Communication networks, in particular the Internet, have become a pivotal part of our life. Since their beginnings, a key aspect of their applicability has been the performance. Safety-critical applications, for example, can sometimes only be implemented in a responsible manner if guarantees about their end-to-end delay can be made. A mathematical modeling and performance evaluation of communication networks requires a powerful set of tools that is able to incorporate their increasing complexity. The stochastic network calculus (SNC) is a versatile, mathematical framework that allows for a calculation of probabilistic end-to-end performance bounds of distributed systems. Its flexibility enables to incorporate a large class of different schedulers as well as different models of traffic processes beyond the assumption of Poisson arrivals that is predominant in queueing theory-based analyses. It originates in the so-called deterministic network analysis (DNC) in the 90's of the 20th century that was introduced to provide deterministic, ``hard'' guarantees that are of relevance, e.g., in the context of real-time systems. While the DNC of today can be used to calculate fast and accurate delay bounds of arbitrary feedforward networks, the SNC is still in a significantly earlier stage. In particular, method-pertinent dependencies, i.e., a phenomenon that occurs when independent flows become stochastically dependent after sharing resources in the network, can be considered a major challenge in the SNC with moment-generating functions (MGFs). This thesis argues to contribute to the SNC in several ways. First, we show that the ``pay multiplexing only once'' (PMOO) analysis known from the DNC is also possible in the SNC. Not only does it significantly improve end-to-end delay bounds, it also needs to consider less method-pertinent dependencies. Therefore, complexity and runtimes of the calculation are greatly reduced. Second, we introduce the concept of negative dependence to the SNC with MGFs and give numerical evidence that this can further lead to better performance bounds. Third, for the larger problem of end-to-end performance bounds of tree networks, we introduce so-called ''h-mitigators'', a modification in the calculation of MGF output bounds. It is minimally invasive, all existing results and procedures are still applicable, and improves performance bounds. As a fourth contribution, we conduct extensive numerical evaluations to substantiate our claims. Moreover, we made the respective code, the ''SNC MGF toolbox'', publicly available to ensure that the results are reproducible. At last, we conduct different stochastic analyses of a popular fair scheduler, generalized processor sharing (GPS). We give an overview of the state-of-the-art analyses in the SNC and substantiate the comparison through numerical evaluations.

This dissertation was developed in the context of the BMBF and EU/ECSEL funded projects GENIAL! and Arrowhead Tools. In these projects the chair examines methods of specifications and cooperations in the automotive value chain from OEM-Tier1-Tier2. Goal of the projects is to improve communication and collaborative planning, especially in early development stages. Besides SysML, the use of agreed vocabularies and on- tologies for modeling requirements, overall context, variants, and many other items, is targeted. This thesis proposes a web database, where data from the collaborative requirements elicitation is combined with an ontology-based approach that uses reasoning capabilities. For this purpose, state-of-the-art ontologies have been investigated and integrated that entail domains like hardware/software, roadmapping, IoT, context, innovation and oth- ers. New ontologies have been designed like a HW / SW allocation ontology and a domain-specific "eFuse ontology" as well as some prototypes. The result is a modular ontology suite and the GENIAL! Basic Ontology that allows us to model automotive and microelectronic functions, components, properties and dependencies based on the ISO26262 standard among these elements. Furthermore, context knowledge that influences design decisions such as future trends in legislation, society, environment, etc. is included. These knowledge bases are integrated in a novel tool that allows for collabo- rative innovation planning and requirements communication along the automotive value chain. To start off the work of the project, an architecture and prototype tool was developed. Designing ontologies and knowing how to use them proved to be a non-trivial task, requiring a lot of context and background knowledge. Some of this background knowledge has been selected for presentation and was utilized either in designing models or for later immersion. Examples are basic foundations like design guidelines for ontologies, ontology categories and a continuum of expressiveness of languages and advanced content like multi-level theory, foundational ontologies and reasoning. Finally, at the end, we demonstrate the overall framework, and show the ontology with reasoning, database and APPEL/SysMD (AGILA ProPErty and Dependency Descrip- tion Language / System MarkDown) and constraints of the hardware / software knowledge base. There, by example, we explore and solve roadmap constraints that are coupled with a car model through a constraint solver.

With this thesis, I contribute to the research field of uncertainty visualization, considering parameter dependencies in multi valued fields and the uncertainty of automated data analysis. Like uncertainty visualization in general, both of these fields are becoming more and more important due to increasing computational power, growing importance and availability of complex models and collected data, and progress in artificial intelligence. I contribute in the following application areas: Uncertain Topology of Scalar Field Ensembles. The generalization of topology-based visualizations to multi valued data involves many challenges. An example is the comparative visualization of multiple contour trees, complicated by the random nature of prevalent contour tree layout algorithms. I present a novel approach for the comparative visualization of contour trees - the Fuzzy Contour Tree. Uncertain Topological Features in Time-Dependent Scalar Fields. Tracking features in time-dependent scalar fields is an active field of research, where most approaches rely on the comparison of consecutive time steps. I created a more holistic visualization for time-varying scalar field topology by adapting Fuzzy Contour Trees to the time-dependent setting. Uncertain Trajectories in Vector Field Ensembles. Visitation maps are an intuitive and well-known visualization of uncertain trajectories in vector field ensembles. For large ensembles, visitation maps are not applicable, or only with extensive time requirements. I developed Visitation Graphs, a new representation and data reduction method for vector field ensembles that can be calculated in situ and is an optimal basis for the efficient generation of visitation maps. This is accomplished by bringing forward calculation times to the pre-processing. Visually Supported Anomaly Detection in Cyber Security. Numerous cyber attacks and the increasing complexity of networks and their protection necessitate the application of automated data analysis in cyber security. Due to uncertainty in automated anomaly detection, the results need to be communicated to analysts to ensure appropriate reactions. I introduce a visualization system combining device readings and anomaly detection results: the Security in Process System. To further support analysts I developed an application agnostic framework that supports the integration of knowledge assistance and applied it to the Security in Process System. I present this Knowledge Rocks Framework, its application and the results of evaluations for both, the original and the knowledge assisted Security in Process System. For all presented systems, I provide implementation details, illustrations and applications.

Highly assisted driving and autonomous vehicles require a detailed and accurate perception of the environment. This includes the perception of the 3D geometry of the scene and the 3D motion of other road users. The estimation of both based on images is known as the scene flow problem in computer vision. This thesis deals with a solution to the scene flow problem that is suitable for application in autonomous vehicles. This application imposes strict requirements on accuracy, robustness, and speed. Previous work was lagging behind in at least one of these metrics. To work towards the fulfillment of those requirements, the sparse-to-dense concept for scene flow estimation is introduced in this thesis. The idea can be summarized as follows: First, scene flow is estimated for some points of the scene for which this can be done comparatively easily and reliably. Then, an interpolation is performed to obtain a dense estimate for the entire scene. Because of the separation into two steps, each part can be optimized individually. In a series of experiments, it is shown that the proposed methods achieve competitive results and are preferable to previous techniques in some aspects. As a second contribution, individual components in the sparse-to-dense pipeline are replaced by deep learning modules. These are a highly localized and highly accurate feature descriptor to represent pixels for dense matching, and a network for robust and generic sparse-to-dense interpolation. Compared to end-to-end architectures, the advantage of deep modules is that they can be trained more effciently with data from different domains. The recombination approach applies a similar concept as the sparse-to-dense approach by solving and combining less diffcult, auxiliary sub-problems. 3D geometry and 2D motion are estimated separately, the individual results are combined, and then also interpolated into a dense scene flow. As a final contribution, the thesis proposes a set of monolithic end-to-end networks for scene flow estimation.

Industrial manufacturing companies have different IT control functions that can be represented with a so-called hierarchical automation pyramid. While these conventional software systems especially support the mass production with consistent demand, the future project “Industry 4.0” focuses on customer-oriented and adaptable production processes. In order to move from conventional production systems to a factory of the future, the control levels must be redistributed. With the help of cyber-physical production systems, an interoperable architecture must be, implemented which removes the hierarchical connection of the former control levels. The accompanied digitalisation of industrial companies makes the transition to modular production possible. At the same time, the requirements for production planning and control are increasing, which can be solved with approaches such as multi-agent systems (MASs). These software solutions are autonomous and intelligent objects with a distinct collaborative ability. There are different modelling methods, communication and interaction structures, as well as different development frameworks for these new systems. Since multi-agent systems have not yet been established as an industrial standard due to their high complexity, they are usually only tested in simulations. In this bachelor thesis, a detailed literature review on the topic of MASs in the field of production planning and control is presented. In addition, selected multi-agent approaches are evaluated and compared using specific classification criteria. In addition, the applicability of using these systems in digital and modular production is assessed.

Recommender systems recommend items (e.g., movies, products, books) to users. In this thesis, we proposed two comprehensive and cluster-induced recommendation-based methods: Orthogonal Inductive Matrix Completion (OMIC) and Burst-induced Multi-armed Bandit (BMAB). Given the presence of side information, the first method is categorized as context-aware. OMIC is the first matrix completion method to approach the problem of incorporating biases, side information terms and a pure low-rank term into a single flexible framework with a well-principled optimization procedure. The second method, BMAB, is context-free. That is, it does not require any side data about users or items. Unlike previous context-free multi-armed bandit approaches, our method considers the temporal dynamics of human communication on the web and treats the problem in a continuous time setting. We built our models' assumptions under solid theoretical foundations. For OMIC, we provided theoretical guarantees in the form of generalization bounds by considering the distribution-free case: no assumptions about the sampling distribution are made. Additionally, we conducted a theoretical analysis of community side information when the sampling distribution is known and an adjusted nuclear norm regularization is applied. We showed that our method requires just a few entries to accurately recover the ratings matrix if the structure of the ground truth closely matches the cluster side information. For BMAB, we provided regret guarantees under mild conditions that demonstrate how the system's stability affects the expected reward. Furthermore, we conducted extensive experiments to validate our proposed methodologies. In a controlled environment, we implemented synthetic data generation techniques capable of replicating the domains for which OMIC and BMAB were designed. As a result, we were able to analyze our algorithms' performance across a broad spectrum of ground truth regimes. Finally, we replicated a real-world scenario by utilizing well-established recommender datasets. After comparing our approaches to several baselines, we observe that they achieved state-of-the-art results in terms of accuracy. Apart from being highly accurate, these methods improve interpretability by describing and quantifying features of the datasets they characterize.

Data is the new gold and serves as a key to answer the five W’s (Who, What, Where, When, Why) and How’s of any business. Companies are now mining data more than ever and one of the most important aspects while analyzing this data is to detect anomalous patterns to identify critical patterns and points. To tackle the vital aspects of timeseries analysis, this thesis presents a novel hybrid framework that stands on three pillars: Anomaly Detection, Uncertainty Estimation, and Interpretability and Explainability. The first pillar is comprised of contributions in the area of time-series anomaly detection. Deep Anomaly Detection for Time-series (DeepAnT), a novel deep learning-based anomaly detection method, lies at the foundation of the proposed hybrid framework and addresses the inadequacy of traditional anomaly detection methods. To the best of the author’s knowledge, Convolutional Neural Network (CNN) was used for the first time in Deep Anomaly Detection for Time-series (DeepAnT) to robustly detect multiple types of anomalies in the tricky and continuously changing time-series data. To further improve the anomaly detection performance, a fusion-based method, Fusion of Statistical and Deep Learning for Anomaly Detection (FuseAD) is proposed. This method aims to combine the strengths of existing wellfounded statistical methods and powerful data-driven methods. In the second pillar of this framework, a hybrid approach that combines the high accuracy of the deterministic models with the posterior distribution approximation of Bayesian neural networks is proposed. In the third pillar of the proposed framework, mechanisms to enable both HOW and WHY parts are presented.

