Mobility in the urban and maritime domains hugely impacts the global economy, generating data at high rates from an increasing number of moving objects. Management of the complete lifecycle of such data implies that trustworthy and privacy-preserving infrastructures need to be put in place, so that reliable and secure data operations can be provided. Meanwhile, the mobility data exploitation has still a wide potential due to the emerging applications and the environmental footprint caused by mobility (e.g., carbon emissions, energy consumption). Motivated by these pressing needs, MobiSpaces delivers an end-to-end mobility-aware and mobility-optimized data governance platform with key differentiating factor that the outcomes of mobility analytics will be utilized to optimize the complete data path, in terms of efficient, reliable, secure, fair and trustworthy data processing. MobiSpaces promises the extraction of actionable insights from ubiquitous mobile sensor data and IoT devices in a decentralized way, offering intelligent transportation services, enforcing privacy constraints at the expected point of action. XAI techniques will be applied at the level of data management and machine learning, supporting the creation of comprehensive and interpretable prediction models, while all the research outcomes will be validated through five use cases: 1) intelligent public transportation services in urban environments, 2) intelligent infrastructure traffic sensing for smart cities, 3) vessel tracking for non-cooperative vessels, 4) decentralized processing on-board of vessels, and 5) enhanced nautical maps via crowdsourced bathymetry vessels data. These actions will be continuously adapted and monitored for their environmental sustainability, whereas MobiSpaces will create a widely accepted standard of data processing and analytics, alongside a data-rich ecosystem providing trustworthy and actionable data that is vital for enabling the growth of the EU digital economy.

TENSOR will provide LEAs a platform that facilitates the biometric evidence extraction, sharing and storage in cross border environments allowing them to share best practices in an automated, robust, secure, privacy-preserving and scalable manner. In addition, the full potential of biometrics technologies will be exploited and their fusion in case of partial evidence gathered in the crime scenes by forensic institutes strengthening their acceptance in the court of justice. More particularly, TENSOR will equip security practitioners with novel tools focusing on (a) Extraction of biometrics and other more or less distinctive features validated in forensic conditions that assist LEAs in identification, identity verification, intelligence and investigation processes and can be leveraged to unlock criminals’ mobile devices; (b) Sharing of biometrics through secure, automated, scalable exchange of biometric intelligence and court-proof evidence among LEAs in a cross-border manner, enhancing interoperability among legacy systems owned by security practitioners and Forensic Institutes; and (c) Storage of biometrics in a privacy-preserving way through a biometric data protection mechanism enabling revocability of biometric templates. TENSOR will also introduce the one-of-its-kind European Biometric Data Space creating a common ground among LEAs, Forensic Institutes and Security Researchers assisting in the faster adoption of modern biometric solutions.

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

TANGO will establish a stronger cross-sector data sharing, in a citizen-centric, secure and trustworthy manner, by developing innovative solutions while addressing environmental degradation and climate change challenges. The overall outcome is a novel platform exhibiting the following capabilities: user-friendly, secure, trustworthy, compliant, fair, transparent, accountable and environmentally sustainable data management, having at its core technology components for distributed, privacy preserving and environmentally sustainable data collection, processing, analysis, sharing and storage. This platform will promote trustworthy and digitally enabled interactions across society, for people as well as for businesses. TANGO will leverage the power of emerging digital technologies to strengthen the privacy for citizens and private/public organisations, reduce costs and improve productivity. It will unlock the innovation potential of digital technologies for decentralised, privacy-preserving applications, while making accessible and demonstrating this potential within the GAIA-X and EOSC ecosystem. With 37 key partners from 13 countries, TANGO, is uniquely positioned to provide a high impact solution within the transport, e-commerce, finance, public administration, tourism and industrial domains supporting numerous beneficiaries across Europe. Through the provision of TANGO technologies, a trustworthy environment will be designed acting as a gatekeeper to information and data flows. Citizens and public/private organisations will be empowered to act and interact providing data both online and offline. TANGO will focus its activities on 3 main pillars: (i) the deployment of trustworthy, accountable and privacy-preserving data-sharing technologies and platforms; (ii) the creation of data governance models and frameworks; (iii) the improvement of data availability, quality and interoperability – both in domain-specific settings and across sectors.

A significant, highly complex class of artificial intelligence applications are sequential decision-making problems, where a sequence of actions needs to be planned and taken to achieve a desired goal. Examples include routing problems, which involve a sequence of steps from source to destination; the control of manufacturing processes, which consist of a variable sequence of operations; or active learning problems, in which machine learning algorithms query human users for a sequence of inputs. We address the compelling scientific and technological goal of tackling users' lack of trust in AI, which currently often hinders the acceptance of AI systems. We break down this problem into two complementary aspects. First, users do not understand current AI systems well, with a lack of transparency leading to misinterpretations and mistrust. Second, current AI systems do not understand users well, offering solutions that are inadequately tailored to the users' needs and preferences. PEER will focus on how to systematically put the user at the centre of the entire AI design, development, deployment, and evaluation pipeline, allowing for truly mixed human-AI initiatives on complex sequential decision-making problems. The central idea is to enable a two-way communication flow with enhanced feedback loops between users and AI, leading to improved human-AI collaboration, mutual learning and reasoning, and thus increased user trust and acceptance. As an interdisciplinary project between social sciences and artificial intelligence, PEER will facilitate novel ways of engagement by end-users with AI in the design phase; will create novel AI planning methods for sequential settings which support bidirectional conversation and collaboration between users and AI; will develop an AI acceptance index for the evaluation of AI systems from a human-centric perspective; and will conduct an integration and evaluation of these novel approaches in several real-world use cases.