Plagiarism Detection System Research Articles

Design and Implementation of a Final Project Plagiarism Detection System Using Cosine Similarity Method

Plagiarism is an act of taking ideas, taking research results, acquiring research results, and summarizing writing without mentioning the source, either intentionally or unintentionally. The cosine similarity method can be used to calculate the score of similarities between documents by comparing existing documents on the database with uploaded ones to gain a similarity percentage. External plagiarism detection (EPD) is used to compare the contents of articles. The design and implementation of the system will be carried out in several stages in the hope that the system can work optimally and detect text similarities accurately. This research objective is to check the plagiarism rate of the document using the cosine similarity method as a method of calculating word equations called Kipcheck. The purpose of checking the level of plagiarism is to ensure that the documents created have a minimum level of fraud to avoid academic sanctions. Kipcheck uses three application system tests; maximum word calculation that can be processed; comparing application and manual calculation; and testing the consistency of application calculation results based on two different schemes.

Cross-Language Text Document Plagiarism Detection System Using Winnowing Method

Currently, there are many text documents such as journals scattered on the internet, both Indonesian and English-language journals. With this, it is possible to act plagiarism by copying from foreign journals that are translated into other languages or copying directly without being changed from the original language. One way that can suppress these actions is to build a plagiarism detection system for cross-language text documents. The method that can be used to detect document plagiarism is the Winnowing method. Winnowing method is a method where text input will be processed to produce a hash value called a fingerprint. This study aims to build a system that can detect plagiarism of text documents in different languages using the Winnowing method. Text documents that can be tested are input text and PDF files. Documents used in system testing are journals that have the same topic. The results of the highest level of accuracy produced between the calculation of the Jaccard Coefficient with the Plagiarism Checker X application are in the fourth scenario with an average percentage value of 84.7%.

An external plagiarism detection system based on part-of-speech (POS) tag n-grams and word embedding

The aim of this paper is to present an automatic plagiarism detection system to identify plagiarized passages of documents. Our plagiarism detection system uses both syntactic and semantic similarities to identify plagiarized passages. Our proposed method is a novel contribution because of its usage of part-of-speech tag n-grams (POSNG) which are able to show syntactic similarities between source and suspicious sentences. Each source document is indexed according to part-of-speech (POS) tag n-grams by a search engine in order to access rapidly to sentences that are possible plagiarism candidates. Even though our plagiarism detection system obtains very good results just using POS tag n-grams, its performance is further improved with the usage of semantic similarities. The semantic relatedness between words is measured with the word embedding technique called Word2Vec and the longest common subsequence approach is used to measure the semantic similarity between source and suspicious sentences. There are several types of plagiarism such as verbatim, paraphrasing, source-code, and cross-lingual. The high obfuscation paraphrasing is a type of plagiarism and its detection is one of the most difficult plagiarism detection tasks. Our proposed method, which is based on POS tag n-grams, improves the detection performance of the high obfuscation paraphrasing type and is the main contribution of this paper. For this study, we use the large dataset called PAN-PC-11 which is created for the evaluation of automatic plagiarism detection algorithms. Our experiments are conducted with the four types of paraphrasing in PAN-PC-11 which are none, low, high and simulated obfuscation paraphrasing types. We defined various threshold and parameter settings in order to assess the diversity of our results. We compared the performance of our method with the plagiarism detectors in the 3rd International Competition on Plagiarism Detection (PAN11). According to the experimental results, the proposed method achieved the best performance in terms of plagdet measure in the types of high and low obfuscation paraphrasing and produced competitive results in the other paraphrasing types.

