2018. No. 4 (46)

This article offers a long short-term memory (LSTM) based structured prediction model taking into account existing approaches to sequence tagging tasks and allowing for extraction of user opinions from reviews. We propose a model configuration and state transition rules which allow us to use past predictions of the model alongside sentence features. We create a body of annotated user reviews about mobile phones from Amazon for model training and evaluation. The model trained on reviews corpus with recommended hyperparameter values. Experiment shows that the proposed model has a 4.51% increase in the F1 score for aspects detection and a 5.44% increase for aspect descriptions compared to the conditional random field (CRF) model with the use of LSTM when F1 spans are matched strictly.
      The extraction of user opinions on mobile phones from reviews outside of the collected corpus was conducted as practical confirmation of the proposed model. In addition, opinions from other product categories like skin care products, TVs and tablets were extracted. The examples show that the model can successfully extract user opinions from different kinds of reviews. The results obtained can be useful for computational linguists and machine learning professionals, heads and managers of online stores for consumer preference determination, product recommendations and for providing rich catalog searching tools.

This study was conducted under government order of the Ministry of Education and Science of Russia, project No. 8.8184.2017/8.9

      This work focuses on the relevant task of increasing the efficiency of forecasting the dynamics of forest fires spreading in real time. To address the problem, it was proposed to develop a method for operational forecasting the forest fire spread dynamics in the context of unsteadiness and uncertainty based on some advanced information technologies, i.e. artificial intelligence and deep machine learning (the convolutional neural network). As part of the research, both domestic and foreign models for the spread of forest fires were evaluated, and the key limitations of using models in real fire conditions were identified (high degree of dynamism and uncertainty of input parameters, the need to ensure minimum collection time and input parameters, as well as minimum response time of the model). Based on the data obtained, the need to use artificial neural network tools to solve the problem of predicting the forest fire’s spread dynamics was substantiated. A general logic diagram of the method for forecasting the forest fire dynamics in real time has been developed, the main feature of which is the construction of a tree of convolutional neural networks. To enhance the quality of learning convolutional neural networks that implement the function of predicting the spread of forest fires, we propose to create a database of forest fire dynamics.

This study was supported by the Russian Foundation for Basic Research, project No. 18-37-00035 “On the dependence of the dynamics of the development of forest fires on the influence of environmental factors, the nature of forest plantations and the type of fire under conditions of nonstationarity and uncertainty”

      In the international field of public services, the competence approach is used as a basis for developing productivity, innovation and responsibility among employees. In Russia, the competence approach is central to legislative and regulatory documents but has not yet become a working tool. Russia’s transition to the digital economy in accordance with the Federal Program necessitates the transformation of professional qualities and qualification requirements for positions of the state civil service. The development of a single information space of the State Civil Service and the widespread introduction of e-government technologies impose increased requirements on public servants’ competencies in the field of information and communication technologies. However, studies have shown that until now, Russian civil servants consider as a primary priority only those competencies that focus on results, discipline, time and stress management skills, and to a lesser degree adaptability, willingness to change, creativity, initiative, and adopting new ideas and innovations. Management by competences requires an individual approach, taking into account the characteristics of each employee, as well as the development and implementation of competence models, in which all aspects of work in the digital world should be reflected.
      The aim of the study is to develop guidelines for improving official regulations of state civil servants in terms of qualification requirements for competencies in the field of information and communication technologies (ICT). The use of comparative analysis methods in the study of the content of official regulations of state civil service in various subjects of the Russian Federation, as well as an expert survey on the content and current level of development of ICT competencies of civil servants allowed the authors to identify “basic,” “advanced” and “special” components in the structure of competencies. We also propose methodological recommendations for the transformation of ICT competences into digital components that provide an expanded set of knowledge and skills required for the digitalization of the civil service. These changes will allow the HR services of public authorities of the Russian Federation to provide a unified approach to the formation of requirements for the maturity level of digital competencies of applicants seeking positions in the state civil service. It also will help to implement a targeted approach in the formation of programs for the development of personnel potential, taking into account the requirements of digital literacy.

