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Recent advances in Natural Language Preprocessing (NLP) allow for a fully automatic extraction of character networks for an incoming text. These networks serve as a compact and easy to grasp representation of literary fiction. They offer an aggregated view of the text, which can be used during distant reading approaches for the analysis of literary hypotheses. In their core, the networks consist of nodes, which represent literary characters, and edges, which represent relations between characters. For an automatic extraction of such a network, the first step is the detection of the references of all fictional entities that are of importance for a text. References to the fictional entities appear in the form of names, noun phrases and pronouns and prior to this work, no components capable of automatic detection of character references were available. Existing tools are only capable of detecting proper nouns, a subset of all character references. When evaluated on the task of detecting proper nouns in the domain of literary fiction, they still underperform at an F1-score of just about 50%. This thesis uses techniques from the field of semi-supervised learning, such as Distant supervision and Generalized Expectations, and improves the results of an existing tool to about 82%, when evaluated on all three categories in literary fiction, but without the need for annotated data in the target domain. However, since this quality is still not sufficient, the decision to annotate DROC, a corpus comprising 90 fragments of German novels was made. This resulted in a new general purpose annotation environment titled as ATHEN, as well as annotated data that spans about 500.000 tokens in total. Using this data, the combination of supervised algorithms and a tailored rule based algorithm, which in combination are able to exploit both - local consistencies as well as global consistencies - yield an algorithm with an F1-score of about 93%. This component is referred to as the Kallimachos tagger. A character network can not directly display references however, instead they need to be clustered so that all references that belong to a real world or fictional entity are grouped together. This process widely known as coreference resolution is a hard problem in the focus of research for more than half a century. This work experimented with adaptations of classical feature based machine learning, with a dedicated rule based algorithm and with modern techniques of Deep Learning, but no approach can surpass 55% B-Cubed F1, when evaluated on DROC. Due to this barrier, many researchers do not use a fully-fledged coreference resolution when they extract character networks, but only focus on a more forgiving subset- the names. For novels such as Alice's Adventures in Wonderland by Lewis Caroll, this would however only result in a network in which many important characters are missing. In order to integrate important characters into the network that are not named by the author, this work makes use of automatic detection of speaker and addressees for direct speech utterances (all entities involved in a dialog are considered to be of importance). This problem is by itself not an easy task, however the most successful system analysed in this thesis is able to correctly determine the speaker to about 85% of the utterances as well as about 65% of the addressees. This speaker information can not only help to identify the most dominant characters, but also serves as a way to model the relations between entities. During the span of this work, components have been developed to model relations between characters using speaker attribution, using co-occurrences as well as by the usage of true interactions, for which yet again a dataset was annotated using ATHEN. Furthermore, since relations between characters are usually typed, a component for the extraction of a typed relation was developed. Similar to the experiments for the character reference detection, a combination of a rule based and a Maximum Entropy classifier yielded the best overall results, with the extraction of family relations showing a score of about 80% and the quality of love relations with a score of about 50%. For family relations, a kernel for a Support Vector Machine was developed that even exceeded the scores of the combined approach but is behind on the other labels. In addition, this work presents new ways to evaluate automatically extracted networks without the need of domain experts, instead it relies on the usage of expert summaries. It also refrains from the uses of social network analysis for the evaluation, but instead presents ranked evaluations using Precision@k and the Spearman Rank correlation coefficient for the evaluation of the nodes and edges of the network. An analysis using these metrics showed, that the central characters of a novel are contained with high probability but the quality drops rather fast if more than five entities are analyzed. The quality of the edges is mainly dominated by the quality of the coreference resolution and the correlation coefficient between gold edges and system edges therefore varies between 30 and 60%. All developed components are aggregated alongside a large set of other preprocessing modules in the Kallimachos pipeline and can be reused without any restrictions.

