Liga Paura and Irina Arhipova
References  I. Rudusa and L. Berzina, Statistical software for statistics teaching and data analyzing: Improving the Teaching and Learning of Mathematics and Informatics , January 23-25, 2012, Kaunas, Lithuania. Kaunas: Aleksandro Stulginskio universitetas, 2012, pp. 24-26.  L. Paura and L. Berziņa, The Biometrics course content in agricultural study program: Improving the Teaching and Learning of Mathematics and Informatics , January 23-25, 2012, Kaunas, Lithuania. Kaunas: Aleksandro Stulginskio universitetas
Dmitry Osipov and Dmitry Titov
and Ordered Statistics Decoding in a Multiple Access System Enabling Wireless Coexistence. – In: Proc. of 6th International Workshop, MACOM’2013, Vilnius, Lithuania, 16-17 December 2013, pp. 33-38. 8. Wolf, J. Efficient Maximum Likelihood Decoding of Linear Block Codes Using a Trellis. – IEEE Transactions on Information Theory, Vol. 24 , 1964, No 1, pp. 76-80.
Boris Melnikov and Aleksandra Melnikova
automaton, J. of Applied Math. and Computing (The Korean J. of Comp. and Appl. Math.), 9, 1 (2002) 131-150. ⇒227, 228  B. Melnikov, A. Melnikova, A new algorithm of constructing the basis finite automaton, Informatica (Lithuanian Acad. Sci. Ed.), 13, 3 (2002) 299-310. ⇒ 227  B. B. Melnikov, N. Sciarini-Guryanova, Possible edges of a finite automaton defining a given regular language, J. of Applied Math. and Computing (The Korean J. of Comp. and Appl. Math.), 9, 2 (2002) 475-485. ⇒232  A.Vakhitova, The basis
. Johansson, R., L. N. Pina. Embedding a Semantic Network in a Word Space. – HLT-NAACL, 2015. 22. Johansson, R., L. N. Pina. Combining Relational and Distributional Knowledge for Word Sense Disambiguation. – In: Proc. of 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, 11-13 May 2015, Vilnius, Lithuania, No 109, Linköping University Electronic Press, 2015. 23. Sascha, R., H. Schütze. Autoextend: Extending Word Embeddings to Embeddings for Synsets and Lexemes. – arXiv preprint arXiv:1507.01127, 2015. 24. Josu, G., A. Soroa, E. Agirre
Renata Walczak, Marlena Piekut, Magdalena Kludacz-Alessandri, Biruta Sloka, Ligita Šimanskiene and Tiiu Paas
After joining the European Union in 2004, the post-communist countries have dramatically changed their structure of expenditure for medical services. The cause of this is legislative and ownership changes in the new economy. The study analyzed the expenditure on medical services in the European Union with a special focus on Poland, Latvia, Lithuania and Estonia. The European Union countries were divided into clusters using different methods, that is, Ward’s, Two Step and Centroid Clustering. In the paper, the structure and changes in health expenses were presented according to the types of expenditures over the years 2004-2015. Countries were assigned to clusters based on three variables: medical products, appliances and equipment, outpatient services and hospital services. Variables were considered as a percentage of household budget. In Lithuania, Latvia and Estonia, there is a clear increase in the outpatient services spending compared to the hospital services expenditure.
.  Wang H., Fan W., Yu P.S. and Han J., Mining Concept-drifting Data Streams Using Ensemble Classifiers, Proceedings ACM SIGKDD, p. 226-235, 2003.  Widmer G., Kubat M., Learning in the Presence of Concept Drift and Hidden Contexts, Machine Learning, vol. 23, p. 69-101, 1996.  Zliobaite I., Learning Under Concept Drift: An Overview, Technical Report, Faculty of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania, 2009.  Zliobaite I., Bakker J., Pechenizkiy M., OMFP: An Approach for Online
An assessment of the degree of the development of the digital economy in Poland in comparison to chosen European countries is the main purpose of the paper. The methodology of the conducted research is based on the analysis of secondary sources and applying statistical methods. In order to make the comparison in methodically correct manner, synthetic measures of the development of the e-economy were used in the form of two indexes: NRI (Networked Readiness Index) and DESI (Digital Economy and Society Index). On the basis of available statistical data, four European countries were confronted with Poland. Results of the analysis indicate a relatively unfavorable situation of Poland.
Emanuel Kulczycki and Przemysław Korytkowski
Czech Republic, Denmark, Finland, Norway, and Poland. Secondly, models can be differentiated according to how much the procedure is formalized. In other words, in Denmark, Finland, Flanders (Belgium), Lithuania, Poland and Spain, evaluation is formalized because of some type of publisher classifications and quality labels, or formal criteria for books are formalized and used. In non-formalized systems, like in Serbia, France, Italy, Latvia, Israel, Portugal and Switzerland, evaluation is based on expert panels or committees. Thirdly, the analyzed European models