Plagiarism Of Ideas Research Articles

Types and Definitions of Plagiarism: An Overview

Plagiarism, the act of using someone else's work without proper attribution, is a pervasive issue in academia, professional fields, and creative endeavors. This paper provides a comprehensive overview of the various types and definitions of plagiarism, shedding light on its nuanced manifestations and implications. By categorizing plagiarism into distinct forms such as verbatim plagiarism, paraphrasing plagiarism, mosaic plagiarism, self-plagiarism, and idea plagiarism, this study aims to offer clarity and insight into the complexity of this ethical transgression. Additionally, it explores the evolving nature of plagiarism in the digital age, considering factors such as the ease of access to information, the proliferation of online content, and the challenges posed by emerging technologies. Through an examination of case studies and scholarly perspectives, this paper highlights the importance of promoting academic integrity and fostering a culture of originality and ethical scholarship. Moreover, it discusses strategies for plagiarism prevention and detection, emphasizing the role of education, awareness, and robust citation practices in addressing this critical issue

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications.

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document's idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document's main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model's ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10-15% for each increase in the number of samples in the dataset.

Named entity recognition and dependency parsing for better concept extraction in summary obfuscation detection

Summary obfuscation is a type of idea plagiarism where a summary of a text document is inserted into another text document so that it is more difficult to detect with ordinary plagiarism detection methods. Various methods have been developed to overcome this problem, one of which is based on genetic algorithms. This paper proposes a new approach for summary obfuscation detection based on named entity recognition and dependency parsing, which is straightforward but accurate and easy to analyze compared to genetic algorithm-based methods. The proposed method successfully detects summary obfuscation at the document level more accurately than existing genetic algorithm-based methods. Our method produced accuracy at sentence level up to more than 84% for specific benchmark and threshold cases. In addition, we have also tested our proposed method on other types of plagiarism, and the resulting accuracy is excellent.

Implementation of e-commerce in improving the competitiveness of vocational secondary education student entrepreneurship products

This study aimed to identify problems, describe, analyze, and evaluate the use of e-commerce to increase the competitiveness of innovative and entrepreneurial products produced by students of State Vocational High School 3 South Tangerang, Banten province. This research had three main stages, namely the preparation stage, the implementation stage, and the evaluation stage. The research location was the State Vocational High School 3 South Tangerang, Banten province, Indonesia, due to the school being outstanding in the field of Motorcycle and Animation expertise. The resource persons and respondents in this study were teaching teachers and students in classes 10-12 majoring in culinary, automotive, and animation. Implementing e-commerce activities at the production unit of State Vocational High School 3 South Tangerang, has increased the image of the production unit of State Vocational High School 3 South Tangerang. This study was a descriptive qualitative with the questionnaire, interview, and practice method. This study revealed that the application of e-commerce using the Shopee seller marketplace provided significant benefits and impacts for the production unit of State Vocational High School 3 South Tangerang. When the production unit implemented e-commerce, sales turnover increases rapidly, and the number of consumers, as a means of promotion, ease of transaction did not need to meet directly with buyers and can expand the business. However, there were also several obstacles, like weak human resources, intense competition, and the emergence of plagiarism of ideas and products.

ПЛАГИАТ В СОВЕТСКОЙ ИСТОРИЧЕСКОЙ НАУКЕ ВТОРОЙ ПОЛОВИНЫ 1940–1950-Х ГОДОВ

The study of ethical issues of research activitiesis an actual problem in historical science. These issues include value orientations and virtues of academics, as well asdeviations from norms, conflicts, and academic violations. The paper is devoted to the problem of plagiarism in Soviet historical science after 1945on the basis ofmaterials from the Archive of the Russian Academy of Sciences (RAS).The article examines the conflicts of Soviet research-es: N.M.Druzhinin, T.D.Shoinbaev, L.V.Cherepnin, A.M.Karasik, A.L.Sidorov, A.I.Gukovskiy, L.I.Zubok, I.S.Zvavich, P.Ya.Bukshpan and I.A.Fedosov.The author analyzes the activities of special commissions that con-sidered cases of plagiarismand discusses what the Soviet scholars meant speaking about plagiarism. The commis-sions’ expertisewasrelated tothe academic genre/type of historiographical source. The author definestwo types of deliberateunfair borrowing: textual plagiarism and plagiarism of ideas. Attention is paid to the issue of punishment for intellectual theftand tothe requirements of the accusers.Forming commissions is regarded as a reaction of the research community to ethical conflicts, and the attempt to resolve issues related to the suspicion of plagiarism in academia.The further perspective of thetopic is connected withthe study of the motivation of scholars-plagiarists

