Browsing by Author "Raznosilin, Valentyn V."

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    Automated Monitoring of Content Demand in Distance Learning
    (CEUR-WS Team, Aachen, Germany, 2021) Shynkarenko, Viktor I.; Raznosilin, Valentyn V.; Snihur, Yuliia
    ENG: In this paper the research of means and the development of software for matching the student’s gaze focus with the structure of information on the computer monitor during distance learning is presented. Widespread hardware is envisaged to be used. Primary processing of the face image, eye regions separation is performed by means of the OpenCV library. An appropriate algorithm to calculate the center of the eye’s pupil has been developed. The influence of the system calibration process with different schemes of calibration point display, its delay time on the screen and location of the additional camera according the accuracy of the calculation the coordinates of the gaze focus is investigated. Based on the performed experiments, it was defined that the error of gaze focus recognition with using two cameras can be reduced to 4-10%. The proposed approach makes it possible for objective measurement the working time of each student with one or another part of content. The lecturer will have the opportunity to improve the content by highlighting significant parts that receive little attention and simplifying those elements that students process for an unreasonable amount of time. It is planned to integrate the developed software with the LMS Moodle in the future.
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    Data Stochastic Preprocessing for Sorting Algorithms
    (CEUR Workshop Proceedings, 2022) Shynkarenko, Viktor I.; Doroshenko, Anatoliy Yu.; Yatsenko, Olena A.; Raznosilin, Valentyn V.; Halanin, Kostiantyn K.
    ENG: The possibilities of improving sorting time parameters through preprocessing by stochastic sorting were investigated. The hypothesis that preprocessing by stochastic sorting can significantly improve the time efficiency of classical sorting algorithms has been experimentally confirmed. Sorting with different computational complexity is accepted as classical sorting algorithms: shaker sorting with computational complexity O(n2), insertions O(n2), Shell O(n·(log n)2) ... O(n3/2), fast with optimization of ending sequences O(n·log n). The greatest effect is obtained when performing comparisons using stochastic sorting in the amount of 160 percent of the array’s size. Indicators of the exchange efficiency of two elements and a series of comparisons with exchanges are proposed, which made it possible to establish the greatest efficiency of data preprocessing by stochastic sorting when one element for comparison is selected from the first part of the array, and the other from the second. For algorithms with a computational complexity of O(n2) the improvement in time efficiency reached 70–80 percent. However, for Shell sort and quick sort, the stochastic presort has no positive effect, but instead increases the total sorting time, which is apparently due to the initial high efficiency of these sorting methods. The hypothesis about increasing the time efficiency of quick sorting combined with sorting by insertions on the final sections due to the use of preliminary stochastic processing of such sections has not been confirmed. However, according to the experiment, the recommended size of the array was established, at which it is necessary to switch to insert sorting in the modified quick sort. The optimal length of the ending sequences is between 60 and 80 elements. Given that algorithm time efficiency is affected by computer architecture, operating system, software development and execution environment, data types, data sizes, and their values, time efficiency indicators should be specified in each specific case.