Персона:
Артамонов, Алексей Анатольевич

Научные группы

Научная группа

Центр анализа и прогнозирования мирового научно-технологического развития

Организационные подразделения

Организационная единица

Цель ИМО и стратегия развития - системная подготовка высококвалифицированных кадров, способных решать нестандартные задачи при реализации международных научно-технологических и торгово-промышленных проектов для компаний и корпораций ключевых секторов экономики страны.

Фамилия

Артамонов

Имя

Алексей Анатольевич

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Теперь показываю 1 - 10 из 12

Только метаданные
Information Analysis Support for Decision-Making in Scientific and Technological Development
(2020) Onykiy, B.; Antonov, E.; Artamonov, A.; Tretyakov, E.; Артамонов, Алексей Анатольевич
This paper presents the development of an information and analytical system to foster scientific and technological development in a given scientific field. In this work, the main software tools for implementing distributed computing, which involves a set of software components for collecting, processing, and analyzing large amounts of data, are considered. In addition, various approaches for task coordination between different sets of software are discussed and techniques for storing large amounts of data are described. The system architecture and database schema are designed and tested. Nowadays, the intellectualization of individual software agents is a key aspect of a new generation of multiagent systems. For this reason, this paper develops an approach that can organize activities of a large number of software agents to increase system intellectualization through swarm intelligence at the level of individual agents. Three remote servers were used to build and test the system deployment, comprising such components as a platform for monitoring and scheduling workflow, data storage, and a graphical user interface that enables data retrieval and interaction on the Internet.
Только метаданные
Graph visualization of the characteristics of complex objects on the example of the analysis of politicians
(2020) Ulizko, M.; Tukumbetova, R.; Antonov, E.; Artamonov, A.; Антонов, Евгений Вячеславович; Артамонов, Алексей Анатольевич
© 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).The paper considers the task of analyzing complex interconnected objects using graph construction. There is no unified tool for constructing graphs. Some solutions can build graphs limited by the number of nodes, while others do not visually display data. The Gephi application was used to construct graphs for the research. Gephi has great functionality for building and analyzing graphs. The subject of research is a politician with a certain set of characteristics. In the paper an algorithm that enables to automate data collection on politicians was developed. One of the main methods of data collecting on the Internet is web scraping. Web scraping software may access the World Wide Web directly using the HTTP, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a software agent. The data was necessary for constructing graphs and their analysis. The use of graphs enables to see various types of relationships, including mediate. This methodology enables to change the attitude towards the analysis of multidimensional objects.
Только метаданные
Complex Objects Identification and Analysis Mechanisms
(2021) Ulizko, M.; Tukumbetova, R.; Tretyakov, E.; Pronicheva, L.; Artamonov, A.; Проничева, Лариса Владимировна; Артамонов, Алексей Анатольевич
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Currently the volume of information on the Internet global network is inclined to increase. It affects various areas of activity. The paper presents the issue of identifying and analyzing complex objects in the context of information sources devoted to project activity of the US National Institutes of Health (NIH). As part of the solution to the problem, information sources relevant to the provided topic were found, and the data were downloaded with the use of agent techniques. The methods of primary analysis and data processing were developed to create a data storage structures in SQL and NoSQL models. The analysis of the presented database models was conducted, their advantages and disadvantages were revealed. As a result software tools have been developed that provide data representation of a complex object and organization of work with it by web interface.
Только метаданные
TRACER (TRACe route ExploRer): A tool to explore OSG/WLCG network route topologies
(2021) Grigorieva, M.; Tretyakov, E.; Artamonov, A.; Klimentov, A.; McKee, S.; Vukotic, I.; Артамонов, Алексей Анатольевич
© 2021 World Scientific Publishing Company.The experiments at the Large Hadron Collider (LHC) rely upon a complex distributed computing infrastructure (WLCG) consisting of hundreds of individual sites worldwide at universities and national laboratories, providing about half a billion computing job slots and an exabyte of storage interconnected through high speed networks. Wide Area Networking (WAN) is one of the three pillars (together with computational resources and storage) of LHC computing. More than 5 PB/day are transferred between WLCG sites. Monitoring is one of the crucial components of WAN and experiments operations. In the past years all experiments have invested significant effort to improve monitoring and integrate networking information with data management and workload management systems. All WLCG sites are equipped with perfSONAR servers to collect a wide range of network metrics. We will present the latest development to provide the 3D force directed graph visualization for data collected by perfSONAR. The visualization package allows site admins, network engineers, scientists and network researchers to better understand the topology of our Research and Education networks and it provides the ability to identify nonreliable or/and nonoptimal network paths, such as those with routing loops or rapidly changing routes.
