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To date, the way that academic misconduct is manifested in undergraduate mathematics coursework has been unexamined in the literature, with the consequence that policy and preventive education can fail to address it appropriately. This paper describes the particular features of the responses expected in mathematical tasks and provides concrete examples of what lapses of integrity look like in this context. The intent is to lay groundwork for discussion and a response to this issue from within our discipline.

The article informs about case study of a serious and multiple ethical misconduct after an author submitted a paper to five journals for publication and committed some form of plagiarism. Topics include mathematics and in sciences in general, multiple submissions of the same results or parallel papers are prohibited as serious ethical misconduct; and mathematics simultaneous or concurrent submission of a manuscript describing the same research publication in the electronic online systems.

Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead. [ABSTRACT FROM AUTHOR]

The road from bipolar fuzzy sets to equilibrium-based mathematical abstraction is surveyed. A continuing historical debate on bipolarity and isomorphism is outlined. Related literatures are critically reviewed to counter plagiarism, distortion, renaming, and sophistry. Based on the debate, the term "isomorphistry" is coined. It is concluded that if isomorphism is used correctly it can be helpful in mathematics. If abused it may become isomorphistry—a kind of historical, socially constructed, entrenched, and "noble" hypocrisy hindering major scientific advances. It is shown that isomorphistry can be motivated by "denying" the originality of bipolar fuzzy sets and aimed at "justifying" plagiarism and distortion. Thus, isomorphistry is sophistry on isomorphism. Some (-,+)-bipolar isomorphistry behaviors are critiqued. YinYang vs. YangYin are distinguished. The geometrical and logical basis of equilibrium-based AI&QI computing machinery is introduced as a new computing paradigm with logically definable causality for mind-body unity. A philosophical joke on sophistry is appended. [ABSTRACT FROM AUTHOR]

Over the history of mankind, textual records change. Sometimes due to mistakes during transcription, sometimes on purpose, as a way to rewrite facts and reinterpret history. There are several classical cases, such as the logarithmic tables, and the transmission of antique and medieval scholarship. Today, text documents are largely edited and redistributed on the Web. Articles on news portals and collaborative platforms (such as Wikipedia), source code, posts on social networks, and even scientific publications or literary works are some examples in which textual content can be subject to changes in an evolutionary process. In this scenario, given a set of near-duplicate documents, it is worthwhile to find which one is the original and the history of changes that created the whole set. Such functionality would have immediate applications on news tracking services, detection of plagiarism, textual criticism, and copyright enforcement, for instance. However, this is not an easy task, as textual features pointing to the documents’ evolutionary direction may not be evident and are often dataset dependent. Moreover, side information, such as time stamps, are neither always available nor reliable. In this paper, we propose a framework for reliably reconstructing text phylogeny trees, and seamlessly exploring new approaches on a wide range of scenarios of text reusage. We employ and evaluate distinct combinations of dissimilarity measures and reconstruction strategies within the proposed framework, and evaluate each approach with extensive experiments, including a set of artificial near-duplicate documents with known phylogeny, and from documents collected from Wikipedia, whose modifications were made by Internet users. We also present results from qualitative experiments in two different applications: text plagiarism and reconstruction of evolutionary trees for manuscripts (stemmatology). [ABSTRACT FROM AUTHOR]

Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets.