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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.
The research papers published in this reader were presented to an audience of academicians and practitioners at the 2011 Global Business Management Research Conference at Fulda University. The conference was conducted under the topic “Recent Developments in Business Management Research in an International and Regional Context”.
Abstract:
There is still little experience in Germany in employing peers in social psychiatric institutions and services. Based on the European Leonardo da Vinci project „Experienced Involvement" from 2005-2007 pioneering work took long to broaden ist influence.
The presented work focused on the employment situation of ExIn recovery accompaniments in Germany and used a mixed methods design for this. On one hand a complete survey with a questonaire was used. This focused on the type and scope of Experienced Involvement as well as fields of application of ExIn recovery support and asked for reasons for non-employment and potential perspectives for future engagement. To find out about the subjective perspectives qualitative research methodes were used. This started with the implementation of focus groups to bring in the perspective of prospective ExIn recovery accompaniments. Further on guideline-based interviews were conducted with ExIn recovery accompaniments and their teammates on the experience of professional action, the conditions for this and the effects on the services and themselves.
This article looks critically at the explanation provided by the so‐called ‘normalisation’
literature for Germany’s apparent reluctance and closefisted approach to the Eurozone crisis.
In contrast to the ‘normalisation’ argument which attributes the handling of the crisis to a more self‐interested and assertive stance in Germany’s European policy, this paper emphasizes the role of economic ideas as an explanatory factor. Based on the economic school of ordoliberalism in Germany, the crisis is perceived of as a debt and institutional crisis rather than a financial crisis, as suggested by ‘normalisation’ proponents. Consequently, a profound long‐term solution is thought to require policies of debt reduction and supply‐side reform as well as amendments to Eurozone institutions. In addition, unconditional financial bail‐outs are deemed inadequate to fight the source of the crisis. While the ‘normalisation’ literature stresses change in Germany’s approach to European integration relative to the early 1990s, the view outlined here suggests that there is a significant degree of continuity with the founding period of the European Economic and Monetary Union. Both then and now, Germany is primarily concerned with sound money, fiscal discipline and the institutional set‐up of the Eurozone, all based on principles derived from ordoliberal economic thinking.
Good governance and redistribution in health financing : Pro-poor effects and general challenges
(2017)
Good governance has increasingly attained priority in international cooperation and health-system performance. Governance refers to all steering activities by public entities to influence the behaviour and activities of stakeholders involved. In the health sector, governance refers to a wide range of functions related to guidance and rule-making carried out by governments or other public decision-makers. More specifically, governance in the health-financing system applies to two different aspects: in addition to the approaches, strategies and policies determining how financial flows are implemented, managed and supervised according to rules- or outcome-based indicators, health-financing governance encompasses the question of how far resource generation, pooling and allocation are organised in an equitable, fair and sustainable manner. Individual and collective financial sustainability, burden sharing and social coherence or solidarity are essential parts of health-financing governance and depend deeply on societal priorities and values. Fairness of financing, transparent risk pooling and accountable purchasing of health services are intrinsic elements of governance in health financing and critical for achieving universal health coverage. The government is ultimately responsible for implementing an appropriate framework for a transparent, accountable and reliable health-financing system, for ensuring that the intermediate institutions can perform their functions, for executing effective and powerful supervision, and for providing civil society with the means to demand transparency and good financial governance.
Health-financing indicators show the system’s ability to effectively mobilise and allocate resources, implement social protection and pooling schemes, and distribute the financial burden of care equitably. Essentially two groups of indicators exist for assessing governance in the health financing system: rules-based approaches consider the existence of appropriate policies, strategies, and codified approaches for governance; outcome-based indicators measure whether rules and procedures are effectively implemented or enforced and health-financing targets achieved.
