The REXASI-PRO project aims to release a novel engineering framework. The REXASI-PRO project aims to release a novel engineering framework to develop greener and Trustworthy Artificial Intelligence solutions. In the methodology, safety, security, and explainability are entangled. In addition, throughout the entire lifecycle of the framework, ethics aspects will be continuously monitored. To this end, the REXASI-PRO project introduces several novelties. The project will develop in parallel the design of novel trustworthy-by-construction solutions for social navigations and a methodology to certify the robustness of AI-based autonomous vehicles for people with reduced mobility. The trustworthy-by-construction social navigation algorithms will exploit mathematical models of social robots. The robots will be trained by using both implicit and explicit communication. REXASI-PRO methodology augments existing system-level and item-level engineering frameworks by leveraging novel eXplainability methods to improve the entire system's robustness. REXASIPRO will release additional verification and validation approaches for safety and security with the AI in the loop. Among the other developments, a novel learning paradigm embeds safety requirements in Deep Neural Network for planning algorithms, runtime monitoring based on conformal prediction regions, trustable sensing, and secure communication. The methodology will be used to certify the robustness of both autonomous wheelchairs and flying robots. The flying robots will be equipped with unbiased machine learning solutions for people detection that will be reliable also in an emergency. Thus, REXASI-PRO will make the AI solutions greener. To this end, both an AI-based orchestrator to augment the intelligence of the robots and topological methods will be developed. The REXASI-PRO framework will be demonstrated by enabling the collaboration among autonomous wheelchairs and flying robots to help people with reduced mobility.

The latest cancer statistics highlight encouraging advances in decreasing cancer-related mortality. However, given that one in two people will be diagnosed with cancer in their lifetime, and due to the growing and ageing population, the absolute number of people living with cancer is set to keep increasing substantially in the near future. The main objective of ASCAPE is to take advantage of the recent ICT advances in Big Data, Artificial Intelligence and Machine Learning to support cancer patients’ quality of life and health status. To achieve its objective, ASCAPE will create an open AI infrastructure that will enable health stakeholders (hospitals, research institutions, companies, etc.) to deploy and execute its AI algorithms locally on their private data. Any new knowledge produced by this process will be sent back to the open AI infrastructure. This way the knowledge will be shared among everyone while the medical data will still remain private. The services to be designed, piloted and deployed inside this project will include intelligent interventions for physiological and psychological support, improved patient and family counselling and guidance, early diagnosis and forecasts of ill-health, identification of disease trajectories and relapse, improved health literacy etc. ASCAPE will focus the training of the AI in two types of cancer, breast and prostate. This way, it will achieve sufficient coverage across genders as well as age groups, hence facilitating its ongoing improvements and applicability towards any type of cancer in the future. The ASCAPE project will be developed in 36 months by a competitive consortium of 15 partners from 7 countries, which corresponds to a well-balanced structure, involving big companies, SMEs, research centres and universities. Despite the great diversity of entities within the proposal, ASCAPE partners bring state-of-the-art complementary skills ensuring the ability of the consortium to develop the proposed solutions.

Buildings constitute a vast, yet currently untapped, source of energy demand flexibility that can provide invaluable services to the energy system. This flexibility currently remains unattainable due to the lack of a technological framework that can connect the multitude of buildings and building systems with the energy system in a cost-effective manner as well as the reluctance of energy consumers to enroll in demand response programs. DRIMPAC offers a comprehensive solution to empower consumer to become active participants in the energy markets. It comprises three main pillars: a) A legacy and Standards-compliant interoperability framework to interconnect building energy loads/appliances and expose their demand flexibility as price-responsive demand to the grid or for market actors to aggregate and bid in ancillary service markets. b) A human-centric, intelligent building energy management system that will lift the burden of demand response from the consumers shoulders and reduce reluctance and fear of participation in DR programs. It will infer user comfort preferences and dynamically control building loads to minimize energy cost and use for the building occupant leveraging dynamic prices, while always preserving comfortable and healthy indoor conditions. c) Innovative business models and service offering for energy retailers in order to facilitate their transformation from commodity suppliers to digital energy service suppliers and kick-start the deployment of the DRIMPAC solution in the market. The DRIMPAC technological framework and business models will be validated by four retailers supplying three energy carriers – electricity, natural gas and district heating – in four different national markets across the EU - France, Cyprus, Germany and Spain. Pilot demonstrations will take place in a range of building types, including residential, office, educational and others, in order to validate the DRIMPAC benefits across most building typologies.