Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Decision SupporT and self-mAnagement system for stRoke survivoRs (STARR)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Active ageing and self-management of health
Action type: Research and Innovation actions
Countries involved: Spain,Spain,Germany,Spain,Sweden,Serbia,France,France,France,United Kingdom,Luxembourg
Call: Self management of health and disease and decision support systems based on predictive computer modelling used by the patient him or herself (H2020-PHC-2015-single-stage, 2015)
Start date: 2016-02-01
End date: 2019-10-31
Duration (months): 45.0
Total project cost (EUR): 4354568.25
EU funding (EUR): 4354568.25
Participants: 13
Project status: CLOSED

Stroke a leading cause of death and disability, with an estimated total cost of €65 billion per year in Europe. Even though preventive measures are in place to reduce the incidence of stroke, the number of persons having a stroke in Europe is likely to increase from 1.1 million/year in 2000 to more than 1.5 million/year in 2025 because of the increasing ageing population. Secondary stroke carries with it a greater risk than first-ever stroke for death and disability. Also, as mortality from first strokes has decreased recently, the number of people at risk for a secondary stroke has increased, with an associated increase in healthcare costs. In order to reduce these stroke statistics and the associated cost, the self-management of stroke risk factors is particularly suitable and necessary for the following reasons: 1) risk factors for stroke are well-known, and 2) 90% of strokes or secondary stroke events are preventable if the risk factors are managed appropriately. The Decision SupporT and self-mAnagement system for stRoke survivoRs (STARR) project and the system developed in it are targeting the self-management of stroke risk factors. Based on existing computational predictive models of stroke risk, we will develop a modular, affordable, and easy-to-use system, which will inform stroke survivors about the relation between their daily activities (e.g., medication intake, physical and cognitive exercises, diet, social contacts) and the risk of having a secondary stroke. This will lead to better prevention and a reduction of the number of secondary stroke events, as well as to a more efficient participation of patients in medical decision-making. A multidisciplinary consortium has been built for achieving the objectives of this ambitious project, involving stroke survivors’ associations, healthcare actors, sensing and human-machine interfaces experts. The consortium also comprises 3 European companies which will exploit the results of the project after its end.

Keywords: stroke,self management,co decision making,predictive models,participative design,human computer interaction
Artificial Intelligence without Bias (NoBIAS)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Austria,Italy,Germany,Germany,Germany,Belgium,Greece,United Kingdom,United Kingdom
Call: Innovative Training Networks (H2020-MSCA-ITN-2019, 2019)
Start date: 2020-01-01
End date: 2023-12-31
Duration (months): 48.0
Total project cost (EUR): 3989295.61
EU funding (EUR): 3989295.6100000003
Participants: 9
Project status: SIGNED

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime entailing risks, such as being denied a credit, a job, a medical treatment, or specific news. Businesses might miss chances, because biases make AI-driven decisions underperform; much worse, they may contravene human rights when treating people unfairly. Bias may arise at all stages of AI-based decision making processes: (i) when data is collected, (ii) when algorithms turn data into decision making capacity, or (iii) when results of decision making are used in applications. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in the training, design and deployment of AI algorithms to ensure social good while still benefiting from the potential of AI. NoBIAS will develop novel methods for AI-based decision making without bias by taking into account ethical and legal considerations in the design of technical solutions. The core objectives of NoBIAS are to understand legal, social and technical challenges of bias in AI-decision making, to counter them by developing fairness-aware algorithms, to automatically explain AI results, and to document the overall process for data provenance and transparency. We will train a cohort of 15 ESRs (Early-Stage Researchers) to address problems with bias through multi-disciplinary training and research in computer science, data science, machine learning, law and social science. ESRs will acquire practical expertise in a variety of sectors from telecommunication, finance, marketing, media, software, and legal consultancy to broadly foster legal compliance and innovation. Technical, interdisciplinary and soft-skills will give ESRs a head start towards future leadership in industry, academia, or government.

