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International audience; Hyperspectral imagery classification is a challenging task due to the large number of spectral bands, and low number of labeled samples. To overcome these issues, we propose a novel approach for hyperspectral image classification based on feature selection and semi-supervised hypergraph convolutional network working with small number of labeled samples. Firstly, we propose a new unsupervised feature selection method based on an information theoretic criterion. Relevant spectral features are automatically selected while preserving the physical properties of hyperspectral data. Secondly, we construct a spectro-spatial hypergraph in order to represent the complex relationships between pixels. Finally, we propose a semi-supervised hypergraph convolutional network which integrates local vertex features and hypergraph topology in the convolutional layers. The aim of this step is to preserve the spectro-spatial features and to cope with the high correlation between hypernodes during classification. The main advantage of the proposed approach is to allow the automatic selection of relevant spectral bands while preserving the spatial and spectral features. In addition, by accounting for the relationship between pixels leads to improved classification results even when the number of labeled samples is low. Experiments are conducted on two real hyperspectral images show that the proposed approach reaches competitive good performances, and achieves better classification performances compared to state-of-the-art methods.

FNEGE 4, HCERES C; International audience; Robotic Process Automation (RPA) is an emerging technology that has been applied in several sectors of the economy such as industry, healthcare and finance. Today, it represents an opportunity for actors in the insurance sector who are considering how to evaluate the benefits of this technology in the medium term, taking into account their specific context. In this article, we will first present a summary of academic work on the contributions of RPA for companies and the stakes of its implementation. The case of Allianz France’s Flow Department is then presented, showing how this organization approaches the issue of assessing the potential benefits of RPA solutions for improving documentary management. We will present the theoretical contributions of the case as well as our experience feedback on its animation. Finally, we will discuss other issues that could also be addressed with this case.; La Robotic Process Automation (RPA) est une technologie émergente utilisée dans plusieurs secteurs de l’économie tels que l’industrie, la santé et la finance. Elle représente aujourd’hui une opportunité pour les acteurs du secteur de l’assurance qui s’interrogent sur la manière d’évaluer les bénéfices de cette technologie à moyen terme en tenant compte de leur contexte spécifique. Dans cet article, nous présentons en premier lieu une synthèse des travaux académiques sur les apports de la RPA pour les entreprises et les enjeux de sa mise en place. Le cas de la Direction des Flux d’Allianz France est ensuite présenté, en montrant la manière dont cette organisation aborde la question de l’évaluation des bénéfices potentiels des solutions de RPA pour l’amélioration de la gestion documentaire. Nous exposons les apports théoriques du cas ainsi que notre retour d’expérience sur son animation. Enfin, nous évoquons d’autres questions qui pourraient aussi être traitées avec ce cas.

International audience; Deep learning architectures, albeit successful in most computer vision tasks, were designed for data with an underlying Euclidean structure, which is not usually fulfilled since pre-processed data may lie on a non-linear space. In this paper, we propose a geometric deep learning approach using rigid and non-rigid transformations, named KShapenet, for 2D and 3D landmark-based human motion analysis. Landmark configuration sequences are first modeled as trajectories on Kendall's shape space and then mapped to a linear tangent space. The resulting structured data are then input to a deep learning architecture, which includes a layer that optimizes over rigid and non-rigid transformations of landmark configurations, followed by a CNN-LSTM network. We apply KShapenet to 3D human landmark sequences for action and gait recognition, and 2D facial landmark sequences for expression recognition, and demonstrate the competitiveness of the proposed approach with respect to state-of-the-art.

