17th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

?

17th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

IEEE, 2019.

Under the general editorship: L. Kovács, L. Vokorokos

Voice command recognition in intelligent systems using deep neural networks

Sokolov A., Savchenko A., , in: 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI).: IEEE, 2019. Ch. 19 P. 113–116.

In this article, we focus on the isolated voice command recognition for autonomous man-machine and intelligent robotic systems. We propose to create a grammar model for a small testing command set with self-loops for each state to return blank symbols for noise and out-of-vocabulary words. In addition, we use single arc connected beginning and ending ...

Added: October 21, 2019

Research target: Computer Science

Priority areas: IT and mathematics

Language: English

Full text

Text on another site

Keywords: искусственный интеллект машинное обучение интеллектуальные системы machine learning intelligent system artificial intelligence

17th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

Влияние нейронных сетей на современную фотографию: от инструмента до нейроавтоматона

Fadeeva T., Staruseva-Persheeva A., В кн.: Первый Российско-Китайский форум. Теория и практика художественного образования: вызовы современности, традиции и национальные школы XXI века : Коллективная монография по материалам международной научной конференции.: М.: Российский государственный художественно-промышленный университет им. С.Г. Строганова, 2024.

This study is devoted to the analysis of the influence of neural networks on contemporary photography in technological and aesthetic aspects. The range of application of machine learning technologies in contemporary photography is wide: from the use of creative tools in photo processing (a neural network can remove “color noise”, increase detail, change the size ...

Added: November 14, 2026

Траектории ставок как сигнал сговора: LSTM-классификация аукционов государственных закупок

Ефимов К. Д., Журнал Новой экономической ассоциации 2026 № 72 С. 105–114

This study proposes a method for automatic detection of collusive behavior in electronic public procurement auctions based on analysis of the full bid trajectory. The empirical dataset comprises 508 auction histories from 2016–2020 collected from the RTS tender e-procurement platform; the positive class is labeled using case materials of the Federal Antimonopoly Service (FAS Russia), ...

Added: November 13, 2026

Model-Dependent Prompt-Component Ablation for Medical LLM Decision Support: Application to ENT Biologics Screening

Sergey N. Koltcov, Budkovaia M., Surkov A. et al., IEEE Journal of Biomedical and Health Informatics 2026 P. 1–10

Large language models (LLMs) show promise for clinical decision support, but privacy constraints and unreliable free-text decisions limit deployment. We present Medotor, a locally deployed system for screening patients with chronic rhinosinusitis with nasal polyps (CRSwNP) for biologic therapy, and evaluate prompt design on 163 retrospective cases (132 eligible and 31 ineligible). Five models were ...

Added: November 13, 2026

Юридическая хроника

Ivanov N., Diskin E., Елаев А. А. et al., Закон 2026 № 10 С. 110–130

В октябрьской «Юридической хронике» эксперты комментируют решение Конституционного совета Франции о признании не соответствующей Конституции статьи 1 законопроекта о защите несовершеннолетних от рисков, связанных с использованием социальных сетей; недавние нормативные акты о награждении государственными наградами, свидетельствующие об обесценивании наград в России; статью 10 Федерального закона от 26 июля 2026 года № 243-ФЗ «О поддержке развития ...

Added: November 13, 2026

Impact of 5G NR-V2X Message Losses on Connected Vehicle Warning Functions

Физулин А. В., Stepanyants V., Amerikanov A. et al., FUTURE TRANSPORTATION 2026 Vol. 6 No. 6 Article 264

Cooperative warning functions depend on whether V2X information becomes available in time to influence vehicle control. This article presents an event-based co-simulation framework coupling SUMO, ns-3 (ms-van3t), 5G-LENA NR sidelink and application records, with Sionna RT enabled for the intersection scenario. In 140 emergency-vehicle-warning runs, reducing mean packet reception ratio (PRR) from 0.9412 to 0.0675 ...

Added: November 12, 2026

Structural model of decision support system for sustainable development of oil and gas companies

Stoianova A. D., Trofimets V. Y., Matrokhina K. V., International Journal of Engineering, Transactions A: Basics 2025 Vol. 38 No. 4 P. 701–709

The article considers the issue of managerial decision-making in an oil and gas company, which is related to the class of multi-criteria choice problems. To improve the validity of decisions in the context of companies’ sustainable development it is proposed to implement a Decision Support System (DSS). A brief review of the existing methods of ...

