-\section{Detailed Document Comparison}
+\section{Detailed Document Comparison}~\label{yenya}
+\label{detailed}
-\subsection{General Approach}
+The detailed comparison task of PAN 2012 consisted in a comparison
+of given document pairs, with the expected output being the annotation of
+similarities found between these documents.
+The submitted program has been run in a controlled environment
+separately for each document pair, without the possibility of keeping any
+data between runs.
-Our approach in PAN 2012 Plagiarism detection---Detailed comparison sub-task
-is loosely based on the approach we have used in PAN 2010 \cite{Kasprzak2010}.
+%In this section, we describe our approach in the detailed comparison
+%task. The rest of this section is organized as follows: in the next
+%subsection, we summarise the differences from our previous approach.
+%In subsection \ref{sec-alg-overview}, we give an overview of our approach.
+%TODO napsat jak to nakonec bude.
-%The algorithm evaluates the document pair in several stages:
-%
-%\begin{itemize}
-%\item intrinsic plagiarism detection
-%\item language detection of the source document
-%\begin{itemize}
-%\item cross-lingual plagiarism detection, if the source document is not in English
-%\end{itemize}
-%\item detecting intervals with common features
-%\item post-processing phase, mainly serves for merging the nearby common intervals
-%\end{itemize}
-
-%\subsection{Intrinsic plagiarism detection}
-%
-%Our approach is based on character $n$-gram profiles of the interval of
-%the fixed size (in terms of $n$-grams), and their differences to the
-%profile of the whole document \cite{pan09stamatatos}. We have further
-%enhanced the approach with using gaussian smoothing of the style-change
-%function \cite{Kasprzak2010}.
-%
-%For PAN 2012, we have experimented with using 1-, 2-, and 3-grams instead
-%of only 3-grams, and using the different measure of the difference between
-%the n-gram profiles. We have used an approach similar to \cite{ngram},
-%where we have compute the profile as an ordered set of 400 most-frequent
-%$n$-grams in a given text (the whole document or a partial window). Apart
-%from ordering the set, we have ignored the actual number of occurrences
-%of a given $n$-gram altogether, and used the value inveresly
-%proportional to the $n$-gram order in the profile, in accordance with
-%the Zipf's law \cite{zipf1935psycho}.
-%
-%This approach has provided more stable style-change function than
-%than the one proposed in \cite{pan09stamatatos}. Because of pair-wise
-%nature of the detailed comparison sub-task, we couldn't use the results
-%of the intrinsic detection immediately, therefore we wanted to use them
-%as hints to the external detection.
+\subsection{Differences Against PAN 2010}
-\subsection{Cross-lingual Plagiarism Detection}
+Our approach in this task
+is loosely based on the approach we have used in PAN 2010 \cite{Kasprzak2010}.
+The main difference is that instead of looking for similarities of
+one type (for PAN 2010, we have used word 5-grams),
+we have developed a method of evaluating multiple types of similarities
+(we call them {\it common features}) of different properties, such as
+density and length.
-%For language detection, we used the $n$-gram based categorization \cite{ngram}.
-%We have computed the language profiles from the source documents of the
-%training corpus (using the annotations from the corpus itself). The result
-%of this approach was better than using the stopwords-based detection we have
-%used in PAN 2010. However, there were still mis-detected documents,
-%mainly the long lists of surnames and other tabular data. We have added
-%an ad-hoc fix, where for documents having their profile too distant from all of
-%English, German, and Spanish profiles, we have declared them to be in English.
-
-%For cross-lingual plagiarism detection, our aim was to use the public
-%interface to Google translate if possible, and use the resulting document
-%as the source for standard intra-lingual detector.
-%Should the translation service not be available, we wanted
-%to use the fall-back strategy of translating isolated words only,
-%with the additional exact matching of longer words (we have used words with
-%5 characters or longer).
-%We have supposed that these longer words can be names or specialized terms,
-%present in both languages.
-
-%We have used dictionaries from several sources, like
-%{\it dicts.info}\footnote{\url{http://www.dicts.info/}},
-%{\it omegawiki}\footnote{\url{http://www.omegawiki.org/}},
-%and {\it wiktionary}\footnote{\url{http://en.wiktionary.org/}}. The source
-%and translated document were aligned on a line-by-line basis.