Keywords: bias,artificial intelligence,decision making,big data,algorithmic bias,algorithm discrimination
Industrial scenarios for connected factories (ConnectedFactories)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Coordination and support actions
Countries involved: Germany,Germany,Germany,Spain,Spain,Italy,Belgium,Greece,Sweden,Sweden,Belgium,Finland,France,Netherlands,United Kingdom,United Kingdom
Call: Digital automation (H2020-FOF-2016, 2016)
Start date: 2016-09-01
End date: 2019-11-30
Duration (months): 39.0
Total project cost (EUR): 1919391.25
EU funding (EUR): 1919391.25
Participants: 18
Project status: CLOSED

There is a consensus among industry and policymakers that Europe is currently in the middle of an industrial revolution initiated by the digitisation of industrial processes. Cyber Physical Systems (CPS), the Industrial Internet and the Internet of Things (IoT) connect people, devices, machines and enterprises like never before. For many years companies and research organisations have been addressing these challenges and opportunities both at European level (through the Factories of the Future PPP and related activities) and at national level. The ConnectedFactories project will build upon these activities and consolidate their expert networks, aiming at industrial consensus building across Europe. The ConnectedFactories project will establish and maintain a structured overview of available and upcoming technological approaches and best practices that are needed for mastering this paradigm shift. Present and future needs and challenges of the manufacturing industries will be identified in order to then identify possible scenarios of how digital platforms will enable the digital integration and interoperability of manufacturing systems and processes. Developing these scenarios will require detailed work with companies and other stakeholders through a series of workshops at national and regional level, in order to reach a necessary industrial consensus on aspects such as: innovative technology proposals, business models, aspects, standardization etc… while considering the establishment of industrial eco-systems where multi-sided IT platforms create value. This work will enhance the awareness among companies of the use of digital technologies in the manufacturing sector and equip them with knowledge to make informed decisions regarding technology and business model choices. It will reinforce the European manufacturing industries’ position in the international scene.

Keywords: industrial internet,interoperability
Identifying the source of unknown volcanic eruptions in Late Antiquity (VEILA)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2019-02-01
End date: 2021-03-09
Duration (months): 24.0
Total project cost (EUR): 183454.8
EU funding (EUR): 183454.8
Participants: 1
Project status: CLOSED

Volcanism has shaped the human trajectory as a result of the regional impacts of ash deposition and through global climate change caused by stratospheric veils of volcanic sulphate aerosols. Volcanic forcing of climate is suspected to be implicated in centennial scale climatic cooling during the Common Era, notably in triggering the Little Ice Age (LIA, circa 1300-1850 CE), and the shorter but cooler Late Antique Little Ice Age (LALIA, circa 530-680 CE). The latter episode would have contributed to major societal reorganisations that occurred across Eurasia, including the rise and demise of empires, migrations and political upheavals. The LALIA coincides with a series of large volcanic eruptions of unknown source, only identified by sulphur-rich horizons in polar ice cores, left by aerosols trapped in the ice after dispersal through the high atmosphere. This project aims to locate the volcanic sources of these unknown 6th and 7th century eruptions, by characterising and correlating their geochemical fingerprints in global archives, including Antarctic and Arctic ice cores and equatorial lake sediment cores. An interdisciplinary approach at the interface of glaciology, tephrochronology, dendrochronology, and climatology will be used to extract and analyse micrometric particles of far-travelled ashes and to identify correlations between different the samples and potentially to identify the source. The results of the proposed research will provide methodological advances of wider interest to tephrochronologists, and will facilitate modelling of the climate impacts of these eruptions. The outputs will provide a better understanding of the role of volcanism in centennial scale climate cooling during the Common Era, and inform the extent to which volcanism and associated climate forcing played a role in Eurasian history in the Late Antique period. They will also be relevant to understanding the potential impacts of future temporal clustering of climate-forcing eruptions.

Keywords: volcanic eruption,ice cores,sediment cores,tephrochronology,late antique little ice age
Fuse-based segmentation design: Avoiding failure propagation in building structures (Endure)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Products and processes engineering
Panel description: Product and process design, chemical, civil, environmental, mechanical, vehicle engineering, energy processes and relevant computational methods
Countries involved: Spain
Call: ERC CONSOLIDATOR GRANTS (ERC-2020-COG, 2020)
Start date: 2022-01-01
End date: 2026-12-31
Duration (months): 60.0
Total project cost (EUR): 2509375.0
EU funding (EUR): 2509375.0
Participants: 1
Project status: SIGNED

