Digital transition

Development of solutions for the major cybersecurity challenges specific to Cloud environments, to ensure the confidentiality, integrity and availability of data, applications and services.

In healthcare, cybersecurity is at the heart of the challenges of artificial intelligence (AI) empowered by massive multiscale data and new medical practices. It is also imposed by means of a complex broad set of strict ethic and legislative regulations. On one hand, the security of data should be ensured whatever their nature, transmission, processing and transformations they went through. On the other hand, methods that will be applied to exploit these data must themselves be safe. Healthcare AI systems are considered as of high risk by EU.

The healthcare sector (public and private) generates a vast amount of data from various sources, including electronic health records, advanced imaging techniques, high throughput sequencing, wearable devices, and population health data. The use of massive datasets, or “big data,” analyzed using sophisticated machine learning algorithms, has the potential to inform the development of more effective and personalized treatments, interventions, and policies, and to improve healthcare delivery and outcomes.However, the sensitive nature of personal health data, cybersecurity risks, biases in the data, and the lack of robustness of machine learning algorithms are all factors that currently limit the widespread use and exploitation of this data. These limitations thus hinder the potential benefits that can be obtained from massive health data analysis for the individuals and society.

A hardware infrastructure for the scientific study of the intersecting issues of IT infrastructures supporting artificial intelligence (from the IoT to the Cloud) and their energy consumption.

MATISSE is a European HORIZON-KDT-JU research project bringing together over 30 partners from 7 countries in order to develop an advanced approach for efficient engineering and validation of industrial systems using Digital Twins (DTs). By integrating DTs with state-of-the-art technologies, MATISSE aims to better simulate, test, and predict system behaviours. This innovative approach helps companies optimise their industrial processes, reduce errors, and boost productivity, ultimately simplifying complex operations. To this end, MATISSE proposes to create a framework incorporating methods and tools for the efficient and continuous engineering and validation of industrial systems that are supported by DTs. The project notably leverages the advantages of model-based, data-driven, and cloud techniques to enable validation and verification services that improve productivity and quality significantly.

Because of their necessary openness and economic value, networks are prime targets for attackers. The HiSec project is developing new methods and tools to secure the networks of the future. 

The SuperviZ project is part of the "system security" axis of the PEPR cybersecurity program. It addresses the field of "system, software and network security". More precisely, it targets the detection, response and remediation to computer attacks, subjects grouped under the name of "security supervision".

The digitization of all infrastructures makes it almost impossible today to secure all systems a priori, as it is too complex and too expensive. Supervision seeks to reinforce preventive security mechanisms and to compensate 
 

The Train-Cyber-Expert project aims to build teaching resources, in the form of digital content and technology platforms, organized by skill block, with a view to modularity, reusability and skills-based teaching leading to certification.
These resources will be deployed by partners to enhance or reinforce their training offerings. As the core business of the academic partners is the awarding of Master's degrees (Bac+5, level 7), their main training offer will be geared towards this level of qualification. They will therefore develop a range of master's degrees, specialized master's degrees, specialized engineers in cybersecurity and post-master's degrees, with an emphasis on work-study programs to facilitate the professional integration of participants.

The industry of the future invites itself into the metaverse

The metaverse is no longer limited to online gaming or social interaction: it is becoming a key technology for industry. Through the 5GMetaverse project, five Institut Mines-Télécom schools are seeking to adapt tomorrow's networks to the needs of augmented and virtual reality. In particular, the project aims to develop concrete solutions for industrial process optimization, remote assistance and man-machine collaboration.

 

The main objective of the DISPLAY project is the development of a proof of concept of two demonstrators of a new energy-saving LCD technology that is based on the ultra-low elasticity recently discovered with bent-shaped liquid-crystalline compounds.

MODES focus on the secure evolution of complex software-intensive systems (e.g., smart factories, Cloud, IoT and AI based systems) that operate in uncertain, ever-changing environments. Any system may need to review its security with respect to a set of evolution events. Including: the emergence of new functional and non-functional requirements; execution context changes; configuration changes; Dealing with such evolution events in the aforementioned complex systems require a switch from the opportunistic and isolated evolution of system artefacts (architecture models, threat models, change management policies, formal verification models, etc.) to a process of planned evolution able to take into account the system as a whole. MODES fill this gap by proposing a new model-based evolution process capable of synchronizing security-related changes across the different artefacts representing a system, calculate impacts with respect to security properties and propose countermeasures.
 