-
-In the final form of the detailed comparison sub-task, the results of machine
-translation of the source documents were provided to the detector programs
-by the surrounding environment, so we have discarded the language detection
-and machine translation from our submission altogether, and used only
-line-by-line alignment of the source and translated document for calculating
-the offsets of text features in the source document. We have then treated
-the translated documents the same way as the source documents in English.
-
-\subsection{Multi-feature Plagiarism Detection}
-
-Our pair-wise plagiarism detection is based on finding common passages
-of text, present both in the source and in the suspicious document. We call them
-{\it common features}. In PAN 2010, we have used sorted word 5-grams, formed from
-words of three or more characters, as features to compare.
-Recently, other means of plagiarism detection have been explored:
-stopword $n$-gram detection is one of them
+As a proof of concept, we have used two types of common features: word
+5-grams and stop-word 8-grams, the later being based on the method described in
\cite{stamatatos2011plagiarism}.
-We propose the plagiarism detection system based on detecting common
-features of various types, for example word $n$-grams, stopword $n$-grams,
-translated single words, translated word bigrams,
-exact common longer words from document pairs having each document
-in a different language, etc. The system
-has to be to the great extent independent of the specialities of various
-feature types. It cannot, for example, use the order of given features
-as a measure of distance between the features, as for example, several
-word 5-grams can be fully contained inside one stopword 8-gram.
-
-We therefore propose to describe the {\it common feature} of two documents
-(susp and src) with the following tuple:
-$\langle
-\hbox{offset}_{\hbox{susp}},
-\hbox{length}_{\hbox{susp}},
-\hbox{offset}_{\hbox{src}},
-\hbox{length}_{\hbox{src}} \rangle$. This way, the common feature is
-described purely in terms of character offsets, belonging to the feature
-in both documents. In our final submission, we have used the following two types
-of common features:
+In addition to the above, we have made several minor improvements to the
+algorithm, such as parameter tuning and improving the detections
+merging in the post-processing stage.
+
+\subsection{Algorithm Overview}
+\label{sec-alg-overview}
+
+The algorithm evaluates the document pair in several stages:
\begin{itemize}
-\item word 5-grams, from words of three or more characters, sorted, lowercased
-\item stopword 8-grams, from 50 most-frequent English words (including
- the possessive suffix 's), unsorted, lowercased, with 8-grams formed
- only from the seven most-frequent words ({\it the, of, a, in, to, 's})
- removed
+\item tokenizing both the suspicious and source documents
+\item forming {\it features} from some tokens
+\item discovering {\it common features}
+\item making {\it valid intervals} from common features
+\item postprocessing
\end{itemize}
-We have gathered all the common features of both types for a given document
-pair, and formed {\it valid intervals} from them, as described
-in \cite{Kasprzak2009a}. A similar approach is used also in
-\cite{stamatatos2011plagiarism}.
-The algorithm is modified for multi-feature detection to use character offsets
+\subsection{Tokenization}
+
+We tokenize the document into words, where word is a sequence of one
+or more characters of the {\it Letter} Unicode class.
+With each word, two additional attributes, needed for further processing,
+are associated: the offset where the word begins, and the word length.
+
+The offset where the word begins is not necessarily the first letter character
+of the word itself. We have discovered that in the training corpus,
+some plagiarized passages were annotated including the preceding
+non-letter characters. We have used the following heuristics to add
+parts of the inter-word gap to the previous, or the next adjacent word:
+
+\begin{itemize}
+\item when the inter-word gap contains interpunction (any of the dot,
+semicolon, colon, comma, exclamation mark, question mark, or quotes),
+add the characters up to, and including the interpunction, to the previous
+word, ignore the space character(s) after the interpunction, and add
+the rest to tne next word.
+\item otherwise, when the inter-word gap contains newline, add the character
+before the first newline to the previous word, ignore the first newline
+character, and add the rest to the next word.
+\item otherwise, ignore the inter-word gap characters altogether.