Added: November 10, 2026

EdFitter: a hybrid course recommendation system using large language models

Sergei Koltcov, Ryumin D., Alexandr Axyonov et al., PeerJ Computer Science 2026 Article 12:e4141

Educational institutions face increasing pressure to adapt their curricula to the rapidly evolving demands of the job market, and recommender systems can be important tools to guide students in selecting courses that align with their career goals. We present EdFitter, a hybrid course recommendation system that combines pretrained text embeddings for retrieval with large language ...

Added: November 10, 2026

Assessing density equilibration in confined Liquid–Liquid interfaces

Guskov T., Gerke K., Kondratyuk N. et al., Molecular Simulation 2026 Vol. 52 No. 7 P. 546 – 555

Equilibration is often assumed rather than tested in molecular dynamics studies of confined liquid–liquid interfaces, even though different physical properties may relax on different time scales. In this work, equilibration is examined using an automated truncation method based on the marginal standard error rule. Interfacial geometry, wetting behaviour, and density-related properties are analysed for liquid–liquid ...

Added: November 10, 2026

Influence of nuclear quantum effects on metastable states of warm dense hydrogen

Lukyanchuk V., Norman G., Kondratyuk N. et al., Scientific Reports 2026 Vol. 16 No. 1 Article 16435

Previously, it has been effectively shown that nuclear quantum effects (NQE) considerably influence the prediction of the warm dense hydrogen phase diagram. Here, we study the influence of NQE on the parameters of the conducting state formation in warm dense hydrogen using path integral molecular dynamics (PIMD) within the framework of the finite temperature density ...

Added: November 10, 2026

Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track. European Conference, ECML PKDD 2026, Naples, Italy, September 7–11, 2026, Proceedings

Cham: Springer, 2027.

This volume is Part VIII of the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, September 7–11, 2026. It contains 40 papers from the Research Track and the Applied Data Science Track and is published as volume 16948 of Lecture Notes in ...

Added: November 9, 2026

Artificial Intelligence for Software Engineering (AI4SE): A Review of Capabilities, Evaluation, and Verification Challenges

Avdoshin S.M., Pesotskaya E.Y., Proceedings of the Institute for System Programming of the RAS 2026 Vol. 38 No. 6, part 2 P. 133–146

Artificial intelligence has significantly reshaped contemporary software engineering practice, changing how software systems are designed, implemented, tested, and maintained. Recent advances in large language models and AI-assisted development tools have expanded the role of AI from supporting isolated tasks to participating in multiple stages of the software lifecycle. In many development settings, software is now created from high-level specifications ...

Added: November 8, 2026

MFL-ScrollNet: Recurrent Sliding-Window Detection for Panoramic Pipeline Magnetic Flux Leakage Images

Aleksei Samarin, Belous F., Торопов А. Г. et al., Technologies 2026 Vol. 14 No. 11 P. 1–21

Abstract Automated interpretation of magnetic flux leakage (MFL) data is central to condition assessment of long-distance oil and gas pipelines. Yet, full-resolution panoramas are difficult to process without either suppressing small anomalies through resizing or fragmenting elongated objects at sliding-window boundaries. We propose MFL-ScrollNet, a recurrent sliding-window detector that formulates panoramic analysis as an ordered sequence ...

Added: November 8, 2026

Proceedings 2026 IEEE International Conference on Joint Cloud Computing JCC 2026

IEEE Computer Society (Conference Publishing Services, CPS), 2026.

Added: November 7, 2026

Detecting the undetectable: benchmarking AI-generated text

Sergey Avdoshin, Elena Pesotskaya, Saleh H. et al., Procedia Computer Science 2026 Vol. 292 P. 1332–1341

The rapid development of generative large language models (LLMs) has intensified the need for reliable methods capable of distinguishing AI-generated text from human writing, especially in academic and professional settings. This paper presents the results of a systematic benchmarking study evaluating the performance and limitations of contemporary LLM-text detectors applied to Russian-language academic content. We introduce ...

Added: November 7, 2026

PrimeKV: Preserving critical KV cache without attention scores for long-context inference

Zhang Z., Ilvovsky D., Neurocomputing 2027 Vol. 710

Efficient management of the Key-Value (KV) cache is essential for deploying large language models (LLMs) in long-context scenarios, where memory requirements often exceed the capacity of resource-constrained hardware. Many existing training-free compression algorithms typically rely on attention-driven heuristics, but exhibit two key limitations. First, they require explicit attention score computation, which is difficult to reconcile ...

Added: November 5, 2026

A Multimodal Framework for Aphasia Severity Classification in Russian

Kolmogorova A., Ekaterina Yavshitz, Margolina A. et al., Association for Computational Linguistics, 2026.

