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Bilingualer Sachfachunterricht gilt wohl als eine der bedeutsamsten Veränderungen im deutschen Schulsystem. Vor allem weiterführende Schulen wie Gymnasien und Realschulen nutzen diese didaktischen Neuerungen zur Weiterentwicklung ihres Schulprofils sowie zur sprachlichen und kognitiven Förderung ihrer Schülerschaft. Aber auch an der Mittelschule insbesondere im M-Zug, kann diese Form des Unterrichts durchaus einen Mehrwert bieten. Mit dieser Arbeit soll die didaktische Wirksamkeit des bilingualen Religionsunterrichts wissenschaftlich erforscht und weiterentwickelt werden. Mittels der praktischen Umsetzung religionspädagogischer und religionsdidaktischer sowie bilingualer Theorien, soll ein Prototyp des bilingualen Religionsunterrichts geschaffen werden, welcher die Fremdsprache Religion zu entschlüsseln vermag. In der iterativen Durchführung bilingualer Religionsunterrichtseinheiten gilt es sodann herauszufinden, inwieweit der Einsatz einer fremden Sprache die Möglichkeit einer kognitiven Durchdringung und Erschließung von Glaubenswissen bieten kann. Zielführend dabei ist jedoch nicht die Erstellung einer empirischen Studie. Vielmehr soll die vorliegende Feldstudie dazu dienen, Hypothesen und Theorien aufzustellen, die dann in einem nächsten Schritt einer empirischen Überprüfung unterzogen werden können.
Die vorliegende Arbeit erforscht den Einsatz von Ekphrasen in audiovisuellen Texten. Der ursprünglich aus der antiken Rhetoriklehre stammende Begriff bezeichnet die literarische Beschreibung von bildender Kunst und wird im Rahmen dieser Untersuchung auf den Film übertragen. Ziel der Analyse filmischer Beschreibungen von Kunst, speziell Malerei, ist es zu eruieren, wie Kunstwerke im Sinne der Bedeutungsvermittlung semantisch aufgeladen respektive funktionalisiert werden. Zentrale Forschungsgegenstände bilden demnach die Medien Bild und Film – somit ist die gesamte Arbeit in den Kontext des Intermedialitätsdiskurses eingebettet. Als Korpus dient ein Konglomerat an Texten, das neben zentralen Schlüsselwerken insbesondere jüngere – zwischen 2011 und 2016 entstandene –, wenig bis kaum erforschte Filme umfasst.
Whenever software faults can endanger human life, property, or the environment, the absence of faults must be ensured with utmost care and the best technologies available. Evidence is needed showing that all requirements are satisfied and that the risk of faults is reduced. One technique to conduct such a verification task—composed of the software to verify, the specification to check, and a model of the environment—is software model checking.
To conduct a verification task with a model checker, different models of the task are constructed. We distinguish between two types of task models: syntactic task models and semantic task models, which define the respective syntactic structure (control flow) and semantic structure (state transitions, invariants) of the verification task. When constructing such models, we can observe that similar structures and substructures reappear within and among different verification tasks. For example, the same assertions to check can appear in different functions, or the same predicate can be part of different invariants to describe sets of program states. Similarities that appear during the model construction process can be the result of solving similar reasoning problems, often solved using computationally expensive procedures (as typical for model checking), over and over again. Not reusing results of solving similar problems, not having a means for conducting repeated efforts automatically, or not trying to reduce the number of similar reasoning efforts, is a waste of precious resources.
