16 research outputs found
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
Detecting Machine-obfuscated Plagiarism
Related dataset is at https://doi.org/10.7302/bewj-qx93 and also listed in the dc.relation field of the full item record.Research on academic integrity has identified online paraphrasing tools as a severe threat to the effectiveness of plagiarism detection systems. To enable the automated identification of machine-paraphrased text, we make three contributions. First, we evaluate the effectiveness of six prominent word embedding models in combination with five classifiers for distinguishing human-written from machine-paraphrased text. The best performing classification approach achieves an accuracy of 99.0% for documents and 83.4% for paragraphs. Second, we show that the best approach outperforms human experts and established plagiarism detection systems for these classification tasks. Third, we provide a Web application that uses the best performing classification approach to indicate whether a text underwent machine-paraphrasing. The data and code of our study are openly available.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152346/1/Foltynek2020_Paraphrase_Detection.pdfDescription of Foltynek2020_Paraphrase_Detection.pdf : Foltynek2020_Paraphrase_Detectio
Enabling Viewpoint Learning through Dynamic Label Generation
Optimal viewpoint prediction is an essential task in many computer graphics
applications. Unfortunately, common viewpoint qualities suffer from two major
drawbacks: dependency on clean surface meshes, which are not always available,
and the lack of closed-form expressions, which requires a costly search
involving rendering. To overcome these limitations we propose to separate
viewpoint selection from rendering through an end-to-end learning approach,
whereby we reduce the influence of the mesh quality by predicting viewpoints
from unstructured point clouds instead of polygonal meshes. While this makes
our approach insensitive to the mesh discretization during evaluation, it only
becomes possible when resolving label ambiguities that arise in this context.
Therefore, we additionally propose to incorporate the label generation into the
training procedure, making the label decision adaptive to the current network
predictions. We show how our proposed approach allows for learning viewpoint
predictions for models from different object categories and for different
viewpoint qualities. Additionally, we show that prediction times are reduced
from several minutes to a fraction of a second, as compared to state-of-the-art
(SOTA) viewpoint quality evaluation. We will further release the code and
training data, which will to our knowledge be the biggest viewpoint quality
dataset available