Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
A revolutionary Influencer Marketing solution that creates a new sales channel for SMEs and self-employment opportunities for Social Media influencers (OCTOLY REVIEWS Plugin)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: SME instrument
Countries involved: France
Call: Open Disruptive Innovation Scheme (H2020-SMEINST-2-2016-2017, 2016)
Start date: 2017-01-01
End date: 2019-06-30
Duration (months): 30.0
Total project cost (EUR): 2500312.5
EU funding (EUR): 1750218.0
Participants: 1
Project status: CLOSED

Advertising & marketing is experiencing a huge revolution. Millennials spend more time watching videos on YouTube than watching TV, and they are armed with ad-blockers and skip-ad buttons that mean traditional advertising barely reaches them. At the same time, millennials actively search for product reviews online, published peer-to-peer on social media and in YouTube videos. Around the world, these social media “influencers” upload 300 hours of videos (beauty tutorials, video game reviews…) every minute and collectively reach audiences of billions. On social media, brands’ “official” message makes-up less than 10% of overall visibility – more than 90% of mentions of their brand are made up of influencers talking about their products. Brands have realised that they must work with influencers, but struggle to mobilise them at scale: how can they collaborate with millions of influencers? To tackle this, OCTOLY has already created the “Free-Store”: a platform where brands can distribute free products to influencers and see them featured in millions of videos. Just 1 year after launch, we have become the biggest influencer intermediary in the world: over 48K YouTube videos have been created with Free Store products, generating over 206 million views for our client brands. The aim of the OCTOLY Marketplace project is to go “beyond the views”. We will make it possible for OCTOLY influencers to create their own “Influencer Store”, where fans can purchase the products they mention in their videos. This means that influencers can generate commission fees and make a living from their passion - and brands (particularly SMEs) gain access to a powerful new sales channel. The project involves creating the technical foundations of the new solution, related features and working with beta-testers to prepare market launch. At the end of the project, our revolutionary new influencer marketing solution will reinforce our position as a market leader in Europe and the US.

Keywords: influencer marketing,smes,entrepreneurship,marketing,advertising,social media
Beyond seasonal suffering: Effects of Pollen on Cardiorespiratory Health and Allergies (EPOCHAL)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Prevention, Diagnosis and Treatment of Human Diseases
Panel description: Medical technologies and tools for prevention, diagnosis and treatment of human diseases, therapeutic approaches and interventions, pharmacology, preventative medicine, epidemiology and public health, digital medicine
Countries involved: Switzerland
Call: ERC Starting Grant (ERC-2019-STG, 2019)
Start date: 2020-08-01
End date: 2025-07-31
Duration (months): 60.0
Total project cost (EUR): 1381931.5
EU funding (EUR): 1381931.5
Participants: 1
Project status: SIGNED

As climate change increases the duration and intensity of the pollen season, allergies to airborne pollen are increasingly common in Europe. Yet, it is not well recognized that high pollen concentrations may increase respiratory and cardiovascular events, leading to mortality and excess hospitalizations. I aim to investigate how short-term exposure to pollen is related to mortality, hospitalization and allergic symptoms, both on its own and synergistically with air pollution and weather. I will develop spatiotemporal exposure models of pollen for the years 2003-2022 based on a network of 14 pollen measurements stations in Switzerland. Taking advantage of large, real-world datasets without selection bias (Swiss National Cohort) and the efficient case-crossover study design, I will investigate the population effects of pollen on daily respiratory and cardiovascular mortality and hospitalization, also accounting for variation in air pollution and weather conditions. To explore individual sensitivity, I will conduct repeated measurements of lung function and airway inflammation in a dedicated panel of 400 allergic patients complemented with opportunistic repeated accounts of self-reported symptoms from the “e-symptoms” app by Swiss Allergy Centre. To provide personalized prevention recommendations and enhance quality of life for the allergic population, I will derive exposure-response relationships based on prevalent pollen, air pollution and weather triggers and individual symptom reports, allowing me to ultimately forecast symptom severity using machine learning techniques. This highly innovative project utilizes available nationwide health datasets and systematic novel data collection methods (in the in-depth panel study), to better understand the role of pollen in respiratory and cardiovascular diseases at both personalized and population levels. The project will prevent and reduce health effects due to pollen, which constitute a large burden on health and economy.

