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Our Projects Portfolio

<p class="font_8">TELEMETRY will provide trustworthy tools that enable the continuous assessment of heterogenous, interlinked components &amp; systems that constitute IoT ecosystems (interconnected IoT devices with hardware, software, services and communications infrastructure). Addressing all aspects of their lifecycle, the TELEMETRY holistic methodology and toolkit incorporates: testing for component development, testing &amp; monitoring for component integration into systems, testing &amp; monitoring for operation of systems.</p> <p class="font_8"><br></p> <p class="font_8">TELEMETRY will deliver advances in cybersecurity testing and runtime monitoring through the use of novel machine learning models and algorithms for real-time anomaly detection; dynamic risk assessment to simulate likelihood and severity of threat consequences; reputation management and privacy-preserving data sharing across independent entities (e.g. supply chains), IoT device emulation and analysis environment and lightweight approaches for trusted updates; all of which that promotes a cycle of continuous improvement and assurance across design and runtime phases.</p> <p class="font_8"><br></p> <p class="font_8">TELEMETRY will leverage 3 exemplar use cases representing diverse, complex IoT ecosystems and IoT supply chains in aerospace, smart manufacturing and telecommunications domains to drive the design and validation of the proposed tools and methodologies. This will lead to significant improvements with respect to accuracy of threat and vulnerability detection, response time and cost of testing and verification of IoT ecosystems.&nbsp;</p> <p class="font_8"><br></p> <p class="font_8">TELEMETRY will promote open source and knowledge sharing through engagement with relevant communities throughout the project for consultation, dissemination and exploitation of its results.</p>

TELEMETRY

Trustworthy methodologies, open knowledge & automated tools for security testing of IoT software, hardware & ecosystems

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<p class="font_8">THEMIS 5.0 draws researchers and practitioners from diverse disciplines in order to secure that AI-driven hybrid decision support is trustworthy and takes place in accordance with the particular human user needs and moral values as well as adhere with the key success indicators of the embedding socio-technical environment. It implements an AI-driven, human-centered Trustworthiness Optimisation Ecosystem that users can use to achieve fairness, transparency, and accountability.</p> <p class="font_8"><br></p> <p class="font_8">In THEMIS 5.0, the trustworthiness vulnerabilities of the AI-systems are determined using an AI-driven risk assessment approach, which, effectively, translates the directions given in the Trustworthy AI Act and relevant standards into technical implementations. THEMIS 5.0 will innovate in its consideration of the human perspective as well as the wider socio-technical systems’ perspective in the risk management-based trustworthiness evaluation. An innovative AI-driven conversational agent will productively engage humans in intelligent dialogues capable of driving the execution of continuous trustworthiness improvement cycles.</p> <p class="font_8"><br></p> <p class="font_8">THEMIS 5.0 adopts the European human-centric approach to the design, development, deployment and operation of the THEMIS 5.0 ecosystem and, in this respect, THEMIS 5.0 will base the implementation of its AI-driven ecosystem on strong co-creation processes. THEMIS 5.0 will pilot and evaluate the humancentric ecosystem using 3 well-defined use cases, each addressing a specific high priority and critical application and industrial sectors.</p> <p class="font_8"><br></p> <p class="font_8">The THEMIS 5.0 solution enhances and accelerates the shift towards more trusted AI-enabled services by unlocking the power of humans to evaluate the trustworthiness of AI solutions and provide feedback on how to improve the AI systems. Users can now better challenge AI systems, pinpoint any biases or problems, embed their own values and norms, and provide feedback to AI developers and providers for improvement.</p>

THEMIS 5.0

Human-centered trustworthiness optimisation in hybrid decision support

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<p class="font_8">In parallel to the current developments in the so-called narrow artificial intelligence (AI) realm, there is an urgent demand for more universal, general AI approaches that can operate across a wider spectrum of application domains with varying data characteristics.</p> <p class="font_8"><br></p> <p class="font_8">It is expected that the emerging sustainable AI methods can be efficiently deployed in the edge-cloud continuum on different hardware platforms and computing infrastructure depending on the real-world task scenarios and constraints including the limited energy budget. In response to this growing demand and emerging trends we propose to adopt a brain-like approach to AI system design due to its promising potential for functional flexibility, hardware friendliness as well as energy efficiency among others.</p> <p class="font_8"><br></p> <p class="font_8">To this end, EXTRA-BRAIN is aimed at developing a new generation of AI solutions based on brain-like neural networks that enable us to overcome key limitations of the current state-of-the-art methods, exemplified by deep learning, such as limited cross-task generalisation and extrapolation to novel domains (bounded reliability), excessive dependence on costly annotated data as well as extensive training and validation processes with heavy demand for compute resources at high energy cost, to name a few.</p> <p class="font_8"><br></p> <p class="font_8">The core brain-like neural network design in our approach derives from the accumulated computational neuroscience insights into the brain's working principles of information processing, key learning schemes and neuroanatomical structures that underlie the brain's perceptual/cognitive phenomena and its functional flexibility.</p> <p class="font_8"><br></p> <p class="font_8">Furthermore, these novel models are supported by data optimisation pipelines, which improve data quality, security and reduce the costs of assembling suitable training data, and an explainability framework to empower the human user. The proposed EXTRA-BRAIN framework will be examined in a diverse set of use cases with different hardware demands in the edge-cloud continuum.</p>

