report has user evaluation without statistics
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\begin{document}
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\maketitle
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\tableofcontents
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\listoffigures
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\newpage
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\section{Introduction}
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documents (lines 25 and 23 -- remember the definition of the \texttt{text} field).
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\section{User interface}
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\subsection{UI flow}
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Figure \ref{fig:ui} illustrates the IR system's UI showing its features.
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\begin{figure}[H]
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\label{fig:ui}
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\end{figure}
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\subsection{Technical details}
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The UI has been implemented using HTML5, vanilla CSS and vanilla JS, with the
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exception of the \textit{FoamTree} library from the Carrot2 project to handle
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displaying the clustering bar to the left of search results.
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Event handlers offered by \textit{FoamTree} allowed for the implementation of
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the results filtering feature when a clustering in filtered.
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This is a single page application, i.e. all updates to the UI happen without
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making the page refresh. This was achieved by using AJAX requests to interact
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with Solr.
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directory. In order to run the UI, a ``CORS Everywhere'' extension must be
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installed on the viewing browser. See the installation instructions for details.
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\subsection{Clustering Component}
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Event handlers offered by \textit{FoamTree} allowed for the implementation of
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the results filtering feature when a clustering in filtered.
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\section{User evaluation}
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The user evaluation was conducted remotely using Microsoft Teams by selecting
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three of my colleagues and making them install and run my project on their local
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system. The examination approximately took 20 minutes for each test subject,
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including installation.
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The questionnaire was implemented using Qualtrics at \href{TBD}{this link}.
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Data for the evaluation was collected using a questionnaire with a ``before
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test'' and an ``after test'' section.
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In the ``before test'' section, users expressed their agreement to test
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procedures and stated their level of familiarity with image search text
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retrieval systems, stating in particular if they ever searched for user created
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images or stock photos. All participants stated they were mostly familiar with
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TR image search systems, and they had the chance to search for user created
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images. Only one participant never searched for stock photos.
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\begin{figure}[H]
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\begin{tabular}{p{4cm}|p{3.5cm}|p{3.5cm}|p{3.5cm}}
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Question & Subject 1 & Subject 2 & Subject 3 \\
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\hline
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\textsc{metadata:} Start time & 2020-12-06 14:52 & 2020-12-07 13:35 & 2020-12-07 13:54\\
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\textsc{metadata:} End time & 2020-12-06 14:58 & 2020-12-07 13:48 & 2020-12-07 14:05\\
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Familiarity with image search TR systems & 4 & 4 & 5 \\
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Has searched for user images & Yes & Yes & Yes \\
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Has searched for stock photos & Yes & No & Yes \\
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The UI was easy to use & 6 & 6 & 7 \\
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Accurate results for ``find a person sneezing'' & 6 & 7 & 7 \\
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Accurate results for ``find Varenna'' & 5 & 7 & 7 \\
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Personal task & ``Churchill Pfeil'' & Eiffel tower from query ``France'' &
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``Italian traditional masks'' \\
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Accurate results for personal task & 7 & 7 & 7 \\
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Clustering was helpful & 6 & 7 & 7 \\
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Irrelevant results were a lot and distracting & 4 & 5 & 2 \\
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Suggestions & Missing Search button & Did not understand clustering was a
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filter & \textit{Great survey background image} \\
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\end{tabular}
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\caption{Data collected from the questionnaire.}
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\label{fig:qs}
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\end{figure}
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Figure \ref{fig:qs} illustrates the data gathered from the questionnaire.
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Numeric values represent a 5-tier likert scale for the ``Familiarity with image
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TR systems question (from 1 to 5, ``Not familiar at all'', ``Slightly
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familiar'', ``Moderately familiar'', ``Very familiar'', ``Extremely familiar'')
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and a 7-tier likert scale on all other questions (from 1 to 7, ``Strongly
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Disagree'', ``Disagree'', ``Slightly disagree'', ``Neither agree or disagree'',
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``Somewhat agree'', ``Agree'', ``Strongly agree''). The start and end times are
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expressed in CEST without DST (corresponding to the local time of participants).
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All participants started the questionnaire before they started effectively using
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the IR system, so this data can also be used to measure the experiment's length.
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\end{document}
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