AI for smart and secure territories

Research activity of Axis 4 during the period 2019-2021

Research topics

The concept of smart and secure territories refers to sustainable cities, non-urban areas and communities with networked intelligent systems that place the users at their core and deliver personalized services and resources suited to a wide range of behaviors, constraints, and preferences. Such intelligent systems should help sense complex situations, make decisions, predict risks, extract knowledge from data, and report back to users. In addition, they should address the challenges of secure territories: reliability, safety, security, legality, trust, and acceptability.

3IA Chairs

A total of 8 principal researchers have contributed to Axis 4: • 3 Chairs since 2019 (M. Teller, M. Önen and P. Alliez) • 4 Chairs since 2020 (E. Di Bernardino, D. Gesbert, P. Goatin, C. Richard) • 1 Affiliate Chair (A. Lhéritier) since 2020. In addition, this axis leverages the research results developed in Axis 1.

Chairs 2019

Pierre Alliez - Inria

3D modeling of large-scale environments for the smart territory We are exploring the generation of rich 3D vector maps with semantic attributes from raw measurement data. We plan to learn geometric priors and error metrics that locally adapt to the semantic class of objects. We are developing a pliant approach with the capability to model the wide range of objects, which abound in open environments of the smart territories.

Melek Önen - Eurecom

Privacy-preserving machine learning

Machine learning has become popular due to cloud computing technology and the increasing number of datasets. Outsourcing computations poses a risk to data privacy. Therefore, our goal is to explore privacy-preserving variants of machine learning techniques while leveraging novel cryptographic methods.

Marina Teller - Université Côte d'Azur

Deep law for tech (DL4T)

The DL4T team is building the legal framework for deep technologies. Starting from the observation that law is often perceived as a force of resistance to innovation, we want to position our research upstream of technology, to support the emergence of technical standards and to promote a convergence between law and AI.

CHAIRS 2020

Elena Di Bernardino - Université Côte d'Azur

Territorial Security through environmental risks management

This project deals with risk assessments related to environmental extreme events. Analyses and predictions of floods, summer heatwaves, and storms are significant questions facing statisticians and risk assessors. Such environmental risks are the result of a long chain of casualties, involving several aleas, often correlated, with complex spatio-temporal dependent structures among extremes. Our contributions in the prevention and management of environmental risks, will be twofold: 1/ Proposing novel and realistic definitions of risks indicators in environmental contexts.2/ Studying in-depth their statistical inference, i.e. specifying more accurately the associated uncertainties. In this project, the skills required to handle the modeling of these uncertainties are stochastic processes and random fields, spatio-temporal models, multivariate extreme theory, as well as practical expertise on spatial and environmental data gathered from firms in 3IACôte d’Azur.

David Gesbert - Eurecom

Internet of Learning Thing, a machine learning approach to future IoT networks

In this chair, we develop cooperative forms of decision making, that can be implemented on distributed IoT devices, and not relying on the assumption that all data is centralized in the cloud. IoT devices can learn to coordinate with each other in their usage of the wireless spectrum, energy, and other resources while dealing with arbitrary noise uncertainties in their observation data. Cooperative machine learning will bring a profound evolution in IoT system design, both at the level of radio access, as well as in the manner services will be orchestrated and how resources will be allocated.

Paola Goatin - Inria

Data driven traffic management

This project aims at contributing to the transition to intelligent mobility management practices through an efficient use of available resources and information, fostering data collection and provision. We focus on improving traffic flow on road networks by using advanced mathematical models and statistical techniques leveraging the information recovered by real data. We are committed in creating a network of local stakeholders sharing knowledge and expertise.

Cédric Richard - Université Côte d'Azur

Distributed dark fiber optic sensing for smart cities monitoring

Optical fiber, in addition to being a means of transmitting information, is also a material that is very sensitive to environmental variations. When a laser light pulse travels through an optical fiber, it interacts with tiny impurities in the material and optical backscattering occurs. The round-trip time of the light provides the locations of interactions and allows us to infer a backscattering profile along the fiber. Processing this response provides estimates of the local variations in temperature, deformation or acoustic pressure along the fiber. This technique, called Distributed Fiber Optic Sensing (DFOS), is currently experiencing growing interest. The goal of our project is to develop a breakthrough framework for smart cities monitoring based on DFOS over existing dark fibers, and Artificial Intelligence.

