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Haftung im Vereinsrecht
(2008)
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
Grenzenlose Liebe
(2008)
Grenzen und Chancen der Neuverteilung von Instandhaltungsaufgaben bei einem Massenstahlproduzenten
(1995)
Goal Driven Business Modelling - Supporting Decision Making within Information System Development
(1995)
Gleichstellungsabrede – Gestaltungsmöglichkeiten und -notwendigkeiten für die betriebliche Praxis
(2007)
A explicação do conceito científico de Gestão de competências mostra o valor de desenvolvimento dos subordinarios. Ferramentas práticas explicam o conceito através de experiências de empresas européias. As Competência sao os seguintes: Competência especializada, Competência Metódica, Competência social, Competência individual.
Generating synthetic LiDAR point cloud data for object detection using the Unreal Game Engine
(2024)
Object detection based on artificial intelligence is ubiquitous in today’s computer vision research and application. The training of the neural networks for object detection requires large and high-quality datasets. Besides datasets based on image data, datasets derived from point clouds offer several advantages. However, training datasets are sparse and their generation requires a lot of effort, especially in industrial domains. A solution to this issue offers the generation of synthetic point cloud data. Based on the design science research method, the work at hand proposes an approach and its instantiation for generating synthetic point cloud data based on the Unreal Engine. The point cloud quality is evaluated by comparing the synthetic cloud to a real-world point cloud. Within a practical example the applicability of the Unreal Game engine for synthetic point cloud generation could be successfully demonstrated.