Keywords: pollen,spatiotemporal modelling,respiratory disease,cardiovascular disease,mortality,allergic symptoms
SaaS platform applying Artificial Intelligence and collaborative learning to the automation of Machine Vision-based manufacturing processes (UniMind)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Spain
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-03-01
End date: 2019-08-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

UniMind is a Deep Learning SaaS platform for the intelligent automation of manufacturing processes, minimizing the combination of low-value+high-repetitiveness tasks for human workers. Automating these processes is expensive (i.e. unattainable for SMEs), time-consuming and most of all: inflexible. All solutions are custom-made and not adaptable to different contexts. UniMind significantly lowers the time needed to learn and implement a new process. The algorithm’s learning process is fostered by incremental collaborative learning: the algorithms engaged in different processes and working in different companies are all connected to the cloud, so the work experience of one solution feeds all the others. UniMind has many possible applications, and we are starting for Visual Quality Control and Random Bin Picking, since both are highly in-demand processes and ideal to train UniMind for other applications. Existing solutions are partial and tailored to clients’ particular needs, not scalable to new applications, shop floor changes and/or scenarios. They are also slow in their deployment, dependent on evolutive maintenance and expensive. Worldwide revenues in the Machine Vision market have consistently grown since 2015, for all applications (€ 8.77 bn in 2017, growing at a 7.7% CAGR up to € 16.08bn in 2025). BitMetrics has defined a Total Available Market of € 8.47bn, of which it will obtain 0.29% share by 2025, having reached a cumulative EBITDA of € 33.64M. Our affordable subscription-based pricing strategy will lead us to reach these numbers. Our product is offered through a SaaS model. As a result, SMEs will be able to affordably automate their processes for the first time in the history. UniMind targets the manufacturing industry, specifically the Automotive, Electronics and Food & Beverages sectors. Nissan Motors (as client) and Siemens (as distributor) have already shown interest in UniMind.

Keywords: random bin picking,visual quality control,incremental collaborative learning,edge computing
Multivariate analysis for the Imaging of Neuronal activity using Deep architectureS (MINDS)

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: Denmark
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2016-01-11
End date: 2018-01-10
Duration (months): 24.0
Total project cost (EUR): 212194.8
EU funding (EUR): 212194.8
Participants: 1
Project status: CLOSED

Functional magnetic resonance imaging (fMRI) is the dominating approach to research in the mapping of neural activity in the human brain. State of the art data analysis techniques employ a statistical parametric mapping (SPM) strategy to convert raw signal into interpretable images by processing data in a pipeline of task-specific modules. This approach, despite its simplicity and reliability, presents a set of inconveniences, including low interconnectivity among modules, resulting in suboptimal solutions. In this project we aim at making a major contribution to the field by replacing the step-by-step data processing pipeline by a deep neural network. We hypothesise that this will achieve better solutions by propagating the effects of module-based decisions through the network, jointly optimizing the whole processing pipeline. Moreover, fMRI low temporal resolution will be alleviated by means of a post-processing treatment, where advanced interpolation techniques will be used. We will release a freely accessible software tool that integrates with SPM, supplying an easy-to-use framework including advanced techniques for an automatic multivariate non-linear data analysis. The generated deep network solution will be applied in a multidisciplinary study in neurofeedback, where subjects will learn relaxation strategies guided by fMRI technology. At the end of the project, we expect our tool to become a useful standard practise in the field.

Keywords: functional magnetic resonance imaging,human brain states,deep neural networks,neurofeedback,data processing pipeline
Directed networks as a novel approach for improving the management of cardiac arrhythmias (SMARTHEART)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Prevention, Diagnosis and Treatment of Human Diseases
Panel description: Medical technologies and tools for prevention, diagnosis and treatment of human diseases, therapeutic approaches and interventions, pharmacology, preventative medicine, epidemiology and public health, digital medicine
Countries involved: Belgium
Call: ERC STARTING GRANTS (ERC-2020-STG, 2020)
Start date: 2021-02-01
End date: 2027-01-31
Duration (months): 72.0
Total project cost (EUR): 1498125.0
EU funding (EUR): 1498125.0
Participants: 1
Project status: SIGNED

