Many human activities today depend critically on systems where substantial functionality has been realized using complex software. Therefore, appropriate means to achieve a sufficient degree for dependability are required, which use the available information about the software components and the system architecture. For the special case of service-based architectures,ext-- an architecture proposed to cope with the complexity and dynamics of today's systems -- we identify in this paper a set of architectural principles which can be used to improve dependability. We then describe how the identified architectural principles have been used in a realized service-based architecture which extends Jini. The dependable operation of the infrastructure services of the architecture further enables to systematically control and configure some dependability attributes of application services. We present a qualitative and quantitative evaluation of the dependability for a configuration of multiple identical services which are executed with the architecture and show how the different parameters effect the dependability. Additionally, a scheme for the dynamic control of the required dependability of the application services in the case of changing failure characteristics of the environment is outlined. Finally, we present a first evaluation of the developed architecture and its dynamic control of dependability.

Model transformation plays a central role in Model-Driven Engineering (MDE) and supporting bidirectionality is a current challenge with important applications. Triple Graph Grammars (TGGs) are a formally founded, bidirectional model transformation language shown by numerous case studies to be promising and useful in practice. TGGs have been researched for more than 15 years and multiple TGG tools are under active development. Although a common theoretical foundation is shared, TGG tools differ considerably concerning expressiveness applicability, efficiency, and the underlying translation algorithm. There currently exists neither a quantitative nor a qualitative overview and comparison of TGG tools and it is quite difficult to understand the different foci and corresponding strengths and weaknesses. Our contribution in this paper is to develop a set of criteria for comparing TGG tools and to provide a concrete quantitative and qualitative comparison of three TGG tools.

In this chapter we present a new learning algorithm, Leave-One-Out (LOO -) SVMs and its generalization Adaptive Margin (AM-) SVMs, inspired by a recent upper bound on the leave-one-out error proved for kernel classifiers by Jaakkola and Haussler. The new approach minimizes the expression given by the bound in an attempt to minimize the leave-one-out error. This gives a convex optimization problem which constructs a sparse linear classifier in feature space using the kernel technique. As such the algorithm possesses many of the same properties as SVMs and Linear Programming (LP-) SVMs. These former techniques are based on the minimization of a regularized margin loss, where the margin is treated equivalently for each training pattern. We propose a minimization problem such that adaptive margins for each training pattern are utilized. Furthermore, we give bounds on the generalization error of the approach which justifies its robustness against outliers. We show experimentally that the generalization error of AM-SVMs is comparable to SVMs and LP-SVMs on benchmark datasets from the UCI repository.

The multi-agent paradigm promises to provide systems with the ability to adapt to changing external contexts. In this paper, we propose an approach for the model-driven design of context-aware multi-agent systems. We provide a classification for differentiating between different aspects of context and present a specification technique for modeling these aspects. We finally discuss the difficult transition from general requirements to implemented solution and propose some techniques.

Aerial images represent a fundamental type of geodata with a broad range of applications in GIS and geovisualization. The perception and cognitive processing of aerial images by the human, however, still is faced with the specific limitations of photorealistic depictions such as low contrast areas, unsharp object borders as well as visual noise. In this paper we present a novel technique to automatically abstract aerial images that enhances visual clarity and generalizes the contents of aerial images to improve their perception and recognition. The technique applies non-photorealistic image processing by smoothing local image regions with low contrast and emphasizing edges in image regions with high contrast. To handle the abstraction of large images, we introduce an image tiling procedure that is optimized for post-processing images on GPUs and avoids visible artifacts across junctions. This is technically achieved by filtering additional connection tiles that overlap the main tiles of the input image. The technique also allows the generation of different levels of abstraction for aerial images by computing a mipmap pyramid, where each of the mipmap levels is filtered with adapted abstraction parameters. These mipmaps can then be used to perform level-of-detail rendering of abstracted aerial images. Finally, the paper contributes a study to aerial image abstraction by analyzing the results of the abstraction process on distinctive visible elements in common aerial image types. In particular, we have identified a high abstraction straction potential in landscape images and a higher benefit from edge enhancement in urban environments.

Systems medicine is an interdisciplinary approach in medicine that relies on computational models based on data from a variety of sources. Typically, such sources include clinical and biomedical data with heterogeneous data definitions that are sometimes not even structured in a useful way. Consequently, the systematic management of data is an important element for the successful implementation of systems medicine in both research and clinical application. In this article, we provide an overview over the following selected aspects of data management:•Integration of multiple data sources•IT infrastructures•Data protection regulations•Data history and data quality•Data sharing/FAIR principles•Use and access policies The presented best practices and experiences result from several systems medicine projects in which the authors have participated. They can be considered as recommendations for future projects in order to quickly set up data management infrastructures for systems medicine.

Today software reaches into almost every aspect of our lives, with mobile devices and their apps a major source of our everyday software experience. This trend has shifted our expectations toward a more user-friendly, intuitive and easy to use user experience. Thus appealing user interfaces and an excellent usability become key to successful software products and services.

Design Thinking (DT) is an established approach to conceptualize software products before starting the product development work. Research suggests that software development can benefit from a continuous integration of DT throughout Agile development processes. However, practitioners and researchers lack an in-depth understanding of which tools from the ever-growing DT toolbox are suited to support software development teams and their processes and how these tools can be applied to the teams’ daily work. As initial steps towards closing this knowledge gap, we present our experiences from testing five different DT tools from a previously developed toolbox with four Agile software development teams. Each team chose three tools to apply to their product, problem, and context during a workshop. We present summarised findings regarding the use cases, benefits, and challenges of these tools as experienced by the participants. Overall, the teams welcomed the DT tools and were able to independently apply them to achieve the desired effects, e.g., to highlight user needs, find product issues, and discover team challenges.