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Simon Klüttermann 2021-10-11 17:34:41 +02:00
parent b90794cb49
commit a05699269c
51 changed files with 936 additions and 460 deletions
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assigns.py Normal file
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from plt2 import *
import numpy as np
#probability that one thesis is not going to be picket
students=15
topics=15
def by_picks(picks=3):
return (1-(((topics-picks)/topics)**students))**topics
#print(by_picks(1))
#print(by_picks(2))
#print(by_picks(3))
picks=np.arange(1.0,5.0,0.1)
probs=[by_picks(pick) for pick in picks]
plt.plot(picks,probs)
plt.title("Probability of everybody being assigned a favorable topic")
plt.ylabel("Probability")
plt.xlabel("Average number of favored topics")
plt.savefig("assigns.png")
plt.savefig("assigns.pdf",format="pdf")
plt.show()

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compile.sh
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python3 gentopic.py
lualatex --shell-escape slides
biber slides
lualatex --shell-escape slides

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dics/benedikt1.json
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{
"index":9,
"index":10,
"title":"Interpretation",
"paper":"Contextual Outlier Interpretation",
"by": "Liu et al",
"when": "2017",
"who": "Benedikt Böing",
"mail": "benedikt.boeing@cs.uni-dortmund.de",
"note1":"Interpretabile AD: Create Trust",
"note2":"Understand by surrounding",
"ext": "png"
}

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dics/bin1.json
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"when": "2021",
"who": "Bin Li",
"mail": "bin.li@tu-dortmund.de",
"note1":"Pretransformations (like Rotations) can improve ML",
"note2":"For Time-Series: Make learnable",
"ext": "png"
}

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dics/bin2.json
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{
"index":11,
"index":12,
"title":"Application: Interpretable Device Failure",
"paper":"Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure",
"by": "Sipple et al",
"when": "2020",
"who": "Bin Li",
"mail": "bin.li@tu-dortmund.de",
"note1":"real world data is messy",
"note2":"detect IOT devices that fail",
"ext": "png"
}

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dics/chiara.json
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{
"index":10,
"index":11,
"title":"Explainability",
"paper":"Additive Explanations for Anomalies Detected from Multivariate Temporal Data ",
"by": "Giurgiu, Schumann et al",
"when": "2019",
"who": "Chiara Balestra",
"mail": "chiara.balestra@cs.uni-dortmund.de",
"note1":"Measure contributions of features",
"note2":"Your own Literature Research is extra important",
"ext": "jpeg"
}

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dics/daniel1.json
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"when": "2000",
"who": "Daniel Wilmes",
"mail": "daniel.wilmes@cs.uni-dortmund.de",
"note1":"Classical AD algorithm",
"note2":"Good for a less experienced student",
"ext": "jpeg"
}

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dics/daniel2.json
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"when": "2012",
"who": "Daniel Wilmes",
"mail": "daniel.wilmes@cs.uni-dortmund.de",
"note1":"Filter out useless Features",
"note2":"Cowritten by Prof. Müller, migth be related to a future thesis",
"ext": "png"
}

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{
"index":14,
"title":"Application: Spacecraft monitoring",
"paper":"Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding",
"by": "Hundman et al",
"when": "2018",
"who": "Daniel Wilmes",
"mail": "daniel.wilmes@cs.uni-dortmund.de",
"note1":"Another Application Paper",
"note2":"This time to supervise Spacecrafts",
"ext": "jpg"
}

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dics/jelle1.json
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{
"index":12,
"title":"Application: Video Data",
"paper":"Fast Unsupervised Anomaly Detection in Traffic Videos",
"by": "Doshi et al",
"when": "2020",
"index":1,
"title":"Anomaly Detection for Monitoring",
"paper":"Anomaly Detection for Monitoring",
"by": "Schwartz, Jinka",
"when": "2015",
"who": "Jelle Hüntelmann",
"mail": "jelle.hüntelmann@cs.uni-dortmund.de",
"ext": "jpeg"
"note1":"Time Series analysis",
"note2":"Less of a Paper, more a Book: great for less experienced People",
"ext": "png"
}