The article is based on the results of studies carried out using budget funds and the 2018 Financial University State Mission “Improvement of information support for personnel management system based on the competence approach and individual career tracking of civil servants”, state registration number AAA-A18-118052490063-1

      At a time when the digital economy is emerging, the preservation and multiplication of intellectual capital, which is currently a key factor in social development, are paramount. The national economy’s orientation toward the use of modern achievements of the digital industry will contribute to a faster transition to the global information society. Digitalization of education will make it possible to use the latest scientific achievements for the development of other areas of life in society.
      This study presents a model that applies distributed registry technologies based on blockchains and smart contracts for the reliable storage and efficient use of date relating to the population’s professional competencies. This model aims to create a unified information environment for interaction between all the actors of the economic system. The authors developed the model for registering professional competencies of the population and their developmental paths based on modern digital technologies. We substantiate the efficiency and security assured by blockchain technology for information storage and transmission. Educational institutions of all levels, governmental authorities controlling education, and people taking part in the educational process are the basic actors of the system presented.
      The proposed model presents the educational level and professional skills of each registered person as an education index (EI), which keeps track of all educational achievements and professional competencies of the participant over their lifetime. When calculating the EI, the authors also propose to consider ratings of the educational institutions responsible for the participant’s professional skills. The implementation of the EI will significantly simplify the process of employing graduates from various educational institutions, as well as the college admissions process. In addition, analytic tools could be used to create ratings of colleges, school departments, and even specific teachers. The registry of professional competencies we developed is directed at the processing and storage of large volumes of data (Big Data). In the future, this will allow us to open access to the registry to employers, pension insurance funds, and other state authorities that require complete and reliable personal data.

This study was supported by the Russian Foundation for Basic Research, project No. 18-410-320002\18 “The concept of innovative management of the development of regional economy in the digitalization era: a project-based approach”

      While evaluating and selecting investment projects, modern companies are confronted with the problem of setting priorities between profitability and riskiness of these projects. Choice of a project on the basis of its profitability significantly increases risks of financial and economic activities and decreases the certainty of achieving the planned financial result. On the other hand, attempts to decrease investment projects risks may not allow one to achieve the desired profitability level. Therefore, it is vital to develop integrated multi-criteria indicators for this purpose.
      This article is the result of the authors’ development of an integral indicator for evaluating investment project efficiency and risks. The developed integral indicator has a matrix form. To compile the integral indicator, three groups of criteria are used: quantitative efficiency criteria, qualitative efficiency criteria and risk evaluation criteria. We propose to divide the qualitative and quantitative criteria into: 1) those defining the commercial (economic) efficiency of projects, 2) those defining their budgetary efficiency; 3) those defining their social efficiency. According to the authors, the list of criteria that define associated risks should include macroeconomic indicators and industry affiliation indicators that provide a comprehensive evaluation of the external economic situation on the corresponding market.
      While evaluating efficiency and riskiness of the given projects, the integral indicator developed by the authors is converted from matrix form into a quantitative indicator that is easy to interpret. The authors propose to use principal component analysis and heuristic methods (including ranking method and hierarchy analysis method) for this purpose.
      The results of this research can be used by companies to select investment projects.

      In this paper we present a decision support system for the sustainable economic growth of the Far Eastern Federal District (FEFD) of the Russian Federation that consists of several regions. Using system dynamics and agent-based modeling methods, a simulation model of the FEFD economy is developed. The model is implemented in the AnyLogic system; it makes it possible to investigate the influence of multiple factors influencing the FEFD economy, for example, increasing rates of investment in fixed assets, average wages rates, subsidies from the federal budget, the forecasted price trends of oil, gas, carbon, diamonds and fishing industry products. One feature of the model is the possibility to analyze the dynamics of development of all regions of the FEFD, as well as taking into account the influence of external macroeconomic factors.
      The decision support system we designed allows us to visualize important characteristics of the FEFD subjects using the map of Russia (GIS) and to save the results of the simulation modelling to the system database. At the same time, we have the possibility of forecasting the dynamics of the Gross Regional Product (a geographic information system) of the Federal District depending on values of the control parameters.
      Different scenarios of the FEFD development are investigated. The realistic scenario assumes stabilization of prices for the main energy resources (oil, gas, coal) and minerals with simultaneous growth of investments in fixed assets. The pessimistic scenario assumes falling prices for energy, diamonds, fishing products, etc., as well as the reduction in the numbers of the economically active population in the Far Eastern Federal district. The optimistic scenario assumes stable increasing demand and prices for the products of all main sectors of the economy of the Federal District, maintaining current growth rates in industry and agriculture.