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8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Liu, Shuang Kabanikhin, Sergey Igorevich Strijhak, Sergei Vladimirovich Novosibirsk State University Akademgorodok Novosibirsk Russia Sobolev Institute of Mathematics Siberian Branch of the Russian Academy of Sciences Novosibirsk Russia Ivannikov Institute of System Programming of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9798400718182

This paper explores the connection between systems of linear algebraic equations (SLAE) and machine learning methods, including regularization techniques, to establish a more novel neural network model based on linear neural networks. The goal is to construct a weight matrix for the neural network, which, by simulating the process of finding pseudo-solutions to SLAE, can generate the optimal answer for any input data. In this new neural network model, linear operations are performed first, followed by nonlinear operations, ultimately yielding an optimized weight matrix that serves as the pseudo-solution to the SLAE. The paper demonstrates how linear neural networks can be simplified to SLAE, how adding nonlinear layers to the linear neural network model can improve accuracy, and how machine learning methods can be used to find pseudo-solutions to SLAE. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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On The Visualization of Subattractor Under Mixed Tidal Forcing

Scientific Visualization

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Scientific Visualization 2025年第1期17卷 138-149页

作者： Elistratov, Stepan But, Ivan Shirshov Institute of Oceanology RAS Moscow Russia Ivannikov Institute for System Programming RAS Moscow Russia Sobolev Institute of Mathematics of Siberian Branch RAS Novosibirsk Russia

One of the principle conditions of a wave attractor appearance is a periodic external forcing. Real forcing in natural basins caused by tidal interaction is more complex than a monochromatic which is usually used in internal wave attractors investigations. Multi-frequency forcing may lead to the multiple wave attractor formation, some of them may be of low energy, which affects their detection. In the article we simulate a mixed forcing for an internal wave attractor flow and visualize subattractor formed due to this forcing type using several methods, including Proper Orthogonal Decomposition. It is shown that the latter method reveals sub-attractor even in case of highly turbulent flow. © 2025 National Research Nuclear University. All rights reserved.

关键词： Visualization

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Influence of wave-maker shape on the wave attractor

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Pramana 2025年第2期99卷 1-9页

作者： Elistratov, Stepan Ilina, Daria Shirshov Institute of Oceanology of RAS Moscow Russia Ivannikov Institute for System Programming of RAS Moscow Russia Sobolev Institute of mathematics of Siberian branch of RAS Novosibirsk Russia

Internal waves in a stratified liquid propagate to fit the dispersion relation, depending only on the frequency and the angle to the gravity. The phenomenon of self-focussing of such waves is known as wave attractor. To study this phenomenon numerically or in a laboratory, it is common to use a wave-maker providing the forcing through one of the boundaries. In this work, we study the influence of the wave-maker forcing on the wave attractor formation and its characteristics.

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Multi-Record Web Page Information Extraction From News Websites

arXiv

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arXiv 2025年

作者： Kustenkov, Alexander Varlamov, Maksim Yatskov, Alexander Ivannikov Institute for System Programming Russian Academy of Sciences Moscow Russia Lomonosov Moscow State University Moscow Russia

In this paper, we focused on the problem of extracting information from web pages containing many records, a task of growing importance in the era of massive web data. Recently, the development of neural network methods has improved the quality of information extraction from web pages. Nevertheless, most of the research and datasets are aimed at studying detailed pages. This has left multi-record"list pages" relatively understudied, despite their widespread presence and practical significance. To address this gap, we created a large-scale, open-access dataset specifically designed for list pages. This is the first dataset for this task in the Russian language. Our dataset contains 13,120 web pages with news lists, significantly exceeding existing datasets in both scale and complexity. Our dataset contains attributes of various types, including optional and multi-valued, providing a realistic representation of real-world list pages. These features make our dataset a valuable resource for studying information extraction from pages containing many records. Furthermore, we proposed our own multi-stage information extraction methods. In this work, we explore and demonstrate several strategies for applying MarkupLM to the specific challenges of multi-record web pages. Our experiments validate the advantages of our methods. By releasing our dataset to the public1, we aim to advance the field of information extraction from multi-record pages. © 2025, CC BY-NC-ND.

关键词： Data collection

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Complexity of the Uniform Membership Problem for Hyperedge Replacement Grammars

arXiv

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arXiv 2025年

作者： Pshenitsyn, Tikhon Steklov Mathematical Institute 8 Gubkina st. Moscow119991 Russia Ivannikov Institute for System Programming 25 Alexander Solzhenitsyn st. Moscow109004 Russia

We investigate complexity of the uniform membership problem for hyperedge replacement grammars in comparison with other mildly context-sensitive grammar formalisms. It turns out that the complexity of the problem considered depends heavily on how one defines a hypergraph. There are two commonly used definitions in the field which differ in whether repetitions of attachment nodes of a hyperedge are allowed in a hypergraph or not. We show that, if repetitions are allowed, then the problem under consideration is EXPTIME-complete even for string-generating hyperedge replacement grammars while it is NP-complete if repetitions are disallowed. We also prove that checking whether a hyperedge replacement grammar is string-generating is EXPTIME-complete. Copyright © 2025, The Authors. All rights reserved.

