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Subject
News
October 5, 2026
‘The Climate Transition Is Not Necessarily a Limitation for Business
Linara Khadimullina works in the field of low-carbon development. In an interview with the Young Scientists of HSE project, she spoke about why nature is not just a beautiful backdrop, her research on the role of sustainable corporate governance in reducing greenhouse gas emissions, and growing plants as a source of inspiration.
October 5, 2026
Africa, Youth, and Civic Dialogue: Public Diplomacy Discussed at HSE University
In late September, HSE University hosted a roundtable discussion titled Civil Society in African Countries and Youth Participation in Public Diplomacy. Representatives of non-governmental organisations from Ghana, Ethiopia, and Russia, along with students from HSE University’s Bachelor’s Programme in Public Administration, discussed how young people without official diplomatic status can influence relations between countries and how the nonprofit sector can remain sustainable amid declining grant funding.
October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.

 

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2023 IX International Conference on Information Technology and Nanotechnology (ITNT)

IEEE, 2023.
Chapters
Effective face recognition based on anomaly image detection and sequential analysis of neural descriptors
Sokolova A., Savchenko A., , in: 2023 IX International Conference on Information Technology and Nanotechnology (ITNT).: IEEE, 2023. P. 1–5.
In this paper, we explore the possibility to improve efficiency of face recognition using information about anomaly input images. Indeed, modern publicly-available datasets typically contain images of mostly middle-aged and Caucasian people, which cause most algorithms to fail on photos of older people or children, rarer ethnicities, poor-quality images, etc. Detection of such anomaly data ...
Added: June 13, 2023
Language: English
Text on another site
Keywords: artificial neural networks
2023 IX International Conference on Information Technology and Nanotechnology (ITNT)
Similar publications
Specialized Non-local Blocks for Recognizing Tumors on Computed Tomography Snapshots of Human Lungs
Aleksei Samarin, Aleksei Toropov, Dzestelova A. et al., , in: Proceedings of the 35th Conference of Open Innovations Association FRUCT, 24-26 April 2024, Tampere, FinlandIssue 1.: FRUCT Oy, 2024. P. 659–664.
This research endeavor is dedicated to the integration of specialized attentional mechanisms within the intricate web of deep neural network architectures aimed at discerning indications of lung carcinoma from monochromatic snapshots derived from computerized axial tomography. Within this exploration, we propose a myriad of adaptations to the traditional non-local blocks, infusing them with bespoke attentional ...
Added: September 19, 2026
Wind Turbines Surface Damage Automatic Detection Using YOLOv8 with Specialized Backbone Modification
Aleksei Samarin, Mamaeva A., Aleksei Toropov et al., , in: Proceedings of the 36th Conference of Open Innovations Association FRUCT, Helsinki, Finland, 30 October - 1 November 2024Vol. 36.: FRUCT Oy, 2024. P. 702–710.
This work is devoted to incorporating specialized self-attention blocks into deep neural network-based models for detecting and quantifying damage across various components of wind turbines using images captured by unmanned aerial vehicle cameras. In our study, we introduce YOLOv8 backbone modification using a specialized self-attention mechanism, tailored to the specific characteristics of the input data. ...
Added: September 19, 2026
Hebb-Inspired Low Rank Adapters for Large Language Models Fine-Tuning
Alexander Demidovskij, Artyom Tugaryov, Igor Salnikov et al., , in: PRICAI 2025: Trends in Artificial Intelligence: 22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025, Wellington, New Zealand, November 17–21, 2025, Proceedings, Part IIIVol. 16453.: Springer, 2026. P. 603–612.
The backpropagation method is the predominant method for pre-training and fine-tuning of Large Language models. At the same time, it is considerably demanding in terms of memory and hardware. Therefore, it makes fine-tuning and pre-training very expensive, harmful for the environment due to the large carbon footprint, and raises the blocks for the development of ...
Added: April 21, 2026
PRICAI 2025: Trends in Artificial Intelligence: 22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025, Wellington, New Zealand, November 17–21, 2025, Proceedings, Part III
Springer, 2026.
