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5th International Conference on control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Kononov, Dmitry Furugyan, Meran Russian State University for the Humanities Institute of Control Sciences of RAS Moscow Russia Federal Research Center Computer Science and Control The Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9798350309126

The computational schemes that are supposed to be used in the processes of processing operational information in real-time systems are considered. Two algorithms for scheduling jobs that do not allow interruption and switching are proposed in real-time multiprocessor computing systems. The first algorithm, based on the branch and bound method, solves the speed problem. Processors may differ in their performance. The main feature of this algorithm is that the lower and upper estimates of the length of the optimal schedule are calculated using recurrent relations, which increases its efficiency. The second algorithm solves the problem of finding an admissible schedule in the case when all processors are identical and the directive intervals of all jobs are the same. The algorithm has pseudopolynomial complexity when the number of processors is fixed. A description of the parallel implementation of the algorithm is given. © 2023 IEEE.

关键词： Real time systems

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Dynamic Multihead Attention for Enhancing Neural Machine Translation Performance 5th

Dynamic Multihead Attention for Enhancing Neural Machine Tra...

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5th International Conference on Trends in Computational and Cognitive Engineering, TCCE 2023

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9789819719228

The research paper delves into the realm of "Attention-Based Neural Machine Translation for Multilingual Communication", focusing on the revolutionary impact of attention mechanisms in enhancing cross-language understanding. Through dynamic allocation of focus to pertinent segments of source sequences, attention mechanisms have significantly improved the precision and coherence of translations, playing a pivotal role in multilingual communication. The paper underscores the importance of this technology in addressing the complexities of aligning words across diverse languages and maintaining grammatical accuracy, crucial for effective cross-lingual interactions. The study also presents a visual flowchart outlining the core components of attention-based NMT, encompassing the encoder, decoder, and attention context. Moreover, the paper extrapolates the future trajectory of attention-based NMT, identifying areas for continued exploration and innovation. researchers are urged to refine attention models to cater to intricate linguistic structures, potentially unveiling novel mechanisms that strike a balance between capturing long-range dependencies and computational efficiency. Furthermore, the paper anticipates the integration of attention-based NMT with multimodal inputs, unlocking the potential for comprehensive communication by incorporating visual and auditory cues. These advancements hold the promise of revolutionising multilingual communication, shaping a future where language barriers are transcended in various domains, from global diplomacy to education and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural machine translation

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Development of Neural Network Approach for Reconstructing the Atmospheric Humidity Profile from Ground-based Microwave Radiometer-spectrometer Data

Development of Neural Network Approach for Reconstructing th...

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2024 Photonics and Electromagnetics research Symposium, PIERS 2024

作者： Egorov, D.P. Verina, Y.V. Kravchenko, O.V. Kotel'Nikov Institute of Radioengineering and Electronics of RAS Russia Bauman Moscow State Technical University Russia Federal Research Center "Computer Science and Control" of RAS Russia

ISBN: (纸本)9798350375909

An original neural network approach based on the mixture of experts has been developed to solve the inverse problem of absolute humidity altitude profile retrieval from atmospheric downwelling radiation brightness temperature obtained in the bands of water vapor resonant absorption near 22.235 and 183.3GHz lines. The software implementation of the neural network model assumes the possibility of its training on GPU. The training dataset has been formed by repeatedly solving the forward problem of simulating the brightness temperature spectra based on around 3000 sessions of radiosonde measurements of meteorological parameters profiles (up to an altitude of 15km) conducted in the city of Dolgoprudny, Moscow Region, from January 2019 to October 2023. These meteorological parameters include air temperature T, atmospheric pressure P, and relative humidity, which is than converted to absolute humidity values ρ. For the dataset augmentation purposes, the temperature profile T varies within±5%, and absolute humidity profile ρ within±20% from the corresponding near-surface values. Herewith, the horizontally homogeneous stratus cloud layer, which vertical extent gradually increases while its liquid water content also increases from 0kg/m2 to more than 5.5kg/m2, has been modeled in the field-of-view of ground-based microwave radiometer antenna. The results of applying the trained model to the test dataset show satisfactory agreement between the reconstructed absolute humidity profiles and the values measured by the radiosonde. © 2024 IEEE.