The two Bretton Woods Sisters – International Monetary Fund (IMF) and World Bank – have been key actors in the international political economy since their inception in 1944. While the IMF was established to support national economies during rather short-term, macroeconomic crises, the World Bank has had a more long-term focus on development and economic growth. In pursuit of their goals, both institutions’ instrumental repertoire includes the provision of information, surveillance, technical assistance and training, policy advice, and – arguably most importantly – lending to those countries that have limited or no access to private capital markets. In this paper, I critically analyse the Bretton Woods sisters’ institutional set-up, their objectives and instruments. Further, various criticisms and challenges of the multilateral system are discussed, including the economic policy conditions imposed on borrowing countries, the Western-dominated governance structure and the under-representation of major emerging economies such as China and India.
The purpose of this report is to determine whether health maintenance organisations (HMOs) can provide a suitable and viable form of financial health protection and service provision in selected West African countries, supplementary to existing healthcare provision and coverage. Burkina Faso, Côte d’Ivoire, The Gambia, Guinea-Bissau, Liberia and Sierra Leone were chosen as country examples. Chapter 1 provides the context for the health and healthcare situation in West Africa as well as specific country profiles, whilst Chapter 2 describes factors to be considered when establishing an HMO. The range of technical di-mensions of an HMO introduced in this report includes: administration, human resources, financing, accreditation, service availability and readiness, the benefits catalogue, paying providers, drugs and quality management. Each of these dimensions is further discussed in Chapters 3 – 10.
The administration of an HMO consists of nine interconnected fields: management dash-board, quality management, IT department, purchasing and coordination, finance and ac-counting, health plan and benefit package, member management, human resources, and marketing. In Chapter 3, the authors give a more in-depth analysis of the fields of marketing and member management. Recommendations provided in this chapter include the use of different marketing approaches to bridge the gap between communities and the HMO by establishing informative advertising (e.g., via a mobile responsive website, social media, posters, flyers, radio, and recorded information).
Chapter 4 focuses on an HMO’s human resources, particularly in regard to staff recruiting, development and retention. Staff development expands staff competence by increasing employees’ motivation and job satisfaction, which leads to an increase in their performance and productivity, thereby improving staff retention. Furthermore, staff retention is important for ensuring a long-term commitment to the HMO. In conclusion, the success of an HMO is crucially dependent on motivating staff and enabling them to exercise, develop and share their skills.
Chapter 5 covers the financial aspects of an HMO, including dimensions related to its target population, financial barriers, funding resources, management of funds, and specific coun-try challenges. In order to calculate the necessary resources, this chapter make clear that an HMO must consider cost projections for the benefit package, infrastructure development, administration, expansion and a reserve.
To establish an accreditation system, HMOs can interact with stakeholders from different fields and levels of service delivery and administration, as examined in Chapter 6. The polit-ical and social conditions of a country must be considered by the HMO in order to effective-ly implement an accreditation system. Besides this, an HMO can seek to improve the per-formance quality of healthcare by supporting the establishment of an accreditation scheme.
Reliable information on service availability and readiness is necessary for successful health systems management as it allows health services to be tracked in terms of how they have responded to changed inputs and processes. In Chapter 7, the authors analyse the Service Availability and Readiness Assessment (SARA) tool, and recommend its application within the HMO, as it offers a standardised approach to monitoring the supply of services by providing a standard set of tracer indicators.
To implement a health benefit package (HBP), the authors assess existing models, such as the one introduced by Glassman et al. (2017) which specifies ten core elements of an HBP design and helps to enable discussions on the most relevant aspects in designing an HBP for an HMO. Chapter 8 presents a coinsurance scheme within the HBP design which will affect the service utilisation of members as well as utilisation management as one method for cost control. In addition, actuarial calculations are proposed using Sierra Leone as a case example.
Chapter 9 describes the pharmaceutical supply chain required by an HMO. Important steps of the HMO’s medicine supply chain include: selection, quantification and forecasting, pro-curement, storage, and distribution of medical products. Medicines provided by the HMO must be safe, available, accessible, and affordable at all times and for all members. Stock-outs must be avoided, and therefore this chapter recommends employing community-based health workers in order to ensure distribution to patients in rural areas.
Quality management is an important field in an HMO analysed in Chapter 10 of this report. It includes patient safety, efficiency, and patient satisfaction; all factors that must be con-sidered during the implementation of an HMO. The chapter concludes by noting that quality is highly subjective and must therefore be applied to the specific context of an HMO within a specific country.
