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News
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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Интернет-занятость в Нижегородской области: социально-демографический портрет нижегородского "фрилансера"

Политика и общество. 2017. № 1. С. 84–91.
Khusyainov T.

The goal of this work is the perception and analysis of certain data from the sociological survey of online employment – the socio-demographic characteristics of the online employees residing in Nizhny Novgorod and Nizhny Novgorod Region. The author highlights the gender and age structure of this category, family status, level of education and its discipline (technical, humanitarian, or natural scientific), as well as defines the fields of activity selected by the online employees depending on the gender and sphere of education. The research was conducted over the period of February-May of 2013; 160 respondents were polled. As demonstrated by the results of the survey, the main category of the online employed population is represented by young people with high education; similar to other spheres, gender margin is gradually fades away, but still evident in the choice of activity: women lean more towards working with the texts, while men are prone to programming. Although, it is not a strict rule.  

Priority areas: sociology
Language: Russian
DOI
Keywords: информационное обществоinformation societyсетевое взаимодействиеteleworkтелеработапостиндустриальное обществофрилансpost-industrial societyatypical employmentfreelanceИнтернет-занятостьатипичная занятостьRemote employmentДистанционная занятостьУдалённая занятостьАтипичные формы трудаonline interactiondistance employmentAtypical forms of laborOnline employment
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