VMEXT: A Visualization Tool for Mathematical Expression Trees

Mathematical expressions can be represented as a tree consisting of terminal symbols, such as identifiers or numbers (leaf nodes), and functions or operators (non-leaf nodes). Expression trees are an important mechanism for storing and processing mathematical expressions as well as the most frequently used visualization of the structure of mathematical expressions. Typically, researchers and practitioners manually visualize expression trees using general-purpose tools. This approach is laborious, redundant, and error-prone. Manual visualizations represent a user's notion of what the markup of an expression should be, but not necessarily what the actual markup is. This paper presents VMEXT - a free and open source tool to directly visualize expression trees from parallel MathML. VMEXT simultaneously visualizes the presentation elements and the semantic structure of mathematical expressions to enable users to quickly spot deficiencies in the Content MathML markup that does not affect the presentation of the expression. Identifying such discrepancies previously required reading the verbose and complex MathML markup. VMEXT also allows one to visualize similar and identical elements of two expressions. Visualizing expression similarity can support support developers in designing retrieval approaches and enable improved interaction concepts for users of mathematical information retrieval systems. We demonstrate VMEXT's visualizations in two web-based applications. The first application presents the visualizations alone. The second application shows a possible integration of the visualizations in systems for mathematical knowledge management and mathematical information retrieval. The application converts LaTeX input to parallel MathML, computes basic similarity measures for mathematical expressions, and visualizes the results using VMEXT.Comment: 15 pages, 4 figures, Intelligent Computer Mathematics - 10th International Conference CICM 2017, Edinburgh, UK, July 17-21, 2017, Proceeding

Plagiarism and new media technologies: Combating 'cut 'n paste' culture

Whilst plagiarism has been around since pen was put to paper, the inextricable relationship that education now enjoys with new media technologies has seen its incidence increase to epidemic proportions. Plagiarism has become a blight on tertiary education, insidiously degrading the quality of degrees, largely thanks to ICTs providing students with ways to seamlessly misappropriate information. Many students are increasingly unsure how to avoid it and are being overseen by educators that cannot agree on what exactly constitutes academic dishonesty and how it should be effectively handled. This paper analyses the issues facing students and academics in light of new media in education and increasing moves to online learning. It considers the issues aggravating the problem; rising financial pressures, ambiguous cultural practices, practices in high school education; and seeks to provide a starting point for consistent, pedagogically sound approaches to the problem

Plagiarism Detection in arXiv

We describe a large-scale application of methods for finding plagiarism in research document collections. The methods are applied to a collection of 284,834 documents collected by arXiv.org over a 14 year period, covering a few different research disciplines. The methodology efficiently detects a variety of problematic author behaviors, and heuristics are developed to reduce the number of false positives. The methods are also efficient enough to implement as a real-time submission screen for a collection many times larger.Comment: Sixth International Conference on Data Mining (ICDM'06), Dec 200

Computers, the internet, and cheating among secondary school students: Some implications for educators

This article investigates in greater depth one particular aspect of cheating within secondary education and some implications for measuring academic achievement. More specifically, it examines how secondary students exploit the Internet for plagiarizing schoolwork, and looks at how a traditional method of educational assessment, namely paper-based report and essay writing, has been impacted by the growth of Internet usage and the proliferation of computer skills among secondary school students. One of the conclusions is that students’ technology fluency is forcing educators to revisit conventional assessment methods. Different options for combating Internet plagiarism are presented, and some software tools as well as non-technology solutions are evaluated in light of the problems brought about by “cyberplagiarism.

Preventing Code Insertion Attacks on Token-Based Software Plagiarism Detectors

Manchen Studierenden fehlt die Zeit oder Arbeitsbereitschaft, ihre Programmieraufgaben selbst zu lösen. Stattdessen plagiieren sie Abgaben ihre Kommilitonen, indem sie deren Code übernehmen und leicht verändern. Um dem vorzubeugen, existieren Programme, die beim Finden von Plagiaten unterstützen. Die geläufigste Art dieser Plagiatserkenner sind Token-basierte Plagiatserkenner. Diese sind resistent gegen viele Verschleierungsversuche, wie beispielsweise Variablenumbenennungen oder Umformatierung des Quelltextes. Sie sind jedoch generell anfällig gegen das Einfügen von Codezeilen, die den Programmfluss und das Ergebnis nicht beeinflussen. Die bisherige Annahme war, dass das erfolgreiche Verschleiern von Plagiaten mehr Zeit und Können erfordert, als die Aufgabe selbst zu lösen. Automatisierte Plagiatsgeneratoren brechen diese Annahme, indem sie die Anfälligkeit gegen Codeeinfügungen ausnutzten, um automatisiert Plagiate zu erstellen. Ziel dieser Arbeit ist es, Mechanismen zu finden, die in bestehende Token-basierte Plagiatserkenner integriert werden können, um die Resilienz gegen Codeeinfügung zu verbessern. Dazu entwerfen wir zunächst Mechanismen, die den negativen Effekt vieler Codeeinfügungen auf die Plagiatserkennung reduzieren können. Anschließend implementieren wir diese Mechanismen prototypisch in einem modernen Token-basierte Plagiatserkenner. Wir evaluieren unsere Implementierung anhand eines Datensatzes aus echten Programmierabgaben und aus Plagiaten, die wir automatisch generiert haben. Damit zeigen wir, dass die Verwendung unserer Mechanismen die gemessene Ähnlichkeit automatisch generierter Plagiate stark erhöht. Dadurch ist der von uns verwendete Plagiatsgenerator nicht mehr in der Lage, verwendbare Plagiate zu erstellen