Furthermore we give some evaluation results from students on the usage of one specific virtual lesson implemented as an eLecture. ![]() In this paper we describe how we applied our infrastructure managing virtual lessons in medical education to our modular based curriculum. With the introduction of version 2 of this interface called MOMOS we have now also a good usability for the administration staff. Since 2008 we consecutively introduced software and interfaces between the learning management system and our central administration system in order to automatize virtual lessons as much as possible and significantly reduce the administrative efforts from the teachers. ![]() In the first years the handling of the virtual lessons was purely manual resulting in a huge amount of administrative efforts for the teachers and the staff. ![]() Hence from the very beginning eLearning was introduced also virtual lessons were a part of the curriculum. The Medical University of Graz fosters a blended learning concept in medical education where classroom and virtual lessons alternate according to a well-planned didactic concept. Techniques as efficient as standard methods for testing purposes, especially when dealing with Boolean expressions, as proved These optimizations promise to make SAT/SMT-based In this paper, we propose several ways to optimize the SAT/SMT-based process of test generation for Boolean expressionsĪnd we compare several solving tools and propositional transformation rules. However, these solvers normally require more timeĪnd a greater amount of memory than classical test generation algorithms, making their applicability not always feasible in Test suites and the support for fault-detecting test generation methods. The main advantages are the capability to deal with constraints over the inputs, the generation of compact (SMT) solvers is becoming an attractive alternative to traditional algorithmic test generation methods, especially when testingīoolean expressions. In the context of automatic test generation, the use of propositional satisfiability (SAT) and Satisfiability Modulo Theories The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms. Experimental results show that the implementation is faster than the most similar previous approaches which used simpler refinement strategies. We implement this approach, with an approximate probability model, as a wrapper around an off-the-shelf SMT solver or SAT solver. We propose an approach inspired by statistical estimation to continually refine a probabilistic estimate of the model count for a formula, so that each XOR-streamlined query yields as much information as possible. ![]() Adding random parity constraints (XOR streamlining) and then checking satisfiability is an effective approximation technique, but it requires a prior hypothesis about the model count to produce useful results. The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms.Īpproximate model counting for bit-vector SMT formulas (generalizing \#SAT) has many applications such as probabilistic inference and quantitative information-flow security, but it is computationally difficult. Approximate model counting for bit-vector SMT formulas (generalizing #SAT) has many applications such as probabilistic inference and quantitative information-flow security, but it is computationally difficult.
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