Cloud-based services have grown from basic computing services to complex ecosystems, comprising (virtual) infrastructure, business processes and application code. These advanced services also increasingly leverage the usage of Artificial Intelligence, including Machine Learning or Natural Language Processing techniques, raising the complexity even higher. Due to the cascade of dependencies among the different products and services, the need arose to bring more agility to the certification process of cloud-based services, e.g., using continuous monitoring and assessment, as evidenced by references to it in the certifications of the EU Cybersecurity Act (EU CSA). To transform the continuous assessment and certification concept into the complete realization of a Certification-as-a-Service (CaaS), several challenges need to be solved: 1) current proposed proofs of concepts for continuous monitoring lack interoperability at technology level, 2) the adoption of cloud and edge computing and the incorporation of regulations on specific topics or domains, such as AI, put significant strain on companies to comply with a multitude of different security schemes, 3) existing market fragmentation for continuous certification (scope, methodologies), hinder transparency and accountability in the provision of European cloud services, 4),smart tools and models need to be adopted to ease the agile application and implementation of the CaaS concept reducing complexity in the whole cloud certification value chain easing the adoption of CaaS by the different stakeholders. To overcome these challenges, the design and implementation of the EMERALD CaaS solution leverages the H2020 project MEDINAs outcomes and advances them to TRL 7 in the EMERALD core. Two PoCs will be provided; one for composite certification and one for mapping requirements to upcoming AI certification schemes. EMERALD will pave the road towards CaaS for continuous certification of harmonized cybersecurity schemes.

The I4MS program in H2020 has been and is a great success for the Digital Transformation of European Manufacturing SMEs. Phase IV of the program was focussing on DIHs and on highly innovative technologies like Digital Twins and AI. In particular, the AI REGIO Innovation Action developed a virtuous alliance between Regions, DIHs, AI solution providers and Manufacturing SMEs, which is materialised by a new methodology for DIHs service portfolio and customer journey analysis, an AI4EU -oriented toolkit of Data and AI resources, a network of Didactic Factories and their TEchnology and REgulatory SAndboxes (TERESA) and an ecosystem of SME-driven experiments and their Digital Transformation pathways. It is time now to align such important outcomes to the evolution of Manufacturing towards Industry 5.0, the evolution of cloud AI Technologies to AI-at-the-Edge, the evolution of H2020 to Horizon and Digital Europe programmes e.g. to EDIH, Data Spaces and AI TEFs (Testing and Experimentation Facilities) for Manufacturing. Some of the AI REGIO I4MS Phase IV motivations are now evolved: it is time for AI REDGIO 5.0 for keeping momentum of AI technologies adoption in Manufacturing SMEs. AI REDGIO 5.0 aims at renovating and extending the H2020 I4MS AI REGIO alliance between Vanguard EU regions and DIHs for a competitive AI-at-the-Edge Digital Transformation of Industry 5.0 Manufacturing SMEs. AI REGIO outcomes (methods and tools for DIHs governance and cross-DIH collaboration; Data Space and AI for Manufacturing toolkit; Didactic Factories network and TERESA facilities; SME-driven experimentations in 14 Vanguard regions) will be i) extended to the I5.0 principles; ii) enabled by the newest trusted technologies along the edge-to-cloud continuum; iii) supported by European open source hw/sw reference implementations, preserving EU values and ethical principles; iv) interconnected with the EDIH network in DEP as well as with the AI TEF nodes and the Data Spaces deployment program.

Cell migration (cell motility) is a fundamental biological process that is pivotal in (i) tissue formation and repair (health) and (ii) tissue invasion during carcinogenesis (disease). Understanding and controlling cell migration will have major clinical impact. Clarifying mechanisms driving cell motility has been challenging due to the complex underlying cellular mechanisms; these involve multiple components coordinated by structural, chemical and physical signals in terms of time and space. To accomplish breakthroughs in this field, researchers are needed who (i) master cutting-edge experimental techniques for monitoring the different cellular processes at high resolution and (ii) have competencies in theoretical science for integrating the resulting data sets into mechanistic mathematical models for predicting motile cell behaviour. The Research Training Network on Integrated Component Cycling in Epithelial Cell Motility (InCeM) aims to endow up-and-coming researchers with exactly these competencies. They will be able to develop and apply innovative devices for microscopic recording, image processing techniques, data analysis tools and modelling procedures for mechanistic understanding of cell migration. InCeM will focus on epithelial cells, since inducing motility in this cell type is clinically relevant for wound healing and cancer invasion. The ultimate goal is to control and manipulate cell migration for clinical applications. A dedicated multidisciplinary team of 11 beneficiaries from universities (4), research institutions (4) and industry (3), based in 5 European countries and Israel, together with 17 associated partners from the public and private sector, will train 15 Early Stage Researchers (ESRs) to use the relevant technologies and sciences and will offer business training to prepare them for successful careers in both academic and non-academic environments.

Smart Manufacturing is believed to play a critical role in maintaining the competitiveness of organisations, by supporting them at different levels such as process optimisation, resource efficiency, predictive maintenance and quality control. Nevertheless, AI technologies which are currently and rapidly penetrating industrial sectors at those levels remain essentially narrow AI systems. This is due to the lack of self-adaptiveness in the AIs capability to assimilate and interpret new information outside of its predefined programmed parameters. This mean that AI systems are tailored for solving specific tasks on a specific predefined setting and changes in the underlying setting usually requires system adaption ranging from fine-grained parameter adaptations to fully-fledged re-design and re-development of AI systems. TEAMING_AI project aims at a human AI teaming framework that integrates the strengths of both, the flexibility of human intelligence and scale-up capability of machine intelligence. Human AI teaming is equally motivated to meet the increased need for flexibility in the maintenance and further evolution of AI systems, driven by the increasing personalization of products and service, as well as tackling the barriers of user acceptance and ethical challenges involved in the collaborative environments where artificial intelligence will be used, in order AI can be considered as “teammate” rather than as a threat. The TEAMING.AI project will be run over 36 months with a work plan divided into 9 Work Packages. Work Packages from 1 to 5 are devoted to the development of new technology to enhance the interaction between human and machine. Furthermore, Work Packages 6 and 7 wrap the development of 3 use case scenarios. Finally, two final Work Packages (8 and 9) will work respectively on the dissemination, exploitation of results and coordination of the project in a transversally way to the above mentioned WPs.