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Statistical Shape Models (SSMs) are one of the most successful tools in 3Dimage analysis and especially medical image segmentation. By modeling the variability of a population of training shapes, the statistical information inherent in such data are used for automatic interpretation of new images. However, building a high-quality SSM requires manually generated ground truth data from clinical experts. Unfortunately, the acquisition of such data is a time-consuming, error-prone and subjective process. Due to this effort, the majority of SSMs is often based on a limited set of this ground truth training data, which makes the models less statistically meaningful. On the other hand, image data itself is abundant in clinics from daily routine. In this work, methods for automatically constructing a reliable SSM without the need of manual image interpretation from experts are proposed. Thus, the training data is assumed to be the result of any segmentation algorithm or may originate from other sources, e.g. non-expert manual delineations. Depending on the algorithm, the output segmentations will contain errors to a higher or lower degree. In order to account for these errors, areas of low probability of being a boundary should be excluded from the training of the SSM. Therefore, the probabilities are estimated with the help of image-based approaches. By including many shape variations, the corrupted parts can be statistically reconstructed. Two approaches for reconstruction are proposed - an Imputation method and Weighted Robust Principal Component Analysis (WRPCA). This allows the inclusion of many data sets from clinical routine, covering a lot more variations of shape examples. To assess the quality of the models, which are robust against erroneous training shapes, an evaluation compares the generalization and specificity ability to a model build from ground truth data. The results show, that especially WRPCA is a powerful tool to handle corrupted parts and yields to reasonable models, which have a higher quality than the initial segmentations.
This thesis is providing an overview over the current topics and influences of mobile components on Enterprise Content Management (ECM). With a literature review the core topics of enterprise mobility and ECM have been identified and projected on the context of using mobile Apps within the environment of ECM. An analysis of three ECM systems and their mobile software lead to an understanding of the functionalities and capabilities mobile systems are providing in the ECM environment. These findings lead to a better un- derstanding for the usage of mobile Enterprise Content Management and is preparation. The thesis focuses the most important topics, which need to be considered for the usage and adoption of mobile Apps in ECM.
One of the main goals of the artificial intelligence community is to create machines able to reason with dynamically changing knowledge. To achieve this goal, a multitude of different problems have to be solved, of which many have been addressed in the various sub-disciplines of artificial intelligence, like automated reasoning and machine learning. The thesis at hand focuses on the automated reasoning aspects of these problems and address two of the problems which have to be overcome to reach the afore-mentioned goal, namely 1. the fact that reasoning in logical knowledge bases is intractable and 2. the fact that applying changes to formalized knowledge can easily introduce inconsistencies, which leads to unwanted results in most scenarios.
To ease the intractability of logical reasoning, I suggest to adapt a technique called knowledge compilation, known from propositional logic, to description logic knowledge bases. The basic idea of this technique is to compile the given knowledge base into a normal form which allows to answer queries efficiently. This compilation step is very expensive but has to be performed only once and as soon as the result of this step is used to answer many queries, the expensive compilation step gets worthwhile. In the thesis at hand, I develop a normal form, called linkless normal form, suitable for knowledge compilation for description logic knowledge bases. From a computational point of view, the linkless normal form has very nice properties which are introduced in this thesis.
For the second problem, I focus on changes occurring on the instance level of description logic knowledge bases. I introduce three change operators interesting for these knowledge bases, namely deletion and insertion of assertions as well as repair of inconsistent instance bases. These change operators are defined such that in all three cases, the resulting knowledge base is ensured to be consistent and changes performed to the knowledge base are minimal. This allows us to preserve as much of the original knowledge base as possible. Furthermore, I show how these changes can be applied by using a transformation of the knowledge base.
For both issues I suggest to adapt techniques successfully used in other logics to get promising methods for description logic knowledge bases.
Science education has been facing important challenges in the recent years: the decline in student’s interest in scientific topics, and moreover, the decrease of students pursuing science beyond their compulsory studies (Bennett, Hogarth, Lubben, 2003). As a result, research has focus on examining different approaches that could attempt to improve the situation. One of these approaches has been the use of context-based problem-solving tasks (Kölbach & Sumfleth, 2011; Bennett, Hogarth, Lubben, 2003). While research into context-based problem-solving tasks suggest that they are very motivating for students, it is still unclear how they influence motivation. Following an experimental pretest-postest design, two studies examined the effects of context-based task characteristics of contextualization, complexity, and transparency, on students’ motivational variables, performance, and metacognitive experiences.
Results from both studies suggest that the task characteristic of contextualization directly influences how students’ interest is triggered and maintained throughout the task. On the other hand, the task characteristics of complexity and transparency had different effects for the other dependent variables of effort, difficulty, and solution correctness.
Moreover, data shows that other motivational variables such as anxiety and success expectancies are strongly influenced by the interaction of the parameters under study. The dissertation concludes that appropriate design and use of context-based task characteristics can benefit students’ learning processes and outcomes.
This habilitation thesis collects works addressing several challenges on handling uncertainty and inconsistency in knowledge representation. In particular, this thesis contains works which introduce quantitative uncertainty based on probability theory into abstract argumentation frameworks. The formal semantics of this extension is investigated and its application for strategic argumentation in agent dialogues is discussed. Moreover, both the computational as well as the meaningfulness of approaches to analyze inconsistencies, both in classical logics as well as logics for uncertain reasoning is investigated. Finally, this thesis addresses the implementation challenges for various kinds of knowledge representation formalisms employing any notion of inconsistency tolerance or uncertainty.
Agriculture covers one third of the world land area and has become a major source of water pollution due to its heavy reliance on chemical inputs, namely fertilisers and pesticides. Several thousands of tonnes of these chemicals are applied worldwide annually and partly reach freshwaters. Despite their widespread use and relatively unspecific modes of action, fungicides are the least studied group of pesticides. It remains unclear whether the taxonomic groups used in pesticide risk assessment are protective for non-target freshwater fungi. Fungi and bacteria are the main microbial decomposers converting allochthonous organic matter (litter) into a more nutritious food resource for leaf-shredding macroinvertebrates. This process of litter decomposition (LD) is central for aquatic ecosystem because it fuels local and downstream food webs with energy and nutrients. Effects of fungicides on decomposer communities and LD have been mainly analysed under laboratory conditions with limited representation of the multiple factors that may moderate effects in the field.
In this thesis a field study was conducted in a German vineyard area to characterise recurrent episodic exposure to fungicides in agricultural streams (chapter 2) and its effects on decomposer communities and LD (chapter 3). Additionally, potential interaction effects of nutrient enrichment and fungicides on decomposer communities and LD were analysed in a mesocosm experiment (chapter 4).
In the field study event-driven water sampling (EDS) and passive sampling with EmporeTM styrene-divinylbenzene reverse phase sulfonated disks (SDB disks) were used to assess exposure to 15 fungicides and 4 insecticides. A total of 17 streams were monitored during 4 rainfall events within the local application period of fungicides in 2012. EDS exceeded the time-weighted average concentrations provided by the SDB disks by a factor of 3, though high variability among compounds was observed. Most compounds were detected in more than half of the sites and mean and maximum peak (EDS) concentrations were under 1 and 3 µg/l, respectively. Besides, SDB disk-sampling rates and a free-software solution to derive sampling rates under time-variable exposure were provided.
Several biotic endpoints related to decomposers and LD were measured in the same sampling sites as the fungicide monitoring, coinciding with the major litter input period. Our results suggest that polar organic fungicides in streams change the structure of the fungal community. Causality of this finding was supported by a subsequent microcosm experiment. Whether other effects observed in the field study, such as reduced fungal biomass, increased bacterial density or reduced microbial LD can be attributed to fungicides remains speculative and requires further investigation. By contrast, neither the invertebrate LD nor in-situ measured gammarid feeding rates correlated with water-borne fungicide toxicity, but both were negatively associated with sediment copper concentrations. The mesocosm experiment showed that fungicides and nutrients affect microbial decomposers differently and that they can alter community structure, though longer experiments are needed to determine whether these changes may propagate to invertebrate communities and LD. Overall, further studies should include representative field surveys in terms of fungicide pollution and physical, chemical and biological conditions. This should be combined with experiments under controlled conditions to test for the causality of field observations.
Six and Gimmler have identified concrete capabilities that enable users to use the Internet in a competent way. Their media competence model can be used for the didactical design of media usage in secondary schools. However, the special challenge of security awareness is not addressed by the model. In this paper, the important dimension of risk and risk assessment will be introduced into the model. This is especially relevant for the risk of the protection of personal data and privacy. This paper will apply the method of IT risk analysis in order to select those dimensions of the Six/Gimmler media competence model that are appropriate to describe privacy aware Internet usage. Privacy risk aware decisions for or against the Internet usage is made visible by the trust model of Mayer et al.. The privacy extension of the competence model will lead to a measurement of the existing privacy awareness in secondary schools, which, in turn, can serve as a didactically well-reasoned design of Informatics modules in secondary schools. This paper will provide the privacy-extended competence model, while empirical measurement and module design is planned for further research activities.
The work presented in this thesis investigated interactions of selected biophysical processes that affect zooplankton ecology at smaller scales. In this endeavour, the extent of changes in swimming behaviour and fluid disturbances produced by swimming Daphnia in response to changing physical environments were quantified. In the first research question addressed within this context, size and energetics of hydrodynamic trails produced by Daphnia swimming in non-stratified still waters were characterized and quantified as a function of organisms’ size and their swimming patterns.
The results revealed that neither size nor the swimming pattern of Daphnia affects the width of induced trails or dissipation rates. Nevertheless, as the size and swimming velocity of the organisms increased, trail volume increased in proportional to the cubic power of Reynolds number, and the biggest trail volume was about 500 times the body volume of the largest daphnids. Larger spatial extent of fluid perturbation and prolonged period to decay caused by bigger trail volumes would play a significant role in zooplankton ecology, e.g. increasing the risk of predation.
The study also found that increased trail volume brought about significantly enhanced total dissipated power at higher Reynolds number, and the magnitudes of total dissipated power observed varied in the range of (1.3-10)X10-9 W.