International audience

Compliant mechanisms (CMs), as a novel type of mechanical systems, utilize the elastic deformation of the builtin elementary flexible members to transfer motion, force and energy. Due to their inherent advantages, CMs have been widely used in various mechanical applications. However, in the current literature, researchers still suffer from the lack of efficient tools and a comprehensive analysis framework for designing CMs. To more systematically and efficiently analyze CMs for application-oriented purposes, this thesis aims to propose a comprehensive methodology of designing, modeling, optimizing and controlling CMs. Detailed derivations have been presented on the aforementioned perspectives, followed by both numerical and experimental validation.; Les mécanismes compliants (CM), en tant que nouveau type de systèmes mécaniques, utilisent la déformation élastique des éléments flexibles intégrés pour transférer le mouvement, la force et l'énergie. En raison de leurs avantages inhérents, les CM sont largement utilisés dans diverses applications mécaniques. Cependant, dans la littérature actuelle, les chercheurs souffrent toujours du manque d'outils efficaces et d'un cadre d'analyse complet pour la conception des CM. Afin d'analyser de manière plus systématique et efficace les CM, cette thèse vise à proposer une méthodologie complète de conception, de modélisation, d'optimisation et de contrôle des CM. Des dérivations détaillées ont été présentées sur les perspectives mentionnées ci-dessus, suivies de validations numériques et expérimentales.

Efficient sampling from a high-dimensional Gaussian distribution is an old but high-stake issue. Vanilla Cholesky samplers imply a computational cost and memory requirements which can rapidly become prohibitive in high dimension. To tackle these issues, multiple methods have been proposed from different communities ranging from iterative numerical linear algebra to Markov chain Monte Carlo (MCMC) approaches. Surprisingly, no complete review and comparison of these methods have been conducted. This paper aims at reviewing all these approaches by pointing out their differences, close relations, benefits and limitations. In addition to this state of the art, this paper proposes a unifying Gaussian simulation framework by deriving a stochastic counterpart of the celebrated proximal point algorithm in optimization. This framework offers a novel and unifying revisit of most of the existing MCMC approaches while extending them. Guidelines to choose the appropriate Gaussian simulation method for a given sampling problem in high dimension are proposed and illustrated with numerical examples., 53 pages, 11 figures

International audience; A photon structure is advanced based on the experimental evidence and the vector potential quantization at a single photon level. It is shown that the photon is neither a point particle nor an infinite wave but behaves rather like a local "wave-corpuscle" extended over a wavelength, occupying a minimum quantization volume and guided by a non-local vector potential real wave function. The quantized vector potential oscillates over a wavelength with circular left or right polarization giving birth to orthogonal magnetic and electric fields whose amplitudes are proportional to the square of the frequency. The energy $\hbar$$\omega$ and momentum $\hbar$$\bar{k}$ are carried by the local wavecorpuscle guided by the non-local vector potential wave function suitably normalized.

International audience; The obsession with how the brain and behavior are related is a challenge for cognitive neuroscience research, for which functional magnetic resonance imaging (fMRI) has significantly improved our understanding of brain functions and dysfunctions. In this paper, we propose a novel multi-modal spatial cerebral graph based on an attention mechanism called MSCGATE that combines both fMRI modalities: task-, and rest-fMRI based on spatial and cerebral features to preserve the rich complex structure between brain voxels. Moreover, it attempts to project the structural-functional brain connections into a new multi-modal latent representation space, which will subsequently be inputted to our trace regression predictive model to output each subject’s behavioral score. Experiments on the InterTVA dataset reveal that our proposed approach outperforms other graph representation learning-based models, in terms of effectiveness and performance.

International audience; In this technology report we present how to design functionalprototypes of smart home systems, based on Augmented Reality(AR) and Brain-Computer Interfaces (BCI). A prototype was designedand integrated into a home automation platform, aiming to illustrate the potential of combining EEG-based interaction with Augmented Reality interfaces for operating home appliances. Our proposed solution enables users to interact with different types of appliances from ”on-off”-based objects like lamps, to multiple command objects like televisions. This technology report presents the different steps of the design and implementation of the system, and proposes general guidelines regarding the future development of such solutions. These guidelines start with the description of the functional and technical specifications that should be met, before the introduction of a generic and modular software architecture that can be maintained and adapted for different types of BCI, AR displays and connected objects. Overall this technology report paves the way to the development of a new generation of smart home systems, exploiting brain activity and Augmented Reality for direct interaction with multiple home appliances.

