Conference Program

The preliminary version of the conference program is available.

WEDNESDAY, OCTOBER 23th

SESSION 1. Data Integration. Room 125. Chair: Dmitrii Shaposhnikov

Sergey Stupnikov. Формальная семантика и верификация программ материализованной интеграции данных на процедурном диалекте SQL (PDF)

Abstract С ростом неоднородности моделей и схем данных все острее проявляется необходимость интеграции данных. Программы интеграции данных могут быть очень сложными, а потому важными становятся вопросы формальной верификации их корректности. В работе предложен метод верификации программ материализованной интеграции данных на процедурном диалекте SQL, основанный на определении их семантики в формальном языке спецификаций, поддержанном средствами автоматизированного доказательства. Метод проиллюстрирован на примере верификации программы мате-риализованной интеграции данных в области управления землепользованием.

Nikolay Skvortsov. Управление качеством данных при решении задач над неоднородными источниками данных (PDF)

Abstract Решение задач над доступными научными данными должно отвечать принципам, обеспечивающим их многократное повторное использование. Показатели качества данных являются важными их характеристиками, отражающимися не только на точности работы методов при решении исследовательских задач, но и на пригодности данных и возможности решения над ними конкретных задач, на выборе методов работы с ними, на их сочетаемости друг с другом и на других аспектах их повторного использования. При этом приходится оценивать различные стороны качества данных на разных уровнях от целых наборов данных до конкретных значений. В настоящем исследовании показан подход к комплексному управлению качеством данных на основе их спецификации в качестве метаданных. Обсуждаются разные измерения в пространстве оценки качества данных, включая их точность, полноту и происхождение. Разработанный подход применён на примере решения задач над множественными источниками данных в области звёздной астрономии.

Андрей Шепелев and Sergey Stupnikov. Сопоставление схем с использованием федеративного обучения на гибридном наборе признаков (PDF)

Abstract Для повышения репрезентативности при анализе данных возникает необходимость извлечения и интеграции данных из различных источников. В данной работе рассматриваются вопросы применения машинного обучения для одного из основных этапов интеграции данных - сопоставления схем. Результатом этого этапа является сопоставление элементов схем источников и элементов целевой схемы. Предложена нейросетевая модель, основанная на сочетании сетей долгой краткосрочной памяти, механизмов внимания и многослойного персептрона. При обучении модели используется гибридный набор признаков, включающий показатели сходства имен, типы данных, выделенные теги, описательные статистики, коэффициенты корреляции для числовых данных. Проведены эксперименты, показывающие, что предложенная модель превосходит по качеству базовую нейросетевую модель и классические методы сопоставления схем. Показано также, что при федеративном обучении, сохраняющем конфиденциальность данных, каче-ство модели практически не падает по сравнению с централизованным обучением.

SESSION 2. Data analysis in astronomy I. Room 126. Chair: Alexey Pozanenko

Ekaterina Malik, Dana Kovaleva, Oleg Malkov, Pavel Kaygorodov and Bernard Debray. Developing the optimal cross-matching algorithm for the Gaia and BDB data (PDF)

Abstract Binary and multiple stellar systems for an important part of stellar population of our Galaxy. Depending on the method of discovery of binarity, such stars are referred to using a number of datasets of observational parameters, dedicated catalogues and systems of identifiers. The task of linking of the identifiers with the proper entities within binary and multiple systems is solved by the index catalogue of the Binary star DataBase BDB (http://bdb.inasan.ru), named Identification List of binaries (ILB). This catalogue needs to be regularly updated, and recently the vast amount of results of the Gaia space mission has been published which contains, among other data, the data for non-single stars. Similarly, based on Gaia data, more than a million of wide binary stars were identified. We are developing the algorithm for cross-identification of the catalogue ILB comprising virtually all data for binary and multiple stars before Gaia, with the data for such stars based on Gaia results.

Евгений Щекотихин, Николай Панков, Алексей Позаненко, Сергей Белкин, Павел Минаев and Алина Вольнова. Применение нейронных сетей для поиска оптических транзиентов на астрономических изображениях методом вычитания (PDF)

Abstract Объектами данного исследования являются методы обработки и вычитания астрономических изображений, полученных в ходе различных оптических обзоров с целью поиска с использованием вычитания изображений оптических транзиентов. В работе рассмотрен новый метод преобразования вычитаемого изображения к поисковому, основанный на обучении модели сверточной нейронной сети соответствующему преобразованию с пары вычитаемых изображений в рамках обучающей маски непосредственно перед вычитанием. На парах фрагментов изображений, полученных на Абастуманской астрофизической обсерватории и фрагментов обзора Pan-STARRS продемонстрирована эффективность метода как для идентификации транзиентных источников, так и для оценки их потока. Актуальность работы заключается в том, что метод может применяться для любых изображений, в том числе для пары изображений полученных на различных телескопах, а также для других задач. Также планируется имплементация алгоритма в действующий программный конвейер для обработки астрономических изображений в широких полях зрения и поиска оптических транзиентов по программе наблюдений космических гамма-всплесков и источников электромагнитного излучения, ассоциированных с гравитационно-волновыми событиями, регистрируемыми детекторами LIGO-Virgo-KAGRA.

Альберт Хабибуллин, Алексей Позаненко and Владимир Лозников. Моделирование кривых блеска космических гамма-всплесков (PDF)

Abstract Работа посвящена решению задачи определения параметров распределения профиля импульсов кривой блеска космических гамма-всплесков. Задача решается с помощью моделирования синтетических кривых блеска, составленных из набора импульсов. Каждый из импульсов определяется аналитической формой, имеющей 4 параметра: амплитуда, положение максимума на кривой блеска, длительность и параметр симметрии. Рассматривается несколько аналитических форм импульсов: двухсторонняя экспонента, логарифмически-нормальная форма и непрерывно дифференцируемая функция Fast Rise Exponential Decay (FRED). Кривые блеска генерируются исходя из предположения, что для каждого параметра импульсов существует некоторое распределение. Исследовано влияние аналитической формы импульса и распределения параметров импульса на спектры мощности ансамбля смоделированных кривых блеска. Показано, что только параметры длительности и симметрии влияют на вид спектра мощности ансамбля кривых блеска, составленных из этих импульсов. Найдены распределения параметров импульсов, которые производят спектр мощности ансамбля кривых блеска, состоящий из трёх степенных участков, а также показано, что для этого необходимо ввести зависимость между параметрами длительности и симметрии импульса. Исследована зависимость между характерными особенностями спектра мощности ансамбля (значениями частоты изломов между степенными участками, и показателями степени участков) и распределением параметров импульсов.

