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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Ivanyuk, Vera Tsvirkun, Anatoly Soloviev, Vladimir Feklin, Vadim Sunchalina, Anna Kravchenko, Oleg Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia Laboratory of Management of the Development of Large-scale Systems V.A. Trapeznikov Institute of Control Sciences of Ras Moscow Russia

The study considers methods for forecasting financial time series. We construct an ensemble forecast based on the linear forecast, ETS forecast, and neural forecast methods. Thereupon, we evaluate the quality of the f... 详细信息

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Time series

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Support System for Choosing an Individual Educational Trajectory at Vasyl' Stus Donetsk National University 9

Support System for Choosing an Individual Educational Trajec...

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9th IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2022

作者： Chaikovskyi, Pavlo Vietrov, Oleh Baiev, Artem Teletska, Yana Strutovskyi, Maksym Vasyl' Stus Donetsk National University Educational and Practical Laboratory of Information Security Vinnytsia Ukraine Vasyl' Stus Donetsk National University Department of Applied Mathematics Vinnytsia Ukraine Vasyl' Stus Donetsk National University Interdepartmental Laboratory of Machine Learning and Intelligent Data Analysis Vinnytsia Ukraine

ISBN: (纸本)9798350398915

Members of Interdepartmental laboratory of machine learning and intelligent data analysis Vasyl' Stus Donetsk national university are developing a software that helps students choose minors and other additional subjects, as well as proper tooling for teachers and staff. Described process of development, its specifics and addressed direction and possible technologies that may serve the scaling of the system. © 2022 IEEE.

关键词： Gale-Shapley algorithm ICS individual educational trajectory

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Applying convolutional neural networks for stock market trends identification

arXiv

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arXiv 2021年

作者： Zolotareva, Ekaterina Data Analysis and Machine Learning Department Financial University under the Government of the Russian Federation 38 Shcherbakovskaya St. Moscow105187 Russia

In this paper we apply a specific type ANNs - convolutional neural networks (CNNs) - to the problem of finding start and endpoints of trends, which are the optimal points for entering and leaving the market. We aim to explore long-term trends, which last several months, not days. The key distinction of our model is that its labels are fully based on expert opinion data. Despite the various models based solely on stock price data, some market experts still argue that traders are able to see hidden opportunities. The labelling was done via the GUI interface, which means that the experts worked directly with images, not numerical data. This fact makes CNN the natural choice of algorithm. The proposed framework requires the sequential interaction of three CNN submodels, which identify the presence of a changepoint in a window, locate it and finally recognize the type of new tendency - upward, downward or flat. These submodels have certain pitfalls, therefore the calibration of their hyperparameters is the main direction of further research. The research addresses such issues as imbalanced datasets and contradicting labels, as well as the need for specific quality metrics to keep up with practical applicability. This paper is the full text of the research, presented at the 20th International Conference on Artificial Intelligence and Soft Computing Web System (ICAISC 2021). © 2021, CC BY-NC-ND.

关键词： Image recognition

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A machine learning Approach to Exploring Ecological Determinacy of Socially Significant Diseases in Moscow

A Machine Learning Approach to Exploring Ecological Determin...

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

作者： Anna Marunko Tatiana Zolotova Faculty of Information Technology and Big Data Analysis Financial University Under the Government of the Russian Federation Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

A study of the relationship between some selected environmental indicators and the presence of socially significant diseases in the population of Moscow was carried out. A statistically significant correlation was confirmed. machine learning models were built during the regression analysis. A web interface was developed to automate the processes of predicting the population’s morbidity.

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learning invariances with Stationary Subspace analysis

Learning invariances with Stationary Subspace Analysis

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Frank C. Meinecke Paul von Bünau Motoaki Kawanabe Klaus-R. Müller Machine Learning Group Department Computer Science Technical University Berlin Berlin Germany Intelligent Data Analysis Group Fraunhofer FIRST (IDA) Berlin Germany

Recently, a novel subspace decomposition method, termed `Stationary Subspace analysis' (SSA), has been proposed by Bu¿nau et al.. SSA aims to find a linear projection to a lower dimensional subspace such that the distribution of the projected data does not change over successive epochs or sub-datasets. We show that by modifying the loss function and the optimization procedure we can obtain an algorithm that is both faster and more accurate. We discuss the problem of indeterminacies and provide a lower bound on the number of epochs that is needed. Finally, we show in an experiment with simulated image patches, that SSA can be used favourably in invariance learning.

关键词： Space stations Cost function Conferences machine learning Computer science learning systems data analysis Brain modeling Feature extraction Brain computer interfaces

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Pitfalls of epistemic uncertainty quantification through loss minimisation 22

Pitfalls of epistemic uncertainty quantification through los...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Viktor Bengs Eyke Hüllermeier Willem Waegeman Institute of Informatics University of Munich (LMU) Institute of Informatics University of Munich (LMU) and Munich Center for Machine Learning Department of Data Analysis and Mathematical Modeling Ghent University

ISBN: (纸本)9781713871088

Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The latter refers to the learner's (lack of) knowledge and appears to be especially difficult to measure and quantify. In this paper, we analyse a recent proposal based on the idea of a second-order learner, which yields predictions in the form of distributions over probability distributions. While standard (first-order) learners can be trained to predict accurate probabilities, namely by minimising suitable loss functions on sample data, we show that loss minimisation does not work for second-order predictors: The loss functions proposed for inducing such predictors do not incentivise the learner to represent its epistemic uncertainty in a faithful way.

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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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Methodology for Applying IIoT Architecture to Design IIOT Systems in the Agricultural Sector of the Russian Federation

Methodology for Applying IIoT Architecture to Design IIOT Sy...

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2023 Seminar on Information Computing and Processing, ICP 2023

作者： Makoveichuk, Krystina A. Olifirov, Alexander V. Petrenko, Sergei A. Makoveichuk, Yan T. Kazak, Anatoliy N. Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia V.I. Vernadsky Crimean Federal University Department of Economics and Finance Yalta Russia Saint Petersburg Electrotechnical University 'LETI' Saint Petersburg Department of Information Security Kazan Russia V.I. Vernadsky Crimean Federal University Department of Management and Tourism Business Yalta Russia

ISBN: (纸本)9798350369830

The article presents the results of the study of Industrial Internet of Things (IIoT) technologies for the agricultural sector, taking into account international experience for the period 2004 - 2021, and proposes a methodology for applying IIoT architecture to design IIOT systems in the agricultural sector of Russia. A two-factor classification of IIoT systems in agriculture has been made. A typical conceptual scheme of intelligent IIoT system for viticulture and winemaking has been developed. The architecture of a typical IIoT system is analyzed, taking into account the observance of information security in data processing and transmission. An analysis of the current state and prospects for the implementation of IoT in the Russian wine industry has been carried out. A business process model is presented in the concept of BPME (BPM Everywhere) for the production of a certain brand of wine. The model in a non-decomposed form shows sub-processes with and without IoT, using traditional 'manual' methods, as well as taking into account the impact of various business processes on production efficiency and product quality. The reasons for the lag and growth drivers for the use of IIoT in agrosector are identified. © 2023 IEEE.

关键词： Wine

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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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