International audience; The use of low-level spatial information, in addition to the rich spectral information of Hyperspectral Image (HSI) with the potential of deep learning-based methods, has provided high performance in improving the HSI classification. With this regard, Gabor Filtering has been successfully applied for HSI pixels analysis due to its ability in extracting representative spatial features. Therefore, a novel methodology based on Gabor filtering and multi-view Convolutional Neural Networks (multi-view CNNs) was proposed in this paper. Firstly, Gabor texture features at different scales and orientations performed on the first three principal components of HSI were extracted. Then, spectral and spatial features are introduced separately through spectral, and spatial CNN, respectively. Afterward, their learned feature maps are combined and fed into our spectral-spatial CNN (SSCNN) to learn a fused multi-view representation. Experiments applied on two real HSI datasets, including Indian Pines, and Salinas yield competitive classification performances compared to other state-of-the-art deep learning methods.

International audience

International audience; Anomaly in Online Social Network can be designated as an unusual or illegal activity of an individual. It can also be considered as an outlier or a surprising truth. Due to the emergence of social networking sites such as Facebook, Instagram, etc., the number of negative impacts of aggressive and bullying phenomena has increased exponentially. Anomaly detection is a problem of crucial importance which has attracted researchers since the 2000s. This problem is often carried out, thanks to deep learning, artificial intelligence and statistics. Several methods have been devoted to solving the problem of detecting abnormal behavior on social media, which are kept under three different types: structural methods which are based on the analysis of graphs of social networks, behavioral methods which are based on the extraction and analysis of user activities and hybrid methods which combine the two types of methods mentioned above. This survey reviews various methods of data mining for the detection of anomalies to provide a better assessment that can facilitate the understanding of this area.

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International audience; Deprecations are a common way to indicate that a given feature or API will not be available in subsequent versions of a library or framework. While raising deprecation warnings lets developers of libraries evolve their APIs, the developers of client applications often have to manually rewrite their applications to adapt to the deprecation (removal, new APIs.. .). Some may use static analysis tools to support the rewriting. However, dynamically-typed languages or the use of reflective features often produce incorrect rewrite candidates. This is a costly activity that can lead to bug introductions. In this article, we present a method deprecation approach and a tool called DEPREWRITER that can automatically rewrite the callers of deprecated methods during program execution. Clients of a deprecated API execute their program and associated tests, and DEPREWRITER dynamically rewrites the source code of methods that called a deprecated API to use the new API. The implementation of DEPREWRITER is based on dynamic program transformation: when a deprecated method is executed, a program transformation engine rewrites and recompiles the caller's code before continuing the execution. The approach presented in this article has been developed by the Pharo consortium. Since 2016, DEPREWRITER is used in production in multiple distributions of the Pharo programming language: Pharo 6, 7, 8, and 9 alpha. This article presents and validates this approach. The validation is done in two steps: first with an analysis of deprecations available in Pharo 8 and second with an open survey of software developers about DEPREWRITER. We studied 367 Pharo 8 deprecations, among which we analyzed the 218 rewriting deprecations that use transformation rules. We identified the validity conditions and reported defects to the community. We also proposed 33 transformation rules to be added to the non-rewriting deprecations. Both contributions were accepted into Pharo 9 alpha. We classified the rules and identified possible points of improvement. In addition, we performed a user survey and collected information from 46 software developers: some of them used existing DEPREWRITER' rules and executed them on their code, others used DEPREWRITER to create rewriting deprecations, and finally, some were not aware of DEPREWRITER. 28 of 46 developers (60%) reported that the rewriting deprecations helped them, while 10 stated the inverse and 8 were uncertain. After discussing the current implementation, we sketch possible implementations for other languages than Pharo, showing that the approach is general enough to be applied to other languages.