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dics/jelle2.json
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{
"index":1,
"title":"High dimensional Data",
"paper":"A comprehensive survey of anomaly detection techniques for high dimensional big data",
"by": "Thudumu et al",
"index":13,
"title":"Application: Video Data",
"paper":"Fast Unsupervised Anomaly Detection in Traffic Videos",
"by": "Doshi et al",
"when": "2020",
"who": "Jelle Hüntelmann",
"mail": "jelle.hüntelmann@cs.uni-dortmund.de",
"ext": "png"
"note1":"Application Paper",
"note2":"Complex input data",
"ext": "jpeg"
}

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dics/simon1.json
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{
"index":2,
"title":"Text Data",
"paper":"Outlier Detection for Text Data",
"by": "Kannan et al",
"when": "2017",
"title":"High dimensional Data",
"paper":"A comprehensive survey of anomaly detection techniques for high dimensional big data",
"by": "Thudumu et al",
"when": "2020",
"who": "Simon Klüttermann",
"mail": "simon.kluettermann@cs.uni-dortmund.de",
"note1":"High dimensional Data becomes hard for AD",
"note2":"Explain why",
"ext": "png"
}

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dics/simon2.json
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{
"index":7,
"index":8,
"title":"Anomaly Ensembles",
"paper":"Unsupervised Anomaly Detection Ensembles using Item Response Theory",
"by": "Kandanaarachchi1",
"when": "2021",
"who": "Simon Klüttermann",
"mail": "simon.kluettermann@cs.uni-dortmund.de",
"note1":"Combine multiple algorithms",
"note2":"Could be combined with the following Paper",
"ext": "jpg"
}

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dics/simon3.json
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"when": "2021",
"who": "Simon Klüttermann",
"mail": "simon.kluettermann@cs.uni-dortmund.de",
"note1":"Graph Data can be super interesting, but also quite complicated",
"note2":"Either split up into two topics or done by two people",
"ext": "png"
}

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dics/simon4.json
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{
"index":8,
"index":9,
"title":"Active Anomaly Detection",
"paper":"Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability",
"by": "Das et al",
"when": "2019",
"who": "Simon Klüttermann",
"mail": "simon.kluettermann@cs.uni-dortmund.de",
"note1":"Active Learning=Learning with humans",
"note2":"Could be combined with the previous Paper",
"ext": "png"
}

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{
"index":7,
"title":"GANs for Anomaly Detection",
"paper":"A Survey on GANs for Anomaly Detection",
"by": "Mattia et al",
"when": "2021",
"who": "Simon Klüttermann",
"mail": "simon.kluettermann@cs.uni-dortmund.de",
"note1":"GANs generally used for data generation",
"note2":"Can also be used for AD, how?",
"ext": "png"
}

