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Tag Archives: Naseeruddin Shah
November 9, 2014 by Rohit Jagadeesan
After a break of close to six months, this blog is back. Henceforth, the aim is to publish the blog on a fortnightly basis. Thank you for your continued support.
The Wind Rises (Kaze tachinu) (Japan, 2014) [ IMDB: 7.9, Rotten tomatoes: 89%]
The swan song of animation legend Hayao Miyazaki (The genius behind classics like Spirited Away, Princess Mononike, Howl's Moving Castle) is itself a tribute to the life and times of the pioneer of Japanese aviation Jiro Horikoshi. True to form, Miyazaki makes sure that the film gets the scale and ambition it deserves. The thing that struck me while watching the movie was the ease with which the story was unfolding. The screenplay is so well written that the movie could have been a fantastic one even if it were shot using real people in real settings. This sense of realism is what separates Japanese animation from the Hollywood set pieces. Hence, the slight disappointment that a vastly inferior 'Frozen' was chosen ahead of this classic at the Oscars.
Grand Budapest Hotel (2014) [ IMDB: 8.2, Rotten Tomatoes: 92%]
Wes Anderson comes up with probably the most engaging film of the year which has its moments of quirkiness which add to the shroud of suspense which camouflages the playfulness of the movie. It tells the story of a concierge in the hotel who has a fetish for older women. Filmed in his signature style, Wes Anderson ensures that the film looks authentic for the inter war period during which it is set. The slick dialogues and accomplished performances from every member of the crew help in adding to one of the best cinematic experiences of 2014.
Begin Again (2014) [ IMDB:7.5 , Rotten Tomatoes: 83% ]
A once famous music producer who has fallen on bad times, meets a neophyte and takes her on the road to super stardom. The songs used in the movie are good. Mark Ruffalo is perfectly cast as down in the dumps music executive and playing perfect foil to him is the delightful Keira Knightley who is vulnerable and yet cheerful. The film is predictable in the turns it takes but as I have always maintained, some predictable movies are very good to watch even when they have some loose points. Watch it for the music and the joie de vivre.
The Fault in Our Stars (2014) [IMDB: 8.1 Roten Tomatoes: 87%]
Based on the NYT bestseller, the novel is made with the teenage audiences in mind. Hazel and Gus meet at a cancer support group and fall in love. They share many memorable experiences together as they await death. There is nothing exceptional about the movie. Since it is based on the impending doom and gloom , the thematic elements are placed to pull at the heart strings which they do with limited success. The movie tries too hard to bring in the sentimentality which is its USP. This is certainly not the type of movie I enjoy watching but I am sure there is a section of the audience which loves such tragedies.
A Most Wanted Man (2014) [IMDB: 7.1, Rotten Tomatoes: 90%]
From the spy novelist John Le Carre comes this thriller focusing on undercover operations against Muslim terrorist groups based in Hamburg, Germany. Philip Seymour Hoffman delights in his final onscreen appearance and simultaneously reminds us of the great talent that we lost. The suspense and intrigue in the movie build up from the first shot and reaches an explosive climax which is difficult to predict. The screenplay focuses on the minute aspects of the story which gives an impression that there is a twist waiting to happen all the time. The director does not let the pace slacken and the actors do full justice to their roles. I immensely enjoyed watching the movie and feel that it is one of the best to have come out in recent months.
Are You Here (2014) [IMDB: 5.3, Rotten Tomatoes: 22%]
Owen Wilson and Zach Galfianakis teaming up was supposed to be funny but when they try to add gravity, when nothing even resembling that is expected, you get the movie disaster of the year. Both of them sleepwalk through their roles and an awfully weak screenplay makes you wonder "What am I being subjected to?". For the record, the story is that of a social misfit who goes to attend his father's funeral and discovers that he has become a millionaire because his dad trusted his dazed and confused son over his selfish and uncaring daughter. At every moment in the movie, there is an anticipation that there could be something funny happening soon but all those hopes are buried along with the funeral in the movie. Watch it if you are learning to be good at anger management.
Finding Fanny (2014) [IMDB: 6.2, Rotten Tomatoes: 51%]
I have immense sympathy for people who paid to see this drivel in a theatre. This is the kind of movie that brings disrepute to good cinema because it is marketed as good cinema intended for the audiences who can see the finer points of filmmaking. What it becomes is storyless story which could have been compressed to a 25 minute episode on television. For all her commercial success and the fixed awards that come with it, Deepika Padukone cannot act to save her life. Even the others look out of touch with thespian Naseeruddin Shah being the only saving grace. He plays the character of a person who is cajoled to go in search of his long lost love. Just like the car they take the trip in, the story meanders along aimlessly and is a pain to watch. I think people should have guessed this early as the main marketing point of the movie during its promotions was the derriere of the actresses. A movie that must be avoided at all costs.
Happy New Year (2014) [IMDB: 6.7, Rotten Tomatoes: 61%]
Thank God that this movie was released during Diwali. If it was released during the new year, it would have ruined the entire year. Now it needs to be just burned along with the the crackers on Diwali. To call this a movie would be the equivalent of calling the winner of the National Spelling Bee a novelist. The movie is a pointless attempt and an insult to the audience. Filled with cheap gags and a story which seems to have been conjured up by an infant in the cradle, the film is a tight slap on the faces of the audiences who throng movies on the first day of release because of the marketing blitzkrieg they are subjected to. It is also a reminder of the fading charisma of an ageing superstar finding it difficult to maintain the aura so frighteningly exposed by those wrinkles. It is an insult to the idea of patriotism when such movies try to play such cards in vain. As for the actors, the cake goes to Abhishek Bachchan who now plays the Uday Chopra equivalent of a side kick he is used to having in the Dhoom series. I hope that the director Farah Khan meets the same fate as her even lousier brother.
Inequality for all (2013) [IMDB: 8.0, Rotten Tomatoes: 90%]
The former US Labour Secretary Robert Reich tries to give a new perspective on the labour market and the distribution of wealth. Highly recommended.
Eagerly Waiting for:'Foxcatcher' because it is a true story of an Olympic winner.
Did you know: In the state of South Dakota it is illegal to show Police getting beaten in the movies.
Tagged A Most Wanted Man, Are You Here, Begin Again, Boman Irani, Deepika Padukone, dimple kapadia, Finding Fanny, Grand Budapest Hotel, Happy New Year, Hayao Miyazaki, IMDB, Inequality for all, Keira Knightley, Mark Ruffalo, Naseeruddin Shah, Philip Seymour Hoffman, Rotten Tomatoes, Shah Rukh Khan, The Fault in Our Stars, Wes Anderson
January 25, 2014 by Rohit Jagadeesan
The Broken Circle Breakdown (Belgium, 2013) [ IMDB: 7.9, Rotten Tomatoes: 87%]
Trailer: http://www.youtube.com/watch?v=3a50DJkCxqw
Academy award nominee for 'Best Foreign Language Film' category this year, this film from Belgium is a devastating tearjerker. It is a heady mix of music, romance and tragedy with moments of sheer brilliance. The wedding scene and the climax are scenes that stand out. The movie follows the challenges faced by a young family and their desperate attempts to overcome them but at the core of the story is the conflicting religious beliefs of the husband and wife which appear beyond reconciliation. I believe that one of the reasons that the film has a nod at the Oscars is because of the way it tears into the George Bush presidency by using actual footage to support the arguments of one of the characters. The good thing is that even if the politics of the movie is disregarded as inconsequential, it is a movie worth your time. Two thumbs up.
The Third Half (Macedonia, 2012) [ IMDB: 8.2, Rotten Tomatoes: Not yet rated]
Trailer: http://www.youtube.com/watch?v=NJq03xHc6mE
Another film on Nazis, which I believe is a theme that has been done to death by Hollywood. Maybe the Macedonians did not figure that out. So they also came up with a mild version to suit their historical narrative. The end product is a movie which is not bad but does not introduce any new elements in a predictable story line. My curiosity stemmed from the fact that it tells the story of a football team trying to get its act together and at the same time bogged down by politics. If you can take another film on Nazis this film is highly recommended. Otherwise, highly forgettable.
Inside Llewyn Davis (2013) [IMDB:7.9, Rotten Tomatoes: 94%]
Trailer: http://www.youtube.com/watch?v=LFphYRyH7wc
Oscar Isaac produces a scintillating performance as the title character of a movie which certainly falls into the drama genre but is neither a tragedy nor a triumph. Llewyn Davis is a folk singer who tries to piece together a living when he is not hopping from one couch to another in his quest to find a place to sleep. (Spoiler ahead) His life is so messed up that he gets to know through a third party that he has become a father and he still does not want to see the child. Llewyn Davis is a musician who is neither great nor ordinary. As a character, he is neither weak nor strong. It is this very quality of a man who cannot be faulted because he is not impractical but at the same time is not practical enough that makes this story a little complex. That also makes the film very engaging to watch as it opens up to unpredictable twists and turns. A special mention must be made of the cinematography of this movie which is a strong contender at the Oscars but may eventually lose out to 'Gravity'. All in all, this is not a movie for everyone but for the discerning few.
All Is Lost (2013) [ IMDB: 7.2, Rotten Tomatoes: 93%]
Trailer: http://www.youtube.com/watch?v=Lk_R04LfUQU
Robert Redford's best movie in years tells the story of a man marooned with his damaged boat in the ocean when it is hit by a stray container. It chronicles his desperate attempts to rescue himself from imminent and painful death. The movie is slow but I believe that the plot dictated the pace of the movie. A standout feature is the background score. At the Oscars this year this category will be a straight shootout between 'All Is Lost' and 'Gravity'. I would love to see an Indian version of this movie because the Hollywood hunks like to appear calm and composed under trying circumstances but imagine Shahrukh Khan in such a role. He would have cried his heart out and appeared vulnerable, making him endearing. Some movies are better off with such men and Robert Redford is not such a man.
Dedh Ishqiya (India, 2014) [IMDB: 7.9, Rooten Tomatoes: 77%]
Trailer: http://www.youtube.com/watch?v=_J3fkfUDkx8
A half decent story with a plausible twist makes this one of the early winners of 2014. All the actors are in fine form and the movie has a good pace to it. It is the story of two con men who think they have found their match in a mysterious duo. The good thing with the movie is the crisp writing which serves as a refresher course in chaste Hindi. Definitely worth a watch.
The Secret Life of Walter Mitty (2013) [IMDB:7.6, Rotten Tomatoes: 49%]
Trailer: http://www.youtube.com/watch?v=c1Xk-yTuFEU
Ben Stiller should stick to comedies. His capabilities as a director are evidently limited and this movie is the best proof for it. He is neither funny nor engaging. The movie about a man with a fertile imagination and pretty much only that, is a damp squib. The story meanders along and is a big yawn. The producers could have donated the money to some charity. At least they wouldn't have to explain to their children their poor decision making abilities.
20 Feet from Stardom (2013) [ IMDB: 7.8, Rotten Tomatoes: 99%]
Trailer: http://www.youtube.com/watch?v=tWyUJcA8Zfo
The story of the unsung chorus singers on stage. Nominated for the Oscar this year. Strong contender. Worth a watch.
Terrence Malick Retrospective
Days of Heaven (1978) [ IMDB: 8.0, Rotten Tomatoes: 93%]
Trailer: http://www.youtube.com/watch?v=LlZDsMCW0U4
A story of passion, intrigue and treachery set in a farm. Competently acted. Beautifully shot.
The Thin Red Line (1998) [ IMDB:7.6, Rotten Tomatoes: 80%]
Trailer: http://www.youtube.com/watch?v=ejm0XvT3rB8
The Army as it is. Sudden death, painful superiors and a low life. Dream casting with too many stars to count.
Badlands (1973) [IMDB: 7.9, Rotten Tomatoes:98%]
Trailer: http://www.youtube.com/watch?v=qKykxE7CBbc
A couple like to kill. A study in psychology.
Eagerly Waiting for: 'The Amazing Spiderman 2' because it is going to be the first blockbuster of 2014.
Did you know: The sounds made by the Brachiosaurs in 'Jurassic Park' were a combination of whale and donkey sounds.
Tagged Abhishek Chaubey, Anne-Marie Loop, Arshad Warsi, Ben Stiller, Brooke Adams, Carey Mulligan, Daniel Pennac, Darko Mitrevski, Darlene Love, Ethan Coen, Felix Van Groeningen, Gabrielle Vincent, J.C. Chandor, Jim Caviezel, Joel Coen, Johan Heldenbergh, John Goodman, Jon Daly, Katarina Ivanovska, Kristen Wiig, Lambert Wilson, Lisa Fischer, Madhuri Dixit, Martin Sheen, Merry Clayton, Morgan Neville, Naseeruddin Shah, Nell Cattrysse, Nick Nolte, Oscar Isaac, Pauline Brunner, Richard Gere, Richard Sammel, Robert Redford, Sam Shepard, Sasko Kocev, Sean Penn, Sissy Spacek, Stéphane Aubier, Terrence Malick, The Secret Life of Walter Mitty, Veerle Baetens, Vincent Patar, Vishal Bhardwaj, Warren Oates


text:
Q: Switch to **insert** mode from a user-defined command in Vim I'm defining my own command in my vimrc:
command! Tcs :normal lvf`hc