The semantics of programming languages assign a meaning to the written program syntax. Currently, the meaning of synchronous programming languages, which are especially designed to develop programs for reactive and embedded systems, is based on a formal semantics definition similar to Fitting`s fixpoint semantics for logic programs. Nevertheless, it is possible to write a synchronous program code that does not evaluate to concrete values with the current semantics, which means those programs are currently seen to be not constructive. In the last decades, the theoretical knowledge and representation of semantics for logic programming has increased, but not all theoretical results and achievements have found their way to practice and application in system design. This thesis, in a first part, focuses on extensions to the semantics of synchronous programming languages to an evaluation similar to a well-founded semantics as defined in logic programming by van Gelder, Ross and Schlipf and to the stable model semantics as defined by Gelfond and Lifschitz. Particularly, this allows an evaluation for some of the currently not constructive programs where the semantics based on Fitting`s fixpoint fails. It is shown that the extension to well-founded semantics is a conservative extension of Fitting`s semantics, so that the meaning for programs which were already constructive does not change. Finally, it is considered how one can still generate circuits that implement the considered synchronous programs with the well-founded semantics. Again, this is a conservative approach that does not modify the circuits generated by the so-far used synthesis procedures. Answer set programming and the underlying stable model semantics describe problems by constraints and the related answer set solvers give all solutions to that problem as so-called answers. This allows the formulation of searching and planning problems as well as efficient solutions without having the need to develop special and possibly error-prone algorithms for every single application. The semantics of the synchronous programming language Quartz is also extended to the stable model semantics. For this extension, two alternatives are discussed: First of all, a direct extension similar to the extension to well-founded semantics is discussed. Second, a transformation of synchronous programs to the available answer set programming languages is given, as this allows to directly use answer set solvers for the synthesis and optimization of synchronous systems. The second part of the thesis contains further examples of the use of answer set programming in system design to emphasis their benefits for system design in general. The first example is hereby the generation of optimal/minimal interconnection-networks which allow non-blocking connections between n sources and n targets in parallel. As a second example, the stable model semantics is used to build a complete compiler chain, which transforms a given program to an optimal assembler code (called move code) for the new SCAD processor architecture which was developed at the University of Kaiserslautern. As a final part, the lessons learned from the two examples are shown by the means of some enhancement ideas for the synchronous programming language paradigm.

Medical cyber-physical systems (MCPS) emerged as an evolution of the relations between connected health systems, healthcare providers, and modern medical devices. Such systems combine independent medical devices at runtime in order to render new patient monitoring/control functionalities, such as physiological closed loops for controlling drug infusion or optimization of alarms. Despite the advances regarding alarm precision, healthcare providers still struggle with alarm flooding caused by the limited risk assessment models. Furthermore, these limitations also impose severe barriers on the adoption of automated supervision through autonomous actions, such as safety interlocks for avoiding overdosage. The literature has focused on the verification of safety parameters to assure the safety of treatment at runtime and thus optimize alarms and automated actions. Such solutions have relied on the definition of actuation ranges based on thresholds for a few monitored parameters. Given the very dynamic nature of the relevant context conditions (e.g., the patient’s condition, treatment details, system configurations, etc.), fixed thresholds are a weak means for assessing the current risk. This thesis presents an approach for enabling dynamic risk assessment for cooperative MCPS based on an adaptive Bayesian Networks (BN) model. The main aim of the approach is to support continuous runtime risk assessment of the current situation based on relevant context and system information. The presented approach comprises (i) a dynamic risk analysis constituent, which corresponds to the elicitation of relevant risk parameters, risk metric building, and risk metric management; and (ii) a runtime risk classification constituent, which aims to analyze the current situation risk, establish risk classes, and identify and deploy mitigation measures. The proposed approach was evaluated and its feasibility proved by means of simulated experiments guided by an international team of medical experts with a focus on the requirements of efficacy, efficiency, and availability of patient treatment.

Business-relevant domain knowledge can be found in plain text across message exchanges among customer support tickets, employee message exchanges and other business transactions. Decoding text-based domain knowledge can be a very demanding task since traditional methods focus on a comprehensive representation of the business and its relevant paths. Such a process can be highly complex, time-costly and of high maintenance effort, especially in environments that change dynamically. In this thesis, a novel approach is presented for developing hybrid case-based reasoning (CBR) systems that bring together the benefits of deep learning approaches with CBR advantages. Deep Knowledge Acquisition Framework (DeepKAF) is a domain-independent framework that features the usage of deep neural networks and big data technologies to decode the domain knowledge with the minimum involvement from the domain experts. While this thesis is focusing more on the textual data because of the availability of the datasets, the target CBR systems based on DeepKAF are able to deal with heterogeneous data where a case can be represented by different attribute types and automatically extract the necessary domain knowledge while keeping the ability to provide an adequate level of explainability. The main focus within this thesis are automatic knowledge acquisition, building similarity measures and cases retrieval. Throughout the progress of this research, several sets of experiments have been conducted and validated by domain experts. Past textual data produced over around 15 years have been used for the needs of the conducted experiments. The text produced is a mixture between English and German texts that were used to describe specific domain problems with a lot of abbreviations. Based on these, the necessary knowledge repositories were built and used afterwards in order to evaluate the suggested approach towards effective monitoring and diagnosis of business workflows. Another public dataset has been used, the CaseLaw dataset, to validate DeepKAF when dealing with longer text and cases with more attributes. The CaseLaw dataset represents around 22 million cases from different US states. Further work motivated by this thesis could investigate how different deep learning models can be used within the CBR paradigm to solve some of the chronic CBR challenges and be of benefit to large-scale multi-dimensional enterprises.

This work presents a visual analytics-driven workflow for an interpretable and understandable machine learning model. The model is driven by a reverse engineering task in automotive assembly processes. The model aims to predict the assembly parameters leading to the given displacement field on the geometries surface. The derived model can work on both measurement and simulation data. The proposed approach is driven by the scientific goals from visual analytics and interpretable artificial intelligence alike. First, a concept for systematic uncertainty monitoring, an object-oriented, virtual reference scheme (OOVRS), is developed. Afterward, the prediction task is solved via a regressive machine learning model using adversarial neural networks. A profound model parameter study is conducted and assisted with an interactive visual analytics pipeline. Further, the effects of the learned variance in displacement fields are analyzed in detail. Therefore a visual analytics pipeline is developed, resulting in a sensitivity benchmarking tool. This allows the testing of various segmentation approaches to lower the machine learning input dimensions. The effects of the assembly parameters are investigated in domain space to find a suitable segmentation of the training data set’s geometry. Therefore, a sensitivity matrix visualization is developed. Further, it is shown how this concept could directly compare results from various segmentation methods, e.g., topological segmentation, concerning the assembly parameters and their impact on the displacement field variance. The resulting databases are still of substantial size for complex simulations with large and high-dimensional parameter spaces. Finally, the applicability of video compression techniques towards compressing visualization image databases is studied.

In recent decades, there has been increasing interest in analyzing the behavior of complex systems. A popular approach for analyzing such systems is a network analytic approach where the system is represented by a graph structure (Wassermann&Faust 1994, Boccaletti et al. 2006, Brandes&Erlebach 2005, Vespignani 2018): Nodes represent the system’s entities, edges their interactions. A large toolbox of network analytic methods, such as measures for structural properties (Newman 2010), centrality measures (Koschützki et al. 2005), or methods for identifying communities (Fortunato 2010), is readily available to be applied on any network structure. However, it is often overlooked that a network representation of a system and the (technically applicable) methods contain assumptions that need to be met; otherwise, the results are not interpretable or even misleading. The most important assumption of a network representation is the presence of indirect effects: If A has an impact on B, and B has an impact on C, then A has an impact on C (Zweig 2016, Brandes et al. 2013). The presence of indirect effects can be explained by ”something” flowing through the network by moving from node to node. Such network flows (or network processes) may be the propagation of information in social networks, the spread of infections, or entities using the network as infrastructure, such as in transportation networks. Also several network measures, particularly most centrality measures, assume the presence of such a network process, but additionally assume specific properties of the network processes (Borgatti 2005). Then, a centrality value indicates a node’s importance with respect to a process with these properties. While this has been known for several years, only recently have datasets containing real-world network flows become accessible. In this context, the goal of this dissertation is to provide a better understanding of the actual behavior of real-world network processes, with a particular focus on centrality measures: If real-world network processes turn out to show different properties than those assumed by classic centrality measures, these measures might considerably under- or overestimate the importance of nodes for the actual network flow. To the best of our knowledge, there are only very few works addressing this topic. The contributions of this thesis are therefore as follows: (i) We investigate in which aspects real-world network flows meet the assumptions contained about them in centrality measures. (ii) Since we find that the real-world flows show considerably different properties than assumed, we test to which extent the found properties can be explained by models, i.e., models based on shortest paths or random walks. (iii) We study whether the deviations from the assumed behavior have an impact on the results of centrality measures. To this end, we introduce flow-based variants of centrality measures which are either based on the assumed behavior or on the actual behavior of the real-world network flow. This enables systematic evaluation of the impact of each assumption on the resulting rankings of centrality measures. While–on a large scale–we observe a surprisingly large robustness of the measures against deviations in their assumptions, there are nodes whose importance is rated very differently when the real-world network flow is taken into account. (iv) As a technical contribution, we provide a method for an efficient handling of large sets of flow trajectories by summarizing them into groups of similar trajectories. (v) We furthermore present the results of an interdisciplinary research project in which the trajectories of humans in a network were analyzed in detail. In general, we are convinced that a process-driven perspective on network analysis in which the network process is considered in addition to the network representation, can help to better understand the behavior of complex systems.

Visual analytics has been widely studied in the past decade both in academia and industry to improve data exploration, minimize the overall cost, and improve data analysis. In this chapter, we explore the idea of visual analytics in the context of simulation data. This would then provide us with the capability to not only explore our data visually but also to apply machine learning models in order to answer high-level questions with respect to scheduling, choosing optimal simulation parameters, finding correlations, etc. More specifically, we examine state-of-the-art tools to be able to perform these above-mentioned tasks. Further, to test and validate our methodology we followed the human-centered design process to build a prototype tool called ViDAS (Visual Data Analytics of Simulated Data). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regards to visual analysis of simulated data.

