Ms
Elena Yasinovskaya
(Plekhanov Russian University of Economics)
9/10/18, 1:30 PM
11. Big data Analytics, Machine learning
Sectional reports
The application of semantic integration methods meets challenges,
which arise during collaboration between IT-specialists and domain
experts at the model building stage. These challenges can
affect correct formalization of the domain as well as the outcome
of the integration in distributed information systems as a whole.
The creation of a collaborative platform for semantic...
Ms
Diana Koshlan
(JINR, LIT)
9/10/18, 1:45 PM
11. Big data Analytics, Machine learning
Sectional reports
Stages of development and operation of specialized agent system concerning collection and analysis of the BRICS countries' scientific publications are considered in this paper. The data are extracted from more than 60 sources of authoritative publications in fields of Chemistry, Physics, Genetics, Biochemistry, Ecology, Geology etc. Algorithms for data analysis used in the system directed to...
Maksim Gubin
(Tomsk Polytechnic University)
9/10/18, 2:00 PM
11. Big data Analytics, Machine learning
Sectional reports
Every modern scientific experiment deals with the processing of large amounts of experimental data (up to exabytes) employing millions of computing processes and delivering corresponding scientific payloads. It raises the task of the efficient management of the batch processing of payloads, that should consider the well-defined grouping mechanism.
Understanding naturally occurring...
Mr
Kamil Khamitov
(Lomonosov Moscow State Univercity)
9/10/18, 2:15 PM
11. Big data Analytics, Machine learning
Sectional reports
One of the most computation complicated tasks during Neural networks development is a training process. It could be considered as high dimensional numerical optimization tasks.
In the modern MapReduce systems, like Apache Spark, it's hard to efficiently implement traditional for Neural networks training gradient-based algorithms or Quasi-newton L-BFGS method, because there are too...
Mr
Sergey Belov
(Joint Institute for Nuclear Research)
9/10/18, 3:00 PM
11. Big data Analytics, Machine learning
Sectional reports
The project aims to create a database of companies and company data and an automated analytical system based on this data. The development of the system will allow credit institutions to obtain information about the links between companies, to carry out a policy of "Know your customer" - to identify the final beneficiaries, to assess risks, to identify relationships between customers. For the...
Sergey Belov
(Joint Institute for Nuclear Research)
9/10/18, 3:15 PM
11. Big data Analytics, Machine learning
Sectional reports
Last years, the prospects for digital transformation of economic processes were actively discussed. It is quite a complex problem having no solution with traditional methods. Opportunities of the qualitative development of the transformation are illustrated by the example of use of Big Data analytics, in particular intellectual text analysis, for the assessment of the needs of regional labour...
Prof.
Vladimir Dimitrov
(University of Sofia)
9/10/18, 3:30 PM
11. Big data Analytics, Machine learning
Sectional reports
В работе исследованы вопросы многомерного анализа данных на основе технологии семейства Business Intelligence и применение этой технологии для анализа продаж. Изучены OLAP технологии и требования к ним, способы реализации, на примере Business Intelligence. Рассмотрены основные положения технологии бизнес интеллекта в Visual Studio, внутренние интерфейсы Microsoft SQL Server. Разработана...
Mr
Mikchail Berezhkov
(Stankin), Prof.
eugene Shchetinin
(Financial University)
9/10/18, 3:45 PM
11. Big data Analytics, Machine learning
Sectional reports
Clustering is a well-known machine learning algorithm which enables the determination of underlying groups in datasets. In electric power systems it has been traditionally utilized for different purposes like defining consumer individual profiles, tariff designs and improving load forecasting.A new age in power systems structure such as smart grids determined the wide investigations of...
Dr
Valery Grishkin
(SPbGU)
9/13/18, 1:30 PM
11. Big data Analytics, Machine learning
Sectional reports
The paper proposes an algorithm for segmentation of text, applied or presented in photorealistic images, characterized by a complex background. Because of its application, the exact location of image regions containing text is determined. The algorithm implements the method for semantic segmentation of images, while the text symbols serve as detectable objects. The original images are...
Prof.
Alexander Bogdanov
(St.Petersburg State University)
9/13/18, 1:45 PM
11. Big data Analytics, Machine learning
Sectional reports
Algorithms for predicting the dynamics of stock options and other assets derivatives for both small times (where one plays on market fluctuations), and medium ones (where trade is stressed at the beginning and closing moments) are well developed, and trading robots are actively used for these purposes.
Analysis of the dynamics of assets for very long time-frames (of several months order) is...
Anna Shaleva
(Saint-Petersburg State University)
9/13/18, 2:00 PM
11. Big data Analytics, Machine learning
Sectional reports
The paper examines the practical issues in developing a speech-to-text system using deep neural networks. The development of a Russian-language speech recognition system based on DeepSpeech architecture is described. The Mozilla company’s open source implementation of DeepSpeech for the English language was used as a starting point.
The system was trained in a containerized environment using...