The aim is that I go on a backtick and it will delete the inner content from here to the next backtick. Like:
`hi there` -> ``

Problem is that is remains in normal mode, I would like to replicate the c command behaviour so that I can start typing immediately in insert mode.
I've tried command! Tcs :normal lvf`hc :startinsert but whatever I put after the last c will be typed directly in the editor.
Thanks

A: From :help :normal
>

        :norm[al][!] {commands}                   *:norm* *:normal*
        (...)
        {commands} should be a complete command.  If
        {commands} does not finish a command, the last one
        will be aborted as if <Esc> or <C-C> was typed.

This implies that an insert command must be completed.
       (...)

In your case the command normal has been aborted that's why you still in normal-mode. You can still try :normal! i
To get what you want instead you can do :
command! Tcs execute "normal lvf`hd" | :startinsert

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}
Authorship analysis is relevant in many kinds of disputes, cases of crime, and acts of manipulation:
The originator of hate mail or blackmail messages has to be identified unequivocally for being sentenced.
Claims of responsibility for attacks and the authenticity of these claims are important aspects in solving that crime.
False insurance claims might not be written by the declared claimant but by another person involved in the fraud.
The originality of a testament and hence the allocation of heritage can be contested.
Online reviews, comments, opinion statements, and fake news by sock puppets are used to influence the public appearance, popularity, or success of products, political missions, etc. Hence, authorship analysis is a highly important field of forensics.

According to Koppel et al. \cite{KoppelFundamentalProblem:2012} \av (\AV) is the \emph{fundamental problem} of authorship analysis.
The goal of \AV is to determine if, given writing examples from an author $\A$, each text in fact was written by $\A$ \cite{SteinAuthorshipVerification:2008}.
If this is the case, we can also reformulate the problem to decide if, given $\Arefset$ (a set of reference documents written by $\A$),
a questioned document $\Dunk \notin \Arefset$ is also written by $\A$ \cite{PAN13Stamatatos:2013}.

Compression Models (\CMs) have been used widely across different domains such as computer science, biology
or (computational) linguistics, for more than two decades. In recent years, it can be observed on several bibliographic databases and indexes such as \textit{DBLP\footnote{\url{http://dblp.uni-trier.de}}}, \textit{Google Scholar\footnote{\url{https://scholar.google.com}}} or \textit{Microsoft Academic\footnote{\url{https://academic.microsoft.com}}}, that \CMs attract more and more researchers, especially in the field of text classification (\eg \cite{YuvalMartonTC:2005, PereiraCompressionTC:2015, SaikrishnaCompressionTC:2016} just to mention a few). We adapt compression models to \AV which, from a technical point of view, is an instance of text classification problems,
where the subject is to classify the text regarding its writing style, rather than its content.

We propose a new intrinsic \AV method based on \CMs, which offers many benefits and contributions.
Our method yields recognition results very similar to a number of current state-of-the-art approaches,
but without relying on sophisticated machine learning techniques (\eg support vector machines or neural networks) or
natural language processing components (\eg part-of-speech taggers or dependency parsers).
Instead, our method requires only a compressing algorithm\footnote{Since for almost any popular programming language (\eg C/C++/C\#, Java or Python), existing compression libraries are available, reimplementation is unnecessary.}, a simple dissimilarity measure as well as
a strategy to determine a threshold that serves as the authorship acceptance criterion.
In contrast to the majority of existing traditional \AV methods, our approach avoids the entire feature engineering process including the definition,
extraction and selection of features, which further reduces its complexity.
Therefore, the proposed method can be reimplemented easily by the community or even by those who are not familiar with the field of \AV.
Besides the threshold determination (which is obligatory for almost any \AV approach), our method does not generate a model and thus, implies lazy learning.
Moreover, the proposed method features a very fast\footnote{This fact is surprisingly contradictory to the claim of Yuval et al., who state that compression based classification methods suffer from slow running time \cite{YuvalMartonTC:2005}.} runtime (for example, an evaluation of a corpus comprising 500 \av cases
is processed in few seconds on an off-the-shelf laptop (Intel$\textsuperscript{\textregistered}$ Core$\textsuperscript{\texttrademark}$ i5-3210M processor, 16\,GB RAM).
Hence, our method is applicable in scenarios (or environments) where runtime matters.

Another advantage of our method is its ability to solve \AV cases, where the questioned and the reference document
differ in terms of topic or genre (so-called cross-topic/genre problems) and, furthermore, are short regarding their text lengths.
Both cases are considered challenging in the field of \AV \cite{BrocardoShortAV:2013, PAN15Stamatatos:2015}.
In addition to the proposed method, we also offer a number of (cross- and mixed-topic) corpora that enable to reproduce
our results and can also be used to train and evaluate new \AV methods.

The rest of this paper is structured as follows.
In Section~\ref{RelatedWork} we review related work and existing approaches that, partially, serve as baselines in the later experiments.
In Section~\ref{ProposedMethod} we introduce our \AV method, where we explain in detail all necessary steps to reproduce our results.
In Section~\ref{Experiments} we present our experiments based on the proposed \AV method and the introduced baselines,
and discuss our observations. Finally, we draw our conclusions in Section~\ref{Conclusions} and provide several ideas for future work.
\section{Related work} \label{RelatedWork}
In the following sub-sections we give an overview of noteworthy \AV approaches, which performed quite well in the context of three PAN\footnote{PAN (\url{http://pan.webis.de})  is a series of scientific events and shared tasks on digital text forensics. Note, the shared task ``Author Identification'' deals with \AV.)} competitions. A part of the introduced \AV methods, marked with ($\ast$) in the sub-section titles, serve as baselines in the later experiments.
Note that some of the introduced approaches model \AV as a binary-class-classification problem.
For reasons of readability, we use the following notation: The target class (the known or reference author) is denoted by $\A$,
while the outlier class (all other possible authors than $\A$) is denoted by $\notA$. The set of documents belonging to $\A$ is
denoted by $\Arefset$, whereas the set of documents belonging to $\notA$ is given by $\mathcal{O}$.
\subsection{Methods by Veenman and Li}

One of the few attempts that adapt \CMs to \av was introduced by Veenman and Li \cite{VeenmanPAN13:2013}.
Overall, the authors proposed three \AV methods based on \CMs, where they transformed the \AV task from a one-class to a two-class classification problem.
More concretely, the authors do not focus on the \textit{recognition}, but instead, on the \textit{discrimination} between $\A$ and $\notA$.

All three methods are \textit{extrinsic}, which means that they rely on external documents (examples of other authors) to model $\notA$.
However, since external documents not exist beforehand ($\mathcal{O} = \emptyset$) Veenman and Li collected external data, where they used
the following procedure: Since they knew that the documents within the training corpus were (approx. 1,000 words long)
fragments of engineering text books, the authors searched for similar books on the web (by using substrings) and gathered 66 books authored by 46 different authors.
After preprocessing, they created 2--75 documents per text book with roughly 1,000 words (6,000--8,000 characters),
which were added to $\mathcal{O}$ \cite{VeenmanPAN13:2013}.

Veenman and Li chose the \textit{Prediction by Partial Matching} (PPMd) algorithm to compress the documents within the training corpus and the
\textit{Compression-based Dissimilarity Measure} (CDM, see Section~\ref{ProposedMethod}) to measure the nearness between the questioned document
$\Dunk$ and the documents in $\Arefset$ and $\mathcal{O}$.

In the first method \cite[Sect.~4.1]{VeenmanPAN13:2013} denoted as \textit{Nearest Neighbor with Compression Distances},
the authors compute the dissimilarity scores between the questioned document $\Dunk$ and all documents in $\Arefset$ and $\mathcal{O}$.
If the document with the smallest dissimilarity score $\D$ belongs to $\Arefset$ then $\Dunk$ is believed to be written by
the reference author $\A$. Otherwise, if $\D$ belongs to $\mathcal{O}$ then $\Dunk$ is believed to be written by some other author $\notA$ \cite{VeenmanPAN13:2013}.

For the second method \cite[Sect.~4.2]{VeenmanPAN13:2013}, the authors used the \textit{Lowest Error in a Sparse Subspace} (LESS) classifier developed by Veenman and Tax \cite{VeenmanTax:2005} in order to discriminate between $\A$ and $\notA$. They select a number of ``prototypes'' from $\mathcal{O}$,
and create a real-valued vector for each document by calculating the CDM between the document and all prototypes.
These vectors are then used as input for the LESS classifier, which is quite similar to a linear classifier with a quadratic kernel.

The third \AV method \cite[Sect.~4.3]{VeenmanPAN13:2013} uses the same classification method as the second one, but the number of documents
in $\Arefset$ is increased artificially by resampling (bootstrapping) new documents from the given reference documents.
This reduces the error rate compared to the second approach. However, the first method performed best in their experiments.

In the later experiments we will provide a differentiation between our method and the first approach of Veenman and Li,
which performed amongst the highest-ranking methods in the PAN 2013 \AV competition \cite{PAN13Stamatatos:2013}.

\subsection{Impostors Method (\texorpdfstring{$\ast$}{*})}
One of the most successful \AV approaches is the well-known \KoppelMethod, proposed by Koppel and Winter \cite{KoppelImpostor:2014}.
The method works in two stages. First, documents needs to be gathered according to a data collection procedure (\eg by using a search engine) that serve as impostors.
Second, feature randomizations are used to iteratively measure the cosine similarity between pairs of documents.
If, given this measure, a suspect is picked out from among the impostor set with sufficient salience, then the suspect is considered
as the author $\A$ of the questioned document $\Dunk$ \cite{KoppelImpostor:2014}. Thus, Koppel and Winter transform the AV problem
(in the same way as Veenman and Li \cite{VeenmanPAN13:2013} do) from a one-class to a multi-class classification
problem ($\A$ against $\notA = $ impostors).

