Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Epidemiological study to quantify risks for paediatric computerized tomography and to optimise doses. (EPI-CT)

Framework: FP7 — Euratom
Programme: Nuclear fission and radiation protection
Theme: Radiation protection
Destination: Radiation Protection
Action type: Collaborative project
Countries involved: France,Germany,Finland,Sweden,United Kingdom,France,Denmark,Netherlands,Belgium,France,Luxembourg,Norway,Belgium,Germany,Spain
Call:  (FP7-Fission-2010, 2010)
Start date: 2011-02-01
End date: 2017-01-31
Duration (months): 72.0
Total project cost (EUR): 7039081.4
EU funding (EUR): 2998594.0
Participants: 20
Project status: CLOSED

Diagnostic radiation represents an indispensable, sometimes life-saving, tool in modern medicine. However, the growing use of computerized tomography (CT) is a topic of concern in radiological protection, especially for children and adolescents. Children are generally more sensitive to the carcinogenic effects of ionizing radiation than adults. In addition, they have a longer life-span to express any effect and, because of their smaller mass, they may receive higher radiation doses from a CT scan than an adult. A large-scale multinational collaborative study will be set up with the objective of providing guidance towards optimisation of doses from paediatric CT scans. We have the following specific aims: 1) describe the pattern of use of CT in different countries and over time; 2) derive individual estimates of organ doses ; 3) assess biological markers of CT-irradiation effects; 4) directly evaluate radiation-related risk of cancer following CT; and 5) characterize the quality of CT images in relation to the estimated doses in order to better inform CT imaging practice. Scientists from nine European countries with expertise in epidemiology, clinical practice, radiology, dosimetry, biology and public health will contribute to the project with the objective of provide recommendations for a “harmonized” approach to CT dose optimisation for paediatric patients in Europe. Results of this research will serve to increase awareness of the scientific and medical communities about public health aspects related to the use of diagnostic radiation and to provide recommendations on the use of valuable diagnostic tools, with lowering the risk of its potential hazards as much as possible.

Keywords: computerized tomography,radiation risk,low dose,individual sensitivity,paediatric
Robotics UBIquitous COgnitive Network (RUBICON)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Cognitive systems, interaction, robotics
Action type: Collaborative project
Countries involved: Ireland,Italy,Sweden,Spain,Spain,United Kingdom
Call:  (FP7-ICT-2009-6, 2009)
Start date: 2011-04-01
End date: 2014-03-31
Duration (months): 36.0
Total project cost (EUR): 3308140.0
EU funding (EUR): 2506767.0
Participants: 10
Project status: CLOSED

This project will create a self-learning robotic ecology, called RUBICON (for Robotic UBIquitous COgnitive Network), consisting of a network of sensors, effectors and mobile robot devices. Enabling robots to seamlessly operate as part of these ecologies is an important challenge for robotics R&D, in order to support applications such as ambient assisted living, security, etc. Current approaches heavily rely on models of the environment and on human configuration and supervision and lack the ability to smoothly adapt to evolving situations. These limitations make these systems hard and costly to deploy and maintain in real world applications, as they must be tailored to the specific environment and constantly updated to suit changes in both the environments and in the applications where they are deployed. A RUBICON ecology will be able to teach itself about its environment and learn to improve the way it carries out different tasks. The ecology will act as a persistent memory and source of intelligence for all its participants and it will exploit the mobility and the better sensing capabilities of the robots to verify and provide the feedback on its own performance. As the nodes of a RUBICON ecology will mutually support one another’s learning, the ecology will identify, commission and fulfil tasks more effectively and efficiently. The project builds on many years of experience across a world-leading consortium. It combines robotics, multi-agent systems, novelty detection, dynamic planning, statistical and computational neuroscience methods, efficient component & data abstraction, robot/WSN middleware and three robotic test-beds. Validation will take place using two application scenarios Impact: The project will reduce the amount of preparation and pre-programming that robotic and/or wireless sensor network (WSN) solutions require when they are deployed. In addition, RUBICON ecologies will reduce the need to maintain and re-configure already-deployed systems, so that changes in the requirements of such systems can be easily implemented and new components can be easily accommodated. The relative intelligence and mobility of a robot, when compared to those of a typical wireless sensor node, means that WSN nodes embedded in a RUBICON ecology can learn about their environment and their domain application, through the ‘training’ that is provided by the robot. This means that the quality of service which is offered by WSNs can be significantly improved, without the need for extensive human involvement.

