In recent years, there has been a substantial amount of research on quantum computers. Such computers would be a major threat for all the public key cryptosystems used in practice, since all these systems rely on the hardness of integer factoring or discrete logarithms, and these problems are easy on a quantum computer. This has prompted the NIST to release at the end of last year a call for standardizing quantum resistant alternatives to those cryptosystems. This call concerns all three major cryptographic primitives, namely public key cryptosytems, key exchange protocols and digital signature schemes. The deadline for this call is November 30, 2017. NIST expects to perform multiple rounds of evaluation, over a period of three to five years. The goal of this process is to select a number of acceptable candidate cryptosystems for standardization. The first round of evaluation will last approximatively twelve to eighteen months. Before the second round, the submitters of the algorithms will have the option of providing updated optimized implementations and to patch small deficiencies discovered during the evaluation process. The most promising techniques today for addressing this issue are code-based cryptography, lattice-based cryptography, mutivariate cryptography, and hash-based cryptography. Our project will propose candidates to the NIST call for all three primitives. We will consider two different, but related, techniques to achieve this purpose: the Hamming metric and the rank metric. For both these techniques, schemes can be designed whose security relies partially, sometimes solely, on the hardness of decoding problem, that is finding a word close, for the Hamming or rank metric, to some code (i.e. a vector space) over a finite alphabet. Our project does not deal solely with the NIST call. We will also develop some other code-based solutions: these will be either primitives that are not mature enough to be proposed in the first NIST call or whose functionalities are not covered by the NIST call, such as identity-based encryption, broadcast encryption, attribute based encryption or functional encryption. A third goal of this project is of a more fundamental nature: namely to lay firm foundations for code-based cryptography by developing thorough and rigorous security proofs together with a set of algorithmic tools for assessing the security of code-based cryptography. More generally, the main objectives of the project are the following: - NIST competition: submission and follow up of our proposals, cryptanalysis of concurrent schemes; - Development of other basic and advanced code-based primitives; - General study of the security of code-based schemes for the Hamming and rank metrics.

The fields of industry 5.0 and defense are increasingly based on systems of systems where robotic agents must adapt to humans with whom interactions take place. The use of heterogeneous fleets of agents with perception devices is a godsend that allows, after merging individual information, to propose solutions to the problems of optimizing fleet operations, securing the convoy, improving safety and security for human operators, as well as increasing flexibility following the reconfiguration of situations or the environment. The mutualization of information allows to produce a global view of the situation resulting from the individual perceptions of each robotic or non-robotic agent. Each individual perception module produces an interpretation of the scene which is by nature tainted with uncertainty. The consequences of a fleet deployment in complex or hostile environments must also be considered. The communication link required for information exchange is subject to a bandwidth that can be very limited or even non-existent when the link is broken even temporarily. The viewpoint positions required to create the situation view are also dependent on the quality of the location source information when available. The SCOPES project proposes to develop a solution for the production of a situation view augmented by uncertainty as a source of decision information. The contributions of the project will be : - A representation formalism of the situation view, integrating the different sources of uncertainties, allowing an interpretation by humans. - A robust localization method based on the graph paradigm and the semantic information provided by each agent. - A functional specification and associated datasets for objective and quantitative evaluation of collaborative perception situations thanks to the exploitation of the outstanding technological platforms of the project partners. The SCOPES project will lead to TRL 4 level productions. The interest of the project for the economic actors was recognized by the labeling of the project by NAE.

