Plagiarism detection

About: Plagiarism detection is a research topic. Over the lifetime, 1790 publications have been published within this topic receiving 24740 citations.

Combating unethical publications with plagiarism detection services

Abstract: About 3,000 new citations that are highly similar to citations in previously published manuscripts that appear each year in the biomedical literature (Medline) alone. This underscores the importance for the opportunity for editors and reviewers to have detection system to identify highly similar text in submitted manuscripts so that they can then review them for novelty. New software-based services, both commercial and free, provide this capability. The availability of such tools provides both a way to intercept suspect manuscripts and serve as a deterrent. Unfortunately, the capabilities of these services vary considerably, mainly as a consequence of the availability and completeness of the literature bases to which new queries are compared. Most of the commercial software has been designed for detection of plagiarism in high school and college papers; however, there is at least 1 fee-based service (CrossRef) and 1 free service (etblast.org), which are designed to target the needs of the biomedical publication industry. Information on these various services, examples of the type of operability and output, and things that need to be considered by publishers, editors, and reviewers before selecting and using these services is provided.

The Instructional Challenges of Student Plagiarism

Abstract: The focus of this article is university teachers’ and students’ views of plagiarism, plagiarism detection, and the use of plagiarism detection software as learning support. The data were collected from teachers and students who participated in a pilot project to test plagiarism detection software at a major university in Finland. The data were analysed through factor analysis, T-tests and inductive content analysis. Three distinct reasons for plagiarism were identified: intentional, unintentional and contextual. The teachers did not utilise plagiarism detection to support student learning to any great extent. We discuss the pedagogical implications and suggest that the contextual reasons for plagiarism require focus primarily on study strategies, whereas the intentional reasons require profound discussion about attitudes and conceptions of good learning and university-level study habits.

Plagiarism detection and prevention: Are we putting the cart before the horse

Abstract: Th e increasing ease of detecting internet plagiarism has intensifi ed debate in Australia, as well as the UK and the USA, on eff ective deterrents in the face of increasing evidence of plagiarism. Many universities are re-vamping their plagiarism policies and some conferences have themes entirely devoted to the subject of academic integrity. Policies and conference discussions relating to academic values and integrity have focussed on improved information on the rules of citation and attribution, coupled with more systematic vigilance and disciplinary procedures. Th e literature has also become increasingly insistent that information on rules of citation and attribution needs to be coupled with an appropriate apprenticeship into the conventions and language of academic writing. Yet there is a fi rst step that is still being overlooked, the initial induction of students into the research-led, evidence-based culture of academic endeavour. By focussing on rules and strategies for avoiding plagiarism, but ignoring the basic reasons for these requirements, we have put the cart before the horse. Th is paper suggests that tertiary induction of new students needs to focus fi rstly on developing an appreciation of the culture of enquiry that characterises learning at the tertiary level and that success is more likely if the students' goal is something positive: to achieve a new approach to learning, than if it is something negative: to avoid 'committing' plagiarism.

A Survey on Plagiarism Detection Systems

Abstract: Being a growing problem, plagiarism is generally defined as "literary theft" and "academic dishonesty" in the literature, and it is really has to be well-informed on this topic to prevent the problem and stick to the ethical principles. This paper presents a survey on plagiarism detection systems, a summary of several plagiarism types, techniques, and algorithms is provided. Common feature of deferent detection systems are described. At the end of this paper authors propose a web enabled system to detect plagiarism in documents , code and images, also this system could be used in E-Learning, E-Journal, and E-Business.

A blockchain-based code copyright management system

Abstract: With the increasing number of open-source software projects, code plagiarism has become one of the threats to the software industry. However, current research on code copyright protection mostly focuses on the approach for code plagiarism detection, failing to fundamentally solve the problem of copyright confirmation and protection. This paper proposes a blockchain-based code copyright management system. Firstly, an Syntax Tree-based code originality verification model is constructed. The originality of the uploaded code is determined through its similarity to other original codes. Secondly, the Peer-to-Peer blockchain network is designed to store the copyright information of the original code. The nodes in the blockchain network can verify the originality of the code based on the code originality verification model. Then, through the construction of blocks and legitimacy validation and linking of blocks, the blockchain-based code copyright management structure is built. The whole process guarantees that the copyright information is traceable and will not be tampered with. According to the experiments, the accuracy and processing time of the code originality verification model are shown to meet code originality verification requirements. The experiment also shows that the best storage type of the code copyright information is the code fingerprint which is a 256bits hash value converted from code eigenvalues. It performs better in both response speed and storage efficiency. Moreover, because of the uniqueness and irreversibility of the result from the SHA256 algorithm, the code fingerprint storage yields a better level of storage security. In summary, this paper proposes a blockchain-based code copyright management system which provides better response speed and storage efficiency.