To go with the current trend of using smart environments or Internet of Things (IoT) and to make the human life easier, many systems have been developed up to date which use special purpose sensing or direct sensing approaches. However, these approaches are expensive due to the need to affix sensors on each and every equipment and also they lack flexibility of usage due to being battery powered. As a solution for these issues, based on the concept of synthetic sensors introduced by a previous research work, a general purpose sensing approach is discussed by the present study. The system can be placed in a corner of a room and will only be taking a maximum of three sensors into account to detect many events happening across the room making it rather cost-effective. Furthermore, performance of several machine learning (ML) algorithms is analysed by this work. With real time testing of the proposed system, the possibility of applying this approach as a cost-effective solution to be used in the day-to-day work is proven.

Creating new lectures and aligning the contents to the students and their knowledge can be a challenging work for all levels of education: Knowledge dependencies between the different blocks of lectures are not always visible. The problem increases if content from third parties like colleagues or from the Internet is being used. The content curation tool described in this poster aims on this challenge and offers a way of automatic, skill-based lecture creation and dependency management. The objective is that the lecturer can enter the required skills as well as the type of teaching materials and the system will generate a complete course from available contents.