A Practical Guide for Plagiarism Detection Through the Coordinated Use of Software and (Human) Hardware

AbstractDuplication of content, whether it be text, figures, tables, or ideas, without duly referencing the original source of such content is called as plagiarism. Plagiarism of text can be flagged by similarity checking software. Careful curating of content before it is put into similarity checking software for screening as well as a thorough understanding of how such software works are essential to appropriately interpret such similarity check reports. Mere similarity is not plagiarism. Drawing an inference regarding plagiarism based on the output of similarity checking software requires considerable human input from editors and reviewers. Identification of plagiarism of figures, tables, and ideas almost entirely depends on the efforts of editors or reviewers rather than being detectable automatically. A thorough understanding of plagiarism is essential for prospective authors to safeguard against this academic sin.

Plagiarism detection of figure images in scientific publications

Plagiarism is stealing others' work using their words directly or indirectly without a credit citation. Copying others' ideas is another type of plagiarism that may occur in many areas but the most serious one is the academic plagiarism. Therefore, technical solutions are urgently required for automatic detection of idea plagiarism. Detection of figure plagiarism is a particularly challenging field of research, because not only the text analytics but also graphic features need to be analysed. This paper investigates the issues of idea and figure plagiarism and proposes a detection method which copes with both text and structure change. The procedure depends on finding similar semantic meanings between figures by applying image processing and semantic mapping techniques. The figures were compared using the representation of shape features based on detailed comparisons between the components of figures. This is an improvement over existing methods, which only compare the numbers and types of shapes inside figures.

Plagiarism detection and prevention: a primer for researchers.

Plagiarism is an ethical misconduct affecting the quality, readability, and trustworthiness of scholarly publications. Improving researcher awareness of plagiarism of words, ideas, and graphics is essential for avoiding unacceptable writing practices. Global editorial associations have publicized their statements on strategies to clean literature from redundant, stolen, and misleading information. Consulting related documents is advisable for upgrading author instructions and warning plagiarists of academic and other consequences of the unethical conduct. A lack of creative thinking and poor academic English skills are believed to compound most instances of redundant and “copy-and-paste” writing. Plagiarism detection software largely relies on reporting text similarities. However, manual checks are required to reveal inappropriate referencing, copyright violations, and substandard English writing.Medical researchers and authors may improve their writing skills and avoid the same errors by consulting the list of retractions due to plagiarism which are tracked on the PubMed platform and discussed on the Retraction Watch blog.

Idea plagiarism detection with recurrent neural networks and vector space model

PurposeNatural languages have a fundamental quality of suppleness that makes it possible to present a single idea in plenty of different ways. This feature is often exploited in the academic world, leading to the theft of work referred to as plagiarism. Many approaches have been put forward to detect such cases based on various text features and grammatical structures of languages. However, there is a huge scope of improvement for detecting intelligent plagiarism.Design/methodology/approachTo realize this, the paper introduces a hybrid model to detect intelligent plagiarism by breaking the entire process into three stages: (1) clustering, (2) vector formulation in each cluster based on semantic roles, normalization and similarity index calculation and (3) Summary generation using encoder-decoder. An effective weighing scheme has been introduced to select terms used to build vectors based on K-means, which is calculated on the synonym set for the said term. If the value calculated in the last stage lies above a predefined threshold, only then the next semantic argument is analyzed. When the similarity score for two documents is beyond the threshold, a short summary for plagiarized documents is created.FindingsExperimental results show that this method is able to detect connotation and concealment used in idea plagiarism besides detecting literal plagiarism.Originality/valueThe proposed model can help academics stay updated by providing summaries of relevant articles. It would eliminate the practice of plagiarism infesting the academic community at an unprecedented pace. The model will also accelerate the process of reviewing academic documents, aiding in the speedy publishing of research articles.

