Prof. Dr.-Ing.

Bauckhage, Christian

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Now showing 1 - 3 of 3
  • Publication
    Anonymization of German financial documents using neural network-based language models with contextual word representations
    ( 2022-03)
    Biesner, David
    ;
    Ramamurthy, Rajkumar
    ;
    Loitz, Rüdiger
    ;
    Lübbering, Max
    ;
    Hillebrand, Lars Patrick
    ;
    Ladi, Anna
    ;
    Stenzel, Robin
    ;
    Pielka, Maren
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    The automatization and digitalization of business processes have led to an increase in the need for efficient information extraction from business documents. However, financial and legal documents are often not utilized effectively by text processing or machine learning systems, partly due to the presence of sensitive information in these documents, which restrict their usage beyond authorized parties and purposes. To overcome this limitation, we develop an anonymization method for German financial and legal documents using state-of-the-art natural language processing methods based on recurrent neural nets and transformer architectures. We present a web-based application to anonymize financial documents and a large-scale evaluation of different deep learning techniques.
  • Publication
    Early drought stress detection in cereals: Simplex volume maximization for hyperspectral image analysis
    ( 2012)
    Römer, Christoph
    ;
    Wahabzada, Mirwaes
    ;
    Ballvora, Agim
    ;
    Pinto, Francisco
    ;
    Rossini, Micol
    ;
    Cinzia, Panigada
    ;
    Behmann, Jan
    ;
    Léon, Jens
    ;
    Thurau, Christian
    ;
    Bauckhage, Christian
    ;
    Kersting, Kristian
    ;
    Rascher, Uwe
    ;
    Plümer, Lutz
    Early water stress recognition is of great relevance in precision plant breeding and production. Hyperspectral imaging sensors can be a valuable tool for early stress detection with high spatio-temporal resolution. They gather large, high dimensional data cubes posing a significant challenge to data analysis. Classical supervised learning algorithms often fail in applied plant sciences due to their need of labelled datasets, which are difficult to obtain. Therefore, new approaches for unsupervised learning of relevant patterns are needed. We apply for the first time a recent matrix factorisation technique, simplex volume maximisation (SiVM), to hyperspectral data. It is an unsupervised classification approach, optimised for fast computation of massive datasets. It allows calculation of how similar each spectrum is to observed typical spectra. This provides the means to express how likely it is that one plant is suffering from stress. The method was tested for drought stress, applied to potted barley plants in a controlled rain-out shelter experiment and to agricultural corn plots subjected to a two factorial field setup altering water and nutrient availability. Both experiments were conducted on the canopy level. SiVM was significantly better than using a combination of established vegetation indices. In the corn plots, SiVM clearly separated the different treatments, even though the effects on leaf and canopy traits were subtle.
  • Publication
    Loveparade 2010: Automatic video analysis of a crowd disaster
    ( 2012)
    Krausz, Barbara
    ;
    Bauckhage, Christian
    On July 24, 2010, 21 people died and more than 500 were injured in a stampede at the Loveparade, a music festival, in Duisburg, Germany. Although this tragic incident is but one among many terrible crowd disasters that occur during pilgrimage, sports events, or other mass gatherings, it stands out for it has been well documented: there were a total of seven security cameras monitoring the Loveparade and the chain of events that led to disaster was meticulously reconstructed. In this paper, we present an automatic, video-based analysis of the events in Duisburg. While physical models and simulations of human crowd behavior have been reported before, to the best of our knowledge, automatic vision systems that detect congestions and dangerous crowd turbulences in real world settings were not reported yet. Derived from lessons learned from the video footage of the Loveparade, our system is able to detect motion patterns that characterize crowd behavior in stampedes. Based on our analysis, we propose methods for the detection and early warning of dangerous situations during mass events. Since our approach mainly relies on optical flow computations, it runs in real-time and preserves privacy of the people being monitored.