Computer Tools in Education

Dynamic Cooperative Game Theoretic Models of Opinion Control

Nailya Galieva — 2025-04-20

In this paper we consider dynamic cooperative game theoretic models of control on networks. We suppose that all strong subgroups are determined in the stage of analysis of the influence digraph, and the control impact is exerted only to the members of those subgroups because they determine all stable final opinions. An agent’s opinion is interpreted as his expenses for buying goods (services) of a firm. We show that due to the model assumptions the characteristic functions by Neumann-Morgenstern, Petrosyan-Zaccour, and Gromova-Petrosyan coincide. We find Shapley value for this common characteristic function, prove its time inconsistency, and built an imputation distribution procedure. We compared the components of Shapley value with players’ payoffs for different forms of non-cooperative behavior.

Unified Constructions of the Regular Heptagon and Triskaidecagon

Helmut Ruhland — 2025-04-20

Constructions of regular heptagon and triskaidecagon by trisection of an angle are well known. An elegant construction of the heptagon by S. Adlaj shows a 3-fold symmetry related to a Galois group. Based on the latter construction, in this article one more for the heptagon and two more for the triskaidecagon are presented, all using angle trisection.

Analysis of Image Processing Methods in the Context of a Basis for Recognizing Small Objects. Image processing methods for object recognition

Alexander Popov — 2025-04-20

The article analyzes the existing methods of detecting small objects from images in the channels of technical vision against a background of noise. It is shown that the method of detecting images of small objects based on the calculation of the likelihood ratio using the estimation of the mathematical expectation of samples of spatially subband vectors and their covariance matrices is promising for further research. To solve the problem of detecting small objects from images of technical vision channels, as well as to prepare data for subsequent stages (recognition and identification), a subband analysis method based on the use of new basic functions is proposed as the main tool. An experimental assessment of the quality of detection of small-sized objects using the above-described method has been carried out, showing that acceptable indicators of the probability of correct detection (0.95) with a false alarm probability of 10^−4 are achieved with a signal-to-noise ratio of more than 14. Based on the fact that noise in images is not always statically independent and additive, the assessment of the influence of spatial spectral characteristics of noise is subject to further investigation. The analysis of the influence of a statistically independent additive noise process on the quality of detection and cognition is carried out. In this case, a set of source images containing images of small objects of the type of unmanned aerial vehicles was used. To search for an object in the analyzed image, a reference image of the object was used. In the course of the study, it was found that the family of dependencies of the probability of correct detection on the signal-to-noise ratio, taking into account the given probability of a false alarm when exposed to additive white noise, is a classic type characteristic of object detection algorithms. The necessary signal-to-noise ratio has been identified, which makes it possible to achieve an acceptable probability of correct detection. The results will allow us to form a way to compare the detection quality of small objects in images for various detection algorithms. Keywords: photo and video images, methods and algorithms for synthesis, analysis and improvement of image quality, machine vision, subband methods.

Integration of Neural Network Technologies into Modern Education

Anastasia Berezina — 2025-04-20

The article considers an approach to the integration of neural network technologies to eliminate the imbalance between the development of hard (hard skills) and flexible (soft skills) skills in the educational process.The modern labor market requires high communication, creative and logical skills from specialists, but traditional educational programs do not sufficiently focus on soft skills. The authors of the article, based on research by RBC, Google and RSM, emphasize the importance of flexible skills for successful careers and social adaptation, noting that many people have difficulties with communication skills as the fundamental soft skills. To solve this problem, an innovative approach is proposed using neural network technologies to diagnose and develop communication skills. The authors analyze existing deep learning models and propose their own convolutional recurrent neural network (CRNN) structure for diagnosing speech deficiencies in the Russian language. The developed model evaluates pronunciation defects and provides personalized learning materials. The authors propose an interactive educational platform that implements the created model within the framework of hard skills training technology in conjunction with soft skills development programs. The neural network algorithms of the platform optimize the learning process, adapting it to the individual characteristics of the student, and can be used both independently and in addition to classes with a tutor.

Application of Transformers and Machine Learning Methods in the System of Recommendations of Academic Supervisor

Elizaveta Budilo — 2025-04-20

This paper proposes a recommendation system for academic supervisor selection based on transformer architecture and modern machine learning methods. The system analyzes a student’s academic data, including courses studied and grades for them, as well as the professional characteristics of teachers. The results of the experiment demonstrate a significant superiority of the proposed approach over traditional methods: during testing, the achieved accuracy of recommendations was 0.3230 versus 0.1106 (method based on the frequency of positive grades) and 0.1637 (approach using classification through machine learning). The obtained results confirm the effectiveness of the system in optimizing the process of selecting a supervisor, which helps to improve the quality of students’ research activities through a personalized comparison of competencies.