The best-performing \AV approaches of the PAN competitions 2013 and 2014 were based on the \KoppelMethod \cite{PAN13Stamatatos:2013, PAN14Stamatatos:2014}.
But even outside of the PAN competitions the \KoppelMethod gained a lot of attraction by researchers in the \AV field, where it has been reimplemented
and extended in various ways. However, despite of the success of the \KoppelMethod, it also suffers from a number of drawbacks.
For example, it seems to be considerably harder to distinguish same-author and different-author pairs if both differ
in terms of genre and topic \cite{KoppelImpostor:2014}. That might be challenging when generating impostors regarding specific genres or topics
which are not available. Furthermore, since the \KoppelMethod is extrinsic (construction of impostors requires external documents),
it must be guaranteed that impostor documents are not written by the authors of the document pair which are subject of verification \cite{KoppelImpostor:2014}.
Another drawback, highlighted by researchers \cite{KhonjiIraqiAV:2014, SebastianRuderCNNbasedAA:2016} who reimplemented the method,
is the computational expense, which makes the \KoppelMethod unsuitable in scenarios where runtime matters (\eg in web applications).

\subsection{Profile-Based Method (\texorpdfstring{$\ast$}{*})}
The \StamatatosMethod is an intrinsic \AV approach, proposed by Stamatatos and Potha \cite{StamatatosPotha:2014}.
The authors reshaped the original method \cite{KeseljAAProfiles:2003}, which initially was used in the context of authorship attribution, a related field of \AV.
The procedure of the method is quite simple: First, all documents of $\A$ are concatenated into one long document $\DA$, which represents the profile of $\A$.
Next, the $L_k$ most frequent character $n$-grams are extracted from $\DA$. Simultaneously, the $L_u$ most frequent character $n$-grams are extracted from the questioned document $\Dunk$. The generated feature vectors are then compared to each other, given an appropriate dissimilarity function.
If the resulting dissimilarity score exceeds a threshold, learned during training, then $\Dunk$ is believed to be written by $\A$.

The \StamatatosMethod offers a number of advantages that led to the decision why we chose it as a baseline in our experiments.
Regarding the PAN 2013 \AV competition the \StamatatosMethod was able to outperform every single participant in terms of the $F_{1}$
measure\footnote{This measure is explained formally in Section~\ref{PerformanceMeasure}.} \cite{StamatatosPotha:2014}.
Furthermore, it benefits from its compactness, not only from the algorithmic point of view, but also in terms of the involved parameters.
Besides these, the method is quite fast, which makes it applicable in real-time \AV scenarios.

\subsection{GLAD (\texorpdfstring{$\ast$}{*})}
The intrinsic \av system GLAD (Groningen Lightweight Authorship Detection) proposed by Hürlimann et al. \cite{GLAD:2015} uses a compression feature among a diverse set of features. In total, they use 29 features representing seven different kinds of information extracted from the documents. Their compression feature is a modified version of the CDM (see Section~\ref{ProposedMethod}). Compared to the CDM, they use a reciprocal formula and insert the compression ratio instead of the length of compressed documents into that formula. The GLAD system classifies \AV problems with a binary SVM, trained on a set of positive and negative example problems. The classification of a problem itself with the trained SVM is intrinsic, \ie it does not use any additional documents outside the problem.

The authors evaluated their system regarding the strength of the different feature groups, and their measurements show that their compression feature has only moderate performance \cite[Fig.\,3]{GLAD:2015} and has only a small contribution to the performance of the full feature set \cite[Fig.\,2]{GLAD:2015}. The GLAD system was among the best systems in the PAN 2015 competition except for the English language, where it had moderate performance \cite{PAN15Stamatatos:2015}. However, our experiments reveal that the opposite is the case. Note that Hürlimann et al. made their \AV method publicly available\footnote{\url{https://github.com/pan-webis-de/glad}} and, thus, a reimplementation was not needed beforehand.

\section{Proposed \av method} \label{ProposedMethod}
This section provides a detailed outline of our \AV approach. As a first step we formalize the \AV problem, which is the key subject of our method.
In the next step, we describe how the data is represented. Next, we elaborate how the similarities regarding the documents are computed. Afterwards, we explain our strategy to determine the threshold and, based on it, how the verification (acceptance/rejection of the authorship) is performed.

\paragraph{Formalization of the \AV Problem:} We define a given \AV problem $\rho$ formally as $\rho = (\Dunk, \Arefset)$. Here, $\Dunk$ denotes the questioned document of an author $\Aunk$, who claims (or is suspected) to be $\A$, while $\Arefset = \{\D_{1, \A}, \D_{2, \A},\ldots \}$ denotes the set of sample documents that in fact
are written by the known/reference author $\A$.

\paragraph{Data representation:} We concatenate all sample documents in $\Arefset$ into a single document $\Dknown$, such that the problem
is simplified from $\rho = (\Dunk, \Arefset)$ to $\rho = (\Dknown, \Dunk)$. Next, we make use of a predefined compression algorithm in order to compress
$\Dknown$ to $C(\Dknown)$, $\Dunk$ to $C(\Dunk)$, and the concatenation $\Dknown\Dunk$ to $C(\Dknown\Dunk)$.
In total, we used for our experiments five (already implemented\footnote{For PPMd we used the \textit{SharpCompress} library by Adam Hathcock: \url{https://github.com/adamhathcock/sharpcompress} and for the remaining compressors \textit{SharpZipLib}, developed and maintained by Mike Krüger, John Reilly, David Pierson and Neil McNeight: \url{https://github.com/icsharpcode/SharpZipLib}.}) compressors: PPMd, Gzip, BZip2, Zip and LZW.

\paragraph{Feature engineering:} Since our approach bypass the definition, extraction and selection of features, this step is skipped.

\paragraph{Computing similarities:} Given the compressed files, we need an appropriate measure to determine the nearness between $\Dknown$ and $\Dunk$.
In total, we implemented three dissimilarity measures designed for \CMs, which measure dissimilarity in terms of the length of compressed documents. The length of a compressed document $C(x)$ is -- for convenience and in accordance to the cited publications -- also denoted as $C(x)$. The dissimilarity measures were also used in plenty of previous research works.

The first measure, the \textit{Normalized Compression Distance} (NCD) developed by Cilibrasi, Li et al. \cite{SimNCDCilibrasi:2005, SimNCDLi:2004}, is defined as:
\begin{equation}
\mathrm{NCD}(x,y) = \frac{C(xy) - \min\{C(x),C(y)\}}{\max\{C(x),C(y)\}}.
\end{equation}
This dissimilarity measure is derived from the universal Normalized Information Distance (NID) \cite{SimNCDLi:2004}, which is defined in terms of the Kolmogorov complexity, and hence the NCD has the most elaborated theoretical foundation among the considered dissimilarity measures. If a compressor is a \textit{normal}, as defined by Cilibrasi and Vitányi \cite[Definition~3.1]{SimNCDCilibrasi:2005}, the NCD maps into the interval $[0;1]$ and satisfies the axioms of a metric up to small deviations caused by the imperfections (allowed errors) in the normality of the compressor \cite[Theorem~6.2]{SimNCDCilibrasi:2005}. Cilibrasi and Vitányi state that the NCD maps ``in practice'' into the interval $[0;1+\varepsilon]$ with $\varepsilon \approx 0.1$ or less \cite[Remark~6.2]{SimNCDCilibrasi:2005}.

The second measure, the \textit{Compression-based Cosine} (CBC) proposed by Sculley and Brodley \cite{SimCosSSculley:2006}, is defined as:
\begin{equation}
\mathrm{CBC}(x,y) = 1 - \frac{C(x) + C(y) - C(xy)}{\sqrt{C(x)C(y)}}.
\end{equation}
This dissimilarity function maps into the interval $[0;1]$, but it is not a metric because it violates the triangle inequality.

The third measure, called \textit{Chen-Li metric} (CLM) by Sculley and Brodley \cite{SimCosSSculley:2006} crediting it to Chen, Li et al. \cite{SimCLMChen:1999, SimCLMLi:2001}, is defined as:
\begin{equation}
\mathrm{CLM}(x,y) = 1 - \frac{C(x) - C(x|y)}{C(xy)}.
\end{equation}
The term $C(x|y)$ is the (length of the) conditional compression of $x$ given $y$ as auxiliary input for compression and decompression. Standard implementations of compression algorithms must be enhanced for using $y$ as auxiliary input, but according to Sculley and Brodley \cite{SimCosSSculley:2006}, the approximation $C(x|y) \approx C(xy) - C(y)$ can be used in order to calculate the CLM with ``off-the-shelf compressors''. Cilibrasi and Vitányi even define $C(x|y) = C(xy) - C(y)$ since they do not consider compressors with the capability of conditional compression \cite[Definition~3.2]{SimNCDCilibrasi:2005}. The CLM maps into the interval $[0;1]$. Furthermore, it is a metric except for minor errors. This is proven by Li et al. for compression in terms of the Kolmogorov complexity \cite[Theorem~1]{SimCLMLi:2001}, but their proof can be extended to compression with normal compressors as defined by Cilibrasi and Vitányi \cite[Definition~3.1]{SimNCDCilibrasi:2005}.

Many authors also use the \emph{Compression-based Dissimilarity Measure} (CDM) proposed by Keogh et al. \cite{Keogh:2004} due to the simplicity of the formula:
\begin{equation}
\mathrm{CDM}(x,y) = \frac{C(xy)}{C(x) + C(y)}.
\end{equation}
This dissimilarity score lies in the interval $[0.5;1]$, and it is related to the (approximated) CLM by the order-preserving mapping
\begin{equation}
\textrm{CLM} = 2 - \frac{1}{\textrm{CDM}}. \label{eq:CDM-CLM-equiv}
\end{equation}
Thus, both measures are equivalent for our purpose. We decided to work with the CLM because it ranges from 0 to 1.

All four dissimilarity measures are closely related, as shown by Sculley and Brodley \cite{SimCosSSculley:2006}: Each measure can be written in the form
\begin{equation}
S(x,y) = 1 - \frac{C(x) + C(y) - C(xy)}{N(x,y)},
\end{equation}
where $N(x,y)$ is a normalization term \cite[Table~1 in Sect.~3.2]{SimCosSSculley:2006}. Based on this, any of the dissimilarity scores will exceed 1 if and only if $C(x) + C(y) - C(xy) < 0$. This can happen due to imperfections in the normality of the compressor. The normalization terms of the dissimilarity measures obey the following relations:
\begin{equation}
\begin{alignedat}{2}
&& N_\mathrm{CBC}(x,y) &= \sqrt{C(x)C(y)} \\
& \leq {} & N_\mathrm{NCD}(x,y) &= \max\{C(x),C(y)\} \\
& \leq {} & N_\mathrm{CLM}(x,y) &= C(xy) \\
& \leq {} & N_\mathrm{CDM}(x,y) &= C(x) + C(y).
\end{alignedat}
\end{equation}
Note that $C(xy)$ can violate the inequalities due to imperfections of the compressor. Any dissimilarity score can also fall below the respective lower bound of its nominal range, which is 0.5 for the CDM and 0 for the other measures, due to imperfections of the compressor. This happens most likely for the CBC, as it has the smallest normalization factor. The CDM falls below 0.5 if and only if the CLM falls below 0 due to the equivalence observed above \eqref{eq:CDM-CLM-equiv}. We wish to highlight that in our experiments we did not observe any case, where a score determined with one of the used measures was outside the interval $[0;1]$.