Drahtlose Kommunikationssysteme dringen in immer mehr Anwendungsbereiche vor. Für einige Anwendungsszenarien, wie etwa die Prozessautomatisierung in Fabriken und Industrieanlagen, ist die Zuverlässigkeit vieler drahtloser Kommunikationssysteme wie IEEE 802.11 (WLAN) oder Bluetooth aber noch unzureichend. Daher wurden für diese Anwendungsbereiche spezielle Kommunikationssysteme wie WirelessHART oder ISA 100.11a entwickelt. Diese basieren meist auf Time Division Multiple Access (TDMA) und erreichen durch exklusive Reservierungen deterministische Zuverlässigkeit, falls kein anderes Kommunikationssystem die genutzten Kanäle stört. Diese Arbeit behandelt geeignete Protokolle und Algorithmen, um die Zuverlässigkeit drahtloser Kommunikationssysteme zu verbessern. Im ersten Teil der Arbeit werden Verfahren für TDMA-basierte Kommunikationssysteme betrachtet. Basierend auf IEEE 802.15.4 werden mehrere Funktionalitäten für ProNet 4.0, einem an der Arbeitsgruppe Vernetzte Systeme der TU Kaiserslautern entwickelten Kommunikations-Stack für Industrie 4.0, entworfen und auf Imote 2 Sensorknoten implementiert. Zuverlässige Kommunikation bedarf Kenntnis von sowohl der Kommunikationstopologie, über die Knoten miteinander kommunizieren können, als auch der Interferenztopologie, die angibt, wie Knoten sich gegenseitig stören können. Dazu stellt die Arbeit mit dem Automatic Topology Discovery Protocol (ATDP) ein Verfahren zur automatischen Topologieerkennung vor. Anschließend wird QoS Multicast Routing betrachtet und mit dem QoS Multicast Routing Protocol (QMRP) ein Verfahren für partiell mobile Netzwerke entwickelt. Weiterhin wird mit ProMid eine Kommunikations-Middleware beschrieben, die ein hohes Abstraktionslevel aufweist und die darunter liegenden Schichten steuert. Die dienstorientierte Architektur nutzt eine verteilte Service Registry, wobei die Auswahl der Registry-Knoten anhand eines dafür entwickelten Clustering-Algorithmus erfolgt. Das Heterogeneous Network Clustering (HNC) genannte Verfahren berücksichtigt ein heterogenes Netzwerkmodell mit Knoten, die Clusterhead bzw. Gateway werden müssen, können bzw. nicht dürfen. Der zweite Teil der Arbeit behandelt Protokolle und Algorithmen für zuverlässige wettbewerbsbasierte Kommunikationssysteme. Die in diesem Kapitel vorgestellten Verfahren sind in einem auf WLAN basierenden Kommunikations-Stack implementiert und evaluiert worden. Zunächst wird ein Verfahren für die Topologieerkennung in WLAN-Netzwerken vorgestellt. Anschließend wird ein auf dem Token Bucket-Mechanismus basierendes Verfahren zur Verkehrskontrolle entwickelt. Daraus wird mit der Unusable Wasted Bandwidth Ratio eine Metrik abgeleitet, die es erlaubt, die Auslastung des Mediums abzuschätzen. Aufbauend auf dem Verfahren zur Verkehrskontrolle wird eine kooperative faire Bandbreitenskalierung für WLAN vorgestellt. Das Verfahren verteilt die Bandbreite fair unter den internen Knoten unter Berücksichtigung der Quality of Service (QoS) Anforderungen. Dabei reagiert es dynamisch auf Änderungen des externen Verkehrs und verhindert so Überlastsituationen. Letztlich wird ein Clustering-Protokoll vorgestellt, welches durch das Anwendungsszenario der Überwachung von Güterzügen motiviert ist und Linientopologien bildet sowie dynamisch repariert. Das auf Bluetooth LE aufbauende Verfahren dient dazu, Energie einzusparen, und wurde in einer Kooperation mit der Bosch Engineering GmbH entwickelt.

Multidisciplinary optimizations (MDOs) encompass optimization problems that combine different disciplines into a single optimization with the aim of converging towards a design that simultaneously fulfills multiple criteria. For example, considering both fluid and structural disciplines to obtain a shape that is not only aerodynamically efficient, but also respects structural constraints. Combined with CAD-based parametrizations, the optimization produces an improved, manufacturable shape. For turbomachinery applications, this method has been successfully applied using gradient-free optimization methods such as genetic algorithms, surrogate modeling, and others. While such algorithms can be easily applied without access to the source code, the number of iterations to converge is dependent on the number of design parameters. This results in high computational costs and limited design spaces. A competitive alternative is offered by gradient-based optimization algorithms combined with adjoint methods, where the computational complexity of the gradient calculation is no longer dependent on the number of design parameters, but rather on the number of outputs. Such methods have been extensively used in single-disciplinary aerodynamic optimizations using adjoint fluid solvers and CAD parametrizations. However, CAD-based MDOs leveraging adjoint methods are just beginning to emerge. This thesis contributes to this field of research by setting up a CAD-based adjoint MDO framework for turbomachinery design using both fluid and structural disciplines. To achieve this, the von Kármán Institute’s existing CAD-based optimization framework cado is augmented by the development of a FEM-based structural solver which has been differentiated using the algorithmic differentiation tool CoDiPack of TU Kaiserslautern. While most of the code could be differentiated in a black-box fashion, special treatment is required for the iterative linear and eigenvalue solvers to ensure accuracy and reduce memory consumption. As a result, the solver has the capability of computing both stress and vibration gradients at a cost independent on the number of design parameters. For the presented application case of a radial turbine optimization, the full gradient calculation has a computational cost of approximately 3.14 times the cost of a primal run and the peak memory usage of approximately 2.76 times that of a primal run. The FEM code leverages object-oriented design such that the same base structure can be reused for different purposes with minimal re-differentiation. This is demonstrated by considering a composite material test case where the gradients could be easily calculated with respect to an extended design space that includes material properties. Additionally, the structural solver is reused within a CAD-based mesh deformation, which propagates the structural FEM mesh gradients through to the CAD parameters. This closes the link between the CAD shape and FEM mesh. Finally, the MDO framework is applied by optimizing the aerodynamic efficiency of a radial turbine while respecting structural constraints.

Scaling up conventional processor architectures cannot translate the ever-increasing number of transistors into comparable application performance. Although the trend is to shift from single-core to multi-core architectures, utilizing these multiple cores is not a trivial task for many applications due to thread synchronization and weak memory consistency issues. This is especially true for applications in real-time embedded systems since timing analysis becomes more complicated due to contention on shared resources. One inherent reason for the limited use of instruction-level parallelism (ILP) by conventional processors is the use of registers. Therefore, some recent processors bypass register usage by directly communicating values from producer processing units to consumer processing units. In widely used superscalar processors, this direct instruction communication is organized by hardware at runtime, adversely affecting its scalability. The exposed datapath architectures provide a scalable alternative by allowing compilers to move values directly from output ports to the input ports of processing units. Though exposed datapath architectures have already been studied in great detail, they still use registers for executing programs, thus limiting the amount of ILP they can exploit. This limitation stems from a drawback in their execution paradigm, code generator, or both. This thesis considers a novel exposed datapath architecture named Synchronous Control Asynchronous Dataflow (SCAD) that follows a hybrid control-flow dataflow execution paradigm. The SCAD architecture employs first-in-first-out (FIFO) buffers at the output and input ports of processing units. It is programmed by move instructions that transport values from the head of output buffers to the tail of input buffers. Thus, direct instruction communication is facilitated by the architecture. The processing unit triggers the execution of an operation when operand values are available at the heads of its input buffers. We propose a code generation technique for SCAD processors inspired by classical queue machines that completely eliminates the use of registers. On this basis, we first generate optimal code by using satisfiability (SAT) solvers after establishing that optimal code generation is hard. Heuristics based on a novel buffer interference analysis are then developed to compile larger programs. The experimental results demonstrate the efficacy of the execution paradigm of SCAD using our queue-oriented code generation technique.

In today’s computer networks we see an ongoing trend towards wireless communication technologies, such as Wireless LAN, Bluetooth, ZigBee and cellular networks. As the electromagnetic spectrum usable for wireless communication is finite and largely allocated for exclusive use by respective license holders, there are only few frequency bands left for general, i.e. unlicensed, use. Subsequently, it becomes apparent, that there will be an overload situation in the unlicensed bands, up to a point where no communication is possible anymore. On the other hand, it has been observed that licensed frequency bands often go unused, at least at some places or over time. Mitola combined both observations and found the term Cognitive Radio Networks [Mit00], denoting a solution for spectrum scarcity. In this concept, so called Secondary Users are allowed to also use licensed bands (attributed to a Primary User) as long as it is vacant. In such networks, all obligations reside with Secondary Users, especially, they must avoid any interference with the Primary User. They must therefore reliably sense the presence of Primary Users and must decide which available spectrum to use. These two functionalities are called Spectrum Sensing and Spectrum Mobility and describe 2 out of 4 core functionalities of Cognitive Radio Networks and are considered in this thesis. Regarding Spectrum Sensing, we present our own approach for energy detection in this thesis. Energy detection essentially works by comparing measured energy levels to a threshold. The inherent problem is on how to find such thresholds. Based on existing work we found in literature, we improve techniques and assert the effectiveness of our additions by conducting real world experiments. Regarding Spectrum Mobility, we concentrate on the point, where the Primary User shows up. At this point, nodes must not use the current channel anymore, i.e. they also have no possibility to agree on another channel to switch to. We solve this problem by employing channel switching, i.e. we change channels proactively, following a schedule shared by all nodes of the network. The main contribution of this thesis is on how to synthesize those schedules to guarantee robust operation under changing conditions. For integration, we considered three dimensions of robustness (of time, of space and of channel) and, based on our algorithms and findings, defined a network protocol, which addresses perturbation within those dimensions. In an evaluation, we showed that the protocol is actually able to maintain robust operation, even if there are large drops in channel quality.

In the avionics domain, “ultra-reliability” refers to the practice of ensuring quantifiably negligible residual failure rates in the presence of transient and permanent hardware faults. If autonomous Cyber- Physical Systems (CPS) in other domains, e.g., autonomous vehicles, drones, and industrial automation systems, are to permeate our everyday life in the not so distant future, then they also need to become ultra-reliable. However, the rigorous reliability engineering and analysis practices used in the avionics domain are expensive and time consuming, and cannot be transferred to most other CPS domains. The increasing adoption of faster and cheaper, but less reliable, Commercial Off-The-Shelf (COTS) hardware is also an impediment in this regard. Motivated by the goal of ultra-reliable CPS, this dissertation shows how to soundly analyze the reliability of COTS-based implementations of actively replicated Networked Control Systems (NCSs)—which are key building blocks of modern CPS—in the presence of transient hard- ware faults. When an NCS is deployed over field buses such as the Controller Area Network (CAN), transient faults are known to cause host crashes, network retransmissions, and incorrect computations. In addition, when an NCS is deployed over point-to-point networks such as Ethernet, even Byzantine errors (i.e., inconsistent broadcast transmissions) are possible. The analyses proposed in this dissertation account for NCS failures due to each of these error categories, and consider NCS failures in both time and value domains. The analyses are also provably free of reliability anomalies. Such anomalies are problematic because they can result in unsound failure rate estimates, which might lead us to believe that a system is safer than it actually is. Specifically, this dissertation makes four main contributions. (1) To reduce the failure rate of NCSs in the presence of Byzantine errors, we present a hard real-time design of a Byzantine Fault Tolerance (BFT) protocol for Ethernet-based systems. (2) We then propose a quantitative reliability analysis of the presented design in the presence of transient faults. (3) Next, we propose a similar analysis to upper-bound the failure probability of an actively replicated CAN-based NCS. (4) Finally, to upper-bound the long-term failure rate of the NCS more accurately, we propose analyses that take into account the temporal robustness properties of an NCS expressed as weakly-hard constraints. By design, our analyses can be applied in the context of full-system analyses. For instance, to certify a system consisting of multiple actively replicated NCSs deployed over a BFT atomic broadcast layer, the upper bounds on the failure rates of each NCS and the atomic broadcast layer can be composed using the sum-of-failure-rates model.

