Rectangle packing with additional restrictions

We formulate a generalization of the NP-complete rectangle packing problem by parameterizing it in terms of packing density, the ratio of rectangle areas, and the aspect ratio of individual rectangles. Then we show that almost all restrictions of this problem remain NP-complete and identify some cases where the answer to the decision problem can be found in constant time.     

Facial expression recognition based on Haar-like feature detection

In this paper we propose a novel approach for facial feature detection in color image sequences using Haar-like classifiers. The feature extraction is initialized without manual input and has the capability to fulfill the real time requirement. For facial expression recognition, we use geometrical measurement and simple texture analysis in detecting facial regions based on the prior detected facial feature points. For expression classification we used a three layer feed forward artificial neural network. The efficiency of the suggested approach is demonstrated under real world conditions. The text was submitted by the authors in English. Axel Panning was born in Magdeburg, Germany, in 1980. He received his Masters Degree (Dipl.-Ing.) in Computer Science at the University of Magdeburg, Germany, in 2006. He is currently working on a PhD thesis focusing on image processing, tracking, and pattern recognition. Ayoub K. Al-Hamadi was born in Yemen in 1970. He received his Masters Degree (Dipl.-Ing.) in Electrical Engineering and Information Technology in 1997 and his PhD in Technical Computer Science at the Ottovon-Guericke-University of Magdeburg, Germany, in 2001. Since 2002 he has been Assistant Professor and Junior-Research-Group-Leader at the Institute for Electronics, Signal Processing, and Communications at the Otto-von-Guericke-University Magdeburg. His research work concentrates on the field of image processing, tracking analysis, pattern recognition, and artificial neural networks. Dr. Al-Hamadi is the author of more than 60 articles. Robert Niese was born in Halberstadt, Germany, in 1977. He received his Masters Degree (Dipl.-Ing.) with distinction in computer science at the Otto-von-Guericke-University Magdeburg, Germany, in 2004. He gathered broad experience in several international internship investigations on medical image and data analysis, including MRI, CT, and EEG. He is currently working at Magdeburg University on his PhD thesis, which focuses on 3D, image processing, tracking, and pattern recognition. Robert Niese is the author of more than 15 publications. Bernd Michaelis was born in Magdeburg, Germany, in 1947. He received a Masters Degree in Electronic Engineering from the Technische Hochschule Magdeburg in 1971 and his first PhD in 1974. Between 1974 and 1980 he worked at the Technische Hochschule Magdeburg and was granted a second doctoral degree in 1980. In 1993 he became Professor of Technical Computer Science at the Otto-von-Guericke University Magdeburg. His research work concentrates on the field of image processing, artificial neural networks, pattern recognition, processor architectures, and microcomputers. Professor Michaelis is the author of more than 200 papers.     

An experiment on the role of graphical elements in architecture visualization

The evolution and maintenance of large-scale software systems requires first an understanding of its architecture before delving into lower-level details. Tools facilitating the architecture comprehension tasks by visualization provide different sets of configurable, graphical elements to present information to their users. We conducted a controlled experiment that exemplifies the critical role of such graphical elements when aiming at understanding the architecture. In our setting, a different configuration of graphical elements had significant influence on program comprehension tasks. In particular, a 63% gain in effectiveness in architectural analysis tasks was achieved simply by changing the configuration of the graphical elements of the same tool. Based on the results, we claim that significant effort should be spent on the configuration of architecture visualization tools and that configurability should be a requirement for such tools.

The impact of cultural differences in offshore outsourcing—Case study results from German–Indian application development projects

Offshore outsourcing to vendors in foreign countries causes unique challenges which need to be understood and managed effectively. This paper explores cultural differences in IS offshoring arrangements involving German client organizations that outsource application development activities to Indian vendors. For this purpose, a research framework is developed based on both theoretical considerations and specific empirical observations from multiple case studies. The goal is to (1) explore the nature of cultural differences in offshore outsourcing arrangements in depth and to (2) analyze the relationship between those cultural differences and offshore outsourcing success. Based on the case findings, implications and practices for the management of offshore development projects are outlined. The results indicate that cultural differences in terms of power distance, IS designer values, and an active versus passive working attitude critically affect several dimensions of relationship quality, thereby influencing offshore outsourcing success. A clear definition of roles and mechanisms, strong leadership, and an active management of culture by adapting to either the client’s or the vendor’s national culture appeared to be effective ways to manage cultural differences.

An approach to quality engineering of TTCN-3 test specifications

Experience with the development and maintenance of large test suites specified using the Testing and Test Control Notation (TTCN-3) has shown that it is difficult to construct tests that are concise with respect to quality aspects such as maintainability or usability. The ISO/IEC standard 9126 defines a general software quality model that substantiates the term “quality” with characteristics and subcharacteristics. The domain of test specifications, however, requires an adaption of this general model. To apply it to specific languages such as TTCN-3, it needs to be instantiated. In this paper, we present an instantiation of this model as well as an approach to assess and improve test specifications. The assessment is based on metrics and the identification of code smells. The quality improvement is based on refactoring. Example measurements using our TTCN-3 tool TRex demonstrate how this procedure is applied in practise. B. Zeiss is supported by a Ph.D. scholarship from Siemens AG, Corporate Technology.     

Efficient and Flexible Matching of Recursive Types

Equality and subtyping of recursive types were studied in the 1990s by Amadio and Cardelli; Kozen, Palsberg, and Schwartzbach; Brandt and Henglein; and others. Potential applications include automatic generation of bridge code for multilanguage systems and type-based retrieval of software modules from libraries. In this paper, we present an efficient decision procedure for a notion of type equality that includes unfolding of recursive types, and associativity and commutativity of product types. Advocated by Auerbach, Barton, and Raghavachari, these properties enable flexible matching of types. For two types of size at most n, our algorithm takes O(n) iterations each of which takes O(n) time, for a total of O(n²) time.     

ClearView: An interactive context preserving hotspot visualization technique

Volume rendered imagery often includes a barrage of 3D information like shape, appearance and topology of complex structures, and it thus quickly overwhelms the user. In particular, when focusing on a specific region a user cannot observe the relationship between various structures unless he has a mental picture of the entire data. In this paper we present ClearView, a GPU-based, interactive framework for texture-based volume ray-casting that allows users which do not have the visualization skills for this mental exercise to quickly obtain a picture of the data in a very intuitive and user-friendly way. ClearView is designed to enable the user to focus on particular areas in the data while preserving context information without visual clutter. ClearView does not require additional feature volumes as it derives any features in the data from image information only. A simple point-and-click interface enables the user to interactively highlight structures in the data. ClearView provides an easy to use interface to complex volumetric data as it only uses transparency in combination with a few specific shaders to convey focus and context information.     

Reinforcement learning to adjust parametrized motor primitives to new situations

Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR?6.