SESSION 3. Conceptual Modeling and Ontologies. Room 125. Chair: Nikolay Skvortsov

Stepan Vinnikov, Anatoly Nardid and Yuriy Gapanyuk. Metagraph Operations using Bigraph Representation (PDF)

Abstract In this article, we propose an efficient implementation of operations on metagraphs by using an alternative definition of a metagraph. Definitions of the metagraph structures are given. Alternative definition of a metagraph is proposed. Metagraph operations definition based on alternative definition of a metagraph are discussed. Operations on hierarchical metagraphs are proposed. Nesting binary relation is discussed. Elementary operations on metagraphs are proposed. Complex operations on metagraphs are discussed. The example of using operations over metagraph is given.

Nikolay Kalinin and Nikolay Skvortsov. Подход к анализу предметной области информационной безопасности для построения исследовательских инфраструктур (PDF)

Abstract В последние годы наблюдается устойчивый рост интенсивности исследований, связанных с информационной безопасностью. Возросший интерес научного сообщества сделал более актуальной проблему доступности, повторного использования научных данных и результатов в данной области. В статье рассматриваются вопросы, связанные с разработкой исследовательских инфраструктур в области информационной безопасности, призванной решить упомянутые проблемы и способствовать проведению более эффективных исследований.

[short] Olga Ataeva, Vladimir Serebryakov, Natalia Tuchkova and Ivan Strebkov. Онтология и граф знаний математической физики в семантической библиотеке (PDF)

Abstract В работе обсуждаются проблемы конструирования семантической библиотеки для ресурсов, посвященных математике и математической физике на основе энциклопедий. Исследуется механизм интеграции энциклопедий в контент семантической библиотеки и объединение математической энциклопедии и энциклопедии математической физики. В процессе интеграции обнаруживаются пересечения множеств статей этих энциклопедий, а также взаимное обогащение их терминов. В результате граф знаний семантической библиотеки насыщается новыми узлами и связями, что в свою очередь приводит к обогащению предметных областей самой семантической библиотеки и предметных областей интегрированных научных публикаций.

[short] Yury Zagorulko, Galina Zagorulko and Elena Sidorova. Approach to Developing a Machine Learning Ontology (PDF)

Abstract The paper describes an approach to developing a machine learning ontology, based on the methodology for constructing ontologies of scientific subject domains, developed in A.P. Ershov Institute of Informatics Systems. A brief overview of the basic concepts and terms of machine learning (ML) and known developed ontologies related to this field is given. The paper also pro-vides a brief description of the methodology for constructing ontologies of scientific subject domains, and describes the ontology design patterns developed within the framework of this methodology to represent the basic concepts of the ML subject domain. The developed ML ontology will be used to build an intelligent scientific Internet resource on machine learning that will provide content-based access to systematized knowledge and data in the field of ML, helping users in choosing methods, models and data sets necessary to solve their practical problems.

SESSION 4. Data analysis in astronomy II. Room 126. Chair: Alina Volnova

Matwey Kornilov, Vladimir Korolev, Konstantin Malanchev, Anastasia Lavrukhina, Etienne Russeil, Timofey Semenikhin, Emmanuel Gangler, Emille Ishida, Maria Pruzhinskaya, Alina Volnova and Sreevarsha Sreejith. Coniferest: an active anomaly detection framework (PDF)

Abstract We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) and Pineforest algorithms are available to tackle active anomaly detection problems. The algorithms and package performance are evaluated on a series of synthetic datasets. We also describe a few success cases which resulted from applying the package to real astronomical data in active anomaly detection tasks within the SNAD project.

Timofey Semenikhin, Matwey Kornilov, Maria Pruzhinskaya, Anastasia Lavrukhina, Etienne Russeil, Emmanuel Gangler, Emille Ishida, Vladimir Korolev, Konstantin Malanchev, Alina Volnova and Sreevarsha Sreejith. Real-bogus classification for ZTF data releases: two approaches (PDF)

Abstract We compared two fundamentally different approaches to real-bogus classification within the Zwicky Transient Facility survey data. The first approach is based on neural networks that take sequences of object images as input. The second approach uses features extracted from light curves and classical machine learning methods. Several models for both approaches were tested. Quality metrics were evaluated using k-fold cross-validation. We found that models based on classical machine learning algorithms outperform the neural network approach in both computational performance and quality. The code written during the study is available on GitHub.

Art Prosvetov, Sergey Grebenev, Sergey Belkin and Alexey Pozanenko. Correlations and Classification in Light Curves of Type Ic Supernovae with GRB Association (PDF)

Abstract In this study, we conducted an analysis of long-term light curves for 56 type Ic supernovae, with a dedicated focus on those accompanied by gamma-ray bursts (GRBs). Our results did not confirm the correlations between the full-width at half-maximum (FWHM) and peak bolometric luminosity previously suggested in smaller samples. However, we identified a relationship between the decay rate and growth rate of luminosity. This relationship enables the reconstruction of light curve profiles and the estimation of peak bolometric luminosity. Additionally, we developed a method for distinguishing between GRB supernovae and type Ic supernovae based on light curve parameters. Future research should aim to expand the sample size to refine and validate the proposed methods.

SESSION 5. Machine learning methods. Room 125. Chair:

Dmitrii Khliustov and Dmitry Kovalev. Aggregation Of Regression Models For Variance Minimization (PDF)

Abstract In this work a novel method of weighting regression models is presented. In order to minimize the variance of residual between predictions and true values of a given parameter, optimization problem is posed. It's solution invokes the use of covariance matrix, which can be reliably estimated from data. The suggested approach is tested on open source dataset, containing information on concentration of several chemical elements in various spatial locations. The performance of algorithm under study is compared to that of other algorithms, including Bootstrap aggregation (bagging), which is often considered a standard one. It is shown that from theoretical point of view the novel approach outperforms bagging, while in practice it gives better results only in some settings, which is attributed to numerical difficulties in matrix inversion.

