories: A survey. Cybernetics and Computer Engineering. 2017, N 2 (188), pp. 5–35
20. Frolov A.A., Rachkovskij D.A., Husek D. On information characteristics of Willshaw-like auto-associative memory. Neural Network World. 2002. Vol. 12, No 2, pp. 141–158.
21. Frolov A.A., Husek D., Rachkovskij D.A. Time of searching for similar binary vectors in associative memory. Cybernetics and Systems Analysis. 2006, Vol. 42, N 5, pp. 615–623.
22. Fusi S. Memory capacity of neural network models. arXiv:2108.07839. 21 Dec 2021.
23. Steinberg J., Sompolinsky H. Associative memory of structured knowledge. 2022. https://doi.org/10.1101/2022.02.22.481380
24. Liang J.C., Erez J., Zhang F., Cusack R., Barense M.D. Experience transforms conjunctive object representations: Neural evidence for unitization after visual expertise. Cerebral Cortex. 2020. Vol. 30, N 5, pp. 2721–2739.
25. Li A.Y., Fukuda K., Barense M.D. Independent features form integrated objects: Using a novel shape-color “conjunction task” to reconstruct memory resolution for multiple object features simultaneously. Cognition. 2022, Vol. 223, Article 105024.
26. Andermane N., Joensen B.H., Horner A.J. Forgetting across a hierarchy of episodic representations. Current Opinion in Neurobiology. 2021, Vol. 67, pp. 50–57.
27. Michelmann S., Hasson U., Norman K.A. Event boundaries are steppingstones for memory retrieval. 2021. Preprint DOI 10.31234/osf.io/k8j94.
28. Schneegans S., McMaster J.M.V., Bays P.M. Role of time in binding features in visual working memory. Psychological Review. 2022. https://doi.org/10.1037/rev0000331
29. Geerligs L., van Gerven M., Campbell K.L., Güçlü U. A nested cortical hierarchy of neural states underlies event segmentation in the human brain. Neuroscience. 2021. https://doi.org/10.1101/2021.02.05.429165
30. Peer M., Brunec I.K., Newcombe N.S., Epstein R.A. Structuring knowledge with cognitive maps and cognitive graphs. Trends in Cognitive Sciences. 2021, Vol. 25, N 1, pp. 37–54.
31. Rachkovskij D.A. Shift-equivariant similarity-preserving hypervector representations of sequences. arXiv:2112.15475. 2021.
32. Rachkovskij D.A. Representation and processing of structures with binary sparse distributed codes. IEEE Trans. Knowledge Data Engineering. 2001, Vol. 13, N 2, pp. 261–276.
33. Rachkovskij D.A., Slipchenko S.V. Similarity-based retrieval with structure-sensitive sparse binary distributed representations. Comput. Intelligence. 2012,Vol. 28, N. 1, pp. 106–129.
34. Papadimitriou C.H., Vempala S.S., Mitropolsky D., Collins M.J., Maass W. Brain computation by assemblies of neurons. Proceedings of the National Academy of Sciences. 2020, Vol. 117, N 25, pp. 14464–14472.
35. Müller M.G., Papadimitriou C.H., Maass W., Legenstein R. A model for structured information representation in neural networks of the brain. eNeuro. 2020, Vol. 7, N 3, pp. 1–17. 
36. Mitropolsky D., Collins M.J., Papadimitriou C.H. A biologically plausible parser. Transactions of the Association for Computational Linguistics. 2021, Vol. 9, pp. 1374–1388. 
37. Papadimitriou C.H., Friederici A.D. Bridging the gap between neurons and cognition through assemblies of neurons. Neural Computation. 2022, Vol. 34, N 2, pp. 291–306.
38. Rachkovskij D.A., Misuno I.S., Slipchenko S.V. Randomized projective methods for the construction of binary sparse vector representations. Cybernetics and Systems Analysis. 2012, Vol 48, N 1, pp. 146–156.
39. Rachkovskij D.A. Vector data transformation using random binary matrices. Cybernetics and Systems Analysis. 2014, Vol. 50, N 6, pp. 960–968. 