Analysis of duplicated publications in Russian journals

The article presents a study of publication ethics violations in Russian-language scientific publications related to the duplicated publication. The aim of the study is to assess the frequency of the above-noted violations in the data of eLIBRARY.RU, the largest aggregator of full texts of Russian-language scientific publications. For the purposes of the study, we used the tools of the “Antiplagiat” plagiarism detection system. Out of the almost 12 million full-text publications on the eLIBRARY.RU platform, more than 3.8 million scientific articles were selected for analysis. The study identified 70 406 cases of duplication of publications. In each of the detected cases, the same – or significantly similar – texts were published two or more (up to 73) times. An analysis of the most significant cases by number of publications is presented along with a detailed discussion of examples of the identified violations. A significant increase in the number and proportion of duplicated publications was identified in the period from 2014 to 2017. Conclusions are presented concerning shortcomings in editorial processes that allow duplication of publications along with the factual impossibility of detecting duplication in cases of simultaneous submission of the manuscript to different journals, the insufficient use by journal editors of means for detecting inappropriate borrowings and the need to retract a significant number of articles in connection with the identified violations.

Application of machine learning methods in plagiarism detection systems in order to solve the problem of highly specialized concepts

Application of machine learning methods in plagiarism detection systems in order to solve the problem of highly specialized concepts

Uji Plagiarism pada Tugas Mahasiswa Menggunakan Algoritma Winnowing

Plagiarism is the act of taking or plagiarizing the work, ideas or ideas of others either intentionally or unintentionally and claiming to be one's own work without mentioning the source or author. Often students plagiarize assignments given by the lecturer so that sometimes students just copy other people's assignments to complete the assignment. So that this can lead to dependence on other people in doing assignments and not being able to independently carry out assignments given by the lecturer. Currently, many plagiarism detection systems have been created to help reduce the level of plagiarism, one of which is the winnowing algorithm. In this study the authors used the winnowing algorithm to detect plagiarism in student assignments, namely programming source code, from the results of research conducted on 10 student assignments using the winnowing algorithm produced various similarity values as a percentage of similarity between two students tasks compared. With an average value of the overall similarity of the 10 tasks, namely 75.12%.

Semantic-Based Integrated Plagiarism Detection Approach for English Documents

The proposed work models a novel plagiarism detection system based on the semantic features to uncover the cases of plagiarism. The system constructs the dynamic relation matrix for each suspicious and source sentence pair to measure the degree of similarity using semantic features. Two Weighted Inverse Distance and GlossDice procedures show several text properties (synonyms, shortest path, etc.) to overcome the limitations of the existing features and new similarity metric for plagiarism detection are presented in this paper. Moreover, this research investigates the independent performance of various features to detect plagiarized cases and combine the best features by assigning different weight contributions to further enhance the system performance. Weighted Inverse Distance integrated with SynJaccard boosts the system performance and shows promising results. Initially, all the experiments were performed on PAN-PC-11dataset, and then PAN-14 text alignment dataset was used to validate the results of the proposed system. The effectiveness of the proposed system has been measured using standard performance measures i.e. Precision, Recall, F-measure, Granularity, and Plagdet score. The proposed system has outperformed the other baseline systems with precision (0.9459), recall (0.8861), f-measure (0.8917), and plagdet (0.8857) on the PAN-PC-11 dataset. For PAN-14 text alignment, the system exhibits precision (0.9257), recall (0.9055), f-measure (0.8931), and plagdet (0.8806).

Perancangan Sistem Deteksi Plagiarisme Skripsi (Judul Dan Abstrak) Berbasis Matlab Menggunakan Algoritma Winnowing

Plagiarism is an act of plagiarizing the work of others who will then acknowledge the work as one's own work without mentioning the source of the work. This research aims to create a plagiarism detection system using the winnowing algorithm in MATLAB to prevent plagiarism in the final project of the Mathematics Department students. In order to get the best k-gram value and window size that will be used in the system, a testing process is carried out between document I (100% data) and document II (80% data) by using variations in k-gram values ​​and window sizes. The test results show that the best k-gram ​​and window size are 12 and 4.

