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Model-driven software development requires techniques to consistently propagate modifications between different related models to realize its full potential. For large-scale models, efficiency is essential in this respect. In this paper, we present an improved model synchronization algorithm based on triple graph grammars that is highly efficient and, therefore, can also synchronize large-scale models sufficiently fast. We can show, that the overall algorithm has optimal complexity if it is dominating the rule matching and further present extensive measurements that show the efficiency of the presented model transformation and synchronization technique.
Persistenz im Exportverhalten : Kann punktuelle Exportförderung langfristige Auswirkungen haben?
(2009)
We give a survey on the calculus of (pseudo-differential) boundary value problems with the transmision property at the boundary, and ellipticity in the Shapiro-Lopatinskij sense. Apart from the original results of the work of Boutet de Monvel we present an approach based on the ideas of the edge calculus. In a final section we introduce symbols with the anti-transmission property.
Modern acquisition of seismic data on receiver networks worldwide produces an increasing amount of continuous wavefield recordings. Hence, in addition to manual data inspection, seismogram interpretation requires new processing utilities for event detection, signal classification and data visualization. Various machine learning algorithms, which can be adapted to seismological problems, have been suggested in the field of pattern recognition. This can be done either by means of supervised learning using manually defined training data or by unsupervised clustering and visualization. The latter allows the recognition of wavefield patterns, such as short-term transients and long-term variations, with a minimum of domain knowledge. Besides classical earthquake seismology, investigations of temporal patterns in seismic data also concern novel approaches such as noise cross-correlation or ambient seismic vibration analysis in general, which have moved into focus within the last decade. In order to find records suitable for the respective approach or simply for quality control, unsupervised preprocessing becomes important and valuable for large data sets. Machine learning techniques require the parametrization of the data using feature vectors. Applied to seismic recordings, wavefield properties have to be computed from the raw seismograms. For an unsupervised approach, all potential wavefield features have to be considered to reduce subjectivity to a minimum. Furthermore, automatic dimensionality reduction, i.e. feature selection, is required in order to decrease computational cost, enhance interpretability and improve discriminative power. This study presents an unsupervised feature selection and learning approach for the discovery, imaging and interpretation of significant temporal patterns in seismic single-station or network recordings. In particular, techniques permitting an intuitive, quickly interpretable and concise overview of available records are suggested. For this purpose, the data is parametrized by real-valued feature vectors for short time windows using standard seismic analysis tools as feature generation methods, such as frequency-wavenumber, polarization, and spectral analysis. The choice of the time window length is dependent on the expected durations of patterns to be recognized or discriminated. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure, which is particularly suitable for high-dimensional data sets. Using synthetics composed of Rayleigh and Love waves and three different types of real-world data sets, we show the robustness and reliability of our unsupervised learning approach with respect to the effect of algorithm parameters and data set properties. Furthermore, we approve the capability of the clustering and imaging techniques. For all data, we find improved discriminative power of our feature selection procedure compared to feature subsets manually selected from individual wavefield parametrization methods. In particular, enhanced performance is observed compared to the most favorable individual feature generation method, which is found to be the frequency spectrum. The method is applied to regional earthquake records at the European Broadband Network with the aim to define suitable features for earthquake detection and seismic phase classification. For the latter, we find that a combination of spectral and polarization features favor S wave detection at a single receiver. However, SOM-based visualization of phase discrimination shows that clustering applied to the records of two stations only allows onset or P wave detection, respectively. In order to improve the discrimination of S waves on receiver networks, we recommend to consider additionally the temporal context of feature vectors. The application to continuous recordings of seismicity close to an active volcano (Mount Merapi, Java, Indonesia) shows that two typical volcano-seismic events (VTB and Guguran) can be detected and distinguished by clustering. In contrast, so-called MP events cannot be discriminated. Comparable results are obtained for selected features and recognition rates regarding a previously implemented supervised classification system. Finally, we test the reliability of wavefield clustering to improve common ambient vibration analysis methods such as estimation of dispersion curves and horizontal to vertical spectral ratios. It is found, that in general, the identified short- and long-term patterns have no significant impact on those estimates. However, for individual sites, effects of local sources can be identified. Leaving out the corresponding clusters, yields reduced uncertainties or allows for improving estimation of dispersion curves.
In normal everyday viewing, we perform large eye movements (saccades) and miniature or fixational eye movements. Most of our visual perception occurs while we are fixating. However, our eyes are perpetually in motion. Properties of these fixational eye movements, which are partly controlled by the brainstem, change depending on the task and the visual conditions. Currently, fixational eye movements are poorly understood because they serve the two contradictory functions of gaze stabilization and counteraction of retinal fatigue. In this dissertation, we investigate the spatial and temporal properties of time series of eye position acquired from participants staring at a tiny fixation dot or at a completely dark screen (with the instruction to fixate a remembered stimulus); these time series were acquired with high spatial and temporal resolution. First, we suggest an advanced algorithm to separate the slow phases (named drift) and fast phases (named microsaccades) of these movements, which are considered to play different roles in perception. On the basis of this identification, we investigate and compare the temporal scaling properties of the complete time series and those time series where the microsaccades are removed. For the time series obtained during fixations on a stimulus, we were able to show that they deviate from Brownian motion. On short time scales, eye movements are governed by persistent behavior and on a longer time scales, by anti-persistent behavior. The crossover point between these two regimes remains unchanged by the removal of microsaccades but is different in the horizontal and the vertical components of the eyes. Other analyses target the properties of the microsaccades, e.g., the rate and amplitude distributions, and we investigate, whether microsaccades are triggered dynamically, as a result of earlier events in the drift, or completely randomly. The results obtained from using a simple box-count measure contradict the hypothesis of a purely random generation of microsaccades (Poisson process). Second, we set up a model for the slow part of the fixational eye movements. The model is based on a delayed random walk approach within the velocity related equation, which allows us to use the data to determine control loop durations; these durations appear to be different for the vertical and horizontal components of the eye movements. The model is also motivated by the known physiological representation of saccade generation; the difference between horizontal and vertical components concurs with the spatially separated representation of saccade generating regions. Furthermore, the control loop durations in the model suggest an external feedback loop for the horizontal but not for the vertical component, which is consistent with the fact that an internal feedback loop in the neurophysiology has only been identified for the vertical component. Finally, we confirmed the scaling properties of the model by semi-analytical calculations. In conclusion, we were able to identify several properties of the different parts of fixational eye movements and propose a model approach that is in accordance with the described neurophysiology and described limitations of fixational eye movement control.
