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+\documentclass[conference]{IEEEtran}
+\usepackage{cite}
+\usepackage{amsmath,amssymb,amsfonts}
+\usepackage{algorithmic}
+\usepackage{graphicx}
+\usepackage{textcomp}
+\usepackage{xcolor}
+\def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em
+    T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
+\begin{document}
+
+\title{Similarity Modeling 1/2 Abstracts}
+
+\author{\IEEEauthorblockN{Tobias Eidelpes}
+\IEEEauthorblockA{\textit{TU Wien}\\
+Vienna, Austria \\
+e1527193@student.tuwien.ac.at}
+}
+
+\maketitle
+
+% \begin{abstract}
+% \end{abstract}
+
+% \begin{IEEEkeywords}
+% component, formatting, style, styling, insert
+% \end{IEEEkeywords}
+
+\section{Setting}
+
+To understand the term \emph{Similarity Modeling} and what it encompasses, it is
+first important to know how we as humans perceive and understand the things we
+pick up. An illustrative example for this process is the process of seeing
+(\emph{detecting}) a face, \emph{recognizing} it and deriving the emotion
+attached to it. These three steps are placed on a figurative \emph{semantic
+ladder}, where detecting a face sits on the bottom and recognizing emotion on
+the top. Face detection thus carries a relatively low semantic meaning, whereas
+recognizing emotion is a much more sophisticated process. All three steps are
+only possible to be carried out by humans, because they have internal models for
+the faces they see, whether they have seen them before and if there is a known
+emotion attached to how the face looks. These models are acquired from a young
+age through the process of learning. Visual stimuli and models alone are not
+enough to be able to conclude that a certain face appears similar or not. The
+process connecting stimuli and models is comparing the two, also called
+\emph{looking for similarities}. Together, modeling and looking for
+similarities, they can be summarized under the term \emph{Similarity Modeling}.
+
+The goal of Similarity Modeling is usually to find a \emph{class} for the object
+of interest. The flow of information thus starts with the stimulus, continues on
+to the modeling part, where we derive a model of the stimulus and—after finding
+similarities to existing knowledge—ends in a class or label. As mentioned
+previously, the existing knowledge is fed during the modeling process which
+describes the feedback loop we call learning. The difficult part lies in
+properly modeling the input stimulus. It is impossible to store every stimulus
+verbatim into our existing knowledge base, because it would be too much data if
+every variety of a stimulus would have to be saved. Therefore, classification
+systems need the modeling step to \emph{break down} the stimulus into small
+components which generalize well. The similarity part is generally the same for
+various domains. Once a proper model of a stimulus exists, checking for
+similarities in the preexisting knowledge base follows the same patterns,
+regardless of the type of stimulus. 
+
+Common problems that arise when engineers try to model and classify stimuli come
+from the fact that there is a wide variety of input signals. This variety is
+represented by signals which can be local and have large and sudden increases or
+drops. Others are smooth and the defining characteristic is the absence of
+sudden variations. Still different signals can have recurring patterns (e.g.
+EEG) or none at all (e.g. stocks). After detection the most crucial problem
+remains, which is understanding semantics (also known as the \emph{semantic
+gap}). The next problem is getting away from the individual samples to be able
+to construct a model. This is known as the \emph{gravity of the sample}. Another
+problem is commonly referred to as the \emph{curse of dimensionality}, where we
+end up with a huge parameter space and have to optimize those parameters to find
+good models. The last problem is bad data. This can be missing data, misleading
+data or noisy data.