+\end{itemize}
+
+When the detection program has been presented three different
+files instead of two (meaning the third one is machine-translated
+version of the second one), we have tokenized the translated document instead
+of the source one. We have make use of the line-by-line alignment of the
+source and machine-translated documents to transform the word offsets
+and lengths in the translated document to the terms of the source document.
+
+\subsection{Features}
+
+We have used features of two types:
+
+\begin{itemize}
+\item Lexicographically sorted word 5-grams, formed of words at least
+three characters long, and
+\item unsorted stop-word 8-grams, formed from 50 most frequent words in English,
+as described in \cite{stamatatos2011plagiarism}. We have further ignored
+the 8-grams, formed solely from the six most frequent English words
+(the, of, and, a, in, to), or the possessive {\it'{}s}.
+\end{itemize}
+
+We have represented each feature with the 32 highest-order bits of its
+MD5 digest. This is only a performance optimization, targeted for
+larger systems. The number of features in a document pair is several orders
+of magnitude lower than $2^{32}$, so the probability of hash function
+collision is low. For pair-wise comparison, it would be feasible to compare
+the features directly instead of their MD5 sums.
+
+Each feature has also the offset and length attributes.
+Offset is taken as the offset of the first word in a given feature,
+and length is the offset of the last character in a given feature
+minus the offset of the feature itself.
+
+\subsection{Common Features}
+
+For further processing, we have taken into account only the features
+present both in source and suspicious document. For each such
+{\it common feature}, we have created the list of
+$(\makebox{offset}, \makebox{length})$ pairs for the source document,
+and a similar list for the suspicious document. Note that a given feature
+can occur multiple times both in source and suspicious document.
+
+\subsection{Valid Intervals}
+
+To detect a plagiarized passage, we need to find a set of common features,
+which map to a dense-enough interval both in the source and suspicious
+document. In our previous work, we have presented the algorithm
+for discovering these {\it valid intervals} \cite{Kasprzak2009a}.
+A similar approach is used also in \cite{stamatatos2011plagiarism}.
+Both of these algorithms use the features of a single type, which
+allows to use the ordering of features as a measure of distance.
+
+When we use features of different types, there is no natural ordering
+of them: for example, a stop-word 8-gram can span multiple sentences,
+which can contain several word 5-grams. The assumption of both of the
+above algorithms, that the last character of the previous feature
+is before the last character of the current feature, is broken.
+
+We have modified the algorithm for computing valid intervals with
+multi-feature detection to use character offsets
only instead of feature order numbers. We have used valid intervals
-consisting of at least 5 common features, with the maximum allowed gap
+consisting of at least 4 common features, with the maximum allowed gap
inside the interval (characters not belonging to any common feature
-of a given valid interval) set to 3,500 characters.
-
-%We have also experimented with modifying the allowed gap size using the
-%intrinsic plagiarism detection: to allow only shorter gap if the common
-%features around the gap belong to different passages, detected as plagiarized
-%in the suspicious document by the intrinsic detector, and allow larger gap,
-%if both the surrounding common features belong to the same passage,
-%detected by the intrinsic detector. This approach, however, did not show
-%any improvement against allowed gap of a static size, so it was omitted
-%from the final submission.
+of a given valid interval) set to 4000 characters.
\subsection{Postprocessing}
+\label{postprocessing}
In the postprocessing phase, we took the resulting valid intervals,
and made attempt to further improve the results. We have firstly
bigger than number of features per character in the possible joined interval.
\end{itemize}
-These parameters were fine-tuned to achieve the best results on the training corpus. With these parameters, our algorithm got the total plagdet score of 0.73 on the training corpus.
+\subsection{Results}
+
+These parameters were fine-tuned to achieve the best results on the training
+corpus. With these parameters, our algorithm got the total plagdet score
+of 0.7288 on the training corpus. The details of the performance of
+our algorithm are presented in Table \ref{table-final}.
+In the PAN 2012 competition, we have acchieved the plagdet score
+of 0.6827, precision 0.8932, recall 0.5525, and granularity 1.0000.
