Cloud computing offers a flexible pay-as-you-go model for provisioning application resources, which enables applications to scale on-demand based on the current workload. In many cases, though, users face the single vendor lock-in effect, missing opportunities for optimal and adaptive application deployment across multiple clouds. Several cloud modelling languages have been developed to support multi-cloud resource management, but still they lack holistic cloud management of all aspects and phases. This work defines the Cloud Application Modelling and Execution Language (CAMEL), which (i) allows users to specify the full set of design time aspects for multi-cloud applications, and (ii) supports the models@runtime paradigm that enables capturing an application’s current state facilitating its adaptive provisioning. CAMEL has been already used in many projects, domains and use cases due to its wide coverage of cloud management features. Finally, CAMEL has been positively evaluated in this work in terms of its usability and applicability in several domains (e.g., data farming, flight scheduling, financial services) based on the technology acceptance model (TAM).

While the economic impact of major passive sport events is well documented, the contribution of active sports events is scarcely examined. This reflective practice paper contributes towards bridging the gap by examining, in a case study, the economic impact of one of the first big active sport events in Cyprus, organised using the state-of-the-art technology. The paper presents the first commercial web platform and recommender system dedicated to active sports events, which provide all necessary services to customers and thus simplify, and ultimately increase participation in the event. We find strong evidence in support of the idea that active sport events have a strong positive impact on the local and countrywide economy, while the technology can contribute and enable further promotion and better organisation of such events.

Connected and Autonomous Vehicles (CAV) depend on satellite systems, such as the Global Positioning System (GPS), for location awareness. Location data are streamed in real-time to the CAV’s perception engine from its onboard GPS receiver for autonomous driving and navigation. However, these receivers are vulnerable to location spoofing attacks that can be easily launched using Commercial-Off-The-Self (COTS) equipment and open-source software. Existing data-driven attack detection solutions typically require data associated with ‘normal’ and ‘attack’ labels. The latter are hard to collect in operational conditions or even in controlled experiments. To this end, we formulate the GPS location spoofing attack detection as an outlier detection problem. The proposed solution based on Machine Learning (ML) relies solely on normal location data for training during attack-free operation. Our solution demonstrates more than 98% detection accuracy according to standard metrics on realistic data produced with the CARLA driving simulator and outperforms by 15% another (non ML-based) state-of-the-art solution.

This paper explains the Virtual Alliances for Learning Society European Project technological approach to support a virtual placements management process. Also, this paper shows the first results of the practical part of the project, the Semester of Code, explaining the detected problems, the issues, the challenges, and some actions to improve the development of these kinds of virtual placements programs. In order to allow the readers to better comprehend the approach and its results, this paper also describes three of the main virtual placements programs around the world, regarding both their organization and technological approach.