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6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

作者： Kuc, Alexander Batmanova, Alisa Maksimenko, Vladimir Immanuel Kant Baltic Federal University Baltic Center for Artificial Intelligence and Neurotechnology Kaliningrad Russia Financial University The Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Innopolis University Neuroscience and Cognitive Technology Lab Republic of Tatarstan Russia

ISBN: (数字)9781665461740

ISBN: (纸本)9781665461740

We have trained an artificial neural network (ANN) to predict correct and erroneous interpretations of complex visual stimuli, Necker cubes with different levels of ambiguity. For the selected configuration of ANN, the classification accuracy was 88%. These results are prospective for developing the new-generation assistive technologies. © 2022 IEEE.

关键词： Neural networks

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Information Security Control Engineering for an Industrial Company with “1C” Software

Information Security Control Engineering for an Industrial C...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Alexey Yu. Zalozhnev Dmitry V. Chistov Active Systems Laboratory V.A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University Moscow Russia

The issues related to information security control engineering for an industrial company with “1C” software are considered in this paper. The article discusses the types of threats to the company's information resources, including collective knowledge stored on electronic media. The paper contains standards and other documents governing the development and operation of an industrial company's information security. A brief description of the 1C software platform and 1C ERP is given. To prevent unauthorized access to the IT infrastructure and misappropriation of information important to the client, a set of needed organizational and technical measures with the use of 1C software products is considered. Some practical conclusions are also given.

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Forecasting Time Series of Financial Indicators Using Artificial Neural Networks

Forecasting Time Series of Financial Indicators Using Artifi...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Savostyanov Aleksandr Grineva Natalia Stroeva Ekaterina Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russia

This paper explores the possibility of using several different architectures of artificial neural networks to construct forecasts for financial indicators of a group of countries. The analysis is based on data collect...

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Econometric Modeling of the Company's Intellectual Capital in the Context of Digitalization

Econometric Modeling of the Company's Intellectual Capital i...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Natalia Grineva Svetlana Mikhailova Natalia Kontsevaya Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Department of Mathematics Financial University Under the Government of the Russian Federation Moscow Russia

An approach to assessing the value of intellectual capital has been developed. Models demonstrating the impact of intellectual capital and its components on the financial performance of companies implementing a digital transformation strategy have been constructed. The influence of R&D investments on company performance has been evaluated, and strategic modeling of enterprise revenue growth rates has been conducted.

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BLESSEMFLOOD21: ADVANCING FLOOD analysis WITH A HIGH-RESOLUTION GEOREFERENCED dataSET FOR HUMANITARIAN AID SUPPORT

arXiv

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arXiv 2024年

作者： Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. Computer Vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery. © 2024, CC BY-NC-ND.

关键词： Floods

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Regional Migration in Russia: Dynamics, Features, Expectations 14

Regional Migration in Russia: Dynamics, Features, Expectatio...

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Borisova, Ludmila Zhukova, Galina University under the Government of the Russian Federation Department of Mathematics Financial Moscow Russia Financial University Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9781665412308

The paper examines the new features and dynamics of internal migration processes in the Russian Federation at the present time (2015-2020). The results of a comparative statistical study on the regions of Russia are presented. In the context of global integration, citizens of all countries, including Russia, have the opportunity to study and analyze in more detail issues related to the financial, economic, social, labor and other policies of various regions and states, and other aspects of life. Therefore, there was a desire, and sometimes a need to change the place of their further residence (both within the country and abroad). At present, internal migration should be considered as a global social phenomenon, a powerful source of demographic improvement. Various aspects of migration processes that are currently observed are studied by many authors. This is understandable: in the life of any state, population migration plays an important role, affecting all spheres of life of the peoples of the countries. Migration processes for Russia are one of the main factors that determine the socio-economic development of modern Russian society, its demographic well-being. Internal migration of the population affects the socio-economic relations in the country, solves the issues of labor and employment of the population, as it allows millions of Russians to find new jobs. Issues of population migration should be regularly monitored and analyzed in order to correctly determine the reserves and plan the growth of the country's population on this basis, and to rationally and responsibly conduct the state's migration policy. The paper examines the new features and dynamics of internal migration processes in the Russian Federation at the present time (2015-2020). The results of a comparative statistical study on the regions of Russia are presented. © 2021 IEEE.