Dmitry Selivanov
(Saint-Petersburg State University)
9/13/18, 2:15 PM
11. Big data Analytics, Machine learning
Sectional reports
The paper describes a solution that reconstructs the texture in 3D models of archeological monuments and performs their visualization. The software we have developed allows to model the outward surface of objects in various states of preservation. Drawings and photographs of preserved wall fragments and stonework elements are used in the modelling process. Our work resulted in development of a...
Mr
Aleksey Kulnevich
(Dmitrievich)
9/13/18, 2:30 PM
11. Big data Analytics, Machine learning
Sectional reports
There are two popular algorithms for text vector extraction: bag of words and skip-gram. The intuition behind it is that a word can be predicted by context and context can be predicted from a word. The vector size of a word is the number of neurons in the hidden layer.
The task of named entity recognition can be solved by using LSTM neural networks. The features for every word can be...
Anna Shaleva
(Saint-Petersburg State University)
9/13/18, 2:45 PM
11. Big data Analytics, Machine learning
Sectional reports
Currently there are hardly any open access corpora of transcribed speech in Russian that can be effectively used to train those speech recognition systems that are based on deep neural networks—e.g., DeepSpeech.
This paper examines the methods to automatically build massive corpora of transcribed speech from open access sources in the internet, such as radio transcripts and subtitles to...
267.
Particle identification in ground-based gamma-ray astronomy using convolutional neural networks
Evgeny Postnikov
(SINP MSU)
9/13/18, 3:30 PM
11. Big data Analytics, Machine learning
Sectional reports
Modern detectors of cosmic gamma rays are a special type of imaging telescopes (Cherenkov telescopes) supplied with cameras with relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA telescope has 560 pixels of hexagonal structure. Images in such cameras can be analyzed by various deep learning techniques to extract numerous physical and geometrical...
Mr
Vladislav Radishevskiy
(Leonidovich)
9/13/18, 3:45 PM
11. Big data Analytics, Machine learning
Sectional reports
Extraction of information from texts is a crucial task in the area of Natural Language Processing. It includes such tasks as named-entity recognition, relationship extraction, coreference resolution, etc. These problems are being resolved using two approaches. The first one is the rules-based approach and the second one is machine learning. Solutions based on machine learning are currently...
alexey Stankus
(-)
9/13/18, 4:00 PM
11. Big data Analytics, Machine learning
Sectional reports
The use of neural networks significantly expands the possibilities of analyzing financial data and improves the quality indicators of the financial market.
In article we examine various aspects of working with neural networks and Frame work TensorFlow, such as choosing the type of neural networks, preparing data and analyzing the results. The work was carried out on the real data of the...
alexey Stankus
(-)
9/13/18, 4:15 PM
11. Big data Analytics, Machine learning
Sectional reports
In most cases, explicit methods are used for the tasks of financial forecasting The modern possibilities of computer technology already allow the use of neural networks for such problems, but the volumes of their application for forecasting are still not large.
This article compares explicit methods, such as the Capital Asset Pricing Model (CAPM) and linear time series, with the results of...
Dr
Alexander Uzhinskiy
(Dr.)
9/13/18, 4:30 PM
11. Big data Analytics, Machine learning
Sectional reports
We present an approach to predict atmospheric heavy metals contamination by statistical models and machine learning algorithms. The source of the field contamination data is ICP Vegetation Data Management System (DMS). DMS is a cloud platform developed at the Joint Institute of Nuclear Research (JINR) to manage ICP Vegetation data. The aim of the UNECE International Cooperative Program (ICP)...
Mr
Kirill Koshelev
(Viktorovich)
9/13/18, 4:45 PM
11. Big data Analytics, Machine learning
Sectional reports
Распознавания образов – научная дисциплина, целью которой является классификация объектов. Сами объекты называются образами или паттернами. Возможность распознавания опирается на схожесть однотипных объектов. Несмотря на то, что все предметы и ситуации уникальны в строгом смысле, между некоторыми из них всегда можно найти сходства по тому или иному признаку. Отсюда возникает понятие...
Mr
Anton Vorontsov
(-)
9/13/18, 5:00 PM
11. Big data Analytics, Machine learning
Sectional reports
In this paper the usage of convolution neural networks considers for solving the problem of emotion recognition by face expression images. Emotion recognition is a complex task and the result of recognition is highly dependent on the choice of the neural network architecture. In this paper various architectures of convolutional neural networks were reviewed and there were selected the most...
Dr
Alexey Averkin
(Plekhanov Russian University of Economics), Mr
Sergey Yarushev
(Plekhanov Russian University of Economics)
9/13/18, 5:15 PM
11. Big data Analytics, Machine learning
Sectional reports
In this paper we consider approach to data analysis and time series forecasting based on hybrid models. This models contains a Deep NN models and Neuro-Fuzzy networks. We are show an overview of new approaches for data science field - time series and data analysis. Also, we propose our models of DL and Neuro-Fuzzy Networks for this task. Finally we show possibility of using this models for...