A two-phase plagiarism detection system based on multi-layer long short-term memory networks

Finding plagiarism strings between two given documents are the main task of the plagiarism detection problem. Traditional approaches based on string matching are not very useful in cases of similar semantic plagiarism. Deep learning approaches solve this problem by measuring the semantic similarity between pairs of sentences. However, these approaches still face the following challenging points. First, it is impossible to solve cases where only part of a sentence belongs to a plagiarism passage. Second, measuring the sentential similarity without considering the context of surrounding sentences leads to decreasing in accuracy. To solve the above problems, this paper proposes a two-phase plagiarism detection system based on multi-layer long short-term memory network model and feature extraction technique: (i) a passage-phase to recognize plagiarism passages, and (ii) a word-phase to determine the exact plagiarism strings. Our experiment results on PAN 2014 corpus reached 94.26% F-measure, higher than existing research in this field.

The multiple uses of iThenticate in doctoral education: Policing malpractice or improving research writing?

This article provides a description and analysis of the way in which research degree students and their supervisors at one Australian university (the University of South Australia) use a popular online plagiarism-detection system, iThenticate. The study identifies how these two groups use iThenticate by analysing usage data together with data from an anonymous online survey conducted 12 months after the university took out a pilot subscription to the system. One hundred and nineteen students and 26 supervisors responded to the survey, representing 61% and 43% of the active users in each category. The survey found that the two groups of respondents used the system differently but that, while for both groups iThenticate’s regulatory function in preventing plagiarism (whether international or accidental) was important, the system’s potential educational function in improving research writing capability and publication was equally important. The study highlights the value of regarding the use of anti-plagiarism software so as to encourage a move way from a simple focus on its punitive regulatory dimension and towards its educational possibilities and suggests directions for future research on the relationship between this type of software and the ways scholars work with other people’s texts to recreate meanings and develop original contributions.
 Implications for practice or policy:
 
 Online plagiarism detection systems (such as iThenticate) can be used either negatively to police doctoral students’ practice or positively to improve their research writing practice.
 Academic developers should promote a positive approach, aimed at improving research writing practice, as the preferable pedagogy in using online plagiarism-detection systems.

Plagiarism Detection Methods and Tools: An Overview

Plagiarism Detection Systems play an important role in revealing instances of a plagiarism act, especially in the educational sector with scientific documents and papers. The idea of plagiarism is that when any content is copied without permission or citation from the author. To detect such activities, it is necessary to have extensive information about plagiarism forms and classes. Thanks to the developed tools and methods it is possible to reveal many types of plagiarism. The development of the Information and Communication Technologies (ICT) and the availability of the online scientific documents lead to the ease of access to these documents. With the availability of many software text editors, plagiarism detections becomes a critical issue. A large number of scientific papers have already investigated in plagiarism detection, and common types of plagiarism detection datasets are being used for recognition systems, WordNet and PAN Datasets have been used since 2009. The researchers have defined the operation of verbatim plagiarism detection as a simple type of copy and paste. Then they have shed the lights on intelligent plagiarism where this process became more difficult to reveal because it may include manipulation of original text, adoption of other researchers' ideas, and translation to other languages, which will be more challenging to handle. Other researchers have expressed that the ways of plagiarism may overshadow the scientific text by replacing, removing, or inserting words, along with shuffling or modifying the original papers. This paper gives an overall definition of plagiarism and works through different papers for the most known types of plagiarism methods and tools.

Paraphrase type identification for plagiarism detection using contexts and word embeddings

Paraphrase types have been proposed by researchers as the paraphrasing mechanisms underlying acts of plagiarism. Synonymous substitution, word reordering and insertion/deletion have been identified as some of the common paraphrasing strategies used by plagiarists. However, similarity reports generated by most plagiarism detection systems provide a similarity score and produce matching sections of text with their possible sources. In this research we propose methods to identify two important paraphrase types – synonymous substitution and word reordering in paraphrased, plagiarised sentence pairs. We propose a three staged approach that uses context matching and pretrained word embeddings for identifying synonymous substitution and word reordering. Our proposed approach indicates that the use of Smith Waterman Algorithm for Plagiarism Detection and ConceptNet Numberbatch pretrained word embeddings produces the best performance in terms of hbox {F}_1 scores. This research can be used to complement similarity reports generated by currently available plagiarism detection systems by incorporating methods to identify paraphrase types for plagiarism detection.

