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SCA-2023: A TWO-PART DATASET FOR BENCHMARKING THE METHODS OF IMAGE PRECOMPENSATION FOR USERS WITH REFRACTIVE ERRORS
Proceedings - European Council for Modelling and Simulation. 2023. Vol. 2023-June. P. 298 – 305.
Алкзир Н., Nikolaev I., Nikolaev D.
Aleksei Samarin, Nazarenko A., Nazarenko D. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 10 Article 330
Automated microscopic image analysis is essential for quantitative microbiology, yet manual acquisition and generic recognition models struggle with morphological variability and scene artifacts. We address both ends of this pipeline. First, we present a low-cost, open-source acquisition system, based on an Arduino UNO R3 microcontroller and NEMA 17 stepper motors, that automates focusing, magnification, and ...
Added: October 14, 2026
Trofimova E., Shamina Z., Selifanova M. et al., , in: Proceedings of the Generative Code Intelligence Workshop (GeCoIn 2026), co-located with the 35th International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026)Vol. 4238.: CEUR-WS.org, 2026.
We introduce ML2B, the first benchmark for evaluating cross-lingual task comprehension in end-to-end ML pipeline generation by large language models. Despite growing global AI adoption, no systematic evaluation exists for ML pipeline generation beyond English task descriptions. ML2B addresses this gap with 35 Kaggle competitions spanning tabular, text, and image domains, translated into 14 languages ...
Added: August 20, 2026
Golyadkin M., Saraev S., Makarov I., IEEE Access 2025 Vol. 13 P. 7526–7537
Manga colorization in augmented reality (AR) environments presents unique challenges, particularly when colorizing manga pages captured in photos under various real-world conditions. Testing models in AR settings for manga colorization has been a significant challenge, primarily because of the absence of suitable datasets tailored for this task. To address this, we propose a benchmark for ...
Added: April 29, 2025
Golyadkin M., Saraev S., Makarov I., , in: 2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct).: IEEE, 2024. P. 608–611.
This paper introduces an innovative approach to manga colorization within augmented reality (AR) environments, focusing on the unique challenges posed by colorizing photos of manga books. We present a novel method using diffusion models to generate a synthetic dataset that accurately replicates photographed manga pages. Additionally, we have compiled a dataset of real manga photographs, ...
Added: April 29, 2025
Алкзир Н., Yarykina n., Nikolaev D. et al., Neuroscience and Behavioral Physiology 2024
Added: April 28, 2025
Taktasheva E., Bazhukov M., Koncha K. et al., , in: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.: Association for Computational Linguistics, 2024. P. 9268–9299.
Minimal pairs are a well-established approach to evaluating the grammatical knowledge of language models. However, existing resources for minimal pairs address a limited number of languages and lack diversity of language-specific grammatical phenomena. This paper introduces the Russian Benchmark of Linguistic Minimal Pairs (RuBLiMP), which includes 45k pairs of sentences that differ in grammaticality and ...
Added: January 2, 2025
Алкзир Н., Nikolaev I., Nikolaev D., Proceedings of SPIE - The International Society for Optical Engineering 2024 Vol. 13072
Added: June 11, 2024
Makarov I. S., D. S. Osipov, Acoustical Physics 2024 Vol. 70 No. 1 P. 182–188
The information content of the parameters of a spectral voice source model in an automatic voice identity recognition problem is studied. For the voice parameters, the identity recognition error was 20.8%; using these parameters together with the pitch period reduced the error to 13.8%. Lastly, the combined use of the spectral model parameters with the ...
Added: May 16, 2024