EXTRA-BRAIN

Explainable Trustworthy brain-like AI for Data Intensive Applications

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<p class="font_8">DS2 draws researchers and practitioners from diverse disciplines to secure that complex lifecycles of inter-sector data sharing, aggregation and provenance take place in a human-centric and trusted way, with common structures, exportability and insight, whilst protecting the sovereign rights of data owners and complying with European data regulations.</p> <p class="font_8"><br></p> <p class="font_8">DS2 provides a modular software infrastructure to connect data sources (Data Spaces/data silos/data lakes) together for the purpose of cross-sector data sharing. Once connected, data consumers and data providers will be able to structure and execute efficient complex data lifecycles that respect the technical and governance related requirements of the participating data sources.</p> <p class="font_8"><br></p> <p class="font_8">It will do this via an IDT (Intersector DataSpace Toolkit) which is deployed at each data source/space and network connected to any other IDT-enabled data source. The IDT Toolkit is composed of a Broker which manages the fail-safe network operation with no central point of control. Plugged into this is a set of modules for the execution of complex data lifecycles, e.g. filtering, labelling, both automated and catering for where human-in-the loop is required.</p> <p class="font_8"><br></p> <p class="font_8">DS2 will pilot and evaluate its technology using 3 well-defined, inter-sector use cases, (City Scape, Green Deal, Precision Agriculture). The DS2 solution enhances and accelerates the shift towards the data economy by addressing the challenges, pain-points, and requirements with respect to the execution of complex data lifecycle. Data consumers and data providers can now orchestrate, manage and securely execute complex data lifecycles to realize cross-sectorial data driven applications.</p>

DS2

DataSpace, DataShare 2.0

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<p class="font_8">PUREMIND will deliver the first <strong>Integrated Mental Healthcare Ecosystem</strong> (IMHE) to predict/early identify <strong>Mental Health Disorders</strong> (MHD) and create more optimal personalised interventions to prevent MHD and thereby improving wellbeing in children, adolescents, and young adults (0–25 years). The ecosystem will mitigate key challenges associated with MHDs, specifically:</p> <ol class="font_8"> <li><p class="font_8"><strong>Detection</strong>: 1 in 7 undetected and untreated MHD cases,</p></li> <li><p class="font_8"><strong>Efficiency of treatment</strong>: lack of tools and guidelines for personalised multidisciplinary interventions that work,</p></li> <li><p class="font_8"><strong>Access to Support and Care</strong>: long waiting times for care due to lack of trained personnel,&nbsp;</p></li> <li><p class="font_8"><strong>Inequality</strong>: disparate and inadequate inter- and intra-country mental healthcare services and&nbsp;</p></li> <li><p class="font_8"><strong>Modern Cultures and Lifestyle</strong>: the increasingly recognised risk factor of digital addiction resulting in sedentary, socially isolated lifestyle contributing to MHD.</p></li> </ol> <p class="font_8">These will be addressed by PUREMIND’s approach of combining analytical modelling of dynamical systems in the cyber-physical context of a subject’s personal natural environment-gene-gut-brain-MHD interactions process creating a much-needed personalised intervention synthesis framework that will be exploited by an AI engine to deliver optimised personalised intervention, and a unique digital twin educating young people how MHD might develop depending on their environment and lifestyle.</p> <p class="font_8">These 2 components will be combined in an accessible/ adaptable mobile app MyMind to measure at scale and allow a subject to seamlessly access a 4-tier service architecture involving family, community, clinical and emergency services, to support prediction/early detection of MHD, empowering them to monitor their own mental health at home/educational/community settings and taking preventive actions against MHD.</p> <p class="font_8">The IMHE will be designed using Implementation Science approach and will be validated in a 12month pilot study in 7 European countries and 2 LMICs covering different socioeconomic and cultural scenarios.</p>

PUREMIND

Personal natural environment-gene-gut-brain interactions based ecosystem for prevention of Mental Illness in Children, Adolescents and Young Adults

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