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\abx@aux@read@bbl@mdfivesum{F7F239C736CF01AC6CE7BD829F9B3C7B}
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[0] Config.pm:312> INFO - This is Biber 2.15
[0] Config.pm:315> INFO - Logfile is 'slides.blg'
[84] biber:330> INFO - === Mi Okt 6, 2021, 21:31:34
[100] Biber.pm:415> INFO - Reading 'slides.bcf'
[182] Biber.pm:952> INFO - Found 0 citekeys in bib section 0
[190] Utils.pm:395> WARN - The file 'slides.bcf' does not contain any citations!
[199] bbl.pm:651> INFO - Writing 'slides.bbl' with encoding 'UTF-8'
[199] bbl.pm:754> INFO - Output to slides.bbl
[199] Biber.pm:128> INFO - WARNINGS: 1
[53] biber:330> INFO - === Mo Okt 11, 2021, 17:26:11
[63] Biber.pm:415> INFO - Reading 'slides.bcf'
[115] Biber.pm:952> INFO - Found 0 citekeys in bib section 0
[119] Utils.pm:395> WARN - The file 'slides.bcf' does not contain any citations!
[125] bbl.pm:651> INFO - Writing 'slides.bbl' with encoding 'UTF-8'
[126] bbl.pm:754> INFO - Output to slides.bbl
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This is LuaHBTeX, Version 1.13.0 (TeX Live 2021/Arch Linux) (format=lualatex 2021.6.8) 6 OCT 2021 21:31
This is LuaHBTeX, Version 1.13.0 (TeX Live 2021/Arch Linux) (format=lualatex 2021.6.8) 11 OCT 2021 17:26
system commands enabled.
**slides
(./slides.tex
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@ -43,6 +43,10 @@
\item Some Formal Stuff
\item Short Overview of the Topics
\end{itemize}
\begin{center}
\includegraphics[height=4cm]{assigns.pdf}
\end{center}
\end{column}
\begin{column}{.475\textwidth}
@ -62,7 +66,7 @@
\begin{frame}
\begin{itemize}
\item This seminar is overbooked and there are dozens of students that we were not able to assign a seminar this semester
\item Many of you are also not fullfilling the suggested requirements and should have time to hear a Seminar next year, while staying in regular study time.
\item Many of you should also have time to hear a Seminar next year, while staying in regular study time.
\item So if youre not sure that you want to participate in this course, please consider that you are taking the place of a potentially very motivated student!
\item If you want to make place for another Student: No hard feelings, just write an email to Simon.Kluettermann@cs.tu-dortmund.de.
@ -91,14 +95,15 @@
\end{frame}
\begin{frame}
\frametitle{Grading}
\frametitle{Timeline}
\begin{enumerate}
\item Presentation in Class
\item Writing of your Report
\item Discussion of your Findings
\item Presentation in Class (Blockseminar, 26-28.01.2022 13:00-17:00)
\item Discussion of your Findings (afterwards)
\item Writing of your Report (till 30.01.2022 23:59)
\item Critizise the Paper of another random Student (till 06.02.2022 23:59)
\end{enumerate}
\begin{itemize}
\item All parts required and graded equally
\item All parts required!
\end{itemize}
\vspace{3em}
\begin{itemize}
@ -108,19 +113,23 @@
\begin{frame}
\frametitle{Tasks of this Seminar}
\begin{itemize}
\item Required: willingless to learn something new
\item Also useful: A basic understanding of machine learning, statistics and linear algebra
%\begin{itemize}
% \item Required: willingless to learn something new
% \item Also useful: A basic understanding of machine learning, statistics and linear algebra
%\end{itemize}
\begin{enumerate}
\item Choose a couple of Topics from our list, you will be assigned to one of them
\item Read and understand the Paper given to you
\item Find, read and understand related literature. It is probably impossible to get a good picture about your Topic from just one Paper
\item Critically analyze the suggested Ideas and compare them to the literature
\item Choose a couple of topics from our list, you will be assigned to one of them
\item Read and understand the paper given to you
\item Find, read and understand related literature. It is probably impossible to get a good picture about your topic from just one paper
\item Critically analyze the suggested ideas and compare them to the literature
\end{enumerate}
\begin{itemize}
