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

Do Disease Stories Need a Hero? Effects of Human Protagonists on a Narrative Visualization about Cerebral Small Vessel Disease

Authors use various media formats to convey disease information to a broad audience, from articles and videos to interviews or documentaries. These media often include human characters, such as patients or treating physicians, who are involved with the disease. While artistic media, such as hand-crafted illustrations and animations are used for health communication in many cases, our goal is to focus on data-driven visualizations. Over the last decade, narrative visualization has experienced increasing prominence, employing storytelling techniques to present data in an understandable way. Similar to classic storytelling formats, narrative medical visualizations may also take a human character-centered design approach. However, the impact of this form of data communication on the user is largely unexplored. This study investigates the protagonist's influence on user experience in terms of engagement, identification, self-referencing, emotional response, perceived credibility, and time spent in the story. Our experimental setup utilizes a character-driven story structure for disease stories derived from Joseph Campbell's Hero's Journey. Using this structure, we generated three conditions for a cerebral small vessel disease story that vary by their protagonist: (1) a patient, (2) a physician, and (3) a base condition with no human protagonist. These story variants formed the basis for our hypotheses on the effect of a human protagonist in disease stories, which we evaluated in an online study with 30 participants. Our findings indicate that a human protagonist exerts various influences on the story perception and that these also vary depending on the type of protagonist.publishedVersio

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

Herstellung und Festigkeitsuntersuchungen nach DIN-Norm von Osteosyntheseschrauben aus boviner Kompakta (CB-Schrauben)

Schrauben sind das am häufigsten verwendete Implantat bei der Osteosynthese. Meist haben sie nur eine temporäre Funktion und werden in einer Zweitoperation wieder entfernt. Biodegradable Schrauben ersparen die Metallentfernung und ermöglichen eine allmähliche Lastübertragung auf den heilenden Knochen. Seit den sechziger Jahren werden Schrauben aus resorbierbaren Polymeren angewandt, jedoch werden Fremdkörperreaktionen und lange Abbauzeiten beobachtet. Seit 1918 werden Anwendungen von Schrauben, hergestellt aus Knochen zur Osteosynthese beschrieben. In der Literatur sind Schrauben aus boviner Kompakta bisher ungenügend mechanisch charakterisiert. Als erster wesentlicher Teil der Dissertaton wurden 200 CB-Schrauben (CB= Compact Bone) mit verschiedenen Gewindedurchmessern und -längen aus kortikalem Hintermittelfussknochen von Rinderbullen hergestellt, acetonbehandelt und per Autoklavierung 20. min bei 121°C thermosterilisiert. Um Festigkeitsuntersuchungen an den Schrauben wie bei der DIN-Normtestung vornehmen zu können, wurden als zweiter wichtiger Teil der Dissertation Prüfstände für einen Torsions-, Zug-, und Scherversuch entwickelt und hergestellt. Im dritten Teil der Dissertation wurden Bruchdrehmoment, Bruchzugkraft und Zugfestigkeit, sowie Bruchscherkraft und Scherfestigkeit ermittelt. Aus den Untersuchungsergebnissen lässt sich ableiten, dass die CB-Schrauben von der mechanischen Belastbarkeit her geeignet erscheinen, als Zugschrauben zur Stabilisierung von Low-stress Frakturen zu fungieren. Beim Eindrehen der Schrauben wird ein Drehmomentschlüssel empfohlen

AI Usage Cards: Responsibly Reporting AI-generated Content

<p>Posterpräsentation im Rahmen des 2. Text+ Plenarys: Connecting People and Data am 28./29. September an der SUB Göttingen.</p&gt