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Introduction The scholarly monograph has been compared to the Hapsburg monarchy in that it seems to have been in decline forever! It was in 2002 that Stephen Greenblatt, in his role as president of the US Modern Language Association, urged his membership to recognise what he called a ‘crisis in scholarly publication’. It is easy to forget now that this crisis, as he then saw it, had nothing to do with the rise of digital technologies, e-publishing, or open access. Indeed, it puts his words in...

International audience; This paper focuses on reinforcement learning (RL) with limited prior knowledge. In the domain of swarm robotics for instance, the expert can hardly design a reward function or demonstrate the target behavior, forbidding the use of both standard RL and inverse reinforcement learning. Although with a limited expertise, the human expert is still often able to emit preferences and rank the agent demonstrations. Earlier work has presented an iterative preference-based RL framework: expert preferences are exploited to learn an approximate policy return, thus enabling the agent to achieve direct policy search. Iteratively, the agent selects a new candidate policy and demonstrates it; the expert ranks the new demonstration comparatively to the previous best one; the expert's ranking feedback enables the agent to refine the approximate policy return, and the process is iterated. In this paper, preference-based reinforcement learning is combined with active ranking in order to decrease the number of ranking queries to the expert needed to yield a satisfactory policy. Experiments on the mountain car and the cancer treatment testbeds witness that a couple of dozen rankings enable to learn a competent policy.

International audience; The current progress of remote sensing systems, based on airborne and spaceborne platforms and involving active and passive sensors, provides an unprecedented wealth of information about the Earth surface for environmental monitoring, sustainable resource management, disaster prevention, emergency response, and defense. In this framework, mathematical models for image processing and analysis play fundamental roles. Effectively exploiting the potential conveyed by the availability of remote sensing data requires automatic or semi-automatic techniques capable of suitably characterizing and extracting thematic information of interest while minimizing the need for user intervention. The current development of mathematical models and methods for image processing and computer vision allows multiple remote sensing information extraction problems to be addressed successfully, accurately, and efficiently. In this introductory chapter, first, general characteristics of sensors and systems for Earth observation are summarized to define the basic terminology that will be used consistently throughout the book. Remote sensing image acquisition through passive and active sensors on-board spaceborne and airborne platforms is recalled together with the basic concepts of spatial, spectral, temporal, and radiometric resolution. Then, an overview of the main families of mathematical models and methods within the scientific field of two-dimensional remote sensing image processing is presented. The overall structure and organization of the book are also described.

International audience; IoT devices are ubiquitous and widely adopted by end-users to gather personal and environmental data that often need to be put into context in order to gain insights. In particular, location is often a critical context information that is required by third parties in order to analyse such data at scale. However, sharing this information is i) sensitive for the user privacy and ii) hard to capture when considering indoor environments.This paper therefore addresses the challenge of producing a new location hash, named IndoorHash, that captures the indoor location of a user, without disclosing the physical coordinates, thus preserving their privacy. This location hash leverages surrounding infrastructure, such as WiFi access points, to compute a key that uniquely identifies an indoor location.Location hashes are only known from users physically visiting these locations, thus enabling a new generation of privacy-preserving crowdsourcing mobile applications that protect from third parties re-identification attacks. We validate our results with a crowdsourcing campaign of 30 mobile devices during 4 weeks of data collection.

Abstract The progress in the area of embedded systems has favored the emergence of so called “smart objects” or “Things”. These ones incorporate, in a context of low energy consumption, various wireless communication capabilities combined with a micro-controller driving sensors and/or actuators. Smartphones, connected TVs, smart watches, and so on, are concrete examples of smart objects belonging to our everyday life. The Internet of Things (IoT) conceptualizes this new environment based on traditional networks connected with objects as specific components of the real world. Building however a global ecosystem gathering the different IoT environments, where Things can communicate seamlessly is a difficult task. Since each IoT platform uses its own stack of communication protocols, they usually are not able to work across the many available networking interfaces, which creates silos of users and Things. Web of Things (WoT) has the ambition to provide a single universal application layer protocol enabling the various Things to communicate with each other in a seamless way by using the standards and the APIs of the web as a universal platform. The articulation between objects and Internet if it represents a strong point of the WoT, leads also this one to inherit all the problems of security and privacy already present in Internet. These problems rest with stronger acuity in this new environment, because of its particular characteristics. It is therefore important to analyze the way in which traditional security and privacy requirements can be declined in this new environment. In this chapter, we will try to give a global overview of the currently proposed architectures for securing the WoT. This overview covers an analysis of the different threats and vulnerabilities that an IoT, eventually a WoT, architecture can be exposed to. It covers also the solutions proposed to solve the problematics related to the identity management, data confidentiality, the authorization and the access control in a WoT system.