Finally, Chapters 11 and 12 of the report include implementation challenges of an HMO in West Africa, as well next steps that should be followed. Although similar challenges con-cerning the social, political, or structural environment can be found in most West African countries, direct transfer of elaborated information to other countries and healthcare situa-tions is not always possible. As well as these situational challenges, HMOs encounter dif-ferent questions such as how to balance the scope of available services against the cover-age of diverse geographical areas, engagement of various stakeholders and reflection of respective values, interests and perspectives of local populations.
Limitations of the report include a lack of specificity in general, and the use of many specific country settings, as observations and examples for one HMO dimension may not always be transferable to other regions and healthcare situations. Therefore, this report is not meant to provide concrete conclusions or solutions in regard to the implementation of an HMO in a specific country setting.
In conclusion, this report states that HMOs have the potential to play a substantial role in healthcare system strengthening, provision of quality healthcare services and the preven-tion of financial burden due to ill-health. As a result, an HMO can support West African countries in their role to fulfil their obligation of protecting the health of their citizens. Addi-tionally, the authors strongly believe that an HMO must reflect the cultural, societal and political environment in which it is implemented. Therefore, it is essential that research be conducted prior to its implementation in addition to including the relevant local stakeholders as early as possible in the process.
Currently, process control in automation technology is mostly regulated by fixed process parameters as a compromise between several identically constructed systems or by plant operators, who are often guided by intuition based on decades of experience. Some operators are not able to pass on their knowledge to the next generation due to societal developments, e.g. academization or increased desire for self-actualization. In contrast, the vision of Smart Factories includes intelligent machining processes that should ultimately lead to self-optimization and adaptation to uncontrollable variables. To consistently implement this vision of self-optimizing machines, a defined quality criterion must be automatically monitored and act as a feedback for continual, autonomous and safe optimization. The term safe refers to the compliance with process quality standards, which must always be maintained. In a very conservative branch such as automation technology, no risks whatsoever are allowed through random experiments for data generation in production operations, since, for example, an unscheduled downtime leads to serious financial losses. Furthermore, machine-driven decisions may at no time pose a threat. Thus, decisions under uncertainty may only be taken where the amount of uncertainty can be considered uncritical. Additionally, industrial applications require a guaranteed real-time capability in terms of reaction to ensure that the actions can be taken in time whenever needed. Since economic aspects are often crucial for decisions in industry, necessary experiments under laboratory conditions, for example, should also be as avoidable as possible, while the effort required for integration into a field application should be as simple as possible.
The aim of this work is the scientific investigation of the integration of learning feedback
for intelligent decision making in the control of industrial processes. The successful integration enables data-driven process optimization. To get closer to the vision of self-optimizing machines, safe optimization methods for industrial applications on the process level are investigated and developed. Here, considering the given restrictions of the automation industry is critical. This work addresses several fields including technical, algorithmic and conceptual aspects. The algorithmic refinements are essential for enabling a wider use of safe optimization for industrial applications. They allow, e.g., the automatic handling of the majority of hyper-parameters and the solution of complex problems by increased computational efficiency. Furthermore, the trade-off between exploration and exploitation of safe optimization in high-dimensional spaces is improved. To account for changeable states perceived via sensor data, contextual Bayesian optimization is modified so that safety requirements are met and real-time capability is satisfied. A software application for industrial safe optimization is implemented within a real-time capable control to be able to interact with other software modules to reach an intelligent decision. Further contributions cover recommendations regarding technical requirements with focus on edge control devices and the conceptual inclusion of machine learning to industrial process control.
To emphasize the application relevance and feasibility of the presented concepts, real world lighthouse projects are realized in the course of this work, indented to reduce skepticism and thus initiate the breakthrough of self-optimizing machines.
This article guides you through the development of a successful moderated and collaborative e-learning course on the basis of an e-learning pattern template. The created patterns are a blueprint of the learning activity which could be implemented by using different web-based communication tools. The “e-learning pattern template” takes the special context of online-courses (compared to face-to-face teaching) into account, with a development focus on the participants’ motivation.