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Recognition of Church Slavonic Texts Using Machine Learning Methods 使用机器学习方法识别教会斯拉夫语文本
IF 1 Q3 Computer Science Pub Date : 2024-04-10 DOI: 10.1134/s105466182401019x
A. A. Smirnova, N. G. Grafeeva, M. A. Tokman

Abstract

This study explores an approach to recognizing texts of ancient printed books written in Church Slavonic, utilizing computer vision and neural network methods. We achieved a classification accuracy of 99.13% for Church Slavonic alphabet characters, including punctuation marks, and 98.58% for superscript signs. The study developed an application variant enabling the conversion of Church Slavonic text images into an editable format. We formed datasets comprising image samples of letters and superscript signs, featuring no fewer than 200 examples per letter and at least 150 images per sign.

摘要 本研究探讨了一种利用计算机视觉和神经网络方法识别教会斯拉夫语古代印刷书籍文本的方法。我们对教会斯拉夫语字母字符(包括标点符号)的分类准确率达到 99.13%,对上标符号的分类准确率达到 98.58%。这项研究开发了一种应用程序变体,可将教会斯拉夫语文本图像转换为可编辑格式。我们建立了由字母和上标符号图像样本组成的数据集,每个字母不少于 200 个示例,每个符号不少于 150 个图像。
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引用次数: 0
Polynomial Time Turing Mitoticity and Arithmetical Hierarchy 多项式时间图灵 Mitoticity 和算术层次结构
IF 1 Q3 Computer Science Pub Date : 2024-04-10 DOI: 10.1134/s1054661824010127
A. H. Mokatsian

Abstract

Let (omega ) be the set of all nonnegative integers. Let P be a class of problems recognized by deterministic Turing machines, which run in polynomial time. It is known that effective enumeration of the sets of the class P (namely, ({{P}_{0}},{{P}_{1}}), …, ({{P}_{i}}), …) exists and thus ({mathbf{P}} = { {{P}_{i}},|,i in omega } .) Note that for each (i), ({{P}_{i}}) is a set of strings that are sequences of 0s and 1s. Based on available numbering of computably enumerable (c.e.) sets ({{{ {{W}_{i}}} }_{{i in omega }}}), a sequence of sets of non-negative numbers ({{hat {P}}_{i}}) is constructed such that there is an effective enumeration of them. Let us define ({mathbf{hat {P}}}) as follows: (~{mathbf{hat {P}}} = { {{hat {P}}_{i}},|,i in omega } ). It’s obvious that it is possible to define such relations between the elements of the set of mentioned strings and between the elements of the set of nonnegative integers that these two sets will be isomorphic (with respect to the relations in question). The article shows that it is possible to define such relations between the elements of ({mathbf{P}}) and between the elements of (hat {{mathbf{P}}}) that there will be homomorphic mappings from ({mathbf{P}}) to (hat {{mathbf{P}}}) and vice versa, from (hat {{mathbf{P}}}) to ({mathbf{P}}) (with respect to the relations in question). Based on the notions of T-mitoticity and T-autoreducibility, Ambos-Spies introduced the notions of P‑T-mitoticity, weakly P-T-mitoticity and P-T-autoreducibility. By analogy with the mentioned notions we introduce the notions of (hat {P})-T-mitoticity, weakly (hat {P})-T-mitoticity and (hat {P})-T-autoreducibility. It is proved in the article that the index sets {({text{z}},|,{{{text{W}}}_{{text{z}}}}) is ({{hat {P}}})-T-mitotic}, ({text{{ z}},|,{{{text{W}}}_{{text{z}}}}) is weakly ({{hat {P}}})-T-mitotic}, ({text{{ }}~{text{z}},|,{{{text{W}}}_{{text{z}}}}) is ({{hat {P}}})-T-autoreducible} and ({text{{ z}},|,{{{text{W}}}_{{text{z}}}} in {mathbf{hat {P}}}} ) are ({{{mathbf{Sigma }}}_{3}})-complete.

