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Kulturtechniken spielend erlernen : Über die Integrationspotenziale des vereinsorgansierten Sports
(2006)
Living in a world of plenty?
(2020)
Inequality in the distribution of economic wealth within populations has been rising steadily over the past century, having reached unprecedented highs in many Western societies. However, this development is not reflected in people’s perceptions of wealth inequality, as the public tends to underestimate it. Research suggests that inequality estimates are derived from personal reference groups, which, as we propose, are expanded by social network site (SNS) use. As content on SNSs frequently revolves around events of consumption, signaling enhanced overall population wealth, this study tests the hypothesis that SNS use distorts inequality perceptions downward, i.e., increases the perception of societal equality. Responses of 534 survey participants in the United States confirm that SNS use negatively predicts perceived inequality. The relationship is stronger the more SNS users perceive the content they encounter online as real, supporting the assumption that observing other people’s behavior online lowers estimates of nationwide wealth inequality. These findings provide novel insights on inequality misperceptions by suggesting individuals’ SNS use as a new predictor of perceived wealth inequality.
In recent years, computer vision algorithms based on machine learning have seen rapid development. In the past, research mostly focused on solving computer vision problems such as image classification or object detection on images displaying natural scenes. Nowadays other fields such as the field of cultural heritage, where an abundance of data is available, also get into the focus of research. In the line of current research endeavours, we collaborated with the Getty Research Institute which provided us with a challenging dataset, containing images of paintings and drawings. In this technical report, we present the results of the seminar "Deep Learning for Computer Vision". In this seminar, students of the Hasso Plattner Institute evaluated state-of-the-art approaches for image classification, object detection and image recognition on the dataset of the Getty Research Institute. The main challenge when applying modern computer vision methods to the available data is the availability of annotated training data, as the dataset provided by the Getty Research Institute does not contain a sufficient amount of annotated samples for the training of deep neural networks. However, throughout the report we show that it is possible to achieve satisfying to very good results, when using further publicly available datasets, such as the WikiArt dataset, for the training of machine learning models.
Landschaften im Wandel : Auswirkungen der globalen Erwärmung auf das Uvs-Nuur-Becken (NW-Mongolei)
(2004)
Sin pragmática no sería posible la comunicación, puesto que no podríamos interpretar enunciados lingüísticos. A cualquier aprendiente de una lengua que no domina, no le basta con ser competente lingüísticamente, puesto que su fin es comunicarse con otras personas y en contextos determinados. Solo una enseñanza que facilite la habilidad de producir y comprender enunciados para realizar actos de lengua, seleccionando los más apropiados para un contexto determinado, podrá preciarse de ser eficiente.
El trabajo que aquí se presenta pretende dar a conocer a la comunidad científica y, en especial, a los y las involucradas directa e indirectamente en el proceso de enseñanza, el concepto de pragmática verbal y contrastarlo con otros como gramática, cultura o interculturalidad, así como concienciarlos de la importancia y de la necesidad imperiosa del establecimiento de la pragmática como disciplina relevante en el proceso comunicativo y, en especial, de su inclusión sistemática y manifiesta en los libros de texto de español como lengua extranjera elaborados para el contexto escolar. Para ello se investiga la presencia de elementos pragmáticos y el fomento de la competencia pragmática en libros de texto para principiantes, por ser estos el material utilizado por excelencia en las escuelas y por su relevancia a la hora de especificar contenidos, tipo de progresión y metodología.
Modular and incremental global model management with extended generalized discrimination networks
(2023)
Complex projects developed under the model-driven engineering paradigm nowadays often involve several interrelated models, which are automatically processed via a multitude of model operations. Modular and incremental construction and execution of such networks of models and model operations are required to accommodate efficient development with potentially large-scale models. The underlying problem is also called Global Model Management.
In this report, we propose an approach to modular and incremental Global Model Management via an extension to the existing technique of Generalized Discrimination Networks (GDNs). In addition to further generalizing the notion of query operations employed in GDNs, we adapt the previously query-only mechanism to operations with side effects to integrate model transformation and model synchronization. We provide incremental algorithms for the execution of the resulting extended Generalized Discrimination Networks (eGDNs), as well as a prototypical implementation for a number of example eGDN operations.
Based on this prototypical implementation, we experiment with an application scenario from the software development domain to empirically evaluate our approach with respect to scalability and conceptually demonstrate its applicability in a typical scenario. Initial results confirm that the presented approach can indeed be employed to realize efficient Global Model Management in the considered scenario.