One of the effects of the radically changing energy market is the construction of more and more offshore wind turbines. Many new companies with different levels of experience are entering the market to meet the demand of renewable energy. To this end, we introduce a distributed simulative approach for verifying a safety concept for offshore missions to support the involved companies. In this paper, we present our vision of the emerging simulation environment and introduce the components that we currently work on. More precisely, we introduce a Physical World Simulator (PWS) that allows the simulation of the environment, persons, and resources of offshore operations. The simulator can be interconnected to other simulators and to a Failure and Hazard Observer (FHO) which we also present in the paper. The observer tracks the occurrence of hazards and thus checks if they have been considered while creating a safety concept for an offshore mission. We use the High Level Architecture (HLA) as the simulator communication interface for which we developed a helper library which also extends its features while maintaining compatibility with the standard. The simulation framework enables us to automatically verify the safety concept by performing simulation runs and injecting identified failures.

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Users nowadays increasingly use their smart phones to take photos and videos. Many of the users would like to create multimedia products such as physical photo books out of their photos or videos on the phone, however the screen of the phone is small for complicated editing and publishing process. In this demo paper we introduce an application that allows the user to automatically create a photo product out of his videos on the smart phone. The user can select one or several videos. The scenes of each video are detected using visual features, and for each scene a distinct good quality frame is selected. The videos are usually not very long and on average a video is 2 mins length in our dataset of users. We enrich the extracted representing frames with related photos from both the phone and from social networks like Facebook. We measure the similarity based on visual, metadata and social features. We apply a face recognition phase to enhance the accuracy of these features. At the end we compile the selected media content in an appealing photo product, that the user can edit later from any device. This process allows the user to make use of the phone videos to create appealing media products with little effort that suits the small screen.