AbstractLet (omega )是所有非负整数的集合。设 P 是一类由确定性图灵机识别的问题,图灵机在多项式时间内运行。众所周知,类 P 的有效枚举集合(即 ({{P}_{0}},{{P}_{1}}), ..., ({{P}_{i}}), ... )是存在的,因此 ({mathbf{P}} = {{P}_{i}},|,i in omega } .注意,对于每个 (i),({{P}_{i}})是一组字符串,它们是 0 和 1 的序列。基于可计算可枚举(c.e. )集合的可用编号 ({{{{W}_{i}}} }_{{i in omega }}}), 非负数集合的序列 ({{hat {P}}_{i}}) 被构造出来,从而对它们进行有效的枚举。让我们定义 ({mathbfhat {P}}) 如下:~{mathbf{hat {P}} = {{hat {P}}_{i}},|,i in omega } }).很明显,我们可以在所述字符串集合的元素与非负整数集合的元素之间定义这样的关系,即这两个集合是同构的(就有关关系而言)。这篇文章表明,有可能在({mathbf{P}})的元素之间和(hat {{mathbf{P}})的元素之间定义这样的关系,即从({mathbf{P}})到(hat {{mathbf{P}})会有同构映射,反之亦然、从({hat {{mathbf{P}}})到({mathbf{P}}})(关于相关关系)。安博斯-斯皮尔斯(Ambos-Spies)在T-缄默性和T-自斥性概念的基础上引入了P-T-缄默性、弱P-T-缄默性和P-T-自斥性的概念。通过与上述概念的类比,我们引入了 (hat {P})-T 有丝分裂性、弱 (hat {P})-T 有丝分裂性和(hat {P})-T 自斥性的概念。文章中证明了索引集 {({text{z}},|,{{text{W}}_{{text{z}}}}) is({{hat {P}})-T-mitotic},({text{z}},|,{{text{W}}_{{text{z}}}}) is weakly ({hat {P}})-T-mitotic}、({text{{ }}~{text{z}},|,{{text{W}}}_{{text{z}}}}) is ({{hat {P}}})-T-autoreducible} and({text{{ z}}、|都是({{{mathbf{{hat {P}}}} )-完全的。
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引用次数: 0
Large Cycles in Graphs around Bondy’s and Jung’s Conjectures – Modifications, Sharpness, and Perspectives 围绕邦迪猜想和荣格猜想的图中大循环--修正、锐度和视角
IF 1 Q3 Computer Science Pub Date : 2024-04-10 DOI: 10.1134/s1054661824010140
Zh. G. Nikoghosyan

Abstract

In 1980, Bondy conjectured a common generalization (depending on λ) of some well-known degree-sum conditions for a graph ensuring the existence of Hamilton cycles for λ = 1 (Ore, 1960) and dominating cycles for λ = 2 (Bondy, 1980) as special cases. The reverse (long-cycle) version of Bondy’s conjecture was proposed in 2001 due to Jung. The importance of these two conjectures in the field is motivated by the fact that they (as starting points) give rise to all (with few exceptions) further developments through various additional extensions and limitations. In this paper, we briefly outline all known notable achievements towards solving the problem: (i) confirmation (by the author) of Bondy’s and Jung’s conjectures for some versions that are very close to the original versions; and (ii) significant improvements (by the author) of results (i), inspiring a number of improved versions of original conjectures of Bondy and Jung. Next we derive a number of modifications from improvements in (ii), which are also very close to the original versions, but do not follow directly from the Bondy’s and Young’s conjectures. Finally, all results (both old and new) are shown to be best possible in a sense based on three types of sharpness, indicating the intervals in 0 < λ < δ + 1 where the result is sharp and the intervals where the result can be further improved, where δ denotes the minimum degree.

摘要 1980 年,Bondy 猜想了一些著名的图的度和条件的普通一般化(取决于 λ),确保在 λ = 1 时存在 Hamilton 循环(Ore,1960 年),在 λ = 2 时存在支配循环(Bondy,1980 年)作为特例。邦迪猜想的反向(长周期)版本是由荣格于 2001 年提出的。这两个猜想在该领域的重要性在于,它们(作为起点)通过各种额外的扩展和限制引起了所有(除少数例外)进一步的发展。在本文中,我们简要概述了为解决这一问题而取得的所有已知显著成就:(i) (作者)证实了邦迪和荣格猜想的某些版本与原始版本非常接近;(ii) (作者)对结果(i)进行了重大改进,激发了邦迪和荣格原始猜想的许多改进版本。接下来,我们从第(ii)项的改进中推导出一些修改,这些修改也非常接近原始版本,但并不直接来自邦迪和杨的猜想。最后,所有结果(包括新旧结果)都被证明是基于三种尖锐度的最佳结果,这三种尖锐度分别表示 0 < λ < δ + 1 中结果尖锐的区间和结果可以进一步改进的区间,其中 δ 表示最小度。
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引用次数: 0
Promoting Origination of Noncellular Cognizers 促进非细胞认知者的起源
IF 1 Q3 Computer Science Pub Date : 2024-04-10 DOI: 10.1134/s1054661824010164
Edward Pogossian

