added short presentation of my bachelors thesis

presentations/Bachelor-Short/LaTeX/Makefile

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+SOURCE = bachelor-short
+
+make:
+	#latexmk -pdf -pdflatex="pdflatex -interactive=nonstopmode" -use-make $(SOURCE).tex
+	pdflatex -shell-escape $(SOURCE).tex -output-format=pdf #shellescape wird fürs logo benötigt
+	pdflatex -shell-escape $(SOURCE).tex -output-format=pdf # nochmaliges ausführen wegen Inhaltsverzeichnissen
+	make clean
+
+clean:
+	rm -rf  $(TARGET) *.class *.html *.log *.aux *.out *.glo *.glg *.gls *.ist *.xdy *.1 *.toc *.snm *.nav *.vrb *.fls *.fdb_latexmk *.pyg

presentations/Bachelor-Short/LaTeX/bachelor-short.tex

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+\documentclass[hyperref={pdfpagelabels=false},usepdftitle=false]{beamer}
+\usepackage{../templates/myStyle}
+
+\begin{document}
+\selectlanguage{english}
+
+\title{\titleText}
+\subtitle{Bachelor's thesis of Martin Thoma}
+\author{\tutor}
+\date{5th of June, 2014}
+%\subject{Programmieren}
+
+\frame{\titlepage}
+
+\frame{
+    \frametitle{Contents}
+    \setcounter{tocdepth}{1}
+    \tableofcontents
+    \setcounter{tocdepth}{2}
+}
+
+%\AtBeginSection[]{
+%    \InsertToC[sections={\thesection}]  % shows only subsubsections of one subsection
+%}
+
+\section{What is my Bachelor's thesis about?}
+\input{introduction}
+
+\section{What did I do so far?}
+\input{work-done}
+
+\section{What will I do next?}
+\input{will-do}
+
+\section{End}
+\subsection{End}
+\input{sources}
+\framedgraphic{Thanks for Your Attention!}{../images/xi.png}
+
+\end{document}

presentations/Bachelor-Short/LaTeX/introduction.tex

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+\subsection{Online and offline recognition}
+
+\begin{frame}{What is my Bachelor's thesis about?}
+    \begin{itemize}
+        \item Handwriting recogntion of mathematical formulas
+        \item On-line recognition, not OCR!
+        \item Given a series of points $(x(t), y(t), b)$\\
+              I want to get the proper \LaTeX{} code.
+    \end{itemize}
+\end{frame}
+
+\begin{frame}{Why do I work on this topic?}
+    \begin{itemize}
+        \item \LaTeX{} is easy as soon as you know the \textbackslash{}codes.
+        \item It's hard to find the \LaTeX{} code of single symbols.
+        \item It's much harder to find complete formulas.
+    \end{itemize}
+
+    I want to
+    \begin{itemize}
+        \item provide a tool that enables beginners to get the best \LaTeX{} code
+              for their formula,
+        \item find out what works best for symbol recognition
+        \item and provide data and a platform to test new ideas for classifiers
+    \end{itemize}
+
+    As soon as symbol recognition works good in terms of classification time and
+    performance, I will continue with formula recognition.
+\end{frame}

presentations/Bachelor-Short/LaTeX/sources.tex

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+\subsection{Image Sources}
+\begin{frame}{Image Sources}
+    \begin{itemize}
+	\item \href{https://commons.wikimedia.org/wiki/File:Server-multiple.svg}{Server} by RRZEicons
+    \item \href{https://commons.wikimedia.org/wiki/File:Computer-aj_aj_ashton_01.svg}{Desktop Computer} by Ed g2s,
+          Ironbrother, Kierancassel and Msgj
+    \item \href{https://commons.wikimedia.org/wiki/File:Server_by_mimooh.svg}{Server} by Mimooh
+    \end{itemize}
+\end{frame}

presentations/Bachelor-Short/LaTeX/will-do.tex

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+\subsection{What will I do next?}
+\begin{frame}{What will I do next?}
+    \begin{itemize}
+        \item Get classification performance with cross-validation
+        \item Implement neural net for classification
+        \begin{itemize}
+            \item preprocessing: compute cubic spline for each line
+            \begin{itemize}
+                \item equi-spaced points or
+                \item get equi-timed points
+            \end{itemize}
+            \item 5 - 20 input neurons for each line
+            \item 1076 output neurons (one for each symbol)
+        \end{itemize}
+        \item Get a language model (e.g. by parsing Wikipedia)
+        \item Use ANN with HMM (?)
+    \end{itemize}
+\end{frame}
+
+\subsection{Far future}
+\begin{frame}{What could be done?}
+    \begin{itemize}
+        \item Make use of audio data in a multimodal approach\\
+              e.g. $R$ and $\mathcal{R}$
+        \item Currently, the Lecture Translation system doesn't recognize math.\\
+              You get \enquote{integral of e raised to the power of x d x} instead
+              of $\int e^x \mathrm{d} x$.
+        \item Spoken math is ambigous: $\sqrt{a+b}$ vs. $\sqrt{a} + b$
+        \item The language model I create could help to find probable formulas
+        \item The platform could be used to get more input data of users
+    \end{itemize}
+\end{frame}

presentations/Bachelor-Short/LaTeX/work-done.tex

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+\subsection{Write Math}
+
+\begin{frame}{write-math.com}
+    \begin{itemize}
+        \item a website where users can add labeled training data
+        \item works with desktop computers and touch devices
+        \item symbol recognition can be done by multiple classifiers
+        \item users can contribute formulas
+        \item users can vote for formulas
+        \item user who wrote the formula can accept one formula
+    \end{itemize}
+\end{frame}
+
+\framedgraphic{Classify}{../images/classify.png}
+\framedgraphic{Workflow}{../images/workflow.png}
+\framedgraphic{User page}{../images/user-page.png}
+\framedgraphic{Information about handwritten-data}{../images/view.png}
+\framedgraphic{Non-mathematical symbols}{../images/yinyang.png}
+\framedgraphic{Training}{../images/train.png}
+\framedgraphic{Ranking}{../images/ranking.png}
+\framedgraphic{Symbol page}{../images/symbol.png}
+
+\begin{frame}{Statistics}
+    \begin{itemize}
+        \item 40 users
+        \item 1076 symbols
+        \item 5519 handwritten symbols (e.g. 195 times the letter \enquote{A})
+        \begin{itemize}
+            \item only 264 have 4 lines
+            \item only 36 have 5 lines
+            \item only 16 have 6 lines
+            \item only 19 have 7 lines or more
+            \item none has more than 12 lines
+        \end{itemize}
+    \end{itemize}
+\end{frame}
+
+\begin{frame}{First classification worker}
+    \begin{itemize}
+        \item preprocessing: Scale to fit into unit square while keeping the aspect
+              ratio
+        \item applies dynamic time warping
+        \item compares a new handwritten symbol with every handwritten symbol
+              in the database
+        \item[$\Rightarrow$] Classification time is in $\mathcal{O}(\text{handwritten symbols})$,
+              but we rather would like $\mathcal{O}(\text{symbols})$
+        \item the current server / workflow can only handle about 4000 handwritten
+              symbols
+        \item[$\Rightarrow$] Another way to classify is necessary
+    \end{itemize}
+\end{frame}