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  • КЛАССИФИКАТОР ДЛЯ ПРОГНОЗИРОВАНИЯ ОЦЕНКИ ПОЛЬЗОВАТЕЛЕМ ИЕРАРХИЧЕСКОГО ТЕКСТОВОГО МЕНЮ ПО ШКАЛЕ "НРАВИТСЯ/НЕ НРАВИТСЯ"
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News
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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КЛАССИФИКАТОР ДЛЯ ПРОГНОЗИРОВАНИЯ ОЦЕНКИ ПОЛЬЗОВАТЕЛЕМ ИЕРАРХИЧЕСКОГО ТЕКСТОВОГО МЕНЮ ПО ШКАЛЕ "НРАВИТСЯ/НЕ НРАВИТСЯ"

Вестник Рязанского государственного радиотехнического университета. 2016. № 58. С. 94–99.
Varnavsky A., Губко М. В.

Приводится результат построения классификатора для прогнозирования оценки пользователем иерархического текстового меню по шкале «нравится/не нравится». Целью работы является исследование влияния структурных параметров иерархических текстовых меню и индивидуальных психофизиологических характеристик пользователя на удовлетворенность пользователя работой с данным меню и построение классификатора по результатам исследования. Проведен эксперимент, в котором участники работали с 10-ю разными разработанными иерархическими текстовыми меню, после чего оценивали удобство работы в каждом меню по шкале «Нравится/Не нравится». Также испытуемые проходили психофизиологическое тестирование. На основе полученных экспериментальных данных построен классификатор - логистическая регрессия для оценки того, понравится ли меню с заданными параметрами пользователю с определенными значениями показателей памяти, внимания и уровня логического мышления. Полученная модель позволяет верно классифицировать 78 % оценок различных меню. Результаты работы могут быть использованы при разработке иерархических текстовых меню для различных программ.

Research target: Engineering and Technology
Priority areas: IT and mathematics engineering science
Language: Russian
Keywords: классификатор
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