关键词： Context sensitive grammars

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Decentralised convex optimisation with probability-proportional-to-size quantization

arXiv

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arXiv 2025年

作者： Pasechnyuk, D.A. Dvurechensky, P. Uribe, C.A. Gasnikov, A.V. Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Ivannikov Institute for System Programming the Russian Academy of Sciences Russia Weierstrass Institute for Applied Analysis and Stochastics Germany Rice University United States Moscow Institute of Physics and Technology Russia

Communication is one of the bottlenecks of distributed optimisation and learning. To overcome this bottleneck, we propose a novel quantization method that transforms a vector into a sample of components’ indices drawn from a categorical distribution with probabilities proportional to values at those components. Then, we propose a primal and a primal-dual accelerated stochastic gradient methods that use our proposed quantization, and derive their convergence rates in terms of probabilities of large deviations. We focus on affine-constrained convex optimisation and its application to decentralised distributed optimisation problems. To illustrate the work of our algorithm, we apply it to the decentralised computation of semi-discrete entropy regularized Wasserstein *** Codes 65K10 © 2025, CC BY.

关键词： Optimization algorithms

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Neuron Coverage Maximization for Effective Test Set Construction with Respect to the Model

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Journal of Mathematical Sciences (United States) 2024年第2期285卷 189-200页

作者： Kushchuk, D. Ryndin, M. Ivannikov Institute for System Programming of the RAS Moscow Russian Federation

Real world data is not stationary and thus models must be monitored in production. One way to be sure in a model’s performance is regular testing. If the labels are not available, the task of minimizing the labeling cost can be formulated. In this work, we investigate and develop various ways to construct a minimum test set for a given trained model, in a fashion where the accuracy of the model calculated on the chosen subset is as close to the real one as possible. We focus on the white box scenario and propose a novel approach that uses neuron coverage as a observable functional to maximize in order to minimize the number of samples. We evaluate the proposed approach and compare it to Bayesian methods and stratification algorithms that are the main approaches to solve this task in literature. The developed method shows approximately the same level of performance but has a number of advantages over its competitors. It is deterministic, thus eliminating the dispersion of the results. Also, this method can give one a hint on the optimal budget. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

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Realistic Adversarial Attacks on Object Detectors Using Generative Models

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Journal of Mathematical Sciences (United States) 2024年第2期285卷 245-254页

作者： Shelepneva, D. Arkhipenko, K. Ivannikov Institute for System Programming of the RAS Moscow Russian Federation

An important limitation of existing adversarial attacks on real-world object detectors lies in their threat model: adversarial patch-based methods often produce suspicious images while image generation approaches do not restrict the attacker’s capabilities of modifying the original scene. We design a threat model where the attacker modifies individual image segments and is required to produce realistic images. We also develop and evaluate a white-box attack that utilizes generative adversarial nets and diffusion models as a generator of malicious images. Our attack is able to produce high-fidelity images as measured by the Fréchet inception distance (FID) and reduces the mAP of Faster R-CNN model by over 0.2 on Cityscapes and COCO-Stuff datasets. A PyTorch implementation of our attack is available at https://***/DariaShel/gan-attack. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： adversarial examples diffusion models generative adversarial networks object detectors

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Automatic verification of the text layer correctness in PDF documents

Automatic verification of the text layer correctness in PDF ...

引用

2024 ivannikov Memorial Workshop, IVMEM 2024

作者： Belyaeva, Oksana V. Golodkov, Aleksandr Bukhatov, Bekzat Programming of the RAS Ivannikov Institute for System Moscow Russia

ISBN: (纸本)9798331504663

PDF documents can contain incorrect textual layers due to low scanning quality, font embedding errors or other reasons. An incorrect text layer can significantly hamper automatic document processing and limit document parsing. To help tackle the problem, this work analyzed the causes of incorrect text layers (gibberish characters) and described a method to automatically extract text information by detecting the correct text layer of PDF files, using OCR and a PDF extractor. To determine the correctness/incorrectness of the text layer obtained from PDF extractors in the method, we trained a binary classifier on the generated *** carried out experiments and assessed the quality of various classification models for text correctness, tested the method on real data, thereby confirming the effectiveness of the developed approach. The obtained results can be useful for further development of methods for automatically checking textual layers and improving the quality of automatic processing PDF documents. © 2024 IEEE.

关键词： Automatic PDF text extraction Corrupted PDF Gibberish text detection Machine learning Reasons for text incorrectness Text classification

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Towards Symbolic Pointers Reasoning in Dynamic Symbolic Execution

Towards Symbolic Pointers Reasoning in Dynamic Symbolic Exec...

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2021 ivannikov Memorial Workshop, IVMEM 2021

作者： Kuts, Daniil Ivannikov Institute for System Programming RAS Moscow Russia

ISBN: (纸本)9781665423274

Dynamic symbolic execution is a widely used technique for automated software testing, designed for execution paths exploration and program errors detection. A hybrid approach has recently become widespread, when the main goal of symbolic execution is helping fuzzer increase program coverage. The more branches symbolic executor can invert, the more useful it is for fuzzer. A program control flow often depends on memory values, which are obtained by computing address indexes from user input. However, most DSE tools don't support such dependencies, so they miss some desired program branches. We implement symbolic addresses reasoning on memory reads in our dynamic symbolic execution tool Sydr. Possible memory access regions are determined by either analyzing memory address symbolic expressions, or binary searching with SMT-solver. We propose an enhanced linearization technique to model memory accesses. Different memory modeling methods are compared on the set of programs. Our evaluation shows that symbolic addresses handling allows to discover new symbolic branches and increase the program coverage. © 2021 IEEE.

关键词： Software testing

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