Персона: Тихомирова, Дарья Валерьевна
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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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- ПубликацияОткрытый доступVisualization and Classification of Human Movements Based on Skeletal Structure: A Neural Network Approach to Sport Exercise Analysis and Comparison of Methodologies(НИЯУ МИФИ, 2024) Kuzevanov, V. O.; Tikhomirova, D. V.; Тихомирова, Дарья ВалерьевнаThe authors of the paper review and compare different existing approaches to Human Action Recognition (HAR), analyze the advantages and disadvantages of platforms for extracting human skeletal structure from video stream, and evaluate the importance of visual representation in the motion analysis process. This paper presents an example implementation of one of the approaches to HAR based on the use of interpretability and visual expressiveness inherent in skeletal structures. In this work, an ad hoc network with Long Short-Term Memory (LSTM) for human activity classification is designed and implemented, which has been trained and tested in the domain of sports exercises. LSTM incorporation of memory cells and gating mechanisms not only mitigates the vanishing gradient problem but also enables LSTMs to selectively retain and utilize relevant information over extended sequences, making them highly effective in tasks with complex temporal dependencies. The problem with a fading gradient is quite common in deep neural networks and is that if the error is back propagated during the training of the network, the gradient can decrease strongly as it travels through the layers of the network to the initial layers. This can lead to the fact that the weights in the initial layers are practically not updated, which makes training of these layers impossible or slows down its process. The resulting solution can be used to create a real-time virtual fitness assistant. The resulting solution can be used to create a real-time virtual fitness assistant. In addition, this approach will make it possible to create interactive training applications with visualization of human skeletal structure, motion analysis and monitoring systems in the field of medicine and rehabilitation, as well as for the development of security systems with access control based on the analysis of visual data on the movement of human body parts.
- ПубликацияОткрытый доступDeep Learning for Effective Visualization and Classification of Recyclable Material Labels(НИЯУ МИФИ, 2024) Kuzevanov, V. O.; Tikhomirova, D. V.; Тихомирова, Дарья ВалерьевнаThis paper presents an example of a system to improve the process of sorting recyclables by using deep learning techniques to automatically detect, classify and visualize recycling codes on product packages. In this paper, the authors discuss various approaches to optical character recognition and object detection in a video stream or image. The authors have developed and proposed a combination of neural networks for detection and classification of recycling codes. The proposed neural network system is designed to facilitate efficient recycling processes by automating the identification of recycling symbols, thereby facilitating the sorting and processing of recyclables.
- ПубликацияОткрытый доступRecognition of emotions in verbal messages based on neural networks(2021) Malova, I. S.; Tikhomirova, D. V.; Тихомирова, Дарья Валерьевна© 2020 Elsevier B.V.. All rights reserved.Emotion detection and recognition by text is an under-researched area of natural language processing (NLP), which can provide valuable input in various fields. Speech and Emotion Recognition (Speech Emotion Recognition SER) has potentially wide applications, such as interaction with robots, banking, call centers, car onboard systems, computer games, etc.
- ПубликацияОткрытый доступVisualization and Classification of Human Movements Based on Skeletal Structure: A Neural Network Approach to Sport Exercise Analysis and Comparison of Methodologies(2024) Kuzevanov, V. O.; Tikhomirova, D. V.; Тихомирова, Дарья ВалерьевнаThe authors of the paper review and compare different existing approaches to Human Action Recognition (HAR), analyze the advantages and disadvantages of platforms for extracting human skeletal structure from video stream, and evaluate the importance of visual representation in the motion analysis process. This paper presents an example implementation of one of the approaches to HAR based on the use of interpretability and visual expressiveness inherent in skeletal structures. In this work, an ad hoc network with Long Short-Term Memory (LSTM) for human activity classification is designed and implemented, which has been trained and tested in the domain of sports exercises. LSTM incorporation of memory cells and gating mechanisms not only mitigates the vanishing gradient problem but also enables LSTMs to selectively retain and utilize relevant information over extended sequences, making them highly effective in tasks with complex temporal dependencies. The problem with a fading gradient is quite common in deep neural networks and is that if the error is back propagated during the training of the network, the gradient can decrease strongly as it travels through the layers of the network to the initial layers. This can lead to the fact that the weights in the initial layers are practically not updated, which makes training of these layers impossible or slows down its process. The resulting solution can be used to create a real-time virtual fitness assistant. The resulting solution can be used to create a real-time virtual fitness assistant. In addition, this approach will make it possible to create interactive training applications with visualization of human skeletal structure, motion analysis and monitoring systems in the field of medicine and rehabilitation, as well as for the development of security systems with access control based on the analysis of visual data on the movement of human body parts.
- ПубликацияОткрытый доступVirtual Listener: A Turing-like test for behavioral believability(2020) Chubarov, A. A.; Tikhomirova, D. V.; Shirshova, A. V.; Veselov, N. O.; Samsonovich, A. V.; Тихомирова, Дарья Валерьевна; Самсонович, Алексей Владимир© 2020 The Authors. Published by Elsevier B.V.Virtual Listener (VL) is a generalized prototype of a virtual character based on the principles of cognitive architecture eBICA, which uses facial expressions and body language (eyes movements, head rotation) to keep social and emotional contact with the user. Such contact also implies that VL needs to perceive user's facial expression and gaze, and in the long term- A lso intonation of the user's voice, the sentiment and content of user's speech. In this work, we present an approach to modeling a perceptive 3D virtual listener with emotional capabilities. The virtual character has a 3D face that performs real-time, realistic and believable facial expression dynamics. Our primary goal in this study was to evaluate the concept: E.g., to find out whether a virtual-agent-generated behavior can engender feelings of rapport in human speakers comparable to those that a real human listener can cause? At the same time, this article serves a limited purpose and only describes our current progress so far in addressing this question.
- ПубликацияОткрытый доступSocial–Emotional Conversational Agents Based on Cognitive Architectures and Machine Learning(2024) Dolgikh,A.A.; Samsonovich,A.V.; Tikhomirova,D.V.; Долгих, Анатолий Андреевич; Самсонович, Алексей Владимир; Тихомирова, Дарья Валерьевна