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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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Психометрическое моделирование: систематическая критика базовых допущений и некоторые альтернативы

Сибирский психологический журнал. 2022. № 86. С. 6–25.
Tyumeneva Y. A.

Due to the popularity of psychometrics, where probabilistic modelling takes a leading position, it seems useful to present a critical view of these techniques, which, unfortunately, is not extensively discussed, especially in the Russian-language literature. This paper is a systematic review of the growing criticism of several assumptions of psychometrics: 1) the existence of unobservable personality traits and abilities which are supposed to determine the performance of test items; 2) the stochastic nature of traits, and as a consequence, the need for probabilistic models to study them; 3) aggregate data represent individual characteristics; 4) as in the natural sciences, models in psychometrics help reveal psychological reality that is not directly observable; 5) if testing the agreement of empirical data with a model yields a positive result, then the model correctly describes reality. These assumptions are criticized on the basis of the circular nature of the definitions of the traits themselves, logical errors ingrained in the assumptions, the methodological mixture of variability and randomness regarding behavior, the lack of a causal link between inter-individual variation in responses and individual responses; superficial analogies with models in the natural science to which psychometrics refers, as well as the substitution of scientific tasks for instrumental and pragmatic ones. We conclude that modelling in psychometrics is counterproductive if used as a method of exploring the psychological reality behind the test. Some alternative practices of quantitative research are discussed. For example, testing the existence of variation at the level of the individual and the experimental search for its explanation. There are other possible alternatives, such as, a network perspective on psychological phenomena; faceted theory; or observation-oriented modeling. Although this kind of research is much more difficult to implement than standard “goodness-of-fit” tests, it is probably this kind of research that can provide an increase in psychological knowledge.

Research target: Psychology
Language: Russian
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Keywords: моделированиереализмmeasurementrealismрепрезентацияrepresentationпсихометрикаmodellingpsychometricsдопущениячерта личности измерениеlatent constructtraitassumptionsлатентный конструкт
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