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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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Связь результатов Единого государственного экзамена и успеваемости в вузе

Вопросы образования. 2014. № 1. С. 176–199.
Khavenson T., Soloveva A. A.

This paper analyzes the possibility of predicting efficiency of learning in a higher education institution based on results of the Unified State Exam (USE). In particular, the authors test the hypothesis that USE results in different subjects are equally efficient predictors of further student performance. Methods of regression analysis have been used to assess how preliminary examinations (both total USE points and points in specific subjects) affect academic performance in higher education. The research involved about 19,000 students enrolled at five Russian higher education institutions between 2009 and 2011. As long as the sample included institutions of different profiles, individual regressions were calculated for each faculty. A meta-analysis of regression coefficients was performed later to bring the data together. Average firstyear grade was used as the key university performance factor. It was found out that USE points were only related to performance in the second and the subsequent years through performance in the first year, i. e. indirectly. The research results allow to conclude that predictive capacity of total USE points is high enough to accept this examination as a valid student selection tool. The explained variation in university performance varies from 15 to 35% in different faculties. Predictive capacity of particular subjects making the USE total points is relatively the same, but USE points in mathematics and Russian are often the best predictors of performance. The paper also analyzes the relation between USE points and another student selection tool — results of academic competitions in specific subjects

Research target: Education
Language: Russian
Full text
Keywords: Единый государственный экзамен (ЕГЭ)олимпиады школьниковвступительные экзаменыentrance examinationRussian Unified State Exammeta-analysisмета-анализПрогностическая валидностьpredictive validityhigh school academic competitionsperformance in higher education institutionуспеваемость в ВУЗе
Publication based on the results of:
Detailed Analysis of the Results of Education Quality Research in the Context of Economic Indicators in Education (2013)
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