Персона: Широкий, Владимир Романович
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Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
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Владимир Романович
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- ПубликацияОткрытый доступSingle trial eeg classification of tasks with dominance of mental and sensory attention with deep learning approach(2019) Knyazeva, I.; Efitorov, A.; Boytsova, Y.; Danko, S.; Shiroky, V.; Широкий, Владимир Романович© Springer Nature Switzerland AG 2019. In this paper, we present classification algorithms based on single-trial ElectroEncephaloGraphy (EEG) during the performance of tasks with the dominance of mental and sensory attention. Statistical data analysis showed numerous significant differences of EEG wavelet spectra density during this task at the group level. We decided to use wavelet power spectral density (PSD) computed in each channel for single trial as the source of feature extraction for the classification task. To obtain a low-dimensional representation of PSD image convolutional autoencoder (CNN) was trained. With this encoded representation binary classification for each subject with multilayer perceptron (MLP) were performed. The classification error varies depending on the subject with the average true classification rate is 83.4%, and the standard deviation is 6.6%. So this approach potentially could be used in the tasks where pattern classification is used, such as a clinical decision or in Brain-Computer Interface (BCI) system.
- ПубликацияТолько метаданныеUsing Domain Knowledge for Feature Selection in Neural Network Solution of the Inverse Problem of Magnetotelluric Sounding(2021) Isaev, I.; Obornev, E.; Obornev, I.; Rodionov, E.; Shirokiy, V.; Широкий, Владимир Романович© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.In the present study, using the inverse problem (IP) of magnetotelluric sounding (MTS) as an example, we consider the use of neural networks to solve high-dimensional coefficient inverse problems. To reduce the incorrectness, a complex approach is considered related to the use of narrow classes of geological models, with prior selection of the model class by solving the classification problem by MTS data. Within the framework of this approach, the actual direction of work is to reduce the volume of calculations when re-building the system for another set of geological models. This goal can be achieved by selecting the essential features. The present paper is devoted to the study of the applicability of various selection methods to the MTS IP. Also, in this paper we consider taking into account domain knowledge about the studied object in the process of selection of essential features using methods such as wrapper.
- ПубликацияТолько метаданныеThe Loop of Nonverbal Communication Between Human and Virtual Actor: Mapping Between Spaces(2021) Vladimirov, R. D.; Dolenko, S. A.; Shirokiy, V. R.; Tikhomirova, D. V.; Samsonovich, A. V.; Широкий, Владимир Романович; Тихомирова, Дарья Валерьевна; Самсонович, Алексей Владимир© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.There is a question about the appropriate emotional and expressive language of a virtual actor. In this paper we study facial expressions. We investigate the transformations between the space of Action Units and the standard affective space in the loop of nonverbal communication between a person and a virtual actor using facial expressions [1]. We are mapping both dimensions into each other using various machine learning algorithms. Action Units space was mapped into emotional space directly using artificial neural networks. Emotional space was mapped into Action Units space with help of dimensionality reduction followed by clusterization of the latter. After the final synthesis, the facial expression of virtual actor can be determined.
- ПубликацияТолько метаданныеThe Solution to the Problem of Classifying High-Dimension fMRI Data Based on the Spark Platform(2021) Efitorov, A.; Orlov, V.; Dolenko, S.; Shiroky, V.; Ushakov, V. L.; Широкий, Владимир Романович; Ушаков, Вадим Леонидович© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.This paper compares approaches to solving the classification problem based on fMRI data of the original dimension using the big data platform Spark. The original data is 4D fMRI time series with time resolution (TR) = 0.5 s for one sample recording. Participants have to solve 6 tasks, requiring activating various types of thinking, during 30 min session. A large number of subjects and a short time resolution generated the dataset with more than 86 000 samples, which allowed applying machine learning methods to solve this problem, instead of classical statistical maps. The random forest model was used to solve the binary classification problem. The paper analyzes model performance dependence upon time during the problem solving sessions. Evidence has been obtained that there is some limited time required for solving the same type of problems, and if more time is spent, this is due to the fact that the brain does not instantly get involved in the work on the proposed task, but it is still staying at resting state for some time.