Experimental Study of the Cloud Architecture Selection for Effective Big Data Processing

Big data dictate their requirements to the hardware and software. Simple migration to the cloud data processing, while solving the problem of increasing computational capabilities, however creates some issues: the need to ensure the safety, the need to control the quality during data transmission, the need to optimize requests. Computational cloud does not simply provide scalable resources but also requires network infrastructure, unknown routes and the number of user requests. In addition, during functioning situation can occur, in which you need to change the architecture of the application - part of the data needs to be placed in a private cloud, part in a public cloud, part stays on the client

The SVM Classifier Based on the Modified Particle Swarm Optimization

The problem of development of the SVM classifier based on the modified particle swarm optimization has been considered. This algorithm carries out the simultaneous search of the kernel function type, values of the kernel function parameters and value of the regularization parameter for the SVM classifier. Such SVM classifier provides the high quality of data classification. The idea of particles' {\guillemotleft}regeneration{\guillemotright} is put on the basis of the modified particle swarm optimization algorithm. At the realization of this idea, some particles change their kernel function type to the one which corresponds to the particle with the best value of the classification accuracy. The offered particle swarm optimization algorithm allows reducing the time expenditures for development of the SVM classifier. The results of experimental studies confirm the efficiency of this algorithm.Comment: 9 page

Engagement Assessment for the Educational Web-Service Based on Largest Lyapunov Exponent Calculation for User Reaction Time Series

Contemporary digital platforms provide a large number of web services for learning and professional growth. In most cases, educational web services only control access when connecting to resources and platforms. However, for educational and similar resources (internet surveys, online research), which are characterized by interactive interaction with the platform, it is important to assess user engagement in the learning process. A fairly large body of research is devoted to assessing learner engagement based on automatic, semi-automatic, and manual methods. Those methods include self-observation, observation checklists, engagement tracing based on learner reaction time and accuracy, computer vision methods (analysis of facial expressions, gestures, and postures, eye movements), methods for analyzing body sensor data, etc. Computer vision and body sensor methods for assessing engagement give a more complete objective picture of the learner’s state for further analysis in comparison with the methods of engagement tracing based on learner’s reaction time, however, they require the presence of appropriate sensors, which may often not be applicable in a particular context. Sensory observation is explicit to the learner and is an additional stressor, such as knowing the learner is being captured by the webcam while solving a problem. Thus, the further development of the hidden engagement assessment methods is relevant, while new computationally efficient techniques of converting the initial signal about the learner’s reaction time to assess engagement can be applied. On the basis of the hypothesis about the randomness of the dynamics of the time series, the largest Lyapunov exponent can be calculated for the time series formed from the reaction time of learners during prolonged work with web interfaces to assess the learner’s engagement. A feature of the proposed engagement assessment method is the relatively high computational efficiency, absence of high traffic loads in comparison with computer vision as well as secrecy from the learner coupled with no processing of learner’s personal or physical data except the reaction time to questions displayed on the screen. The results of experimental studies on a large amount of data are presented, demonstrating the applicability of the selected technique for learner’s engagement assessment. © 2023 by the authors.Russian Science Foundation, RSFThis study was supported by a grant (No. 17-78-30028) from the Russian Science Foundation

Economic evaluation of efficiency of herbicides application in the technology of corn cultivation for grain

The aim of the work is to evaluate the efficiency of application of plant protection chemicals as one of the component of energy-saving technologies of corn cultivation for grain. The evaluation of the efficiency of the use of leaf and soil herbicides as well as their combined effect on agrophytocenosis of corn for grain is given in the article. The relevance of the researches is in the use of soil herbicides in its pure form in the system of chemical protection of corn plants which reduce the level of chemical load on crops and contribute to the formation of a high level of productivity. It is determined that the use of herbicides (Kraterr, Fronter Optima and Titus) in the technology of corn cultivation for grain is more effective in its pure form as well as economically reasonable and expediently. It is proved in practice that in order to increase the protective period of influence of the soil herbicides they could be applied at the early stages of weeds growth or immediately after germination of cultivated plants. As a result a decrease in total weed infestation of corn crops by 89.5% at application of Fronter Optima (1.3 l/ha) and by 94.5% - Kraterr (3.0 l/ha), compared to the control variant has been determined. Field horsetail (Equisetum arvense) is marked as a weak-susceptible to these soil herbicides. The application of soil herbicide Kraterr in a dose 3 l/ha in its pure form allow to reduce the cost price of growing of 1 ton of corn grain to 2.31 thousand rubles; achieve cost recovery at the level of 30 rubles per every additional expended ruble as well as improving the profitability of technology to 248% due to more effective chemical weeding and containment of growth processes of weed component and as a result to get high crop yields

Computerized tools in psychology: cross cultural and genetically informative studies of memory

In this article we presented the computerized tools for psychological studies of memory. The importance of implementing computerized automated tools for psychological studies is discussed. It has been shown that this tools can be used both for cross-cultural and genetically informative studies. The validity of these tools for cross-cultural and genetically informative studies of memory can be seen as the first step to use automated computerized tools for big data collection in psychology