Distributed machine learning for IoT

In the modern world, big data is used in machine learning, which is quite difficult to process on a single computer, so various methods for parallel processing of such data are being developed. But what about microcontrollers? In a cloud system, microcontrollers are often found, thanks to which they make pacification of various devices, and sometimes you have to work with big data. In microcontrollers, the memory is quite small and the processor is not as productive as on modern supercomputers. Therefore, many scientists propose various methods for parallel processing of big data for embedded systems, one of such methods is proposed by the author of this article

Information Transmission Concept Based Model of Wave Propagation in Discrete Excitable Media

A new information transmission concept based model of excitable media with continuous outputs of the model’s cells and variable excitation time is proposed. Continuous character of the outputs instigates infinitesimal inaccuracies in calculations. It generates countless number of the cells’ excitation variants that occur in front of the wave even in the homogenous and isotropic grid. New approach allows obtain many wave propagation patterns observed in real world experiments and known simulation studies. The model suggests a new spiral breakup mechanism based on tensions and gradually deepening clefts that appear in front of the wave caused by uneven propagation speed of curved and planar segments of the wave. The analysis hints that the wave breakdown and daughter wavelet bursting behavior possibly is inherent peculiarity of excitable media with weak ties between the cells, short refractory period and granular structure. The model suggested is located between cellular automaton with discrete outputs and differential equation based models and gives a new tool to simulate wave propagation patterns in applied disciplines. It is also a new line of attack aimed to understand wave bursting, propagation and annihilation processes in isotropic homogenous media

URGENT EDUCATION PROBLEMS OF KLAIPĖDA REGION IN THE 1ST HALF OF 20TH CENTURY

Baltic tribes had much in common, though differences between them also existed – they used to be separated by rivers, marshlands, lakes, wastelands and woods. Therefore, tribes received different names: Prussians, Jatvingians, Lithuanians (lowland and upland Lithuanians), Curonians, Semigallians, Selonians, Lettigallians. Presumably, before 18th century Prussians and Jatvingians were assimilated by Lithuanians, Germans and Slavs. As a nation Lithuanians formed out of lowland and upland Lithuanians, southern Curonians, Selonians and Semigallians

POSSIBILITIES OF APPLYING INNOVATIVE TEACHING TECHNOLOGIES WITHIN HIGH EDUCATION DIDACTIC SYSTEM

Thearticle discusses importance and significance of innovative teaching technologieswithin didactic system of higher education and possibilities for employment ofactive training methods in lectures. It introduces to modern concept ofinnovative teaching technologies, analyzes theoretical level of exploration inemployment of innovative teaching technologies within didactic system of highereducation and explores variety of activating training methods in lectures fromthe viewpoint of pedagogues. KEYWORDS:  innovative teaching technologies,training method, activating method, upbringing technologies

MANAGEMENT OF PEDAGOGICAL TEACHING (PRACTICE) IN TRAINING OF PEDAGOGUES FOR SUBJECT RELATED EDUCATION

The article analyzes experience, problems and perspectives in management of pedagogical teaching (practice) and training subject pedagogues in academic studies. It also reviews major trends in organization of patterns for pedagogical teaching at Lithuanian universities, when training subject pedagogues, introduces to conception of pedagogical teaching in the study programme of subject pedagogy. It analyzes topicalities in pedagogical teaching, organized at base schools, introduces to data of research about students’ approach to management of pedagogical teaching in the process of practical training. KEYWORDS: pedagogical teaching, subject related education, management of pedagogical teaching, base schools, mentor, tutor, social partners

Combining Multiple Classifiers with Dynamic Weighted Voting

When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In this paper, new functions for dynamic weighting in classifier fusion are introduced. Experimental results demonstrate the advantages of these novel strategies over the simple voting scheme

The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement

In the pattern recognition literature, Huang and Suen introduced the "multinomial" rule for fusion of multiple classifiers under the name of Behavior Knowledge Space (BKS) method [1]. This classifier fusion method can provide very good performances if large and representative data sets are available

A new three-step class of iterative methods for solving nonlinear systems

[EN] In this work, a new class of iterative methods for solving nonlinear equations is presented and also its extension for nonlinear systems of equations. This family is developed by using a scalar and matrix weight function procedure, respectively, getting sixth-order of convergence in both cases. Several numerical examples are given to illustrate the efficiency and performance of the proposed methods.This research has been partially supported by both Generalitat Valenciana and Ministerio de Ciencia, Investigacion y Universidades, under grants PROMETEO/2016/089 and PGC2018-095896-B-C22 (MCIU/AEI/FEDER, UE), respectively.Capdevila-Brown, RR.; Cordero Barbero, A.; Torregrosa Sánchez, JR. (2019). A new three-step class of iterative methods for solving nonlinear systems. Mathematics. 7(12):1-14. https://doi.org/10.3390/math712122111471