Dmitrii Shaposhnikov Minimax. Approach for Using the Qualitative Preferences in the Multicriteria Evaluation (PDF)

Abstract One of the main tasks of big data processing and analysis systems is to obtain an integral numerical multicriteria evaluation of grouped or ungrouped objects of a population for all or some subset of quality indicators. The article considers one of the approach to solving this problem by forming a multicriteria (multiobject) evaluation of quality of objects or groups using weighting coefficients of indicator preference. The proposed approach assumes that in the case when the assignment of exact values of weighting coefficients is dicult or impossible, the use of qualitative (verbal) information on the relative importance of parameters presented by the analyst in the form of a preference graph, which may be incomplete. The minimax method for assigning weighting coefficients of relative preference is considered based on the fundamental principle of different values of weighting coefficients for different objects of a population while maintaining the preference system of the entire set of objects. For each object or group, weighting coefficients are calculated automatically based on qualitative preferences according to the minimax principle by solving an optimization problem using generalized logical criteria of maximum risk and maximum caution. For special cases of preference systems, analytical relationships and algorithms for calculating weighting coefficients are given.

short] Олег Валентинович Сенько, Александр Александрович Докукин and Федор Александрович Мельник. Использование ансамблей с увеличенной дивергенцией в пространстве прогнозов в рекомендательных системах (PDF)

Abstract В работе представлены результаты применения метода дивергентного решающего леса при решении задач классификации, возникающих при создании рекомендательных систем. Метод является авторской разработкой, основанной на достижении более высокой дивергенции в пространстве прогнозов по сравнению со стандартным случайным решающим лесом.

SESSION 6. Machine learning methods and applications II. Room 126. Chair:

Irina Dvoretskaya, Alexey Semenov and Alexander Uvarov. Exploring Patterns Of Information Literacy Development In Schools: Application Of Multilevel Latent Class Analysis To School Students Survey Data (PDF)

Abstract While the literature on digital transformation in education has searched for evidence based practices to improve ICT uptake in school settings, we know little about how schools differ in their approaches. This study aims to overcome the absence of standardised tools that could help to assess the stages and progress of ICT integration in educational settings. By using the example of information literacy development tasks assignment in class-room, we applied a latent class analysis to the survey data obtained from the monitoring the digital transformation of schools in the 2020-21 aca-demic year. Based on the survey data from monitoring the digital transformation of schools, four types of students' patterns were identified, depending on the information skills tasks assigned to them by their teachers at school. Based on the distribution of students' patterns of working with information, three typical patterns of schools were identified with the use of multilevel latent class analysis. This study provides evidence for how the development of information literacy differs across schools contexts. As with advent of digital technolo-gies education becomes data intensive domain, new approaches to the big data analysis are encouraged and it can help educators and education policy makers to improve decision-making.

Alexander Varnavsky. Regression models for the AI gaming chatbot for learning programming based on Wordle-type puzzles (PDF)

Abstract Programming is one of the most important skills of the 21st century. However, it is often a challenge to keep students interested and engaged when learning pro-gramming. It is believed that digital games can solve this problem. One of the types of games that are well suited for the field of computer science are puzzle games, which are aimed, among other things, at the development of working memory and thinking. The aim of the work is to create the gaming chat-bot for learning programming based on Wordle-type puzzle and developing regression models that will make it intelligent. This type of game is chosen because of its worldwide popularity. Artificial intelligence in the chatbot is necessary for con-trolling the expediency and appropriate time for its use, as well as adaptive for-mation of the level of puzzles. In this paper, the gaming chatbot was created and used by students. Based on the collected data from the results of its use, models of the influence of factors on the level of interest and difficulty of tasks were built. The developed models formed the basis of a new version of the gaming chatbot with artificial intelligence. When using the chatbot with such models, it is possible to retrain the models and adjust the obtained values of coefficients. Ap-probation of the obtained chatbot has shown great interest in its use among stu-dents learning programming.

[short] Maxim Maron, Arkadiy Maron and Danila Tet'Kov. Определения вероятностей реализации рисков по прогнозным и фактическим данным (PDF)

Abstract Актуальность исследуемой проблемы обусловлена тем, что деятельность компаний в современном быстро меняющемся мире неизбеж-но связана с рисками. Необходимо эти риски обоснованно оценивать в условиях дефицита релевантных статистических данных. Цель статьи за-ключается в разработке метода определения вероятностей рисков, которые привели к отклонению целевых показателей эффективности деятельности компании от целевых значений. В качестве подхода к исследованию данной проблемы выбран метод максимальной энтропии – информационный метод Джейнса. В результате предложен метод, позволяющий для каждого целе-вого показателя компании и для каждого риска определить, вероятность того, что реализация именно этого риска привела к отклонению от целевого значения. В качестве данных используются прогноз отклонений и фактиче-ские значения. Материалы статьи будут полезными в первую очередь для руководства компаний. Также они могут быть полезны научным работни-кам и аспирантам математических и экономических специальностей.

THURSDAY, OCTOBER 24th

SESSION 7. Image Analysis I. Room 125. Chair:

Aleksei Samarin, Alexander Savelev, Aleksei Toropov, Artem Nazarenko, Alexandr Motyko, Elena Mikhailova, Egor Kotenko, Alina Dzestelova and Valentin Malykh. Modernized Non-Local Blocks for Infrared Camera Image Segmentation of the Human Eye (PDF)

Abstract This investigation delves into the integration of specialized self-attention modules within deep learning models, specifically focusing on the task of delineating human iris and pupil regions in infrared imagery. In this research endeavor, we introduce several adaptations of non-local blocks that imbue the essence of self-attentiveness, while taking into account the unique attributes inherent in infrared image data. Employing these tailored enhancements, we have witnessed remarkable strides in the performance metrics of the underlying deep neural net- work framework, manifesting notable refinement of segmentation out- comes (advancing from 0.945 to 0.983 in mIoU and from 0.951 to 0.988 in mDice) upon rigorous evaluation against a representative subset of the infrared image dataset. This progress opens doors to diverse applications in the realm of infrared image analysis, promising novel avenues for research and innovation.

Aleksei Samarin, Alina Dzestelova, Egor Kotenko, Valentin Malykh, Elena Mikhailova, Alexandr Motyko, Artem Nazarenko, Alexander Savelev, Aleksei Toropov and Aleksandra Dozortseva. Automated Feature Engineering Based Approach for Micrococci Microscopic Image Classification and Taxonomic Characteristics Determination (PDF)

Abstract In this paper, we describe our research on creating classifiers for microbial images (micrococci microscopy images) obtained from images of unfixed microscopic scenes. In our work, we propose an AutoML approach based on the automatic generation and analysis of the feature space for constructing the most optimal descriptors of microorganism images for subsequent classification. This makes it possible to use interpretable taxonomic features based on the geometric features of the visual series of images of microorganisms of various species, which is important for the microbiology domain environment. To demonstrate the effectiveness of our method, we publish an annotated dataset we created consisting of microbial images of unfixed microscopic scenes. Using the presented data set, we compare the classification efficiency of our method and various types of classifiers, including those based on deep neural network models. The method we proposed demonstrated the best results among those studied (F1-score = 0.997).