To address these problems, we present a common conceptual and technical foundation for sharing syntactic and semantic task artifacts for reuse, within and among verification runs. Both the syntactic construction of a verification task and the construction of its semantic model—which describes all possible behaviors and states—are covered. We study how commonalities and regularities in the task models can be taken into account to facilitate the process of sharing task artifacts for reuse, and to make the overall verification process more efficient and effective. We introduce abstract transducers as the theoretical foundation of this thesis: a type of finite-state transducers with an inherent notion of abstraction for states, the input alphabet, and its output alphabet. Abstracting these transducers allows us to widen both the set of input words for that they produce output and the sets of output words. Abstract transducers are instantiated as task artifact transducers to map from program structures to task artifacts to share. We show that the notion of abstraction provides a means for increasing the scope for that task artifacts are shared for reuse. We present two instances of task artifact transducers: Yarn transducers and precision transducers. We use Yarn transducers for providing code to weave into the control-flow structure of a computer program, and present the Loom analysis as a means for orchestrating the weaving process. Precision transducers provide a means for sharing abstraction precisions for reuse, thus aid in defining the level of abstraction of a semantic task model. For both types of transducers, we provide empirical evidence on their practical applicability, for example, to verify Linux kernel modules, and show that they can help in increasing the verification performance.
Das Hauptanliegen dieser Dissertation ist es einerseits, zu untermauern, dass sich mit der niederösterreichischen „Musiklandschaft“ ein überregional relevantes Erfolgsmodell kultureller Entwicklungs- und Aufbauarbeit präsentiert, und andererseits zu erforschen, welche konkreten Rahmenbedingungen und Erfolgsfaktoren zu diesem positiven Befund beigetragen haben. Die zentrale Frage in diesem Zusammenhang lautet: Wie war es möglich, ausgerechnet in Niederösterreich, dem rund um die österreichische Hauptstadt mit ihrem intensiven Kulturangebot und international beachteten Musikleben gelegenen Bundesland, eine eigenständige und ebenfalls hochwertige Musikszene und Festivaldichte – zwar im Spannungsfeld Wiens aber dennoch unabhängig von den Musikaktivitäten der Hauptstadt – zu entwickeln und langfristig zu etablieren?
The amount of audio, video and image data on the Web is immensely growing, which leads to data management problems based on the hidden character of Multimedia. Therefore the interlinking of semantic concepts and media data with the aim to bridge the gap between the Internet of documents and the Web of Data has become a common practice. However, the value of connecting media to its semantic meta data is limited due to lacking access methods and the absence of an adapted query language specialized for media assets and fragments. This thesis aims to extend the standard query language for the Semantic Web (SPARQL) with media specific concepts and functions. The main contributions of the work are an exhaustive survey on Multimedia query languages of the last 3 decades, the SPARQL extension specification itself and an approach for the efficient evaluation of the new query concepts. Additionally I elaborate and evaluate a meta data based media fragment similarity approach, which provides a basis for further language extensions.
We have proposed a strategy for the creation of attributes based on hidden Markov models (HMM) characterizing the transaction from different points of view. This strategy makes it possible to integrate a broad spectrum of sequential information into the attributes of transactions. In fact, we model the authentic and fraudulent behavior of merchants and card holders according to two univariate characteristics: the date and the amount of transactions. In addition, attributes based on HMMs are created in a supervised manner, thereby reducing the need for expert knowledge for the creation of the fraud detection system. Ultimately, our HMM-based multi-perspective approach allows automated data pre-processing to model time correlations to complement and eventually replace transaction aggregation strategies to improve detection efficiency. Experiments carried out on a large set of credit card transaction data from the real world (46 million transactions carried out by Belgian card holders between March and May 2015) have shown that the strategy proposed for data preprocessing based on HMM can detect more fraudulent transactions when combined with the strategy of preprocessing reference data based on expert knowledge for the detection of credit card fraud.
Nowadays, consumers are often required to disclose private data in various contexts such as while surfing the internet, downloading a mobile application, or engaging in a business relationship with a firm. Privacy-related decision-making research has so far mainly investigated data disclosure as a cognitive risk-benefit trade-off analysis. While this cognitive approach might be appropriate for situations where consumers have the opportunity for cognitive evaluations, there are many situations in the modern landscape where consumers cannot or do not want to engage in cognitive processing. Decision-making under stress or data disclosure to a business network of collaborating firms, for example, constitute challenges to purely cognitive decision-making approaches, calling for an extension of the established paradigm of cognitive privacy-related decision making. This dissertation advocates for the crucial role of affective processing in many modern data disclosure situations, where consumers do not engage in purely cognitive processing due to external hindrances or a lack of personal involvement in the data disclosure situation.
