#### Inna Konstantinovna Shingareva (University of Sonora) :

##### Articles:

doi: 10.18698/2309-3684-2021-2-96116

The problems of estimating the similarity index of inhomogeneous scientific publications containing equations and formulas are discussed for the first time. It is shown that the presence of equations and formulas (as well as figures, drawings, and tables) is a complicating factor that significantly complicates the study of such texts. It has been proved that the method for determining the similarity index of publications, based on taking into account individual mathematical symbols and parts of equations and formulas, is ineffective and can lead to erroneous and even completely absurd conclusions. Possibilities of the most popular analytical systems Antiplagiat and iThenticate, currently used in scientific journals, are investigated for detecting plagiarism and self–plagiarism. The results of processing by the iThenticate system of specific examples and specific test problems containing equations and formulas are presented. It has been established that this analytical system, when analyzing heterogeneous texts, is often unable to distinguish self– plagiarism from pseudo-self-plagiarism, seeming real (but false and imaginary) self– plagiarism. A model complex situation is considered, in which the identification of self–plagiarism requires the involvement of highly qualified specialists of a narrow profile. Various ways to improve the work of analytical systems for comparing inhomogeneous texts are proposed. This article will be useful to researchers and university teachers in physics, mathematics, and engineering, programmers dealing with problems in image recognition and research topics of digital image processing, as well as a wide range of readers who are interested in issues of plagiarism and self–plagiarism.

Полянин А.Д., Шингарева И.К. Индекс подобия математических и других научных публикаций с уравнениями и формулами и проблема идентификации самоплагиата. Математическое моделирование и численные методы, 2021, № 2, с. 96–116.