Background: The use of health apps to support the treatment of chronic pain is gaining importance. Most available pain management apps are still lacking in content quality and quantity as their developers neither involve health experts to ensure target group suitability nor use gamification to engage and motivate the user. To close this gap, we aimed to develop a gamified pain management app, Pain-Mentor. Objective: To determine whether medical professionals would approve of Pain-Mentor’s concept and content, this study aimed to evaluate the quality of the app’s first prototype with experts from the field of chronic pain management and to discover necessary improvements. Methods: A total of 11 health professionals with a background in chronic pain treatment and 2 mobile health experts participated in this study. Each expert first received a detailed presentation of the app. Afterward, they tested Pain-Mentor and then rated its quality using the mobile application rating scale (MARS) in a semistructured interview. Results: The experts found the app to be of excellent general (mean 4.54, SD 0.55) and subjective quality (mean 4.57, SD 0.43). The app-specific section was rated as good (mean 4.38, SD 0.75). Overall, the experts approved of the app’s content, namely, pain and stress management techniques, behavior change techniques, and gamification. They believed that the use of gamification in Pain-Mentor positively influences the patients’ motivation and engagement and thus has the potential to promote the learning of pain management techniques. Moreover, applying the MARS in a semistructured interview provided in-depth insight into the ratings and concrete suggestions for improvement. Conclusions: The experts rated Pain-Mentor to be of excellent quality. It can be concluded that experts perceived the use of gamification in this pain management app in a positive manner. This showed that combining pain management with gamification did not negatively affect the app’s integrity. This study was therefore a promising first step in the development of Pain-Mentor.

In recent years, the Internet has become a major source of visual information exchange. Popular social platforms have reported an average of 80 million photo uploads a day. These images, are often accompanied with a user provided text one-liner, called an image caption. Deep Learning techniques have made significant advances towards automatic generation of factual image captions. However, captions generated by humans are much more than mere factual image descriptions. This work takes a step towards enhancing a machine's ability to generate image captions with human-like properties. We name this field as Affective Image Captioning, to differentiate it from the other areas of research focused on generating factual descriptions. To deepen our understanding of human generated captions, we first perform a large-scale Crowd-Sourcing study on a subset of Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M). Three thousand random image-caption pairs were evaluated by native English speakers w.r.t different dimensions like focus, intent, emotion, meaning, and visibility. Our findings indicate three important underlying properties of human captions: subjectivity, sentiment, and variability. Based on these results, we develop Deep Learning models to address each of these dimensions. To address the subjectivity dimension, we propose the Focus-Aspect-Value (FAV) model (along with a new task of aspect-detection) to structure the process of capturing subjectivity. We also introduce a novel dataset, aspects-DB, following this way of modeling. To implement the model, we propose a novel architecture called Tensor Fusion. Our experiments show that Tensor Fusion outperforms the state-of-the-art cross residual networks (XResNet) in aspect-detection. Towards the sentiment dimension, we propose two models:Concept & Syntax Transition Network (CAST) and Show & Tell with Emotions (STEM). The CAST model uses a graphical structure to generate sentiment. The STEM model uses a neural network to inject adjectives into a neutral caption. Achieving a high score of 93% with human evaluation, these models were selected as the top-3 at the ACMMM Grand Challenge 2016. To address the last dimension, variability, we take a generative approach called Generative Adversarial Networks (GAN) along with multimodal fusion. Our modified GAN, with two discriminators, is trained using Reinforcement Learning. We also show that it is possible to control the properties of the generated caption-variations with an external signal. Using sentiment as the external signal, we show that we can easily outperform state-of-the-art sentiment caption models.

With the technological advancement in the field of robotics, it is now quite practical to acknowledge the actuality of social robots being a part of human's daily life in the next decades. Concerning HRI, the basic expectations from a social robot are to perceive words, emotions, and behaviours, in order to draw several conclusions and adapt its behaviour to realize natural HRI. Henceforth, assessment of human personality traits is essential to bring a sense of appeal and acceptance towards the robot during interaction. Knowledge of human personality is highly relevant as far as natural and efficient HRI is concerned. The idea is taken from human behaviourism, with humans behaving differently based on the personality trait of the communicating partners. This thesis contributes to the development of personality trait assessment system for intelligent human-robot interaction. The personality trait assessment system is organized in three separate levels. The first level, known as perceptual level, is responsible for enabling the robot to perceive, recognize and understand human actions in the surrounding environment in order to make sense of the situation. Using psychological concepts and theories, several percepts have been extracted. A study has been conducted to validate the significance of these percepts towards personality traits. The second level, known as affective level, helps the robot to connect the knowledge acquired in the first level to make higher order evaluations such as assessment of human personality traits. The affective system of the robot is responsible for analysing human personality traits. To the best of our knowledge, this thesis is the first work in the field of human-robot interaction that presents an automatic assessment of human personality traits in real-time using visual information. Using psychology and cognitive studies, many theories has been studied. Two theories have been been used to build the personality trait assessment system: Big Five personality traits assessment and temperament framework for personality traits assessment. By using the information from the perceptual and affective level, the last level, known as behavioural level, enables the robot to synthesize an appropriate behaviour adapted to human personality traits. Multiple experiments have been conducted with different scenarios. It has been shown that the robot, ROBIN, assesses personality traits correctly during interaction and uses the similarity-attraction principle to behave with similar personality type. For example, if the person is found out to be extrovert, the robot also behaves like an extrovert. However, it also uses the complementary attraction theory to adapt its behaviour and complement the personality of the interaction partner. For example, if the person is found out to be self-centred, the robot behaves like an agreeable in order to flourish human-robot interaction.

Although today’s bipeds are capable of demonstrating impressive locomotion skills, in many aspects, there’s still a big gap compared to the capabilities observed in humans. Partially, this is due to the deployed control paradigms that are mostly based on analytical approaches. The analytical nature of those approaches entails strong model dependencies – regarding the robotic platform as well as the environment – which makes them prone to unknown disturbances. Recently, an increasing number of biologically-inspired control approaches have been presented from which a human-like bipedal gait emerges. Although the control structures only rely on proprioceptive sensory information, the smoothness of the motions and the robustness against external disturbances is impressive. Due to the lack of suitable robotic platforms, until today the controllers have been mostly applied to simulations. Therefore, as the first step towards a suitable platform, this thesis presents the Compliant Robotic Leg (CARL) that features mono- as well as biarticular actuation. The design is driven by a set of core-requirements that is primarily derived from the biologically-inspired behavior-based bipedal locomotion control (B4LC) and complemented by further functional aspects from biomechanical research. Throughout the design process, CARL is understood as a unified dynamic system that emerges from the interplay of the mechanics, the electronics, and the control. Thus, having an explicit control approach and the respective gait in mind, the influence of each subsystem on the characteristics of the overall system is considered carefully. The result is a planar robotic leg whose three joints are driven by five highly integrated linear SEAs– three mono- and two biarticular actuators – with minimized reflected inertia. The SEAs are encapsulated by FPGA-based embedded nodes that are designed to meet the hard application requirements while enabling the deployment of a full-featured robotic framework. CARL’s foot is implemented using a COTS prosthetic foot; the sensor information is obtained from the deformation of its main structure. Both subsystems are integrated into a leg structure that matches the proportions of a human with a size of 1.7 m. The functionality of the subsystems, as well as the overall system, is validated experimentally. In particular, the final experiment demonstrates a coordinated walking motion and thereby confirms that CARL can produce the desired behavior – a natural looking, human-like gait is emerging from the interplay of the behavior-based walking control and the mechatronic system. CARL is robust regarding impacts, the redundant actuation system can render the desired joint torques/impedances, and the foot system supports the walking structurally while it provides the necessary sensory information. Considering that there is no movement of the upper trunk, the angle and torque profiles are comparable to the ones found in humans.

As visualization as a field matures, the discussion about the development of a theory of the field becomes increasingly vivid. Despite some voices claiming that visualization applications would be too different from each other to generalize, there is a significant push towards a better understanding of the principles underlying visual data analysis. As of today, visualization is primarily data-driven. Years of experience in the visalization of all kinds of different data accumulated a vast reservoir of implicit knowledge in the community of how to best represent data according to its shape, its format, and what it is meant to express. This knowledge is complemented by knowledge imported to visualization from a variety of other fields, for example psychology, vision science, color theory, and information theory. Yet, a theory of visualization is still only nascent. One major reason for that is the field's too strong focus on the quantitative aspects of data analysis. Although when designing visualizations major design decisions also consider perception and other human factors, the overall appearance of visualizations as of now is determined primarily by the type and format of the data to be visualized and its quantitative attributes like scale, range, or density. This is also reflected by the current approaches in theoretical work on visualization. The models developed in this regard also concentrate primarily on perceptual and quantitative aspects of visual data analysis. Qualitative considerations like the interpretations made by viewers and the conclusions drawn by analysts currently only play a minor role in the literature. This Thesis contributes to the nascent theory of visualization by investigating approaches to the explicit integration of qualitative considerations into visual data analysis. To this end, it promotes qualitative visual analysis, the explicit discussion of the interpretation of artifacts and structures in the visualization, of efficient workflows designed to optimally support an analyst's reasoning strategy and capturing information about insight provenance, and of design methodology tailoring visualizations towards the insights they are meant to provide rather than to the data they show. Towards this aim, three central qualitative principles of visual information encodings are identified during the development of a model for the visual data analysis process that explicitly includes the anticipated reasoning structure into the consideration. This model can be applied throughout the whole life cycle of a visualization application, from the early design phase to the documentation of insight provenance during analysis using the developed visualization application. The three principles identified inspire novel visual data analysis workflows aiming for an insight-driven data analysis process. Moreover, two case studies prove the benefit of following the qualitative principles of visual information encodings for the design of visualization applications. The formalism applied to the development of the presented theoretical framework is founded in formal logics, mathematical set theory, and the theory of formal languages and automata. The models discussed in this Thesis and the findings derived from them are therefore based on a mathematically well-founded theoretical underpinning. This Thesis establishes a sound theoretical framework for the design and description of visualization applications and the prediction of the conclusions an analyst is capable of drawing from working with the visualization. Thereby, it contributes an important piece to the yet unsolved puzzle of developing a visualization theory.

