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News
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.
May 22, 2026
HSE Graduates AI Project Wins at TECH & AI Awards
Daria Davydova, graduate of the HSE Graduate School of Business and Head of the AI Implementation Unit at the Artificial Intelligence Department of Alfa-Bank, received a prize at the TECH & AI Awards. She was awarded for the best AI solution for optimising business processes. The winners were determined as part of the VII Russian Summit and Awards on Digital Transformation (CDO/CDTO Summit & Awards).
May 20, 2026
HSE University Opens First Representative Office of Satellite Laboratory in Brazil
HSE University-St Petersburg opened a representative office of the Satellite Laboratory on Social Entrepreneurship at the University of Campinas in Brazil. The platform is going to unite research and educational projects in the spheres of sustainable development, communications and social innovations.

 

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ОБРАБОТКА И АНАЛИЗ РЕЗУЛЬТАТОВ МОНИТОРИНГОВ ДЛЯ УПРАВЛЕНИЯ ФОРМИРОВАНИЕМ УСЛОВИЙ КАЧЕСТВЕННОГО ОБРАЗОВАНИЯ

Моделирование, оптимизация и информационные технологии. 2017. Т. 5. № 4. С. 1–18.
Shvindt A.

The article reviews models and procedures for processing and evaluation of monitoring results, including student participation, focused on intellectual support of administrative managerial decisions when developing of conditions and corresponding resources for the achievement of applicable regulatory requirements for the quality of university education. The first stage of processing is normalization of factors which characterize the conditions for quality education, indicators of graduate employability and student-oriented monitoring. The next stage is aimed at building the models of integral evaluation of factors and indicators. With the chosen structure of a model in the form of additive convolution, a parametric identification procedure based on the comparison of results of neural network modeling, visual information and subsequent expert evaluation is proposed. Evaluation of monitoring results consists of ranking and clustering of higher education institutions on the basis of integral assessments of education quality. The sequence of steps in the clustering procedure, which determines probabilistic estimates with the use of expert's views, according to which a great number of higher education institutions are divided into a given number of clusters, is described.

Research target: Education
Priority areas: state and public administration
Language: Russian
Text on another site
Keywords: кластеризацияранжированиеclusteringrankingнормированиеrationingintegral evaluationadditive convolutionинтегральное оцениваниеаддитивная свертка
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