SEED, which stands for Societal, Energy, Environmental, industrial and Digital transitions, is a 60-month interdisciplinary, international and intersectoral doctoral training programme offered by IMT Atlantique and co-funded by the European Union. The programme itself is designed to nurture four key dimensions: thesis interdisciplinarity, internationality, cross-sector experience, and promotion of innovation. It offers 40 fully funded early-stage researcher (ESR) positions within three different tracks. Each track builds on the same fundamental excellence trainings implementing a 4i approach (Interdisciplinarity, Internationality, Intersectorial, Innovation), while providing a different degree of mobility and focus:
 

MaaS to increase the European industry’s competitiveness

Issues in surgery can be related to collaboration problems such as miscommunication and misunderstanding. Analysis of these collaborative situations can be done by experts, but it is a long and complex process. Moreover, this approach does not allow for real-time analysis and feedback on the collaboration. Some recent approaches aim to assess and model the quality of collaboration using measures that can be computed in real-time (e.g., non-verbal, gaze, physiological data). Considering some of the challenges related to the evaluation and modeling of collaboration, the SAAC project aims to compute metrics in real-time to report on the quality of collaboration within a group. The ultimate goal of SAAC is for the system to be seen as a collaborating system by the team, able to interact with the team when detecting miscommunication or miscoordination to prevent some adverse events.

The CyberSecDome project offers a proactive solution to protect digital infrastructures against cyber threats. It comprises four key elements: a digital infrastructure, digital twins, AI-powered security tools and a VR user interface. This approach ensures business continuity despite potential cyberattacks, facilitates safe training and testing, and enhances response capabilities through an interactive VR interface. Interconnected CyberSecDomes form a global network for effective collaboration, threat identification and comprehensive response strategies, while guaranteeing secure, privacy-friendly data exchange.

The HyMES project explores hybrid modeling solutions to address the growing complexity of multi-energy systems and networks. By combining physical and data-based models, the project aims to improve the representation of energy dynamics and address the challenges of uncertainties and non-linearities in system models. HyMES also aims to develop a reference model for multi-energy networks.

"Intelligent" video surveillance, facial recognition, predictive mapping, etc. are all devices that renew police action. They are supposed to build “safe cities” / “safe cities” thanks to the contributions of artificial intelligence (AI). Three main questions articulate the research:

1) Following the hypothesis of a "scientification" of police knowledge, we will wonder how algorithmic processes change the modes of knowledge and representation of crime phenomena: do we go from a police of information to "big data policing"?

2) Do these tools lead public security policies towards predictive action or are they above all tools for rationalizing police activity (by providing the police hierarchy with more precise indicators on the action field police officers)?

3) Finally, it is a question of analyzing how the debate on AI tools for urban security purposes – raising strong issues of public freedoms and the protection of personal data – pushes researchers and IT developers to integrate political and social issues into their scientific and technical productions.

The HOPES project funded by the ANR 2021 with 270 KE is led by a young researcher RESTREPO RUIZ Maria-Isabel on the planning of work schedules and employee rounds over a multi-period horizon for home-based services. HOPES will propose innovative decision support tools to solve these complex problems by taking into account work rules, individual preferences and different sources of uncertainty. To do so, we will rely on the formulation and design of new optimization approaches integrating techniques from data science and deterministic and stochastic optimization. Instead of proposing a method that works only for a specific application, HOPES aims to propose a general framework that can be extended to solve different variants of the problem. The decision support tools developed are expected to improve the quality of service for users, the well-being of employees, and to improve the planning and execution of home service operations.

The ProPruNN project aims to use structured pruning in a  hardware-algorithm co-design methodology to improve hardware  implementation of Convolutional Neural Networks (CNNs). The first  sub-hypothesis of the project suggests that designing hardware  architectures that take advantage of structured pruning leads to  significant gains in latency, throughput, and power metrics. However,  this may be complicated in more complex networks like ResNets and  DenseNets, which require rearrangements after filter removal. The second  sub-hypothesis is that it is possible to predict the performance of  pruned networks in terms of accuracy and power, throughput, and latency  metrics. The final sub-hypothesis is that prediction models can be used  to design state-of-the-art networks and push the Pareto frontier of  accuracy vs implementation performances of current literature. The project aims to improve the results of this method by  using a finer and more efficient pruning approach.

SLICES is a unique initiative in the digital sciences. It aims to provide a large-scale scientific instrument to support research activities addressing issues around the design and management of digital infrastructures. SLICES entered the ESFRI roadmap at the end of 2021, successfully moving from the design phase to the preparation phase. More importantly, the vibrant SLICES community is growing, attracting increasing interest and support from key players in research and industry. SLICES-PP ("preparatory phase") addresses all key issues concerning the legal, financial, and technical issues leading to the establishment of the new SLICES research infrastructure and securing the commitment of the Member States/Associated Countries to its long-term operation and use in all fields of science.

