Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
eWall for Active Long Living (EWALL)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: ICT for Health, Ageing Well, Inclusion and Governance
Action type: Collaborative project
Countries involved: Denmark,Italy,Croatia,Netherlands,Austria,Greece,Romania,North Macedonia,Montenegro,Bulgaria,Germany
Call:  (FP7-ICT-2013-10, 2013)
Start date: 2013-11-01
End date: 2016-10-31
Duration (months): 36.0
Total project cost (EUR): 8838005.0
EU funding (EUR): 5980000.0
Participants: 14
Project status: CLOSED

Independent living of senior citizens is one of the main challenges linked to the ageing population, due to the impact on: (a) the life of the elderly people, (b) the national health systems, (c) the insurance companies, (d) the relatives and (e) the care-givers. Senior citizens may suffer from a number of diseases, including the decline in cardiopulmonary conditions, weaker muscle functions and a declined neuromuscular control of the movements, which result in a higher risk of fall and a higher vulnerability for cardiovascular and pulmonary diseases. With respect to cognitive functions, senior citizens may suffer from a decline of memory function, less ability to orientate and a declined ability to cope with complex situations. Mild dementia is another disease affecting this population, which requires either the institutionalization or the constant support from care-givers. eWALL will be an affordable, easy-to-install prefabricated wall that can be mounted on an existing wall and includes, into the background, all the ICT technology needed to enable a number of services for the senior citizen to cover the major ontologies of Active and Healthy Ageing. The project will carry out high-risk and multi-disciplinary research and will have a large-scale demonstrator exercise for validating the concept with solid clinical evidence. This will include both technical-, user- and legal-evaluation, to measure with advanced tools and methodologies the impact on the QoL. The eWALL system will extend the state-of-the-art of Assistive Platforms and will significantly increase the independent living of seniors. The project will also perform socio-economic studies to deliver recommendations for the health sector that will result in mid- and long-term benefits for the sustainability of national health systems.

Keywords: active ageing,independent living,ict,copd,dementia,epsos
Radio technologies for broadband mm-wave satellite communication systems (DORADA)

Framework: FP7 — People
Programme: Marie-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Industry-Academia Partnerships and Pathways
Action type: Support for training and career development of researchers (Marie Curie)
Countries involved: United Kingdom,Italy
Call:  (FP7-PEOPLE-2013-IAPP, 2013)
Start date: 2014-01-01
End date: 2017-12-31
Duration (months): 48.0
Total project cost (EUR): 1542151.6
EU funding (EUR): 1542151.6
Participants: 2
Project status: CLOSED

An on-going and forecasted explosive growth of broadcast and broadband multimedia satellite communication services is putting extreme pressure to increase throughput. To meet the ever-increasing demand, future systems must exploit higher carrier frequencies and reconfigurable radio techniques. These systems are particularly demanding on the performance of RF front-ends, which are increasingly required to combine agility and adaptability with superior performance at ever higher frequencies, setting an extremely challenging landscape for the existing RF technology base while offering major prospects for high-end technology supply in both home and global markets. DORADA has the aim of fostering a strategic intersectorial alliance capable to deliver the technological leadership and innovation in future mm-wave satcom developments that will underpin new services to the consumer and strengthen the position of the EU space industry globally. Bringing together Space Engineering - a European pioneer in satcom RTD - with world leading expertise in the areas of microsystems manufacturing and microwave engineering available in the institute of sensors, signals and systems of Heriot-Watt University, DORADA nurtures an environment suitable to deliver transformative research exploiting complementarities and knowledge transfer. The main S&T goal of DORADA is to develop and implement novel antenna architectures and innovative passive and active mm-wave circuitry which will overcome present technological bottlenecks related to the development of future mm-wave very small aperture terminals (VSATs). Significantly, DORADA will benefit from and be linked to Europe’s and the World’s most recent experimental mm-wave civil communication payload, the Technology Demonstrator Payload 5 on board the ALPHASAT satellite. A key deliverable of DORADA will be a test bed demonstrator that will enable on-the-air verification of the developed technology exploiting this infrastructure.

