Contributions in Merlin

In outdoor environments, mobility, adaptability and reliability of a robot are more important than its speed and precise trajectory. From a practical point of view, tracked robots have an advantage over wheeled robots in outdoor applications. The tracked robot is frequently operated by using the remote controller, but the remote operation is not effective for all cases. To overcome some complex obstacles such as rocks or stairs, the information related to the robot posture is required. However, the sensor information is not intuitive to the user to control the robot. In this research, a multi-active crawler robot (MACbot) was developed and the autonomous stair climbing algorithm was implemented to deal with those problems. Various experiments show that the MACbot can climb the stair autonomously.

Many mobile learning projects aim to support new learning forms like situated learning in a real world environment. Situated and explorative learning should be active learning, engaging students in the environment. We tested four different tasks designs in two large field tests with the mExplorer system. Two kinds of engaging tasks were observed. Interactive tasks with high context integration led to knowledge about specific aspects of an environment. Creative tasks led to a familiarization with the environment. We also analyzed other projects with situated real world learning scenarios to see what types of tasks they were using. We found that instead of sup-porting active learning, many of these projects still focus on transmissive elements and do not use the full potential of situated and explorative learning. To optimize this, we propose four design recommendations for tasks and de-scribe the circumstances under which specific types of m-learning tasks should be used.

Futures contracts on the New York Mercantile Exchange are the most liquid instruments for trading crude oil, which is the world’s most actively traded physical commodity. Under normal market conditions, traders can easily find counterparties for their trades, resulting in an efficient market with virtually no return predictability. Yet even this extremely liquid instrument suffers from liquidity shocks that induce periods of increased volatility and significant return predictability. This paper identifies an important and recurring cause of these shocks: the accumulation of extreme and opposing positions by the two main trader classes in the market, namely hedgers and speculators. As positions become extreme, approaching their historical limits, counterparties for trades become scarce and prices must adjust to induce trade. These liquidity-induced price adjustments are found to be driven by systematic speculative behavior and are determined to be significant.

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Although network management has always played a key role for industry, it only recently received a similar level of attention from many research communities, accelerated by funding opportunities from new initiatives, including the FP7 Program in Europe and GENI/FIND in the United States. Work is ongoing to assess the state of the art and identify the challenges for future research in the field, and this article contributes to this discussion. It presents major findings from a two-day workshop organized jointly by the IRTF/NMRG and the EMANICS Network of Excellence, at which researchers, operators, vendors, and technology developers discussed the research directions to be pursued over the next five years. The workshop identified several topic areas, including management architectures, distributed real-time monitoring, data analysis and visualization, ontologies, economic aspects of management, uncertainty and probabilistic approaches, as well as understanding the behavior of managed systems.

This chapter introduces the general vision of the Social Semantic Desktop (SSD) and details it in the context of the NEPOMUK project. It outlines the typical SSD requirements and functionalities that were identified from real world scenarios. In addition, it provides the design of the standard SSD architecture together with the ontology pyramid developed to support it. Finally, the chapter gives an overview of some of the technical challenges that arise from the actual development process of the SSD.