The unstoppable proliferation of novel computing and sensing device technologies, and the ever-growing demand for data-intensive applications in the edge and cloud, are driving a paradigm shift in computing around dynamic, intelligent and yet seamless interconnection of IoT, edge and cloud resources, in one single computing system to form a continuum. Many research initiatives have focused on deploying a sort of management plane intended to properly manage the continuum. Simultaneously, several solutions exist aimed at managing edge and cloud systems through not suitably addressing the whole continuum challenges though. The next step is, with no doubt, the design of an extended, open, secure, trustable, adaptable, technology agnostic and much more complete management strategy, covering the full continuum, i.e. IoT-to-edge-to-cloud, with a clear focus on the network connecting the whole stack, leveraging off-the-shell technologies (e.g., AI, data, etc.), but also open to accommodate novel services as technology progress goes on. The ICOS project aims at covering the set of challenges coming up when addressing this continuum paradigm, proposing an approach embedding a well-defined set of functionalities, ending up in the definition of an IoT2cloud Operating System (ICOS). Indeed, the main objective of the project ICOS is to design, develop and validate a meta operating system for a continuum, by addressing the challenges of: i) devices volatility and heterogeneity, continuum infrastructure virtualization and diverse network connectivity; ii) optimized and scalable service execution and performance, as well as resources consumptions, including power consumption; iii) guaranteed trust, security and privacy, and; iv) reduction of integration costs and effective mitigation of cloud provider lock-in effects, in a data-driven system built upon the principles of openness, adaptability, data sharing and a future edge market scenario for services and data.

AI is transforming law enforcement, offering new tools for policing but also enabling advanced criminal tactics that challenge traditional methods. The global nature of crime, including cyber threats, trafficking, and terrorism, calls for innovative solutions as LEAs face vast data volumes and increasingly sophisticated criminal activities. AI has raised concerns with deepfakes—highly realistic but fake audio, video, or text that can depict individuals saying or doing things they never did. Deepfakes pose serious risks, impacting politics, economy, and social trust. Examples include fabricated videos of political figures and voice-cloned audio for financial fraud, often spread through social networks to deceive and defraud on a large scale. Forensic institutes and courts struggle to differentiate authentic evidence from AI fabrications, especially in cases involving national security. Despite promising detection research, existing methods fall short as current models rely on limited, non-diverse datasets and produce results with limited legal admissibility. The DETECTOR initiative aims to address these challenges, supporting LEAs and forensic experts in analyzing altered media. It offers an integrated solution through cross-border collaboration among AI researchers, LEAs, forensic scientists, legal experts, and ethicists. DETECTOR’s goals include: developing specialized tools for detecting media manipulation, creating comprehensive datasets, researching digital evidence exchange across borders, engaging stakeholders, informing policymakers, and training forensic experts in digital media and AI. Through these efforts, DETECTOR seeks to safeguard digital evidence authenticity and enhance forensic capabilities to counter AI-driven media manipulation across Europe

MORPHEMIC proposes a unique way of adapting and optimizing Cloud computing applications by introducing the novel concepts of polymorph architecture and proactive adaptation. The former is when a component can run in different technical forms, i.e. in a Virtual Machine (VM), in a container, as a big data job, or as serverless components, etc. The technical form of deployment is chosen during the optimization process to fulfil the user’s requirements and needs. The quality of the deployment is measured by a user defined and application specific utility. Depending on the application’s requirements and its current workload, its components could be deployed in various forms in different environments to maximize the utility of the application deployment and the satisfaction of the user. Proactive adaptation is not only based on the current execution context and conditions but aims to forecast future resource needs and possible deployment configurations. This ensures that adaptation can be done effectively and seamlessly for the users of the application. The MORPHEMIC deployment platform will therefore be very beneficial for heterogeneous deployment in distributed environments combining various Cloud levels including Cloud data centres, edge Clouds, 5G base stations, and fog devices. Advanced forecasting methods, including the ES-Hybrid method recently winning the M4 forecasting competition, will be used to achieve the most accurate predictions. The outcome of the project will be implemented in the form of the complete solution, starting from modelling, through profiling, optimization, runtime reconfiguration and monitoring. Then the MORPHEMIC implementation will be integrated as a pre-processor for the existing MELODIC platform extending its deployment and adaptation capabilities beyond the multi-cloud and cross-cloud to the edge, 5G, and fog. This approach allows for a path to early demonstrations and commercial exploitation of the project results.