40. Rachkovskij D.A. Formation of similarity-reflecting binary vectors with random binary projections. Cybernetics and Systems Analysis. 2015, Vol. 51, N 2, pp. 313–323.
41. Rachkovskij D.A. Estimation of vectors similarity by their randomized binary projections. Cybernetics and Systems Analysis. 2015, Vol. 51, N 5, pp. 808–818.
42. Dasgupta S., Stevens C.F., Navlakha S. A neural algorithm for a fundamental computing problem. Science. 2017, Vol. 358, pp. 793–796.
43. Rachkovskij D.A., Gritsenko V.I. Distributed Representation of Vector Data Based on Random Projections. Kyiv: Interservice, 2018. ISBN: 978-617-696-837-5. (in Ukrainian)
44. Rachkovskij D.A. Codevectors: Sparse Binary Distributed Representations of Numerical Data. Kiev: Interservice, 2019. ISBN: 978-617-696-987-7. (in Russian)
45. Gritsenko V.I., Rachkovskij D.A. Methods for Vector Representation of Objects for Fast Similarity Estimation. Kyiv: Naukova Dumka, 2019. ISBN: 978-966-00-1741-2. (In Russian)
46. Rachkovskij D. A. Introduction to fast similarity search. Kyiv: Interservice, 2019, 294 p. ISBN: 978-617-696-904-4. (in Russian)
47. Ghazi B., Panigrahy R., Wang J. Recursive sketches for modular deep learning. Proceedings of the 36th International Conference on Machine Learning. PMLR. 2019, Vol. 97, pp. 2211–2220.
48. Panigrahy R., Wang X., Zaheer M. Sketch based memory for neural networks. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS’21). 2021, San Diego, California, USA. PMLR. 2021, Vol. 130, pp. 3169–3177.
49. Hiratani N., Sompolinsky H. Optimal quadratic binding for relational reasoning in vector symbolic neural architectures. arXiv:2204.07186. 2022, pp. 1–32.
50. Chaitanya R., Hopfield J., Grinberg L., Krotov D. Bio-inspired hashing for unsupervised similarity search. Proceedings of the 37th International Conference on Machine Learning. PMLR. 2020, Vol. 119, pp. 8295–8306.
51. Liang Y., Ryali C., Hoover B., Grinberg L., Navlakha S., Zaki M., Krotov D. Can a fruit fly learn word embeddings? Proc. ICLR’21. 2021.
52. Li W. Modeling winner-take-all competition in sparse binary projections. In: Machine Learning and Knowledge Discovery in Databases. Edited by: F. Hutter, K. Kersting,
    J. Lijffijt, I. Valera. Lecture Notes in Computer Science. Vol. 12457. Cham: Springer, 2021, pp. 456–472. https://doi.org/10.1007/978-3-030-67658-2_26 
53. Li W.Y., Zhang S.Z. Binary random projections with controllable sparsity patterns. Journal of the Operations Research Society of China. 2022. https://doi.org/10.1007/s40305-021-00387-0
54. Rachkovskij D.A., Slipchenko S.V., Misuno I.S., Kussul E.M., Baidyk T.N. Sparse binary distributed encoding of numeric vectors. Journal of Automation and Information Sciences. 2005, Vol. 37, N 11, pp. 47–61.
55. Izawa S., Kitai K., Tanaka S., Tamura R., Tsuda K. Continuous black-box optimization with quantum annealing and random subspace coding. Proc. Adiabatic Quantum Computing (AQC’21), June 22-25, 2021.
56. Izawa S., Kitai K., Tanaka S., Tamura R., Tsuda K. Continuous black-box optimization with an Ising machine and random subspace coding. Phys. Rev. Research. 2022, Vol. 4, N 2. Article 023062.
57. Barclay I. et al. Trustable service discovery for highly dynamic decentralized workflows. Future Generation Computer Systems. 2022. DOI: 10.1016/j.future.2022.03.035
58. Wei Y., Xie P., Zhang L. Tikhonov regularization and randomized GSVD. SIAM J.
    Matrix Analysis Appl. 2016, Vol. 37, pp. 649–675. 
59. Zhang L., Wei Y. Randomized core reduction for discrete ill-posed problem. J. Comput. Appl. Math. 2020, Vol. 375, Article 112797.
