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Bremen, Germany

The German Research Center for Artificial Intelligence is one of the world's largest nonprofit contract research institutes in the field of innovative software technology based on artificial intelligence methods. DFKI was founded in 1988, and has facilities in the German cities of Kaiserslautern, Saarbrücken, Bremen and Berlin.DFKI shareholders include Microsoft, SAP, BMW and Daimler. The current directors are Prof. Wolfgang Wahlster and Dr. Walter G. Olthoff . Wikipedia.

Krestel R.,search Center | Fankhauser P.,German Research Center for Artificial Intelligence
Neurocomputing | Year: 2012

More and more content on the Web is generated by users. To organize this information and make it accessible via current search technology, tagging systems have gained tremendous popularity. Especially for multimedia content they allow to annotate resources with keywords (tags) which opens the door for classic text-based information retrieval. To support the user in choosing the right keywords, tag recommendation algorithms have emerged. In this setting, not only the content is decisive for recommending relevant tags but also the user's preferences.In this paper we introduce an approach to personalized tag recommendation that combines a probabilistic model of tags from the resource with tags from the user. As models we investigate simple language models as well as Latent Dirichlet Allocation. Extensive experiments on a real world dataset crawled from a big tagging system show that personalization improves tag recommendation, and our approach significantly outperforms state-of-the-art approaches. © 2011 Elsevier B.V. Source

Seifert I.,German Research Center for Artificial Intelligence
Information Visualization | Year: 2011

Abstract In this article, I present a novel relational visualization that supports people at exploration of scientific literature in digital libraries. This visualization provides an integrated view of multiple dimensions concealed in the scientific literature. It displays, for example, authors and scientific organizations together with freely defined search queries and highlights the intersections between them. The proposed visual representation introduces interactive drag-and-drop operations for manipulation of queries in order to retrieve further results. These operations enable information seekers to employ efficient online search strategies that involve Boolean AND, OR and NOT operators. In doing so, an information seeker can refine (or relax) various search queries in an interactive way during a focusing or a defocusing phase. The intersections of queries are made explicitly visible to enable the information seeker to build an individual picture of the research area under investigation and to avoid frustrating 'zero hit' situations. © SAGE Publications, 2011. Source

Bongardt B.,German Research Center for Artificial Intelligence
Journal of Geometric Mechanics | Year: 2014

In this article, a novel characterization of the workspace of 3R chains with non-orthogonal, intersecting axes is derived by describing the set of singular orientations as two tori that separate two-solvable from non-solvable orientations within SO(3). Therefore, the tori provide the boundary of the workspace of the axes' constellation. The derived characterization generalizes a recent result obtained by Piovan and Bullo. It is based on a specific, novel representation of rotations, called unit ball representation, which allows to interpret the workspace characterization with ease. In an appendix, tools for dealing with angles and rotations are introduced and the equivalence of unit quaternion representation and unit ball representation is described. © American Institute of Mathematical Sciences. Source

Zuehlke D.,SmartFactoryKL | Zuehlke D.,German Research Center for Artificial Intelligence
Annual Reviews in Control | Year: 2010

In 1991, Mark Weiser described the vision of a future world under the name of Ubiquitous Computing. Since then, many details of the described vision have become reality: Our mobile phones are powerful multimedia systems, our cars computer systems on wheels, and our homes are turning into smart living environments. All these advances must be turned into products for very cost-sensitive world markets in shorter cycles than ever before. Today, the resulting requirements for design, setup, and operation of our factories become crucial for success. In the past, we often increased the complexity in structures and control systems, resulting in inflexible monolithic production systems. But the future must become "lean"-not only in organization, but also in planning and technology!Wemust develop technologies which allow us to speed up planning and setup, to adapt to rapid product changes during operation, and to reduce the planning effort. To meet these challenges we should also make use of the smart technologies of our daily lives. But for industrial use, there are many open questions to be answered. The existing technologies may be acceptable for consumer use but not yet for industrial applications with high safety and security requirements. Therefore, the SmartFactoryKL initiative was founded by industrial and academic partners to create and operate a demonstration and research test bed for future factory technologies. Many projects develop, test, and evaluate new solutions. This presentation describes changes and challenges, and it summarizes the experience gained to date in the SmartFactoryKL. © 2010 Elsevier Ltd. Source

Shafait F.,German Research Center for Artificial Intelligence | Breuel T.M.,University of Kaiserslautern
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2011

Projection methods have been used in the analysis of bitonal document images for different tasks such as page segmentation and skew correction for more than two decades. However, these algorithms are sensitive to the presence of border noise in document images. Border noise can appear along the page border due to scanning or photocopying. Over the years, several page segmentation algorithms have been proposed in the literature. Some of these algorithms have come into widespread use due to their high accuracy and robustness with respect to border noise. This paper addresses two important questions in this context: 1) Can existing border noise removal algorithms clean up document images to a degree required by projection methods to achieve competitive performance? 2) Can projection methods reach the performance of other state-of-the-art page segmentation algorithms (e.g., Docstrum or Voronoi) for documents where border noise has successfully been removed? We perform extensive experiments on the University of Washington (UW-III) data set with six border noise removal methods. Our results show that although projection methods can achieve the accuracy of other state-of-the-art algorithms on the cleaned document images, existing border noise removal techniques cannot clean up documents captured under a variety of scanning conditions to the degree required to achieve that accuracy. © 2006 IEEE. Source

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