HumAIne is a pioneering initiative dedicated to researching, developing, validating, and promoting a revolutionary Operating System (OS) for Human-AI collaboration. The project aims to empower AI solution integrators by creating a platform that facilitates advanced decision-making applications in dynamic and unstructured environments across various industrial sectors.

XR5.0 will build, demonstrate, and validate a novel Person-Centric and AI-based XR paradigm that will be tailored to the requirements and nature of I5.0 applications. The XR5.0 project aims to develop user-friendly Extended Reality (XR) applications for industrial workers using Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Despite improving production and safety, current XR applications often lack personalization for Industry 5.0 (I5.0). To overcome this, the project proposes person-centric XR visualizations considering individual worker traits, skills, and context, addressing challenges in technology, content, flow control, and compliance with European values. XR5.0 focuses on a pioneering XR approach tailored for I5.0, integrating it with innovative “XR-made-in-Europe” technology. This includes human-centered digital twins and advanced AI paradigms. The XR5.0 technologies will be coupled with a cloud-based XR training platform for Operator 5.0 applications, which will enable ergonomic and personalized training of industrial workers on popular processes.

By harnessing AI to create advanced risk prediction models and encouraging healthy lifestyles, SmartCHANGE will fight back against cardiovascular and metabolic ailments all over Europe.
Objectives and Key Results:

Accurate Risk Prediction Models: Develop precise AI-driven models for forecasting the lifetime risk of non-communicable chronic diseases (NCDs) like cardiovascular and metabolic diseases among children and youth. Utilize advanced machine learning techniques, incorporating diverse variables such as behavior, fitness, and NCD biomarkers, to enhance accuracy and adaptability.

Trustworthy AI Tools: Create robust risk-prediction models and AI tools that prioritize data privacy through federated learning, data accuracy through adaptive learning, and explainability via visual analytics. These tools will offer transparent insights into predictions and risk factors, instilling user trust and understanding.

User-Centric Health Enhancement Tools and Stakeholder Engagement: Develop user-friendly tools for healthcare professionals and citizens, enabling visualization of predicted outcomes based on current and modified lifestyles. These tools will provide personalized risk-lowering strategies, empowering users to make informed decisions about their health habits. Engage health professionals, educators, children, and families in participatory design to co-create risk-prediction models, explanations, and tools, harnessing diverse insights to effectively address users’ needs.

Promoting Healthy Lives: Disseminate the SmartCHANGE project’s findings and tools, collaborating with institutions to integrate them into healthcare systems. Work towards raising awareness among healthcare professionals, families, and the broader public about the significance of healthy lifestyle choices and the potential for positive change. By providing accessible tools and actionable insights, the project aims to promote healthier lives among children and youth.

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.

Modern data marketplaces are transforming how data assets are shared, traded, and utilized. Recent European initiatives have made significant strides, particularly in enhancing data monetization, regulatory compliance, and secure data exchange. However, existing centralized marketplaces face challenges that limit broader participation and accessibility. Notable limitations include complex data discovery processes, limited transparency in value-based data monetization, and insufficient integration of trusted, energy-efficient analytics. Addressing these gaps can unlock new data-driven applications in sectors like finance, retail, and smart cities, empowering innovative services that seamlessly integrate financial data. That is where FAME comes in.

FAME is on a mission to create a transformative platform and ecosystem to serve as a federated marketplace reference for the financial sector, closely aligned with the EU Data Strategy. As an EU-funded initiative, FAME combines technical, business, and legal frameworks to establish a unique, open, and publicly accessible federated data marketplace and platform for Embedded Finance (EmFi). The project adheres to emerging European data strategies, ensuring a solution that is secure, energy-efficient, and interoperable, while supporting transparent, programmable trading and pricing for data assets.

STAR is a joint effort of AI and digital manufacturing experts towards enabling the deployment of standard-based secure, safe reliable and trusted human centric AI systems in real-life manufacturing environments. STAR will research, develop, validate and make available to the AI and Industry4.0 communities novel technologies that will enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project will research and provide technologies that will enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. In this way STAR’s solutions will eliminate security and safety barriers against deploying sophisticated AI systems in real-life production lines. The project’s results will be fully integrated into existing EU-wide Industry4.0 and AI initiatives (notably EFFRA and AI4EU), as a means of enabling researchers and the European industry to deploy and fully leverage advanced AI solutions in manufacturing lines.

PHYSICS empowers European CSPs exploit the most modern, scalable and cost-effective cloud model (FaaS), operated across multiple service and hardware types, provider locations, edge, and multi-cloud resources. To this end, it applies a unified continuum approach, including functional and operational management across sites and service stacks, performance through the relativity of space (location of execution) and time (of execution), enhanced by semantics of application components and services. PHYSICS applies this scope via a vertical solution consisting of a:
-Cloud Design Environment, enabling design of visual workflows of applications, exploiting provided generalized Cloud design patterns functionalities with existing application components, easily integrated and used with FaaS platforms, including incorporation of application-level control logic and adaptation to the FaaS model.
-Optimized Platform Level FaaS Service, enabling CSPs to acquire a cross-site FaaS platform middleware including multi-constraint deployment optimization, runtime orchestration and reconfiguration capabilities, optimizing FaaS application placement and execution as well as state handling within functions, while cooperating with provider-local policies
-Backend Optimization Toolkit, enabling CSPs to enhance their baseline resources performance, tackling issues such as cold-start problems, multitenant interference and data locality through automated and multi-purpose techniques.
PHYSICS will produce an Artefacts Marketplace (RAMP), in which internal and external entities (developers, researchers etc) will be able to contribute fine-grained reusable artifacts (functions, flows, controllers etc).
PHYSICS will contribute to open source tools and initiatives/policies (Gaia-X, Green Deal, EOSC, Eur. Strategy for Data), while validating the outcomes in 3 real-world applications (eHealth, Agriculture and Manufacturing), making a business, societal and environmental impact on EU citizen life.

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.