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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
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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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?

Self-adaptive Intelligent System for Mass Evaluation of Real Estate Market in Cities

P. 81–87.
Alexeev A., Alexeeva I., Yasnitsky L.

This article is devoted to the method of creating an intelligent neural
network system. Unlike existing similar systems, the proposed system does not
require frequent updates, because it is able to adapt itself to the constantly
changing state of the economy and to the peculiarities of a particular region.
Besides, the proposed system allows performing scenario forecasting of regional
real estate markets depending on virtually changing economic parameters such
as the dollar rate, the market price of oil, gross domestic product and gross
regional product, the volume of housing construction in the region, the
parameters of the state’s credit policy, etc.

Language: English
Full text
DOI
Text on another site
Keywords: оценка недвижимостиreal estate marketискусственная нейронная сеть scenario forecastingсценарное прогнозирование экономикиartificial neural network

In book

Advances in Intelligent Systems and Computing
Vol. 850: Digital Science. , Switzerland: Springer, 2019.
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