Abstract

According to the hypothesis of abiogenesis, the simplest cellular, uncials, originated from chemical compounds that already existed in nature. Unfortunately, in spite of ongoing intensive research efforts, abiogenesis owns more difficulties and hopes than advances. That is why new hypotheses try to exempt its difficulties. Particularly, successful modeling of cognizing lets us assume that uncials were designed by some cognizers of the Universe, originated in nature as elementary recurrent classifiers, then evolved to attain the power of cognizing, at least, comparable with the highest human one, allowing them to design uncials analogous to the human design of robots nowadays. In parallel, molecular studying assumes that even elementary units of matter are able to communicate through the IDs of classifiers. And since the constituents of uncials are functionally analogous to those of cognizers, while communication is vital for cognizing, it is worth trying to promote the origin of constituents of cognizers by reaching in abiogenesis and communications. Thus, to promote origination of cognizers, we decompose the nuclei of cognizers to constituents, followed by examining the potential impact of constituents of uncials and molecular recurrent classifiers to the origin of functionally analogous ones of cognizers. Then recall algorithms of formation of 1-/2-place classifiers for possible clues to their origination. Finally, address to the origin of dynamicity of the nuclei of cognizers–doers, to trace dynamicity of doers to the dynamics of a variety of cases in sciences as a footstep to more general models.

摘要 根据 "生物起源 "假说,最简单的细胞--单细胞--起源于自然界中已经存在的化合物。遗憾的是,尽管研究工作一直在深入进行,但 "生物起源 "的困难和希望多于进展。这就是为什么新的假说试图免除其困难。特别是,成功的认知模型让我们可以假定,单词是由宇宙中的某些认知者设计的,它们起源于自然界,是基本的循环分类器,然后进化到至少与人类最高认知能力相当的认知能力,使它们能够设计出类似于人类现在设计机器人的单词。与此同时,分子研究认为,即使是物质的基本单位也能够通过分类器的 ID 进行交流。由于 "分类器 "的成分在功能上类似于 "认知器 "的成分,而交流对于认知至关重要,因此,通过生物起源和交流来促进 "认知器 "成分的起源是值得尝试的。因此,为了促进认知者的起源,我们将认知者的核心分解为成分,然后研究单词和分子循环分类器的成分对功能相似的认知者起源的潜在影响。然后,回顾 1 位/2 位分类器的形成算法,寻找其起源的可能线索。最后,探讨认知者-执行者核心动态性的起源,将执行者的动态性追溯到科学中各种情况的动态性,以此作为通向更一般模型的一个步骤。
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引用次数: 0
Sequential Data Classification under Dynamic Emission 动态排放下的序列数据分类
IF 1 Q3 Computer Science Pub Date : 2024-04-10 DOI: 10.1134/s1054661824010048
L. Aslanyan

Abstract

Sequential data are ubiquitous and widely available in a range of applications in almost all areas. We aim at considering medical, metrological and motion capturing type applications in terms of sequential data analytics in general, and classification in particular. Two scenarios are considered. The first starts with a pass through the initial sequential data database, performing training/learning of set of classes–medical conditions of patients in case of the dynamic treatment regime problems. This learned procedure will be used during the automated classification of new patients. Before starting the next, second pass, we form a confusion matrix based on the learned classification algorithm, and we form a transition matrix, which can be obtained in two ways: by the original database and alternatively by the data classified by the trained algorithm. The second pass is designed to correct the original classification with help of an additional hidden Markov type model (HMM), based on the mentioned two matrices as transition and emission matrices. The database (set of trellises, the training set) has a lattice structure. A part of the trellis tracks end at the target class (important in dynamic treatment regime applications, sometime associated with the healthy class). The trained classification, applied to the training set, can change the set of tracks ending at the target class, which forms one of the performance indicators of this algorithm. The next scenario also is based on the HMM type model. If one takes a lattice track, treating it as a sequence of observations, then HMM can improve that sequence by generating a complementary sequence, similar to the sequence of Viterbi states of HMM. It can also change the set of tracks ending at the target class, which forms the next performance measure, this time for the HMM procedure. Convergence to the target class is characterized by the convergence of the degrees of the transition matrices to the simple special case of such matrices. Alternatively, by extracting the root of the convergent matrices, the corresponding characterization of the transition matrix can be obtained so that the convergence is guaranteed. This work is mostly methodological than innovative being a complementary part to our previous work on target class classification topics. In the experimental part of this work we considered a root-oriented directed acyclic graphs that correspond to the target class classification policy. On the model of this graphs, a random set of tracks is generated, forming a so-called synthetic training set, synthetic trellis.

