Детектирование эмоций в речи с использованием долгой краткосрочной памяти
Попова А. С., Рассадин А. Г., Пономаренко А. А.
, , , Интеллектуальные системы. Теория и приложения 2022 Т. 26 № 1 С. 250-254
Currently, video surveillance systems are becoming more widespread. One of the main goals of such systems is to control and track a person’s movement. The solution of this problem allows us to solve such applied problems as tracking the occupancy of various premises (whether shopping facilities or educational and cultural institutions), creating a motion heatmap or organizing control of access to ...
Added: January 31, 2023
, , В кн. : Сборник трудов IV Международной конференции и молодёжной школы "Информационные технологии и нанотехнологии" (ИТНТ 2018). : Самара : Предприятие "Новая техника", 2018. Гл. 124. С. 916-924.
In this paper we examine the age and gender video-based recognition problem using deep convolutional neural networks. The comparative analysis of classifier fusion algorithms to aggregate decisions for individual frames is presented. In order to improve the age and gender identification accuracy we implement the video-based recognition system with several aggregation methods. We provide the ...
Added: October 18, 2018
Распознавание изолированных слов на основе взвешенного голосования дикторозависимых нейросетевых моделей
, Информационные технологии 2020 Т. 26 № 5 С. 290-296
article deals with the problem of isolated words recognition based on deep convolutional neural networks. The use of existing recognition systems in practice is limited by an insufficiently high degree of their reliability functioning in conditions of intense acoustic noise, such as street noise, sounds from passing vehicles, etc. Nowadays, the most accurate recognition methods are characterized by ...
Added: September 2, 2020
, , et al., / arxiv. Series 2110.08626 "Machine Learning". 2021.
The paper considers the problem of velocity model acquisition for a complex media based on boundary measurements. The acoustic model is used to describe the media. We used an open-source dataset of velocity distributions to compare the presented results with the previous works directly. Forward modeling is performed using the grid-characteristic numerical method. The inverse ...
Added: May 24, 2022
, , et al., , in : Proceedings of 4th IEEE World Forum on Internet of Things WF-IoT 2018. : NY : IEEE Computer Society, 2018. P. 625-628.
Autonomous taxies are in high demand for smart city scenario. Such taxies have a well specified path to travel. Therefore, these vehicles only required two important parameters. One is detection parameter and other is control parameter. Further, detection parameters require turn detection and obstacle detection. The control parameters contain steering control and speed control. In ...
Added: February 14, 2018
Association for Computational Linguistics, 2019
The 4th Workshop on Representation Learning for NLP (RepL4NLP) will be hosted by ACL 2019 and held on 2 August 2019. The workshop is being organised by Isabelle Augenstein, Spandana Gella, Sebastian Ruder, Katharina Kann, Burcu Can, Alexis Conneau, Johannes Welbl, Xian Ren and Marek Rei; and advised by Kyunghyun Cho, Edward Grefenstette, Karl Moritz ...
Added: November 1, 2019
, , , Journal of Physics: Conference Series 2021 Vol. 1740 Article 012031
Demographic and population structure inference is one of the most important problems in genomics. Population parameters such as effective population sizes, population split times and migration rates are of high interest both themselves and for many applications, e.g. for genome-wide association studies. Hidden Markov Model (HMM) based methods, such as PSMC, MSMC, coalHMM etc., proved ...
Added: May 17, 2021
Система постановки произношения на основе сверточных нейронных сетей и информационной теории восприятия речи
, Информационные технологии 2019 Т. 25 № 5 С. 313-318
We consider a problem of computer assisted language and pronunciation learning based on the deep learning methods and the information theory of speech perception. In order to improve the efficiency of testing of pronunciation quality, we propose to train a convolutional neural network using the best reference utterances from the user. The experimental results proved ...
Added: May 29, 2019
Deep convolutional neural networks capabilities for binary classification of polar mesocyclones in satellite mosaics
, , et al., Atmosphere 2018 Vol. 9 No. 426 P. 1-23
Polar mesocyclones (MCs) are small marine atmospheric vortices. The class of intense MCs, called polar lows, are accompanied by extremely strong surface winds and heat fluxes and thus largely influencing deep ocean water formation in the polar regions. Accurate detection of polar mesocyclones in high-resolution satellite data, while challenging, is a time-consuming task, when performed ...
Added: November 26, 2020
, , , Optical Memory and Neural Networks (Information Optics) 2018 Vol. 27 No. 1 P. 23-31
We discuss the video classification problem with the matching of feature vectors extracted using deep convolutional neural networks from each frame. We propose the novel recognition method based on representation of each frame as a sequence of fuzzy sets of reference classes whose degrees of membership are defined based on asymptotic distribution of the Kullback–Leibler ...
Added: February 9, 2018
, , et al., , in : WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining. : Association for Computing Machinery (ACM), 2020. P. 528-536.