International audience; This paper proposes a new method to solve the monitoring and anomaly detection problems of Low-power Internet of Things (IoT) devices. However, their performances are constrained by limited processing, memory, and communication, usually using battery-powered energy. Polling driven mechanisms for monitoring the security, performance, and quality of service of these networks should be efficient and with low overhead, which makes it particularly challenging. The present work proposes the design of a novel method based on a Deep Reinforcement Learning (DRL) algorithm coupled with an Unsupervised Learning reward technique to build a pooling monitoring of IoT networks. This combination makes the network more secure and optimizes predictions of the DRL agent in adaptive environments.

International audience; Nowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a diversity of wireless-enabled devices ranging from smartphones, wearables, to sensors, drones, and connected vehicles. The growing number of IoT devices, the increasing complexity of IoT systems, and the large volume of generated data have made the monitoring and management of these networks extremely difficult. Numerous research papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques to overcome these difficulties by building IoT systems with effective and dynamic decision-making mechanisms, dealing with incomplete information related to their environments. The paper first reviews pre-existing surveys covering the application of RL and DRL techniques in IoT communication technologies and networking. The paper then analyzes the research papers that apply these techniques in wireless IoT to resolve issues related to routing, scheduling, resource allocation, dynamic spectrum access, energy, mobility, and caching. Finally, a discussion of the proposed approaches and their limits is followed by the identification of open issues to establish grounds for future research directions proposal.

CNRS 2, FNEGE 2, HCERES A; International audience; Available technologies in organizations can be exploited for creating useful innovative solutions in a rather quick, efficient and economical way. Despite the huge benefits that can be derived by proactively exploiting available technologies and systems for innovation, there is relatively less academic research devoted to the subject. Motivated by this perceptible gap in the digital innovation literature, we theorize and examine the role of one possible structured creative mechanism (design thinking approach) for exploiting existing digital technologies to facilitate innovation. Grounding our arguments in the 'effective use of IS' and 'technology affordance' literature, we conceptualize innovation agent attributes and actualized innovation affordance achieved through the use of ICT, as the two salient facilitators for digital innovation. Innovation agent attributes are operationalized via domain generic and domain specific attributes of 'creative self-efficacy' and 'affect towards ICT use' and actualized innovation affordance is operationalized as the effective use of available technologies through an 'ICT enabled design-thinking approach'. The hypothesized model is empirically tested via a quasi-experimental method comprising data from a two-wave temporally separated survey. Results establish the significant influence of both-innovation agent attributes and actualized innovation affordance for facilitating digital innovation. Moreover, actualized innovation affordance, operationalized through ICT-DT, has a stronger association with digital innovation compared to the innovation agent attributes. This result underlines the need for organizations to proactively focus on using ICT effectively through structured creativity approaches (such as DT) for facilitating innovative outcomes. The study also offers several implications for research and practice.; Les technologies de l’information (TIC) disponibles dans les organisations peuvent être utilisées pour créer des solutions innovantes d’une manière rapide, efficace et économique. Malgré la nécessité de mieux comprendre les modalités permettant d’utiliser les TIC dans les organisations pour favoriser l’innovation, la littérature sur l’innovation numérique/digitale se concentre principalement sur l’étude de la mise en œuvre de TIC existantes dans de nouveaux contextes ou sur la conception et le développement de nouvelles technologies de l’information. Cet article enrichit ces travaux de recherche en étudiant un mécanisme créatif alternatif mobilisant les technologies digitales existantes pour l’innovation. En ancrant nos arguments dans la littérature sur l’usage efficace des SI et « l’affordance de la technologie », nous conceptualisons les caractéristiques des agents d’innovation et l’affordance actualisée de l’innovation basée sur l’utilisation des TIC, comme les deux principaux facilitateurs de l’innovation digitale. Les caractéristiques des agents d’innovation sont opérationnalisées par le biais de caractéristiques génériques et spécifiques au domaine de « l’auto-efficacité créative » et de « l’affect envers l’utilisation des TIC ». L’affordance de l’innovation actualisée est opérationnalisée comme l’utilisation efficace des technologies disponibles grâce à une approche de conception basée sur les TIC ou encore « ICT enabled design-thinking » (ICT-DT). Le modèle proposé est testé empiriquement via une méthode quasi-expérimentale comprenant des données provenant d’une enquête en deux temps de collecte. Les résultats montrent une influence significative à la fois des caractéristiques de l’agent d’innovation et de l’affordance actualisée de l’innovation pour faciliter l’innovation digitale. Cependant, l’affordance actualisée de l’innovation, opérationnalisée par l’approche ICT-DT, a une relation plus forte avec l’innovation digitale par rapport aux caractéristiques de l’agent d’innovation. Cela souligne la nécessité pour les organisations d’intégrer l’utilisation des TIC pour permettre des approches de créativité structurées (telles que le DT) et encourager les productions innovantes. L’étude offre également plusieurs implications pour la recherche et pour la pratique.

