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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
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‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Dark personalities on Facebook: Harmful online behaviors and language

Computers in Human Behavior. 2018. Vol. 78. P. 151–159.
Bogolyubova O., Panicheva P., Tikhonov R., Ivanov V., Ledovaya Y.

*Реализация соц. сети Facebook запрещена на территории России по основаниям осуществления экстремистской деятельности.

The goal of this paper was to assess the connection between dark personality traits and engagement in harmful online behaviors in a sample of Russian Facebook users, and to describe the language they use in online communication. A total of 6724 individuals participated in the study (mean age = 44.96 years, age range: 18–85 years, 77.9% — female). Data was collected via a purpose-built application, which served two purposes: administer the survey and download consenting user's public wall posts, gender and age from the Facebook profile. The survey included questions on engagement in harmful online behaviors and the Short Dark Triad scale; 15,281 wall posts from 1972 users were included in the dataset. These posts were subjected to morphological, lexical and semantic analyses. More than 25% of the sample reported engaging in harmful online behaviors. Males were more likely to send insulting or threatening messages and post aggressive comments; no gender differences were found for disseminating other people's private information. Psychopathy and male gender were the unique predictors of engagement in harmful online behaviors. A number of significant correlations were found between the dark traits and numeric, lexical, morphological and semantic characteristics of the participants' posts.

Research target: Computer Science Philology and Linguistics Psychology
Priority areas: humanitarian IT and mathematics
Language: English
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DOI
Text on another site
Keywords: Facebookdistributional semanticsкластеризация словaggressionдистрибутивная семантикаsemantic clusteringDark TriadТемная триадаФейсбуккиберагрессия
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