This proceedings contain the papers presented at the 22nd Pacific Rim International Conference on Artificial Intelligence (PRICAI), held on November 17–21, 2025 in Wellington, New Zealand. PRICAI 2025 was co-hosted with the 40th International Conference on Image and Vision Computing New Zealand (IVCNZ 2025) and the annual conference of the New Zealand Artificial Intelligence Researchers ...
Added: April 21, 2026
Semi-automatic annotation of brain vessels in magnetic resonance angiography images
Bernadotte A, Elfimov N., Menshikov I., Scientific data 2025 Vol. 13 No. 41
Accurate segmentation of brain vessels in magnetic resonance angiography (MRA) is essential for surgical procedures. Neural networks are powerful tools for medical image segmentation, but their development requires well-annotated datasets. However, publicly available MRA datasets with detailed vessel annotations are scarce. We present a dataset of 100 manually annotated brain MRA images from the IXI ...
Added: February 25, 2026
Тесты как инструменты оценивания в вузах: трудности и решения
Antipkina I., Иванущенко А. В., Калабина И. А. et al., Мир психологии. Научно-методический журнал 2025 № 4(123) С. 295–316
Low-quality test items pose significant risks of biased and inaccurate assessment in higher education. In this study, multi-disciplinary test banks were examined, first, using classical test theory and then using a Large Language Model (Grok). Our findings reveal a number of problems in university test items due to methodological shortcomings rather than content inaccuracies. Based ...
Added: January 22, 2026
Формирование требований к технологическим параметрам серийного производства на основе нейросетевого подхода
Yasnitsky L., Голдобин М. А., Прикладная информатика 2025 Т. 20 № 3(117) С. 85–100
Currently, artificial intelligence methods are widely used in the practice of serial production enterprises. They are used to detect defects, classify and eliminate them, identify the causes of defects, predict the quality and properties of the resulting product, select optimal parameters of the production process, and identify and study its patterns. However, outside the field ...
Added: July 10, 2025
Экономические и социальные аспекты атомной энергетики в условиях развития технологий искусственного интеллекта
Podchufarov A., Galkina A. N., Ванина С. С. et al., Экономика и управление: проблемы, решения 2025 Т. 5 № 4 С. 61–74
Under modern conditions, the introduction of artificial intelligence technologies is becoming a significant factor in the development of high-tech industries. The article presents the results of a study of the prospects for the use of intelligent analytical systems in nuclear energy. The experience of foreign countries is analyzed and the features of successful projects using ...
Added: June 5, 2025
Where Do Large Learning Rates Lead Us?
Sadrtdinov I., Kodryan M., Pokonechny E. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 58445–58479.
Added: February 19, 2025
Big Data Analytics Approach with Multiple Text Types: The Case of the Computer Gaming
Aleksandr Belov, Zakharov F., Litvinenko E. et al., , in: International IoT, Electronics and Mechatronics Conference, Volume 2. Proceedings of IEMTRONICS 2024. LNEE, volume 1228Vol. 1228.: Springer Publishing Company, 2025. P. 275–287.
Added: January 26, 2025
Artificial Neural Networks as a Natural Tool in Solution of Variational Problems in Hydrodynamics
Litvinenko N., IEEE Access 2024
Added: December 9, 2024
ALOE: Boosting Large Language Model Fine-Tuning with Aggressive Loss-Based Elimination of Samples
Demidovskij A., Трутнев А. И., Тугарев А. М. et al., , in: Frontiers in Artificial Intelligence and Applications: 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, SpainVol. 392.: IOS Press Ebooks, 2024. P. 3980–3986.
As modern neural network training and fine-tuning requires a lot of computational resources, there is a huge demand for novel, specialized algorithms for efficient and cost-effective training procedures. Aggressive Loss-based Elimination of Samples (ALOE) is an innovative method that operates with training samples based on losses obtained from a currently trained model or a pre-trained ...