Keywords: robotics,cognition,distributed intelligence and memory,mutually supportive learning
Digital Preservation for Timeless Business Processes and Services (TIMBUS)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Digital libraries and content
Action type: Collaborative project
Countries involved: Germany,Germany,Portugal,Germany,United Kingdom,Austria,Germany,Germany,Ireland
Call:  (FP7-ICT-2009-6, 2009)
Start date: 2011-04-01
End date: 2014-12-31
Duration (months): 45.0
Total project cost (EUR): 11548030.0
EU funding (EUR): 7778616.0
Participants: 13
Project status: CLOSED

The digital preservation problem is well-understood for query-centric information scenarios but has been less explored for scenarios where the important digital information to be preserved is the execution context within which data is processed, analysed, transformed and rendered. Furthermore, preservation is often considered as a set of activities carried out in the isolation of a single domain, without considering the dependencies on third-party services, information and capabilities that will be necessary to validate digital information in a future usage context. TIMBUS will endeavour to enlarge the understanding of DP to include the set of activities, processes and tools that ensure continued access to services and software necessary to produce the context within which information can be accessed, properly rendered, validated and transformed into knowledge. One of the fundamental requirements is to preserve the functional and non-functional specifications of services and software, along with their dependencies.

Keywords: digital preservation,business continuity management,saas,ios,iot,business process
Intelligent observation and execution of Actions and manipulations (INTELLACT)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Cognitive systems, interaction, robotics
Action type: Collaborative project
Countries involved: Denmark,Germany,Spain,Austria,Germany,Slovenia
Call:  (FP7-ICT-2009-6, 2009)
Start date: 2011-03-01
End date: 2014-02-28
Duration (months): 36.0
Total project cost (EUR): 3899781.0
EU funding (EUR): 2959592.0
Participants: 6
Project status: CLOSED

IntellAct addresses the problem of understanding and exploiting the meaning (semantics) of manipulations in terms of objects, actions and their consequences for reproducing human actions with machines. This is in particular required for the interaction between humans and robots in which the robot has to understand the human action and then to transfer it to its own embodiment. IntellAct will provide means to allow for this transfer not by copying movements of the human but by transferring the human action on a semantic level. IntellAct will demonstrate the ability to understand scene and action semantics and to execute actions with a robot in two domains. First, in a laboratory environment (exemplified by a lab in the International Space Station (ISS)) and second, in an assembly process in an industrial context. IntellAct consists of three building blocks: (1) Learning: Abstract, semantic descriptions of manipulations are extracted from video sequences showing a human demonstrating the manipulations; (2) Monitoring: In the second step, observed manipulations are evaluated against the learned, semantic models; (3) Execution: Based on learned, semantic models, equivalent manipulations are executed by a robot. The analysis of low-level observation data for semantic content (Learning) and the synthesis of concrete behaviour (Execution) constitute the major scientific challenge of IntellAct. Based on the semantic interpretation and description and enhanced with low-level trajectory data for grounding, two major application areas are addressed by IntellAct: First, the monitoring of human manipulations for correctness (e.g., for training or in high-risk scenarios) and second, the efficient teaching of cognitive robots to perform manipulations in a wide variety of applications. To achieve these goals, IntellAct brings together recent methods for (1) parsing scenes into spatio-temporal graphs and so-called „semantic Event Chains‟, (2) probabilistic models of objects and their manipulation, (3) probabilistic rule learning, and (4) dynamic motion primitives for trainable and flexible descriptions of robotic motor behaviour. Its implementation employs a concurrent-engineering approach that includes virtual-reality-enhanced simulation as well as physical robots. Its goal culminates in the demonstration of a robot understanding, monitoring and reproducing human action.

Keywords: machine learning,recognition and monitoring,robot execution,probabilistic rule learning,semantic event chain
AXES - Access to Audiovisual Archives (AXES)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Digital libraries and content
Action type: Collaborative project
Countries involved: France,Belgium,United Kingdom,France,Ireland,Germany,Netherlands,United Kingdom,Netherlands,Germany,France,Netherlands
Call:  (FP7-ICT-2009-6, 2009)
Start date: 2011-01-01
End date: 2015-03-31
Duration (months): 51.0
Total project cost (EUR): 8347341.0
EU funding (EUR): 5900000.0
Participants: 13
Project status: CLOSED

