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Antwortmengenprogrammierung
(2003)
Zur Unterstützung von Studierenden in der Studieneingangsphase wurde an der RWTH Aachen ein neuartiger und motivierender Einstieg in den Vorkurs Informatik entwickelt und zum Wintersemester 2011/12 erprobt. Dabei wurde die grafische Programmierung mittels App Inventor eingeführt, die zur Umsetzung anwendungsbezogener Projekte genutzt wurde. In diesem Beitrag werden die Motivation für die Neugestaltung, das Konzept und die Evaluation des Testlaufs beschrieben. Diese dienen als Grundlage für eine vollständige Neukonzeption des Vorkurses für das Wintersemester 2012/2013.
Projektmanagement-Kompetenzen werden von Unternehmen unterschiedlichster Branchen mit wachsender Priorität betrachtet und eingefordert. Als Beitrag zu einer kompetenzorientierten Ausbildung werden in diesem Paper interdisziplinäre Studienmodule als Bestandteil des Wirtschaftsinformatik-Studiums vorgestellt. Zielsetzung der Studienmodule ist die Befähigung der Studierenden, konkrete Projekte unter Nutzung von standardisierten Werkzeugen und Methoden nach dem IPMA-Standard planen und durchführen zu können.
The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy. Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays.
We present the hybrid ASP solver clingcon, combining the simple modeling language and the high performance Boolean solving capacities of Answer Set Programming (ASP) with techniques for using non-Boolean constraints from the area of Constraint Programming (CP). The new clingcon system features an extended syntax supporting global constraints and optimize statements for constraint variables. The major technical innovation improves the interaction between ASP and CP solver through elaborated learning techniques based on irreducible inconsistent sets. A broad empirical evaluation shows that these techniques yield a performance improvement of an order of magnitude.
Although Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances, and is known to depend strongly on algorithm parameters. This problem was addressed by means of a simple, yet effective approach using handmade, uniform, and unordered schedules of multiple solvers in ppfolio, which showed very impressive performance in the 2011 Satisfiability Testing (SAT) Competition. Inspired by this, we take advantage of the modeling and solving capacities of Answer Set Programming (ASP) to automatically determine more refined, that is, nonuniform and ordered solver schedules from the existing benchmarking data. We begin by formulating the determination of such schedules as multi-criteria optimization problems and provide corresponding ASP encodings. The resulting encodings are easily customizable for different settings, and the computation of optimum schedules can mostly be done in the blink of an eye, even when dealing with large runtime data sets stemming from many solvers on hundreds to thousands of instances. Also, the fact that our approach can be customized easily enabled us to swiftly adapt it to generate parallel schedules for multi-processor machines.
In this paper we describe the recent state of our research
project concerning computer science teachers’ knowledge on students’
cognition. We did a comprehensive analysis of textbooks, curricula
and other resources, which give teachers guidance to formulate assignments.
In comparison to other subjects there are only a few concepts
and strategies taught to prospective computer science teachers in university.
We summarize them and given an overview on our empirical
approach to measure this knowledge.
Automatic code generation is an essential cornerstone of today's model-driven approaches to software engineering. Thus a key requirement for the success of this technique is the reliability and correctness of code generators. This article describes how we employ standard model checking-based verification to check that code generator models developed within our code generation framework Genesys conform to (temporal) properties. Genesys is a graphical framework for the high-level construction of code generators on the basis of an extensible library of well-defined building blocks along the lines of the Extreme Model-Driven Development paradigm. We will illustrate our verification approach by examining complex constraints for code generators, which even span entire model hierarchies. We also show how this leads to a knowledge base of rules for code generators, which we constantly extend by e.g. combining constraints to bigger constraints, or by deriving common patterns from structurally similar constraints. In our experience, the development of code generators with Genesys boils down to re-instantiating patterns or slightly modifying the graphical process model, activities which are strongly supported by verification facilities presented in this article.
Die gelungene Durchführung einer Vorlesung „Informatik I – Einführung in die Programmierung“ ist schwierig, trotz einer Vielfalt existierender Materialien und erprobter didaktischer Methoden. Gerade aufgrund dieser vielfältigen Auswahl hat sich bisher noch kein robustes Konzept durchgesetzt, das unabhängig von den Durchführenden eine hohe Erfolgsquote garantiert. An den Universitäten Tübingen und Freiburg wurde die Informatik I aus den gleichen Lehrmaterialien und unter ähnlichen Bedingungen durchgeführt, um das verwendete Konzept auf Robustheit zu überprüfen. Die Grundlage der Vorlesung bildet ein systematischer Ansatz zum Erlernen des Programmierens, der von der PLTGruppe in USA entwickelt worden ist. Hinzu kommen neue Ansätze zur Betreuung, insbesondere das Betreute Programmieren, bei dem die Studierenden eine solide Basis für ihre Programmierfähigkeiten entwickeln. Der vorliegende Bericht beschreibt hierbei gesammelte Erfahrungen, erläutert die Entwicklung der Unterrichtsmethodik und der Inhaltsauswahl im Vergleich zu vorangegangenen Vorlesungen und präsentiert Daten zum Erfolg der Vorlesung.
This paper presents a concept for automated architecture synthesis for adaptive multiprocessors on chip, in particular for Field-Programmable Gate-Array (FPGA) devices. Given a parallel program, the intent is to simultaneously allocate processor resources and the corresponding communication network, and at the same time, to map the parallel application to get an optimum application-specific architecture. This approach builds up on a previously proposed design platform that automates system integration and FPGA synthesis for such architectures. As a result, the overall concept offers an automated design approach from application mapping to system and FPGA configuration. The automated synthesis is based on combinatorial optimization. Automation is possible because a solvable Integer Linear Programming (ILP) model that captures all necessary design trade-off parameters of such systems has been found. Experimental results to study the feasibility of the automated synthesis indicate that problems with sizes that can be encountered in the embedded domain can be readily solved. Results obtained underscore the need for an automated synthesis for design space exploration.
Since 2004, increases in computational power described by Moore's law have substantially been realized in the form of additional cores rather than through faster clock speeds. To make effective use of modern hardware when solving hard computational problems, it is therefore necessary to employ parallel solution strategies. In this work, we demonstrate how effective parallel solvers for propositional satisfiability (SAT), one of the most widely studied NP-complete problems, can be produced automatically from any existing sequential, highly parametric SAT solver. Our Automatic Construction of Parallel Portfolios (ACPP) approach uses an automatic algorithm configuration procedure to identify a set of configurations that perform well when executed in parallel. Applied to two prominent SAT solvers, Lingeling and clasp, our ACPP procedure identified 8-core solvers that significantly outperformed their sequential counterparts on a diverse set of instances from the application and hard combinatorial category of the 2012 SAT Challenge. We further extended our ACPP approach to produce parallel portfolio solvers consisting of several different solvers by combining their configuration spaces. Applied to the component solvers of the 2012 SAT Challenge gold medal winning SAT Solver pfolioUZK, our ACPP procedures produced a significantly better-performing parallel SAT solver.