Von technischen Systemen wird in der heutigen Zeit erwartet, dass diese stets fehlerfrei funktionieren, um einen reibungslosen Ablauf des Alltags zu gewährleisten. Technische Systeme jedoch können Defekte aufweisen, die deren Funktionsweise einschränken oder zu deren Totalausfall führen können. Grundsätzlich zeigen sich Defekte durch eine Veränderung im Verhalten von einzelnen Komponenten. Diese Abweichungen vom Nominalverhalten nehmen dabei an Intensität zu, je näher die entsprechende Komponente an einem Totalausfall ist. Aus diesem Grund sollte das Fehlverhalten von Komponenten rechtzeitig erkannt werden, um permanenten Schaden zu verhindern. Von besonderer Bedeutung ist dies für die Luft- und Raumfahrt. Bei einem Satelliten kann keine Wartung seiner Komponenten durchgeführt werden, wenn er sich bereits im Orbit befindet. Der Defekt einer einzelnen Komponente, wie der Batterie der Energieversorgung, kann hierbei den Verlust der gesamten Mission bedeuten. Grundsätzlich lässt sich Fehlererkennung manuell durchführen, wie es im Satellitenbetrieb oft üblich ist. Hierfür muss ein menschlicher Experte, ein sogenannter Operator, das System überwachen. Diese Form der Überwachung ist allerdings stark von der Zeit, Verfügbarkeit und Expertise des Operators, der die Überwachung durchführt, abhängig. Ein anderer Ansatz ist die Verwendung eines dedizierten Diagnosesystems. Dieses kann das technische System permanent überwachen und selbstständig Diagnosen berechnen. Die Diagnosen können dann durch einen Experten eingesehen werden, der auf ihrer Basis Aktionen durchführen kann. Das in dieser Arbeit vorgestellte modellbasierte Diagnosesystem verwendet ein quantitatives Modell eines technischen Systems, das dessen Nominalverhalten beschreibt. Das beobachtete Verhalten des technischen Systems, gegeben durch Messwerte, wird mit seinem erwarteten Verhalten, gegeben durch simulierte Werte des Modells, verglichen und Diskrepanzen bestimmt. Jede Diskrepanz ist dabei ein Symptom. Diagnosen werden dadurch berechnet, dass zunächst zu jedem Symptom eine sogenannte Konfliktmenge berechnet wird. Dies ist eine Menge von Komponenten, sodass der Defekt einer dieser Komponenten das entsprechende Symptom erklären könnte. Mithilfe dieser Konfliktmengen werden sogenannte Treffermengen berechnet. Eine Treffermenge ist eine Menge von Komponenten, sodass der gleichzeitige Defekt aller Komponenten dieser Menge alle beobachteten Symptome erklären könnte. Jede minimale Treffermenge entspricht dabei einer Diagnose. Zur Berechnung dieser Mengen nutzt das Diagnosesystem ein Verfahren, bei dem zunächst abhängige Komponenten bestimmt werden und diese von symptombehafteten Komponenten belastet und von korrekt funktionierenden Komponenten entlastet werden. Für die einzelnen Komponenten werden Bewertungen auf Basis dieser Be- und Entlastungen berechnet und mit ihnen Diagnosen gestellt. Da das Diagnosesystem auf ausreichend genaue Modelle angewiesen ist und die manuelle Kalibrierung dieser Modelle mit erheblichem Aufwand verbunden ist, wurde ein Verfahren zur automatischen Kalibrierung entwickelt. Dieses verwendet einen Zyklischen Genetischen Algorithmus, um mithilfe von aufgezeichneten Werten der realen Komponenten Modellparameter zu bestimmen, sodass die Modelle die aufgezeichneten Daten möglichst gut reproduzieren können. Zur Evaluation der automatischen Kalibrierung wurden ein Testaufbau und verschiedene dynamische und manuell schwierig zu kalibrierende Komponenten des Qualifikationsmodells eines realen Nanosatelliten, dem SONATE-Nanosatelliten modelliert und kalibriert. Der Testaufbau bestand dabei aus einem Batteriepack, einem Laderegler, einem Tiefentladeschutz, einem Entladeregler, einem Stepper Motor HAT und einem Motor. Er wurde zusätzlich zur automatischen Kalibrierung unabhängig manuell kalibriert. Die automatisch kalibrierten Satellitenkomponenten waren ein Reaktionsrad, ein Entladeregler, Magnetspulen, bestehend aus einer Ferritkernspule und zwei Luftspulen, eine Abschlussleiterplatine und eine Batterie. Zur Evaluation des Diagnosesystems wurde die Energieversorgung des Qualifikationsmodells des SONATE-Nanosatelliten modelliert. Für die Batterien, die Entladeregler, die Magnetspulen und die Reaktionsräder wurden die vorher automatisch kalibrierten Modelle genutzt. Für das Modell der Energieversorgung wurden Fehler simuliert und diese diagnostiziert. Die Ergebnisse der Evaluation der automatischen Kalibrierung waren, dass die automatische Kalibrierung eine mit der manuellen Kalibrierung vergleichbare Genauigkeit für den Testaufbau lieferte und diese sogar leicht übertraf und dass die automatisch kalibrierten Satellitenkomponenten eine durchweg hohe Genauigkeit aufwiesen und damit für den Einsatz im Diagnosesystem geeignet waren. Die Ergebnisse der Evaluation des Diagnosesystems waren, dass die simulierten Fehler zuverlässig gefunden wurden und dass das Diagnosesystem in der Lage war die plausiblen Ursachen dieser Fehler zu diagnostizieren.