+
+\begin{table}
+\begin{center}
+\begin{tabular}{|l|r|r|r|r|}
+\hline
+&plagdet&recall&precision&granularity\\
+\hline
+whole corpus&0.7288&0.5994&0.9306&1.0007\\
+\hline
+01-no-plagiarism &0.0000&0.0000&0.0000&1.0000\\
+02-no-obfuscation &0.9476&0.9627&0.9330&1.0000\\
+03-artificial-low &0.8726&0.8099&0.9477&1.0013\\
+04-artificial-high &0.3649&0.2255&0.9562&1.0000\\
+05-translation &0.7610&0.6662&0.8884&1.0008\\
+06-simulated-paraphrase&0.5972&0.4369&0.9433&1.0000\\
+\hline
+\end{tabular}
+\end{center}
+\caption{Performance on the training corpus}
+\label{table-final}
+\end{table}
+
+\subsection{Other Approaches Explored}
+
+There are several other approaches we have evaluated, but which were
+omitted from our final submission for various reasons. We think mentioning
+them here is worthwhile nevertheless.
+
+\subsubsection{Intrinsic Plagiarism Detection}
+
+We tested the approach based on character $n$-gram profiles of the interval of
+the fixed size (in terms of $n$-grams), and their differences to the
+profile of the whole document \cite{pan09stamatatos}. We have further
+enhanced the approach with using gaussian smoothing of the style-change
+function \cite{Kasprzak2010}. For PAN 2012, we made further improvements
+to the algorithm, resulting in more stable style change function in
+both short and long documents.
+
+We tried to use the results of the intrinsic plagiarism detection
+as hint for the post-processing phase, allowing to merge larger
+intervals, if they both belong to the same passage, detected by
+the intrinsic detector. This approach did not provide improvement
+when compared to the static gap limits, as described in Section
+\ref{postprocessing}, so we have omitted it from our final submission.
+
+%\subsubsection{Language Detection}
+%
+%For language detection, we used the $n$-gram based categorization \cite{ngram}.
+%We computed the language profiles from the source documents of the
+%training corpus (using the annotations from the corpus itself). The result
+%of this approach was better than using the stopwords-based detection we have
+%used in PAN 2010. However, there were still mis-detected documents,
+%mainly the long lists of surnames and other tabular data. We added
+%an ad-hoc fix, where for documents having their profile too distant from all of
+%English, German, and Spanish profiles, we declared them to be in English.
+
+\subsubsection{Cross-lingual Plagiarism Detection}
+
+For cross-lingual plagiarism detection, our aim was to use the public
+interface to Google translate if possible, and use the resulting document
+as the source for standard intra-lingual detector.
+Should the translation service not be available, we wanted
+to use the fall-back strategy of translating isolated words only,
+with the additional exact matching of longer words (we have used words with
+5 characters or longer).
+We have supposed that these longer words can be names or specialized terms,
+present in both languages.
+
+We used dictionaries from several sources, for example
+{\it dicts.info}\footnote{\url{http://www.dicts.info/}},
+{\it omegawiki}\footnote{\url{http://www.omegawiki.org/}},
+and {\it wiktionary}\footnote{\url{http://en.wiktionary.org/}}.
+
+In the final submission, we simply used the machine translated texts,
+which were provided to the running program from the surrounding environment.
+
\subsection{Further discussion}
-As in our PAN 2010 submission, we tried to make use of the intrinsic plagiarism
-detection, but despite making further improvements to the intrinsic plagiarism detector, we have again failed to reach any significant improvement
-when using it as a hint for the external plagiarism detection.
+From our previous PAN submissions, we knew that the precision of our
+system was good, and because of the way how the final score is computed, we
+wanted to exchange a bit worse precision for better recall and granularity.
+So we pushed the parameters towards detecting more plagiarized passages,
+even when the number of common features was not especially high.
-In the full paper, we will also discuss the following topics:
+\subsubsection{Plagdet score}
-\begin{itemize}
-\item language detection and cross-language common features
-\item intrinsic plagiarism detection
-\item suitability of plagdet score\cite{potthastframework} for performance measurement
-\item feasibility of our approach in large-scale systems
-\item discussion of parameter settings
-\end{itemize}
+Our results from tuning the parameters show that the plagdet score\cite{potthastframework}
+is not a good measure for comparing the plagiarism detection systems:
+for example, the gap of 30,000 characters, described in Section \ref{postprocessing},
+can easily mean several pages of text. And still the system with this
+parameter set so high resulted in better plagdet score.