Added: October 28, 2020
Simulating the time projection chamber responses at the MPD detector using generative adversarial networks
, , et al., The European Physical Journal C - Particles and Fields 2021 Vol. 81 Article 599
High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the computing time available to the experiments. In this work, we demonstrate a new approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at ...
Added: July 12, 2021
, , , Siberian Journal of Life Sciences and Agriculture 2021 Т. 13 № 3 С. 103-118
Цель. Разработка модели сверточной нейронной сети для определения вне корневых заболеваний яблонь по фотографиям листьев с мобильного телефона. Методы и материалы исследования. Материалом для исследований по служили размеченные изображения с различными видами внекорневых заболе ваний яблони, опубликованные в открытом доступе платформы Kaggle. Ме тоды исследования: теория проектирования и разработки информационных систем, программирования, методы аугментации и расширения датасетов для задач компьютерного зрения, методы настройки гиперпараметров ...
Added: November 17, 2021
, , Journal of Physics: Conference Series 2018 Vol. 1085 P. 1-6
In the research, a new approach for finding rare events in high-energy physics was tested. As an example of physics channel the decay of \tau -> 3 \mu is taken that has been published on Kaggle within LHCb-supported challenge. The training sample consists of simulated signal and real background, so the challenge is to train ...
Added: December 11, 2017
, , , , in : Pattern Recognition and Machine Intelligence. 7th International Conference, PReMI 2017, Kolkata, India, December 5-8, 2017, Proceedings. Lecture Notes in Computer Science book series (LNCS, volume 10597). : Springer, 2017. P. 351-357.
In this paper, we consider several compression techniques for the language modeling problem based on recurrent neural networks (RNNs). It is known that conventional RNNs, e.g., LSTM-based networks in language modeling, are characterized with either high space complexity or substantial inference time. This problem is especially crucial for mobile applications, in which the constant interaction with ...
Added: October 14, 2018
Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data
, , et al., Remote Sensing 2022 Vol. 14 No. 9 Article 2224
The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The ...
Added: November 14, 2022
, , , , in : Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science. Vol. 11179.: Berlin : Springer, 2018. P. 155-167.
This research is motivated by sustainability problems of oil palm expansion. Fast-growing industrial Oil Palm Plantations (OPPs) in the tropical belt of Africa, Southeast Asia and parts of Brazil lead to significant loss of rainforest and contribute to the global warming by the corresponding decrease of carbon dioxide absorption. We propose a novel approach to ...
Added: January 23, 2019
Piscataway : IEEE, 2020
2020 International Joint Conference on Neural Networks (IJCNN) held virtually, as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020. IJCNN 2020 is jointly organized by the IEEE Computational Intelligence Society (CIS) and the International Neural Network Society (INNS). For IJCNN 2020 (and when WCCI is organized in even-numbered years) IEEE CIS ...
Added: October 15, 2020
, , , in : Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers. Communications in Computer and Information Science. Vol. 1086.: Springer, 2020. P. 154-159.
In this paper, a deep learning method study is conducted to solve a new multiclass text classification problem, identifying user interests by text messages. We used an original dataset of almost 90 thousand forum text messages, labeled for ten interests. We experimented with different modern neural network architectures: recurrent and convolutional, as well as simpler ...
Added: November 7, 2019
, , , , in : Supercomputing: 8th Russian Supercomputing Days, RuSCDays 2022, Moscow, Russia, September 26–27, 2022, Revised Selected Papers. Vol. 13708.: Springer, 2022. P. 397-408.
We explore the possibilities of using neural networks to study phase transitions. The main question is the level of accuracy which can be achieved for the estimates of the critical point and critical exponents of statistical physics models. We generate data for two spin models in two dimensions for which analytical solutions exist, the Ising ...
Added: March 31, 2023
, , , Scientific Reports 2020 Vol. 10 P. 19134
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not ...
Added: December 11, 2020
, , in : 2018 Fifth HCT Information Technology Trends (ITT). : IEEE, 2018. P. 1-6.
The law of accelerating returns can be viewed as a concept that describes acceleration of technological progress. The idea is that tools are used for developing more advanced tools that are applied for creating even more advanced tools etc. A similar idea has been implemented in algorithms for advancing artificial intelligence. In this paper, the ...
Added: February 28, 2019
, , et al., Remote Sensing 2022 Vol. 14 No. 22 Article 5837
Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches ...
Added: June 19, 2023
, , et al., Life Science Alliance 2023 Vol. 6 No. 7 Article e202301962
Identifying roles for Z-DNA remains challenging given their dynamic nature. Here, we perform genome-wide interrogation with the DNABERT transformer algorithm trained on experimentally identified Z-DNA forming sequences (Z-flipons). The algorithm yields large performance enhancements (F1 = 0.83) over existing approaches and implements computational mutagenesis to assess the effects of base substitution on Z-DNA formation. We ...
Added: June 9, 2023