Keywords: satellite communications systems,vsat,microwave and antennas,microfabrication,mm-wave technologies
Proactive Autonomic Management of Cloud Resources (PANACEA)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Pervasive and Trusted Network and Service Infrastructures
Action type: Collaborative project
Countries involved: France,United Kingdom,Spain,Israel,France,Germany
Call:  (FP7-ICT-2013-10, 2013)
Start date: 2013-10-01
End date: 2016-03-31
Duration (months): 30.0
Total project cost (EUR): 3172422.0
EU funding (EUR): 2130000.0
Participants: 7
Project status: CLOSED

The main objective of the project "PANACEA" is to provide Proactive Autonomic Management of Cloud Resources as a remedy to the exponentially growing complexity. If you look at the system resources (Internet) at the bottom of the stack, that system resource can be servers, storage, data centres, and network resources, the concept is then to build a level of virtualization of those resources so that any given event is not tied to one box necessarily or to one storage disk. Once you get that kind of leverage, you can build the set of functions that relate to autonomic self-* properties: configuring, healing, optimizing and protecting. The design that you have to have holistically has to deal with the fact that components are going to fail. The aim of a Cloud Computing platform is to support redundant, self-recovering, highly scalable programming models that allow workloads to recover from many inevitable hardware/software failures and monitoring resource use in real time for providing physical and virtual servers, on which the applications can run. It will propose innovative solutions for autonomic management of cloud resources, which will be based on a set of advanced Machine Learning Techniques and virtualization. A Machine Learning (ML) framework will be created for a proactive autonomic management of cloud resources. It will allow predicting the failure time of software, or user applications running on Virtual Machines (VM) and the violation of expected response time of services. To deal with the vast number of possible resources to monitor, our main approach will consider the use of mobile agents, which will move on the cloud, interacting with other agents, reading computing and network sensors, and making autonomous decisions on what to measure, when to report and to whom. Distributed Machine Learning, based on Reinforcement Learning and Neural Networks, will be used to enforce "self-organizing paths".

Keywords: proactive management,machine learning,virtualization,overlay networks,cloud computing,mobile agents,autonomic,self* properties
Learning to Create (LRN2CRE8)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: ICT for Creativity and Learning
Action type: Collaborative project
Countries involved: United Kingdom,Austria,Denmark,Spain
Call:  (FP7-ICT-2013-10, 2013)
Start date: 2013-10-01
End date: 2016-09-30
Duration (months): 36.0
Total project cost (EUR): 3427387.0
EU funding (EUR): 2499616.0
Participants: 6
Project status: CLOSED

Lrn2Cre8 aims to understand the relationship between learning and creativity by means of practical engineering, theoretical study, and cognitive comparison. We begin from the position that creativity is a function of memory, that generates new structures based on memorised ones, by processes which are essentially statistical. Thus, the project is situated in the tradition of frequentist statistical models of mind, and it builds on statistical understandings of perception of sequence (these are not naive 1st-order statistics) to consider its domain at the level of sequence processing in terms of percepts. Representations of these percepts are also learned, in parallel with the structural information in the data itself, and the guiding principle is one of information efficiency: the representations conspire to produce the most efficient possible representation of the data in memory. In Lre2Cre8, we wish to avoid the problem of extrinsic domain reasoning (e.g., physics in the real world) so we use music as our domain; extrinsic reasoning for music is small in comparison. We propose to build systems that take musical data as input, both in continuous and discrete forms, and learn the necessary representations and structure to memorise it efficiently. We hypothesise that this is a cognitive model of human musical behaviour, and we will test our hypthesis though empirical studies and experiments that compare the behaviour of our computational models with human behaviours. We will study the relationship between our well-understood and cognitively validated learning mechanisms and creative behaviour, in musical composition and performance. We aim to devise new methods for evaluation of creative behaviour in machines and humans, and to apply them, comparatively, to creative processes and outputs of the project. We aim ultimately to produce music which will be of genuine interest to society, and we will launch a record label to promote it as part of our evaluation methods.

Keywords: computational creativity,machine learning,music composition and performance
Semi-Autonomous 3rd Hand (3RD HAND)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Cognitive Systems and Robotics
Action type: Collaborative project
Countries involved: France,Germany,Austria,Germany
Call:  (FP7-ICT-2013-10, 2013)
Start date: 2013-10-01
End date: 2017-09-30
Duration (months): 48.0
Total project cost (EUR): 3379167.0
EU funding (EUR): 2549815.0
Participants: 5
Project status: CLOSED