\paragraph{Threshold determination:} Once we computed the dissimilarity $\M(\Dknown, \Dunk)$ with a fixed measure $\M \in \{ \mathrm{NCD}, \mathrm{CBC}, \mathrm{CLM} \}$,
we need a threshold $\theta$ to judge whether $\Dknown$ and $\Dunk$ were written by the same author $\A$ or not.
Our strategy to define $\theta$ requires a training corpus consisting of $n$ problems equally distributed regarding true (\texttt{Y}) and false (\texttt{N}) authorships.
Given the chosen measure, we compute for each problem $\rho$ in this corpus a dissimilarity score $s_\rho$.
Then, we determine $\theta$ based on the EER (\textit{equal error rate}), \ie we select the threshold where the false acceptance rate and the false rejection rate are equal. The detailed procedure to compute $\theta$ is listed in Algorithm~\ref{DetermineThresholdEER}.

\begin{algorithm}
	\SetKwInput{KwData}{Input}
	\SetKwInput{KwResult}{Output}
	\SetKw{KwBreak}{break}
	\SetKw{KwContinue}{continue}
	\SetKw{KwThrow}{throw}

	\KwData{$Y$ (the dissimilarity scores of the \texttt{Y} problems), $N$  (the dissimilarity scores of the \texttt{N} problems)}
	\KwResult{$\theta$ (the threshold)}
	\BlankLine

	\If{$\mathrm{length}(Y) \neq \mathrm{length}(N)$}
	{
		Exception $\leftarrow$ "Number of Y and N problems mismatch!"\;
		\KwThrow Exception\;
	}
	\BlankLine

	Sort $Y$ and $N$ in ascending order\;
	$\ell \leftarrow \mathrm{length}(Y)$\;
	$i \leftarrow 0$\;
	$j \leftarrow \ell - 1$\;
	\BlankLine

	\For{$k \leftarrow 0$ \KwTo $\ell - 1$}
	{
		\If{$Y_{i} < N_{j}$}
		{
			$i \leftarrow i + 1$\;
			$j \leftarrow j - 1$\;
			\KwContinue\;
		}
		\If{$Y_{i} = N_{j}$}
		{
			$\theta \leftarrow Y_{i}$\;
			\KwBreak\;
		}
		\If{$Y_{i} > N_{j}$}
		{
			\uIf{$i = 0$}
			{
				$\theta \leftarrow \frac{1}{2} (Y_{i} + N_{j})$\;
			}
			\Else
			{
				$\theta \leftarrow \frac{1}{2} \big( \min(Y_{i}, N_{j + 1}) + \max(Y_{i - 1}, N_{j}) \big)$\;
			}
			\KwBreak\;
		}
	}
	\If{$i = \ell$}
	{
		$\theta \leftarrow \frac{1}{2} (Y_{i - 1} + N_{j + 1})$\;
	}
	\BlankLine

	\KwRet{$\theta$}

	\caption{Determine threshold via EER.}
	\label{DetermineThresholdEER}
\end{algorithm}
The training procedure is done once beforehand such that $\theta$ is readily available when deciding about the authorship in $\rho$. Note that our approach only requires a training corpus to determine $\theta$. However, the \AV method itself is intrinsic, which means that it is based exclusively on all documents within $\rho$ rather than on external available texts.

\paragraph{Classification:} The last step is to verify the authorship for the problem $\rho$ based on the dissimilarity score
$s_\rho = \M(\Dknown, \Dunk)$ and the decision threshold $\theta$:
\begin{equation}
\textrm{decision}(\rho) = \begin{cases}
\texttt{Y}\ (\textrm{Yes}) & \textrm{if}\ s_\rho < \theta, \\
\texttt{N}\ (\textrm{No})  & \textrm{if}\ s_\rho \geq \theta, \\
\end{cases}
\end{equation}

Another possibility would be to define a specific range $R = [\theta - \varepsilon, \theta + \varepsilon]$,
such that if $s_\rho \in R$ holds, we output an \texttt{unanswered} decision (rather than \texttt{Y}/\texttt{N}), due to uncertainty.
However, this would require additional training in order to learn a stable $R$ across several corpora. Therefore, we leave this option open for future work.
\section{Experiments} \label{Experiments}
In the following sub-sections we describe a number of experiments. First, we explain which corpora we used, how they were generated and which characteristics they cover. Next, we explain how each corpus was preprocessed especially in terms of deduplication and noise removal. Finally, we present our experiments, where the first one focuses on how to find the optimal compression algorithm as well as the most suitable dissimilarity measure. In the second experiment, we build on the findings of the first experiment and measure how well our \AV method performs on the PAN corpora collection. Here, we compare our results with the official results of all participants from the three PAN competitions 2013--2015, given in \cite{PAN13Stamatatos:2013, PAN14Stamatatos:2014, PAN15Stamatatos:2015}. In the last experiment, we measure our \AV method regarding three additional corpora, where we compare our method against the re-implemented baselines, mentioned in Section~\ref{RelatedWork}).

\subsection{Corpora} \label{Corpora}
Corpora are the key ingredient when it comes to train or measure the quality of \AV methods. In the context of our experiments we made use of seven corpora (splitted into training and evaluation subcorpora). In the following sub-sections we give a brief introduction regarding these corpora, covering their origin, characterization as well as and a compact statistic. All corpora are listed in Table~\ref{tab:AllCorpora}, where the second and third columns denote the number of problems within the training and evaluation subcorpora. Note that regarding our self-compiled corpora\footnote{Available under \url{http://tiny.cc/WSDF2017}} (the last three rows in Table~\ref{tab:AllCorpora}) we splitted the initial corpus into training (20\%) and evaluation (80\%) subcorpora. Since the PAN corpora (the first four rows in Table~\ref{tab:AllCorpora}) were already separated into training and evaluation subcorpora, additional splitting was not needed. Note that all given statistics (number of problems, averaged document lengths, etc.) in the following sub-sections refer to the evaluation corpora.
\begin{table}
	\centering
	\resizebox{\columnwidth}{!}{
		\begin{tabular}{|l|r|r|r|r|r|}
			\hline
		    \textbf{Corpus} $\bm{\mathcal{C}}$  & $\bm{|\mathcal{C}_{\textrm{Train}}|}$ & $\bm{|\mathcal{C}_{\textrm{Eval}}|}$ & \textbf{Genre} & \textbf{Topic} & \textbf{Source} \\ \hline

			$\panThirteen$   & 10  & 30    & textbooks          & same topic   & \cite{PAN13Stamatatos:2013} \\ \hline
			$\panFourteenEs$ & 200 & 200   & essays             & cross-topic  & \cite{PAN14Stamatatos:2014} \\ \hline
			$\panFourteenNo$ & 100 & 200   & novels             & cross-topic  & \cite{PAN14Stamatatos:2014} \\ \hline
			$\panFifteen$    & 100 & 500   & dialog lines       & cross-topic  & \cite{PAN15Stamatatos:2015} \\ \hline
			$\Reddit$        & 200 & 800   & social news        & cross-topic  & self-compiled \\ \hline
			$\Koppel$        & 400 & 1,600 & blog posts         & mixed topics & modified, \cite{KoppelSchlerArgamonPennebaker:2006} \\ \hline
			$\Amazon$        & 646 & 2,582 & product reviews    & cross-topic  & modified, \cite{AmazonReviewCorpus:2015} \\ \hline

		\end{tabular}
	}
	\caption{Statistics and references regarding all corpora.} \label{tab:AllCorpora}
\end{table}

\subsubsection{PAN Corpora Collection}
The PAN corpora\footnote{Available under \url{http://pan.webis.de}} are (to our best knowledge) the most prominent corpora in the field of \AV. For our purpose, we used all English corpora from 2013--2015 \cite{PAN13Stamatatos:2013, PAN14Stamatatos:2014, PAN15Stamatatos:2015}, which we denote as $\panThirteen, \panFourteenEs, \panFourteenNo$ and $\panFifteen$.

The $\panThirteen$ corpus is predominantly compiled from textbooks from computer science and related disciplines. 157 documents, of which 97 are unique, were collected from 16 different authors. The corpus compromises 30 problems (in average five known documents per problem), one of which is unknown, documents sized at $\approx$ 7 KByte \cite{PAN13Stamatatos:2013}. While some of these documents are very narrow regarding their genre, others are more divergent in their content. This aims to prevent authorship detection by a simple content detection and, therefore, makes the verification task more challenging.

The $\panFourteenEs$ corpus was derived from the existing \textit{The Uppsala Student English (USE) corpus}. Each document represents an essay of $\approx$ 5 KByte and falls in one of the following types of writing style: personal, formal, academic. The problems then were constructed so that all documents in one problem were of the same style and their authors were similar in age. In total, 200 problems (each with approximately three known documents) were constructed from 718 documents (625 unique) of 435 authors \cite{PAN14Stamatatos:2014}.

The $\panFourteenNo$ specifies on both a narrow writing style and content. The corpus is focusing on a very small subgenre of speculative and horror fiction, namely ``Cthulhu Mythos''. The special genre, which is based on writings of the American H.P. Lovecraft, is characteristic of florid prose and a vocabulary very unusual for English literature.  43 unique documents were collected from different online sources, including Project Gutenberg and FanFiction. Each problem consist solely of one known and one unknown document. In total, the corpus comprises 200 problems and covers document sizes from 9 to 68 KByte \cite{PAN14Stamatatos:2014}.

The $\panFifteen$ corpus includes dialog lines from plays, excluding named entities like speaker names or lists of characters.
According to \cite{PAN15Stamatatos:2015}, the corpus is considered to be challenging for at least three reasons.
First, for each problem in the corpus only one known document per problem is provided. Second, the documents are quite short (on average 536 words per document).
Third, the known and unknown document differ in terms of topic. In total, the $\panFifteen$ corpus comprises 500 problems covering 1,000 documents, where 83 are unique with a size of $\approx$ 2 KByte.

\subsubsection{Koppel Blogs AV Corpus}
The \emph{Koppel Blogs AV-Corpus} is a derivate of the existing \emph{The Blog Authorship Corpus}, released by Schler et al. \cite{KoppelSchlerArgamonPennebaker:2006}. In its original form, the corpus comprises postings of 19,320 bloggers, gathered from \emph{blogger.com} in August 2004 and incorporates a total of 681,288 posts (in total over 140 million words). Since the corpus was designed for author profiling (a related discipline to \AV) there was a need to apply a transformation regarding the structure, in order to be used in the context of AV. For this, we extracted postings of 2,000 authors (or precisely users, as several users might refer to the same person) and aggregated them in such a way, that for each user there are four documents with an average file size of $\approx$ 4 KByte.

\subsubsection{Amazon Product Data AV Corpus}
The \emph{Amazon Product Data AV Corpus} is also a derivate of an existing corpus, released by McAuley et al. \cite{AmazonReviewCorpus:2015}. The original corpus contains product reviews and metadata from \emph{Amazon}, including 142.8 million reviews, gathered between 1996--2014. More precisely, the corpus includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features) as well as links (also viewed/also bought graphs). However, for the purpose of \av we only used of the reviewer IDs and their associated reviews. In total, the \emph{Amazon Product Data AV Corpus} comprises 3,228 problems (21,534 documents), where for each problem there are $\approx$ 6 known documents ($\approx$ 4 KByte file size  per document). In order to enable a cross-topic property, we constructed the corpus in such a way that all documents within a problem differ from each other in terms of topics. Here, each topic refers to a specific \textit{Amazon} product category. In total, 17 product categories span the entire corpus such as automotive, beauty or musical instruments.

\subsubsection{Reddit Cross-Topic AV Corpus}
In contrast to the former two this corpus was entirely compiled by us and represents the only corpus that was not derived from an existing corpus.
It consists of comments written between 2010--2016 from 1,000 Reddit users and thus, comprises 1,000 problems.
Each problem includes one unknown and four known documents with $\approx$ 7 KByte per document,
where each document represents an aggregation of reviews coined from the same so-called \emph{subreddit}.
However, all documents within a problem are disjunct to each other in terms of \emph{subreddits}.
This was done in the same way to the \emph{Amazon Product Data AV Corpus} in order to enable a cross-topic property for the corpus.
All subreddits cover exactly 1,388 different topics such as books, news, gaming, music, movies, etc.