60. Wei W., Zhang H., Yang X., Chen X. Randomized generalized singular value decomposition. Commun. Appl. Math. Comput. 2021, Vol 3, pp. 137–156. 
61. Zuo Q., Wei Y., Xiang H. Quantum-inspired algorithm for truncated total least squares solution. arXiv:2205.00455. 2022
62. Revunova E.G., Rachkovskij D.A. Using randomized algorithms for solving discrete ill-posed problems. J. Information Theories and Applications. 2009, Vol. 16, N 2, pp. 176–192.
63. Rachkovskij D.A., Revunova E.G. Randomized method for solving discrete ill-posed problems. Cybernetics and Systems Analysis. 2012, Vol. 48, N 4, pp. 621–635.
64. Revunova E.G. Analytical study of the error components for the solution of discrete
    ill-posed problems using random projections. Cybernetics and Systems Analysis. 2015, Vol. 51, N 6, pp. 978–991.
65. Revunova E.G. Model selection criteria for a linear model to solve discrete ill-posed problems on the basis of singular decomposition and random projection. Cybernetics and Systems Analysis. 2016, Vol. 52, N 4, pp. 647–664.
66. Revunova E.G. Increasing the accuracy of solving discrete ill-posed problems by the random projection method. Cybernetics and Systems Analysis. 2018, Vol. 54, N 5, pp. 842–852.
67. Revunova O.G., Tyshchuk O.V., Desiateryk О.О. On the generalization of the random projection method for problems of the recovery of object signal described by models of convolution type. Control Systems and Computers. 2021, N 5–6, pp. 25–34.
68. Sokolov A., Rachkovskij D. Approaches to sequence similarity representation. Int. J. Inf. Theor. Appl. 2006, Vol. 13, N 3, pp. 272–278.
69. Sokolov A. Vector representations for efficient comparison and search for similar strings. Cybernetics and Systems Analysis. 2007, Vol. 43, N 4, pp. 484–498.
70. Rachkovskij D.A. Development and investigation of multilevel assembly neural networks. PhD thesis. Kiev, Ukrainian SSR: V.M. Glushkov Institute of Cybernetics, 1990. (in Russian)
71. Kussul E.M., Baidyk T.N. On information encoding in Associative-Projective Neural Networks. Technical Report 93-3. V.M. Glushkov Institute of Cybernetics, 1993. (in Russian)
72. Kleyko D., Osipov E., De Silva D., Wiklund U., Vyatkin V., Alahakoon D. Distributed representation of n-gram statistics for boosting self-organizing maps with hyperdimensional computing. Proc. PSI’19. 2019, pp. 64–79.
73. Kussul E.M., Baidyk T.N., Wunsch D.C., Makeyev O., Martin A. Permutation coding technique for image recognition system,” IEEE Trans. Neural Networks. 2006, Vol. 17, N 6, pp. 1566–1579.
74. Rachkovskij D.A. Application of stochastic assembly neural networks in the problem
    of interesting text selection. Neural network systems for information processing. 1996, pp. 52–64. (in Russian) 
75. Rachkovskij D.A., Kleyko D. Recursive binding for similarity-preserving hypervector representations of sequences. Proc. IJCNN’22. 2022.
76. Kussul E., Baidyk T. Improved method of handwritten digit recognition tested on MNIST database. Image and Vision Computing. 2004, Vol. 22, pp. 971–981.
77. Makeyev O., Sazonov E., Baidyk T., Martin A. Limited receptive area neural classifier for texture recognition of mechanically treated metal surfaces. Neurocomputing. 2008, Vol. 71, N 7-9, pp. 1413–1421.
78. Rachkovskij D.A. Distance-based index structures for fast similarity search. Cybernetics and Systems Analysis. 2017, Vol. 53, N 4, pp. 636–658.
79. Rachkovskij D.A. Index structures for fast similarity search for binary vectors. Cybernetics and Systems Analysis. 2017, Vol. 53, N 5, pp. 799–820.
80. Kussul E.M., Baidyk T.N., Lukovich V.V., Rachkovskij D.A. Adaptive neural network classifier with multifloat input coding. Proc. NeuroNimes’93, Nimes, France, Oct. 25-29, 1993, pp. 209–216.