Art Prosvetov, Alexandr Govorov, Maxim Pupkov, Alexandr Andreev and Vladimir Nazarov. Illuminating the Moon: Reconstruction of Lunar Terrain Using Photogrammetry, Neural Radiance Fields, and Gaussian Splatting (PDF)

Abstract Accurately reconstructing the lunar surface is critical for scientific analysis and the planning of future lunar missions. This study investigates the efficacy of three advanced reconstruction techniques – photogrammetry, Neural Radiance Fields, and Gaussian Splatting – applied to the lunar surface imagery. The research emphasizes the influence of varying illumination conditions and shadows, crucial elements due to the Moon’s lack of atmosphere. Extensive comparative analysis is conducted using a dataset of lunar surface images captured under different lighting scenarios. Our results demonstrate the strengths and weaknesses of each method based on a pairwise comparison of the obtained models with the original one. The results indicate that using methods based on neural networks, it is possible to complement the model obtained by classical photogrammetry. These insights are invaluable for the optimization of surface reconstruction algorithms, promoting enhanced accuracy and reliability in the context of upcoming lunar exploration missions.

SESSION 8. Data Analysis in Neurophysiology. Room 126. Chair: Mikhail Zymbler

Anastasiia Timofeeva, Tatiana V. Avdeenko and Sergei Alkov. Robust Partial Correlation between EEG Connectivity and Arithmetic Ability (PDF)

Abstract Numerous studies of brain function using EEG data indicate conflicting results. Therefore, the analysis of methods that make it possible to identify stable relationships remains relevant. In this regard, the aim of the present study is to search for features obtained from EEG data that are robust correlated with arithmetic ability. Connectivity graph measures are extracted as features. The problem is that the graph measures are highly correlated with each other, so it is proposed to use partial correlation coefficients based on the Spearman correlation coefficient. The feature with the largest absolute partial correlation coefficient is selected. To exclude the influence of other features on it, a regression is built, based on the coefficients of which the weights of the features are calculated. The resulting linear combination of features is considered as an extracted factor. This approach is compared to the principal component analysis. The result shows that the use of a partial correlation coefficient allows not only to select more significant connectivity metrics, but also to identify new relationships that cannot be detected by standard methods due to spurious correlation.

Almaz Shangareev, Ivan Shanin and Sergey Stupnikov. Отслеживание прогресса чтения c использованием глубокого обучения на данных окулографии с высоким уровнем шума (PDF)

Abstract Работа посвящена исследованию методов отслеживания про- гресса чтения на данных окулографии c использованием нейронных се- тей глубокого обучения. Разработана архитектура автокодирующей нейрон- ной сети, нацеленная на эффективное использование пространственной и временной информации. Предложен метод аугментации данных, который порождает данные с высоким уровнем шума и сохраняет информацию о соответствии каждой фиксации взгляда определённому слову. Проведена экспериментальная оценка качества нейросетевой модели на зашумленных данных.

Margarita Samburova, Albina Lebedeva, Alexander Naumov, Vyacheslav Razin, Nikolay Gromov, Svetlana Gerasimova, Tatiana Levanova and Lev Smirnov. Using two interconnected reservoirs to predict mouse hippocampal local field potentials (PDF)

Abstract The hippocampus plays an important role in various processes in the brain related to memory and information processing. The aim of this paper is to propose and test an approach based on reservoir computings for signal prediction in rodent hippocampus based on received biological input. We compare the prediction results for two reservoir architectures: a single reservoir and two subsequently connected reservoirs. Obtained results can be used in tasks of hippocampal activity restoration using neurohybrid chips.

SESSION 9. Image Analysis II. Room 125. Chair:

Aleksei Samarin, Alexander Savelev, Aleksei Toropov, Artem Nazarenko, Alexandr Motyko, Elena Mikhailova, Egor Kotenko, Alina Dzestelova and Valentin Malykh. ADSAR: Advanced Dual-Stream Attention and Reweighting for Small Object Detection (PDF)

Abstract This paper focuses on advancing the field of small object detection within complex visual environments, leveraging the latest in deep learning technologies. We introduce a novel approach characterized by a dual-stream self-attention mechanism integrated within a multi-head framework, and further refine detection accuracy through an innovative output reweighting technique. The core of our methodology, termed ADSAR (Advanced Dual-Stream Attention and Reweighting), is designed to tackle the challenges posed by small objects that often overlap multiple tokens in feature maps—a common issue in conventional detection models. By dynamically adjusting the scale of attention across different heads, ADSAR allows for detailed feature capture at multiple granularities, significantly enhancing the model’s ability to detect and characterize small objects. The addition of a softmax-based reweighting function selectively emphasizes features crucial for object recognition, thereby sup- pressing irrelevant information and reducing noise. Our proposed model not only outperforms existing state-of-the-art solutions in accuracy but also demonstrates superior efficiency in processing and scalability. These advancements not only contribute to the theoretical understanding of attention mechanisms in deep neural networks but also offer practical improvements in real-world applications where small object detection is critical.

Anna Shiyan, Ivan Kozlov, Ildar Baimuratov and Nataly Zhukova. Plants and their Diseases Recognition: Multiclass and Multilabel Classification Benchmarks (PDF)

Abstract We introduce a comparison of MobileNetV3Small, EfficientNetB0 and DenseNet121 models pre-trained on ImageNet and fine-tuned on Plant Village and PlantDoc datasets for plants and their diseases multiclass and multilabel classification. As a result of the experiments, it was found that the EfficientNetV2B0 model was the most effective for the plant diseases recognition task with accuracy 0.997 on Plant Village dataset and 0.96 on PlantDoc dataset.

[short] Павел Архипов, Сергей Филиппских and Максим Цуканов. Кластерный анализ данных для оптимизации параметров алгоритмов детектирования объектов мобильными нейросетевыми моделями (PDF)

Abstract В статье рассматривается задача выбора оптимальных форм и количества полей привязки для точной настройки алгоритмов детектирования и классификации объектов. Поиск полей привязки сводится к задаче кластеризации, позволяющей выделять группы схожих объектов. Для кла-стеризации были выбраны три алгоритма, основанные на разных принципах: принципе прототипов, иерархических деревьев и графов. Для экспе-риментов с поиском полей привязки выбраны две нейросетевые модели: SSD MobileNet V2 FPNLite 640x640 и SSD ResNet50 V1 FPN 640x640, находящиеся в репозитории TensorFlow 2 Detection Model Zoo. Данные нейросетевые модели были предобучены на наборе данных Microsoft Common Objects in Context 2017, а затем дообучены на наборе данных VisDrone2022. При помощи кластерного анализа вычислены 15 множеств коэффициентов полей привязки. Для каждого множества полученных ко-эффициентов обучена своя нейросетевая модель SSD MobileNet V2 FPNLite 640x640 и произведено сравнение с базовыми моделями SSD MobileNet V2 FPNLite 640x640 и SSD ResNet50 V1 FPN 640x640 со стан-дартными значениями коэффициентов полей привязки. В результате оп-тимизации параметров существенно выросла точность детектирования объектов при помощи модели на базе мобильной нейросетевой архитектуры MobileNet V2. Данный результат имеет большое практическое значение при работе с компактными энергоэффективными системами с ограниченной производительностью и памятью.