Weighted semantic plagiarism detection approach based on AHP decision model

ABSTRACT The increasing rate of academic plagiarism is a social problem that engages institutions and publishers. Plagiarists try to mislead the plagiarism detection system using synonyms and inverted word order. Numerous algorithms tried to overcome these problems using structural and semantic detection. However, most of them focus on overcoming some challenges. Moreover, all of them consider the same significant degree for all terms of the documents. On the other hand, the time complexity is an essential parameter that must be considered. This paper presents an effective way to detect structural and semantic similarity degrees among two papers only using some part of the paper’s content instead of all content, decreasing the time complexity. The similarity is calculated using a set of impressive terms and various combinations to augment plagiarism detection ability even if the word order is changed. Different weight is assigned to the word according to its position in various sections of the paper. Finally, an AHP (Analytical Hierarchy Process) model uses to calculate a weighted similarity. The results indicated that the proposed approach has more ability to detect semantic academic plagiarism, and the runtime is reduced compared to similar ones.

An intelligent decision support system for software plagiarism detection in academia

The act of source code plagiarism is an academic offense that discourages the learning habits of students. Online support is available through which students can hire professional developers to code their regular programming tasks. These facilities make it easier for students to practice plagiarism. First, raw source codes are cleaned from noisy data to extract meaningful codes as the actual logic is more important to the programmers. Second, pre-processing techniques based on tokenization are used to convert filtered codes into meaningful tokens. It breaks the codes into small instances with the number of occurrences known as the frequency. Thirdly, the local and global weighting scheme method is applied to estimate the significance of each feature in an individual or a group of documents. It helps us greatly to zoom in on the importance of each feature of how effective it is for the next phase. Fourth, the single value decomposition method is used to reduce the dimensions of these features by maintaining the actual semantics of the source codes. This technique is used to remove overloaded noise information and collect only those features that are more effective for plagiarism detection. Fifth, the latent semantic analysis (LSA) technique is used to mine the actual semantics of the source codes in the form of latent variables. After that, the LSA features are used as input to cosine similarity to compute the plagiarism among different source codes. To validate the proposed approach, we used the topic modeling approach to group the relevant features into different topics.

Reliability assessment of the automatic plagiarism detection system for various editing patterns in documents containing complex mathematical notation

The paper presents the idea and implementation details of a novel tool intended for detection of plagiarism in mathematical content of LATEX files. The proposed algorithm compares LTeX files and detects possible similarities in mathematical formulas, providing an objective similarity score. It offers two modes of comparison: direct (symbolic) mode and verbalized math mode, where the mathematical expressions are first verbalized to spoken language form and compared as such. The solution has been tested against identified various types of plagiarism specific to mathematical symbolic notation and LATeX features, and proved to be an efficient tool for plagiarism detection.

Проблемы проверки выпускных квалификационных работ на объем заимствованного текста

Presented is analysis of issues of checking the final qualifying works for the amount of borrowed text and problems of controlling circumvention of this checking. Examined are some widely used ways methods to circumvent controlling in documents MS Word. Examined ways of circumvention were used for testing of fairly well-known plagiarism detection systems, such as Antiplagiat, TEXT.RU and RUKONTEXT.

A comparative Plagiarism Detection System methods between sentences

After the era of the World Wide Web, information is easily accessible with a single click. But this progression has drawbacks despite the ease of access to information. Plagiarism has a growing challenge to society, which impact on the academic world, researchers, and students in particular. This work discusses the plagiarism process, types, and detection methodologies. It presents the different plagiarism detection techniques based on syntactic and semantic approaches. The result of this work is a comparative survey of plagiarism detection system methods using the identification of syntactic and semantic similarities based a sentence-to-sentence comparison, and no longer word-to-word like the classical systems because the similarity between the sentences is a complex phenomenon.