\item Final Results:
\begin{itemize}
\item Presentation (30min +10min discussion)
\item Written Report (at least 6 Pages double column, ACM template)
\item Written Report (at least 6 Pages double column, ACM template equivalent)
\item Critique of another written report
\end{itemize}
\end{itemize}
@ -133,7 +142,7 @@
\item Feel free to ask as many Questions as you want
\item If you want to discuss your Topic with somebody, make an appointment with your Supervisor
\item the same holds for your Presentation/Report
\item Also any Feedback is always appreciated
\item Any Feedback is always appreciated
\end{itemize}
\end{frame}
@ -146,7 +155,7 @@
\section{Topics}
%Data Concepts
\subfile{topics/jelle2.tex}
\subfile{topics/jelle1.tex}
\subfile{topics/simon1.tex}
\subfile{topics/simon3.tex}
@ -154,6 +163,7 @@
\subfile{topics/daniel1.tex}
\subfile{topics/daniel2.tex}
\subfile{topics/bin1.tex}
\subfile{topics/simon5.tex}
\subfile{topics/simon2.tex}
\subfile{topics/simon4.tex}
@ -163,13 +173,58 @@
%Application
\subfile{topics/bin2.tex}
\subfile{topics/jelle1.tex}
\subfile{topics/jelle2.tex}
\subfile{topics/daniel3.tex}
\begin{frame}
\begin{center}
\includegraphics[height=7cm]{clock/output.pdf}
\end{center}
\end{frame}
%\begin{frame}
%\begin{center}
%\begin{table}[]
%\begin{tabular}{|l|l|l|}
%\hline
%1: AD for Monitoring & 2: High dim Data & 3: Graph Data \\ \hline
%4: LOF & 5: Subspace prep & 6: Transformations for AD \\ \hline
%7: GANs & 8: Ensembles & 9: Active AD \\ \hline
%10: Interpretation & 11: Explainability & 12: Application: Device Failure \\ \hline
%13: Application: Video Data & 14: Application: Spacecrafts & Any other ideas? \\ \hline
%\end{tabular}
%\end{table}
% \end{center}
% \end{frame}
\begin{frame}
\begin{columns}
\begin{column}{0.475\textwidth}
\begin{itemize}
\item 1: AD for Monitoring
\item 2: High dimensional Data
\item 3: Graph Data (*2?)
\item 4: LOF
\item 5: Subspace preprocessing
\item 6: Transformations
\item 7: GANs for AD
\end{itemize}
\end{column}
\begin{column}{0.475\textwidth}
\begin{itemize}
\item 8: Ensembles
\item 9: Active AD
\item 10: Interpretation
\item 11: Explainability
\item 12: Application: Device Failure
\item 13: Application: Video Data
\item 14: Application: Spacecrafts
\item Any other ideas?
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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Contextual Outliers
This Paper focuses on interpretability of anomaly detection methods. The Method described works by splitting up the set of normal events into groups and tries to relate any abnormal event to its surrounding normal ones. I would say it is more practical and I want to strongly encurage you to implement this algorithm if choosen.
https://arxiv.org/abs/1711.10589
Active AD via Ensembles...
This Paper tries do to a lot. I suggest that you focus on the active learning part. Alternatively we have also a paper on ensembles so if you both want, you can combine these papers to be worked on by two Students. Active AD extends the task of finding anomalies to the case in which the anomaly status of the training events is not clearly defined. Its focus here lies in minimizing the amount of human work needed to classify a given dataset (given some labels, train a model, find those new events that are unclear, classify those, restart).
I want to note here, that great work on an easy topic if for us the same as good work on a hard topic.
https://arxiv.org/pdf/1901.08930
Interpretable AD for Device Failure
This is an Application Paper. Its complexity comes mostly from the fact that real world data is messy and the Paper addresses ways to mitigate this.
https://arxiv.org/pdf/2007.10088
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
While for Image data, certain pre-Transformations(like Rotations) can clearly improve Machine Learning Tasks like Anomaly Detection, this is much less well defined for Time-Series/Tabular data. This Paper tries to solve this by defining learnable Transformations.
https://arxiv.org/pdf/2103.16440
Unsupervised Anomaly Detection Ensembles using Item Response Theory