Robots have been essential for keeping industrial manufacturing in Europe. Most factories have large numbers of robots in a fixed setup and few programs that produce the exact same product hundreds of thousands times. The only common interaction between the robot and the human worker has become the so-called “emergency stop button”. As a result, re-programming robots for new or personalized products has become a key bottleneck for keeping manufacturing jobs in Europe. The core requirement to date has been the production in large numbers or at a high price. Robot-based small series production requires a major breakthrough in robotics: the development of a new class of semi-autonomous robots that can decrease this cost substantially. Such robots need to be aware of the human worker, alleviating him from the monotonous repetitive tasks while keeping him in the loop where his intelligence makes a substantial difference. In this project, we pursue this breakthrough by developing a semi-autonomous robot assistant that acts as a third hand of a human worker. It will be straightforward to instruct even by an untrained layman worker, allow for efficient knowledge transfer between tasks and enable a effective collaboration between a human worker with a robot third hand. The main contributions of this project will be the scientific principles of semi-autonomous human-robot collaboration, a new semi-autonomous robotic system that is able to: i) learn cooperative tasks from demonstration; ii) learn from instruction; and iii) transfer knowledge between tasks and environments. We will demonstrate its efficiency in the collaborative assembly of an IKEA-like shelf where the robot acts as a semiautonomous 3rd-Hand .

Keywords: robot learning,human-robot cooperation,motor learning,active learning,robot transfer learning,hierarchical reinforcement learning,learning from instruction,cooperative systems
Clinical Intervention Modelling, Planning and Proof for Ablation Cancer Treatment (CLINICIMPPACT)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: ICT for Health, Ageing Well, Inclusion and Governance
Action type: Collaborative project
Countries involved: Germany,Netherlands,Germany,Austria,Finland,Ireland
Call:  (FP7-ICT-2013-10, 2013)
Start date: 2014-02-01
End date: 2017-07-31
Duration (months): 42.0
Total project cost (EUR): 3708135.0
EU funding (EUR): 2843000.0
Participants: 8
Project status: CLOSED

The ClinicIMPPACT proposal builds upon the successful completion of the IMPPACT project (Grant No. 223877, completed in February 2012) that resulted in the creation of a physiological radio-frequency ablation (RFA) model for liver cancer treatment. This preliminary RFA model has been tested in porcine animal studies with extensive histological workup and in a clinical study using patient data for retrospective simulations. The main objectives of this proposal are: (i) to bring the existing IMPPACT RFA model for liver cancer treatment into clinical practice; (ii) to verify and refine the model in a small clinical study; (iii) to develop the model into a real-time patient-specific RFA planning and support system for Interventional Radiologists (IR) under special consideration of their clinical workflow needs; (iv) to establish a corresponding training procedure for IR’s; (v) to evaluate the practicality and benefit of the model for routine clinical purposes by analyzing user surveys and running expert forums. The proposed RFA planning and support tool is therefore unique as it offers a validated software environment, where IRs can interact during the RFA treatment with the virtual tumour ablation through the extensive use of simulation and visualisation technology.

Keywords: liver cancer radio-frequency ablation (rfa) rfa simulation model patient-specific rfa protocol visualization interface clinical study and validation training of medical personnel
Making Sense of Human-Human Conversation Data (SENSEI)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Technologies for Digital Content and Languages
Action type: Collaborative project
Countries involved: Italy,France,United Kingdom,United Kingdom,Spain
Call:  (FP7-ICT-2013-10, 2013)
Start date: 2013-11-01
End date: 2016-10-31
Duration (months): 36.0
Total project cost (EUR): 3560044.0
EU funding (EUR): 2650000.0
Participants: 6
Project status: CLOSED

The overall goals of the SENSEI project are twofold. First, SENSEI will develop summarization/analytics technology to help users make sense of human conversation streams from diverse media channels. Second, SENSEI will design and evaluate its summarization technology in ecological environments, aiming to improve task performance and productivity of end-users. Conversational interaction is the most natural and persistent paradigm for business relations with end-customers or users. In contact centres millions of customer spoken conversations are handled daily. On social media platforms hundreds of millions of blog posts are delivered through generalist or proprietary platforms. In both cases, conversations have little impact on the intended target “listeners” due to the volume, velocity and diversity (media, style, social context) of the document streams (spoken conversations and blog posts). Most language analytics technology is limited in that it performs keyword search, which does not provide automatic descriptions of what happened, who said what, which opinions are held on what subject, in a coherent, readable and executable form. In the SENSEI project we plan to go beyond keyword search and sentence based analysis of conversations. We will design and adapt lightweight and large coverage linguistic models of semantic and discourse resources to learn a layered model of conversations. SENSEI will address the issue of multidimensional textual, spoken and metadata descriptors in terms of semantic, para-semantic and discourse structures. The combination of supervised and unsupervised learning techniques will support the scaling and adaptation of such computational models to the diversity of the conversation data. Automated generation of readable analytics documents (summaries) will support end-users in the context of large data analysis tasks. Summarization technology developed in SENSEI will be evaluated with respect to user’s productivity in the context of ecological scenarios, specifically, call centre and social media conversation analysis.