[short] Sergey Stasenko, Andrey Lebedev, Olga Shemagina, Irina Nuidel, Andrey Kovalchuk and Vladimir Yakhno. Adaptive Correction of Multi-Cascade Detectors in Biomorphic AI for Pattern Recognition (PDF)

Abstract This paper investigates the adaptive correction of a multistage detector in a biomorphic artificial intelligence system for pattern recognition problems. A distinctive feature of this system is its imitation of the hierarchical information processing observed in living systems, such as in the visual cortex of the brain. As part of this study, an algorithm for correcting the results of a multistage detector was tested, and conclusions about the outcomes of its application were formulated.

SESSION 10. Data Analysis in Medicine. Room 126. Chair: Nikolay Zolotykh

Vladislav Kuznetsov, Victor Moskalenko and Nikolay Zolotykh. Diagnosis of cardiovascular diseases using recurrent and convolutional neural networks (PDF)

Abstract In this work, we explore methods for diagnosing cardiovascular diseases based on electrocardiogram (ECG) data, using neural networks, as well as machine learning methods. We will use convolutional and recurrent neural networks, as well as the XGBoost algorithm. Our goal is to build a model that can identify a large set of cardiovascular diseases using a signal and segmentation built on that signal with a high degree of accuracy, as well as additionally calculated features. We set the task of determining heart rhythms, hypertrophies, extrasystoles, AV blocks, bundle branch blocks and electrical axes. The signal consists of waves with sampling rates of 100 and 500 Hz and waveform lengths of 10 seconds. The signal will be processed and supplied to the input of the models in a shortened form of 6 and 9 seconds. The metrics used are precision, recall, specificity and F1-measure. Within the framework of the problem under study, both binary and multi-class problems of determining the presence of pathologies will be posed. Some of the diagnoses show good quality metrics on individual models. We will conduct a comparative analysis of the results of each model. Automatic diagnosis of cardiovascular diseases is designed to reduce and optimize the work of cardiologists.

Alexander Varnavsky. Model for assessing the need to involve users of social networks in a healthy lifestyle and giving up bad habits according to the data of a social network (PDF)

Abstract An urgent task is to preserve and maintain the health of the country’s population, including through the promotion of a healthy lifestyle. Since social networks are very popular, especially among young people, it is possible to promote a healthy lifestyle on their basis. Despite the existing research on the influence of social networks on user behaviour, especially to alcohol consumption and smoking, no models are providing personalized recommendations for the user to involve in a healthy lifestyle and quit bad habits. The work aimed to research the young peo-ple’s social networks usage indicators and behaviour to a healthy lifestyle and the construction of personalized models to assess the need to change user behav-iour. To achieve the aim, experimental research was conducted based on a sur-vey of young people and an assessment of their profiles in social networks. An assessment and analysis of the existence of relationships between indicators of self-assessment of health, the presence of diseases, behaviour to a healthy life-style and the behaviour of users in social networks were completed. It was found that self-assessment of health and the presence of chronic diseases are not only interconnected with indicators of behaviour to a healthy lifestyle but also in-terrelated with respondents’ behaviour indicators in social networks. The theory of cognitive processes and cognitive load can explain these relationships. Based on the presence of interrelationships, regression models were built predicting us-ers’ behaviour to a healthy lifestyle. Using such models embedding in social networks will allow issuing personalized recommendations.

[short] Vyacheslav Razin and Alexander Krasnov. Deep learning to detect the presence of heart disease on the PTB-XL dataset (PDF)

Abstract This article describes the use of convolutional and recurrent neural networks to solve the problem of determining the presence of heart disease. Particular atten-tion is paid to constructing various ensembles from trained deep learning methods to improve target metrics. The work also proposes various modifications and methods that may be useful for increasing target metrics.

[short] Maxim Kostyukov, Lev Smirnov and Grigory Osipov. Neuromorphic reservoir computing for ECG heartbeats classification (PDF)

Abstract Brain-inspired reset computing methods attracted a great attention due to their reduced computation complexity by using fixed in- ternal synaptic strengths. In our study we consider a quantized reservoir neural network to be used on a neuromorphic hardware as a feature ex- traction model for solving time-series classification tasks. We conducted experiments on ambulatory ECG recordings. The considered approach demonstrates competitive accuracy and robustness, so it can be regarded for use on wearable devices due its energy efficiency.

FRIDAY, OCTOBER 25th

SESSION 11. Information extraction from text I: Generative and Transformer-Based Models. Room 125. Chair: Boris Dobrov

Anna Glazkova and Dmitry Morozov. Exploring Fine-tuned Generative Models for Keyphrase Selection: A Case Study for Russian (PDF)

Abstract Keyphrase selection plays a pivotal role within the domain of scholarly texts, facilitating efficient information retrieval, summarization, and indexing. In this work, we explored how to apply fine-tuned generative transformer-based models to the specific task of keyphrase selection within Russian scientific texts. We experimented with four distinct generative models, such as ruT5, ruGPT, mT5, and mBART, and evaluated their performance in both in-domain and cross-domain settings. The experiments were conducted on Russian scientific abstract texts from four domains: mathematics and computer science, history, medicine, and linguistics. The use of generative models, namely mBART, led to gains in in-domain performance (up to 4.87% in BERTScore, 8.96% in ROUGE-1, and 12.16% in F1-score) over three keyphrase extraction baselines for the Russian language. Although the results for cross-domain usage were significantly lower, they still demonstrated the capability to surpass baseline performances in several cases, underscoring the promising potential for further exploration and refinement in this domain.

