Phoenix aims to develop a fundamentally novel computational model for reconstructing complex software systems, following some massive internal failure or external infrastructure damage. Recovering system operations is a challenging problem as it may require excessive system reconstruction using a different infrastructure (i.e., computational and communication devices named system cells) from the one that the system was originally designed for. Thus, software functionality may have to be remodularised and allocated onto devices with very different characteristics than the ones originally used but with some generic capabilities. Phoenix aims to develop a bio-inspired paradigm for reconstructing nearly extinct complex software systems based on a novel computational DNA (co-DNA) oriented systems modelling approach. The co-DNA will encapsulate logic and program code and will enable the use of analogues of biological processes for transmitting, transforming, combining, activating and deactivating it across computational and communication devices. The purpose of encoding the co-DNA of a system, and computational analogues of biological processes using it, is to enable other computational devices receiving the co-DNA to act as parts of the system that needs to be reconstructed, realise chunks of its functionality, and spread further the system reconstruction process. The Phoenix approach will bring a breakthrough in the current software system design and engineering paradigm. This will be through, not only a fundamentally new way of engineering mechanisms to support the resilience, continuity and recovery of software systems, but also the initiation of a new paradigm of designing and implementing software systems, based on the encoding of a system co-DNA that can trigger processes of self-regulated and incrementally expanding system functionality.

Advances in robotics and artificial intelligence hold a promise for the radical transformation of healthcare services. Besides, the COVID-19 pandemic demonstrates that robots are crucial allies during pandemics. Yet, the adoption of these technologies is impeded by concerns related to cybersecurity issues. The rise of security incidents is a timely reminder that not only the volume of attacks is increasing but their diversity is also expanding, posing a significant threat towards disrupting clinical care delivery. Ongoing research focuses on the use of robotics operating in healthcare spaces (surgical, service, logistics), while security aspects are not well covered in the research community. RESPECT project objective is to create a sustainable European and inter-sectoral network of organisations working on a joint research programme aiming to design and develop concrete defense strategies to ensure secure, safe, resilient and privacy-preserving operation of indoor mobile robotics solutions for logistic applications in healthcare environments. Specifically the main research objectives of the project are: (i) Explore and identify system-specific cyber-physical weaknesses posing security, privacy, and safety threats, in autonomous mobile robots operating in a healthcare environment; (ii) Analyse surfaced vulnerability issues in conjunction with projected threats and propose defence measures and mitigation strategies towards safeguarding mobile robots operation. (iii) Define and standardize a minimal set of vulnerability testing procedures and guidelines leveraging and extending the Robot Vulnerability Scoring System for safe and autonomous robotic fleet management in a “safety-critical setting”. The project will be implemented through staff exchanges among different organizations with complementary expertise in cybersecurity, healthcare, cloud computing and robotics from 5 countries across EU promoting transfer of knowledge between industry and academia.

The traditional cloud centric IoT has clear limitations, e.g. unreliable connectivity, privacy concerns, or high round-trip times. IntellIoT overcomes these challenges in order to enable NG IoT applications. IntellIoT’s objectives aim at developing a framework for intelligent IoT environments that execute semi-autonomous IoT applications, which evolve by keeping the human-in-the-loop as an integral part of the system. Such intelligent IoT environments enable a suite of novel use cases. IntellIoT focuses on: Agriculture, where a tractor is semi-autonomously operated in conjunction with drones. Healthcare, where patients are monitored by sensors to receive advice and interventions from virtual advisors. Manufacturing, where highly automated plants are shared by multiple tenants who utilize machinery from third-party vendors. In all cases a human expert plays a key role in controlling and teaching the AI-enabled systems. The following 3 key features of IntellIoT’s approach are highly relevant for the work programme as they address the call’s challenges: (1) Human-defined autonomy is established through distributed AI running on intelligent IoT devices under resource-constraints, while users teach and refine the AI via tactile interaction (with AR/VR). (2) De-centralised, semi-autonomous IoT applications are enabled by self-aware agents of a hypermedia-based multi-agent system, defining a novel architecture for the NG IoT. It copes with interoperability by relying on W3C WoT standards and enabling automatic resolution of incompatibility constraints. (3) An efficient, reliable computation & communication infrastructure is powered by 5G and dynamically manages and optimizes the usage of network and compute resources in a closed loop. Integrated security assurance mechanisms provide trust and DLTs are made accessible under resource constraints to enable smart contracts and show transparency of performed actions.

We live in an era of information overload that impairs objective decision making, especially in time-sensitive contexts. Information Visualization (InfoVis) systems have been used to mitigate information overload, yet they have not yet unlocked their potential in critical decision-making scenarios. From emergency rooms and autonomous cars to operational command centres, a clear understanding and rapid assessment based on the available data can make the difference between life and death. SYMBIOTIK envisions an effortless interaction dialogue between human and InfoVis systems to support decision making processes, inspired by known biological principles and guided by artificial intelligence (AI). Critically, this dialogue requires AI solutions with context awareness, emotion sensing, and expressing capabilities. We propose a novel framework where both the human and the machine cooperate towards a common goal and evolve together. Awareness principles will allow us to engineer complex systems, making them more resilient and more human-centric. We will define an integrative approach for awareness engineering and propose a specific open source implementation. Finally, we will demonstrate and validate the role and added-value of such an awareness framework in two scenarios: supporting novice-to-expert transitions and critical decision making. The awareness principles to be developed in this project can support learning, adaptation, and self-development of intelligent systems over long periods of time, not only in the InfoVis domain. Therefore, SYMBIOTIK has potential to achieve the real breakthroughs needed to bring awareness and emotional intelligence for decision-making tasks in computing systems. The results of the project will benefit a range of stakeholders, from human vision and brain researchers, computer scientists, citizens, as well as research funding bodies and policy makers.