+
+Another problem of plagdet can be
+seen in the 01-no-plagiarism part of the training corpus: the border
+between the perfect score 1 and the score 0 is a single false-positive
+detection. Plagdet does not distinguish between the system reporting this
+single false-positive, and the system reporting the whole data as plagiarized.
+Both get the score 0. However, our experience from real-world plagiarism detection systems show that
+the plagiarized documents are in a clear minority, so the performance of
+the detection system on non-plagiarized documents is very important.
+
+\subsubsection{Performance Notes}
+
+We consider comparing the CPU-time performance of PAN 2012 submissions almost
+meaningless, because any sane system would precompute features for all
+documents in a given set of suspicious and source documents, and use the
+results for pair-wise comparison, expecting that any document will take
+part in more than one pair.
+
+Also, the pair-wise comparison without caching any intermediate results
+lead to worse overall performance: in our PAN 2010 submission, one of the
+post-processing steps was to remove all the overlapping detections
+from a given suspicious documents, when these detections were from different
+source doucments, and were short enough. This removed many false-positives
+and improved the precision of our system. This kind of heuristics was
+not possible in PAN 2012.
+
+As for the performance of our system, we split the task into two parts:
+1. finding the common features, and 2. computing valid intervals and
+postprocessing. The first part is more CPU intensive, and the results
+can be cached. The second part is fast enough to allow us to evaluate
+many combinations of parameters.
+
+We did our development on a machine with four six-core AMD 8139 CPUs
+(2800 MHz), and 128 GB RAM. The first phase took about 2500 seconds
+on this host, and the second phase took 14 seconds. Computing the
+plagdet score using the official script in Python took between 120 and
+180 seconds, as there is no parallelism in this script.
+
+When we have tried to use intrinsic plagiarism detection and language
+detection, the first phase took about 12500 seconds. Thus omitting these
+featurs clearly provided huge performance improvement.
+
+The code was written in Perl, and had about 669 lines of code,
+not counting comments and blank lines.
+
+\endinput
+
+- hranice mezi pasazema nekdy zahrnovala whitespace a nekdy ne.
+
+Diskuse plagdet:
+- uzivatele chteji "aby odevzdej ukazovalo 0\% shody", nezajima je
+ co to cislo znamena
+- nezalezi na hranicich detekovane pasaze
+- false-positives jsou daleko horsi
+- granularita je zlo
+
+Finalni vysledky nad testovacim korpusem:
+
+0.7288 0.5994 0.9306 1.0007 2012-06-16 02:23 plagdt recall precis granul
+ 01-no-plagiarism 0.0000 0.0000 0.0000 1.0000
+ 02-no-obfuscation 0.9476 0.9627 0.9330 1.0000
+ 03-artificial-low 0.8726 0.8099 0.9477 1.0013
+ 04-artificial-high 0.3649 0.2255 0.9562 1.0000
+ 05-translation 0.7610 0.6662 0.8884 1.0008
+ 06-simulated-paraphr 0.5972 0.4369 0.9433 1.0000
+
+Vysledky nad souteznimi daty:
+plagdet precision recall granularity
+0.6826726 0.8931670 0.5524708 1.0000000
+
+Run-time:
