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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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Декомпозиция трудности заданий теста читательской грамотности

Вопросы образования. 2023. № 3. С. 92–112.
Ivanova A., Antipkina I.

The current study investigates the question of test difficulty decomposition depending on the characteristics of items (such as: format, belonging to the type of text to which the item belongs) and the reader's actions required to answer it (search for information in the text, simple conclusions, complex conclusions, critical interpretation of the text). The sample of the study consisted of fourth grade elementary school students in Krasnoyarsk, who completed the computerized test of reading literacy "Progress" in the spring of 2022. Research method: psychometric modeling using the LLTM+e model. Research hypothesis: the decomposition of item difficulties will help to prove that the reading actions required to complete the tasks will form a hierarchy of difficulties similar to traditional taxonomies (B. Bloom), that is, reading skills aimed at analyzing, synthesizing, interpreting information will give tasks greater difficulty than simple conclusions, and those, in turn, will make tasks more difficult than the reader's actions to find information in the text. The results show that the assignment of items to the group of reader's actions is a significant factor. The size of the effects does not allow us to speak of a strict hierarchy, but when other attributes are controlled, the tasks for information retrieval in an explicit form are easier for students than the tasks for complex conclusions and for critical understanding of the text.

Research target: Education Psychology
Language: Russian
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Keywords: чтениетестированиеreadingtestingначальная школаelementary schoolLLTMLLTMмоделирование трудности заданий
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