关键词： machine learning

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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix

Method for Finding an Investment Strategy in the Case of a S...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia

An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

关键词： Large-scale systems Sparse matrices Risk management Covariance matrices Stock markets Portfolios Investment

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Can a bifurcation diagram contain loops?

Can a bifurcation diagram contain loops?

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2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021

作者： Palshin, Gleb P. Ryabov, Pavel E. Sokolov, Sergei V. Financial University Department of Data Analysis and Machine Learning Moscow Russia Moscow Institute of Physics and Technology National Research University Department of Theoretical Mechanics Moscow Region Dolgoprudny Russia

ISBN: (纸本)9781665424066

The bifurcation diagram plays a major role in the study of the phase topology of completely Liouville-integrable Hamiltonian systems. In the works of A.T. Fomenko and A.V. Bolsinov, the problem of the permissible form of bifurcation diagrams is formulated. In particular, can a bifurcation diagram contain loops? The answer is positive. In this paper, the critical set of the integral mapping and the bifurcation diagram, which contains or almost contains a loop, is given by the example of the vortex dynamics problem. © 2021 IEEE.

关键词： Bifurcation (mathematics)

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Combining Computer Vision and Word Processing to Classify Film Genres

Combining Computer Vision and Word Processing to Classify Fi...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Nikita Andriyanov Vitaly Dementev Data Analysis and Machine Learning Department Financial University under the Government of the Russian Federation Moscow Russia Radio Engineering Telecommunications and Information Security Department Ulyanovsk State Technical University Ulyanovsk Russia

The paper investigates various deep learning approaches for classifying movies by genre. To prepare the initial dataset, a system for parsing textual information and images from the Internet Movie database, IMDB (https://***/) was implemented. Pandas (Python library) methods were applied to generate the dataset. Particular attention was paid to the preparation of target information and the choice of the learning loss function due to the fact that the problem was reduced to a multi-label classification. A number of transfer learning algorithms have been implemented for convolutional neural network models and transformer architectures when processing movie posters, as well as simple machine learning models and deep learning transformer-type models for text classification. The paper proposes a method for complexing extracted features from heterogeneous data in another neural network to improve classification accuracy. It was possible to achieve an increase in the average cosine distance for the test sample by 3% higher than when using standard models and about 7% in sense of intersection over union metric.

关键词： Measurement Deep learning Computer vision Computational modeling Computer architecture Motion pictures Transformers

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Comparison of MS Custom Vision Auto machine learning with Algorithms Implementation Methods

Comparison of MS Custom Vision Auto Machine Learning with Al...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Nikita Andriyanov Yulia Kamalova Vitaly Dementiev Data Analysis and Machine Learning Department Financial University Under the Government of the Russian Federation Moscow Russia Radio Engineering Telecommunications and Information Protection Department Ulyanovsk State Technical University Ulyanovsk Russia

This study is devoted to the acquaintance, application, and implementation of computer vision models in the automated Microsoft Custom Vision service, as well as comparing the results of such models with the results of work personally assembled using the PyTorch/Tensorflow frameworks. The base models were represented by convolutional neural networks, which are applied to the image classification task. The study showed that the functionality of the Custom Vision service, although not great, but they represent really strong models, because without searching for the architecture, tuning the hyperparameters of the convolutional neural network, it is not possible to achieve results comparable to automatic machine learning solutions. However, the disadvantages of such models are the complexity of their deployment. As data for the study, we used databases of images of pollen grains of various plants, as well as aviation security images recorded by introscopes at airports. The experiment showed that in terms of precision and recall metrics, the Custom Vision solution provides an advantage of about 2-3% on balanced data and up to 15% on unbalanced ones. Thus, it is shown that using machine learning cloud services, you can get the primary baseline model, which programmers can further develop and improve, and other users can try to launch in production without knowledge of machine and deep learning technologies if the model quality estimation is quite high.

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