\subsection{Preprocessing}
During the construction of our three additional corpora, a number of preprocessing steps were necessary, before they could be used in the later experiments. First, we performed deduplication for each corpus in terms of exact and near-duplicates. To identify near-duplicates, we applied on each pair of texts, or more precisely on their tokenized representation $X$ and $Y$, the overlap coefficient similarity measure:
\begin{equation}
\textrm{overlap}(X, Y) = \frac{|X \cap Y|}{\min(|X|, |Y|)}
\end{equation}
Two texts were considered as near-duplicates if their resulting overlap coefficient exceeded 0.25. We choose this value since our empirical observations have shown that two documents with an overlap coefficient below 0.25 are partly similar in terms of function words, but not in terms of content words. After the deduplication process, we removed noise from the remaining documents in the following manner: First, all available texts were converted to \mbox{UTF-8} format. Next, we removed URLs, HTML tags, encoded HTML strings, \mbox{UTF-8} control characters, consecutive symbols and formulas from the texts. As a last step, we replaced newlines, tabs and consecutive whitespace characters by a single space, such that each document resulted in a single-lined string.

\subsection{Performance Measures} \label{PerformanceMeasure}
During the past decades, a number of performance measures have become established in the field of \av. Perhaps the simplest one used in \AV (\eg \cite{KoppelOneClassAV:2004,BrennanGreenstadt:2009}) is the \textit{Accuracy} measure, defined as:
\[
   \textrm{Accuracy} = \frac{\textrm{Number of correct answers}}{\textrm{Total number of problems}}
\]

Another (more sophisticated) performance measure is the ROC-AUC\footnote{For reasons of readability, ROC-AUC is often shortened to AUC in the literature.} (\textit{\textbf{A}rea \textbf{U}nder the ROC \textbf{C}urve}), which was used, for instance, by Stamatatos and Potha in their work \cite{StamatatosPotha:2014}. One significant characteristic of AUC is that it is not bounded to a fixed threshold, which is the case of the accuracy measure. This allows a more reliable comparison among different \AV methods.

Another measure, typically used in information retrieval for binary classification tasks, is $\textrm{F}_1$, defined as follows:
\[ \textrm{F}_{1} = \frac{2 \cdot \textrm{Recall} \cdot \textrm{Precision}} {\textrm{Recall} + \textrm{Precision}} \]

In the PAN-2013 competition \cite{PAN13Stamatatos:2013} the $\textrm{F}_1$ measure was used to evaluate the \av methods of all participants, but non-standard definitions for recall and precision were used:
\[ \textrm{Recall} = \frac{\textrm{Number of correct answers}} {\textrm{Total number of problems}} \]

\[ \textrm{Precision} = \frac{\textrm{Number of correct answers}} {\textrm{Total number of answers}} \]

In recent years, a new performance measure is becoming more and more popular in the AV community, the so-called \auccat that was first introduced in \cite{PAN14Stamatatos:2014}. It represents the product of the already mentioned AUC and the c@1, which is defined as:
\[
   \textrm{c@1} = \frac{1}{n} \biggl( n_c + \left(\frac{n_u \cdot n_c}{n}\right) \biggr)
\]

Here, $n$, $n_c$ and $n_u$ denote the number of problems, the number of correct answers and the number of unanswered problems, respectively. Note that c@1 equals the accuracy measure for the case that an \AV method classifies all problems either as \texttt{Y} or \texttt{N}. If, on the other hand, all problems are left unanswered, then c@1 will be zero. However, c@1 rewards such \AV methods that maintain the same number of correct answers and decrease the number of wrong answers by leaving some problems unanswered \cite{PAN14Stamatatos:2014}. At this point we would like to underline that our \AV method does not benefit from c@1, since (at least in its current form) it does not leave any problem unanswered.

In order to provide the reader a better comparison regarding our approach, we will use all mentioned measures from above in our experiments.
In order to avoid redundancy, we omit the accuracy measure as it is identical to c@1, at least regarding our method.

\subsection{Experiment 1: Determining the optimal compressor/dissimilarity measure}
The goal of the first experiment is to find the optimal combination of a compression algorithm $c(\cdot) \in \{ \mathrm{PPMd}, \mathrm{GZip}, \mathrm{BZip2}, \mathrm{Zip}, \mathrm{LZW} \}$ and a dissimilarity measure $\M \in \{ \mathrm{NCD}, \mathrm{CBC}, \mathrm{CLM} \}$ that lead to the highest performance, in terms of the AUC.
For this, we applied our method on the four PAN training corpora, which results in $60$ different runs. The results regarding these runs are given in Table~\ref{tab:TrainingCorpusMetricCompressor}, where it can be seen in the last row (showing the average AUC among all corpora) that PPMd was able to outperform all other involved compressors, independently of the underlying dissimilarity measure. Moreover, it can be seen in the same row that across all compressors the highest AUC is constantly achieved by the CBC measure. Therefore, we used $c(\cdot)$ = PPMd and $\M$ = CBC for the further experiments. One interesting observation (which, due to a lack of space, did not fit in Table~\ref{tab:TrainingCorpusMetricCompressor}) is the fact, that Zip is by far the fastest among the five compressors. More precisely, Zip is at least twice as fast as the best performing compressor PPMd.

\begin{table*}
	\small
	\centering
	\resizebox{\textwidth}{!}{
	\begin{tabular}{|l||r|r|r||r|r|r||r|r|r||r|r|r||r|r|r|}
		\hline
		& \multicolumn{3}{c||}{\textbf{PPMd}} & \multicolumn{3}{c||}{\textbf{Gzip}}  & \multicolumn{3}{c||}{\textbf{BZip2}}  & \multicolumn{3}{c||}{\textbf{Zip}}  &\multicolumn{3}{c|}{\textbf{LZW}}  \\ \hline
		Corpus              & NCD & CBC & CLM & NCD & CBC & CLM  & NCD & CBC & CLM & NCD & CBC & CLM & NCD & CBC & CLM \\ \hline

		$\panThirteenTr$   & 0.800 & 1.000 & 1.000 & 0.800 & 1.000 & 0.880 & 0.800 & 1.000 & 0.920 & 0.720 & 0.920 & 0.800  & 0.680 & 0.880 & 0.720 \\
		$\panFourteenEsTr$ & 0.541 & 0.545 & 0.545 & 0.566 & 0.587 & 0.576 & 0.541 & 0.527 & 0.540 & 0.549 & 0.547 & 0.552 	& 0.552 & 0.517 & 0.541 \\
		$\panFourteenNoTr$ & 0.596 & 0.705 & 0.634 & 0.584 & 0.652 & 0.624 & 0.581 & 0.656 & 0.611 & 0.534 & 0.549 & 0.557 	& 0.518 & 0.592 & 0.573 \\
		$\panFifteenTr$    & 0.666 & 0.702 & 0.700 & 0.632 & 0.664 & 0.658 & 0.613 & 0.666 & 0.662 & 0.589 & 0.612 & 0.608 & 0.564 & 0.620 & 0.615 \\ \hline
		Average            & 0.651 & \textbf{0.738} & 0.720 & 0.645 & \textbf{0.726} & 0.685 & 0.634 & \textbf{0.712} & 0.683 & 0.598 & \textbf{0.657} & 0.629 & 0.578 & \textbf{0.652} & 0.612 \\ \hline

	\end{tabular}
}
\caption{Results showing (in terms of AUC) how well the compressing algorithms performed regarding the dissimilarity measures on the PAN training corpora.}
	\label{tab:TrainingCorpusMetricCompressor}
\end{table*}

\subsection{Experiment 2: Evaluation (PAN competition)}
The findings in Experiment~1 have shown that the PPMd compressor, together with the CBC metric, outperformed all other (compressor/metric)-combinations. Given these both, we applied our \AV method on each of the four PAN training corpora in order to determine a threshold, based on Algorithm~\ref{DetermineThresholdEER}. Each threshold was then used to test our method on the corresponding four PAN evaluation corpora. In the following, we discuss the results given in Tables~\ref{tab:PAN2015Eval}--\ref{tab:PAN2013EvalAUC}.

\paragraph{Results for $\panFifteenEval$:} As can be seen from Table~\ref{tab:PAN2015Eval}, our method outperforms (with the exception of the winning approach of Bagnall) all approaches of the participating teams , the three baselines \textit{PAN13-BASELINE}, \textit{PAN14-BASELINE-1} and \textit{PAN14-BASELINE-2}, as well as the meta-model \textit{PAN15-ENSEMBLE}, which fuses the output of all submitted \AV methods. Note that Bagnall's sophisticated approach \cite{BagnallRNN:2015}, which is based on multi-headed recurrent neural networks, is included within the meta-model. It is noteworthy to underline the fact, that our method yields results very similar to Bagnall's method but, however, in much less time (7 seconds instead of more than 21 hours). To learn more about the performance of our method, we inspected the verification results on the problem level. Here, we observed that our method successfully solved many \AV problems with small document sizes (for example 1.56 KByte) regarding $\Dknown$ and $\Dunk$. In addition to this, we want to stress that the documents within each problem differ in terms of topic, such that the verification task is even more challenging, as it already is.

\paragraph{Results for $\panFourteenEsEval$:} The results regarding $\panFourteenEsEval$ are given in Table~\ref{tab:PAN2014esEval}, where it, unfortunately, can be seen that our method achieves only moderate results (one rank above the baseline). In order to understand what causes the low recognition performance, we applied a deeper investigation on the $\panFourteenEsEval$ corpus. First, we compared the document lengths regarding the known and unknown document $\Dknown$ and $\Dunk$. For the four shortest texts, we noted document sizes between 2--3 KByte which, as shown in the results for $\panFifteenEval$, seems not to be the primary challenge for the low performance. Therefore, we inspected the common vocabulary between $\Dknown$ and $\Dunk$ for each problem. But it turned out that, on average, all $(\Dknown, \Dunk)$ pairs share a sufficient amount of features (\eg function words or character bigrams) that allow the problems to be successfully solved. Therefore, we leave this challenge for future work.

\paragraph{Results for $\panFourteenNoEval$:} In contrast to $\panFourteenEsEval$, our method seems to perform much better on the $\panFourteenNoEval$ corpus. From the  results, shown in Table~\ref{tab:PAN2014noEval}, one can see that our approach is ranked among the three best-performing methods. One of these three is the approach of Khonji\&Iraqi \cite{KhonjiIraqiAV:2014}. Their approach, a modified version of the state-of-the-art \KoppelMethod, performs better than our method in terms of the AUC, but at the expense of runtime.

\paragraph{Results for $\panThirteenEval$:} For the $\panThirteenEval$ evaluation corpus two tables are provided. Table~\ref{tab:PAN2013EvalAccuracy} and Table~\ref{tab:PAN2013EvalAUC} list the evaluation results in terms of $F_{1}$ and AUC, respectively. The rationale behind this is, that in the initial \AV competition PAN 2013, the participants were requested to provide binary scores, in the form of \texttt{Y}/\texttt{N}, which were mandatory to compute the $F_{1}$ and, optional, real numbers in the interval $[0 ;1]$ in order to compute the AUC. However, since not all participants provided real numbers there was a need for two tables.

As one can see from Table~\ref{tab:PAN2013EvalAccuracy} our approach shares the highest rank together with the methods of Veenman\&Li \cite{VeenmanPAN13:2013} and Seidman \cite{SeidmanPAN13:2013}, in terms of $F_{1}$. When we look on Table~\ref{tab:PAN2013EvalAUC} we can see that Seidman's approach (which, analogous to Khonji\&Iraqi \cite{KhonjiIraqiAV:2014}, is also based on \KoppelMethod) performs somewhat worse than our method, in terms of AUC.

