004 Datenverarbeitung; Informatik
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GraffDok is an application helping to maintain an overview over sprayed images somewhere in a city. At the time of writing it aims at vandalism rather than at beautiful photographic graffiti in an underpass. Looking at hundreds of tags and scribbles on monuments, house walls, etc. it would be interesting to not only record them in writing but even make them accessible electronically, including images.
GraffDok’s workflow is simple and only requires an EXIF-GPS-tagged photograph of a graffito. It automatically determines its location by using reverse geocoding with the given GPS-coordinates and the Gisgraphy WebService. While asking the user for some more meta data, GraffDok analyses the image in parallel with this and tries to detect fore- and background – before extracting the drawing lines and make them stand alone. The command line based tool ImageMagick is used here as well as for accessing EXIF data.
Any meta data is written to csv-files, which will stay easily accessible and can be integrated in TeX-files as well. The latter ones are converted to PDF at the end of the workflow, containing a table about all graffiti and a summary for each – including the generated characteristic graffiti pattern image.
Spotlocator is a game wherein people have to guess the spots of where photos were taken. The photos of a defined area for each game are from panoramio.com. They are published at http://spotlocator. drupalgardens.com with an ID. Everyone can guess the photo spots by sending a special tweet via Twitter that contains the hashtag #spotlocator, the guessed coordinates and the ID of the photo. An evaluation is published for all tweets. The players are informed about the distance to the real photo spots and the positions are shown on a map.
Die Tagungsreihe zur Hochschuldidaktik der Informatik HDI wird vom Fachbereich Informatik und Ausbildung / Didaktik der Informatik (IAD) in der Gesellschaft für Informatik e. V. (GI) organisiert. Sie dient den Lehrenden der Informatik in Studiengängen an Hochschulen als Forum der Information und des Austauschs über neue didaktische Ansätze und bildungspolitische Themen im Bereich der Hochschulausbildung aus der fachlichen Perspektive der Informatik. Diese fünfte HDI 2012 wurde an der Universität Hamburg organisiert. Für sie wurde das spezielle Motto „Informatik für eine nachhaltige Zukunft“ gewählt, um insbesondere Fragen der Bildungsrelevanz informatischer Inhalte, der Kompetenzen für Studierende informatisch geprägter Studiengänge und der Rolle der Informatik in der Hochschulentwicklung zu diskutieren.
In the last decades, there was a notable progress in solving the well-known Boolean satisfiability (Sat) problem, which can be witnessed by powerful Sat solvers. One of the reasons why these solvers are so fast are structural properties of instances that are utilized by the solver’s interna. This thesis deals with the well-studied structural property treewidth, which measures the closeness of an instance to being a tree. In fact, there are many problems parameterized by treewidth that are solvable in polynomial time in the instance size when parameterized by treewidth.
In this work, we study advanced treewidth-based methods and tools for problems in knowledge representation and reasoning (KR). Thereby, we provide means to establish precise runtime results (upper bounds) for canonical problems relevant to KR. Then, we present a new type of problem reduction, which we call decomposition-guided (DG) that
allows us to precisely monitor the treewidth when reducing from one problem to another problem. This new reduction type will be the basis for a long-open lower bound result for quantified Boolean formulas and allows us to design a new methodology for establishing runtime lower bounds for problems parameterized by treewidth.
Finally, despite these lower bounds, we provide an efficient implementation of algorithms that adhere to treewidth. Our approach finds suitable abstractions of instances, which are subsequently refined in a recursive fashion, and it uses Sat solvers for solving subproblems. It turns out that our resulting solver is quite competitive for two canonical counting problems related to Sat.
The aim of our project design space exploration with answer set programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE.
Independent component analysis (ICA) is a tool for statistical data analysis and signal processing that is able to decompose multivariate signals into their underlying source components. Although the classical ICA model is highly useful, there are many real-world applications that require powerful extensions of ICA. This thesis presents new methods that extend the functionality of ICA: (1) reliability and grouping of independent components with noise injection, (2) robust and overcomplete ICA with inlier detection, and (3) nonlinear ICA with kernel methods.