Automatic classification of aphasia severity presents persistent challenges, particularly for languages with limited clinical speech re sources such as Russian. This paper explores a multimodal approach to severity estimation that combines acoustic and semantic representa tions of pathological speech. Acoustic features are extracted using pretrained Wav2Vec 2.0 models, while semantic information is obtained from the ...

Added: November 4, 2026

Findings of the Association for Computational Linguistics: EMNLP 2026

Association for Computational Linguistics, 2026.

Findings of the Association for Computational Linguistics: EMNLP 2026 ...

Added: November 3, 2026

PULSE: Psychological Characteristics Unified Learning via Speech-based Estimation

Abdulkhadirov T., Ryumina E., Ryumin D. et al., Expert Systems with Applications 2026 Vol. 334 P. 1–29

Assessing human psychological characteristics from speech requires models that can jointly capture affective states, expressive behavior, and relatively stable personality traits. However, most existing speech-based frameworks address isolated tasks and use separate task-specific models. This leads to model proliferation and complicates inference in real-world intelligent systems. Unified models are also difficult to develop because few ...

Added: November 3, 2026

Interpretable GNN with Stable Concepts as Neurons

Kuznetsov S., CEUR Workshop Proceedings 2026 Vol. Vol-4247 P. 27–32

A method for constructing interpretable graph neural networks based on an ordered subsets of "interesting" formal concepts is proposed. Interestingness indices for selecting concepts are compared, namely frequency (extent size) and Δ-stability. The experimental results demonstrate that Δ-stability provides superior performance of the generated networks at the inference stage. ...

Added: November 2, 2026

О длине адаптивной различающей последовательности для семейства наблюдаемых автоматов

Бурдонов И. Б., Evtushenko N. V., Косачев А. С., Труды Института системного программирования РАН 2025 Т. 37 № 6-1 С. 7–20

В статье исследуется длина адаптивной различающей последовательности для семейства инициальных полностью определенных наблюдаемых, возможно, недетерминированных автоматов. Приводится верхняя оценка длины такой последовательности и показывается ее достижимость. Обсуждается, каким образом полученные результаты можно применить при построении адаптивных диагностических тестов на основе конечно-автоматной модели. ...

Added: October 31, 2026

К организации системы запросов в распределенной сети с кластерами диаметра не больше 2

Бурдонов И. Б., Evtushenko N. V., Косачев А. С. et al., Труды Института системного программирования РАН 2026 Т. 38 № 4-1 С. 7–24

В статье усовершенствуется предложенная в предыдущей статье авторов модель кластеризованной распределённой сети с опросом, соответствующая сервис-ориентированной архитектуре. Целью опроса является нахождение ближайшего узла, готового оказать требуемую услугу (сервис) как можно раньше. Предлагаемые алгоритмы обеспечивают достижимость узлов, отсутствие дублирования сообщений, и обмен сообщениями с найденным узлом по кратчайшему пути. Кластеризованность сети означает, что в графе сети ...

Added: October 31, 2026

Имитационные игры и искусственный интеллект: возвращение к корням проекта исследований экспертизы и опыта (SEE) третьей волны социологии науки

Ivanov D., ИНТЕРакция. ИНТЕРвью. ИНТЕРпретация 2026 Т. 18 № 2 С. 13–29

This article addresses a methodological contradiction that arises when attempting to in- corporate artificial intelligence (AI) into qualitative sociological research grounded in the in- terpretivist paradigm. While in quantitative research, AI, at least at first glance, seamlessly fits into the natural-scientific ideal of “instrumental distancing”, for the qualitative tradition, which demands contextual sensitivity and proximity ...

Added: October 28, 2026

Predicting multi-component oil viscosity using machine learning methods

Deshchenya V., Nikolay D. Kondratyuk, Sivakova A. et al., Chemical Engineering Sciences 2027 Vol. 337 Article 124616

Accurate prediction of complex formulation viscosity is crucial for the lubricant industry and engineering processes. However, developing generalizable models remains challenging due to the complexity of industrial mixtures and the scarcity of public experimental data, often resulting from corporate proprietary restrictions. This study presents a machine-learning workflow for viscosity estimation from a proprietary pseudonymized dataset, ...

Added: October 28, 2026

Parameterization and calibration of wild blueberry machine learning models to predict fruit-set in the northeast china bog blueberry agroecosystem

Qu H., Xiang R., Obsie E. Y. et al., Agronomy 2021

Data deficiency prevents the development of reliable machine learning models for many agroecosystems, especially those characterized by a dearth of knowledge derived from field data. However, other similar agroecosystems with extensive data resources can be of use. We propose a new predictive modeling approach based upon the concept of transfer learning to solve the problem ...

Added: October 22, 2026