Another observation, which cannot be seen in both tables is, that the consumed runtime of Seidman's method is very high. Note that we cannot tell the runtime for the $\panThirteenEval$ corpus, as the organizers only provided the overall runtime across three corpora \cite[Table~1]{PAN13Stamatatos:2013}, which in case of Seidman's method is $\approx$ 18 hours. If we naively divide the runtime by 3 (due to three corpora), Seidman's method will still need six hours for the $\panThirteenEval$  corpus, whereas our method can process the same corpus in less than a second.

Unfortunately, Veenman and Li did not provide AUC scores for their submitted \AV method, such that a direct comparison to our method is not possible. Therefore, we decided at least to provide a differentiation (Table~\ref{tab:VeenmanVsHalvani}) regarding the characteristics between our and their method.
\begin{table}
	\centering
	\resizebox{\columnwidth}{!}{
		\begin{tabular}{|l|l|l|l|l|l|}
			\hline
			             & \textbf{\AV type} & \textbf{Threshold}     & \textbf{Dis. Measure}  & \textbf{Compressor} & \textbf{Output} \\ \hline

			Our approach & intrinsic             & Yes, via EER  & CBC            & PPMd       & real score + \texttt{Y}/\texttt{N}\\ \hline
			Veenman\&Li  & extrinsic             & Not provided  & CDM            & PPMd       & only \texttt{Y}/\texttt{N} \\ \hline

		\end{tabular}
	}
	\caption{Our approach vs. Veenman and Li's method \cite{VeenmanPAN13:2013}.} \label{tab:VeenmanVsHalvani}
\end{table}

\begin{table}

	\centering
	\scalebox{0.84}{
		\begin{tabular}{|l|r|r|r|r|r|}
			\hline
			\bf Team & \bf FS & \bf AUC & \bf c@1  & \bf UP  & \bf Runtime \\ \hline

			Bagnall & 0.614 & 0.811  & 0.757 &  3  & 21:44:03  \\\hline
			\textbf{Our approach} & \textbf{0.605} & \textbf{0.802} & \textbf{0.754} & \textbf{0} & \textbf{00:00:07} \\\hline
			Castro-Castro et al. & 0.520 & 0.750  & 0.694  & 0 &  02:07:20 \\
			Gutierrez et al.  & 0.513  & 0.739  & 0.694  & 39  & 00:37:06 \\
			Kocher and Savoy  & 0.508 &  0.738 &  0.689  & 94 &  00:00:24 \\
			{\color{gray} PAN15-ENSEMBLE}  & {\color{gray} 0.468}  & {\color{gray} 0.786} &  {\color{gray} 0.596} &  {\color{gray} 0} & {\color{gray} -}  \\
			Halvani  & 0.458  & 0.762  & 0.601  & 25 &  00:00:21 \\
			Moreau et al.  & 0.453 &  0.709 &  0.638  & 0 &  24:39:22 \\
			Pacheco et al.  & 0.438  & 0.763  & 0.574  & 2  & 00:15:01 \\
			H{\"u}rlimann et al.  & 0.412 &  0.648  & 0.636 &  5 &  00:01:46 \\
			{\color{gray} PAN14-BASELINE-2} & {\color{gray} 0.409} & {\color{gray}  0.639} & {\color{gray}  0.640}  &{\color{gray} 0} & {\color{gray} 00:26:19} \\
			{\color{gray} PAN13-BASELINE}  & {\color{gray} 0.404} & {\color{gray}  0.654} & {\color{gray}  0.618} & {\color{gray}  0} & {\color{gray}  00:02:44} \\
			Posadas-Dur$\acute{\mathrm{a}}$n et al.  & 0.400  & 0.680  & 0.588 &  0 &  01:41:50 \\
			Maitra et al.  & 0.347  & 0.602 &  0.577 &  10  & 15:19:13 \\
			Bartoli et al.  & 0.323 & 0.578 & 0.559  & 3 &  00:20:33 \\
			G$\acute{\mathrm{o}}$mez-Adorno et al.  & 0.281  & 0.530  & 0.530  & 0  & 07:36:58 \\
			Sol$\acute{\mathrm{o}}$rzano et al.  & 0.259  & 0.517  & 0.500  & 0  & 00:29:48 \\
			Nikolov et al.  &  0.258  & 0.493  & 0.524  & 16  & 00:01:36 \\
			Pimas et al.   & 0.257  & 0.507  & 0.506 &  0  & 00:07:22 \\
			{\color{gray} PAN14-BASELINE-1}  & {\color{gray} 0.249} & {\color{gray}  0.537}  & {\color{gray} 0.464}  & {\color{gray} 159}  &{\color{gray}  00:01:11} \\
			Mechti et al.  & 0.247 &  0.489 &  0.506 &  0 &  00:04:59 \\
			Sari and Stevenson  & 0.201  & 0.401  & 0.500  & 0 &  00:05:47 \\
			Vartapetiance and G. & 0.000  & 0.500  & 0.000  & 500 & - \\ \hline

	\end{tabular}}
	\caption{PAN 2015 results on the English evaluation corpus. The final score FS denotes \auccat and UP the number of unanswered problems. Table adapted from \cite{PAN15Stamatatos:2015}.}
	\label{tab:PAN2015Eval}
\end{table}

\begin{table}
	\centering
	\scalebox{0.84}{
		\begin{tabular}{|l|r|r|r|r|r|}
			\hline
			\bf Team & \bf FS & \bf AUC & \bf c@1  & \bf UP  & \bf Runtime \\ \hline

			{\color{gray} META-CLASSIFIER}  & {\color{gray} 0.531}  & {\color{gray} 0.781} & {\color{gray} 0.680} & {\color{gray} 0} & {\color{gray} ---} \\
			Frery et al. & 0.513 & 0.723 & 0.710 & 15 & 00:00:54 \\
			Satyam et al. & 0.459 & 0.699 & 0.657 & 2 & 00:16:23 \\
			Moreau et al. & 0.372 & 0.620 & 0.600 & 0 & 00:28:15 \\
			Layton & 0.363 & 0.595 & 0.610 & 0 & 07:42:45 \\
			Modaresi \& Gross & 0.350 & 0.603 & 0.580 & 0 & 00:00:07 \\
			Khonji \& Iraqi & 0.349 & 0.599 & 0.583 & 1 & 09:10:01 \\
			Halvani \& Steinebach & 0.338 & 0.629 & 0.538 & 1 & 00:00:07 \\
			Zamani et al. & 0.322 & 0.585 & 0.550 & 0 & 00:02:03 \\
			Mayor et al. & 0.318 & 0.572 & 0.557 & 10 & 01:01:07 \\
			Castillo et al. & 0.318 & 0.549 & 0.580 & 0 & 01:31:53 \\
			Harvey & 0.312 & 0.579 & 0.540 & 0 & 00:10:22 \\\hline
			\textbf{Our approach} & \textbf{0.312} & \textbf{0.558} & \textbf{0.560} & \textbf{0} & \textbf{00:00:05} \\\hline
			{\color{gray} BASELINE} & {\color{gray} 0.288} & {\color{gray} 0.543} & {\color{gray} 0.530} & {\color{gray} 0} & {\color{gray} 00:03:29} \\
			Jankowska et al. & 0.284 & 0.518 & 0.548 & 5 & 01:16:35 \\
			Vartapetiance \& Gillam & 0.270 & 0.520 & 0.520 & 0 & 00:16:44 \\ \hline

	\end{tabular}}
	\caption{PAN 2014 results on the English essays evaluation corpus. The final score FS denotes \auccat and UP the number of unanswered problems. Table adapted from \cite{PAN14Stamatatos:2014}.}
	\label{tab:PAN2014esEval}
\end{table}

\begin{table}
	\centering
	\scalebox{0.84}{
		\begin{tabular}{|l|r|r|r|r|r|}
			\hline
			\bf Team & \bf FS & \bf AUC & \bf c@1  & \bf UP  & \bf Runtime \\ \hline

			Modaresi \& Gross & 0.508 & 0.711 & 0.715 & 0 & 00:00:07 \\
			Zamani et al. & 0.476 & 0.733 & 0.650 & 0 & 02:02:02 \\
			{\color{gray} META-CLASSIFIER} & {\color{gray} 0.472} & {\color{gray} 0.732} & {\color{gray} 0.645} & {\color{gray} 0} &  \\
			Khonji \& Iraqi & 0.458 & 0.750 & 0.610 & 0 & 02:06:16 \\\hline
			\textbf{Our approach} & \textbf{0.421} & \textbf{0.695} & \textbf{0.605} & \textbf{0} & \textbf{00:00:14} \\\hline
			Mayor et al. & 0.407 & 0.664 & 0.614 & 8 & 01:59:47 \\
			Castillo et al. & 0.386 & 0.628 & 0.615 & 0 & 02:14:11 \\
			Satyam et al. & 0.380 & 0.657 & 0.579 & 3 & 02:14:28 \\
			Frery et al. & 0.360 & 0.612 & 0.588 & 1 & 00:03:11 \\
			Moreau et al. & 0.313 & 0.597 & 0.525 & 12 & 00:11:04 \\
			Halvani \& Steinebach & 0.293 & 0.569 & 0.515 & 0 & 00:00:07 \\
			Harvey & 0.283 & 0.540 & 0.525 & 0 & 00:46:30 \\
			Layton & 0.260 & 0.510 & 0.510 & 0 & 07:27:58 \\
			Vartapetiance \& Gillam & 0.245 & 0.495 & 0.495 & 0 & 00:13:03 \\
			Jankowska et al. & 0.225 & 0.491 & 0.457 & 1 & 02:36:12 \\
			{\color{gray} BASELINE} & {\color{gray} 0.202} & {\color{gray} 0.453} & {\color{gray} 0.445} & {\color{gray} 0} & {\color{gray} 00:08:31} \\ \hline

	\end{tabular}}
	\caption{PAN 2014 results on the English novels evaluation corpus. The final score FS denotes \auccat and UP the number of unanswered problems. Table adapted from \cite{PAN14Stamatatos:2014}.}
	\label{tab:PAN2014noEval}
\end{table}

\begin{table}
	\centering
	\scalebox{0.84}{
		\begin{tabular}{|l|r|r|r|r|r|}
			\hline
			\textbf{Submission} & $\bm{F_1}$ & \textbf{Precision} & \textbf{Recall} \\ \hline

			Veenman\&Li & 0.800 & 0.800 & 0.800 \\\hline
			\textbf{Our approach} & \textbf{0.800} & \textbf{0.800} & \textbf{0.800} \\\hline
			Seidman & 0.800 & 0.800 & 0.800 \\
			Layton et al. & 0.767 & 0.767 & 0.767 \\
			Moreau\&Vogel & 0.767 & 0.767 & 0.767 \\
			Jankowska et al. & 0.733 & 0.733 & 0.733 \\
			Vilariño et al. & 0.733 & 0.733 & 0.733 \\
			Halvani et al.  & 0.700 & 0.700 & 0.700 \\
			Feng\&Hirst & 0.700 & 0.700 & 0.700 \\
			Ghaeini  & 0.691 & 0.760 & 0.633 \\
			Petmanson  & 0.667 & 0.667 & 0.667 \\
			Bobicev  & 0.644 & 0.655 & 0.633 \\
			Sorin & 0.633 & 0.633 & 0.633 \\
			van Dam  & 0.600 & 0.600 & 0.600 \\
			Jayapal\&Goswami  & 0.600 & 0.600 & 0.600  \\
			Kern  & 0.533 & 0.533 & 0.533 \\
			{\color{gray} BASELINE} & {\color{gray} 0.500} & {\color{gray} 0.500} & {\color{gray} 0.500} \\
			Vartapetiance\&Gillam  & 0.500 & 0.500 & 0.500  \\
			Ledesma et al.  & 0.467 & 0.467 & 0.467  \\
			Grozea & 0.400 & 0.400 & 0.400 \\ \hline

