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

Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations

Identifying academic plagiarism is a pressing task for educational and research institutions, publishers, and funding agencies. Current plagiarism detection systems reliably find instances of copied and moderately reworded text. However, reliably detecting concealed plagiarism, such as strong paraphrases, translations, and the reuse of nontextual content and ideas is an open research problem. In this paper, we extend our prior research on analyzing mathematical content and academic citations. Both are promising approaches for improving the detection of concealed academic plagiarism primarily in Science, Technology, Engineering and Mathematics (STEM). We make the following contributions: i) We present a two-stage detection process that combines similarity assessments of mathematical content, academic citations, and text. ii) We introduce new similarity measures that consider the order of mathematical features and outperform the measures in our prior research. iii) We compare the effectiveness of the math-based, citation-based, and text-based detection approaches using confirmed cases of academic plagiarism. iv) We demonstrate that the combined analysis of math-based and citation-based content features allows identifying potentially suspicious cases in a collection of 102K STEM documents. Overall, we show that analyzing the similarity of mathematical content and academic citations is a striking supplement for conventional text-based detection approaches for academic literature in the STEM disciplines.Comment: Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL) 2019. The data and code of our study are openly available at https://purl.org/hybridP

Identificando plágio externo com Locality-sensitive hashing

Heuristic Retrieval task aims to retrieve a set of documents from which the external plagiarism detection identifies plagiarized pieces of text. In this context, we present Minmax Circular Sector Arcs algorithms that treats HR task as an approximate k-nearest neighbor search problem. Moreover, Minmax Circular Sector Arcs algorithms aim to retrieve the set of documents with greater amounts of plagiarized fragments, while reducing the amount of time to accomplish the HR task. Our theoretical framework is based on two aspects: (i) a triangular property to encode a range of sketches on a unique value; and (ii) a Circular Sector Arc property which enables (i) to be more accurate. Both properties were proposed for handling high-dimensional spaces, hashing them to a lower number of hash values. Our two Minmax Circular Sector Arcs methods, Minmax Circular Sector Arcs Lower Bound and Minmax Circular Sector Arcs Full Bound, achieved Recall levels slightly more imprecise than Minmaxwise hashing in exchange for a better Speedup in document indexing and query extraction and retrieval time in high-dimensional plagiarism related datasets.A tarefa de recuperação heurística tem como objetivo resgatar um conjunto de documentos dos quais a identificação de plágio externo identifica de pedaços de texto plagiado. Neste contexto, o presente trabalho apresenta os algoritmos Minmax Circular Sector Arcs que lidam com a tarefa de recuperação heurística como um problema de busca aproximada dos vizinhos mais próximos. Ademais, os algoritmos Minmax Circular Sector Arcs têm como objetivo recuperar documentos com grande quantidade de fragmentos plagiados enquanto reduz a quantidade de tempo para realizar a tarefa recuperação heurística. O ferramental teórico proposto é baseado em dois aspectos: (i) uma propriedade triangular que codifica um conjunto de esbo¸cos em um valor único; e (ii) a propriedade baseada em Arcos de Setores Circulares que melhoram a precisão de (i). Ambas as propriedades foram propostas para lidar com espaços de alta dimensionalidade, representando-os em um número pequendo de valores de hash. Os dois métodos Minmax Circular Sector Arcs aqui propostos, alcunhados de Minmax Circular Sector Arcs Lower Bound e Minmax Circular Sector Arcs Full Bound alcançaram níveis de recall singelamente mais imprecisos que o método Minmaxwise em troca de uma aceleração durante a indexação de documentos e da redução do tempo de extração e busca de consultas em coleções de dados de plágio de alta dimensionalidade