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Introduction of RePriCo’13
(2013)
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.
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.
Gamification and gamified information systems (GIS) apply video game elements to encourage the work on boring and everyday tasks. Meanwhile, several research works provide evidence that gamification increases efficiency and effectivity of such tasks. The paper at hand investigates the health care sector, which is challenged with cost pressure and suffers in process efficiency. We hypothesize that GIS may improve the efficiency and quality of care processes. By applying an interview-based content analysis, the paper at hand evaluates gamification elements in an assisted living environment and provides three research contributions. First, insights into relevant GIS affordances and application examples for assisted living facilities are given. Second, assisted living experts evaluate GIS design guidelines. Both the relevant affordances and design principles comprise a basis for the development of a GIS for social workers in assisted living facilities. Third, potential adoption barriers and design guidelines for GIS in assisted living are presented.
A Gamified Information System (GIS) implements game concepts and elements, such as affordances and game design principles to motivate people. Based on the idea to develop a GIS to increase the motivation of software developers to perform software quality tasks, the research work at hand aims at investigating relevant requirements from that target group. Therefore, 14 interviews with software development experts are conducted and analyzed. According to the results, software developers prefer the affordances points, narrative storytelling in a multiplayer and a round-based setting. Furthermore, six design principles for the development of a GIS are derived.