PROGRAM SYSTEMS: THEORY AND APPLICATIONS

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Online Scientific Journal published by the Ailamazyan Program Systems Institute of the Russian Academy of Sciences

Artificial Intelligence, Intelligence Systems, Neural Networks
Software and Hardware for Distributed Systems and Supercomputers
Mathematical Foundations of Programming
Information Systems in Culture and Education
Healthcare Information Systems
Methods for Optimal Control and Control Theory
Mathematical Modelling

Papers are accepted in the form of a PDF file

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• Содержание выпуска •
• Artificial Intelligence, Intelligence Systems, Neural Networks •
• Software and Hardware for Distributed Systems and Supercomputers •
• Mathematical Foundations of Programming •
• Information Systems in Culture and Education •
• Healthcare Information Systems •
• Methods for Optimal Control and Control Theory •
• Mathematical Modelling •

Artificial Intelligence, Intelligence Systems, Neural Networks

Responsible for the Section: doctor of technical Sciences Vyacheslav Khachumov., candidate of technical Sciences Eugene Kurshev.

On the left: assigned number of the paper, submission date, the number of A5 pages contained in the paper, and the reference to the full-text PDF .

 

Article # 13_2018

31 p.

PDF

submitted on 27th Jule 2018 displayed on website on 01th Nov 2018

Igor Trofimov, Elena Suleymanova, Natalia Vlasova, Alexey Podobryaev
Disambiguation between eventive and non-eventive meaning of nouns

Event extraction is an advanced form of text mining having numerous applications. One of the challenges faced by event extraction systems is the problem of automatic distinguishing between eventive and non-eventive use of ambiguous event nominals. The proposed disambiguation method relies on an automatically generated training set. In order to learn the difference between eventive and non-eventive reading of a target ambiguous nominal, the classifier is trained on two sets of automatically labelled examples featuring unambiguous distributionally similar lexical substitutes for either reading. The method was evaluated on a small sample of 6 ambiguous event-denoting nouns and performed fairly well (77,38% average accuracy, although with more than 20% variation for individual nouns). Suggestions for future work include development of a more advanced distributional model and research towards automated selection of unambiguous substitutes.


Key words: word sense disambiguation, automatic training set generation, distributional semantic model, event, event nominal, event-related information extraction.

article citation

http://psta.psiras.ru/read/psta2018_4_3-33.pdf

DOI

https://doi.org/10.25209/2079-3316-2018-9-4-3-33

Article # 18_2018

42 p.

PDF

submitted on 17th Apr 2018 displayed on website on 14th Nov 2018

Yulia Emelyanova, Vitaly Fralenko
Methods of cognitive-graphical representation of information for effective monitoring of complex technical systems

The methods of cognitive-graphical representation of telemetric information are considered. The analysis of existing methods applicability of multidimensional data visualization for dynamic real-time systems monitoring with a complex hierarchical structure is performed. The final part of the paper presents a table summarizing the results of the methods studied analysis. (In Russian).


Key words: cognitive image, information presentation, monitoring, operator, dynamic system.

article citation

http://psta.psiras.ru/read/psta2018_4_117-158.pdf

DOI

https://doi.org/10.25209/2079-3316-2018-9-4-117-158

Article # 26_2018

13 p.

PDF

submitted on 18th Nov 2018 displayed on website on 17th Dec 2018

Alexander Smirnov, Dmitry Stepanov
The use of convolutional neural networks for recognition of the type of premises using special features of the premises

The article proposes a method for recognizing a convolutional neural network (CNN) of the type / class of internal elements of a building layout using specific features of these elements, such as borders or edges. An algorithm for pre-processing of images is proposed to highlight the borders / edges in the image. Also a database is created with images of various types of interior elements of the building layout, such as a corridor, a door (doorway), corner structures and stairs. The article also discusses the own structure of the SNA, and presents data on the accuracy of recognition of various types of premises. The developed method is proposed to use for the primary navigation of mobile robots. (In Russian).


Key words:  convolutional neural network, premises recognition, image filtering, block-parallel processing, feature extraction.

article citation

http://psta.psiras.ru/read/psta2018_4_279-291.pdf

DOI

https://doi.org/10.25209/2079-3316-2018-9-4-279-291

Article # 32_2018

26 p.

PDF

submitted on 29th Oct 2018 displayed on website on 30th Dec 2018

Nikolai Abramov, Dmitry Makarov, Alexander Talalaev, Vitaly Fralenko
Modern methods for intelligent processing of Earth remote sensing data

The paper presents an overview of modern methods for processing Earth remote sensing data. The analysis of works devoted to solving problems of preliminary analysis of images, the selection and recognition of target objects for their further monitoring is given. Emphasis has been placed on hybrid methods for analyzing images using, among other things, high-performance processing technologies and artificial neural networks. The features, problems and trends in the development of big data processing technologies in various remote sensing applications are shown. (In Russian).


Key words: Earth remote sensing, search, recognition, image processing, artificial neural network, intelligent system, software systems, big data.

article citation

http://psta.psiras.ru/read/psta2018_4_417-442.pdf

DOI

https://doi.org/10.25209/2079-3316-2018-9-4-417-442

Article # 33_2018

18 p.

PDF

submitted on 12th Nov 2018 displayed on website on 30th Dec 2018

Igor Trofimov, Elena Suleymanova
A dependency-based distributional semantic model for identifying taxonomic similarity

Are dependency-based distributional semantic models worth the computational cost and the linguistic resources they require? As our evaluation study suggests, the answer should be "yes" if the task in hand involves distinguishing between feature-based similarity and pure association. After extensive parameter tuning, window-based models still fall behind dependencybased ones when evaluated on our Russian-language similarity/association dataset. (In Russian).


Key words:  distributional semantic model, dependency-based DSM, taxonomic similarity, feature-based similarity, word2vec, skipgram, RuSim1000.

article citation

http://psta.psiras.ru/read/psta2018_4_443-460.pdf

DOI

https://doi.org/10.25209/2079-3316-2018-9-4-443-460

Article # 34_2018

15 p.

PDF

submitted on 15th Nov 2018 displayed on website on 30th Dec 2018

Andrey Vinogradov, Igor Elizavetin, Eugeniy Kurshev, Semen Paramonov, Sergey Belov
Analysis of the differential interferometry methods applicability for geotechnical monitoring of the Arctic zone

We consider the application of space radar differential interferometry methods for solving actual applied problems of geotechnical and geoecological monitoring of Arctic regions. Various directions and problems of using interferometric data are investigated.
The requirements for the formation of a time series of interferometric images were developed and criteria for assessing their suitability for geotechnical monitoring of the Arctic zone were formulated. (In Russian).


Key words: radar image, satellite differential radar interferometry, geotechnical monitoring, earth remote sensing.

article citation

http://psta.psiras.ru/read/psta2018_4_461-475.pdf

DOI

https://doi.org/10.25209/2079-3316-2018-9-4-461-475

• Artificial Intelligence, Intelligence Systems, Neural Networks •
• Software and Hardware for Distributed Systems and Supercomputers •
• Mathematical Foundations of Programming •
• Information Systems in Culture and Education •
• Healthcare Information Systems •
• Methods for Optimal Control and Control Theory •
• Mathematical Modelling •

 

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© Ailamazyan Program System Institute of RAS 2010-2018