+
+\section{Similarity Measurement}
+
+The artificial process of measuring similarity in computers is shaped by the
+same rules and fundamentals which are governing similarity measurements in
+humans. Understanding how similarity measurements work in humans is thus
+invaluable for any kind of measurement done using computers. A concept which
+appears in both domains is the \emph{feature space}. An example for a feature
+space is one where we have two characteristics of humans, gender and age, which
+we want to explore with regards to their relation to each other. Gender exists
+on a continuum which goes from male to female. Age, on the other hand, goes from
+young to old. Because we are only concerned with two characteristics, we have a
+\mbox{two-dimensional} feature space. Theoretically, a feature space can be
+$n$-dimensional, where increasing values for $n$ result in increasing
+complexity. In our brains processing of inputs happens in neurons which receive
+weighted signals from synapses. The neuron contains a summarization operation
+and a comparison to a threshold. If the threshold is exceeded, the neuron fires
+and sends the information to an axon. The weights constitute the dimensions of
+the feature space. In computers we can populate the feature space with samples
+and then do either a distance (negative convolution) or a cosine similarity
+measurement (positive convolution). Since the cosine similarity measurement uses
+the product of two vectors, it is at its maximum when the two factors are the
+same. It is much more discriminatory than the distance measurement. Distance
+measurements are also called \emph{thematic} or \emph{integral}, whereas cosine
+similarity measurements are called \emph{taxonomic} or \emph{separable}. Due to
+the latter exhibiting highly taxonomic traits, questions of high semantics such
+as ``is this person old?'', which require a \emph{true} (1) or \emph{false} (0)
+answer, fit the discriminatory properties of cosine similarity.
+
+The relationship between distance and similarity measurements is described by
+the \emph{Generalization} function. Whenever the distance is zero, the similarity
+measurement is one. Conversely, similarity is at its lowest when the distance is
+at its highest. The relationship in-between the extremes is nonlinear and
+described by the function $g(d)=s=e^{-d}$, which means that only small increases
+in distance disproportionately affect similarity. Generalization allows us to
+convert distance measurements to similarity measurements and vice-versa.
+
+\begin{equation}
+\label{eq:dpm}
+\mathrm{dpm} = \alpha\cdot\vec{s} + (1-\alpha)\cdot
+g(\vec{d})\quad\mathrm{with}\quad\alpha\in[0,1]
+\end{equation}
+
+Both, cosine similarity and distance measurements, can be combined to form
+\emph{Dual Process Models of Similarity} (DPMs). One such example is given in
+\eqref{eq:dpm} where both measurements are weighted and the distance
+measurement is expressed as a similarity measure using the generalization
+function. DPMs model humans' perception particularly well, but are not widely
+used in the computer science domain.
+
+\section{Feature Engineering 500 words}
+
+\section{Classification 500 words}
+
+\section{Evaluation 200 words}
+
+\section{Perception and Psychophysics 600 words}
+
+\section{Spectral Features 600 words}
+
+\section{Semantic Modeling 200 words}
+
+\section{Learning over Time 600 words}
+
+\section*{References}
+
+Please number citations consecutively within brackets \cite{b1}. The 
+sentence punctuation follows the bracket \cite{b2}. Refer simply to the reference 
+number, as in \cite{b3}---do not use ``Ref. \cite{b3}'' or ``reference \cite{b3}'' except at 
+the beginning of a sentence: ``Reference \cite{b3} was the first $\ldots$''
+
+Number footnotes separately in superscripts. Place the actual footnote at 
+the bottom of the column in which it was cited. Do not put footnotes in the 
+abstract or reference list. Use letters for table footnotes.
+
+Unless there are six authors or more give all authors' names; do not use 
+``et al.''. Papers that have not been published, even if they have been 
+submitted for publication, should be cited as ``unpublished'' \cite{b4}. Papers 
+that have been accepted for publication should be cited as ``in press'' \cite{b5}. 
+Capitalize only the first word in a paper title, except for proper nouns and 
+element symbols.
+
+For papers published in translation journals, please give the English 
+citation first, followed by the original foreign-language citation \cite{b6}.
+
+\begin{thebibliography}{00}
+\bibitem{b1} G. Eason, B. Noble, and I. N. Sneddon, ``On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,'' Phil. Trans. Roy. Soc. London, vol. A247, pp. 529--551, April 1955.
+\bibitem{b2} J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68--73.
+\bibitem{b3} I. S. Jacobs and C. P. Bean, ``Fine particles, thin films and exchange anisotropy,'' in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271--350.
+\bibitem{b4} K. Elissa, ``Title of paper if known,'' unpublished.
+\bibitem{b5} R. Nicole, ``Title of paper with only first word capitalized,'' J. Name Stand. Abbrev., in press.
+\bibitem{b6} Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, ``Electron spectroscopy studies on magneto-optical media and plastic substrate interface,'' IEEE Transl. J. Magn. Japan, vol. 2, pp. 740--741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
+\bibitem{b7} M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 1989.
+\end{thebibliography}
+
+\end{document}