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News
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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?

Machine Learning Methods Application to Search for Regularities in Chemical Data

P. 375–380.
Kiselyova N. N., V.A. Dudarev, Stolyarenko A. V.

The possibility of searching for classification regularities in large arrays of chemical information by means of machine learning methods is discussed. Tasks peculiarities in inorganic chemistry and materials science are considered. The short review of these methods applications to inorganic chemistry and materials science is presented. The system for computer-assisted inorganic compounds design based on machine learning methods has been developed. The developed system usage makes it possible to predict new inorganic compounds and estimate some of their properties without experimental synthesis. The results of this information-analytical system application to inorganic compounds design are promising for new materials search.

Language: English
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Keywords: databasemachine learninginorganic chemistrydesign of inorganic compounds

In book

Selected Papers of the XIX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2017) Moscow, Russia, October 9-13, 2017.
CEUR-WS, 2017.
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