The RESONANCE project develops an innovative software framework that provides means for rapid development and plug-and-play deployment of standard-compliant Customer Energy Manager (CEM), Resource Manager (RM), and their aggregation solutions. The CEM, specified in the EN 50491-12 standard family, is the next-generation demand-side flexibility management (DSFM) solution in Europe. CEM is a software agent that automates DSFM by interacting with smart appliances (represented by RMs), aggregators, and the markets to maximize customer benefits. According to the new EN 50491-12-2 standard, CEMs are envisioned to 1) provide a more deterministic demand response, and 2) be able to optimize consumer benefits with respect to multiple incentives and optimization targets. To achieve this, there is a need for accurate models of flexible assets (smart appliances) and model predictive control techniques to automate the decision-making within the customer premises. The RESONANCE Framework will facilitate the adoption of CEMs as the next generation DSFM system by significantly reducing the development efforts and costs. This is achieved with 1) a standard-compliant and modular system architecture, and 2) an innovative modeling pipeline that combines automated machine learning (AutoML) with physics-based modeling to provide accurate and robust models of flexible assets with minimum effort. The project brings together 19 partners (including a cluster with 40 organizations) with inter-disciplinary expertise and forms a basis for a cross-sector energy ecosystem that significantly contributes to the mobilization of DSFM at a large scale. Large scale piloting in six member states with a variety of consumer sectors, flexible assets (e.g. electric vehicles, HVAC systems, and white goods), stakeholders, and market settings (including sector integration with district heating) is utilized for demonstrating and validating the scalability and replication potential of the solutions.

In the domain of Cybersecurity Research and innovation, European scientists hold pioneering positions in fields such as cryptography, formal methods, or secure components. Yet this excellence on focused domains does not translate into larger-scale, system-level advantages. Too often, scattered and small teams fall short of critical mass capabilities, despite demonstrating world-class talent and results. Europe’s strength is in its diversity, but that strength is only materialised if we cooperate, combine, and develop common lines of research. Given today’s societal challenges, this has become more than an advantage – an urgent necessity. Various approaches are being developed to enhance collaboration at many levels. Europe’s framework programs have sprung projects in cybersecurity over the past thirty years, encouraging international cooperation and funding support actions. More recently, the Cybersecurity PPP has brought together public institutions and industrial actors around common roadmaps and projects. While encouraging, these efforts have highlighted the need to break the mould, to step up investments and intensify coordination. The SPARTA proposal brings together a unique set of actors at the intersection of scientific excellence, technological innovation, and societal sciences in cybersecurity. Strongly guided by concrete and risky challenges, it will setup unique collaboration means, leading the way in building transformative capabilities and forming world-leading expertise centres. Through innovative governance, ambitious demonstration cases, and active community engagement, SPARTA aims at re-thinking the way cybersecurity research is performed in Europe across domains and expertise, from foundations to applications, in academia and industry.

Five leading European supercomputing centres are committed to develop, within their respective national programs and service portfolios, a set of services that will be federated across a consortium. The work will be undertaken by the following supercomputing centres, which form the High Performance Analytics and Computing (HPAC) Platform of the Human Brain Project (HBP): ▪ Barcelona Supercomputing Centre (BSC) in Spain, ▪ The Italian supercomputing centre CINECA, ▪ The Swiss National Supercomputing Centre CSCS, ▪ The Jülich Supercomputing Centre in Germany, and ▪ Commissariat à l'énergie atomique et aux énergies alternatives (CEA), France (joining in April 2018). The new consortium will be called Fenix and it aims at providing scalable compute and data services in a federated manner. The neuroscience community is of particular interest in this context and the HBP represents a prioritised driver for the Fenix infrastructure design and implementation. The Interactive Computing E-Infrastructure for the HBP (ICEI) project will realise key elements of this Fenix infrastructure that are targeted to meet the needs of the neuroscience community. The participating sites plan for cloud-like services that are compatible with the work cultures of scientific computing and data science. Specifically, this entails developing interactive supercomputing capabilities on the available extreme computing and data systems. Key features of the ICEI infrastructure are: ▪ Scalable compute resources; ▪ A federated data infrastructure; and ▪ Interactive Compute Services providing access to the federated data infrastructure as well as elastic access to the scalable compute resources. The ICEI e-infrastructure will be realised through a coordinated procurement of equipment and R&D services. Furthermore, significant additional parts of the infrastructure and R&D services will be realised within the ICEI project through in-kind contributions from the participating supercomputing centres.

Ubiquitous AI will soon allow complex systems to drive on our roads, fly over our heads, move alongside us during our daily lives & work in our factories. In spite of this disruptive landscape, deployment and broader adoption of learned-enabled autonomous systems in safety-critical scenarios remains challenging. Continuous engineering (DevOps) can mediate problems when encountering new scenarios throughout the product life cycle. However, the technical foundations and assumptions on which traditional safety engineering principles rely do not extend to learning-enabled autonomous systems engineered under continuous development. FOCETA gathers prominent academic groups & leading industrial partners to develop foundations for continuous engineering of trustworthy learning-enabled autonomous systems. The targeted scientific breakthrough lies within the convergence of “data-driven” and “model-based” engineering, where this convergence is further complicated by the need to apply verification and validation incrementally & avoid complete re-verification & re-validation efforts. FOCETA’s paradigm is built on three scientific pillars: (1) integration of learning-enabled components & model-based components via a contract-based methodology which allows incremental modification of systems including threat models for cyber-security, (2) adaptation of verification techniques applied during model-driven design to learning components in order to enable unbiased decision making, & finally, (3) incremental synthesis techniques unifying both the enforcement of safety & security-critical properties as well as the optimization of performance. FOCETA approach, implemented in open source tools & with open data exchange standards, will be applied to the most demanding & challenging applications such as urban driving automation & intelligent medical devices, to demonstrate its viability, scalability & robustness, while addressing European industry cutting-edge technology needs.