The advent of heterogeneous many-core systems has increased the spectrum of achievable performance from multi-threaded programming. As the processor components become more distributed, the cost of synchronization and communication needed to access the shared resources increases. Concurrent linearizable access to shared objects can be prohibitively expensive in a high contention workload. Though there are various mechanisms (e.g., lock-free data structures) to circumvent the synchronization overhead in linearizable objects, it still incurs performance overhead for many concurrent data types. Moreover, many applications do not require linearizable objects and apply ad-hoc techniques to eliminate synchronous atomic updates. In this thesis, we propose the Global-Local View Model. This programming model exploits the heterogeneous access latencies in many-core systems. In this model, each thread maintains different views on the shared object: a thread-local view and a global view. As the thread-local view is not shared, it can be updated without incurring synchronization costs. The local updates become visible to other threads only after the thread-local view is merged with the global view. This scheme improves the performance at the expense of linearizability. Besides the weak operations on the local view, the model also allows strong operations on the global view. Combining operations on the global and the local views, we can build data types with customizable consistency semantics on the spectrum between sequential and purely mergeable data types. Thus the model provides a framework that captures the semantics of Multi-View Data Types. We discuss a formal operational semantics of the model. We also introduce a verification method to verify the correctness of the implementation of several multi-view data types. Frequently, applications require updating shared objects in an “all-or-nothing” manner. Therefore, the mechanisms to synchronize access to individual objects are not sufficient. Software Transactional Memory (STM) is a mechanism that helps the programmer to correctly synchronize access to multiple mutable shared data by serializing the transactional reads and writes. But under high contention, serializable transactions incur frequent aborts and limit parallelism, which can lead to severe performance degradation. Mergeable Transactional Memory (MTM), proposed in this thesis, allows accessing multi-view data types within a transaction. Instead of aborting and re-executing the transaction, MTM merges its changes using the data-type specific merge semantics. Thus it provides a consistency semantics that allows for more scalability even under contention. The evaluation of our prototype implementation in Haskell shows that mergeable transactions outperform serializable transactions even under low contention while providing a structured and type-safe interface.

Private data analytics systems preferably provide required analytic accuracy to analysts and specified privacy to individuals whose data is analyzed. Devising a general system that works for a broad range of datasets and analytic scenarios has proven to be difficult. Despite the advent of differentially private systems with proven formal privacy guarantees, industry still uses inferior ad-hoc mechanisms that provide better analytic accuracy. Differentially private mechanisms often need to add large amounts of noise to statistical results, which impairs their usability. In my thesis I follow two approaches to improve the usability of private data analytics systems in general and differentially private systems in particular. First, I revisit ad-hoc mechanisms and explore the possibilities of systems that do not provide Differential Privacy or only a weak version thereof. Based on an attack analysis I devise a set of new protection mechanisms including Query Based Bookkeeping (QBB). In contrast to previous systems QBB only requires the history of analysts’ queries in order to provide privacy protection. In particular, QBB does not require knowledge about the protected individuals’ data. In my second approach I use the insights gained with QBB to propose UniTraX, the first differentially private analytics system that allows to analyze part of a protected dataset without affecting the other parts and without giving up on accuracy. I show UniTraX’s usability by way of multiple case studies on real-world datasets across different domains. UniTraX allows more queries than previous differentially private data analytics systems at moderate runtime overheads.

IoT systems consist of Hardware/Software systems (e.g., sensors) that are embedded in a physical world, networked and that interact with complex software platforms. The validation of such systems is a challenge and currently mostly done by prototypes. This paper presents the virtual environment for simulation, emulation and validation of an IoT platform and its semantic model in real life scenarios. It is based on a decentralized, bottom up approach that offers interoperability of IoT devices and the value-added services they want to use across different domains. The framework is demonstrated by a comprehensive case study. The example consists of the complete IoT “Smart Energy” use case with focus on data privacy by homomorphic encryption. The performance of the network is compared while using partially homomorphic encryption, fully homomorphic encryption and no encryption at all.As a major result, we found that our framework is capable of simulating big IoT networks and the overhead introduced by homomorphic encryption is feasible for VICINITY.

Planar force or pressure is a fundamental physical aspect during any people-vs-people and people-vs-environment activities and interactions. It is as significant as the more established linear and angular acceleration (usually acquired by inertial measurement units). There have been several studies involving planar pressure in the discipline of activity recognition, as reviewed in the first chapter. These studies have shown that planar pressure is a promising sensing modality for activity recognition. However, they still take a niche part in the entire discipline, using ad hoc systems and data analysis methods. Mostly these studies were not followed by further elaborative works. The situation calls for a general framework that can help push planar pressure sensing into the mainstream. This dissertation systematically investigates using planar pressure distribution sensing technology for ubiquitous and wearable activity recognition purposes. We propose a generic Textile Pressure Mapping (TPM) Framework, which encapsulates (1) design knowledge and guidelines, (2) a multi-layered tool including hardware, software and algorithms, and (3) an ensemble of empirical study examples. Through validation with various empirical studies, the unified TPM framework covers the full scope of application recognition, including the ambient, object, and wearable subspaces. The hardware part constructs a general architecture and implementations in the large-scale and mobile directions separately. The software toolkit consists of four heterogeneous tiers: driver, data processing, machine learning, visualization/feedback. The algorithm chapter describes generic data processing techniques and a unified TPM feature set. The TPM framework offers a universal solution for other researchers and developers to evaluate TPM sensing modality in their application scenarios. The significant findings from the empirical studies have shown that TPM is a versatile sensing modality. Specifically, in the ambient subspace, a sports mat or carpet with TPM sensors embedded underneath can distinguish different sports activities or different people's gait based on the dynamic change of body-print; a pressure sensitive tablecloth can detect various dining actions by the force propagated from the cutlery through the plates to the tabletop. In the object subspace, swirl office chairs with TPM sensors under the cover can be used to detect the seater's real-time posture; TPM can be used to detect emotion-related touch interactions for smart objects, toys or robots. In the wearable subspace, TPM sensors can be used to perform pressure-based mechanomyography to detect muscle and body movement; it can also be tailored to cover the surface of a soccer shoe to distinguish different kicking angles and intensities. All the empirical evaluations have resulted in accuracies well-above the chance level of the corresponding number of classes, e.g., the `swirl chair' study has classification accuracy of 79.5% out of 10 posture classes and in the `soccer shoe' study the accuracy is 98.8% among 17 combinations of angle and intensity.

In computer graphics, realistic rendering of virtual scenes is a computationally complex problem. State-of-the-art rendering technology must become more scalable to meet the performance requirements for demanding real-time applications. This dissertation is concerned with core algorithms for rendering, focusing on the ray tracing method in particular, to support and saturate recent massively parallel computer systems, i.e., to distribute the complex computations very efficiently among a large number of processing elements. More specifically, the three targeted main contributions are: 1. Collaboration framework for large-scale distributed memory computers The purpose of the collaboration framework is to enable scalable rendering in real-time on a distributed memory computer. As an infrastructure layer it manages the explicit communication within a network of distributed memory nodes transparently for the rendering application. The research is focused on designing a communication protocol resilient against delays and negligible in overhead, relying exclusively on one-sided and asynchronous data transfers. The hypothesis is that a loosely coupled system like this is able to scale linearly with the number of nodes, which is tested by directly measuring all possible communication-induced delays as well as the overall rendering throughput. 2. Ray tracing algorithms designed for vector processing Vector processors are to be efficiently utilized for improved ray tracing performance. This requires the basic, scalar traversal algorithm to be reformulated in order to expose a high degree of fine-grained data parallelism. Two approaches are investigated: traversing multiple rays simultaneously, and performing multiple traversal steps at once. Efficiently establishing coherence in a group of rays as well as avoiding sorting of the nodes in a multi-traversal step are the defining research goals. 3. Multi-threaded schedule and memory management for the ray tracing acceleration structure Construction times of high-quality acceleration structures are to be reduced by improvements to multi-threaded scalability and utilization of vector processors. Research is directed at eliminating the following scalability bottlenecks: dynamic memory growth caused by the primitive splits required for high- quality structures, and top-level hierarchy construction where simple task par- allelism is not readily available. Additional research addresses how to expose scatter/gather-free data-parallelism for efficient vector processing. Together, these contributions form a scalable, high-performance basis for real-time, ray tracing-based rendering, and a prototype path tracing application implemented on top of this basis serves as a demonstration. The key insight driving this dissertation is that the computational power necessary for realistic light transport for real-time rendering applications demands massively parallel computers, which in turn require highly scalable algorithms. Therefore this dissertation provides important research along the path towards virtual reality.

In this thesis, we consider the problem of processing similarity queries over a dataset of top-k rankings and class constrained objects. Top-k rankings are the most natural and widely used technique to compress a large amount of information into a concise form. Spearman’s Footrule distance is used to compute the similarity between rankings, considering how well rankings agree on the positions (ranks) of ranked items. This setup allows the application of metric distance-based pruning strategies, and, alternatively, enables the use of traditional inverted indices for retrieving rankings that overlap in items. Although both techniques can be individually applied, we hypothesize that blending these two would lead to better performance. First, we formulate theoretical bounds over the rankings, based on Spearman's Footrule distance, which are essential for adapting existing, inverted index based techniques to the setting of top-k rankings. Further, we propose a hybrid indexing strategy, designed for efficiently processing similarity range queries, which incorporates inverted indices and metric space indices, such as M- or BK-trees, resulting in a structure that resembles both indexing methods with tunable emphasis on one or the other. Moreover, optimizations to the inverted index component are presented, for early termination and minimizing bookkeeping. As vast amounts of data are being generated on a daily bases, we further present a distributed, highly tunable, approach, implemented in Apache Spark, for efficiently processing similarity join queries over top-k rankings. To combine distance-based filtering with inverted indices, the algorithm works in several phases. The partial results are joined for the computation of the final result set. As the last contribution of the thesis, we consider processing k-nearest-neighbor (k-NN) queries over class-constrained objects, with the additional requirement that the result objects are of a specific type. We introduce the MISP index, which first indexes the objects by their (combination of) class belonging, followed by a similarity search sub index for each subset of objects. The number of such subsets can combinatorially explode, thus, we provide a cost model that analyzes the performance of the MISP index structure under different configurations, with the aim of finding the most efficient one for the dataset being searched.

This manuscript presents a topology-based analysis and visualization framework that enables the effective exploration of feature evolution in large-scale simulations. Such simulations pose additional challenges to the already complex task of feature tracking and visualization, since the vast number of features and the size of the simulation data make it infeasible to naively identify, track, analyze, render, store, and interact with data. The presented methodology addresses these issues via three core contributions. First, the manuscript defines a novel topological abstraction, called the Nested Tracking Graph (NTG), that records the temporal evolution of features that exhibit a nesting hierarchy, such as superlevel set components for multiple levels, or filtered features across multiple thresholds. In contrast to common tracking graphs that are only capable of describing feature evolution at one hierarchy level, NTGs effectively summarize their evolution across all hierarchy levels in one compact visualization. The second core contribution is a view-approximation oriented image database generation approach (VOIDGA) that stores, at simulation runtime, a reduced set of feature images. Instead of storing the features themselves---which is often infeasable due to bandwidth constraints---the images of these databases can be used to approximate the depicted features from any view angle within an acceptable visual error, which requires far less disk space and only introduces a neglectable overhead. The final core contribution combines these approaches into a methodology that stores in situ the least amount of information necessary to support flexible post hoc analysis utilizing NTGs and view approximation techniques.