A deep learning based technique for plagiarism detection: a comparative study

<table width="0" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="593"><p>The ease of access to the various resources on the web-enabled the democratization of access to information but at the same time allowed the appearance of enormous plagiarism problems. Many techniques of plagiarism were identified in the literature, but the plagiarism of idea steels the foremost troublesome to detect, because it uses different text manipulation at the same time. Indeed, a few strategies have been proposed to perform semantic plagiarism detection, but they are still numerous challenges to overcome. Unlike the existing states of the art, the purpose of this study is to give an overview of different propositions for plagiarism detection based on the deep learning algorithms. The main goal of these approaches is to provide a high quality of worlds or sentences vector representation. In this paper, we propose a comparative study based on a set of criterions like: Vector representation method, Level Treatment, Similarity Method and Dataset. One result of this study is that most of researches are based on world granularity and use the word2vec method for word vector representation, which sometimes is not suitable to keep the meaning of the whole sentences. Each technique has strengths and weaknesses; however, none is quite mature for semantic plagiarism detection.</p></td></tr></tbody></table>

Information and Misinformation on COVID-19: a Cross-Sectional Survey Study.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has led to a large volume of publications, a barrage of non-reviewed preprints on various professional repositories and a slew of retractions in a short amount of time.MethodsWe conducted an e-survey using a cloud-based website to gauge the potential sources of trustworthy information and misinformation and analyzed researchers', clinicians', and academics' attitude toward unpublished items, and pre- and post-publication quality checks in this challenging time.ResultsAmong 128 respondents (mean age, 43.2 years; M:F, 1.1:1), 60 (46.9%) were scholarly journal editors and editorial board members. Social media channels were distinguished as the most important sources of information as well as misinformation (81 [63.3%] and 86 [67.2%]). Nearly two in five (62, 48.4%) respondents blamed reviewers, editors, and misinterpretation by readers as additional contributors alongside authors for misinformation. A higher risk of plagiarism was perceived by the majority (70, 58.6%), especially plagiarism of ideas (64.1%) followed by inappropriate paraphrasing (54.7%). Opinion was divided on the utility of preprints for changing practice and changing retraction rates during the pandemic period, and higher rejections were not supported by most (76.6%) while the importance of peer review was agreed upon by a majority (80, 62.5%). More stringent screening by journal editors (61.7%), and facilitating open access plagiarism software (59.4%), including Artificial Intelligence (AI)-based algorithms (43.8%) were among the suggested solutions. Most (74.2%) supported the need to launch a specialist bibliographic database for COVID-19, with information indexed (62.3%), available as open-access (82.8%), after expanding search terms (52.3%) and following due verification by academics (66.4%), and journal editors (52.3%).ConclusionWhile identifying social media as a potential source of misinformation on COVID-19, and a perceived high risk of plagiarism, more stringent peer review and skilled post-publication promotion are advisable. Journal editors should play a more active role in streamlining publication and promotion of trustworthy information on COVID-19.

Role Term-Based Semantic Similarity Technique for Idea Plagiarism Detection

Most of the text mining systems are based on statistical analysis of term frequency. The statistical analysis of term (phrase or word) frequency captures the importance of the term within a document, but the techniques that had been proposed by now still need to be improved in terms of their ability to detect the plagiarized parts, especially for capturing the importance of the term within a sentence. Two terms can have a same frequency in their documents, but one term pays more to the meaning of its sentences than the other term. In this paper, we want to discriminate between the important term and unimportant term in the meaning of the sentences in order to adopt for idea plagiarism detection. This paper introduces an idea plagiarism detection based on semantic meaning frequency of important terms in the sentences. The suggested method analyses and compares text based on a semantic allocation for each term inside the sentence. SRL offers significant advantages when generating arguments for each sentence semantically. Promising experimental has been applied on the CS11 dataset and results revealed that the proposed technique's performance surpasses its recent peer methods of plagiarism detection in terms of Recall, Precision and F-measure.

BHA2

In the last decade, the plagiarism cases were increased and become a topical problem in the modern scientific world, caused by the quantity of textual information available online/offline. The authors' work deals on the development of a new plagiarism detector system called BHA2 which has as input the suspicious text (to be analysed) and the original texts (learning basis). It can detect the different forms of plagiarism based on: Google API to detect the cases of plagiarism with translation; text summarization to detect the plagiarism of idea; conceptual transformation to detect the plagiarism with synonymy; bag of phrases to detect the paraphraser plagiarism; the social worker bees algorithm that was inspired from the lifestyle of social worker bees (forager, guardian, and cleaner) to select the documents source of plagiarism; the output of the authors' system are the plagiarised passages (the copied parts from the original texts) and the plagiarism percentage for each suspicious text. Their experiments were performed on the Pan 09 dataset and using the validation measures (recall, precision, accuracy, error, f-measure, and entropy, FPR, FNR, W-accuracy, ROC and TCR) in order to show the benefit derived from using such idea compared to the result of classical systems existed in literature. A comparative study in term of services was realised between their system and others commercial systems such as (check, Turnitin, and machine learning system) with their system. Finally, a visualization step was achieved for the purpose to see the outcome in graphical form (3d cub and cobweb) with more realism using the functionalities of zooming and rotation.