	\end{tabular}}
	\caption{PAN 2013 results (in terms of $F_{1}$) on the English evaluation corpus. Table adapted from \cite{PAN13Stamatatos:2013}.}
	\label{tab:PAN2013EvalAccuracy}
\end{table}

\begin{table}
	\centering
	\scalebox{0.84}{
		\begin{tabular}{|l|l|r|}
			\hline
			\textbf{Rank} & \textbf{Submission} & \textbf{AUC} \\ \hline

			1 & Jankowska, et al. & 0.842 \\
			2 & Ghaeini & 0.837 \\\hline
			3 & \textbf{Our approach} & \textbf{0.813}  \\\hline
			4 & Seidman & 0.792 \\
			5 & Feng\&Hirst & 0.750 \\
			6 & Petmanson & 0.672 \\
			7 & Bobicev & 0.585 \\
			{\color{gray} -} & {\color{gray} BASELINE} & {\color{gray} 0.500} \\
			8 & Kern & 0.384 \\
			9 & Grozea & 0.342 \\
			10 & Layton et al. & 0.277 \\ \hline

	\end{tabular}}
	\caption{PAN 2013 results (in terms of AUC) on the English evaluation corpus. Table adapted from \cite{PAN13Stamatatos:2013}.}
	\label{tab:PAN2013EvalAUC}
\end{table}

\subsection{Experiment 3: Evaluation (baselines)}
As we have seen in Experiment 2, our \AV method performed quite well regarding three out of four PAN evaluation corpora. The goal of the following experiment is to test our method against three baselines (mentioned in Section~\ref{RelatedWork}) on all seven evaluation corpora (described in Section~\ref{Corpora}). From the results, listed in Table~\ref{tab:HalvaniVsBaselines}, we can see that GLAD outperforms our method as well as the other baselines in four cases. On the other hand, our method outperformed all baselines in two cases and achieved, in comparison to GLAD, similar results regarding $\panThirteenEval$ and $\panFourteenNoEval$, in terms of \auccat (or just AUC).

We can infer from Table~\ref{tab:HalvaniVsBaselines} that the $\panFourteenEsEval$ corpus is quite challenging, not only for our method but also for the \StamatatosMethod and \KoppelMethod. GLAD, in contrast, achieves good results on the same corpus. In fact, a closer look on Table~\ref{tab:PAN2014esEval} reveals that GLAD outperforms all participated \AV approaches (except the meta-model) which indicates its good generalization ability. A similar picture can be seen regarding $\AmazonEval$, where GLAD not only significantly outperformed our method and the two baselines, but also achieved the highest AUC (0.937) across all methods on all seven corpora. Surprisingly, $\panFifteenEval$ is the only corpus where GLAD yielded the lowest recognition result. We assume that the characteristic of $\panFifteenEval$ (one known vs. one unknown document with an average number of 536 word per document \cite{PAN15Stamatatos:2015}) causes the moderate performance of GLAD on it.

Another interesting observation that can be seen in Table~\ref{tab:HalvaniVsBaselines} (AUC column) is that in most cases all involved methods perform better on the three additional corpora than on the four PAN evaluation corpora. In particular, the $\KoppelEval$ corpus seems not to be challenging for all methods as they achieved here the highest results from all seven corpora.

\begin{table*}
	\small
	\centering
	\resizebox{\textwidth}{!}{
		\begin{tabular}{|l||r|r|r|r||r|r|r|r||r|r|r|r|}
			\hline
& \multicolumn{4}{c||}{\textbf{\auccat}} & \multicolumn{4}{c||}{\textbf{AUC}}  & \multicolumn{4}{c|}{\textbf{c@1}} \\ \hline
Corpus & $\AVhalvani$ & $\AVstamatatos$ & $\AVglad$ & $\AVkoppel$ & $\AVhalvani$ & $\AVstamatatos$ & $\AVglad$ & $\AVkoppel$ & $\AVhalvani$ & $\AVstamatatos$ & $\AVglad$ & $\AVkoppel$ \\\hline
$\panThirteenEval$   & 0.650 & 0.461 & 0.664 & \textbf{0.694} & 0.813 & 0.692 & \textbf{0.866} & 0.833 & 0.800 & 0.667 & 0.767 & \textbf{0.833} \\
$\panFourteenEsEval$ & 0.312 & 0.339 & \textbf{0.514} & 0.289 & 0.558 & 0.599 & \textbf{0.751} & 0.540 & 0.560 & 0.565 & \textbf{0.685} & 0.535 \\
$\panFourteenNoEval$ & 0.421 & 0.397 & \textbf{0.449} & 0.354 & 0.695 & 0.702 & \textbf{0.709} & 0.599 & 0.605 & 0.565 & \textbf{0.633} & 0.590 \\
$\panFifteenEval$    & \textbf{0.605} & 0.393 & 0.406 & 0.254 & \textbf{0.802} & 0.719 & 0.644 & 0.504 & \textbf{0.754} & 0.546 & 0.630 & 0.504 \\
$\RedditEval$        & 0.529 & 0.549 & \textbf{0.681} & 0.471 & 0.748 & 0.773 & \textbf{0.868} & 0.698 & 0.708 & 0.710 & \textbf{0.784} & 0.675 \\
$\KoppelEval$        & \textbf{0.789} & 0.646 & 0.764 & 0.589 & \textbf{0.926} & 0.847 & 0.912 & 0.792 & \textbf{0.852} & 0.763 & 0.838 & 0.744 \\
$\AmazonEval$        & 0.631 & 0.577 & \textbf{0.817} & 0.632 & 0.842 & 0.793 & \textbf{0.937} & 0.831 & 0.749 & 0.728 & \textbf{0.872} & 0.760 \\\hline \hline
Average				 & 0.562 & 0.480 & \textbf{0.614} & 0.469 & 0.769 & 0.732 & \textbf{0.812} & 0.685 & 0.718 & 0.649 & \textbf{0.744} & 0.663 \\\hline
		\end{tabular}
	}
	\caption{Our approach ($\AVhalvani$) vs. \StamatatosMethod ($\AVstamatatos$), \GLADMethod ($\AVglad$) and \KoppelMethod ($\AVkoppel$).}
	\label{tab:HalvaniVsBaselines}
\end{table*}

\section{Conclusions} \label{Conclusions}
We presented a simple but quite effective and efficient \av method, based on compression models. The method yields competitive results compared to current state-of-the-art approaches, that are based on sophisticated machine learning models such as support vector machines or neural networks. In contrast to these approaches, our \AV method only relies on a compressing algorithm, a simple dissimilarity measure and a threshold, acting as an acceptance criterion regarding the authorship of a questioned document. Moreover, our method does not require natural language processing concepts (\eg part-of-speech tagging or parsing) or even less complex techniques such as regular expressions, which are used in many traditional \AV approaches.

In a series of experiments we made a number of observations regarding our approach. In the first experiment we have shown that PPMd was able to outperform all other tested compressors. In addition, the CBC measure yielded the highest results, which behaved stable across the five compressors that were tested against all training corpora. In the second experiment we compared our method against a fair number of \AV approaches, which were submitted to three international \AV competitions. With an exception of one out of four evaluation corpora, our method performs amongst the highest-ranked \AV methods. In the same experiment we provided the runtime of our method for comparison, where it turned out that it counts to the fastest approaches. In the third experiment we evaluated our method against three strong baselines on seven evaluation corpora and achieved competitive verification results.

However, despite of the success and the benefits of our \AV method, a number of questions remain open and must be addressed in future work. One important issue, for example, that was not taken into account in our study is a deeper investigation regarding the proposed method. In fact, at this stage, we cannot provide a satisfactory explanation regarding the good performance of our method and which concrete limitations it actually faces. Our experiments revealed that the method was able to solve even complicated \AV tasks such as cross-genre/topic problems which, in addition, were short in terms of text lengths. Hence, we do not consider these as limitations for the method. Besides this, robustness must be covered in future work, including under which conditions the method fails to solve verification problems, which it solved beforehand. And last but not least, we plan to construct an ensemble based on our method and other successful methods (for example GLAD) in order to achieve strong and reliable verification results.
\newpage
\bibliographystyle{plain}

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text:
More info on Adenosine triphosphate
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Question 2: It is produced by photophosphorylation and ________ and used by enzymes and structural proteins in many cellular processes, including biosynthetic reactions, motility, and cell division.
Metabolism Cellular respiration Glycolysis Gluconeogenesis
Question 3: ATP has multiple ionizable groups with different ________.
Solubility equilibrium Chemical equilibrium Acid dissociation constant Equilibrium constant
Question 4: ________ (also called the Krebs cycle or TCA cycle)
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Question 5: In most organisms, this process occurs in the ________, but in some protozoa such as the kinetoplastids, this is carried out in a specialized organelle called the glycosome.
Cytosol Metabolism Mitochondrion Cell (biology)
Question 6: [17] As ATP has several negatively-charged groups in neutral solution, it can ________ metals with very high affinity.
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Question 7: Flow of protons down this potential gradient — that is, from the intermembrane space to the matrix — provides the driving force for ATP synthesis by ________.
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Question 8: ATP consists of ________ — composed of an adenine ring and a ribose sugar — and three phosphate groups (triphosphate).
Adenosine triphosphate Adenosine monophosphate Adenosine diphosphate Adenosine
Question 9: Some proteins that bind ATP do so in a characteristic protein fold known as the Rossmann fold, which is a general ________-binding structural domain that can also bind the cofactor NAD.
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text:
Кледесхольмен () — небольшой городок в коммуне Чёрн лена Вестра-Гёталанд (в исторической провинции Бохуслен, Швеция). Расположен на двух островках — Кледесхольмен и Кухольмен — у юго-западной оконечности острова Чёрн.

Население — 385 жителей (2011).

История
Уже в 1594 году епископ Йенс Нилльсон упоминал Кледесхольмен как старую рыбацкую деревушку. Как и большинству островных поселений в этом районе, его развитию дал толчок приход сельди к берегам Бохуслена в 1748-1808 годах. В этот период его население значительно выросло и прежде всего за счёт сезонных работников, занимавшихся солением рыбы и ватапливанием ворвани. В 1793 году в Кледесхольмене на частные пожертвования была возведена церковь, которая в наши дни является старейшей действующей церковью в коммуне.

В 1808 году сельд ушла, и к 1830 году население Кледесхольмена составило всего три сотни жителей.

Новый рост городок испытал в 1861 году, когда два его жителя узнали, что рыба есть возле Стореггена (Олесунн). В результате этого многие суда отправились в Норвегию. Когда в конце XIX века сельд вновь пришла к бохусленскому побережью, в Кледесхольмене начала возникать промышленность по её переработке.

Достопримечательности
В 1995 году в Кледесхольмене открылся посвящённый истории городка музей "Sillebua", который разместился в бывшем помещении консервного заводика. В музее имеется фотоархив, в котором хранится около 3000 негативов.

Галерея

Примечания

Источники
 Nationalencyklopedin.

Ссылки
 Музей "Sillebua" (швед.)

