SEARCH AND ANALYSIS OF DATA BASED ON SIMILARITY-DIFFERENCE METRIC

Authors

  • O. Shevchenko Kharkiv National University of Radioelectronics
  • I. Khakhanov Kharkiv National University of Radioelectronics
  • V. Hahanov Kharkiv National University of Radioelectronics

Keywords:

computing, cybersocial computing, decision making, unitary data codes, similarity – difference, data retrieval, plagiarism.

Abstract

Models, methods and algorithms for cyber-social computing, machine learning are proposed that use the similarity-difference metric of unitary coded information for processing big data in order to generate adequate actuator signals for controlling cyber-social critical systems. A set-theoretic method of data search is being developed based on the similarity – difference of the frequency parameters of primitive elements, which makes it possible to determine the similarity of objects, the strategy of transforming one object into another, and also to identify the level of community of interests, conflicts. Computational architectures of cyber-social computing and metric search for key data are being created. The definitions of the fundamental concepts in the field of computing are given on the basis of metric relations between interacting processes and phenomena. A software application is proposed for calculating the similarity-differences of objects based on the formation of frequency vectors of two sets of primitive data. A high level of correlation of the application results with the wellknown system for determining plagiarism is shown.

Author Biographies

O. Shevchenko, Kharkiv National University of Radioelectronics

Assistant of the Design Automation Department

I. Khakhanov, Kharkiv National University of Radioelectronics

Kharkiv National University of Radioelectronics

V. Hahanov, Kharkiv National University of Radioelectronics

Doctor of Technical Sciences, Professor, Professor of the Design Automation Department

References

Drozd A. Checkability of the digital components in safety-critical systems: problems and solutions / A. Drozd, V. Kharchenko, S. Antoshchuk et. al. // IEEE East-West Design & Test Symposium. Sevastopol, Ukraine. 2011. P. 411–416.

Drozd O. Development of Models in Resilient Computing / O. Drozd, V. Kharchenko, A. Rucinski et. al. // IEEE International Conference DESSERT. Leeds, UK. 2019. P. 2-7.

Hahanov V. Green Cyber-Physical Computing as Sustainable Development Model / V. Hahanov, E. Litvinova, and S. Chumachenko // In the Book “Green IT Engineering: Components, Networks and Systems Implementation”. Editors V. Kharchenko, Y. Kondratenko, J. Kacprzyk, Springer. 2017.

Tarraf D. Control of Cyber-Physical Systems / D. Tarraf // Workshop held at Johns Hopkins University. March 2013. Springer. 2013.

Guo R. The Method of Similarity-Difference Comprehensive Evaluation on Test Paper Quality in

Colleges and Universities and Its Application / R. Guo, G. Mao, Y. Liu, Y. Liu, J. Wang, R. Cui // 2009 Second International Conference on Education Technology and Training. Sanya, 2009. P. 227-230.

Zhu J. Deep Hybrid Similarity Learning for Person Re-Identification / J. Zhu, H. Zeng, S. Liao, Z. Lei, C. Cai, L. Zheng // IEEE Transactions on Circuits and Systems for Video Technology. Nov. 2018. Vol. 28, no. 11. P. 3183-3193.

Komori T. Real Friendship and Virtual Friendship: Differences in Similarity of Contents/People and Proposal of Classification Models on SNS / T. Komori, Y. Hijikata, T. Tominaga, S. Yoshida, N. Sakata and K. Harada // 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Santiago,. 2018. H. 354-360.

Lin K. New Vague Set Based Similarity Measure for Pattern Recognition / K. Lin // 2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). Toyama, Japan. 2019. P. 15-21.

Hahanov V. Qubit Description of the Functions and Structures for Computing / V. Hahanov, S. Chumachenko, E. Litvinova, and M. Liubarskyi // Proc. of IEEE East-West Design and Test Symposium. Yerevan. Armenia. 14-17 Oct., 2016. P. 88-93.

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Published

2021-03-23

Issue

Vol. 3 No. 1(65) (2021)

Section

Статьи

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