Keywords: human communication,language and behaviour analytics,summarization technology
Sustainable and reliable robotics for part handling in manufacturing automation (STAMINA)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Cognitive Systems and Robotics
Action type: Collaborative project
Countries involved: Denmark,France,France,Germany,Germany,Portugal,United Kingdom
Call:  (FP7-ICT-2013-10, 2013)
Start date: 2013-10-01
End date: 2017-03-31
Duration (months): 42.0
Total project cost (EUR): 6277472.0
EU funding (EUR): 4550000.0
Participants: 8
Project status: CLOSED

Part handling during the assembly stages in the automotive industry is the only task with automation levels below 30% due to the variability of the production and to the diversity of suppliers and parts. The full automation of such task will not only have a huge impact in the automotive industry but will also act as a cornerstone in the development of advanced mobile robotic manipulators capable of dealing with unstructured environments, thus opening new possibilities in general for manufacturing SME's. The STAMINA project will use a holistic approach by partnering with experts in each necessary key fields, thus building on previous R&D to develop a fleet of autonomous and mobile industrial robots with different sensory, planning and physical capabilities for jointly solving three logistic and handling tasks: De-palletizing, Bin-Picking and Kitting. The robot and orchestration systems will be developed in a lean manner using an iterative series of development and validation testes that will not only assess the performance and usability of the system but also allow goal-driven research. STAMINA will give special attention to the system integration promoting and assessing the development of a sustainable and scalable robotic system to ensure a clear path for the future exploitation of the developed technologies. In addition to the technological outcome, STAMINA will allow to give an impression on how a sharing of work and workspace between humans and robots could look in the future.

Keywords: autonomous robot,automation,part handling,manufacturing,planning
Tactile Manipulation (TACMAN)

Framework: FP7 — Cooperation
Programme: Information and Communication Technologies
Destination: Cognitive Systems and Robotics
Action type: Collaborative project
Countries involved: Germany,Germany,Sweden,Italy
Call:  (FP7-ICT-2013-10, 2013)
Start date: 2014-01-01
End date: 2016-12-31
Duration (months): 36.0
Total project cost (EUR): 3159800.0
EU funding (EUR): 2399458.0
Participants: 4
Project status: CLOSED

TACMAN addresses the key problem of developing an information processing and control technology enabling robot hands to exploit tactile sensitivity and thus become as dexterous as human hands. The current availability of the required technology now allows us to considerably advance in-hand manipulation. TACMAN’s goal is to develop fundamentally new approaches which can replace manual labor under inhumane conditions by endowing robots with such tactile manipulation abilities, by transferring insights from human neuroscientific studies into machine learning algorithms. TACMAN will provide an innovative new technology that is key for bringing industrial manufacturing back to Europe. Consider the case of the iPhone, where most mechanical manipulation of the major components is achieved by manual human labor under terrible work conditions and not by advanced industrial robots—despite that millions of iPhones are industrially assembled per month. The reason for this absence of appropriate automation is the lack of manipulation skills of current robots. Commercially available robotic hand-arm systems move more accurately and faster than humans, and their sensors see more and at a higher precision—even the smallest forces and torques can be detected. Despite these impressive sensori-motor abilities, current robots are terrible at manipulation when compared to humans. Neuro- science provides a clear reason for the superiority of human hands: During manipulation, humans make substantial use of the data from tactile sensors, i.e., the information obtained through the feeling in the human’s fingers. Robot hands are lacking this key ability! Hence, the rationale of TACMAN is that this performance gap in manipulation ability can be filled by (1) making such tactile sensory comprehensible, and (2) use the information provided by such sensors intelligently for behavior generation. TACMAN aims to integrate the most robust available tactile sensors into the control of existing modern robot hands, and, based on this control law, develop tactile sensor-based manipulation solutions. To make this innovation tractable in a three year project, we aim only on recognising and handling objects that are already in the hand. The structure of the project is designed to allow quick scaling from straightforward, well-captured scenarios employing a single finger to complex multi-fingered manipulation.

Keywords: manipulation,robotics,tactile sensing