Learning a model for the relationship between the attributes and the annotated labels of data examples serves two purposes. Firstly, it enables the prediction of the label for examples without annotation. Secondly, the parameters of the model can provide useful insights into the structure of the data. If the data has an inherent partitioned structure, it is natural to mirror this structure in the model. Such mixture models predict by combining the individual predictions generated by the mixture components which correspond to the partitions in the data. Often the partitioned structure is latent, and has to be inferred when learning the mixture model. Directly evaluating the accuracy of the inferred partition structure is, in many cases, impossible because the ground truth cannot be obtained for comparison. However it can be assessed indirectly by measuring the prediction accuracy of the mixture model that arises from it. This thesis addresses the interplay between the improvement of predictive accuracy by uncovering latent cluster structure in data, and further addresses the validation of the estimated structure by measuring the accuracy of the resulting predictive model. In the application of filtering unsolicited emails, the emails in the training set are latently clustered into advertisement campaigns. Uncovering this latent structure allows filtering of future emails with very low false positive rates. In order to model the cluster structure, a Bayesian clustering model for dependent binary features is developed in this thesis. Knowing the clustering of emails into campaigns can also aid in uncovering which emails have been sent on behalf of the same network of captured hosts, so-called botnets. This association of emails to networks is another layer of latent clustering. Uncovering this latent structure allows service providers to further increase the accuracy of email filtering and to effectively defend against distributed denial-of-service attacks. To this end, a discriminative clustering model is derived in this thesis that is based on the graph of observed emails. The partitionings inferred using this model are evaluated through their capacity to predict the campaigns of new emails. Furthermore, when classifying the content of emails, statistical information about the sending server can be valuable. Learning a model that is able to make use of it requires training data that includes server statistics. In order to also use training data where the server statistics are missing, a model that is a mixture over potentially all substitutions thereof is developed. Another application is to predict the navigation behavior of the users of a website. Here, there is no a priori partitioning of the users into clusters, but to understand different usage scenarios and design different layouts for them, imposing a partitioning is necessary. The presented approach simultaneously optimizes the discriminative as well as the predictive power of the clusters. Each model is evaluated on real-world data and compared to baseline methods. The results show that explicitly modeling the assumptions about the latent cluster structure leads to improved predictions compared to the baselines. It is beneficial to incorporate a small number of hyperparameters that can be tuned to yield the best predictions in cases where the prediction accuracy can not be optimized directly.
Zur Beherrschung großer Systeme, insbesondere zur Weitergabe und Nutzung von Erfahrungswissen in der frühen Entwurfs- und Planungsphase, benötigt man Abstraktionen für deren Strukturen. Trennt man Software- von Systemstrukturen, kann man mit letzteren Systeme auf ausreichend hohem Abstraktionsgrad beschreiben.Software-Patterns dienen dazu, Erfahrungswissen bezüglich programmierter Systeme strukturiert weiterzugeben. Dabei wird unterschieden zwischen Idiomen, die sich auf Lösungen mit einer bestimmten Programmiersprache beziehen, Design-Patterns, die nur einen kleinen Teil des Programms betreffen und Architektur-Patterns, deren Einfluss über einen größeren Teil oder gar das komplette Programm reicht. Eine Untersuchung von existierenden Patterns zeigt, dass deren Konzepte nützlich zum Finden von Systemstrukturen sind. Die grafische Darstellung dieser Patterns ist dagegen oft auf Software-Strukturen eingeschränkt und ist für die Vermittlung von Erfahrungen zum Finden von Systemstrukturen meist nicht geeignet. Daher wird die Kategorie der konzeptionellen Patterns mit einer darauf abgestimmten grafischen Darstellungsform vorgeschlagen, bei denen Problem und Lösungsvorschlag im Bereich der Systemstrukturen liegen. Sie betreffen informationelle Systeme, sind aber nicht auf Lösungen mit Software beschränkt. Die Systemstrukturen werden grafisch dargestellt, wobei dafür die Fundamental Modeling Concepts (FMC) verwendet werden, die zur Darstellung von Systemstrukturen entwickelt wurden.