To set out future-proof pathways to strengthen democracy through improving accountability, transparency and effectiveness of media production and expanding active and inclusive citizenship, the project aims to clarify the extent to which European media perform their democratic functions. By applying an innovative multi-method design the project will cover (1) perspectives of both representative and participatory notions of democracy as they exist in European societies, (2) the entire range of news media, regardless of distribution channel, ownership and source of financing, (3) the legal and (self-)regulatory framework under which media houses and journalism operate, (4) the media's potential to promote and support political participation (supply side), and (5) the media use patterns, communication needs and democratic attitudes of the audiences (demand side) in the EU Member States.

While machine learning (ML) can lead to great advancements with respect to digital services and applications in the field of medicine, the training process based on real medical patient data is blocked by the fact that uncontrolled access to and exposure of such assets is not allowed by data protection legislation. The EU-funded PAROMA-MED project aims to develop novel technologies, tools, services and architectures for patients, health professionals, data scientists and health domain businesses so that they will be able to interact in the context of data and ML federations according to legal constraints and with complete respect to data owners rights from privacy protection to fine grained governance, without performance and functionality penalties of ML/AI workflows and applications.

One-stop shop to accelerate the digitization and Cybersecurity of SMEs/MCs/PSOs through training, test before invest, support to find investment and EU networking services

Gathering 31 partners from 10 different countries, DECARBOMILE aims to trigger an unprecedented improvement of the green last mile logistics in Europe. To reach that goal, DECARBOMILE relies on a strong experience of decarbonating urban logistics through European initiatives such as CIVITAS. Partners will build upon all previous results to develop improved delivery methods, tools and methodologies, and implement them across Europe.

The MSCA ITN 2018 project Lowcomote will train a generation of experts that will upgrade the current trend of Low-code development platforms (LCPDs) to a new paradigm, Lowcode Engineering Platforms (LCEPs).

EDITO-Model Lab will prepare the next generation of ocean models, complementary to Copernicus Marine Service to be integrated into the EU public infrastructure of the European Digital Twin Ocean [j1] (EDITO) that will ensure access to required input and validation data (from EMODnet, EuroGOOS, ECMWF, Copernicus Services and Sentinels satellite observations) and to high performance and distributed computing facilities (from EuroHPC for High Performance Computing and other cloud computing resources) and that will be consolidated under developments of Destination Earth (DestinE).

Develop skills in artificial intelligence and machine learning, and to explore how learning techniques can contribute to the improvement of code design methods (by using less parameters, more relevant heuristics, producing stronger codes) and decoders (better performance, reduced complexity or energy consumption), on selected scenarios of practical interest for which a full theoretical understanding is still lacking.

The third (3G) and fourth (4G) generation wireless communication systems brought forth the mobile internet which changed our society. The fifth generation wireless communication (5G) and beyond 5G (B5G) systems will also bring their share of society changing technologies, like mobile virtual and augmented reality made possible through high-speed fixed wireless broadband connections.

xCALE is a research project funded by the Agence Nationale de la Recherche (ANR – the French National Research Agency) that will last 42 months and start in april 2021. It gathers the strength of Lab-STICC, LS2N, CREAD and France-IOI.

Statistical physics shows strong benefits when describing multi-scale complex systems such as: fluid turbulence, climate or neural signals.  In particular, Information Theory exhibits strong potentialities in the study of complex systems due to its power to characterize non-linear behaviors. Moreover in the last years, AI models have been strongly developed to deal with a large number of scientific questions, and more particularly complex systems. Thus, SCALES proposes to combine this IT framework with AI models to characterize interactions among the scales of complex systems.

Social anxiety is a global problem with significant personal and societal costs. Virtual reality makes it possible to consider new treatment alternatives, in particular for exposure therapy: a practitioner can gradually confront patients with the situations that are problematic to them. In this context, it is important to afford fine control over the stress experienced by the patient in these virtual situations. However, the parameters - and their interactions - influencing the amount and nature of the stress experienced in these interactions are still poorly understood.

ASSISTANT's main objective is to develop a collection of intelligent digital twins that will automatically adapt to the manufacturing environment. These digital twins will assist in the design and operation of a complex, collaborative, reconfigurable, mixed/multi-model production system based on data collected by IoT devices.

BC4SSI aims at studying and developing new public Blockchain paradigm to handle SSI with the weakest model assumptions.