Different AD algorithms are usually better at finding different types of anomalies. To get a more general algorithm you can combine multiple ones into one using Ensembles.
This Paper could be merged together with "Active AD via Ensembles" to be handled by two students.
https://arxiv.org/pdf/2106.06243
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
A lot of datasets that are interesting to AD (For example Email Communications or Trading Data) can be best represented as graphs. This provides unique challeges for AD algorithms.
This is a paper that could either be handled by two students or split up into two. Maybe one considers anomalous graphs, while the other one considers anomalous nodes in graphs.
https://arxiv.org/pdf/2106.07178
Additive Explanations for Anomalies Detected from Multivariate Temporal Data
Explaining why a given event is anomalous can be as important as detecting it, as it helps to create Trust. This Paper suggests a Method that is based on differentiating between features that contribute more and less.
It is also a quite short paper, so it is extra important to look for other papers.
https://dl.acm.org/doi/abs/10.1145/3357384.3358121
requires vpn, contact me if you have problems with this
Anomaly Detection for Monitoring
This is more a book and less of a Paper. So it should be perfect for you if you have not that much experience. If focusses on Time Series Analyses, namely the Task of detection when a continous datastream becomes anomalous. This is for examle useful for a machine supervised by sensors that at some point stops working (and thus changes the sensor output)
https://assets.dynatrace.com/content/dam/en/wp/Anomaly-Detection-for-Monitoring-Ruxit.pdf
Fast Unsupervised Anomaly Detection in Traffic Videos
This is another Application Paper. Its main complexity is the Input data type, as this uses videos (which are very high dimensional and contain temporal correlations). You will see how good preprocessing can make even a basic algorithm viable for complicated problems.
https://openaccess.thecvf.com/content_CVPRW_2020/papers/w35/Doshi_Fast_Unsupervised_Anomaly_Detection_in_Traffic_Videos_CVPRW_2020_paper.pdf
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking
As most datapoints are quite high-dimensional, it is often the case that some features are useless and could actually take part in hiding the true abnomalities. This Paper suggests a method to select a subspace that filters out unimportant features.
This paper was cowritten by Prof. Müller and might be related to a future Masters thesis.
https://www.ipd.kit.edu/~muellere/publications/ICDE2012.pdf
LOF: identifying Density-Based Local Outliers
LOF is a classical algorithm used in many Applications. This is the original Paper introducing it. As this is a fairly old Paper, you will also find a lot of other sources describing LOF.
https://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf
A comprehensive survey of anomaly detection techniques for high dimensional big data
Anomaly Detection is generally more complicated when you are given higher dimensional data (Curse of dimensionality). This seems a little weird, as usually machine learning improves when you are given more informations. I imagine it as useless features confusing this algorithm. This Paper could be seen as a study of this phenomena.
https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-020-00320-x.pdf
A Survey on GANs for Anomaly Detection
GANs are an advanced ML method, normally used to generate really realistic artificial Images (check out https://thispersondoesnotexist.com/ if you have never done so). But these can also be used for anomaly detection. Your task would be to explain how.
https://arxiv.org/pdf/1906.11632
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
This is another Application Paper, but this time using a more complicated algorithms from recurrent ML. It tries to monitor the evergrowing amount of spacecrafts for anomalous behaviour.
https://arxiv.org/pdf/1802.04431