关键词： Radiosondes

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Text-Image Scene Graph Fusion for Multimodal Named Entity Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2828-2839页

作者： Cheng, Jian Long, Kaifang Zhang, Shuang Zhang, Tian Ma, Lianbo Cheng, Shi Guo, Yinan State Key Laboratory for Intelligent Coal Mining and Strata Control Research Institute of Mine Big Data Chinese Institute of Coal Science Beijing100013 China Northeastern University College of Software Shenyang110819 China Shaanxi Normal University College of Computer Science Xi'an710062 China China University of Mining and Technology Xuzhou221116 China

With the popularity and widespread use of social media platforms, such as Twitter and Facebook, massive amounts of text and image information posted by a variety of users have flooded these social media platforms. Thus, multimodal named entity recognition (MNER) has become a research hotspot for the task of extracting named entities from multimodal data. Empirically, the visual clues unrelated to text data may introduce uncertain or even negative impacts on the named entity recognition. The considerations of the relevance of multimodal data have been ignored in the previous studies. In this article, to effectively measure the relationship between text data and visual cues for improving the accuracy of named entities, we propose a text-image scene graph fusion (TISGF) approach with a text-image similarity assessment module (TISA) and a text-image fusion module (TIF) for MNER. Specifically, we first construct two (visual and textual) scene graphs to exploit the joint features of objects and relations in text and image and encode the two scene graphs separately using a specific encoder pair. In this way, we can obtain both object-level and relationship-level cross-modal features. Subsequently, TISA is used to compute the similarity of the image and text data and to determine the proportion of visual information that will be retained for fusion. Finally, we use TIF to achieve a unified multimodal representation of each word and predict the entity type using conditional random fields. Extensive experiment results on two public datasets demonstrate the effectiveness and competitiveness of our proposed method for the MNER task. © 2020 IEEE.

关键词： Semantics

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Conditionally Eulerian–Lagrangian Method on the Example of the Problem on the Dynamics of an Intrathermocline Vortex Lens

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Mathematical Models and computer Simulations 2023年第3期15卷 539-553页

作者： Zatsepa, S.N. Ivchenko, A.A. Solbakov, V.V. Zubov State Oceanographic Institute Roshydromet Moscow Russian Federation Federal Research Centre Computer Science and Control Russian Academy of Sciences Moscow Russian Federation

Abstract: A new conditionally Eulerian–Lagrangian numerical method is developed to solve a number of problems in which the object of study is concentrated in a region of changing configuration, and the movement within the region is described by a system of equations of a two-dimensional continuous medium. A modification of the computational scheme of particles in cells on adaptive Eulerian grids is presented on the example of solving the problem of the dynamics of an intrathermocline vortex lens (ITL). The basic equations are equations of the shallow water type. The area in which the fields of the lens parameters are determined is the desired one and is determined in the course of the solution. The artificial viscosity of the numerical scheme is studied, and the results of testing the method and examples of computational experiments are presented. © 2023, Pleiades Publishing, Ltd.

关键词： particle-in-cell numerical method scheme viscosity vortex lens

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Planning Maneuvers for Autonomous Driving Based on Offline Reinforcement Learning: Comparative Study 7th

Planning Maneuvers for Autonomous Driving Based on Offline...

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7th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Melkumov, Mikhail Panov, Aleksandr I. Moscow Institute of Physics and Technology Moscow Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031437885

One of the key challenges in developing autonomous vehicles is planning safe and efficient trajectories in complex environments, such as intersections. This paper proposes an offline RL approach for planning trajectories for autonomous vehicles at crossroads with other actors. It enables the possibility of using pre-recorded expert trajectories for algorithm tuning. We study the influence of the quality of collected trajectories on various offline reinforcement learning methods. Our approach has the potential to overcome the limitations of online RL and provide an effective planning solution for autonomous vehicles in dynamic environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Reinforcement learning

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Evaluating the usage of Text to3D scene generation methods in Game-Based Learning 24

Evaluating the usage of Text to3D scene generation methods i...

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Gradinaru, Alexandru Vrejoiu, Mihnea Moldoveanu, Florica Balutoiu, Maria-Anca University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania National Institute for Research and Development in Informatics Bucharest Romania

ISBN: (纸本)9798350313390

computer games as an educational tool significantly improved information retention, student involvement, and motivation. Despite the growing popularity and the demonstrated positive impact, little progress is made to let educators create games without learning game programming or investing massive amounts of time, money, and human resources. This paper considers the usage of procedural content generation in simplifying the generation of game environments. Given that most educational material is in text format, we evaluate the possibility of generating 3D scenes starting from natural language text. A comprehensive survey of the current methods is presented, analyzing whether they can be successfully used in game-based learning. The study discusses several current limitations and possible improvements, highlighting a need for better progress in the domain. © 2023 IEEE.