+12500 sekund tokenizace vcetne sc a detekce jazyka
+2500 sekund bez sc a detekce jazyka
+14 sekund vyhodnoceni valid intervalu a postprocessing
+
+
+TODO:
+- hranici podle hustoty matchovani
+- xml tridit podle this_offset
+
+Tady je obsah souboru JOURNAL - jak jsem meril nektera vylepseni:
+=================================================================
+baseline.py
+0.1250 0.1259 0.9783 2.4460 2012-05-03 06:02 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.8608 0.8609 0.8618 1.0009
+ 03_artificial_low 0.1006 0.1118 0.9979 2.9974
+ 04_artificial_high 0.0054 0.0029 0.9991 1.0778
+ 05_translation 0.0003 0.0002 1.0000 1.2143
+ 06_simulated_paraphr 0.0565 0.0729 0.9983 4.3075
+
+valid_intervals bez postprocessingu (takhle jsem to poprve odevzdal)
+0.3183 0.2034 0.9883 1.0850 2012-05-25 15:25 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9861 0.9973 0.9752 1.0000
+ 03_artificial_low 0.4127 0.3006 0.9975 1.1724
+ 04_artificial_high 0.0008 0.0004 1.0000 1.0000
+ 05_translation 0.0001 0.0000 1.0000 1.0000
+ 06_simulated_paraphr 0.3470 0.2248 0.9987 1.0812
+
+postprocessed (slucovani blizkych intervalu)
+0.3350 0.2051 0.9863 1.0188 2012-05-25 15:27 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9863 0.9973 0.9755 1.0000
+ 03_artificial_low 0.4541 0.3057 0.9942 1.0417
+ 04_artificial_high 0.0008 0.0004 1.0000 1.0000
+ 05_translation 0.0001 0.0000 1.0000 1.0000
+ 06_simulated_paraphr 0.3702 0.2279 0.9986 1.0032
+
+whitespace (uprava whitespaces)
+0.3353 0.2053 0.9858 1.0188 2012-05-31 17:57 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9865 0.9987 0.9745 1.0000
+ 03_artificial_low 0.4546 0.3061 0.9940 1.0417
+ 04_artificial_high 0.0008 0.0004 1.0000 1.0000
+ 05_translation 0.0001 0.0000 1.0000 1.0000
+ 06_simulated_paraphr 0.3705 0.2281 0.9985 1.0032
+
+gap_100: whitespace, + ve valid intervalu dovolim mezeru 100 petic misto 50
+0.3696 0.2305 0.9838 1.0148 2012-05-31 18:07 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9850 0.9987 0.9717 1.0000
+ 03_artificial_low 0.5423 0.3846 0.9922 1.0310
+ 04_artificial_high 0.0058 0.0029 0.9151 1.0000
+ 05_translation 0.0001 0.0000 1.0000 1.0000
+ 06_simulated_paraphr 0.4207 0.2667 0.9959 1.0000
+
+gap_200: whitespace, + ve valid intervalu dovolim mezeru 200 petic misto 50
+0.3906 0.2456 0.9769 1.0070 2012-05-31 18:09 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9820 0.9987 0.9659 1.0000
+ 03_artificial_low 0.5976 0.4346 0.9875 1.0139
+ 04_artificial_high 0.0087 0.0044 0.9374 1.0000
+ 05_translation 0.0001 0.0001 1.0000 1.0000
+ 06_simulated_paraphr 0.4360 0.2811 0.9708 1.0000
+
+gap_200_int_10: gap_200, + valid int. ma min. 10 petic misto 20
+0.4436 0.2962 0.9660 1.0308 2012-05-31 18:11 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9612 0.9987 0.9264 1.0000
+ 03_artificial_low 0.7048 0.5808 0.9873 1.0530
+ 04_artificial_high 0.0457 0.0242 0.9762 1.0465
+ 05_translation 0.0008 0.0004 1.0000 1.0000
+ 06_simulated_paraphr 0.5123 0.3485 0.9662 1.0000
+
+no_trans: gap_200_int_10, + nedetekovat preklady vubec, abych se vyhnul F-P
+0.4432 0.2959 0.9658 1.0310 2012-06-01 16:41 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9608 0.9980 0.9263 1.0000
+ 03_artificial_low 0.7045 0.5806 0.9872 1.0530