Added: November 5, 2024
Frontiers in Artificial Intelligence and Applications: 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain
IOS Press Ebooks, 2024.
The field of AI has grown enormously since 1974, when a summer conference on Artificial Intelligence and Simulation of Behaviour was held in Brighton, UK. This milestone in the history of AI has since come to be thought of as the 1st European Conference on Artificial Intelligence (ECAI). This book presents the proceedings of ECAI-2024, the ...
Added: November 5, 2024
The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time
Leonid N. Yasnitsky, Yasnitsky V., Aleksander O. Alekseev, Complexity 2021 Vol. 2021 Article 5392170
In the modern scientific literature, there are many reports about the successful application of neural network technologies for solving complex applied problems, in particular, for modeling the urban real estate market. There are neural network models that can perform mass assessment of real estate objects taking into account their construction and operational characteristics. However, these ...
Added: February 10, 2024
Моделирование рынков жилой недвижимости крупнейших городов России
Yasnitsky L., Ясницкий В. Л., Alekseev A., Экономика региона 2022 Т. 18 № 2 С. 609–622
The existing mass appraisal models and mathematical tools for predicting the market value of residential property have a number of disadvantages, as they are developed for individual regions. Without considering the constantly changing economic environment, these models quickly become outdated and require constant updating. Thus, they are not suitable for construction business optimisation. The study ...
Added: February 10, 2024
Data Preprocessing and Neural Network Architecture Selection Algorithms in Cases of Limited Training Sets—On an Example of Diagnosing Alzheimer’s Disease
Alekseev A., Kozhemyakin L., Nikitin V. et al., Algorithms 2023 Vol. 16 No. 5 Article 219
This paper aimed to increase accuracy of an Alzheimer’s disease diagnosing function that was obtained in a previous study devoted to application of decision roots to the diagnosis of Alzheimer’s disease. The obtained decision root is a discrete switching function of several variables applicated to aggregation of a few indicators to one integrated assessment presents ...
Added: February 10, 2024
Neural Networks for Speech Synthesis of Voice Assistants and Singing Machines
Pantiukhin D., , in: Integral Robot Technologies and Speech Behavior.: Newcastle upon Tyne: Cambridge Scholars Publishing, 2024. Ch. 9 P. 281–296.
Added: December 10, 2023
Selected Papers from the XXV International Conference on Neuroinformatics, October 23-27, 2023, Moscow, Russia. Advances in Neural Computation, Machine Learning, and Cognitive Research VII (NEUROINFORMATICS 2023)
Frankfurt: Springer, 2023.
Reports on advanced theories and applications of artificial neural networks Focuses on problems in neuroscience, systems biophysics, cognitive research, and adaptive control Merges topics in neurobiology, machine learning, and evolutionary programming ...
Added: November 1, 2023
Latent Stochastic Differential Equations for Change Point Detection
Ryzhikov A., Hushchyn M., Derkach D., IEEE Access 2023 Vol. 11 P. 104700–104711
Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep ...
Added: October 5, 2023
Real-time low latency estimation of brain rhythms with deep neural networks
Ilia Semenkov, Nikita Fedosov, Makarov I. et al., Journal of Neural Engineering 2023 Vol. 20 No. 5 Article 056008
Objective. Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increase the efficacy of such interaction. Novel more efficient approaches capable of tracking brain ...
Added: September 9, 2023
Artificial Intelligence in Music, Sound, Art and Design: 12th International Conference, EvoMUSART 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings
Cham: Springer, 2023.
This book constitutes the refereed proceedings of the 12th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2023, held as part of Evo* 2023, in April 2023, co-located with the Evo* 2023 events, EvoCOP, EvoApplications, and EuroGP. The 20 full papers and 7 short papers presented in this book were carefully reviewed ...
Added: April 4, 2023
Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data
Rukhovich D., Koroleva P., Rukhovich D. et al., Remote Sensing 2022 Vol. 14 No. 9 Article 2224
The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The ...
Added: November 14, 2022
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