摘要 序列数据无处不在,广泛应用于几乎所有领域。我们旨在从顺序数据分析,特别是分类的角度来考虑医疗、计量和运动捕捉类型的应用。我们考虑了两种情况。第一种情况是从通过初始序列数据数据库开始,在动态治疗机制问题的情况下,对一组类别--患者的医疗条件--进行训练/学习。在对新病人进行自动分类时,将使用这一学习程序。在开始下一步,即第二步之前,我们会根据学习到的分类算法形成混淆矩阵,并形成过渡矩阵,过渡矩阵可以通过两种方式获得:通过原始数据库和通过训练算法分类的数据。第二步的目的是借助额外的隐马尔可夫模型(HMM)修正原始分类,该模型以上述两个矩阵为过渡矩阵和排放矩阵。数据库(花架集,即训练集)具有网格结构。部分花架轨迹以目标类别为终点(在动态处理系统应用中很重要,有时与健康类别相关)。应用于训练集的训练分类法可以改变以目标类别为终点的轨迹集,这也是该算法的性能指标之一。下一种情况也是基于 HMM 模型。如果将网格轨迹视为观测序列,那么 HMM 可以通过生成一个互补序列来改进该序列,这与 HMM 的维特比状态序列类似。它还能改变以目标类别为终点的轨迹集,这就形成了下一个性能指标,这次是针对 HMM 程序的。收敛到目标类别的特征是过渡矩阵的度数收敛到这种矩阵的简单特例。或者,通过提取收敛矩阵的根,可以得到过渡矩阵的相应特征,从而保证收敛。这项工作主要是方法论方面的,而不是创新方面的,是对我们之前关于目标类分类主题工作的补充。在这项工作的实验部分,我们考虑了与目标类别分类策略相对应的面向根的有向无环图。在该图的模型上,随机生成一组轨迹,形成所谓的合成训练集,即合成树状图。
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引用次数: 0
Erratum to: Knowledge and Data in Artificial Intelligence: A Duel or a Duo 勘误:人工智能中的知识与数据:对决还是双赢
IF 1 Q3 Computer Science Pub Date : 2024-04-10 DOI: 10.1134/s105466182401022x
T. A. Gavrilova

An Erratum to this paper has been published: https://doi.org/10.1134/S105466182401022X

本文的勘误已发表: https://doi.org/10.1134/S105466182401022X
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引用次数: 0
Descriptive Image Analysis 描述性图像分析
IF 1 Q3 Computer Science Pub Date : 2024-03-20 DOI: 10.1134/s1054661823040181
I. B. Gurevich, V. V. Yashina

Abstract

An overview of the main methods, models, and results of Descriptive Image Analysis is given. Descriptive Image Analysis is a logically organized set of descriptive methods and models designed for image analysis and evaluation. The state of the art and trends in the development of Descriptive Image Analysis are determined by the methods, models, and results of the Descriptive Approach to image analysis and understanding. As the methods and apparatus of the Descriptive Approach to the analysis and understanding of images were developed and refined, its interpretation was proposed, defined as Descriptive Image Analysis. The main goal of Descriptive Image Analysis is to structure and standardize the various methods, processes, and concepts used in image analysis and recognition. Descriptive Image Analysis solves the fundamental problems of formalizing and systematizing methods and forms of information representation in image analysis, recognition, and understanding problems, in particular, associated with automating the extraction of information from images to make intelligent decisions (diagnosis, prediction, detection, assessment, and identification patterns of objects, events and processes). Descriptive Image Analysis makes it possible to solve both problems related to constructing formal descriptions of images as recognition objects and problems of synthesizing procedures for recognizing and understanding images. It is suggested that the processes of analysis and evaluation of information represented in the form of images (problem solution trajectories) can generally be considered a sequence/combination of transformations and calculations of a set of intermediate and final (determining the solution) estimates. These transformations are defined by equivalence classes of images and their representations. The latter are defined descriptively, i.e., using a basic set of prototypes and corresponding generating transformations that are functionally complete with respect to the equivalence class of admissible transformations. As part of Descriptive Image Analysis, the following main results were obtained: (1) new mathematical objects were introduced and studied: image formalization space, descriptive image algebras, descriptive algorithmic schemes; (2) descriptive image analysis models have been defined and studied: image models, image transformation models, models for generating descriptive algorithmic schemes; (3) linguistic and knowledge-oriented tools have been developed to support the automation of image analysis; (4) a number of automated software systems have been developed and axioms for Descriptive Image Analysis proposed. A general description of the provisions of Descriptive Image Analysis is presented, and the main results of research in the first two directions are discussed: new mathematical objects and image analysis models. A comprehensive bibliography on Descriptive Image Analysis is