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topics/benedikt1.tex
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\begin{frame}
\frametitle{Topic 9: Interpretation}
\frametitle{Topic 10: Interpretation}
\Large \textbf{Contextual Outlier Interpretation} (Liu et al, 2017)\\
\LARGE \textbf{Supervisor:} Benedikt Böing (benedikt.boeing@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/benedikt1.png}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/benedikt1.png}
\includegraphics[height=3cm]{clock/output9.pdf}
\includegraphics[height=3cm]{illustrations/benedikt1.png}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Interpretabile AD: Create Trust
\item Understand by surrounding
\end{itemize}
\end{column}
\end{columns}
\end{frame}

19
topics/bin1.tex
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\Large \textbf{Neural Transformation Learning for Deep Anomaly Detection Beyond Images} (Qui et al, 2021)\\
\LARGE \textbf{Supervisor:} Bin Li (bin.li@tu-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/bin1.png}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/bin1.png}
\includegraphics[height=3cm]{clock/output6.pdf}
\includegraphics[height=3cm]{illustrations/bin1.png}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Pretransformations (like Rotations) can improve ML
\item For Time-Series: Make learnable
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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topics/bin2.tex
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\begin{frame}
\frametitle{Topic 11: Application: Interpretable Device Failure}
\frametitle{Topic 12: Application: Interpretable Device Failure}
\Large \textbf{Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure} (Sipple et al, 2020)\\
\LARGE \textbf{Supervisor:} Bin Li (bin.li@tu-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/bin2.png}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/bin2.png}
\includegraphics[height=3cm]{clock/output11.pdf}
\includegraphics[height=3cm]{illustrations/bin2.png}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item real world data is messy
\item detect IOT devices that fail
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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topics/chiara.tex
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\begin{frame}
\frametitle{Topic 10: Explainability}
\frametitle{Topic 11: Explainability}
\Large \textbf{Additive Explanations for Anomalies Detected from Multivariate Temporal Data } (Giurgiu, Schumann et al, 2019)\\
\LARGE \textbf{Supervisor:} Chiara Balestra (chiara.balestra@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/chiara.jpeg}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/chiara.jpeg}
\includegraphics[height=3cm]{clock/output10.pdf}
\includegraphics[height=3cm]{illustrations/chiara.jpeg}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Measure contributions of features
\item Your own Literature Research is extra important
\end{itemize}
\end{column}
\end{columns}
\end{frame}