Только метаданные
Multiagent System for Monitoring, Analysis and Classification of Data from Procurement Services
(2022) Artamonov, A.; Vasilev, M.; Tukumbetova, R.; Ulizko, M.; Артамонов, Алексей Анатольевич; Тукумбетова, Руфина Рашитовна
Только метаданные
Evaluation of Named Entity Recognition Software Packages for Data Mining
(2024) Sokolov, I.; Antonov, E.; Artamonov, A.; Соколов, Иван Дмитриевич; Антонов, Евгений Вячеславович; Артамонов, Алексей Анатольевич
Только метаданные
Designing a System for Monitoring the Publication Activity of the Scientific Organization
(2024) Malugin, M.; Antonov, E.; Artamonov, A.; Малугин, Матвей Игоревич; Антонов, Евгений Вячеславович; Артамонов, Алексей Анатольевич
Открытый доступ
Evaluating the Effectiveness of Machine Learning Methods for Spam Detection
(2021) Kontsewaya, Y.; Antonov, E.; Artamonov, A.; Антонов, Евгений Вячеславович; Артамонов, Алексей Анатольевич
© 2020 Elsevier B.V.. All rights reserved.Technological advances are accelerating the dissemination of information. Today, millions of devices and their users are connected to the Internet, allowing businesses to interact with consumers regardless of geography. People all over the world send and receive emails every day. Email is an effective, simple, fast, and cheap way to communicate. It can be divided into two types of emails: spam and ham. More than half of the letters received by the user - spam. To use Email efficiently without the threat of losing personal information, you should develop a spam filtering system. The aim of this work is to reduce the amount of spam using a classifier to detect it. The most accurate spam classification can be achieved using machine learning methods. A natural language processing approach was chosen to analyze the text of an email in order to detect spam. For comparison, the following machine learning algorithms were selected: Naive Bayes, K-Nearest Neighbors, SVM, Logistic regression, Decision tree, Random forest. Training took place on a ready-made dataset. Logistic regression and NB give the highest level of accuracy - up to 99%. The results can be used to create a more intelligent spam detection classifier by combining algorithms or filtering methods.
Открытый доступ
Parametric and semantic analytical search indexes in hieroglyphic languages
(2020) Fomina, J.; Safikanov, D.; Artamonov, A.; Tretyakov, E.; Артамонов, Алексей Анатольевич
© 2020 The Authors. Published by Elsevier B.V.Nowadays tremendous amounts of heterogeneous information known as Big Data have completely changed the modern scientific landscape. On the one hand, Big Data provides experts with massive opportunities for conducting research in almost every filed of human endeavor, developing novel technologies and disseminating scientific knowledge. On the other hand, a strong need for creating new techniques to handle Big Data has emerged. This paper is devoted to analytical search indexes, which allow researchers to obtain the information of interest from Big Data. Two types of analytical search indexes, namely parametric and semantic, are considered in the paper. Parametric search index will allow researchers to find information about a technology or material with specific physical parameters which values lie in the given interval, as opposed to the substring search, that enables researchers to find only a particular parameter value. The idea behind using semantic search index in this paper is based on the notion of the technology life cycle, which will allow identifying the current state of a particular technology. Existing models of life cycle have been analyzed and a new model has been suggested. The ontology of physical parameters and the life cycle ontology have been developed. The next step has been the development of the algorithm, which uses the corresponding ontologies to split, filter and mark texts and saves the results to the database for further use as search indexes. The Chinese language has been chosen as a hieroglyphic language for conducting this research.
Открытый доступ
Visualization of dataflows: A casestudy of COVID-19 rumors
(2021) Ulizko, M.; Tretyakov, E.; Tukumbetova, R.; Artamonov, A.; Esaulov, M.; Улизко, Михаил Сергеевич; Тукумбетова, Руфина Рашитовна; Артамонов, Алексей Анатольевич; Есаулов, Михаил Николаевич
© 2021 Copyright for this paper by its authors.One of the most significant and rapidly developing works in the field of data analysis is information flow management. Within the analysis targeted and stochastic dissemination patterns are studied. The solving of such problems is relevant due to the global growth in the amount of information and its availability for a wide range of users. The paper presents a study of dissemination of information messages in open networks on the example of COVID-19. The study was conducted with the use of visual analytics. Informational messages from the largest world and Russian information services, social networks and instant messengers were used as sources of information. Due to the large amount of information on the topic, the authors proposed a pattern of the wave-like dissemination of information on the example of topic clusters on the connection of COVID-19, hydroxychloroquine and 5G. The developed methods can be scaled up to analyze information events of various topics.