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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
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.

 

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Автоматизация сбора статистических данных и интегральный подход к анализу и оценке эффективности функционирования системы дополнительного профессионального образования в РФ

Информатизация образования и науки. 2012. № 4. С. 74–89.
Абрамов А. Г., Bulakina M., Иванников А. Д., Кривошеев А. О., Мосичева И. А.

The paper sets out the results of the first stage of the project on creation and development of the automatized system on the collection, analysis and representation of the information about functioning of the structure of the additional professional education (APE) in the Federal subjects of Russia. The kernel functionality and capabilities of the automatized system are discussed, some statistical indicators of education on the APE programs in Russian Federation in 2011 year, calculated on the base of the data loaded, are resented. The methodology for estimating the efficiency of functioning of the APE structure developed subjected to the requirements of the applicable normative-legal documents and on the results of the analysis of the collected statistical data is considered. The formalization of the procedure of the quantitative and intentional evaluation of the indices of the criteria of the efficiency, built into the methodology, is fulfilled. The methods of the estimation of the efficiency of functioning of an individual educational institution and the integral estimating of the APE structure in the Federal subjects of Russia and in the country in total are observed.

Research target: Education Mathematics Computer Science
Priority areas: engineering science
Language: Russian
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Keywords: статистические данныедополнительное профессиональное образованиеstatistical dataавтоматизированная системаadditional professional educationautomatized systemprocedure for estimating efficiencyметодика оценки эффективности
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