Detection of idea plagiarism using syntax–Semantic concept extractions with genetic algorithm

Plagiarism is increasingly becoming a major issue in the academic and educational domains. Automated and effective plagiarism detection systems are direly required to curtail this information breach, especially in tackling idea plagiarism. The proposed approach is aimed to detect such plagiarism cases, where the idea of a third party is adopted and presented intelligently so that at the surface level, plagiarism cannot be unmasked. The reported work aims to explore syntax-semantic concept extractions with genetic algorithm in detecting cases of idea plagiarism. The work mainly focuses on idea plagiarism where the source ideas are plagiarized and represented in a summarized form. Plagiarism detection is employed at both the document and passage levels by exploiting the document concepts at various structural levels. Initially, the idea embedded within the given source document is captured using sentence level concept extraction with genetic algorithm. Document level detection is facilitated with word-level concepts where syntactic information is extracted and the non-plagiarized documents are pruned. A combined similarity metric that utilizes the semantic level concept extraction is then employed for passage level detection. The proposed approach is tested on PAN13-1411http://pan.webis.de/.plagiarism corpus for summary obfuscation data, which represents a challenging case of idea plagiarism. The performance of the current approach and its variations are evaluated both at the document and passage levels, using information retrieval and PAN plagiarism measures respectively. The results are also compared against six top ranked plagiarism detection systems submitted as a part of PAN13-14 competition. The results obtained are found to exhibit significant improvement over the compared systems and hence reflects the potency of the proposed syntax-semantic based concept extractions in detecting idea plagiarism.

On the development of a plagiarism detection model based on citation analysis using a bibliographic database

A relatively new method for the detection of text plagiarism is proposed based on a search for original sources with an identical or similar list of references. First of all, this is applied to the most difficult to detect forms of translated plagiarism and the plagiarism of ideas. This method successfully proved itself in test studies of groups of foreign authors and is a continuation of our studies. The peculiarity of the approach that is proposed by the authors is the use of multidisciplinary bibliographic databases rather than full-text databases, which are often unavailable due to the high price of subscriptions under Russian conditions. The advantage of their use is the access to the maximum possible number of article references, which significantly extends the base for the search for originals while analyzing suspicious scientific publications. The step-by-step algorithm for addressing a query to the Web of Science and Scopus databases and analysis of the obtained data can be recommended for implementation into systems of plagiarism detection as an additional component.

A New Algorithm of Grouping Cockroaches Classifier (GCC) for Textual Plagiarism Detection

In the last decade with the new technology, it is important to allow users to access information freely, while at the same time, restrict them from illegal copying and distribution of information. In the age of information technologies plagiarism has become a topical subject in the digital world and turned into a serious problem. The author's work deals with the development of a new system for combating this phenomenon using a new insect behaviour algorithm called Groping cockroaches classifier GCC. Each suspicious text (cockroach) will be classified (hidden) in a class (shelter) that can be plagiarism or no-plagiarism, using a security function that is based on the attractiveness of each class (calculated using the aggregation operators (shelter darkness, congeners attraction and security quality)) and the displacement probability (calculated using the naive Bayes algorithm). The experimental results performed on the Pan 09 dataset and using the validation measures (recall, precision, f-measure, and entropy), have demonstrated that GCC has clear advantages over others plagiarism detection techniques existed in literature. Finally, a set of service was added in order to detect the different cases of plagiarism such as plagiarism with translation, plagiarism of idea, plagiarism with synonymy, and plagiarism paraphrase.

Guidelines on What Constitutes Plagiarism and Electronic Tools to Detect it.

Plagiarism is a serious ethical problem among scientific publications. There are various definitions of plagiarism, and the major categories include unintentional (unsuitable paraphrasing or improper citations) and intentional. Intentional plagiarism includes mosaic plagiarism, plagiarism of ideas, plagiarism of text, and self-plagiarism. There are many Web sites and software packages that claim to detect plagiarism effectively. A violation of plagiarism laws can lead to serious consequences including author banning, loss of professional reputation, termination of a position, and even legal action.

