Abstract
DAMSS-2019 is the 11th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at
 the end of the year. The same place and the same time every year. History of the workshop starts from 2009 with 16 presentations. The idea
 of such workshop came up at the Institute of Mathematics and Informatics that now is the Institute of Data Science and Digital Technologies
 of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval
 both in the Lithuanian research community and abroad. The number of this year presentations is 77. The number of registered participants
 is 127 from 9 countries. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the
 fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the
 research community. Even 9 companies and institutions supported the workshop this year. This means that the topics of the workshop are
 actual for business, too. Topics of the workshop cover Artificial Intelligence, Big data, Bioinformatics, Blockchain technologies, Business
 Rules Software Engineering, Data Science, Deep Learning, Digital Technologies, High-Performance Computing, Machine Learning, Medical
 Informatics, Modelling Educational Data, Ontological Engineering, Optimization in Data Science, Signal Processing, Visualization Methods for
 Multidimensional Data. A special session and discussions are organized about topical business problems that may be solved together with the
 research community. This book gives an overview of all presentations of DAMSS-2019.
Highlights
Co-Chairmen: Dr Saulius Maskeliūnas (Lithuanian Computer Society) Prof
Creating well performing Convolutional neural networks (CNNs) comes with a price: they require many labelled images for training, which is expensive to obtain in many industries; image features extracted by convolutions are location dependent
The results obtained in our previous study lead us to conclude that speech with the Lombard effect is more recognizable in noisy environments than normal speech
Summary
Co-Chairmen: Dr Saulius Maskeliūnas (Lithuanian Computer Society) Prof. Gintautas Dzemyda (Vilnius University, Lithuanian Academy of Sciences). Olga Kurasova Dr Viktor Medvedev Laima Paliulionienė Dr Martynas Sabaliauskas
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