The management of cardiac arrhythmia remains the largest problem in cardiac electrophysiology. The prevalence of the most frequent arrhythmia, atrial fibrillation (AF), is expected to rise steeply due to the ageing population. In spite of intensive research, the mechanism of atrial fibrillation remains unclear, leading to poor results in its treatment. Ablation of AF often results in complex atrial tachycardia (AT), which are difficult to treat. Also ventricular tachycardias (VT) and fibrillations (VF) are a major cause of sudden cardiac death. Again, eliminating VTs with ablation has achieved only modest success in complex cases. Therefore, there is an urgent need to better understand and localize the sources of arrhythmia in order to improve its treatment. I propose a radical new approach of applying network theory to study the mechanisms of AT, VT and AF. Currently, network theory is known for being the basis for the Google search engine other online social networks, and has myriad applications throughout biology, physics, and social sciences. However, it has never been applied to the heart. In this proposal, based on my invention and preliminary work, I propose to apply network theory to clinical data of cardiac arrhythmia, backed-up by in-silico simulations. A new set of research tools will be created to automatically detect the source of the arrhythmia for complex AT and AF, which will identify possible ablation targets. For VT a substrate analysis is proposed, in order to reveal the structure of the heart to also determine the ablation target. My preliminary results already show that network analysis is able to automatically predict sites of ablation, prior to surgery in AT, largely exceeding the most recent technologies currently used in clinics. Therefore, this translational project will not only provide novel insights into the mechanism of cardiac arrhythmia, but will actually lead to an improved treatment for the patient.

Keywords: network theory,cardiac arrhythmia,computer modeling
Personalized Prediction and Intervention for Behavioral Avoidance and Maladaptive Affective States (Personalized-PredInt)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Social Sciences and Humanities
Countries involved: Belgium,United States
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-10-01
End date: 2023-09-30
Duration (months): 36.0
Total project cost (EUR): 266425.92
EU funding (EUR): 266425.92
Participants: 2
Project status: TERMINATED

Accurate forward prediction of maladaptive behaviors and emotional states involved in various forms of psychopathology offers an intriguing avenue for prevention and intervention science. To date, however, such prediction has been limited by a heterogeneity in factors determining individuals' behaviors and emotions. To address this limitation, the proposed project adopts an idiographic (i.e., person specific) approach while focusing on two exemplar targets that constitute transdiagnostic components in various psychological disorders – namely, behavioral avoidance and maladaptive emotional states. Specifically, the project aims to (a) develop person-specific models for predicting the two targets; and (b) use the models to construct a person-specific just-in-time adaptive intervention (JITAI) system, a novel, accessible, and highly promising means for affecting change. To do so, the project will utilize ecological momentary assessment (EMA) and harness three methodological innovations: (1) an adaptive assessment and personalized item-selection tool which will reduce participant burden; (2) a scalable personalized system that provides accurate forward prediction using brief psychosocial questions and automatically-collected timing/location data; (3) a personalized JITAI app that will inform participants in real time regarding increased likelihood of impending targets, and prompt appropriate interventions based on individual predictors. The project can significantly promote understanding of transdiagnostic maladaptive processes and lead to efficient precision interventions. It builds on the fellow's expertise in EMA and affect research and will develop his statistical and analytic tools for exploring ideographic time-series data, his skills in translating such data into tailored interventions, and his ability to shift back and forth between basic and applied science. The team assembled for this work is ideal in terms of both methodological and theoretical expertise.

Keywords: person specific methods,dynamic modeling,personalized medicine,behavioral avoidance,affect
Aircraft Light Communication (ALC)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: Clean Sky 2
Action type: Innovation actions
Partnership: CS2 (Art-187)
Countries involved: France,France,France,United Kingdom
Call: Secured and performant wireless connection based on light (LiFi) for EFB, headset and other pilot connected devices (H2020-CS2-CFP03-2016-01, 2016)
Start date: 2017-01-01
End date: 2021-12-31
Duration (months): 60.0
Total project cost (EUR): 1451812.86
EU funding (EUR): 1098771.63
Participants: 5
Project status: CLOSED

The ambition of ALC is to show evidence that LiFi connection solutions can successfully address current RF transmission drawbacks and are mature enough to successfully operate connected devices in the highly demanding environment of a cockpit. The targeted breakthroughs to be reached are: - Interfacing an existing LiFi module to the aircraft server - Implementing the currently existing secure and robust LiFi connection used for offices into the severe environmental conditions of an aircraft - Reducing current consumption of the connected devices, to save battery and increase autonomy. As compared to currently implemented solutions (e.g. WiFi, Bluetooth), ALC will demonstrate that LiFi is: - Fully secure for data transmission with no possible threat of malevolent action - Fully reliable with no risk of interference or propagation effects, fully compliant with normal conditions (e.g. sun or artificial lights, obstacles) and abnormal conditions (e.g. smoke) as well as DO160-G compliant for power management The project will therefore deliver, test and validate up to TRL5 applications integrating LiFi communication with: 1/ A flight crew wireless audio headset integrating LiFi connection 2/ An EFB tablet integrating LiFi connection to the A/C 3/ A connected headset, gathering health monitoring and behaviour/motion signals 4/ One or several other LiFi applications, to be identified by FACTEM, PURELIFI and XLIM, replacing existing wired connections, or providing new services or functions For each application, a comprehensive analysis of the impact of the proposed solution versus legacy wireless technology will be performed and quantitative and qualitative indicators will be used as TRL5 pass/fail criteria. The solutions will be standard solutions, i.e. not LiFi proprietary.

