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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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Применение статистик m* и d* для оценки качества данных психологического исследования с помощью закона Бенфорда (на примере измерения времени реакции)

Моделирование и анализ данных. 2024. Т. 14. № 2. С. 23–44.
Kolachev N.

The objective of the proposed study was to examine the properties of statistics used to assess the conformity of the distribution of the first significant digit to Benford’s Law, m* and d*, with relatively modest sample sizes (10≤ n ≤70). A simulation study was conducted to achieve this goal. Data were simulated following a log-normal distribution with parameters that mimic the distribution of reaction time measurements. The distribution of the first significant digit was examined in standardized values raised to the power of γ; 5≤ γ ≤100. It was found that the statistic m* does not depend on the power of the number, unlike d*. Critical values were established for samples ranging from 10 to 70 observations with an increment of h = 10. It turned out that for small n, the critical values of the statistic d^ are close to asymptotic, while the critical values of the statistic m* are significantly larger. The functionality of the established critical values was tested within the framework of an experimental study: one respondent performed the Stroop cognitive test in accordance with the instructions (control case), while another violated them (experimental case). It was discovered that the statistic d* does not allow for differentiation in the behavior of subjects. Conversely, m* proved sensitive to changes in respondent behavior, and in the experimental case, it significantly more often allowed for the rejection of the null hypothesis regarding the conformity of the distribution of the first significant digit of the standardized value of reaction time to Benford’s Law compared to the control. Thus, a preliminary conclusion is made that the statistic m* is more functional compared to d* in studying the quality of data on reaction time with small n.

Research target: Mathematics Psychology
Language: Russian
Full text
DOI
Keywords: время реакции reaction timeBenford’s lawЗакон Бенфордаm* statisticsd* statisticsстатистика m*статистика d*
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