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

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Summary

SOFTWARE SYSTEMS

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

Sepsis Prediction Model Based on Vital Signs Related Features
Do We Really Know How to Measure Software Quality?
Multisensor Data Fusion for Data Analysis
Functional Data Analysis of fNIRS Data
Narrative Detection for Lithuanian Language
Viktoras Bulavas
Approximating the Minimum of a Smooth Gaussian Process
Expanding Convolutional Networks with SIFT Features to Classify Images Better
Andrzej Czyżewski
Paulius Dapkus
Overview of Identification of Phishing Email Messages
New Ideas for Multidimensional Scaling
Aligning Agile Application Software Development with the Archimate Framework
Experimental Investigation of Energy Consumption for Cryptocurrency Mining
Helen Karatza
Fraudulent Transaction Path Detection in Ethereum Blockchain
Yuriy Kharin
Aliaksei Kolesau
Evaluation of Lombard speech models in the context of speech enhancement
Cognitive Mapping Principle for Advanced Survey Analysis
Application of Deep Learning for Credit Scoring
Application of Neural Networks for Cyber Attacks Detection
Dynamic Car Rental Pricing
Pilar Martínez Ortigosa
Investigation of User Fatigue Based on Input Behavior
Gerda Ana Melnik
High Performance Computing Techniques for IMRT Plan Optimization
Gediminas Navickas
Domain Sensitivity Issues in Aspect Based Sentiment Analysis
Game Scene Generation by Ventura Creativity Criteria Model
Álvaro Rocha
Player Recognition by Cursor Movement in Computer Game
Tax Fraud Reduction Using Analytics in an East European Country
Raimundas Savukynas
Creditworthiness Estimation from Aggregated Bank Account Transactions Data
Conclusions
Network Configuration Impact on IoT Performance
Mantas Stankevičius
Eye Blood Vessel Segmentation Using Convolutional Neural Networks
Lukas Vabalas
Full Text
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