Affiliate chair - 2020

Alix Lhéritier - Eurecom | AMADEUS

Improving the Air Travel Experience via Probabilistic Regression with Epistemic Uncertainty Estimation in Adversarial Scenarios

Alix Lhéritier was born in Montevideo, Uruguay, in 1978. He received the Computer Engineer degree and the M.Sc. degree in computer science from the Universidad de la República, Montevideo, Uruguay, in 2004 and 2010, respectively. In 2006, he was a Visiting Research Scholar at the Mathematical Sciences Research Institute, Berkeley, CA and at the Image Processing Laboratory of the University of Minnesota, MN. In 2011, he joined the Algorithms-Biology-Structure (ABS) team at Inria Sophia Antipolis, France as a doctoral student and he received his Ph.D. in computer science from the Université Nice Sophia Antipolis in 2015. He is currently with Amadeus, France, working on machine learning research and applications for the travel industry. His research interests include sequential decision problems, statistical dissimilarity and choice modeling.

Actions and highlights:

• AI & Law: The fast-paced development of AI makes it challenging to regulate, and to develop laws and ethical principles for questions pertaining to AI systems, connected objects, and digital twins. AI methods offer novel opportunities for devising tools capable of improving the quality and efficacy of justice, and contributing to a future era of deep law, digital evidence and predictive justice (Chair: M. Teller). The topics of AI ethics and “ethics by design” are investigated by S. Villata (Axis 1) and 3IA Assistant Professor

A. Nogales. • Secure territories: Privacy and security concerns are raised by the storage, transmission, and processing of sensitive data by ML services. Novel cryptographic methods have been used to devise privacy-preserving neural networks, and robust watermarking solutions have been extended to neural networks (Chair: M. Önen). Privacy issues are also investigated in the medical data domain, in collaboration with

M. Lorenzi (Axis 1) and O. Humbert (Axis 2). Furthermore, ML is used to improve the design of wireless networks for IoT via a federated machine learning approach that provides efficient beam tracking. Novel wireless networks, beyond 5G, are devised to deliver machine learning services (Chair: D. Gesbert). • Smart territories: Unused telecommunication fibers are used as intelligent distributed sensors for predicting earthquakes and monitoring road traffic. Contributions have been made to deep deconvolution and self-supervised learning from acoustic sensing data (Chair: C. Richard). The prevention and modeling of environmental risks such as forest fires is also tackled through spatiotemporal analysis of massive geospatial data recorded over decades. Methods such as statistical inference in large dimension, stochastic processes, multivariate extreme theory and data assimilation are used to model fire dynamics and devise novel weather-informed risk indicators (Chair: E. Di Bernardino). Another sensitive issue deals with the improvement of traffic flow on road networks, which is obtained through the analysis of real-world data, and through multi-scale mathematical modeling and prediction leveraging a novel

Bayesian uncertainty quantification method (Chair: P. Goatin). Finally, contributions have been made to the analysis of satellite images (remote or onboard via low-power neural networks), to the automated generation of vector maps from satellite images, and to the physics-informed geometric reduction of 3D models for the radiative thermal simulation of satellites (Chair: P. Alliez).

Conclusion on the Research Activity during the period 2019-2021

To conclude, through the dynamism of the chairholders and their teams, the Institute has achieved significant results both at the international level (with the organization of scientific events held in major venues for AI and ML, their publication activity and the keynote talks they have been invited to deliver) and at the national level, where it has strengthened ties with local organizations such as the Nice Côte d’Azur Metropolis or the National Park of Mercantour, and hospitals such as the Nice University Hospital and the Antoine Lacassagne Center (CAL). These results are grounded on high-quality scientific contributions to the core elements of AI and their applications in computational medicine and biology, and smart territories. These applications show the concrete impact of AI methods and technologies on society, highlighting its beneficial contribution to the improvement of two main aspects, i.e., AI-assisted healthcare, and smart cities and territories, while always remaining cautious with respect to the related issues of security, confidentiality, and ethics (e.g., three chairholders (Ayache, Ourselin, Teller) are members of the Scientific Advisory Committee of the French Health Data Hub, and S. Villata is a member of the National Committee for Digital Ethics.