Plagiarism Detection Using Deep Based Feature Combined with SynmDict

2021 ◽  
pp. 45-52
Author(s):  
Ashish Varghese Muttumana ◽  
Harsh Goel ◽  
Yash Teotia ◽  
Piyush Bhardwaj
Keyword(s):  
Plagiarism Detection

Analysis of Stylometric Features and Segmentation Strategies in Intrinsic Plagiarism Detection System

2020 ◽  
Vol 4 (5) ◽  
pp. 988-997
Author(s):  
Sylvia Putri Gunawan ◽  
Lucia Dwi Krisnawati ◽  
Antonius Rachmat Chrismanto
Keyword(s):  
Detection System ◽  
Plagiarism Detection ◽  
Development System ◽  
Intrinsic Plagiarism Detection

Two different paradigms in the field of plagiarism detection resulting in External Plagiarism Detection (EPD) and Intrinsic Plagiarism Detection (IPD) systems. The most common applied system is EPD, which requires its algorithm to make a heuristic comparison between a suspicious document with documents in a corpus. In contrast, given a suspicious document only, an algorithm of IPD should be able to find the plagiarism section by looking for text segments having different writing styles. Previous researches for Indonesian texts fell only in the field of the EPD development system. Therefore, this research focuses on and contributes to experimenting and analyzing the stylometric features and segmentation strategies to build an IPD system for Indonesian texts. The experimentation results show that the paragraph segment performs better by scoring 0.92 for Macro Averaged-Accuracy and 0.54 for Macro Averaged-F1. The stylometric features achieving the highest scores of F-1 and Accuracy are the frequency of punctuation, the average paragraph length, and the type-token ratio.  

scholarly journals Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 47
Author(s):  
Fariha Iffath ◽  
A. S. M. Kayes ◽  
Md. Tahsin Rahman ◽  
Jannatul Ferdows ◽  
Mohammad Shamsul Arefin ◽  
...  
Keyword(s):  
Source Code ◽  
Large Data ◽  
Large Data Sets ◽  
Detection Technique ◽  
Data Sets ◽  
Plagiarism Detection ◽  
Source Codes ◽  
Efficient Detection ◽  
Mathematical Problems ◽  
Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

scholarly journals A set theory based similarity measure for text clustering and classification

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ali A. Amer ◽  
Hassan I. Abdalla
Keyword(s):  
Set Theory ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Text Clustering ◽  
Plagiarism Detection ◽  
K Nearest Neighbor ◽  
Single Measure ◽  
Highly Effective ◽  
Clustering And Classification ◽  
Effectiveness And Efficiency

Abstract Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency.

scholarly journals “There must be Someone’s Name Under Every Bit of Text, Even if it is Unimportant or Incorrect”: Plagiarism as a Learning Strategy

2021 ◽  
Author(s):  
Beata Bielska ◽  
Mateusz Rutkowski
Keyword(s):  
Higher Education ◽  
Detection Rate ◽  
Learning Strategy ◽  
Plagiarism Detection ◽  
International Context ◽  
Detection Systems ◽  
Uniform Definition ◽  
Definition Of ◽  
Difficult Situations ◽  
Source Of Information

AbstractThe article offers analyses of the phenomenon of copying (plagiarism) in higher education. The analyses were based on a quantitative survey using questionnaires, conducted in 2019 at one of the Polish universities. Plagiarism is discussed here both as an element of the learning process and a subject of public practices. The article presents students’ definitions of plagiarism, their strategies for unclear or difficult situations, their experiences with plagiarism and their opinions on how serious and widespread this phenomenon is. Focusing on the non-plagiarism norm, that is the rule that students are not allowed to plagiarize, and in order to redefine it we have determined two strategies adopted by students. The first is withdrawing in fear of making a mistake (omitting the norm), which means not using referencing in unclear situations, e.g. when the data about the source of information are absent. The second is reducing the scope of the norm applicability (limiting the norm), characterized by the fact that there are areas where the non-plagiarism norm must be observed more closely and those where it is not so important, e.g. respondents classify works as credit-level and diploma-level texts, as in the credit-level work they “can” sometimes plagiarize since the detection rate is poor and consequences are not severe. The presented results are particularly significant for interpreting plagiarism in an international context (no uniform definition of plagiarism) and for policies aimed at limiting the scale of the phenomenon (plagiarism detection systems1).

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