Alexey Sery, Daria Ilina, Elena Sidorova and Yury Zagorulko. Applying Generative Neural Networks to Extract Argument Relations from Scientific Communication Texts (PDF)

Abstract The study explores methods for extracting argument relations from texts using large generative language models. Experiments were conducted on a Russian-language corpus of texts related to the field of scientific communication. Prompt-engineering methods were used, with prompts developed using various tactics. The Mistral-7B was employed as the generative model. The task of extracting ar-gumentative links was formulated as a binary classification problem of the exist-ence/non-existence of a link between two statements. In constructing the dataset, the data were balanced. Positive examples included statements that were part of a single argument (premise, conclusion), while negative examples were generated from statements in the same paragraph for each positive example. Two methods of creating instructions were considered: using ChatGPT and an expert approach using the Chain-of-Thoughts tactic. The best solutions were obtained based on instructions composed by an expert and including context for each statement of one paragraph size. Instructions generated by ChatGPT, while producing compa-rable results, oftentimes returned incorrect responses. An experimental study was also conducted on an approach, in which the argumentation scheme is predicted immediately, allowing for more precise information about the type of relation to be included in the prompt. This task was also formulated as a binary classification problem. The two most frequent schemes in the examined corpora, “Expert Opin-ion” and “Example,” were explored.

Alisher Rogov and Natalia Loukachevitch. Explaining Transformer-Based Models: a Comparative Study of flan-T5 and BERT Using Post-Hoc Methods (PDF)

Abstract Neural networks have become an integral part of everyday life, finding applications in various domestic and industrial tasks. Generative models based on the Transformer architecture play a particularly significant role in natural language processing. These models have achieved, and in some cases surpassed, human-level performance in several tasks. However, despite their high performance, generative models can sometimes produce unexpected results. Understanding the principles behind the decisions of such models is an important and relevant challenge. In this article, we investigate how effectively the T5 model explains its answers in classification tasks. We also compare its interpretative capabilities with those of the BERT model using well-known interpretation methods such as SHAP, LIME, and the attention mechanism.

Elena Bolshakova and Vladislav Semak. An Experimental Study on Cross-domain Transformer-Based Term Recognition for Russian (PDF)

Abstract Terminologies of specialized problem domains present an important part of knowledge to be extracted for various applications, such as construction of thesauri, ontologies, glossaries and so on. Meanwhile, widely-used automatic term extraction (ATE) methods are mainly statistics-based and show quite aver-age quality, so ways to leverage modern deep learning techniques are currently studied. The paper addresses the task of term recognition based on BERT clas-sifier, considering cross-domain settings for experiments. The dataset constructed for experiments is presented, which contains samples taken from scientific texts in Russian. The results of the experiments with cross-domain term recognition are described, demonstrating comparable or slightly better quality than the most known ATE methods.

SESSION 12. Large Language Models and Applications. Room 126. Chair: Alexander Ponomarenko

Dmitry Namiot and Elena Zubareva. On Open Datasets for LLM Adversarial Testing (PDF)

Abstract This article discusses the issues of testing large language models. Large language models are the most popular form of generative machine learning models. The simple and clear usage model has led to their enormous popularity. However, like other machine learning models, large language models are susceptible to adversarial attacks. One could even say that the success of large language models has greatly increased interest in the security of machine learning models themselves. This direction immediately turned out to affect all users of machine learning systems. This article discusses the use of ready-made datasets for adversarial testing of large language models.

Pujun Xie and Anton Khritankov. An LLM Approach to Fixing Common Code Issues in Machine Learning Projects (PDF)

Abstract Modern empirical research in machine learning largely relies on developing custom software. Often such software is written by researchers and not professional software engineering. As a result, source code issues and the associated technical debt may accumulate and lead to higher programming effort, obstacles to code reuse, hidden software defects affecting the quality of the research itself. In this paper, we investigate if it is possible to apply automatic tools to prevent or remove these source code issues thus alleviating the need for software engineers in research projects. We analyze the source code of 24 open source research projects in machine learning, identify common issues and propose practical techniques to prevent these issues during coding. We also investigate if an application of an LLM coding assistant can fix common code issues automatically. We found out that 1) frequent source code issues largely the same for different machine learning frameworks 2) most of the issues could be eliminated by following simple coding practices 3) most of the issues could be removed by applying an LLM coding assistant.

Vasily Kostyumov, Bulat Nutfullin and Oleg Pilipenko. Uncertainty-Aware Evaluation for Vision-Language Models (PDF)

Abstract Vision-Language Models like GPT-4, LLaVA, and CogVLM have surged in popularity recently due to their impressive performance in several vision-language tasks. Current evaluation methods, however, overlook an essential component: uncertainty, which is crucial for a comprehensive assessment of VLMs. Addressing this oversight, we present a benchmark incorporating uncertainty quantification into evaluating VLMs. Our analysis spans 20+ VLMs, focusing on the multiple-choice Visual Question Answering (VQA) task. We examine models on 5 datasets that evaluate various vision-language capabilities. Using conformal prediction as an uncertainty estimation approach, we demonstrate that the models’ uncertainty is not aligned with their accuracy. Specifically, we show that models with the highest accuracy may also have the highest uncertainty, which confirms the importance of measuring it for VLMs. Our empirical findings also reveal a correlation between model uncertainty and its language model part. The code is available at https://github.com/EnSec-AI/VLM-Uncertainty-Bench. Arxiv Preprint: https://arxiv.org/pdf/2402.14418

SESSION 13. Information extraction from text II: Topic models. Room 125. Chair: Natalia Loukachevitch

Julian Serdyuk, Konstantin Vorontsov and Murat Apishev. Hypergraph topic models of document collections (PDF)

Abstract In this paper, the problem of constructing hypergraph (transactional) topic models on a collection of documents is explored. Such a model allows improve the standard the bag of words approach and take into account the semantic structure of the text: named entities, sentences or entire paragraphs of text. The transactional model assumes that a document is compiled through transactions, each of which adds some unit to the text, be it a word, phrase, sentence, and so on. Experiments have shown that hypergraph topic models have higher quality compared to classical topic modeling models.

Bulat Gizatullin and Olga Nevzorova. Comparative analysis of methods for topic modeling of mathematical documents (PDF)

Abstract The comparison of three topic modeling methods - LDA, NMF, and BERTopic - was conducted across diverse collections of mathematical articles. In the initial experiment using articles from "Izvestiya VUZov. Matematika," LDA exhibited superior performance based on the CV Coherence metric, although NMF also yielded commendable results. Conversely, BERTopic's thematic classes proved less interpretable compared to LDA and NMF. In the subsequent experiment, employing articles from the same journal but with vocabulary derived from OntoMathPRO ontology concepts, LDA again demonstrated favorable metric outcomes. However, BERTopic showcased enhanced interpretability of thematic classes compared to LDA and NMF. The third experiment, conducted on a combined collection from two journals with vocabulary compiled via frequency truncation and OntoMathPRO ontology concepts, reaffirmed LDA's superiority in terms of CV Coherence. Nevertheless, NMF exhibited significantly greater interpretability. Hence, it is evident that each topic modeling method possesses distinct advantages and constraints depending on the context and assumptions. Choosing the appropriate data preprocessing method is pivotal, as it significantly impacts modeling outcomes. Additionally, different data preprocessing approaches influence the interpretability of thematic classes for each method.

