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An example of similarity evaluation of different strings x=“aaabbbccc”, y=“aaabbbccd”, z= “aaabbbccdf” w=“aaabbbcc”’.

Table 4 An example of similarity evaluation of different strings x=“aaabbbccc”, y=“aaabbbccd”, z= “aaabbbccdf” w=“aaabbbcc”’.

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Fig. 1. Plagiarism types with some related detection principals [20].
Fig. 1. Plagiarism types with some related detection principals [20].
Fig. 2. Taxonomy of Plagiarism. citations around the borrowed ideas and citing the origina! author [62, 65].
Fig. 2. Taxonomy of Plagiarism. citations around the borrowed ideas and citing the origina! author [62, 65].
Fig. 7. Literal versus idea plagiarism in a scientific paper. (a) Original paper (Alzahrani and Salim, 2009), (b) Simulated plagiarism based on the importance of different sections/segments in the article.
Fig. 7. Literal versus idea plagiarism in a scientific paper. (a) Original paper (Alzahrani and Salim, 2009), (b) Simulated plagiarism based on the importance of different sections/segments in the article.
NO ki oe Fig. 8. Pattern of idea plagiarism in a scientific article. (a) Original paper (Alzahrani and Salim, 2009), (b) Simulated plagiarism via the adoption of a sequence of key ideas in the choice of the methodology.
NO ki oe Fig. 8. Pattern of idea plagiarism in a scientific article. (a) Original paper (Alzahrani and Salim, 2009), (b) Simulated plagiarism via the adoption of a sequence of key ideas in the choice of the methodology.
TYPES OF TEXT FEATURES WITH COMPUTATIONAL TOOLS REQUIRED FOR THEIR IMPLEMENTATION TABLE I
TYPES OF TEXT FEATURES WITH COMPUTATIONAL TOOLS REQUIRED FOR THEIR IMPLEMENTATION TABLE I
TYPES OF CROSS-LANGUAGE TEXT FEATURES WITH COMPUTATIONAL TOOLS D REQUIRED FOR THEIR IMPLEMENTATION TABLE II
TYPES OF CROSS-LANGUAGE TEXT FEATURES WITH COMPUTATIONAL TOOLS D REQUIRED FOR THEIR IMPLEMENTATION TABLE II
Optional tools are included in brackets. Optional tools are included in brackets.
Optional tools are included in brackets. Optional tools are included in brackets.
An example of similarity evaluation of two vectors x= /0.1, 0.2, 0.3, 0.4] and y= [0.1, 0.2, 0.3, 0.5]. VECTOR SIMILARITY METRICS
An example of similarity evaluation of two vectors x= /0.1, 0.2, 0.3, 0.4] and y= [0.1, 0.2, 0.3, 0.5]. VECTOR SIMILARITY METRICS
The notions in the table indicate the following: M means include/support by evidence from research stated in the references column o means possibility to include/support but need further research for proof. TABLE VI
The notions in the table indicate the following: M means include/support by evidence from research stated in the references column o means possibility to include/support but need further research for proof. TABLE VI
Related topics:
EngineeringInformation RetrievalPragmaticsPlagiarism DetectionComputational LinguisticsFuzzy set theory
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