Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

Performance assessment of a ductless personalized ventilation system using a validated CFD model

Abstract: The aim of this study is twofold: to validate a computational fluid dynamics (CFD) model, and then to use the validated model to evaluate the performance of a ductless personalized ventilation (DPV...

Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness.

Abstract: Predicting the persuasiveness of arguments has applications as diverse as writing assistance, essay scoring, and advertising. While clearly relevant to the task, the personal characteristics of an argument’s source and audience have not yet been fully exploited toward automated persuasiveness prediction. In this paper, we model debaters’ prior beliefs, interests, and personality traits based on their previous activity, without dependence on explicit user profiles or questionnaires. Using a dataset of over 60,000 argumentative discussions, comprising more than three million individual posts collected from the subreddit r/ChangeMyView, we demonstrate that our modeling of debater’s characteristics enhances the prediction of argument persuasiveness as well as of debaters’ resistance to persuasion.

From keywords to keyqueries: content descriptors for the web

Abstract: We introduce the concept of keyqueries as dynamic content descriptors for documents. Keyqueries are defined implicitly by the index and the retrieval model of a reference search engine: keyqueries for a document are the minimal queries that return the document in the top result ranks. Besides applications in the fields of information retrieval and data mining, keyqueries have the potential to form the basis of a dynamic classification system for future digital libraries---the modern version of keywords for content description. To determine the keyqueries for a document, we present an exhaustive search algorithm along with effective pruning strategies. For applications where a small number of diverse keyqueries is sufficient, two tailored search strategies are proposed. Our experiments emphasize the role of the reference search engine and show the potential of keyqueries as innovative document descriptors for large, fast evolving bodies of digital content such as the web.