[short] Alexander Sychev. Анализ тематической модели для размеченной коллекции текстовых сообщений на основе подхода Word2Vec (PDF)

Abstract В статье рассматривается проблема тематического моделирования и оценки тематических моделей, представленных в размеченных наборах текстовых сообщений на основе модели векторного представления слов Word2vec. Построенные в результате анализа векторов слов кластеры могут быть использованы для различных задач, в том числе для диагностики тематической модели, представленной в размеченной коллекции текстовых сообщений. Для этого предложено вычислять матрицу пересечений между кластерами словаря, сформированного для всего корпуса текстов, и словарями тематических подмножеств в корпусе. В работе представлены и обсуждаются результаты машинного эксперимента применительно к коллекции новостных сообщений одного из региональных сетевых изданий. Результаты эксперимента показывают практическую возможность проведения диагностики существующей системы тематических рубрик в коллекции текстовых сообщений и определения направлений ее возможной реорганизации.

SESSION 14. Data Extraction and Storage. Room 126. Chair: Roman Samarev

Alexey Shigarov. Regular Table Language for Data Extraction from Tables Presented in Electronic Documents (PDF)

Abstract The paper presents "Regular Table Language" (RTL), a novel domain-specific language for extracting recordsets from arbitrary tables represented as parts of electronic documents in machine-readable formats (e.g. spreadsheets, text processors, or HTML). It is based on the hypothesis that any of such tables can be matched to a pattern specifying its structure sufficient to extract the required data from it. Moreover, a whole class of tables can be successfully matched to such a pattern. We propose an "Interpretable Table Model" (ITM) as a mediator between source tables and target recordsets. It extends the table structure found in wide-spread sources (spreadsheets, text processors, or HTML) by adding the semantics that enables to automatically inference recordsets from tables. RTL provides a way to formally express some patterns of table structure in a declarative and laconic manner. The semantics missing in a source table is recovered as a result of matching the corresponding instance of ITM with an appropriate RTL-pattern. ITM and RTL have been implemented as main parts of Regtab, an open-source software library that simplifies the development of custom applications for data extraction from arbitrary tables presented in electronic documents.

Evgenii Stepanov and Alexey Mitsyuk. bXES: a Binary Format For Storing and Transferring Software Event Logs (PDF)

Abstract Modern software produces a lot of events which can be analyzed using process mining techniques. The first step in any process mining pipeline is the collection of the event logs. Then, those event logs need to be stored persistently on the disk. The problem is, that software event logs usually consist of many events, each of which can specify tens of attributes. In such a context, the event log stored in XML-based XES format consumes tremendous amount of memory. Moreover, it is not that read-friendly, i.e. does not provide tools with any advantage while reading a XES file. In this paper, we present bXES, a binary format for storing and transferring event logs, especially software event logs. We highlight main characteristics of software event logs which are utilized in the format scheme, next we describe the format. Finally, we conduct experiments in order to demonstrate the bXES compatibility. The experiments are conducted with real-life business process and software event logs.

[short] Евгений Александров, Игорь Александров, Дмитрий Беляков, Наталья Давыдова, Петр Зрелов, Лидия Калмыкова, Мария Любимова, Татьяна Сапожникова, Татьяна Сыресина and Александр Яковлев. Возможности разработанной в ЛИТ информационной системы сопровождения лицензий (PDF)

Abstract Основная цель создания информационной системы сопровождения лицензий (ИССЛ) - автоматизация управления, приобретения, обслуживания и использования лицензионных программных продуктов. Представлены результаты развития системы за последние два года. Реализован механизм согласования заявок, и на его основе созданы различные заявки: заявки пользователя на новую лицензию, на добавление новых программных продуктов в каталог поддерживаемого ПО, заявки аудитора на закупку дополнительных лицензий. Выполнены работы по наполнению базы данных ИССЛ, удобному представлению информации о лицензиях и различной статистической информации, а также интеграции с другими сервисами в рамках Цифровой экосистемы ОИЯИ.

SESSION 15. Information extraction from text III. Room 125. Chair: Natalia Loukachevitch

Евгений Владимирович Волков and Борис Викторович Добров. Автоматическое транскрибирование OOV слов для улучшения распознавания терминов предметной области (PDF)

Abstract Одним из популярных методов улучшения качества распознавания незнакомых модели (OOV, от англ. out of vocabulary – отсутствующих в словаре) слов является метод расширения словаря (vocabulary expansion). OOV слова особенно часто встречаются в предметных областях: это термины и терминоподобные словосочетания. В русской речи многие из них недавно заимствованы из английского языка и еще не имеют принятой записи на русском языке. Это затрудняет добавление таких слов в словарь модели для моделей, не являющихся мультиязычными, что снижает качество их работы на аудио из предметных областей. В данной работе представлен метод улучшения качества распознавания речи, содержащей такие OOV термины, на основе алгоритма автоматического построения для произвольного английского слова т.н. русских транскрипций. Русская транскрипция – это сходно звучащее и записанное русскими буквами слово, которое могло бы заменить исходный термин при добавлении его в словарь модели, и таким образом сделать возможным его корректное распознавание. Результаты проведенных экспериментов позволяют говорить о возможности применения описанного алгоритма для улучшения качества работы ASR систем.

Artem Prosvetov, Alexey Matveev and Alexandr Andreev. Decoding the Past: Building a Comprehensive Glagolitic Dataset for Historical Text Analysis (PDF)

Abstract The Glagolitic script, one of the oldest known Slavic scripts, presents a substantial challenge for historical manuscript decryption due to its intricate glyph forms and limited existing digital resources. This paper introduces a novel dataset of Glagolitic letters aimed at facilitating the application of machine learning algorithms in the decryption of historical documents. The dataset creation process comprised several critical stages: collection of raw data, preparation of images, application of neural networks for letter extraction, clustering of images, training of models to discern noise, and manual validation and annotation of rare letters. The resultant dataset stands as the first publicly accessible Glagolic script resource tailored for deep learning applications in historical document analysis.

