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.

Predicting quality flaws in user-generated content: the case of wikipedia

Abstract: The detection and improvement of low-quality information is a key concern in Web applications that are based on user-generated content; a popular example is the online encyclopedia Wikipedia Existing research on quality assessment of user-generated content deals with the classification as to whether the content is high-quality or low-quality This paper goes one step further: it targets the prediction of quality flaws, this way providing specific indications in which respects low-quality content needs improvement The prediction is based on user-defined cleanup tags, which are commonly used in many Web applications to tag content that has some shortcomings We apply this approach to the English Wikipedia, which is the largest and most popular user-generated knowledge source on the Web We present an automatic mining approach to identify the existing cleanup tags, which provides us with a training corpus of labeled Wikipedia articles We argue that common binary or multiclass classification approaches are ineffective for the prediction of quality flaws and hence cast quality flaw prediction as a one-class classification problem We develop a quality flaw model and employ a dedicated machine learning approach to predict Wikipedia's most important quality flaws Since in the Wikipedia setting the acquisition of significant test data is intricate, we analyze the effects of a biased sample selection In this regard we illustrate the classifier effectiveness as a function of the flaw distribution in order to cope with the unknown (real-world) flaw-specific class imbalances The flaw prediction performance is evaluated with 10,000 Wikipedia articles that have been tagged with the ten most frequent quality flaws: provided test data with little noise, four flaws can be detected with a precision close to 1

Retrieval of the Best Counterargument without Prior Topic Knowledge

Abstract: Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments’ premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60% accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.

Learning behavior models for hybrid timed systems

Abstract: A tailored model of a system is the prerequisite for various analysis tasks, such as anomaly detection, fault identification, or quality assurance. This paper deals with the algorithmic learning of a system's behavior model given a sample of observations. In particular, we consider real-world production plants where the learned model must capture timing behavior, dependencies between system variables, as well as mode switches--in short: hybrid system's characteristics. Usually, such model formation tasks are solved by human engineers, entailing the well-known bunch of problems including knowledge acquisition, development cost, or lack of experience. Our contributions to the outlined field are as follows. (1) We present a taxonomy of learning problems related to model formation tasks. As a result, an important open learning problem for the domain of production system is identified: The learning of hybrid timed automata. (2) For this class of models, the learning algorithm HyBUTLA is presented. This algorithm is the first of its kind to solve the underlying model formation problem at scalable precision. (3) We present two case studies that illustrate the usability of this approach in realistic settings. (4) We give a proof for the learning and runtime properties of HyBUTLA.