Шляхи відновлення текстової інформації, представленої у вигляді логіко-лінгвістичної моделі

The materials of the article substantiate the urgency of solving the problem of identifying meaningful links in electronic text documents in order to further compare their content and improve the operation of plagiarism detection systems. An important step is to assess the reliability of the formed formal models. Therefore, the aim of this article is to study the algorithm of automatic analysis of logic and linguistic models of electronic text documents for the reproduction of textual information, which combines the basic properties of the text and its components. The logic and linguistic model of a text document reflects the main relationships between structural components; it is an ordered quadruple and an array of logic and linguistic models of sentences of natural language, which are included in the text. The author proposes several ways of restoration of textual information, starting from the structure of the logic and linguistic model of an electronic text document, which contains a linguistic and semantic-syntactic component. The article describes the schemes of text information recovery, it chooses the combined method, which provides the analysis of semantic-syntactic component in parallel with the analysis of the text base, in particular, its components - a set of sentences containing connections between logic and linguistic models of sentences of the text within electronic text document. It has been developed an algorithm for recovery of textual information presented in the form of a formal logic and linguistic model of an electronic text document, and there have been described the stages of abovementioned algorithm. All steps of the algorithm are demonstrated on the example of analysis of a specific given logic and linguistic model of a fragment of an electronic text document. The author conducted experiments on the restoration of textual information for scientific style texts. During experiments, it was revealed, that the main factors influencing the restoration of textual information include the removal of homonymy, as well as different interpretations of synonymous constructions and invariant forms of logic and linguistic models of sentences of natural language.

Peer review declaration for the International Conference PhysicA.SPb/2020

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.• Type of peer review: Single-blind Single-anonymous: authors’ identities are known to the reviewers, reviewers’ identities are hidden from authorsDouble-anonymous: author and reviewer identities are hidden to each otherTriple-blind: author and reviewer identities are hidden to each other, and from the Editor(s)Open: author and reviewer identities are known to each other• Describe criteria used by Reviewers when accepting/declining papers. Was there the opportunity to resubmit articles after revisions? The main criteria used by Reviewers when accepting/declining papers were the novelty of the research, its technical merit, scientific value and originality. Additionally, submissions were ranked basing on content, length, and language. In most of cases, the authors had the opportunity to resubmit articles after revisions. • Conference submission management system: https://physicaspb2020.iopconferenceseries.rivervalleytechnologies.com/ • Number of submissions received: 328 • Number of submissions sent for review: 273 • Number of submissions accepted: 254 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 77.4% • Average number of reviews per paper: 1 • Total number of reviewers involved: 391 • Any additional info on review process (ie plagiarism check system): All submissions were checked for plagiarism with Antiplagiat (#1 plagiarism detection system in Russia) https://www.antiplagiat.ru/ • Contact person for queries: Prof Grigorii Sokolovskii gs@mail.ioffe.ru

Similarity Search for Dynamic Data Streams

Nearest neighbor searching systems are an integral part of many online applications, including but not limited to pattern recognition, plagiarism detection, and recommender systems. With increasingly larger data sets, scalability has become an important issue. Many of the most space and running time efficient algorithms are based on locality-sensitive hashing. Here, we view the data set as an $n$ n by $|U|$ | U | matrix where each row corresponds to one of $n$ n users and the columns correspond to items drawn from a universe $U$ U . The de-facto standard approach to quickly answer nearest neighbor queries on such a data set is usually a form of min-hashing. Not only is min-hashing very fast, but it is also space efficient and can be implemented in many computational models aimed at dealing with large data sets such as MapReduce and streaming. However, a significant drawback is that minhashing and related methods are only able to handle insertions to user profiles and tend to perform poorly when items may be removed. We initiate the study of scalable locality-sensitive hashing (LSH) for fully dynamic data-streams. Specifically, using the Jaccard index as similarity measure, we design (1) a collaborative filtering mechanism maintainable in dynamic data streams and (2) a sketching algorithm for similarity estimation. Our algorithms have little overhead in terms of running time compared to previous LSH approaches for the insertion only case, and drastically outperform previous algorithms in case of deletions.