[short] Andrey Lovyagin and Boris Dobrov. Verifying Factographic Content in Narrative Texts (PDF)

Abstract This research thoroughly examines both traditional and modern methods for automating information verification, with a specific focus on analyzing essays and texts containing dated content. It introduces three new techniques — CHECK-S, CHECK-V, and CHECK-U — for analyzing texts with various attributes, and develops a new data storage architecture, the "Reverse Index Tree", to enhance the efficiency of the CHECK-S method. Additionally, this study presents a new approach to contrastive learning, "Refinement Contrastive Learning", which has been tested in competitive environments and has shown substantial improvements over existing methods, setting new performance standards. The findings from this study indicate significant enhancements in effectiveness compared to traditional methods and those used by previous leaders in academic competitions. The new method, along with its underlying data architecture and training approaches, demonstrates considerable advantages, affirming the effectiveness and potential of the proposed solutions in improving automated information verification for essays and historically dated texts.

[short] Olga Gavenko and Sofia Obersht. Development and implementation of software application for comparative analysis of the estimates of the complexity of text data (PDF)

Abstract The complexity of the text is a complex concept consisting of difficultness, readability and comprehensibility and describing the text and its structure depending on how they influence on the processing of information. The determination of text complexity has applied significance in the fields where understanding and processing of information and knowledges are important: educational literature, legislative and other documents, journalistic literature. Subjective parameters of text include empirical data on the reader’s perception of the text, physical and cognitive abilities, knowledge and education of an individual in certain area and experience in general. Objective parameters can be divided into quantitative such as length, frequency of usage or amount of language tokens, and qualitative parameters which are related to the analysis of linguistic means of categorical language levels and their implementation in a definite text. The task becomes more complicated when the complexity of large text data needs to be estimated. Defining text as a character sequence, the estimating model of complexity can be developed, the parameters are the objective parameters of text, the choice of which, as well as methods of complexity estimation can vary depending on the tasks; most of the formulas of readability estimates are based on the linear-regression model. Since the formulas are considered to be universal, the goal of this paper is the development and implementation of software application in Python able to do the comparative analysis of existing readability estimates for text data; the basic formulas for English and adapted for the Russian are observed. School textbooks on Social Studies (5-11 classes) included in the Russian Readability Corpus and textbooks on History (6-11 classes) make the test sample. The experiments with the text corpus data shows the series of incorrect results what can be explained by the fact that the model development based on the texts of different genres and styles having various linguistic means, terminology and structure. The formulas developed for the English language give less accurate results compared to adapted ones, what is due to the difference in languages; in addition, the fact, that quantitative parameters may not be sufficient to obtain reliable results, should be taken into account when expanding corpus data.

SESSION 16. Data Analysis in Earth Sciences. Room 126. Chair: Nikolay Skvortsov

Евгений Вязилов and Наталия Пузова. Перспективы использования средств искусственного интеллекта в гидрометеорологии (PDF)

Abstract Инструменты искусственного интеллекта включают программных роботов, которые могут решать рутинные задачи интенсивной обработки. например, загрузка и контроль данных; проведение аналитики, обнаружение опасных явлений в потоках оперативных данных и принятие решений на основе климатических, прогностических и наблюденных данных. Нейронные сети, машинное обучение, глубокое обучение могут применяться для лучшего понимания и прогнозирования погоды. В статье представлен широкий спектр направлений применения искусственного интеллекта в гидрометеорологии, связанный со сбором, поиском данных на основе метаданных, организацией доступа к данным на основе взаимодействия с чат-ботом, прогнозами гидрометеорологических процессов, возможных воздействии опасных явлений на предприятия и население, обучением населения и руководителей поведению в период прохождения опасных явлений.

[short] Nikolai Lavrentiev, Alexey Akhlyostin, Alexey Privezentsev and Alexander Fazliev. Data quality assessment in large spectral data collections. States and transitions (PDF)

Abstract Two groups of spectral data collections on molecular states and transitions are considered in this work. The main goal of the work is to improve the spectral data quality, to find out into what parts these groups should be divided, and how the groups are related to each other when analyzing the quality of states and transitions collected. The approach to the formation of empirical states is clarified, and a specific problem is considered using collections of data on the basic isotopologue of the water molecule as an example. This paper continues the investigation of spectral data quality started in [1]. To improve data quality, this work applies filtering first using empirical states and then the unique set of states that are not identical to the empirical states. The additional filtering allowed us to add about 500 correct transitions to the empirical states.

[short] Vladimir Budzko and Victor Medennikov. Экосистемный Подход к Стратегическому Управлению на Примере Сельского Хозяйства (PDF)

Abstract Рассматривается трансформация методов и моделей стратегического управления на основе экосистемного подхода в рамках формирования единой цифровой платформы управления экономикой. Экосистемный подход к социально-экономическому развитию общества набирает все большую популярность, продиктованную общемировым социальным заказом на защиту окружающей среды и на бережное отношение к расходованию ограниченных природных ресурсов. Экологические проблемы в сельском хозяйстве России нарастают, в частности из-за активного процесса формирования агропромышленных объединений, в основном в виде агрохолдингов. При этом возникает проблема системного подхода к необходимости применения технологий интеграции всех видов участвующих в производстве ресурсов, с учетом роста числа и значения факторов внешней среды. В качестве основного метода исследования стратегического управления предлагается математическое моделирование, которое в отличие от большинства используемых моделей, носящих при этом зачастую иконографический вид, позволяет учесть значительно большее количество факторов и дает возможность в режиме имитации рассчитать различные варианты развития объектов моделирования. В результате исследований была разработана математическая модель стратегического управления агрохолдинга в целях его устойчивого развития. Показано, что разработка стратегии его развития должна осуществляться в тесной увязке с внедрением соответствующей автоматизированной системы управления холдингом, что приведет к коренному изменению всей системы его управления, а также производства, что позволит объединению при стратегическом целеполагании ориентироваться вслед за мировыми тенденциями в первую очередь на качество, прослеживаемость, другие составляющие конкурентоспособности. Рассмотренная оригинальная авторская математическая модель дает научное обоснование единых методов цифровизации в долгосрочном плане как больших, многоотраслевых аграрных объединений после отработки на некотором множестве их, так и средних и малых хозяйств, которые смогут работать с агрохолдингами на принципах аутсорсинга.


Important dates

Conference
Submission deadline for papers June 17, 2024
Submission deadline for tutorials June 3, 2024
Notification for the first round August 12, 2024
Deadline for revised versions of the papers forwarded to the second round of reviewing September 2, 2024
Final notification of acceptance September 16, 2024
Deadline for camera-ready versions of the accepted papers September 16, 2024
Conference October 23-25, 2024