The systems in industrial automation management (IAM) are information systems. The management parts of such systems are software components that support the manufacturing processes. The operational parts control highly plug-compatible devices, such as controllers, sensors and motors. Process variability and topology variability are the two main characteristics of software families in this domain. Furthermore, three roles of stakeholders -- requirement engineers, hardware-oriented engineers, and software developers -- participate in different derivation stages and have different variability concerns. In current practice, the development and reuse of such systems is costly and time-consuming, due to the complexity of topology and process variability. To overcome these challenges, the goal of this thesis is to develop an approach to improve the software product derivation process for systems in industrial automation management, where different variability types are concerned in different derivation stages. Current state-of-the-art approaches commonly use general-purpose variability modeling languages to represent variability, which is not sufficient for IAM systems. The process and topology variability requires more user-centered modeling and representation. The insufficiency of variability modeling leads to low efficiency during the staged derivation process involving different stakeholders. Up to now, product line approaches for systematic variability modeling and realization have not been well established for such complex domains. The model-based derivation approach presented in this thesis integrates feature modeling with domain-specific models for expressing processes and topology. The multi-variability modeling framework includes the meta-models of the three variability types and their associations. The realization and implementation of the multi-variability involves the mapping and the tracing of variants to their corresponding software product line assets. Based on the foundation of multi-variability modeling and realization, a derivation infrastructure is developed, which enables a semi-automated software derivation approach. It supports the configuration of different variability types to be integrated into the staged derivation process of the involved stakeholders. The derivation approach is evaluated in an industry-grade case study of a complex software system. The feasibility is demonstrated by applying the approach in the case study. By using the approach, both the size of the reusable core assets and the automation level of derivation are significantly improved. Furthermore, semi-structured interviews with engineers in practice have evaluated the usefulness and ease-of-use of the proposed approach. The results show a positive attitude towards applying the approach in practice, and high potential to generalize it to other related domains.

The usage of sensors in modern technical systems and consumer products is in a rapid increase. This advancement can be characterized by two major factors, namely, the mass introduction of consumer oriented sensing devices to the market and the sheer amount of sensor data being generated. These characteristics raise subsequent challenges regarding both the consumer sensing devices' reliability and the management and utilization of the generated sensor data. This thesis addresses these challenges through two main contributions. It presents a novel framework that leverages sentiment analysis techniques in order to assess the quality of consumer sensing devices. It also couples semantic technologies with big data technologies to present a new optimized approach for realization and management of semantic sensor data, hence providing a robust means of integration, analysis, and reuse of the generated data. The thesis also presents several applications that show the potential of the contributions in real-life scenarios. Due to the broad range, growing feature set and fast release pace of new sensor-based products, evaluating these products is very challenging as standard product testing is not practical. As an alternative, an end-to-end aspect-based sentiment summarizer pipeline for evaluation of consumer sensing devices is presented. The pipeline uses product reviews to extract the sentiment at the aspect level and includes several components namely, product name extractor, aspects extractor and a lexicon-based sentiment extractor which handles multiple sentiment analysis challenges such as sentiment shifters, negations, and comparative sentences among others. The proposed summarizer's components generally outperform the state-of-the-art approaches. As a use case, features of the market leading fitness trackers are evaluated and a dynamic visual summarizer is presented to display the evaluation results and to provide personalized product recommendations for potential customers. The increased usage of sensing devices in the consumer market is accompanied with increased deployment of sensors in various other fields such as industry, agriculture, and energy production systems. This necessitates using efficient and scalable methods for storing and processing of sensor data. Coupling big data technologies with semantic techniques not only helps to achieve the desired storage and processing goals, but also facilitates data integration, data analysis, and the utilization of data in unforeseen future applications through preserving the data generation context. This thesis proposes an efficient and scalable solution for semantification, storage and processing of raw sensor data through ontological modelling of sensor data and a novel encoding scheme that harnesses the split between the statements of the conceptual model of an ontology (TBox) and the individual facts (ABox) along with in-memory processing capabilities of modern big data systems. A sample use case is further introduced where a smartphone is deployed in a transportation bus to collect various sensor data which is then utilized in detecting street anomalies. In addition to the aforementioned contributions, and to highlight the potential use cases of sensor data publicly available, a recommender system is developed using running route data, used for proximity-based retrieval, to provide personalized suggestions for new routes considering the runner's performance, visual and nature of route preferences. This thesis aims at enhancing the integration of sensing devices in daily life applications through facilitating the public acquisition of consumer sensing devices. It also aims at achieving better integration and processing of sensor data in order to enable new potential usage scenarios of the raw generated data.

Large-scale distributed systems consist of a number of components, take a number of parameter values as input, and behave differently based on a number of non-deterministic events. All these features—components, parameter values, and events—interact in complicated ways, and unanticipated interactions may lead to bugs. Empirically, many bugs in these systems are caused by interactions of only a small number of features. In certain cases, it may be possible to test all interactions of \(k\) features for a small constant \(k\) by executing a family of tests that is exponentially or even doubly-exponentially smaller than the family of all tests. Thus, in such cases we can effectively uncover all bugs that require up to \(k\)-wise interactions of features. In this thesis we study two occurrences of this phenomenon. First, many bugs in distributed systems are caused by network partition faults. In most cases these bugs occur due to two or three key nodes, such as leaders or replicas, not being able to communicate, or because the leading node finds itself in a block of the partition without quorum. Second, bugs may occur due to unexpected schedules (interleavings) of concurrent events—concurrent exchange of messages and concurrent access to shared resources. Again, many bugs depend only on the relative ordering of a small number of events. We call the smallest number of events whose ordering causes a bug the depth of the bug. We show that in both testing scenarios we can effectively uncover bugs involving small number of nodes or bugs of small depth by executing small families of tests. We phrase both testing scenarios in terms of an abstract framework of tests, testing goals, and goal coverage. Sets of tests that cover all testing goals are called covering families. We give a general construction that shows that whenever a random test covers a fixed goal with sufficiently high probability, a small randomly chosen set of tests is a covering family with high probability. We then introduce concrete coverage notions relating to network partition faults and bugs of small depth. In case of network partition faults, we show that for the introduced coverage notions we can find a lower bound on the probability that a random test covers a given goal. Our general construction then yields a randomized testing procedure that achieves full coverage—and hence, find bugs—quickly. In case of coverage notions related to bugs of small depth, if the events in the program form a non-trivial partial order, our general construction may give a suboptimal bound. Thus, we study other ways of constructing covering families. We show that if the events in a concurrent program are partially ordered as a tree, we can explicitly construct a covering family of small size: for balanced trees, our construction is polylogarithmic in the number of events. For the case when the partial order of events does not have a "nice" structure, and the events and their relation to previous events are revealed while the program is running, we give an online construction of covering families. Based on the construction, we develop a randomized scheduler called PCTCP that uniformly samples schedules from a covering family and has a rigorous guarantee of finding bugs of small depth. We experiment with an implementation of PCTCP on two real-world distributed systems—Zookeeper and Cassandra—and show that it can effectively find bugs.

Wearable activity recognition aims to identify and assess human activities with the help of computer systems by evaluating signals of sensors which can be attached to the human body. This provides us with valuable information in several areas: in health care, e.g. fluid and food intake monitoring; in sports, e.g. training support and monitoring; in entertainment, e.g. human-computer interface using body movements; in industrial scenarios, e.g. computer support for detected work tasks. Several challenges exist for wearable activity recognition: a large number of nonrelevant activities (null class), the evaluation of large numbers of sensor signals (curse of dimensionality), ambiguity of sensor signals compared to the activities and finally the high variability of human activity in general. This thesis develops a new activity recognition strategy, called invariants classification, which addresses these challenges, especially the variability in human activities. The core idea is that often even highly variable actions include short, more or less invariant sub-actions which are due to hard physical constraints. If someone opens a door, the movement of the hand to the door handle is not fixed. However the door handle has to be pushed to open the door. The invariants classification algorithm is structured in four phases: segmentation, invariant identification, classification, and spotting. The segmentation divides the continuous sensor data stream into meaningful parts, which are related to sub-activities. Our segmentation strategy uses the zero crossings of the central difference quotient of the sensor signals, as segment borders. The invariant identification finds the invariant sub-activities by means of clustering and a selection strategy dependent on certain features. The classification identifies the segments of a specific activity class, using models generated from the invariant sub-activities. The models include the invariant sub-activity signal and features calculated on sensor signals related to the sub-activity. In the spotting, the classified segments are used to find the entire activity class instances in the continuous sensor data stream. For this purpose, we use the position of the invariant sub-activity in the related activity class instance for the estimation of the borders of the activity instances. In this thesis, we show that our new activity recognition strategy, built on invariant sub-activities, is beneficial. We tested it on three human activity datasets with wearable inertial measurement units (IMU). Compared to previous publications on the same datasets we got improvement in the activity recognition in several classes, some with a large margin. Our segmentation achieves a sensible method to separate the sensor data in relation to the underlying activities. Relying on sub-activities makes us independent from imprecise labels on the training data. After the identification of invariant sub-activities, we calculate a value called cluster precision for each sensor signal and each class activity. This tells us which classes can be easily classified and which sensor channels support the classification best. Finally, in the training for each activity class, our algorithm selects suitable signal channels with invariant sub-activities on different points in time and with different length. This makes our strategy a multi-dimensional asynchronous motif detection with variable motif length.

The simulation of physical phenomena involving the dynamic behavior of fluids and gases has numerous applications in various fields of science and engineering. Of particular interest is the material transport behavior, the tendency of a flow field to displace parts of the medium. Therefore, many visualization techniques rely on particle trajectories. Lagrangian Flow Field Representation. In typical Eulerian settings, trajectories are computed from the simulation output using numerical integration schemes. Accuracy concerns arise because, due to limitations of storage space and bandwidth, often only a fraction of the computed simulation time steps are available. Prior work has shown empirically that a Lagrangian, trajectory-based representation can improve accuracy [Agr+14]. Determining the parameters of such a representation in advance is difficult; a relationship between the temporal and spatial resolution and the accuracy of resulting trajectories needs to be established. We provide an error measure for upper bounds of the error of individual trajectories. We show how areas at risk for high errors can be identified, thereby making it possible to prioritize areas in time and space to allocate scarce storage resources. Comparative Visual Analysis of Flow Field Ensembles. Independent of the representation, errors of the simulation itself are often caused by inaccurate initial conditions, limitations of the chosen simulation model, and numerical errors. To gain a better understanding of the possible outcomes, multiple simulation runs can be calculated, resulting in sets of simulation output referred to as ensembles. Of particular interest when studying the material transport behavior of ensembles is the identification of areas where the simulation runs agree or disagree. We introduce and evaluate an interactive method that enables application scientists to reliably identify and examine regions of agreement and disagreement, while taking into account the local transport behavior within individual simulation runs. Particle-Based Representation and Visualization of Uncertain Flow Data Sets. Unlike simulation ensembles, where uncertainty of the solution appears in the form of different simulation runs, moment-based Eulerian multi-phase fluid simulations are probabilistic in nature. These simulations, used in process engineering to simulate the behavior of bubbles in liquid media, are aimed toward reducing the need for real-world experiments. The locations of individual bubbles are not modeled explicitly, but stochastically through the properties of locally defined bubble populations. Comparisons between simulation results and physical experiments are difficult. We describe and analyze an approach that generates representative sets of bubbles for moment-based simulation data. Using our approach, application scientists can directly, visually compare simulation results and physical experiments.

