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September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Диалектные различия между востоком и западом на материале данных Диалектологического атласа русского языка: результаты многомерного шкалирования

Гл. 5. С. 236–260.
Марченко И. А., Ronko R.

This paper presents a classification of Russian dialects based on data from the Dialectological Atlas of the Russian Language, using the method of multidimensional scaling. The main outcome of the study is a map of the Russian dialectal space, which identifies six zones (three western and three eastern) and corresponding sets of dialectal features. The authors focuses on the traditional issue in Russian linguistic geography—the division between western and eastern dialects—while placing greater emphasis on the eastern group. The resulting sets of features are compared with the dialect classification proposed by K.F. Zakharova and V.G. Orlova. The identified features differ from those highlighted by Zakharova and Orlova and focus more closely on the connection between the eastern zones and the historical Rostov–Suzdal dialect. Although the method used is not historically based, its results are open to historical interpretation.

Language: Russian
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DOI
Text on another site
Keywords: диалектологиямногомерное шкалированиеdialectologyMultidimensional scaling (MDS)dialectometryдиалектометрияWest Russian dialectsEast Russian dialectsзападнорусские диалектывосточнорусские диалекты

In book

Исследования по славянской диалектологии. Выпуск 25
Т. 25. , М.: Институт славяноведения РАН, 2025.
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