81. Lukovich V.V., Goltsev A.D., Rachkovskij D.A. Neural network classifiers for micromechanical equipment diagnostics and micromechanical product quality inspection. Proc. EUFIT’97, 1997, pp. 534–536.
82. Rachkovskij D.A., Kussul E.M. DataGen: a generator of datasets for evaluation of classification algorithms. Pattern Recognition Letters. 1998, Vol.19, N 7, pp. 537–544.
83. Goltsev A.D. Neural networks with assembly organization. Kyiv: Naukova Dumka. 2005, 200 p. (in Russian)
84. Rachkovskij D. Linear classifiers based on binary distributed representations. J. Inf. Theories Appl. 2007, Vol. 14, N 3, pp. 270–274.
85. Goltsev A., Rachkovskij D. A recurrent neural network for partitioning of hand drawn characters into strokes of different orientations. International Journal of Neural Systems. 2001, Vol. 11, pp. 463–475.
86. Goltsev A., Gritsenko V., Húsek D. Segmentation of visual images by sequential extracting homogeneous texture areas. Journal of Signal and Information Processing. 2020, Vol. 11, N 4, pp. 75–102.
87. Goltsev A. D., Gritsenko V.I. Algorithm of sequential finding the textural features characterizing homogeneous texture segments for the image segmentation task. Cybernetics and Computer Engineering. 2013, N 173, pp. 25–34.
88. Goltsev A., Gritsenko V., Kussul E., Baidyk T. Finding the texture features characterizing the most homogeneous texture segment in the image. Proc. IWANN’15. 2015, pp. 287–300.
89. Volkov O., Komar M., Volosheniuk D. Devising an image processing method for transport infrastructure monitoring systems. Eastern-European Journal of Enterprise Technologies. 2021, Vol. 4, N 2 (112), pp. 18–25. https://doi.org/10.15587/1729-4061.2021.239084
90. Gritsenko V., Volkov O., Komar M., Volosheniuk D. Integral adaptive autopilot for an unmanned aerial vehicle. Aviation. 2018, Vol. 22, N 4, pp. 129–135. http://dx.doi.org/10.3846/ aviation.2018.6413
91. Misuno I. S., Rachkovskij D. A., Slipchenko S. V., Sokolov A. M. Searching for text information with the help of vector representations. Probl. Programming. 2005, N 4, pp. 50–59.
92. Goltsev A., Rachkovskij D. Combination of the assembly neural network with a perceptron for recognition of handwritten digits arranged in numeral strings. Pattern Recognition. 2005, Vol. 38, N 3, pp. 315–322.
93. Kasatkina L.M., Lukovich V.V., Pilipenko V.V. Recognition of the person’s voice by the classifier LIRA. Control Systems and Computers. 2006, N. 3, pp. 67–73.
94. Slipchenko S. V. Distributed representations for the processing of hierarchically structured numerical and symbolic information. System Technologies. 2005, N 6, pp. 134–141. (In Russian)
95. Rachkovskij D.A., Revunova E.G. Intelligent gamma-ray data processing for environmental monitoring. In: Intelligent Data Processing in Global Monitoring for Environment and Security. Kiev-Sofia: ITHEA, 2011, pp. 136–157.
96. Revunova E.G. Rachkovskij D.A. Training a linear neural network with a stable LSP solution for jamming cancellation. Intern. Journal Information Theories and Applications. 2005, Vol.12, N 3, pp. 224–230.
97. Revunova E.G., Rachkovskij D.A. Random projection and truncated SVD for estimating direction of arrival in antenna array. Cybernetics and Computer Engineering. 2018, N 3(193), pp. 5–26.
98. Hinton G. How to represent part-whole hierarchies in a neural network. arXiv:2102.12627. 2021, pp. 1–44.
99. Goyal A., Bengio Y. Inductive biases for deep learning of higher-level cognition. arXiv:2011.15091. 2020, pp. 1–42.
100. Greff K., van Steenkiste S., Schmidhuber J. On the binding problem in artificial neural networks. arXiv:2012.05208. 2020, pp. 1–75.

Received 17.03.2022