19
topics/daniel1.tex
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\Large \textbf{LOF: Identifying Density-Based Local Outliers} (Breunig et al, 2000)\\
\LARGE \textbf{Supervisor:} Daniel Wilmes (daniel.wilmes@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/daniel1.jpeg}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/daniel1.jpeg}
\includegraphics[height=3cm]{clock/output4.pdf}
\includegraphics[height=3cm]{illustrations/daniel1.jpeg}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Classical AD algorithm
\item Good for a less experienced student
\end{itemize}
\end{column}
\end{columns}
\end{frame}

19
topics/daniel2.tex
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\Large \textbf{HiCS: High Contrast Subspaces for Density-Based Outlier Ranking} (Keller, Müller et al, 2012)\\
\LARGE \textbf{Supervisor:} Daniel Wilmes (daniel.wilmes@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/daniel2.png}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/daniel2.png}
\includegraphics[height=3cm]{clock/output5.pdf}
\includegraphics[height=3cm]{illustrations/daniel2.png}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Filter out useless Features
\item Cowritten by Prof. Müller, migth be related to a future thesis
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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\begin{frame}
\frametitle{Topic 14: Application: Spacecraft monitoring}
\Large \textbf{Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding} (Hundman et al, 2018)\\
\LARGE \textbf{Supervisor:} Daniel Wilmes (daniel.wilmes@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/daniel3.jpg}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/daniel3.jpg}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Another Application Paper
\item This time to supervise Spacecrafts
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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topics/jelle1.tex
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\begin{frame}
\frametitle{Topic 12: Application: Video Data}
\frametitle{Topic 1: Anomaly Detection for Monitoring}
\Large \textbf{Fast Unsupervised Anomaly Detection in Traffic Videos} (Doshi et al, 2020)\\
\Large \textbf{Anomaly Detection for Monitoring} (Schwartz, Jinka, 2015)\\
\LARGE \textbf{Supervisor:} Jelle Hüntelmann (jelle.hüntelmann@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/jelle1.png}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/jelle1.jpeg}
\includegraphics[height=3cm]{clock/output12.pdf}
\includegraphics[height=3cm]{illustrations/jelle1.png}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Time Series analysis
\item Less of a Paper, more a Book: great for less experienced People
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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topics/jelle2.tex
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\begin{frame}
\frametitle{Topic 1: High dimensional Data}
\frametitle{Topic 13: Application: Video Data}
\Large \textbf{A comprehensive survey of anomaly detection techniques for high dimensional big data} (Thudumu et al, 2020)\\
\Large \textbf{Fast Unsupervised Anomaly Detection in Traffic Videos} (Doshi et al, 2020)\\
\LARGE \textbf{Supervisor:} Jelle Hüntelmann (jelle.hüntelmann@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/jelle2.jpeg}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/jelle2.png}
\includegraphics[height=3cm]{clock/output1.pdf}
\includegraphics[height=3cm]{illustrations/jelle2.jpeg}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Application Paper
\item Complex input data
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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topics/shema.tex
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\Large \textbf{##paper##} (##by##, ##when##)\\
\LARGE \textbf{Supervisor:} ##who## (##mail##)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/##name##.##ext##}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/##name##.##ext##}
\includegraphics[height=3cm]{clock/output##index##.pdf}
\includegraphics[height=3cm]{illustrations/##name##.##ext##}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item ##note1##
\item ##note2##
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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topics/simon1.tex
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\begin{frame}
\frametitle{Topic 2: Text Data}
\frametitle{Topic 2: High dimensional Data}
\Large \textbf{Outlier Detection for Text Data} (Kannan et al, 2017)\\
\Large \textbf{A comprehensive survey of anomaly detection techniques for high dimensional big data} (Thudumu et al, 2020)\\
\LARGE \textbf{Supervisor:} Simon Klüttermann (simon.kluettermann@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/simon1.png}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/simon1.png}
\includegraphics[height=3cm]{clock/output2.pdf}
\includegraphics[height=3cm]{illustrations/simon1.png}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item High dimensional Data becomes hard for AD
\item Explain why
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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topics/simon2.tex
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\begin{frame}
\frametitle{Topic 7: Anomaly Ensembles}
\frametitle{Topic 8: Anomaly Ensembles}
\Large \textbf{Unsupervised Anomaly Detection Ensembles using Item Response Theory} (Kandanaarachchi1, 2021)\\
\LARGE \textbf{Supervisor:} Simon Klüttermann (simon.kluettermann@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/simon2.jpg}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/simon2.jpg}
\includegraphics[height=3cm]{clock/output7.pdf}
\includegraphics[height=3cm]{illustrations/simon2.jpg}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Combine multiple algorithms
\item Could be combined with the following Paper
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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topics/simon3.tex
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\Large \textbf{A Comprehensive Survey on Graph Anomaly Detection with Deep Learning} (Ma et al, 2021)\\
\LARGE \textbf{Supervisor:} Simon Klüttermann (simon.kluettermann@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/simon3.png}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/simon3.png}
\includegraphics[height=3cm]{clock/output3.pdf}
\includegraphics[height=3cm]{illustrations/simon3.png}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Graph Data can be super interesting, but also quite complicated
\item Either split up into two topics or done by two people
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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topics/simon4.tex
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\begin{frame}
\frametitle{Topic 8: Active Anomaly Detection}
\frametitle{Topic 9: Active Anomaly Detection}
\Large \textbf{Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability} (Das et al, 2019)\\
\LARGE \textbf{Supervisor:} Simon Klüttermann (simon.kluettermann@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/simon4.png}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/simon4.png}
\includegraphics[height=3cm]{clock/output8.pdf}
\includegraphics[height=3cm]{illustrations/simon4.png}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item Active Learning=Learning with humans
\item Could be combined with the previous Paper
\end{itemize}
\end{column}
\end{columns}
\end{frame}

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\begin{frame}
\frametitle{Topic 7: GANs for Anomaly Detection}
\Large \textbf{A Survey on GANs for Anomaly Detection} (Mattia et al, 2021)\\
\LARGE \textbf{Supervisor:} Simon Klüttermann (simon.kluettermann@cs.uni-dortmund.de)\\
%\begin{center}
%\includegraphics[height=3cm]{illustrations/simon5.png}
%\end{center}
\begin{columns}
\begin{column}{.675\textwidth}
\begin{center}
\includegraphics[height=3cm]{illustrations/simon5.png}
\end{center}
\end{column}
\begin{column}{.275\textwidth}
\begin{itemize}
\item GANs generally used for data generation
\item Can also be used for AD, how?
\end{itemize}
\end{column}
\end{columns}
\end{frame}