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Institute
Abstract knowledge is deeply grounded in many computer-based applications. An important research area of Artificial Intelligence (AI) deals with the automatic derivation of knowledge from data. Machine learning offers the according algorithms. One area of research focuses on the development of biologically inspired learning algorithms. The respective machine learning methods are based on neurological concepts so that they can systematically derive knowledge from data and store it. One type of machine learning algorithms that can be categorized as "deep learning" model is referred to as Deep Neural Networks (DNNs). DNNs consist of multiple artificial neurons arranged in layers that are trained by using the backpropagation algorithm. These deep learning methods exhibit amazing capabilities for inferring and storing complex knowledge from high-dimensional data.
However, DNNs are affected by a problem that prevents new knowledge from being added to an existing base. The ability to continuously accumulate knowledge is an important factor that contributed to evolution and is therefore a prerequisite for the development of strong AIs. The so-called "catastrophic forgetting" (CF) effect causes DNNs to immediately loose already derived knowledge after a few training iterations on a new data distribution. Only an energetically expensive retraining with the joint data distribution of past and new data enables the abstraction of the entire new set of knowledge. In order to counteract the effect, various techniques have been and are still being developed with the goal to mitigate or even solve the CF problem. These published CF avoidance studies usually imply the effectiveness of their approaches for various continual learning tasks.
This dissertation is set in the context of continual machine learning with deep learning methods. The first part deals with the development of an application-oriented real-world evaluation protocol which can be used to investigate different machine learning models with regard to the suppression of the CF effect. In the second part, a comprehensive study indicates that under the application-oriented requirements none of the investigated models can exhibit satisfactory continual learning results. In the third part, a novel deep learning model is presented which is referred to as Deep Convolutional Gaussian Mixture Models (DCGMMs). DCGMMs build upon the unsupervised approach of Gaussian Mixture Models (GMMs). GMMs cannot be considered as deep learning method and they have to be initialized in a data-driven manner before training. These aspects limit the use of GMMs in continual learning scenarios.
The training procedure proposed in this work enables the training of GMMs by using Stochastic Gradient Descent (SGD) (as applied to DNNs). The integrated annealing scheme solves the problem of a data-driven initialization, which has been a prerequisite for GMM training. It is experimentally proven that the novel training method enables equivalent results compared to conventional methods without iterating their disadvantages. Another innovation is the arrangement of GMMs in form of layers, which is similar to DNNs. The transformation of GMMs into layers enables the combination with existing layer types and thus the construction of deep architectures, which can derive more complex knowledge with less resources.
In the final part of this work, the DCGMM model is examined with regard to its continual learning capabilities. In this context, a replay approach referred to as Gaussian Mixture Replay (GMR) is introduced. GMR describes the generation and replay of data samples by utilizing the DCGMM functionalities. Comparisons with existing CF avoidance models show that similar continual learning results can be achieved by using GMR under application-oriented conditions. All in all, the presented work implies that the identified application-oriented requirements are still an open issue with respect to "applied" continual learning research approaches. In addition, the novel deep learning model provides an interesting starting point for many other research areas.
Kurzfassung
Fehlverhalten in Unternehmen bzw. durch deren Mitarbeiter kann in der Praxis zu Verlusten durch Umsatzeinbußen, Geldstrafen oder gar zu Haftstrafen führen. Ein Imageschaden kann zudem die Abwanderung von Kunden und Mitarbeitern zur Folge haben und somit wirtschaftliche Schäden vergrößern. Dies gilt für Großunternehmen ebenso wie für kleine und mittelständische Unternehmen, wenngleich KMUs dieser Problematik bislang eine geringere Bedeutung beigemessen haben.
Compliance Management als Ansatz zur Bewältigung dieser Herausforderungen wird daher in KUMs in geringerem Maße als in den Konzernen umgesetzt, zumal keine explizite rechtliche Verpflichtung besteht. Eine Auseinandersetzung mit dem Konzept empfiehlt sich dennoch, um dessen Erfolgspotenzial ausschöpfen zu können. Zudem sollten sich KMUs als Marktpartner der Großen auf die Erwartungen der Konzerne vorbereiten.
Unter Berücksichtigung der begrenzten finanziellen, personellen und zeitlichen Kapazitäten kleiner und mittelständischer Unternehmen werden hier die Anforderungen an ein erfolgsträchtiges Implementieren von Compliance Management aufgezeigt. Konkret werden das Aufbauen einer Compliance-Struktur sowie das Schaffen einer Compliance-Kultur beschrieben. Damit haben KMUs die Gelegenheit, Compliance Management zum Erfolgsfaktor zu entwickeln und sich zudem als integrer Marktpartner zu profilieren.
CHANCE is a project funded by the EU-programme GRUNDTVIG/ “Lifelong Learning Programme” conducted from December 2007 to November 2009. Partners from the participating countries presented their individual project results at the 2nd international meeting on June 12th 2009, in Fulda, Germany. CHANCE describes new pathways to enhance and support people in the long term to be well-informed and to take responsibility for their own health. The focus of the project was based on the following questions: - What resources are offered by the community to live healthy or healthier and what are the barriers that need to be resolved? - Are there cultural differences in health behaviours and in the perception of health information? - What health information is perceived in general and by whom? - What information and health interventions are required? CHANCE shows how people in different European cities and communities live, perceive information with regard to health and process it. The inhabitants of the communities were motivated to participate actively in the improvement of local interventions with regard to consumer education in health. The community approach aims to reach socially, culturally or economically disadvantaged groups such as elderly people, migrants and single parents.