We present a study comparing the effect of real-time wearable feedback with traditional training methods for cardiopulmonary resuscitation (CPR). The aim is to ensure that the students can deliver CPR with the right compression speed and depth. On the wearable side, we test two systems: one based on a combination of visual feedback and tactile information on a smart-watch and one based on visual feedback and audio information on a Google Glass. In a trial with 50 subjects (23 trainee nurses and 27 novices,) we compare those modalities to standard human teaching that is used in nurse training. While a single traditional teaching session tends to improve only the percentage of correct depth, it has less effect on the percentage of effective CPR (depth and speed correct at the same time). By contrast, in a training session with the wearable feedback device, the average percentage of time when CPR is effective improves by up to almost 25%.

Novel image processing techniques have been in development for decades, but most of these techniques are barely used in real world applications. This results in a gap between image processing research and real-world applications; this thesis aims to close this gap. In an initial study, the quantification, propagation, and communication of uncertainty were determined to be key features in gaining acceptance for new image processing techniques in applications. This thesis presents a holistic approach based on a novel image processing pipeline, capable of quantifying, propagating, and communicating image uncertainty. This work provides an improved image data transformation paradigm, extending image data using a flexible, high-dimensional uncertainty model. Based on this, a completely redesigned image processing pipeline is presented. In this pipeline, each step respects and preserves the underlying image uncertainty, allowing image uncertainty quantification, image pre-processing, image segmentation, and geometry extraction. This is communicated by utilizing meaningful visualization methodologies throughout each computational step. The presented methods are examined qualitatively by comparing to the Stateof- the-Art, in addition to user evaluation in different domains. To show the applicability of the presented approach to real world scenarios, this thesis demonstrates domain-specific problems and the successful implementation of the presented techniques in these domains.

In recent years, enormous progress has been made in the field of Artificial Intelligence (AI). Especially the introduction of Deep Learning and end-to-end learning, the availability of large datasets and the necessary computational power in form of specialised hardware allowed researchers to build systems with previously unseen performance in areas such as computer vision, machine translation and machine gaming. In parallel, the Semantic Web and its Linked Data movement have published many interlinked RDF datasets, forming the world’s largest, decentralised and publicly available knowledge base. Despite these scientific successes, all current systems are still narrow AI systems. Each of them is specialised to a specific task and cannot easily be adapted to all other human intelligence tasks, as would be necessary for Artificial General Intelligence (AGI). Furthermore, most of the currently developed systems are not able to learn by making use of freely available knowledge such as provided by the Semantic Web. Autonomous incorporation of new knowledge is however one of the pre-conditions for human-like problem solving. This work provides a small step towards teaching machines such human-like reasoning on freely available knowledge from the Semantic Web. We investigate how human associations, one of the building blocks of our thinking, can be simulated with Linked Data. The two main results of these investigations are a ground truth dataset of semantic associations and a machine learning algorithm that is able to identify patterns for them in huge knowledge bases. The ground truth dataset of semantic associations consists of DBpedia entities that are known to be strongly associated by humans. The dataset is published as RDF and can be used for future research. The developed machine learning algorithm is an evolutionary algorithm that can learn SPARQL queries from a given SPARQL endpoint based on a given list of exemplary source-target entity pairs. The algorithm operates in an end-to-end learning fashion, extracting features in form of graph patterns without the need for human intervention. The learned patterns form a feature space adapted to the given list of examples and can be used to predict target candidates from the SPARQL endpoint for new source nodes. On our semantic association ground truth dataset, our evolutionary graph pattern learner reaches a Recall@10 of > 63 % and an MRR (& MAP) > 43 %, outperforming all baselines. With an achieved Recall@1 of > 34% it even reaches average human top response prediction performance. We also demonstrate how the graph pattern learner can be applied to other interesting areas without modification.

Computational problems that involve dynamic data, such as physics simulations and program development environments, have been an important subject of study in programming languages. Recent advances in self-adjusting computation made progress towards achieving efficient incremental computation by providing algorithmic language abstractions to express computations that respond automatically to dynamic changes in their inputs. Selfadjusting programs have been shown to be efficient for a broad range of problems via an explicit programming style, where the programmer uses specific primitives to identify, create and operate on data that can change over time. This dissertation presents implicit self-adjusting computation, a type directed technique for translating purely functional programs into self-adjusting programs. In this implicit approach, the programmer annotates the (toplevel) input types of the programs to be translated. Type inference finds all other types, and a type-directed translation rewrites the source program into an explicitly self-adjusting target program. The type system is related to information-flow type systems and enjoys decidable type inference via constraint solving. We prove that the translation outputs well-typed self-adjusting programs and preserves the source program’s input-output behavior, guaranteeing that translated programs respond correctly to all changes to their data. Using a cost semantics, we also prove that the translation preserves the asymptotic complexity of the source program. As a second contribution, we present two techniques to facilitate the processing of large and dynamic data in self-adjusting computation. First, we present a type system for precise dependency tracking that minimizes the time and space for storing dependency metadata. The type system improves the scalability of self-adjusting computation by eliminating an important assumption of prior work that can lead to recording spurious dependencies. We present a type-directed translation algorithm that generates correct selfadjusting programs without relying on this assumption. Second, we show a probabilistic-chunking technique to further decrease space usage by controlling the fundamental space-time tradeoff in self-adjusting computation. We implement implicit self-adjusting computation as an extension to Standard ML with compiler and runtime support. Using the compiler, we are able to incrementalize an interesting set of applications, including standard list and matrix benchmarks, ray tracer, PageRank, sparse graph connectivity, and social circle counts. Our experiments show that our compiler incrementalizes existing code with only trivial amounts of annotation, and the resulting programs bring asymptotic improvements to large datasets from real-world applications, leading to orders of magnitude speedups in practice.

If gradient based derivative algorithms are used to improve industrial products by reducing their target functions, the derivatives need to be exact. The last percent of possible improvement, like the efficiency of a turbine, can only be gained if the derivatives are consistent with the solution process that is used in the simulation software. It is problematic that the development of the simulation software is an ongoing process which leads to the use of approximated derivatives. If a derivative computation is implemented manually, it will be inconsistent after some time if it is not updated. This thesis presents a generalized approach which differentiates the whole simulation software with Algorithmic Differentiation (AD), and guarantees a correct and consistent derivative computation after each change to the software. For this purpose, the variable tagging technique is developed. The technique checks at run-time if all dependencies, which are used by the derivative algorithms, are correct. Since it is also necessary to check the correctness of the implementation, a theorem is developed which describes how AD derivatives can be compared. This theorem is used to develop further methods that can detect and correct errors. All methods are designed such that they can be applied in real world applications and are used within industrial configurations. The process described above yields consistent and correct derivatives but the efficiency can still be improved. This is done by deriving new derivative algorithms. A fixed-point iterator approach, with a consistent derivation, yields all state of the art algorithms and produces two new algorithms. These two new algorithms include all implementation details and therefore they produce consistent derivative results. For detecting hot spots in the application, the state of the art techniques are presented and extended. The data management is changed such that the performance of the software is affected only marginally when quantities, like the number of input and output variables or the memory consumption, are computed for the detection. The hot spots can be treated with techniques like checkpointing or preaccumulation. How these techniques change the time and memory consumption is analyzed and it is shown how they need to be used in selected AD tools. As a last step, the used AD tools are analyzed in more detail. The major implementation strategies for operator overloading AD tools are presented and implementation improvements for existing AD tools are discussed.\ The discussion focuses on a minimal memory consumption and makes it possible to compare AD tools on a theoretical level. The new AD tool CoDiPack is based on these findings and its design and concepts are presented. The improvements and findings in this thesis make it possible, that an automatic, consistent and correct derivative is generated in an efficient way for industrial applications.

The identification of entities that play an important role in a system is one of the fundamental analyses being performed in network studies. This topic is mainly related to centrality indices, which quantify node centrality with respect to several properties in the represented network. The nodes identified in such an analysis are called central nodes. Although centrality indices are very useful for these analyses, there exist several challenges regarding which one fits best for a network. In addition, if the usage of only one index for determining central nodes leads to under- or overestimation of the importance of nodes and is insufficient for finding important nodes, then the question is how multiple indices can be used in conjunction in such an evaluation. Thus, in this thesis an approach is proposed that includes multiple indices of nodes, each indicating an aspect of importance, in the respective evaluation and where all the aspects of a node’s centrality are analyzed in an explorative manner. To achieve this aim, the proposed idea uses fuzzy operators, including a parameter for generating different types of aggregations over multiple indices. In addition, several preprocessing methods for normalization of those values are proposed and discussed. We investigate whether the choice of different decisions regarding the aggregation of the values changes the ranking of the nodes or not. It is revealed that (1) there are nodes that remain stable among the top-ranking nodes, which makes them the most central nodes, and there are nodes that remain stable among the bottom-ranking nodes, which makes them the least central nodes; and (2) there are nodes that show high sensitivity to the choice of normalization methods and/or aggregations. We explain both cases and the reasons why the nodes’ rankings are stable or sensitive to the corresponding choices in various networks, such as social networks, communication networks, and air transportation networks.