Города Швеции

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text:
Dolly Parton Releases 44th Studio Album, "I Believe In You"
Danielle Van Witzenberg October 19, 2017 Tagged Danielle Van Witzenberg, Dolly Parton, Reviews
By: Danielle Van Witzenberg//
Photo: Dolly Records/RCA Nashville
Dolly Parton, the self-proclaimed "Backwoods Barbie," has entertained the world for generations. Be it her illustrious music and film career or her notable charity work, Dolly has always been unapologetically, well… Dolly! With the release of her 44th studio album, "I Believe In You" it's only fitting to take a look back over her 50-year career.
Right out of high school, Parton moved to Nashville to pursue her country music aspirations. She got her television debut in 1967 on The Porter Wagoner Show. Her partnership with Wagoner would lead to success with songs like, "Just Because I'm a Woman" and "In the Good Old Days (When Times Were Bad)." Parton's infamous 1971 song "Coat of Many Colors" outlined what it was like growing up "dirt poor" in a small, East Tennessee town with eleven siblings. Dolly has always been a humble girl from Tennessee, which can be seen in her relatable and down-home lyrics. It wasn't until 1973 with the release of "Jolene" that Parton skyrocketed onto Billboard's Hot 100 list. The following year Parton made the difficult decision to dissolve her business partnership with Wagoner and wrote her number one hit "I Will Always Love You" about their separation.
In the mid to late seventies Parton's career took a detour from her country roots and headed towards a more mainstream, pop direction. Her career dived back into her television roots in 1977 when she appeared on Barbara Walters' Special, Cher's television special and a joint special with Carol Burnett and herself called, Carol and Dolly in Nashville. Parton's 1978 Grammy win for Best Female Country Vocal Performance for her album Here You Come Again made her visibility outside of the country world increase.
Parton hit real mainstream success when she wrote the theme song to the 1980 feature film 9 to 5 that she also starred in along-side Jane Fonda and Lily Tomlin. Parton received an Academy Award nomination for Best Original Song for "9 to 5."
The eighties brought Parton continued success as a songwriter and actress when she re-recorded "I Will Always Love you" for the feature film that she also starred in, The Best Little Whorehouse in Texas. The Best Little Whorehouse in Texas marked the return to Parton's country roots. Parton had success in film and music throughout her career, but nothing tops the success of Whitney Houston's version of "I Will Always Love You" off of the soundtrack for the 1992 feature film The Bodyguard.
In Parton's 50 years of making music she has lent her voice to country, bluegrass, and pop music. She has been seen on both the big and small screens. She is an icon among some of the greatest pop and country music stars. She has 44 studio albums with the addition of "I Believe In You." So what can we expect from her newest album release?
Well, If you're a fan of Parton's you will be delighted to hear the sweet, bubbly, country tone she is known for. What makes this album different from her others is that "I Believe In You" is Parton's first ever children's album. "I Believe In You" is filled with inspirational songs—much like the title track—that will fill everyone, young and old, with joy.
The album also features a new recording of "Coat of Many Colors" and a bonus track of Dolly reading an excerpt of her book "Coat of Many Colors," as well. Not only is this album full of optimism and joy, all of the proceeds of this album will go to Dolly Parton's Imagination Library. Founded in 1995 the Imagination Library is Parton's charity dedicated to her father who never learned to read and write. The charity donates high-quality, age-appropriate books each month to children around the world.
Featured Image: Kristopher Harris
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Right before Thanksgiving, my children and I finished a book about Eric Liddell probably known best through the Chariots of Fire movie. In the book, I was impressed with how upbeat and positive Eric was in all circumstances including being in a Japanese War camp. I was also convicted about how grouchy I had been since I had started to work. Yes, I was tired. Yes, I was overwhelmed. But circumstances were dictating my attitude. My children were used to seeing their mommy grouchy more then in a good mood. I had the habit of being a grouch. So the Friday before Thanksgiving I knew I needed to break this habit with God's help. I also needed some incentive. The deal was if I could go a whole week without being grouchy, my husband would take me on a date. My biggest breakthrough was first realizing I was a grouch and confessing that to God. Next I found so many things I was missing and took the time to be thankful. I decided to set my mind on things above for once. I did make it a whole week and had a wonderful date with my husband. I also haven't been perfect since then but being a grouch is no longer the norm. Hopefully being positive and thankful will soon be the habit of choice.
Dear God, Thank you for your forgiveness and love. Thank you for changing us from the inside out remaking us in the image of your son.
I often recall the part in the movie where Jackson Schultz passes a piece of paper to Eric at the starting line of the 400 meter final at the Olympics. The paper read "In the Good Book it says 'If any man will serve Me, him will My Father honor.'" That inspires me to once again call upon the Father to help me be the man he created me to be.


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Pardons and Paroles says postponed hearing count now 627
Bureau cites notification issues for delays
Pardons and Paroles says postponed hearing count now 627 Bureau cites notification issues for delays Check out this story on montgomeryadvertiser.com: https://www.montgomeryadvertiser.com/story/news/2019/09/11/pardons-and-paroles-director-says-hearings-could-resume-nov-1/2288415001/
Brian Lyman, Montgomery Advertiser Published 12:05 p.m. CT Sept. 11, 2019 | Updated 4:37 p.m. CT Sept. 11, 2019
The Bureau of Pardons and Paroles said Wednesday it was aiming to resume parole hearings "on or about Nov. 1."
Director Charles Graddick on Friday postponed 113 hearings scheduled for this week. The Bureau said Wednesday the decision will delay a total of 627 parole hearings in September and October. That figure includes the 113 previously postponed.
Graddick, who recently stepped down as a Circuit Judge in Mobile County, will take the position on Sept. 1, according to a statement from Gov. Kay Ivey on Friday. (Photo: Provided)
A statement by the bureau on Wednesday said the issue was whether "all interested parties had been notified of the hearings as prescribed by law." A law passed by the legislature and signed by Gov. Kay Ivey earlier this year tightened notification requirements for victims of crimes and required the governor and attorney general to get 30 days advance notice of a parole hearing. Each could file objections within 14 days of notification.
"This uncalled-for situation is a disappointment to me and to our hardworking employees but mostly I feel sorry for the victims and other interested parties who have been forced to wait to testify before the parole board," Graddick said in a statement. "We put all (the) resources possible toward repairing this breakdown."
More: Pardons and Paroles says 'weeks' before hearings rescheduled
The law passed in the springtime also put specific requirements for parole in the law and required at least one member of the board to be an active or retired law enforcement officer. The law also gave the governor the power to appoint the director of Pardons and Paroles; she named Graddick, a former Alabama attorney general and Mobile circuit judge, this summer.
Graddick's move caught several people off guard, and brought criticism that it could exacerbate overcrowding in Alabama. Ebony Howard, a senior supervising attorney for the Southern Poverty Law Center, said in a statement Wednesday that they felt for the individuals "who have engaged in rehabilitative efforts to correct their past wrongs, but will not have their expected parole hearing this week to demonstrate that they are prepared to rejoin the community."
"We hope that the more than 100 people who were scheduled to have a hearing this week receive fair treatment and that Director Graddick gives them as much consideration in overseeing this process as he has afforded the interested parties he identified," the statement said.
The Bureau said in a statement Wednesday afternoon that the 627 cases would be "rescheduled for hearings that will begin no later than November 1."
Updated at 1:12 p.m. with comment from the Southern Poverty Law Center's Ebony Howard. Updated at 4:31 p.m. with further information from the Bureau.
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Summer - you can't lick it
SCORCHING summer sunshine left office workers reaching melting-point as temperatures soared into the eighties in Rossendale.
HANNAH Smethurst (7) cools off with an ice cream from the kiosk in Whitaker Park
But it's not good news for everyone and pet owners have been warned to take care of their animals and don't take them out in the midday sun.
One office in Rawtenstall shut up shop and gave workers a chance to enjoy the heat.
Ice cream sellers did a roaring trade. By midweek in Rawtenstall town centre there was only one outlet with a supply - and Maji Newsagents in Bank Street was down to the 'last couple of ice lollies'!
If you can't lick a lolly or ice cream, the next best way to cool off, many found, was enjoying a swim at the Marl Pits pool in Rawtenstall. Manager John Riddy said: 'It is good news for us and long may it continue.'
But people were not bowing out of holiday's abroad because at Bacup Travel Centre, Burnley Road, Bacup, Ann Blair said they were rushed off their feet.
She said: 'We have been really very busy and in fact I can't talk to you because we are both serving at the minute!'
At Bellusci men's and women's clothing shop in Bank Street, Rawtenstall the doors were shut on Wednesday afternoon and a sign announced: 'Too hot - gone to beach!'
Asda's ice cream shelves were cleared out as fast as they could be re-filled - and there was no shortage of volunteers willing to work in the walk-in storage freezers at the back of the store in temperatures of minus 18C to minus 28C!
Sun lotion - especially brands for children - has been 'walking' off the shelves.
Richard Wood, Boots store manager at Rawtenstall said: 'We were closed for two weeks for a refurbishment but since we re-opened and the good weather returned, we are probably up 30 per cent on sales of sun cream and our Soltan kids range which this year is green is proving very successful.'


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Arcueil () is a commune in the Val-de-Marne department in the southern suburbs of Paris, France. It is located  from the center of Paris.

Name

The name Arcueil was recorded for the first time in 1119 as Arcoloï, and later in the 12th century as Arcoïalum, meaning "place of the arches" (Latin radical arcus, "arch", and Celtic suffix -ialo, "clearing, glade", "place of"), in reference to the Roman aqueduct carrying water to the Roman city of Lutetia (modern Paris).

History

The arches of the Roman aqueduct crossed the Bièvre valley near Arcueil and are still visible at the Chateau des Arcs.

Between 1613 and 1624 a bridge-aqueduct over 1300 ft. long was constructed to convey water from the spring of Rungis, south of Arcueil, across the river Bièvre to the Luxembourg Palace in Paris. Between 1868 and 1872 another aqueduct, still longer, was superimposed above that of the 17th century, forming part of the system conveying water from the river Vanne to Paris.

The commune of Arcueil was officially renamed Arcueil-Cachan in 1894, after the hamlet of Cachan located within the commune. On 26 December 1922, Cachan seceded from the commune of Arcueil-Cachan and became a commune in its own right. The reduced commune of Arcueil-Cachan was renamed simply Arcueil.

Economy
Orange France, formerly France Télécom S.A., has its headquarters in Arcueil.

Geography
The commune of Arcueil covers an area of . Its highest elevation is , its lowest point is .

Arcueil is served by two stations on Paris RER line B: Laplace and Arcueil-Cachan.

Education
Primary schools:
 Five preschools: Henri Barbusse, Danielle Casanova, Jules Ferry, Olympe de Gouges, and Pauline Kergomard
 Five elementary schools: Henri Barbusse, Jules Ferry, Olympe de Gouges, Aimé Césaire, and Jean Macé

There is one junior high school, Collège Dulcie September and an engineering College, the École supérieure d'ingénieurs des travaux de la construction de Paris.

Residents are served by the Lycée intercommunal Darius-Milhaud in Le Kremlin-Bicêtre.

Personalities
 Jean-Antoine de Baïf (1532–1589), member of the "Pléiade".
 Adrienne Bolland (1895–1975), first woman to fly an airplane across the Andes, was born in Arcueil.
 Claude Louis Berthollet (1748–1822), chemist.
 Michel Bulteau, writer and cult film maker, is a native of Arcueil.
 Pierre and Marie Curie installed at Arcueil an annex of the Institut du Radium for the chemical treatment of radioactive elements.
 Jean-Paul Gaultier, fashion designer.
 Pierre-Simon de Laplace (1749–1827), mathematician, astronomer and physicist.
 Henri Rousseau (1844–1910) also called "Douanier Rousseau", notable naive painter.
 The Marquis de Sade, writer and libertine.
 Erik Satie, composer, lived in Arcueil from 1898 to 1925.  He is buried in the town.
 Dulcie September, of the African National Congress, when living in France lodged in Arcueil; an Arcueil high school is named after her.

Population
Data before 1922 in the table and graph below refer to the old commune Arcueil(-Cachan), before the commune of Cachan was separated.

See also
 Society of Arcueil
 Communes of the Val-de-Marne department
 :fr:Aqueducs d'Arcueil et de Cachan French Wiki article on the history of the 3 aqueducts

References

External links
 Official website
 Arcueil Business Theatre

Communes of Val-de-Marne