The goal of AXES is to develop tools that provide various types of users with new engaging ways to interact with audiovisual libraries, helping them discover, browse, navigate, search and enrich archives. In particular, apart from a search-oriented scheme, we will explore how suggestions for audiovisual content exploration can be generated via a myriad of information trails crossing the archive. This will be approached from three perspectives (or axes): users, content, and technology. Within AXES innovative indexing techniques are developed in close cooperation with a number of user communities through tailored use cases and validation stages. Rather than just starting new investments in technical solutions, the co-development is proposed of innovative paradigms of use and novel navigation and search facilities. We will target media professionals, educators, students, amateur researchers, and home users. Based on an existing Open Source service platform for digital libraries, novel navigation and search functionalities will be offered via interfaces tuned to user profiles and workflow. To this end, AXES will develop tools for content analysis deploying weakly supervised classification methods. Information in scripts, audio tracks, wikis or blogs will be used for the cross-modal detection of people, places, events, etc., and for link generation between audiovisual content. Users will be engaged in the annotation process: with the support of selection and feedback tools, they will enable the gradual improvement of tagging performance. AXES technology will open up audiovisual digital libraries, increasing their cultural value and their exposure to the European public and academia at large. The consortium is a perfect match to the multi-disciplinary nature of the project, with professional content owners, academic and industrial experts in audiovisual analysis, retrieval, and user studies, and partners experienced in system integration and project management.

Keywords: experiencing audiovisual digital libraries,multimodal content analysis,collaborative use
Spatial & Personal Adaptive Communication Environment: Behaviors & Objects & Operations & Knowledge (SPACEBOOK)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Cognitive systems, interaction, robotics
Action type: Collaborative project
Countries involved: Sweden,United Kingdom,Sweden,United Kingdom,Spain
Call:  (FP7-ICT-2009-6, 2009)
Start date: 2011-03-01
End date: 2014-04-30
Duration (months): 38.0
Total project cost (EUR): 3674051.0
EU funding (EUR): 2799551.0
Participants: 7
Project status: CLOSED

The SpaceBook project will prototype a speech-driven, hands-free, eyes-free device for pedestrian navigation and exploration. SpaceBook will be developed as open source and progress will be bench marked through controlled task-based experiments with real users in central Edinburgh. The SpaceBook project will generate concrete technical and scientific advances for eyes-free, hands-free navigation and exploration systems which will support applications in tourism, elderly care and tools for urban workers. In addition to advances in navigation and exploration systems, SpaceBook provides a task environment in which more fundamental scientific and technical knowledge will be generated. Specifically we seek to advance the state of the art in model-based approaches to plan generation and recognition, statistical learning techniques for interaction management and machine learning of semantic analysis components. The interdisciplinary SpaceBook team brings a wealth of complementary expertise necessary to realize the SpaceBoook vision. Umeå University (Sweden), the project's overall coordinator, brings expertise in natural language interfaces to database systems and spatial databases. The University of Edinburgh (UK) brings expertise in location-based services, dynamic 3D modelling, geographical information systems, system evaluation methodology, discourse processing, and information access and delivery (including question answering). Heriot Watt University (UK) brings experience in machine learning, spoken dialogue systems, data-collection, and evaluation of interactive systems. KTH (Sweden) brings expertise in dialogue management and the design and development of spoken dialogue systems. Our industrial partner, Liquid Media (Sweden), brings software engineering and commercial exploitation expertise and experience. Cambridge University (UK) brings expertise in the combination of linguistic theory and machine learning and the development of real-world language processing applications. Finally, the AI group from the Universitat Pompeu Fabra (Spain) brings expertise in the areas of autonomous behavior, and in particular, model-based methods for plan generation and plan recognition.

Keywords: machine learning,behaviour recognition,spatial cognitive models,adaptivity,modular architectures,robustness,location-based services
Promoting and Monitoring Biomedical Informatics in Europe (INBIOMEDVISION)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Towards sustainable and personalised healthcare
Action type: Coordination and support actions
Countries involved: Spain,Denmark,Netherlands,Spain,United Kingdom
Call:  (FP7-ICT-2009-6, 2009)
Start date: 2011-02-01
End date: 2013-07-31
Duration (months): 30.0
Total project cost (EUR): 1165389.0
EU funding (EUR): 998000.0
Participants: 7
Project status: CLOSED

Biomedical Informatics deals with the integrative management and synergic exploitation of the wide ranging and inter-related scope of information that is generated and needed in healthcare settings, biomedical research institutions and health-related industry. Some key present challenges of Biomedical Informatics are: - The effective and synergic integration between computational methods and technologies supporting life sciences research (Bioinformatics) and the informatics supporting healthcare and medical research (Health or Medical Informatics). - The development of effective translational knowledge management approaches facilitating the application of knowledge arising from basic biomedical research into clinical practice. - The integration and joint exploitation of heterogeneous information stored in widespread repositories and diverse formats. - The development of innovative methods for the modelling and simulation of complex biological phenomena, as well as the corresponding computational applications. - The intersection between neuroscience and informatics (neuroinformatics). In this context, INBIOMEDvision aims to become a European-wide initiative intended to monitor the evolution of the Biomedical Informatics field and address its scientific challenges by means of collaborative efforts performed by a broad group of experts with complementary perspectives on the field. These efforts will certainly contribute to the strength and expansion of the Biomedical Informatics scientific community, particularly in Europe. INBIOMEDvision will develop a series of services and activities to serve the aforementioned purposes (inventory of resources and initiatives, state of the art reviews, prospective analyses, community-building actions and dissemination and training activities).