Extreme events often cause local-initial damage to the critical elements of building structures, followed by a cascade of further failures in the rest of the building; a phenomenon known as “progressive collapse”. Current design philosophies are based on giving buildings extensive continuity, so that when a critical element fails its load can be re-distributed among the rest of the structure. However, in certain situations (e.g. initial failure of several columns) this extensive continuity introduces undesirable effects and actually increases the risk of progressive collapse. Segmenting a building into individual units connected only by means of fuses would avoid a failure in one zone propagating to others. While such fuses would provide continuity for normal loads or small local-initial failure, they would “isolate” the different parts of the building when otherwise the forces generated by the initial failure would pull down the rest of the structure. Although fuse segmentation is probably the only alternative that can fill the gaps in the present design philosophies, so far, no studies have been carried out on the possibility of applying it to buildings. Endure’s overall aim is to develop a novel fuse-based segmentation design approach to limit or arrest the propagation of failures in building structures subjected to extreme events. The project will be multidisciplinary and highly ambitious, and will achieve its overall aim by: 1) Developing a performance-based approach for the design of fuse-segmented buildings; 2) Designing, manufacturing and testing fuses for segmenting buildings; and 3) Implementing fuses in segmented realistic building prototypes and testing and validating the new fuse-based approach in these structures. Endure will open up a new research area and design approach, and also deliver novel construction procedures. The project will lead to safer buildings, especially in the case of extreme events with severe consequences for building integrity.

Keywords: structural engineering,buildings,resilience,secure societies,failure
DataTrackerTM – Artificial Intelligence Enabling a Secure and Compliant Cloud Adoption (Data TrackerTM)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Israel
Call: SME instrument (H2020-SMEInst-2018-2020-2, 2018)
Start date: 2019-04-01
End date: 2021-03-31
Duration (months): 24.0
Total project cost (EUR): 3084695.0
EU funding (EUR): 2159286.5
Participants: 1
Project status: CLOSED

The basis for properly protecting data relies on being able to dynamically identify it and understanding the way it behaves, however companies fail in achieving this today. Current solutions and technology constraints compel chief data officers (CDO) and chief information security officers (CISO) to spend massive amount of money and resources to define and identify sensitive data. MinerEye has developed a proprietary technology to match data to a reference set using advanced technics applied originally in computer vision applications and machine learning solutions, to automatically categorize, classify and track unstructured data. This set of algorithms read the bytes of a given file and represent its content by creating a single mathematical vector on the fly, that is constant in size and very small (few k’s) per each file. From this point on, the system (DataTracker™) uses this vector called “signal”, that is extracted from every file it scans, for its unsupervised clustering and further analysis tasks. This capability allows the system to process unprecedented amount of data in a very short period (~1TB/3hrs.). The system does not dispose files from their original storage / location, opens or changes the file but rather reads their byte stream, creates the signal on the fly and sends it out to the server. This saves enormous network load when learning the sensitive data patterns and attributes and strengthens the non-intrusive nature of the system. The signal can be refreshed on any scheduled basis since the data does not change in such a pace that would affect its accuracy in the clustering process. Additionally, the scheduled scan is incremental. Powered by Interpretive AI™ Technology, MinerEye sees beyond form and file, tracking sensitive data by its essence. MinerEye does not just see numbers, names, and file types. It sees contracts, customers’ personal data, chemical patents, most sensitive designs and sketches, and more.

Keywords: interpretive ai technology,gdpr,data privacy regulation,dark data,classifying data,tracking data,secured cloud migration,data mapping,data minimization
Elastic Metamaterial-aided impulse aerodynamic Force measurement techniques (EMiFT)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Belgium
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2021-11-01
End date: 2025-04-29
Duration (months): 24.0
Total project cost (EUR): 178320.0
EU funding (EUR): 178320.0
Participants: 1
Project status: CLOSED

Wind tunnels play an important role in the early era of developing high-speed aircrafts. And reproducing high-speed flight conditions above Mach 8 on the ground is usually restricted to impulse wind tunnels, whose test time lasts only several milliseconds. However conventional force measurement techniques requires a longer time. Aerodynamic force measurement in such a short time is a great challenge, since measuring aerodynamic forces is crucial in wind tunnel tests. The stress wave force measurement technqiue shows to be quite suitable for force measurement in impulse facilities. However, decoupling response signals to stress waves is still phenomenologically processed, resulting in rough force measurement results. Indeed, that the elastic waves propagate in the sound speed, which is almost equal to the flow speed, is the basis for the stress wave balance (SWB). So separating elastic waves and shielding reflected waves, the key in designing SWB, are an elastic wave propagation problem. Elastic metamaterial (EMM), a kind of artificial material, has the designable frequency bandgaps, which can be used to guide waves. Hence EMM will be a good choice for developing new SWB to accurately measure impulse aerodynamic forces.Then the challenging scientific and technical problem is two inverse problems: one is how to inversely design EMM, the other is how to inversely estimate forces. With a forward-modelling knowledge on EMM, a parameterized objective function will be generated and a multi-objective optimization algorithm will be applied to solve the problem of inversely designing EMM. When the response signals are decoupled, identifying aerodynamic forces will be a typical inverse problem. A fuzzy inference method will be developed to solve such an inverse problem with the aid of impulse response functions that acquired by calibration tests. Experiments in lab will be used to validate and optimize the inversely designed EMM and force identification scheme.