Informatischen Hochleistern über die Schulter geschaut : was können schwache Problemlöser lernen
(2009)
Leistungsstarke Problemlöser zeigen ein deutlich anderes Vorgehen beim Bearbeiten von typischen Informatikproblemen als schwächere Problemlöser, das hat eine Studie mit Hochleistern und einer Vergleichsgruppe mit Informatikstudierenden ergeben. Auffällig war das Fehlen informatikspezifischer Herangehensweisen bei der Problembearbeitung durch die Vergleichsgruppe, obwohl ihnen die dazu notwendigen Werkzeuge wie etwa Baumstrukturen oder Rekursion durchaus bekannt waren. Die Frage, wie nun die deutlich effizienteren Strategien der Hochleister didaktisch aufgearbeitet werden können, um sie an schwache Problemlöser zu vermitteln, wird im vorliegen Beitrag anhand der Konzeption eines Lehrvideos diskutiert.
Mit hochschuldidaktischer Forschung zur Informatik soll aus einem traditionellen Hardwarepraktikum ein attraktives Entwurfs- und Anwendungspraktikum für Mikrosysteme (MSE) werden, das ein unverzichtbarer Bestandteil des Informatikstudiums ist. Diese Neugestaltung der Lehre wurde aufgrund des Bologna-Prozesses und der zunehmenden Präsenz multifunktionaler eingebetteter Mikrosysteme (EMS) im täglichen Leben notwendig. Ausgehend von einer Lehrveranstaltungsanalyse werden Vorschläge für die Kompetenzorientierung abgeleitet. Es wird gezeigt, dass für eine Verfeinerung des Ansatzes ein wissenschaftlich fundiertes Verständnis der erwarteten Kompetenzen erforderlich ist. Für den aufgezeigten Forschungsbedarf werden ein Ansatz zur Beschreibung des notwendigen Mikrosystemverständnisses dargestellt und Forschungsfelder zu Aspekten des Kompetenzbegriffs im Kontext der Lehrveranstaltung beschrieben.
Ein handlungsorientiertes, didaktisches Training für Tutoren im Bachelorstudium der Informatik
(2009)
Die didaktisch-pädagogische Ausbildung studentischer Tutoren für den Einsatz im Bachelorstudium der Informatik ist Gegenstand dieser Arbeit. Um die theoretischen Inhalte aus Sozial- und Lernpsychologie handlungsorientiert und effizient zu vermitteln, wird das Training als Lehrform gewählt. Die in einer Tutorübung zentrale Methode der Gruppenarbeit wird dabei explizit und implizit vermittelt. Erste praktische Erfahrungen mit ihrer zukünftigen Rolle gewinnen die Tutoren in Rollenspielen, wobei sowohl Standardsituationen als auch fachspezifisch und pädagogisch problematische Situationen simuliert werden. Während die Vermittlung der genannten Inhalte und die Rollenspiele im Rahmen einer Blockveranstaltung vor Beginn des Semesters durchgeführt werden, finden während des Semesters Hospitationen statt, in der die Fähigkeiten der Tutoren anhand eines standardisierten Bewertungsbogens beurteilt werden.
Die nachhaltige Integration von Blended Learning in den Informatik-Lehrbetrieb von Präsenzhochschulen und die Qualitätssicherung der Lehre mit digitalen Medien beruht nicht nur auf der Verfügbarkeit von Lernmaterialien und dem Zugang zu Lernplattformen, sondern erfordert auch Qualifizierungsmaßnahmen für die Lehrenden. Am Beispiel der Gestaltung von vorlesungsbegleitenden Übungen in der universitären Informatikausbildung wird ein Konzept für die Schulung von Tutoren vorgestellt, das sich an den Erfordernissen des Übungsbetriebs und den unterschiedlichen Arbeits- und Lernkontexten der künftigen Tutoren orientiert. Das Konzept basiert auf mehrjährigen Praxiserfahrungen mit Schulungsworkshops für Tutoren in der Informatik, die nun aufgrund aktueller didaktischer Konzepte zum Blended Learning und unter Berücksichtigung von Umfrageergebnissen unter studentischen Tutoren modifiziert wurden. Das neu entwickelte modulare Konzept zur Tutorenschulung befindet sich aktuell in einer erweiterten Pilotphase.