Furthermore, this study provided strong evidence that swimming speed of Daphnia and total dissipated power in Daphnia trails exceeded those of some other selected zooplankton species.
In recognizing turbulence as an intrinsic environmental perturbation in aquatic habitats, this thesis also examined the response of Daphnia to a range of turbulence flows, which correspond to turbu-lence levels that zooplankton generally encounter in their habitats. Results indicated that within the range of turbulent intensities to which the Daphnia are likely to be exposed in their natural habitats, increasing turbulence compelled the organisms to enhance their swimming activity and swim-ming speed. However, as the turbulence increased to extremely high values (10-4 m2s-3), Daphnia began to withdraw from their active swimming behaviour. Findings of this work also demonstrated that the threshold level of turbulence at which animals start to alleviate from largely active swimming is about 10-6 m2s-3. The study further illustrated that during the intermediate range of turbu-lence; 10-7 - 10-6 m2s-3, kinetic energy dissipation rates in the vicinity of the organisms is consistently one order of magnitude higher than that of the background turbulent flow.
Swarming, a common conspicuous behavioural trait observed in many zooplankton species, is considered to play a significant role in defining freshwater ecology of their habitats from food exploitation, mate encountering to avoiding predators through hydrodynamic flow structures produced by them, therefore, this thesis also investigated implications of Daphnia swarms at varied abundance & swarm densities on their swimming kinematics and induced flow field.
The results showed that Daphnia aggregated in swarms with swarm densities of (1.1-2.3)x103 L-1, which exceeded the abundance densities by two orders of magnitude (i.e. 1.7 - 6.7 L-1). The estimated swarm volume decreased from 52 cm3 to 6.5 cm3, and the mean neighbouring distance dropped from 9.9 to 6.4 body lengths. The findings of this work also showed that mean swimming trajectories were primarily horizontal concentric circles around the light source. Mean flow speeds found to be one order of magnitude lower than the corresponding swimming speeds of Daphnia. Furthermore, this study provided evidences that the flow fields produced by swarming Daphnia differed considerably between unidirectional vortex swarming and bidirectional swimming at low and high abundances respectively.
The publication of open source software aims to support the reuse, the distribution and the general utilization of software. This can only be enabled by the correct usage of open source software licenses. Therefore associations provide a multitude of open source software licenses with different features, of which a developer can choose, to regulate the interaction with his software. Those licenses are the core theme of this thesis.
After an extensive literature research, two general research questions are elaborated in detail. First, a license usage analysis of licenses in the open source sector is applied, to identify current trends and statistics. This includes questions concerning the distribution of licenses, the consistency in their usage, their association over a period of time and their publication.
Afterwards the recommendation of licenses for specific projects is investigated. Therefore, a recommendation logic is presented, which includes several influences on a suitable license choice, to generate an at most applicable recommendation. Besides the exact features of a license of which a user can choose, different methods of ranking the recommendation results are proposed. This is based on the examination of the current situation of open source licensing and license suggestion. Finally, the logic is evaluated on the exemplary use-case of the 101companies project.
In Part I: "The flow-decomposition problem", we introduce and discuss the flow-decomposition problem. Given a flow F, this problem consists of decomposing the flow into a set of paths optimizing specific properties of those paths. We introduce different types of decompositions, such as integer decompositions and alpha-decompositions, and provide two formulations of the set of feasible decompositions.
We show that the problem of minimizing the longest path in a decomposition is NP-hard, even for fractional solutions. Then we develop an algorithm based on column generation which is able to solve the problem.
Tight upper bounds on the optimal objective value help to improve the performance.
To find upper bounds on the optimal solution for the shortest longest path problem, we develop several heuristics and analyze their quality. On pearl graphs we prove a constant approximation ratio of 2 and 3 respectively for all heuristics. A numerical study on random pearl graphs shows that the solutions generated by the heuristics are usually much better than this worst-case bound.
In Part II: "Construction and analysis of evacuation models using flows over time", we consider two optimization models in the context of evacuation planning. The first model is a parameter-based quickest flow model with time-dependent supply values. We give a detailed description of the network construction and of how different scenarios are modeled by scenario parameters. In a second step we analyze the effect of the scenario parameters on the evacuation time. Understanding how the different parameters influence the evacuation time allows us to provide better advice for evacuation planning and allows us to predict evacuation times without solving additional optimization problems. To understand the effect of the time-dependent supply values, we consider the quickest path problem with time-dependent supply values and provide a solution algorithm. The results from this consideration are generalized to approximate the behavior of the evacuation times in the context of quickest flow problems.
The second model we consider is a path-based model for evacuation in the presence of a dynamic cost function. We discuss the challenges of this model and provide ideas for how to approach the problem from different angles. We relate the problem to the flow-decomposition problem and consider the computation of evacuation paths with dynamic costs for large capacities. For the latter method we provide heuristics to find paths and compare them to the optimal solutions by applying the methods to two evacuation scenarios. An analysis shows that the paths generated by the heuristic yield close to optimal solutions and in addition have several desirable properties for evacuation paths which are not given for the optimal solution.