International audience; The security issues of devices, used in the Internet of Things (IoT) for example, can be considered in two contexts. On the one hand, these algorithms can be proven secure mathematically. On the other hand, physical attacks can weaken the implementation. In this work, we want to compare these attacks between them. A tool to evaluate and compare different physical attacks, by separating the theoretical attack path and the experimental parts of the attacks, is presented.

International audience; Digital technologies have impacted almost every aspect of our society, including how people participate in activities that matter to them. Indeed, digital participation allows people to be involved in different societal activities at an unprecedented scale through the use of Information and Communication Technologies (ICT). Still, enabling participation at scale requires making it seamless for people to: interact with a variety of software platforms, get information from connected physical objects and software services, and communicate and collaborate with their peers. Toward this objective, this paper introduces and formalizes the concept of Social Participation Network, which captures the diverse participation relationships-between people, digital services and connected things-supporting participatory processes. The paper further presents the design of an associated online service to support the creation and management of Social Participation Networks. The design advocates the instantiation of Social Participation Networks within distinct participation contexts-spanning, e.g., private institutions, neighbor communities, and governmental institutions-so that the participants' information and contributions to participation remain isolated and private within the given context.

We assume that both living creatures and robots must face the same fundamental difficulty: incompleteness (and its direct consequence uncertainty). Any model of a real phenomenon is incomplete: there are always some hidden variables, not taken into account in the model, that influence the phenomenon. The effect of these hidden variables is that the model and the phenomenon never behave exactly alike. Both living organisms and robotic systems must face this central difficulty: how to use an incomplete model of their environment to perceive, infer, decide and act efficiently. These difficulties may be clearly explained by taking the robotics field as an example. The dominant paradigm in robotics may be illustrated by Fig. 1. The programmer of the robot has an abstract conception of its environment. He or she can describe the environment in geometrical terms because the shapes of objects and the map of the world can be specified. The environment may be described in analytical terms because the laws of physics that govern this world are known. The environment may also be described in symbolic terms because both the objects and their characteristics can be named. The programmer uses this abstract representation to program the robot. The programs use these geometric, analytic and symbolic notions. In a way, the programmer imposes on the robot his or her own abstract conception of the environment. The difficulties of this approach appear when the robot must link these abstract concepts with the raw signals that it obtains from its sensors and the outputs that it sends to its actuators. The central origin of these difficulties is the irreducible incompleteness of the models. Hidden variables, which influence the sensory inputs or bias the motor outputs but are not taken into account by the program, prevent the robot from relating the abstract concepts and the raw sensory–motor data reliably. This problem has been identified for many years in artificial intelligence and robotics under many different names, one of the most well known being the “symbol grounding problem” (see Harnad (1989) and Harnad (1990)).

The characteristics of a digitization of a Euclidean planar shape depends on the digitization process but also on the shape border regularity. The notion of Local Turn Boundedness (LTB) was introduced by the authors in Le Quentrec, E. et al.: Local Turn-Boundedness: A curvature control for a good digitization, DGCI 2019 so as to have multigrid convergent perimeter estimation on Euclidean shapes. If it was proved that the par-regular curves are locally turn bounded, the relation with the quasi-regularity introduced in Ngo, P.et al.: Convexity-Preserving Rigid Motions of 2D Digital Objects, DGCI 2017 had not yet been explored. Our paper is dedicated to prove that for planar shapes, local turn-boundedness implies quasi-regularity.

International audience; Intelligent Autonomous Robots deployed in human environments must have understanding of the wide range of possible semantic identities associated with the spaces they inhabit – kitchens, living rooms, bathrooms, offices, garages, etc. We believe robots should learn this information through their own exploration and situated perception in order to uncover and exploit structure in their environments – structure that may not be apparent to human engineers, or that may emerge over time during a deployment. In this work, we combine semantic web-mining and situated robot perception to develop a system capable of assigning semantic categories to regions of space. This is accomplished by looking at web-mined relationships between room categories and objects identified by a Convolutional Neural Network trained on 1000 categories. Evaluated on real-world data, we show that our system exhibits several conceptual and technical advantages over similar systems, and uncovers semantic structure in the environment overlooked by ground-truth annotators.