In our paper we investigate the role of civil society organisations (CSOs) in the provision of services and in forming advocacy coalitions for illegalized migrants in Bern and Vienna. We analyse the variety of CSOs which actively challenge policies of exclusion at the urban level. We examine the political and social practices of CSOs in local welfare arrangements and their organizational structures, the way they build up solidarity relations, networks and alliances, and their relations to municipality and urban authorities. By focusing on varieties of practices and strategies of CSOs, we shed light on civil society’s crucial role concerning the construction of urban infrastructure of solidarity and aim to show how local arrangements for illegalized migrants are co-produced and negotiated by a variety of actors within urban settings.
This guideline is a result of the project CHANCE, funded by the EU-programme GRUNDTVIG / “Lifelong Learning Programme” conducted from December 2007 to November 2009.
The project focuses on the approach of “CommunityBuilding“, which is beyond counselling and education campaigns designed for the social and environmental circumstances and aims to initiate the build-up of networks and local communities.
The manual is based on the interdisciplinary view of health (holistic according to the WHO), community and social environment (promotion of personal and structural potential).
After the introduction with regard to the subject matter, the manual presents 13 fundamental guidelines and illustrates project examples from the participating countries.
This guideline is a result of the project CHANCE, funded by the EU-programme GRUNDTVIG / “Lifelong Learning Programme” conducted from December 2007 to November 2009. The project focuses on the approach of “CommunityBuilding“, which is beyond counselling and education campaigns designed for the social and environmental circumstances and aims to initiate the build-up of networks and local communities. The manual is based on the interdisciplinary view of health (holistic according to the WHO), community and social environment (promotion of personal and structural potential). After the introduction with regard to the subject matter, the manual presents 13 fundamental guidelines and illustrates project examples from the participating countries.
This guideline is a result of the project CHANCE, funded by the EU-programme GRUNDTVIG / “Lifelong Learning Programme” conducted from December 2007 to November 2009. The project focuses on the approach of “CommunityBuilding“, which is beyond counselling and education campaigns designed for the social and environmental circumstances and aims to initiate the build-up of networks and local communities. The manual is based on the interdisciplinary view of health (holistic according to the WHO), community and social environment (promotion of personal and structural potential). After the introduction with regard to the subject matter, the manual presents 13 fundamental guidelines and illustrates project examples from the participating countries.
This guideline is a result of the project CHANCE, funded by the EU-programme GRUNDTVIG / “Lifelong Learning Programme” conducted from December 2007 to November 2009.
The project focuses on the approach of “CommunityBuilding“, which is beyond counselling and education campaigns designed for the social and environmental circumstances and aims to initiate the build-up of networks and local communities.
The manual is based on the interdisciplinary view of health (holistic according to the WHO), community and social environment (promotion of personal and structural potential).
After the introduction with regard to the subject matter, the manual presents 13 fundamental guidelines and illustrates project examples from the participating countries.
This guideline is a result of the project CHANCE, funded by the EU-programme GRUNDTVIG / “Lifelong Learning Programme” conducted from December 2007 to November 2009. The project focuses on the approach of “CommunityBuilding“, which is beyond counselling and education campaigns designed for the social and environmental circumstances and aims to initiate the build-up of networks and local communities. The manual is based on the interdisciplinary view of health (holistic according to the WHO), community and social environment (promotion of personal and structural potential). After the introduction with regard to the subject matter, the manual presents 13 fundamental guidelines and illustrates project examples from the participating countries.
Background: In 2021, a significant proportion of adult deaths in Germany, comprising of 447,473 individuals, occurred within hospital settings, representing nearly half of the total deaths in the country, which numbered 1,023,687 (statistika 2021). Consequently, nurses play a pivotal role as primary caregivers for this patient group, necessitating comprehensive education to address their specific needs. Existing literature suggests that nursing students often lack the adequate preparation to provide care for this group, with factors such as insufficient theoretical knowledge and suboptimal mentoring during clinical placements (Bloomfield et al. 2015; Gillan et al. 2014; Leighton 2009, Leighton/Dubas 2009). The use of simulation has proven effective in bridging the theory-practice gap, particularly in the context of End-of-Life Care. The objective of this study was to assess nursing students' perceptions of the use of simulation in learning about End-of-Life Care (Gillan et al. 2014; Moreland et al. 2012).
Methods: Over a three-year period, three cohorts of third-year nursing students at Fulda University of Applied Sciences engaged in simulated experiences involving a dying patient and one or more family members. The authors created three different scenarios in which the students had to perform oral care, break bad news to family members and administer palliative pain medication. During the simulations, the family member(s) confronted the students with questions concerning spiritual care and improving the quality of life at this stage. This project utilized a qualitative design. After the simulation and debriefing sessions, semi-structured interviews and group discussion were conducted. After transcribing, the interviews were analyzed using open and axial coding, after the Glaser and Strauss approach to Grounded Theory.
Results: The process of theoretical coding yielded five results: Simulation revealed to be a good tool to learn about End-of-Life Care (1), simulation focused on communication (2), the importance of spiritual care (3), the aspect of realism (4) and a lack of theoretical knowledge (5).
Conclusion: Simulation-based learning seems to be a valuable tool in the teaching of End-of-Life-Care especially with a focus on communication.