摘要 概述了描述性图像分析的主要方法、模型和结果。描述性图像分析法是一套逻辑严密的描述性方法和模型,用于图像分析和评估。描述性图像分析法的方法、模型和结果决定了描述性图像分析法的发展现状和趋势。随着描述性方法分析和理解图像的方法和设备的发展和完善,人们提出了对其的解释,并将其定义为描述性图像分析。描述性图像分析法的主要目标是将图像分析和识别中使用的各种方法、过程和概念结构化和标准化。描述性图像分析解决了图像分析、识别和理解问题中信息表示方法和形式的形式化和系统化的基本问题,特别是与自动从图像中提取信息以做出智能决策(诊断、预测、检测、评估和识别物体、事件和过程的模式)相关的问题。描述性图像分析既可以解决与构建图像作为识别对象的形式描述有关的问题,也可以解决识别和理解图像的综合程序问题。有人认为,对以图像形式表示的信息(问题解决方案轨迹)进行分析和评估的过程,一般可视为一系列中间和最终(确定解决方案)估计值的变换和计算的序列/组合。这些变换由图像的等价类及其表示法定义。后者的定义是描述性的,即使用一组基本原型和相应的生成变换,这些变换在功能上与可允许变换的等价类是完整的。作为描述性图像分析的一部分,取得了以下主要成果:(1) 引入并研究了新的数学对象:图像形式化空间、描述性图像代数、描述性算法方案;(2) 定义并研究了描述性图像分析模型:图像模型、图像变换模型、生成描述性算法方案的模型;(3) 开发了支持图像分析自动化的语言和知识导向工具;(4) 开发了一些自动化软件系统,并提出了描述性图像分析公理。本文对描述性图像分析的规定进行了总体描述,并讨论了前两个方向的主要研究成果:新的数学对象和图像分析模型。还提供了有关描述性图像分析的综合参考书目。
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引用次数: 0
From the Editor-in-Chief 主编的话
IF 1 Q3 Computer Science Pub Date : 2024-03-20 DOI: 10.1134/s1054661823040466
Igor’ A. Sokolov
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引用次数: 0
On Fast Computing of Neural Networks Using Central Processing Units 论使用中央处理器快速计算神经网络
IF 1 Q3 Computer Science Pub Date : 2024-03-20 DOI: 10.1134/s105466182304048x
A. V. Trusov, E. E. Limonova, D. P. Nikolaev, V. V. Arlazarov

Abstract

This work is devoted to methods for creating fast and accurate neural network algorithms for central processors, which were proposed by scientists of the V.L. Arlazarov’s scientific school. It outlines general principles and approaches to improving computational efficiency and discusses specific examples: tensor convolution decompositions that simplify convolutional neural networks; bounded nonlinear activation function ratio, which is calculated faster than exponential activation functions; and p-im2col convolution algorithm, which allows you to achieve a balance between computational efficiency and RAM consumption. Particular attention is paid to quantized (8- and 4-bit integer) neural networks, their training, implementation, and limitations on some central processor architectures, such as Elbrus.

内容提要 本著作专门介绍为中央处理器创建快速准确的神经网络算法的方法,这些方法是由 V.L. Arlazarov 科学流派的科学家们提出的。它概述了提高计算效率的一般原则和方法,并讨论了具体实例:简化卷积神经网络的张量卷积分解;有界非线性激活函数比,它比指数激活函数计算得更快;p-im2col 卷积算法,它允许你在计算效率和 RAM 消耗之间取得平衡。本书特别关注量化(8 位和 4 位整数)神经网络、其训练、实施以及在某些中央处理器架构(如 Elbrus)上的限制。
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引用次数: 0
The St. Petersburg Scientific School of Pattern Recognition, Signal Analysis, and Biotechnical Systems 圣彼得堡模式识别、信号分析和生物技术系统科学学校
IF 1 Q3 Computer Science Pub Date : 2024-03-20 DOI: 10.1134/s1054661823040326
A. P. Nemirko, V. V. Geppener, Z. M. Yuldashev, D. M. Klionsky

Abstract

The history of development and the results of research by scientific groups in the field of pattern recognition, signal analysis, and biotechnical systems are described. Teams associated with the St. Petersburg Electrotechnical University “LETI” have been considered.

摘要 介绍了模式识别、信号分析和生物技术系统领域科研小组的发展历史和研究成果。其中包括与圣彼得堡电工技术大学 "LETI "相关的团队。
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引用次数: 0
期刊
PATTERN RECOGNITION AND IMAGE ANALYSIS
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