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<title>Master of Science in Computer and Information Science (CIS)</title>
<link>https://dspace.aua.am/xmlui/handle/123456789/122</link>
<description/>
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<rdf:li rdf:resource="https://dspace.aua.am/xmlui/handle/123456789/2143"/>
<rdf:li rdf:resource="https://dspace.aua.am/xmlui/handle/123456789/2141"/>
<rdf:li rdf:resource="https://dspace.aua.am/xmlui/handle/123456789/2140"/>
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<dc:date>2026-04-17T17:14:07Z</dc:date>
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<item rdf:about="https://dspace.aua.am/xmlui/handle/123456789/2149">
<title>Web development: frontend using CSS with modern techniques</title>
<link>https://dspace.aua.am/xmlui/handle/123456789/2149</link>
<description>Web development: frontend using CSS with modern techniques
Abajyan, Anna
Web technologies are so fast that it is becoming a tough job for businesses as well as developers to keep up the pace. New tools pop up every year letting the old ones go sideways and frontend development is a major part of the web. Businesses are focusing more on front-end development to enhance user interaction, site efficiency, interactivity and look &amp; feel. And of course the popularity of front-end development tools is on the rise due to ever changing web techs. So developing my project I‘m using some front-end development tools which are included in 2018 top list: Chrome Developer Tools, NPM Node JS, SASS and CodePen. Besides I‘m using some CSS modern technics which are not supported by all browsers. So my goal is to show the power of CSS, what effects and what can be done with modern technics. And further you will see the final result of the website for a fictional company using only CSS. (No JavaScript and no Bootstrap).
Thesis and thesis presentation
</description>
<dc:date>2018-01-01T00:00:00Z</dc:date>
</item>
<item rdf:about="https://dspace.aua.am/xmlui/handle/123456789/2143">
<title>Data augmentation for natural language text datasets</title>
<link>https://dspace.aua.am/xmlui/handle/123456789/2143</link>
<description>Data augmentation for natural language text datasets
Vartanian, Anooshik
In this thesis, I explored the idea of using data augmentation for text datasets&#13;
through developing a python application. Chapter one explains the problem&#13;
statement and what I planned to achieve. Chapter two provides background&#13;
information on definitions and tools used during the study. Chapter three focuses&#13;
on the structure and contents of the python program as well as the features it&#13;
provides. Chapter four is about the result of using the application on three datasets.&#13;
Chapter five presents the summary and conclusion to the thesis, as well as some&#13;
suggestions and improvements for future.
Thesis and thesis presentation
</description>
<dc:date>2018-01-01T00:00:00Z</dc:date>
</item>
<item rdf:about="https://dspace.aua.am/xmlui/handle/123456789/2141">
<title>Problems regarding fuzzy string processing</title>
<link>https://dspace.aua.am/xmlui/handle/123456789/2141</link>
<description>Problems regarding fuzzy string processing
Harmandayan, Arevik
This thesis presents the solutions to the fuzzified dotted string matching and fuzzified string&#13;
distance problems. These algorithms are modifications of known string matching and string&#13;
distance algorithms. For each of the problems two cases are presented. For the fuzzified dotted string matching&#13;
problem the cases are matching a string with a fuzzy pattern and fuzzy matching of a string with&#13;
a pattern. For the fuzzified string distance problem the cases are distance between a string and a&#13;
fuzzy pattern and distance with fuzzy matching.
Thesis and thesis presentation
</description>
<dc:date>2018-01-01T00:00:00Z</dc:date>
</item>
<item rdf:about="https://dspace.aua.am/xmlui/handle/123456789/2140">
<title>Robust principal component analysis</title>
<link>https://dspace.aua.am/xmlui/handle/123456789/2140</link>
<description>Robust principal component analysis
Minasyan, Arshak
One of the most famous dimensionality reduction methods is Principal Component&#13;
Analysis (PCA), which is successfully used worldwide. However this method is sensitive&#13;
to outliers and hence a few number of them cause bias in the resulting subspace. There&#13;
are a number of techniques now for the robustification of PCA, but we stick to the&#13;
version introduced in [ 30 ]. The numerical technique for optimization in [ 30 ] relied on&#13;
Iteratively Reweighted Least Squares (IRLS) method. In the present paper we adopted&#13;
the Conjugate Gradient Descent algorithm with orthogonal matrix constraints from [ 18 ]&#13;
for solving the nonconvex matrix optimization problem. We discuss the arising&#13;
computational and convergence problems and compare effectiveness of the methods.
Thesis and a thesis presentation entitled “Minimization over Stiefel manifolds: Robust PCA and eigenvalue problem”.
</description>
<dc:date>2018-01-01T00:00:00Z</dc:date>
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