Keywords: biomedical informatics,translational bioinformatics,personalised medicine,virtual physiological human,scientific monitoring,state-of-the-art analysis,scientific prospective analysis
Airway Disease PRedicting Outcomes through Patient Specific Computational Modelling (AIRPROM)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Towards sustainable and personalised healthcare
Action type: Collaborative project
Countries involved: United Kingdom,Germany,Netherlands,United Kingdom,United Kingdom,United Kingdom,United Kingdom,United Kingdom,France,United Kingdom,Spain,Belgium,United Kingdom,Poland,Switzerland,United Kingdom,Italy,Hungary,France,Sweden,Sweden,Italy,Belgium,Poland,Sweden,Belgium,Belgium,Germany
Call:  (FP7-ICT-2009-6, 2009)
Start date: 2011-03-01
End date: 2016-06-30
Duration (months): 64.0
Total project cost (EUR): 15535205.0
EU funding (EUR): 11713194.0
Participants: 38
Project status: CLOSED

The airways diseases asthma and chronic obstructive pulmonary disease affect over 400 million people world-wide and cause considerable morbidity and mortality. Airways disease costs the European Union in excess of €56 billion per annum. Current therapies are inadequate and we do not have sufficient tools to predict disease progression or response to current or future therapies. Our consortium, Airway Disease PRedicting Outcomes through Patient Specific Computational Modelling (AirPROM), brings together the exisiting clinical consortia (EvA FP7, U-BIOPRED IMI and BTS Severe Asthma), and expertise in physiology, radiology, image analysis, bioengineering, data harmonization, data security and ethics, computational modeling and systems biology. We shall develop an integrated multi-scale model building upon existing models. This airway model will be comprised of an integrated ‘micro-scale’ and ‘macro-scale’ airway model informed and validated by ‘omic data and ex vivo models at the genome-transcriptome-cell-tissue scale and by CT and functional MRI imaging coupled to detailed physiology at the tissue-organ scale utilising Europe’s largest airway disease cohort. Validation will be undertaken cross-sectionally, following interventions and after longitudinal follow-up to incorporate both spatial and temporal dimensions. AirPROM has a comprehensive data management platform and a well-developed ethico-legal framework. Critically, AirPROM has an extensive exploitation plan, involving at its inception and throughout its evolution those that will ‘develop’ and ‘use’ the technologies emerging from this project. AirPROM therefore will bridge the critical gaps in our clinical management of airways disease, by providing validated models to predict disease progression and response to treatment and the platform to translate these patient-specific tools, so as to pave the way to improved, personalised management of airways disease.

Keywords: modelling,asthma,copd,airways,mri,personalized medicine,predictive,cloud computing
Crop Monitoring as an E-agriculture tool in developing countries (E-AGRI)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Horizontal support actions
Action type: Collaborative project
Countries involved: Belgium,Netherlands,Italy,European organisations area,Morocco,China,Kenya
Call:  (FP7-ICT-2009-6, 2009)
Start date: 2011-02-01
End date: 2014-01-31
Duration (months): 36.0
Total project cost (EUR): 2302113.0
EU funding (EUR): 1618000.0
Participants: 9
Project status: CLOSED

This proposal is introduced to address the “Target Outcome B” stated in the Objective ICT-2009.1, namely the support to the uptake of European ICT research results in developing economies. The objective can be realized by setting up an advanced European E-agriculture service in two developing economies, Morocco and China, by means of crop monitoring. The activities of capacity building will be carried out in the third developing country, Kenya, to raise the interest of local stakeholders on European E-agricultural practices and to pave the way for an eventual technological transfer in the future. The European research institutions including VITO, Alterra, JRC and University of Milan, have developed series of agricultural monitoring approaches to support European Common Agriculture Policy (CAP). These approaches are based on the European Information and Communication technologies including space-based Earth Observation (EO), geographical information systems and agro-meteorological modelling. The transfer, adaptation and local application of these E-agriculture practices will assist the policy makers of developing countries in their challenge to sustain agriculture’s productivity growth. It helps to achieve the food security, increase farmer incomes and protect local farmer interest, in the context of agricultural liberalization, which is a fiercely disputed issue in the forums of the World Trade Organization. On the other hand, the feedback from this proposed action will enhance the applicability of European crop production forecasting technology on a global scale, thus ultimately strengthen its capacity in global monitoring of food security.

Keywords: european ict research,e-agriculture,crop monitoring,agricultural intelligency