Optical Character Recognition (OCR) system plays an important role in digitization of data acquired as images from a variety of sources. Although the area is very well explored for Latin languages, some of the languages based on Arabic cursive script are not yet explored. It is due to many factors: Most importantly are the unavailability of proper data sets and complexities posed by cursive scripts. The Pashto language is one of such languages which needs considerable exploration towards OCR. In order to develop such an OCR system, this thesis provides a pioneering study that explores deep learning for the Pashto language in the field of OCR. The Pashto language is spoken by more than $50$ million people across the world, and it is an active medium both for oral as well as written communication. It is associated with rich literary heritage and contains huge written collection. These written materials present contents of simple to complex nature, and layouts from hand-scribed to printed text. The Pashto language presents mainly two types of complexities (i) generic w.r.t. cursive script, (ii) specific w.r.t. Pashto language. Generic complexities are cursiveness, context dependency, and breaker character anomalies, as well as space anomalies. Pashto specific complexities are variations in shape for a single character and shape similarity for some of the additional Pashto characters. Existing research in the area of Arabic OCR did not lead to an end-to-end solution for the mentioned complexities and therefore could not be generalized to build a sophisticated OCR system for Pashto. The contribution of this thesis spans in three levels, conceptual level, data level, and practical level. In the conceptual level, we have deeply explored the Pashto language and identified those characters, which are responsible for the challenges mentioned above. In the data level, a comprehensive dataset is introduced containing real images of hand-scribed contents. The dataset is manually transcribed and has the most frequent layout patterns associated with the Pashto language. The practical level contribution provides a bridge, in the form of a complete Pashto OCR system, and connects the outcomes of the conceptual and data levels contributions. The practical contribution comprises of skew detection, text-line segmentation, feature extraction, classification, and post-processing. The OCR module is more strengthened by using deep learning paradigm to recognize Pashto cursive script by the framework of Recursive Neural Networks (RNN). Proposed Pashto text recognition is based on Long Short-Term Memory Network (LSTM) and realizes a character recognition rate of $90.78\%$ on Pashto real hand-scribed images. All these contributions are integrated into an application to provide a flexible and generic End-to-End Pashto OCR system. The impact of this thesis is not only specific to the Pashto language, but it is also beneficial to other cursive languages like Arabic, Urdu, and Persian e.t.c. The main reason is the Pashto character set, which is a superset of Arabic, Persian, and Urdu languages. Therefore, the conceptual contribution of this thesis provides insight and proposes solutions to almost all generic complexities associated with Arabic, Persian, and Urdu languages. For example, an anomaly caused by breaker characters is deeply analyzed, which is shared among 70 languages, mainly use Arabic script. This thesis presents a solution to this issue and is equally beneficial to almost all Arabic like languages. The scope of this thesis has two important aspects. First, a social impact, i.e., how a society may benefit from it. The main advantages are to bring the historical and almost vanished document to life and to ensure the opportunities to explore, analyze, translate, share, and understand the contents of Pashto language globally. Second, the advancement and exploration of the technical aspects. Because, this thesis empirically explores the recognition and challenges which are solely related to the Pashto language, both regarding character-set and the materials which present such complexities. Furthermore, the conceptual and practical background of this thesis regarding complexities of Pashto language is very beneficial regarding OCR for other cursive languages.

Collaboration aims to increase the efficiency of problem solving and decision making by bringing diverse areas of expertise together, i.e., teams of experts from various disciplines, all necessary to come up with acceptable concepts. This dissertation is concerned with the design of highly efficient computer-supported collaborative work involving active participation of user groups with diverse expertise. Three main contributions can be highlighted: (1) the definition and design of a framework facilitating collaborative decision making; (2) the deployment and evaluation of more natural and intuitive interaction and visualization techniques in order to support multiple decision makers in virtual reality environments; and (3) the integration of novel techniques into a single proof-of-concept system. Decision making processes are time-consuming, typically involving several iterations of different options before a generally acceptable solution is obtained. Although, collaboration is an often-applied method, the execution of collaborative sessions is often inefficient, does not involve all participants, and decisions are often finalized with- out the agreement of all participants. An increasing number of computer-supported cooperative work systems (CSCW) facilitate collaborative work by providing shared viewpoints and tools to solve joint tasks. However, most of these software systems are designed from a feature-oriented perspective, rather than a human-centered perspective and without the consideration of user groups with diverse experience and joint goals instead of joint tasks. The aim of this dissertation is to bring insights to the following research question: How can computer-supported cooperative work be designed to be more efficient? This question opens up more specific questions like: How can collaborative work be designed to be more efficient? How can all participants be involved in the collaboration process? And how can interaction interfaces that support collaborative work be designed to be more efficient? As such, this dissertation makes contributions in: 1. Definition and design of a framework facilitating decision making and collaborative work. Based on examinations of collaborative work and decision making processes requirements of a collaboration framework are assorted and formulated. Following, an approach to define and rate software/frameworks is introduced. This approach is used to translate the assorted requirements into a software’s architecture design. Next, an approach to evaluate alternatives based on Multi Criteria Decision Making (MCDM) and Multi Attribute Utility Theory (MAUT) is presented. Two case studies demonstrate the usability of this approach for (1) benchmarking between systems and evaluates the value of the desired collaboration framework, and (2) ranking a set of alternatives resulting from a decision-making process incorporating the points of view of multiple stake- holders. 2. Deployment and evaluation of natural and intuitive interaction and visualization techniques in order to support multiple diverse decision makers. A user taxonomy of industrial corporations serves to create a petri network of users in order to identify dependencies and information flows between each other. An explicit characterization and design of task models was developed to define interfaces and further components of the collaboration framework. In order to involve and support user groups with diverse experiences, smart de- vices and virtual reality are used within the presented collaboration framework. Natural and intuitive interaction techniques as well as advanced visualizations of user centered views of the collaboratively processed data are developed in order to support and increase the efficiency of decision making processes. The smartwatch as one of the latest technologies of smart devices, offers new possibilities of interaction techniques. A multi-modal interaction interface is provided, realized with smartwatch and smartphone in full immersive environments, including touch-input, in-air gestures, and speech. 3. Integration of novel techniques into a single proof-of-concept system. Finally, all findings and designed components are combined into the new collaboration framework called IN2CO, for distributed or co-located participants to efficiently collaborate using diverse mobile devices. In a prototypical implementation, all described components are integrated and evaluated. Examples where next-generation network-enabled collaborative environments, connected by visual and mobile interaction devices, can have significant impact are: design and simulation of automobiles and aircrafts; urban planning and simulation of urban infrastructure; or the design of complex and large buildings, including efficiency- and cost-optimized manufacturing buildings as task in factory planning. To demonstrate the functionality and usability of the framework, case studies referring to factory planning are demonstrated. Considering that factory planning is a process that involves the interaction of multiple aspects as well as the participation of experts from different domains (i.e., mechanical engineering, electrical engineering, computer engineering, ergonomics, material science, and even more), this application is suitable to demonstrate the utilization and usability of the collaboration framework. The various software modules and the integrated system resulting from the research will all be subjected to evaluations. Thus, collaborative decision making for co-located and distributed participants is enhanced by the use of natural and intuitive multi-modal interaction interfaces and techniques.

Crowd condition monitoring concerns the crowd safety and concerns business performance metrics. The research problem to be solved is a crowd condition estimation approach to enable and support the supervision of mass events by first-responders and marketing experts, but is also targeted towards supporting social scientists, journalists, historians, public relations experts, community leaders, and political researchers. Real-time insights of the crowd condition is desired for quick reactions and historic crowd conditions measurements are desired for profound post-event crowd condition analysis. This thesis aims to provide a systematic understanding of different approaches for crowd condition estimation by relying on 2.4 GHz signals and its variation in crowds of people, proposes and categorizes possible sensing approaches, applies supervised machine learning algorithms, and demonstrates experimental evaluation results. I categorize four sensing approaches. Firstly, stationary sensors which are sensing crowd centric signals sources. Secondly, stationary sensors which are sensing other stationary signals sources (either opportunistic or special purpose signal sources). Thirdly, a few volunteers within the crowd equipped with sensors which are sensing other surrounding crowd centric device signals (either individually, in a single group or collaboratively) within a small region. Fourthly, a small subset of participants within the crowd equipped with sensors and roaming throughout a whole city to sense wireless crowd centric signals. I present and evaluate an approach with meshed stationary sensors which were sensing crowd centric devices. This was demonstrated and empirically evaluated within an industrial project during three of the world-wide largest automotive exhibitions. With over 30 meshed stationary sensors in an optimized setup across 6400m2 I achieved a mean absolute error of the crowd density of just 0.0115 people per square meter which equals to an average of below 6% mean relative error from the ground truth. I validate the contextual crowd condition anomaly detection method during the visit of chancellor Mrs. Merkel and during a large press conference during the exhibition. I present the approach of opportunistically sensing stationary based wireless signal variations and validate this during the Hannover CeBIT exhibition with 80 opportunistic sources with a crowd condition estimation relative error of below 12% relying only on surrounding signals in influenced by humans. Pursuing this approach I present an approach with dedicated signal sources and sensors to estimate the condition of shared office environments. I demonstrate methods being viable to even detect low density static crowds, such as people sitting at their desks, and evaluate this on an eight person office scenario. I present the approach of mobile crowd density estimation by a group of sensors detecting other crowd centric devices in the proximity with a classification accuracy of the crowd density of 66 % (improvement of over 22% over a individual sensor) during the crowded Oktoberfest event. I propose a collaborative mobile sensing approach which makes the system more robust against variations that may result from the background of the people rather than the crowd condition with differential features taking information about the link structure between actively scanning devices, the ratio between values observed by different devices, ratio of discovered crowd devices over time, team-wise diversity of discovered devices, number of semi- continuous device visibility periods, and device visibility durations into account. I validate the approach on multiple experiments including the Kaiserslautern European soccer championship public viewing event and evaluated the collaborative mobile sensing approach with a crowd condition estimation accuracy of 77 % while outperforming previous methods by 21%. I present the feasibility of deploying the wireless crowd condition sensing approach to a citywide scale during an event in Zurich with 971 actively sensing participants and outperformed the reference method by 24% in average.

Computational simulations run on large supercomputers balance their outputs with the need of the scientist and the capability of the machine. Persistent storage is typically expensive and slow, its peformance grows at a slower rate than the processing power of the machine. This forces scientists to be practical about the size and frequency of the simulation outputs that can be later analyzed to understand the simulation states. Flexibility in the trade-offs of flexibilty and accessibility of the outputs of the simulations are critical the success of scientists using the supercomputers to understand their science. In situ transformations of the simulation state to be persistently stored is the focus of this dissertation. The extreme size and parallelism of simulations can cause challenges for visualization and data analysis. This is coupled with the need to accept pre partitioned data into the analysis algorithms, which is not always well oriented toward existing software infrastructures. The work in this dissertation is focused on improving current work flows and software to accept data as it is, and efficiently produce smaller, more information rich data, for persistent storage that is easily consumed by end-user scientists. I attack this problem from both a theoretical and practical basis, by managing completely raw data to quantities of information dense visualizations and study methods for managing both the creation and persistence of data products from large scale simulations.

We study high dimensional integration in the quantum model of computation. We develop quantum algorithms for integration of functions from Sobolev classes \(W^r_p [0,1]^d\) and analyze their convergence rates. We also prove lower bounds which show that the proposed algorithms are, in many cases, optimal within the setting of quantum computing. This extends recent results of Novak on integration of functions from Hölder classes.

The research focus of this PhD thesis is on the various types of existing Security Requirements Sources (SRSs), particularly on their categorization and structuring. A model is proposed in order to address the current challenges in the state of the art and the state of the practice.

We consider the computation of the mean of sequences in the quantum model of computation. We determine the query complexity in the case of sequences which satisfy a \(p\)-summability condition for \(1\le p<2\). This settles a problem left open in Heinrich (2001).

Die dreidimensionale Darstellung hybrider Datensätze hat sich in den letzten Jahren als ein wichtiger Teilbereich der wissenschaftlichen Visualisierung etabliert. Hybride Datensätze enthalten sowohl diskrete Volumendaten als auch durch geometrische Primitive definierte Objekte. Bei der visuellen Verarbeitung einer gegebenen Szene spielen Schatteninformationen eine wichtige Rolle, indem sie die Beziehungen von Objekten untereinander verständlich machen. Wir beschreiben ein einfaches Verfahren zur Berechnung von Schatteninformation, das in ein bestehendes System zur Visualisierung hybrider Datensätze integriert wurde. An einem Beispiel aus der klinischen Anwendung werden die Ergebnisse illustriert.