Developing teaching materials is a time-consuming and expensive activity. Thus, over the years, several consortia created joint materials to benefit from them. It is expected that shared teaching materials are a means to save efforts for its development, to transfer methodological and technical knowledge between different university staff, and to exchange experience in practical application. However, does it really pay off considering the diversity of different educational environments and the difficulties of using externally produced materials rather than dedicated individual ones? This paper reports on the experience gained in a multi-country project. Success factors as well as problems will be outlined.

Understanding grows with active commitment: to "do" something, to master it, provides a deeper understanding. Experiencing with own experiments is of course an important prerequisite for studies in Robotics and Artificial Intelligence as well. But experimenting with real robots is difficult not only because of expensive hardware. Maintaining the robots and set ups for experiments are very time consuming even for experienced people. Experiments at home as needed for e-learning require a deep technical understanding by the students, i.e. experiences that they are just going to learn. So it is not surprising that simple hardware is still broadly used in robot experiments, hardware which is far behind the recent technical developments, not to talk about e.g. complex humanoid robots.
Simulated robots in simulated environments can be used as an alternative for complex hardware. The RoboCup community has 15 years of experiences with real and simulated robots in the field of soccer playing robots. Soccer playing robots have been established as a challenging test field for the progress in scientific research and technical developments. Robots have to be able to control their bodies and their motions according to soccer play, they must perceive a dynamically changing environment and they have to choose successful actions out of many options in real time. They have to cooperate with team mates and to pay attention to opponents. Several thousand scientists and students are participating in the annual RoboCup competitions in different leagues with different types of real and simulated robots. The humanoid robot Nao of the French Company Aldebaran is used in the Standard Platform league, while its simulated version is used in the so-called 3D-simulation league.
The official RoboCup 3D simulator SimSpark provides an excellent environment for experiments with simulated complex robots: It provides a physical simulation using ODE for the body dynamics of the robot Nao and the soccer environment. Users can program their own robot controls as “agents” which communicate by messages with the SimSpark simulation server. The agents could be considered as the “brains” of the robots. They perceive sensory information from the server and send action commands back to control the motors of the robot. Unfortunately, the technical details of the simulation server are still a barrier for inexperienced users which is now tackled by our RoboNewbie project.
The RoboNewbie Agent is a basic framework written in JAVA for the development of simulated humanoid robots. It provides easy understandable interfaces to simulated sensors and effectors of the robot as well as a simple control structure. It serves as an inspiration for beginners and behinds that it provides room for many challenging experiments at schools as well as at universities. It runs in the environment of the SimSpark simulation server, thus is can but need not be used for soccer playing robots. Users can develop their own motions, e.g. for dancing, gymnastics or kicking a ball. They will get insights into the complex phenomena of coordinated limb control, of cinematics and sensor-actor control. They can experiment with problems of perception, action planning, and coordination with other robots. The framework can also be used for Machine Learning, where many runs can be performed to train behaviors – much more than ever possible with real robots.
The talk will give an introduction to the framework and its backgrounds. It will also report about experiences with its usage so far.

TEL stands for Technology-Enhanced Learning. It refers to the support of any learning activity through technology. It encompasses dozens of research topics, such as personalization, learner modeling and adaptation, context-aware learning systems, Social Semantic Web and learning, mobile technologies for learning, network infrastructures and architectures for TEL, ubiquitous learning, data mining and information retrieval, recommender systems for TEL, learning analytics, problem- and project-based learning / Inquiry based learning, computer supported collaborative learning, collaborative knowledge building, game-based and simulation-based learning, and many more.
TEL is a thriving research area, and draws substantial funding. This talk surveys several recent and ongoing TEL research projects and indicates important issues in developing and running such projects.

Software is the cornerstone of the modern society, which can hardly tolerate failures and service discontinuity. At the same time, software systems are rapidly changing, and often rely on dynamically linked modules and services that may not be even available at design time. Classic off-line verification that require access to the whole software system, and stop-and-go maintenance that works off line badly adapt to these new needs of modern software systems. Self-healing technology addresses the new demands of software systems by moving some V&V activities from design to runtime. Self-healing systems can detect failures, diagnose, locate and correct faults fully automatically and at runtime, thus guaranteeing rapid recovery and resilience to software failures. In this talk, I discusses how systems can detect and heal failures and faults that are unknown at runtime without additional human intervention, identify intrinsic software redundancy as a great opportunity that can be exploited to automatically deal with emerging problems at runtime, and indicate how self-healing technology can impact on classic maintenance approaches.

Periodic data play a major role in many application domains, spanning from manufacturing to office automation and from scheduling to data broadcasting. In many of such domains, the huge number of repetitions makes the goal of explicitly storing and accessing such data very challenging. In this talk a new methodology based on an implicit representation of periodic data will be explained. It will be shown that the proposed model captures the notion of periodic granularity, provided by the temporal database glossary, and is an extension of the TSQL2 temporal relational data model. At first, the algebraic operators will be defined and then access algorithms will be introduced. Finally, results from extensive experimental evaluation will be presented, which demonstrate that the implicit representation of periodic data outperforms the explicit approach.

Modern workflow systems are (a) highly distributed, (b) cross the boundaries of many enterprises. This renders them to be

• Security-Critical – as it may need to access data of various enterprises;
• Context-Aware – as activities may be adapted to conform with the context that it occupies and
• Time-Critical - some of their activities are inherently temporal with hard timing constraints.

Current modelling techniques are not adequate to cope with these characteristics in a unified fashion. In this talk, a new computational model is presented which supported by a modelling notation (CS-Flow) which has the following features:

• support for concurrency;
• context and context awareness are first-class citizen;
• support for mobility as activities can move from one context to another;
• has the ability to express timing constrains: delay, deadlines, priority and schedulability;
• allows the expressibility of (access control) security policies without the need for an extra linguistic complexities; and
• enjoy sound formal semantics that allows us to animate design and compare various designs.

CS-Flow is illustrated on modelling of the next generation of hospital ward system, known as CAW (Context-Aware Ward).