In high-performance computing, one primary objective is to exploit the performance that the given target hardware can deliver to the fullest. Compilers that have the ability to automatically optimize programs for a specific target hardware can be highly useful in this context. Iterative (or search-based) compilation requires little or no prior knowledge and can adapt more easily to concrete programs and target hardware than static cost models and heuristics. Thereby, iterative compilation helps in situations in which static heuristics do not reflect the combination of input program and target hardware well. Moreover, iterative compilation may enable the derivation of more accurate cost models and heuristics for optimizing compilers. In this context, the polyhedron model is of help as it provides not only a mathematical representation of programs but, more importantly, a uniform representation of complex sequences of program transformations by schedule functions. The latter facilitates the systematic exploration of the set of legal transformations of a given program.
Early approaches to purely iterative schedule optimization in the polyhedron model do not limit their search to schedules that preserve program semantics and, thereby, suffer from the need to explore numbers of illegal schedules. More recent research ensures the legality of program transformations but presumes a sequential rather than a parallel execution of the transformed program. Other approaches do not perform a purely iterative optimization.
We propose an approach to iterative schedule optimization for parallelization and tiling in the polyhedron model. Our approach targets loop programs that profit from data locality optimization and coarse-grained loop parallelization. The schedule search space can be explored either randomly or by means of a genetic algorithm.
To determine a schedule's profitability, we rely primarily on measuring the transformed code's execution time. While benchmarking is accurate, it increases the time and resource consumption of program optimization tremendously and can even make it impractical. We address this limitation by proposing to learn surrogate models from schedules generated and evaluated in previous runs of the iterative optimization and to replace benchmarking by performance prediction to the extent possible.
Our evaluation on the PolyBench 4.1 benchmark set reveals that, in a given setting, iterative schedule optimization yields significantly higher speedups in the execution of the program to be optimized. Surrogate performance models learned from training data that was generated during previous iterative optimizations can reduce the benchmarking effort without strongly impairing the optimization result. A prerequisite for this approach is a sufficient similarity between the training programs and the program to be optimized.
Internet browsers include Application Programming Interfaces (APIs) to support Web applications that require complex functionality, e.g., to let end users watch videos, make phone calls, and play video games. Meanwhile, many Web applications employ the browser APIs to rely on the user's hardware to execute intensive computation, access the Graphics Processing Unit (GPU), use persistent storage, and establish network connections.
However, providing access to the system's computational resources, i.e., processing, storage, and networking, through the browser creates an opportunity for attackers to abuse resources. Principally, the problem occurs when an attacker compromises a Web site and includes malicious code to abuse its visitor's computational resources. For example, an attacker can abuse the user's system networking capabilities to perform a Denial of Service (DoS) attack against third parties. What is more, computational resource abuse has not received widespread attention from the Web security community because most of the current specifications are focused on content and session properties such as isolation, confidentiality, and integrity.
Our primary goal is to study computational resource abuse and to advance the state of the art by providing a general attacker model, multiple case studies, a thorough analysis of available security mechanisms, and a new detection mechanism. To this end, we implemented and evaluated three scenarios where attackers use multiple browser APIs to abuse networking, local storage, and computation. Further, depending on the scenario, an attacker can use browsers to perform Denial of Service against third-party Web sites, create a network of browsers to store and distribute arbitrary data, or use browsers to establish anonymous connections similarly to The Onion Router (Tor). Our analysis also includes a real-life resource abuse case found in the wild, i.e., CryptoJacking, where thousands of Web sites forced their visitors to perform crypto-currency mining without their consent. In the general case, attacks presented in this thesis share the attacker model and two key characteristics: 1) the browser's end user remains oblivious to the attack, and 2) an attacker has to invest little resources in comparison to the resources he obtains.
In addition to the attack's analysis, we present how existing, and upcoming, security enforcement mechanisms from Web security can hinder an attacker and their drawbacks. Moreover, we propose a novel detection approach based on browser API usage patterns. Finally, we evaluate the accuracy of our detection model, after training it with the real-life crypto-mining scenario, through a large scale analysis of the most popular Web sites.