Interacting with space is a constant challenge for Visually Impaired People (VIP) since spatial information in Humans is typically provided by vision. Sensory Substitution Devices (SSDs) have been promising Human-Machine Interfaces (HMI) to assist VIP. They re-code missing visual information as stimuli for other sensory channels. Our project redirects somehow from SSD’s initial ambition for a single universal integrated device that would replace the whole sense organ, towards common encoding schemes for multiple applications. SAM-Guide will search for the most natural way to give online access to geometric variables that are necessary to achieve a range of tasks without eyes. Defining such encoding schemes requires selecting a crucial set of geometrical variables, and building efficient and comfortable auditory and/or tactile signals to represent them. We propose to concentrate on action-perception loops representing target-reaching affordances, where spatial properties are defined as ego-centered deviations from selected beacons. The same grammar of cues could better help VIP to get autonomy along with a range of vital or leisure activities. Among such activities, the consortium has advances in orienting and navigating, object locating and reaching, laser shooting. Based on current neurocognitive models of human action-perception and spatial cognition, the design of the encoding schemes will lay on common theoretical principles: parsimony (minimum yet sufficient information for a task), congruency (leverage existing sensorimotor control laws), and multimodality (redundant or complementary signals across modalities). To ensure an efficient collaboration all partners will develop and evaluate their transcoding schemes based on common principles, methodology, and tools. An inclusive user-centered “living-lab” approach will ensure constant adequacy of our solutions with VIP’s needs. Five labs (three campuses) comprising ergonomists, neuroscientists, engineers, and mathematicians, united by their interest and experience with designing assistive devices for VIP, will duplicate, combine and share their pre-existing SSDs prototypes: a vibrotactile navigation belt, an audio-spatialized virtual guide for jogging, and an object-reaching sonic pointer. Using those prototypes, they will iteratively evaluate and improve their transcoding schemes in a 3-phase approach: First, in controlled experimental settings through augmented-reality serious games in motion capture (virtual prototyping indeed facilitates the creation of ad-hoc environments, and gaming eases the participants’ engagement). Next, spatial interaction subtasks will be progressively combined and tested in wider and more ecological indoor and outdoor environments. Finally, SAM-Guide’s system will be fully transitioned to real-world conditions through a friendly sporting event of laser-run, a novel handi-sport, which will involve each subtask. SAM-Guide will develop action-perception and spatial cognition theories relevant to nonvisual interfaces. It will provide guidelines for the efficient representation of spatial interactions to facilitate the emergence of spatial awareness in a task-oriented perspective. Our portable modular transcoding libraries are independent of hardware consideration. The principled experimental platform offered by AR games will be a tool for evaluating VIP spatial cognition, and novel strategies for mobility training. Systems and libraries will provide solutions for more VIP autonomy in indoor and outdoor activities, including sports (e.g. laser-run). SAM-Guide’s results could also contribute to industries relying on Human-Machine interactions, such as cobotics, smart prosthesis & wheelchairs, and to the development of more immersive AR or VR games. However, our primary concern is VIP’s quality of life, by increased autonomy, access to new forms of leisure, and easier participation in our society.

The automatic segmentation of medical images plays an important role in diagnosis and therapy. Deep convolutional neural networks (CNN) represent the state of the art, but have limitations, particularly on the plausibility of the generated segmentations. Our hypothesis is that the improvement of segmentations will come from the addition of external information, via medical knowledge for example, and auxiliary tasks, such as registration, which will guide and constrain the segmentation. On the other hand, the uninterpretable nature of CNN hinders their use in the medical field. If there are explicability methods for classification, everything remains to be done for segmentation. We will aim to develop such methods, in order to understand the mechanisms underlying the addition of knowledge and tasks. Although our developments will be generic, we will target use cases to demonstrate the impact of the results on clinical practice.

In Europe, the history of urban and suburban populations between the end of the 19th century and the Second World War is poorly known even though it was a time of profound transformation, largely linked to industrialisation and urbanisation. In France, historical demography has, until now, largely focused on the 1750–1830 period and especially on villages. Cities are less well understood, and even less their suburbs, because the size of their populations makes data collection time-consuming. With their very wide variety of populations, Paris and its suburbs offer an ideal setting to undesrtand better the rise of love marriages, the increase in divorce, and major changes in gender relations between 1880 and 1940 that seem to appear first in large cities. Thanks to a collaboration with specialists in machine learning, the EXO-POPP project will develop a database of 300,000 marriage certificates from Paris and its suburbs between 1880 and 1940. These marriage certificates provide a wealth of information about the bride and groom, their parents and their marriage witnesses, that will be analysed from a host of new angles made possible by the new dataset. These studies of marriage, divorce, kinship and social networks covering a span 60 years will also intersect with transversal issues such as gender, class and origin. The geolocation of data will provide a rare opportunity to work on places and relocations within the city, and linkage with two other databases will make it possible to follow people from birth to death. Building such a database by hand would take at least 50,000 hours of work. But, thanks to the recent developments in deep learning and machine learning, it is now possible to construct huge databases with automated reading systems including handwriting recognition and natural language understanding. Indeed, because of these recent advances, optical printed named entity recognition (OP-NER) is now perfoming very well when analysing regular texts such as financial yearbooks and old newspapers, and similar performance is now expected with printed marriage certificates from 1923 to 1940. On the other hand, while handwriting recognition by machine has become a reality, also thanks to deep learning, optical handwritten named entity recognition (OH-NER) has not received much attention. OH-NER is expected to achieve promising results on handwritten marriage certificates dating from 1880 to 1922. This project’s research questions will focus on the best strategies for word disambiguation for handwritten named entity recognition. We will explore end-to-end deep learning architectures for OH-NER, writer adaptation of the recognition system, and named entity disambiguation by exploiting the French mortality database (INSEE) and the French POPP database. An additional benefit of this study is that a unique and very large dataset of handwritten material for named entity recognition will be built. The EXO-POPP dataset will be a rich new asset in the field. In addition to its major contribution toward better understanding of research questions about marriage, migration, family and friend networks, divorce and separation, among many others, between 1880 and 1940, the EXO-POPP project will foster new collaborations between computer scientists and researchers in the humanities and social sciences to improve the recognition and the optic of characters and handwriting, which are now essential to provide valuable new tools for the processing of data sources, especially historical ones.