a b s t r a c tRecent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.

Modern connected cities are more and more leveraging advances in ICT to improve their services and the quality of life of their inhabitants. The data generated from different sources, such as environmental sensors, social networking platforms, traffic counters, are harnessed to achieve these end goals. However, collecting, integrating, and analyzing all the heterogeneous data sources available from the cities is a challenge. This article suggests a data lake approach built on Big Data technologies, to gather all the data together for further analysis. The platform, described here, enables data collection, storage, integration, and further analysis and visualization of the results. This solution is the first attempt to integrate a diverse set of data sources from four pilot cities as part of the CUTLER project (Coastal urban development through the lenses of resiliency). The design and implementation details, as well as usage scenarios are presented in this paper.

New technologies enable novel types of learning activities that differ radically from traditional approach of visiting lectures and doing homework assignments. Namely, these technologies support transforming our everyday environments into learning environments. This concept is referred to in the literature as ubiquitous learning. Enriching ubiquitous learning systems with adaptive functionality facilitates personalization of learning activities by adapting them to learners' progress and situation. In this article, we identify needs of four user roles in ubiquitous learning systems, i.e. learner, instructor, developer, and researcher. We analyze the state of the art in ubiquitous learning and find that roles other than learners have not received much attention in the literature. Finally, we propose supporting different needs identified for four user roles by adding meta-level functionality to ubiquitous learning systems. This proposal adds self-introspective capabilities to such systems to serve their users better.

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