Citation‐based plagiarism detection: Practicability on a large‐scale scientific corpus

The automated detection of plagiarism is an information retrieval task of increasing importance as the volume of readily accessible information on the web expands. A major shortcoming of current automated plagiarism detection approaches is their dependence on high character‐based similarity. As a result, heavily disguised plagiarism forms, such as paraphrases, translated plagiarism, or structural and idea plagiarism, remain undetected. A recently proposed language‐independent approach to plagiarism detection, Citation‐based Plagiarism Detection (CbPD), allows the detection of semantic similarity even in the absence of text overlap by analyzing the citation placement in a document's full text to determine similarity. This article evaluates the performance of CbPD in detecting plagiarism with various degrees of disguise in a collection of 185,000 biomedical articles. We benchmark CbPD against two character‐based detection approaches using a ground truth approximated in a user study. Our evaluation shows that the citation‐based approach achieves superior ranking performance for heavily disguised plagiarism forms. Additionally, we demonstrate CbPD to be computationally more efficient than character‐based approaches. Finally, upon combining the citation‐based with the traditional character‐based document similarity visualization methods in a hybrid detection prototype, we observe a reduction in the required user effort for document verification.

Analysis of Contradictions in Online Collaborative Learning using Activity Theory as Analytical Framework

While previous studies have cited the benefits of incorporating online collaborative learning (OCL) in teaching and learning, there are also shortcomings of OCL that should not be ignored. This study aimed to investigate the constraints of incorporating OCL in a Malaysian tertiary classroom using Activity Theory as analytical framework. Activity Theory proved to be useful, particularly for OCL, because it provided a structure for identifying internal contradictions, also referred as tensions or constraints as result of interaction by the components of the OCL. Hence, the objectives of this paper are twofold: to identify constraints of OCL as perceived by students; and to investigate their suggestions or recommendations for improvement of OCL. The findings indicated two keys constraints: technology-related contradictions, which are related to desire for synchronous feedback in forum discussions, cut and paste and plagiarism of ideas, and other technological distractions; and group discussion contradictions. These refer to repetitive and mixed-up postings, clashes on topics of discussion, and discussions being too formal. Suggestions for improvement are reported by students regarding aspects of personalizing an online collaborative learning template; and providing additional support for collaborating online.

Policy of the Indian Journal of Psychiatry on the problem of plagiarism.

Byline: T. Sathyanarayana Rao, Chittaranjan. Andrade Editors of journals frequently face academic and administrative problems, and sometimes ethical problems, as well; plagiarism, though uncommon, is one of these. Plagiarism is most simply defined as the misrepresentation of another's ideas or words as one's own, without acknowledgement of the original source. [sup][1] The concept is broad, and includes plagiarism of concepts as well as plagiarism of text. Plagiarism of original material may be total, or the guilty author may interlace his own words and ideas with those from the source, constituting what is known as mosaic plagiarism. Authors may also reproduce their own, previously published material; this is called self-plagiarism, and duplicate publication is an extreme example of such an act. [sup][2] The temptation to plagiarize may at least in part be driven by the pressure to publish for academic survival, poor language and composition abilities, lack of time, or a combination of these factors. Opportunities for plagiarism are increasing because of the greater availability of source materials through online resources. Whereas the full extent of plagiarism in biomedical literature is unknown because only glaring examples tend to be spotted, it is now becoming easier to detect copy-paste plagiarism of text through the use of software, some applications for which are freely available. [sup][2] Curiously, a large proportion of journals does not have a policy in place to address plagiarism. In December 2011, Bosch et al . [sup][3] surveyed the misconduct policies of 399 high-impact biomedical journals in 27 biomedical categories of the Journal Citation Reports. They found that plagiarism was explicitly mentioned by only 56% of the journals, and plagiarism-checking services were used by only 28%. The Indian Journal of Psychiatry did not have a formal plagiarism policy, earlier. However, there were two events which led to a revision of editorial policy. One was the identification of an almost word-for-word reproduction of an entire article on poverty, social stress, and mental health from one Indian journal [sup][4] to another. [sup][5] The other was the identification of the reproduction of significant quantities of text from a previously published book into a review in the Indian Journal of Psychiatry, earlier this year. Fortunately, the latter instance of plagiarism was detected immediately after the journal was posted online and before the print issues were commissioned, and so it was possible for the article to be removed from both online and print editions of the journal. In order that we do not face similar problems in the future, the Indian Journal of Psychiatry is instituting a policy on plagiarism. The Journal defines plagiarism as follows: *Plagiarism of ideas is the presentation of the ideas of another author or group of authors without reference to the source. *Plagiarism of text is the presentation of one or more sentences of text from another source without reference to the source. *Self-plagiarism is the reproduction of one's own ideas, data, or text without reference to the source. In this context, plagiarism will include a full sentence that is identical with that in another source, or several sentences in sequence that are substantially identical with those in another source. …