+ 04_artificial_high 0.0457 0.0242 0.9762 1.0465
+ 05_translation 0.0000 0.0000 0.0000 1.0000
+ 06_simulated_paraphr 0.5123 0.3485 0.9662 1.0000
+
+
+swng_unsorted se stejnym postprocessingem jako vyse "whitespace"
+0.2673 0.1584 0.9281 1.0174 2012-05-31 14:20 plagdt recall precis granul
+ 01_no_plagiarism 0.0000 0.0000 0.0000 1.0000
+ 02_no_obfuscation 0.9439 0.9059 0.9851 1.0000
+ 03_artificial_low 0.3178 0.1952 0.9954 1.0377
+ 04_artificial_high 0.0169 0.0095 0.9581 1.1707
+ 05_translation 0.0042 0.0028 0.0080 1.0000
+ 06_simulated_paraphr 0.1905 0.1060 0.9434 1.0000
+
+swng_sorted
+0.2550 0.1906 0.4067 1.0253 2012-05-30 16:07 plagdt recall precis granul
+ 01_no_plagiarism 0.0000 0.0000 0.0000 1.0000
+ 02_no_obfuscation 0.6648 0.9146 0.5222 1.0000
+ 03_artificial_low 0.4093 0.2867 0.8093 1.0483
+ 04_artificial_high 0.0454 0.0253 0.4371 1.0755
+ 05_translation 0.0030 0.0019 0.0064 1.0000
+ 06_simulated_paraphr 0.1017 0.1382 0.0814 1.0106
+
+sort_susp: gap_200_int_10 + postprocessing tridim intervaly podle offsetu v susp, nikoliv v src
+0.4437 0.2962 0.9676 1.0308 2012-06-01 18:06 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9641 0.9987 0.9317 1.0000
+ 03_artificial_low 0.7048 0.5809 0.9871 1.0530
+ 04_artificial_high 0.0457 0.0242 0.9762 1.0465
+ 05_translation 0.0008 0.0004 1.0000 1.0000
+ 06_simulated_paraphr 0.5123 0.3485 0.9662 1.0000
+
+post_gap2_16000: sort_susp, + sloucit dva intervaly pokud je < 16000 znaku a mezera je jen polovina velikosti tech intervalu (bylo 4000)
+0.4539 0.2983 0.9642 1.0054 2012-06-01 18:09 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9631 0.9987 0.9300 1.0000
+ 03_artificial_low 0.7307 0.5883 0.9814 1.0094
+ 04_artificial_high 0.0480 0.0247 0.9816 1.0078
+ 05_translation 0.0008 0.0004 1.0000 1.0000
+ 06_simulated_paraphr 0.5133 0.3487 0.9721 1.0000
+
+post_gap2_32000: sort_susp, + sloucit intervaly < 32000 znaku a mezera aspon polovina velikosti
+0.4543 0.2986 0.9638 1.0050 2012-06-01 18:12 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9628 0.9987 0.9294 1.0000
+ 03_artificial_low 0.7315 0.5893 0.9798 1.0085
+ 04_artificial_high 0.0480 0.0247 0.9816 1.0078
+ 05_translation 0.0008 0.0004 1.0000 1.0000
+ 06_simulated_paraphr 0.5138 0.3487 0.9763 1.0000
+
+post_gap2_64000: sort_susp, + sloucit intervaly < 32000 znaku a mezera aspon pol
+ovina velikosti
+0.4543 0.2988 0.9616 1.0050 2012-06-01 18:21 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9603 0.9987 0.9248 1.0000
+ 03_artificial_low 0.7316 0.5901 0.9782 1.0085
+ 04_artificial_high 0.0480 0.0247 0.9816 1.0078
+ 05_translation 0.0008 0.0004 1.0000 1.0000
+ 06_simulated_paraphr 0.5138 0.3487 0.9763 1.0000
-\nocite{pan09stamatatos}
-%\nocite{ngram}
+post_gap1_2000: post_gap2_32000, + spojit bez podminek veci co maji mezeru pod 2000 (bylo 600)
+0.4543 0.2986 0.9635 1.0050 2012-06-01 18:29 plagdt recall precis granul
+ 01_no_plagiarism 1.0000 1.0000 1.0000 1.0000
+ 02_no_obfuscation 0.9628 0.9987 0.9294 1.0000
+ 03_artificial_low 0.7315 0.5895 0.9794 1.0085
+ 04_artificial_high 0.0480 0.0247 0.9816 1.0078
+ 05_translation 0.0008 0.0004 1.0000 1.0000
+ 06_simulated_paraphr 0.5138 0.3487 0.9763 1.0000
https://www.fi.muni.cz/~kas/git / pan12-paper.git / blobdiff