The overall aim of CODECO is to contribute to a smoother and more flexible support of services across the Edge-Cloud continuum via the creation of a novel, cognitive Edge-Cloud management framework. To achieve this aim, CODECO proposes a unique, smart, and cross-layer orchestration between the decentralised data flow, computation, and networking services, to address Edge-Cloud challenges derived from the rising Internet and IoT service decentralisation.

The AI4Gov project is aimed at exploring the possibilities of Artificial Intelligence (AI) and Big Data technologies for developing evidence-based innovations, policies, and policy recommendations to harness the public sphere, political power, and economic power for democratic purposes. The project intends to contribute to the research landscape that addresses ethical, trust, discrimination, and bias issues, and provide solutions to the challenges faced by stakeholders in modern democracies.

LeADS research and educational program trains a new interdisciplinary professional figure that we call Legality Attentive Data Scientist or LeADS. An expert in data science and law expected to work within and across the two disciplines, a leader in bridging scientific skills with the ethico-legal constraints of their operating environment. LeADS will develop a data science capable of maintaining its innovative solutions within the borders of law – by design and by default – and of helping expand the legal frontiers in line with innovation needs, for instance, preventing the enactments of legal rules technologically unattainable. LeADS research will set the theoretical framework and
the practical implementation template of a common language for co-processing and joint-controlling key notions for both data scientists and jurists. Its outcomes will produce also a comparative and interdisciplinary lexicon that draws experts from these fields to define important crossover concepts. Through a broad, interdisciplinary, inter-sectoral network of academic and non-academic partners, LeADS will provide a cross-disciplinary training to ESRs who will work as (e.g.) data scientists or researchers in general, sales managers, project or general managers management at private entities (tech companies, consultancies and legal advisories) and public entities (research centres, universities and administration). LeADS answers to the lack of programs blending experiential learning and research in the area, offers an innovative curriculum linked to pioneering research results. It includes 6 interdisciplinary modules, sectoral courses; joint mentoring on individual research projects; TILL modules and Discussion Games to help ESRs develop pioneering soft skills; challenging secondments. Overall, LeADS research and training aims at changing the regulatory and business approach to information while training the experts able to drive the process that every data driven society needs to employ

The focus of iHELP is on early identification and mitigation of the risks associated with Pancreatic Cancer based on the application of advance AI-based learning and decision support techniques on the historic (primary) data of Cancer patients gathered from established data banks and cohorts. This analysis helps to (i) determine key risks associated with Pancreatic Cancer, (ii) develop predictive models for identified risks, and (iii) develop adaptive models for targeted prevention and intervention measures. Based on the identification of key risks and availability of respective models, the project selects high-risk individuals (from hospital records and other sources) that are invited to take part in the pilot activities or digital trials. The digital trials are carried out through user-centric mobile and wearable applications that apply proven usability principles to offer more engaging experience for health monitoring, risk assessment and personalised decision support. Close collaboration between clinical and AI experts focus on drawing decision support from the prevention and intervention models against identified/predicted risks and providing personalised recommendations (e.g. lifestyle changes, behavioural nudges, screening test etc) to the participants in the digital trials. In addition to providing the personalised monitoring, alerting and decision support mechanisms, the iHELP (mobile and wearable) technology solutions help in validating iHELP solutions and raising health related awareness at individual level. The (secondary) data gathered through the mobile and wearable applications (concerning life style, behavioural, social interactions and response to targeted prevention and intervention measures) is integrated with primary data in the standardised HHR format – within a big data platform. Frugal AI-based learning techniques are developed to provide near real-time risk assessment based on the integration and availability of primary and secondary data in the standardised HHR format. The availability of HHRs provide opportunities to validate iHELP outcomes (e.g. improvements in quality of life, reduced risks etc) through advance analytic functions. iHELP solutions also help in policy making by providing decision support and social analysis on the design of new screening programs and new guidelines for bringing improvements in clinical, lifestyle and behavioural aspects of the fight against Cancer.