关键词： computer games

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Probabilistic Back-ends for Online Speaker Recognition and Clustering 48

Probabilistic Back-ends for Online Speaker Recognition and C...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sholokhov, Alexey Kuzmin, Nikita Lee, Kong Aik Chng, Eng Siong Federal Research Center "Computer Science and Control"of the Russian Academy of Sciences Moscow Russia Nanyang Technological University Singapore A∗STAR Institute for Infocomm Research Singapore

ISBN: (纸本)9781728163277

This paper focuses on multi-enrollment speaker recognition which naturally occurs in the task of online speaker clustering, and studies the properties of different scoring back-ends in this scenario. First, we show that popular cosine scoring suffers from poor score calibration with a varying number of enrollment utterances. Second, we propose a simple replacement for cosine scoring based on an extremely constrained version of probabilistic linear discriminant analysis (PLDA). The proposed model improves over the cosine scoring for multi-enrollment recognition while keeping the same performance in the case of one-to-one comparisons. Finally, we consider an online speaker clustering task where each step naturally involves multi-enrollment recognition. We propose an online clustering algorithm allowing us to take benefits from the PLDA model such as the ability to handle uncertainty and better score calibration. Our experiments demonstrate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Calibration

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Stability of artificial intelligence methods in computed tomography metal artifacts reduction task 5

Stability of artificial intelligence methods in computed tom...

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5th Symposium on Pattern Recognition and Applications, SPRA 2024

作者： Gilmanov, Marat Kirkicha, Andrey Ingacheva, Anastasia Chukalina, Marina Nikolaev, Dmitrii Smart Engines Service LLC Moscow117312 Russia Institute for Information Transmission Problems RAS Moscow Russia Federal Research Center Computer Science and Control RAS Moscow Russia

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

Reducing image artifacts in modern computed tomography is one of the most important directions, which also receives a research boost from the rapid development of artificial intelligence and machine learning. The neural network based methods often allow the achievement of the high artifact reduction quality unattainable by classical approaches. Simultaneously, these models tend to be less robust and generalize worse than algorithmic approaches. The output result may degrade severely under the use in cases not represented in learning datasets. In the present work, the robustness and generalization properties of neural network models are studied in the task of metal-like artifact reduction for computed tomography reconstruction. The pipeline for data augmentation is developed for the metal artifacts severity and geometry variations parameters. The pipeline is validated by augmentation of DICDNet model test dataset and evaluating of the corresponding pre-trained DICDNet model. The generated data consisted of 192000 samples for 12×8 combinations of experiment parameters. It was demonstrated that DICDNet is reasonably robust to geometry variations with a maximum deviation of PSNR≈1.5%. At the same time, even small artifact severity growth leads to rapid quality degradation and blurring of studied model output. The degradation reaches ≈22% PSNR deterioration from the baseline algorithmic approach, which is also one of the inputs for the studied model. Copyright © 2025 SPIE.

关键词： computerized tomography

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Real-time reconstruction on micro-CT setup 5

Real-time reconstruction on micro-CT setup

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5th Symposium on Pattern Recognition and Applications, SPRA 2024

作者： Gilmanov, Marat Bulatov, Konstantin Bugai, Oleg Ingacheva, Anastasia Chukalina, Marina Nikolaev, Dmitrii Arlazarov, Vladimir Smart Engines Service LLC Moscow117312 Russia Institute for Information Transmission Problems RAS Moscow Russia Federal Research Center Computer Science and Control RAS Moscow Russia

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

Dose reduction in computed tomography is one of the most urgent questions with a deep research history. One of the rising potentially powerful tools for dose reduction is monitored tomographic reconstruction. This promising concept heavily relies on utilizing real-time reconstruction from currently available data, which is the main topic of the present paper. The main result of achieving a real-time reconstruction on a commercially available micro-CT setup utilizing SmartTomoEngine SDK is highlighted and presented in more detail. The new acquisition order is proposed, allowing for faster convergence of intermediate reconstruction results. An optimization of reconstruction from partial data is proposed, which allowed the production of 385 and 440 reconstructions for standard and proposed acquisition orders correspondingly during a single acquisition of 512 projections, achieving a real-time reconstruction. The results are produced with the use of only a single previous-generation consumer-grade graphical processing unit. The study results demonstrate that with proposed optimizations monitored tomographic reconstruction can be effectively utilized for practical applications using the current generation of existing setups in the micro CT application field. Copyright © 2025 SPIE.

关键词： computerized tomography

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