Keywords: performant robust and secure wireless communication,lifi,aircraft environment,electronic devices,headset,health monitoring bracelet,aircraft to ground communication,flight entertainment
The Demography of Sustainable Human Wellbeing (EmpoweredLifeYears)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: The Social World and Its Diversity
Panel description: Sociology, social psychology, social anthropology, education sciences, communication studies
Countries involved: Austria
Call: ERC Advanced Grant (ERC-2016-ADG, 2016)
Start date: 2017-11-01
End date: 2023-10-31
Duration (months): 72.0
Total project cost (EUR): 1819250.0
EU funding (EUR): 1819250.0
Participants: 1
Project status: SIGNED

This project will apply two distinctly demographic concepts to research questions that go far beyond demography. The wellbeing indicators proposed here will be based on life table methods and the recently operationalized concept of Demographic Metabolism – modelling social change through the replacement of generations – will be used to get a quantitative analytical handle on the temporal dynamics of improving human wellbeing. The project will theoretically develop, empirically estimate, test and forecast indicators of human wellbeing that are based on life table methods and hence reflect the basic – but often overlooked fact – that being alive is a necessary prerequisite for enjoying any quality of life. But since mere survival is not sufficient as an ultimate goal for most people the person years lived at each age will be weighted with four different dimensions of empowerment: health, literacy, happiness and being out of poverty. These are four dimensions of an indicator tentatively called ELY (Empowered Life Years). ELY will also serve as the explanandum of a global level econometric estimation of the determinants of wellbeing considering human, manufactured and natural capitals as well as knowledge and institutions. The global level analysis is complemented by a set of strategically chosen in-depth systems-analytical case studies in Namibia/Western Cape, Nepal, Costa Rica and historical Finland modelling the population-development-environment (PDE) interactions including feed-backs e.g. from environmental degradation to wellbeing and taking the trends of ELY in different sub-populations as sustainability criteria. They will also include stake holder involvement and science-policy interactions. This innovative inter-disciplinary cross-fertilisation can potentially make an important contribution to the current discussions about operationalizing the criteria and end goal of sustainable development and developing better human wellbeing based metrics of progress.

Keywords: indicators of quality of life,demographic metabolism,human capital formation
Innovative model-based design and operational optimization of Dissolved Air Flotation (InnoDAF)

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: Belgium
Call: Individual Fellowships (H2020-MSCA-IF-2018, 2018)
Start date: 2019-09-01
End date: 2021-08-31
Duration (months): 24.0
Total project cost (EUR): 178320.0
EU funding (EUR): 178320.0
Participants: 1
Project status: CLOSED

Water and resources recovery from sewage stand at the foreground of circular economy and technological innovation in the wastewater industry 4.0. The approach of up-concentration of municipal effluent upon arrival at the wastewater treatment facilities followed by anaerobic digestion allows closing cycles and is an alternative solution to conventional activated sludge processes, which have little or no reuse. Dissolved air flotation (DAF) has great potential as an up-concentration process, a first priority of the above-mentioned combo system. To bring the technology readiness level of DAF for up-concentration of sewage and A-sludge to a higher level to make it ready for the market, the knowledge gap in fluid mechanisms of flocculation and hydraulic performance in DAF will be addressed based on the computational fluid dynamics (CFD) modelling and integrated model framework of CFD and PBM (population balance model) and XDLVO (extended Derjaguin-Laudau-Verwey-Overbeek) forces, a totally complete bottom-up approach. Extensive validation experiments of fluid flow velocity, bubble and floc property (density, size distribution, interfacial force, etc.) and residence time distribution in bench- and pilot-scale DAF will be carried out together with the modeling work to build a simulation platform for reliable hydrodynamic prediction in DAF. Based on this platform, optimization of DAF will be carried out in terms of design and operation. A major reduction in the pretreatment flocculation times and an increase of floc stability will be achieved by optimizing contact zone, flocculator pipes and chemical dosage in sewage up-concentration. A major increase in the hydraulic loadings with flow pattern optimization will be pursued by modifying the configuration of contact and separation zone and by varying operations.

Keywords: water reuse,resource recovery,dissolved air flotation,computational fluid dynamics,population balance model,up concentration,circular economy,simulation,optimization,anaerobic digestion