Keywords: elastic metamaterial,impulse aerodynamic force,inverse problem,impulse wind tunnel,stress wave balance
Robustness in sparse random-like graphs (RobSparseRand)

Framework: Horizon 2020 — Spreading excellence and widening participation
Programme: Widening participation and spreading excellence
Theme: Other Widening
Action type: Marie Skłodowska-Curie actions
Countries involved: Croatia
Call: Widening Fellowships (H2020-WF-03-2020, 2020)
Start date: 2021-09-20
End date: 2023-09-19
Duration (months): 24.0
Total project cost (EUR): 147463.68
EU funding (EUR): 147463.68
Participants: 1
Project status: SIGNED

Sparse, highly connected, random-like graphs are currently a focal point in discrete mathematics, theoretical computer science and network science, motivated by the insight that networks of this type are ubiquitous in computing, biology, economics, physics, social science, etc., and by the theoretical challenges of this setting. Random graph models used in statistical modelling of real-world networks include the Molloy-Reed model for scale-free graphs and the Watts-Strogatz model (designed to simultaneously exhibit the small-world phenomenon and formation of hubs, cited 43000 times). Barabási devotes a chapter of his classic network science book to robustness of random graphs, specifically addressing robustness against adversarial attack. Extremal combinatorics includes the fundamental study of sparse networks. Our line of enquiry is sparse Ramsey theory, concerning sparse graphs that are robust in a strong sense, with respect to adversarial edge-partitioning. This notion is also of interest in theoretical computer science. An archetypal robust graph in Ramsey theory is the Erdős-Rényi random graph. Our project addresses some limitations of this paradigm. This projects aims to (1) solve important open problems in Ramsey theory, shedding light on a surprising synergy between structural and Ramsey-type properties of graphs, (2) open up new frontiers in sparse Ramsey theory by using a random Cayley graph (RCG) as a much-needed alternative sparse robust graphs to the Erdős-Rényi model, (3) advance the essential tools in random graph theory (sparse regularity framework, concentration bounds, embedding and colouring techniques) by taking them into an entirely new algebraic setting, (4) illuminate potentially useful classes of expander graphs with both structure and randomness.

Keywords: ramsey theory,random graphs,cayley graphs,sparse regularity,hypergraph containers,expander,discrete fourier analysis,robustness
Objective home-based EEG prediction of aMCI: Identification of a predictive electrophysiological model of cognitive function in amnesic mild cognitive impairment. (ID-earlyMCI)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Society and Enterprise panel
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-10-01
End date: 2022-09-30
Duration (months): 24.0
Total project cost (EUR): 184590.72
EU funding (EUR): 184590.72
Participants: 1
Project status: CLOSED

Dementia is an umbrella term for age-related brain disease, of which Alzheimer’s Disease (AD) is the most common. Around 5-7% of adults over 60 years suffer dementia worldwide, with approx. 8.7 m people in the EU. A frequent precursor of AD is amnestic mild cognitive impairment (aMCI), a clinical condition characterised by declines in memory skills. By predicting aMCI progression, health-care services will have new opportunities to deliver early interventions that could delay AD onset. This will ultimately promote functional independence in vulnerable adults and meet the societal challenge Health, Demographic Change and Wellbeing of Horizon 2020. Clinical outcomes of aMCI patients are influenced by the severity of cognitive (dys-) function. However, these deficits may occur at an advanced stage of neurodegeneration. This fellowship aims to identify a predictive model of cognitive function based on brain activity measured with electro-encephalography (EEG). Previous studies suggest that the capacity to learn a new task (practice effects) can help classify a person into healthy, aMCI or AD. Also, cross-sectional studies using EEG have found differences between normal controls, aMCI and AD patients during rest and cognitive tasks. The behavioural and EEG evidence combined shows the potential of using behavioural practice and EEG measures to predict cognitive function. This potential will be investigated and exploited in this fellowship via advanced machine learning methods on a large EEG data sample. This fellowship will take place in BrainWaveBank (BWB), an innovative company developing the largest database of EEG data in older adults along with cutting-edge analytics. This fellowship will allow the researcher to apply her experience on neural engineering and expand her knowledge and expertise to machine learning and clinical neuroscience in BWB. This will build the researcher’s independence and build prospects for a career in the medical technology sector.

Keywords: amci,dementia,machine learning,prediction,cognitive function,longitudinal designs,behavioural practice,disease progression,home based