Insect microbiota plays an essential role on the hosts’ health and fitness, regulating their development, nutrition and immunity. The natural microbiota of bees, in particular, has been given much attention, largely because of the globally reported bee population declines. However, although the worker honey bee has been associated with distinctive and specialized microbiota, the microbiota of solitary bees has not been examined in detail, despite their enormous ecological importance. The main objectives of the present thesis were a) the bacterial community description for various solitary bee species, b) the association of the solitary bee microbiota with ecological factors such as landscape type, c) the relation of the bee foraging preferences with their nest bacterial microbiota, d) the examination of the nest building material contribution to the nest microbiota, e) the isolation of bacterial strains with beneficial or harmful properties for the solitary bee larvae and f) the pathological investigation of bacteria found in deceased solitary bee larvae. The findings of the present study revealed a high bacterial biodiversity in the solitary bee nests. At the same time, the bacterial communities were different for each bee host species. Furthermore, it was shown that the pollen bacterial communities underwent compositional shifts reflecting a reduction in floral bacteria with progressing larval development, while a clear landscape effect was absent. The examination of the nest pollen provisions showed different foraging preferences for each included bee species. Both the pollen composition and the host species identity had a strong effect on the pollen bacteria, indicating that the pollen bacterial communities are the result of a combinatory process. The introduced environmental material also contributed to the nest natural microbiome. However, although the larval microbiota was significantly influenced by the pollen microbiota, it was not much associated with that of the nest material. Two Paenibacillus strains isolated from O. bicornis nests showed strong antifungal activities, while several isolated strains were able to metabolize various oligosaccharides which are common in pollen and nectar. Screening for potential pathogenic bacteria in the nests of O. bicornis unveiled bacterial taxa, which dominated the bacterial community in deceased larvae, while at the same time they were undetectable in the healthy individuals. vi Finally, larvae which were raised in vitro developed distinct bacterial microbiomes according to their diet, while their life span was affected. The present thesis described aspects of the microbiota dynamics in the nests of seven megachilid solitary bee nests, by suggesting which transmission pathways shape the established bacterial communities and how these are altered with larval development. Furthermore, specific bacterial taxa were associated with possible services they might provide to the larvae, while others were related with possible harmful effects. Future studies should integrate microbiota examination of different bee generations and parallel investigation of the microbiota of the nests and their surrounding environment (plant community, soil) to elucidate the bacterial transmission paths which establish the nest microbiota of solitary bees. Functional assays will also allow future studies to characterize specific nest bacteria as beneficial or harmful and describe how they assist the development of healthy bees and the fitness of bee populations.

The goal of the white paper at hand is as follows. The definitions of the terms build a framework for discussions around the hype topic ‘crowdsourcing’. This serves as a basis for differentiation and a consistent view from different perspectives on crowdsourced network measurements, with the goal to provide a commonly accepted definition in the community. The focus is on the context of mobile and fixed network operators, but also on measurements of different layers (network, application, user layer). In addition, the white paper shows the value of crowdsourcing for selected use cases, e.g., to improve QoE or regulatory issues. Finally, the major challenges and issues for researchers and practitioners are highlighted. This white paper is the outcome of the Würzburg seminar on “Crowdsourced Network and QoE Measurements” which took place from 25-26 September 2019 in Würzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms “crowdsourced network measurements” and “crowdsourced QoE measurements”, describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is – to our knowledge – the first one covering the topic of crowdsourced network and QoE measurements.

Bridge-local latency computation is often regarded with caution, as historic efforts with the Credit-Based Shaper (CBS) showed that CBS requires network wide information for tight bounds. Recently, new shaping mechanisms and timed gates were applied to achieve such guarantees nonetheless, but they require support for these new mechanisms in the forwarding devices. This document presents a per-hop latency bound for individual streams in a class-based network that applies the IEEE 802.1Q strict priority transmission selection algorithm. It is based on self-pacing talkers and uses the accumulated latency fields during the reservation process to provide upper bounds with bridge-local information. The presented delay bound is proven mathematically and then evaluated with respect to its accuracy. It indicates the required information that must be provided for admission control, e.g., implemented by a resource reservation protocol such as IEEE 802.1Qdd. Further, it hints at potential improvements regarding new mechanisms and higher accuracy given more information.