The Angular Resolution Limit (ARL) is a fundamental statistical metric to quantify our ability to resolve two closely-spaced narrowband far-field complex sources. This statistical quantity, is defined as the minimal angular deviation between the two sources to be separated for a prefixed detection-based performance. In this work, we assume that the sources of interest are corrupted by a compound-Gaussian noise. In the standard literature, denoting with δ the true distance between the two sources, the derivation of the ARL is based on the statistical distribution of the Generalized Likelihood Ratio Test (GLRT) for a binary test where there is only one source under the null hypothesis (i.e., δ = 0 ) and two sources under the alternative hypothesis δ ≠ 0. In literature, the true angular distance (TAD) is generally considered as an unknown deterministic parameter, then a maximum likelihood-based estimation of δ is exploited in the GLRT. In this paper, breaking away from existing contributions, we suppose that the TAD is a random variable, Gaussian distributed, meaning that δ ∼ N ( δ 0 , σ δ 2 ) . The TAD uncertainty can have many causes as for instance moving sources or/and platform, antenna calibration error, etc. In this work, a generic and flexible (but common) statistical model of the uncertain knowledge of the TAD is preferred instead of a too much specified error model. The degree of randomness (or uncertainty) is quantified by the ratio ξ = δ 0 2 / σ δ 2 . The standard framework of the GLRT is no longer feasible for our problem formulation. To cope with the compound Gaussian noise modeling and the random model of the TAD, a powerful upper bound from information/geometry theory is exploited in this work. More precisely, a new expected Chernoff Upper Bound (CUB) on the minimal error probability is introduced. Based on the analysis of this upper bound, we show that the expected-CUB is highly dependent on the degree of uncertainty, ξ. As a by-product, the optimal s-value in the Chernoff divergence for which the expected-CUB is the tightest upper bound is analytically studied and the role of the mean value δ0 in the ARL context is analyzed.

International audience; This paper proposes a novel approach based on adaptive dimensionality reduction (ADR) and a semi-supervised 3-D convolutional neural network (3-D CNN) for the spectro-spatial classification of hyperspectral images (HSIs). It tackles the problem of curse of dimensionality and the limited number of training samples by selecting the most relevant spectral bands. The selected bands should be informative, discriminative and distinctive. They are fed into a semi-supervised 3-D CNN feature extractor, then a linear regression classifier to produce the classification map. In fact, the proposed semi-supervised 3-D CNN model seeks to extract the deep spectral and spatial features based on convolutional encoder-decoder to enhance the HSI classification. It uses several 3-D convolution and max-pooling layers to extract these features from the selected relevant bands. The main advantage of the proposed approach is to reduce the high dimensionality of HSI, preserve the relevant spectro-spatial information and enhance the classification using few labeled training samples. Experimental studies are carried out on three real HSI data sets: Indian Pines, Pavia University, and Salinas. The obtained results show that the proposed approach performs better than other deep learning-based methods including CNN-based methods, and significantly improves the classification accuracy of HSIs.

Internet of Mobile Things (IoMT) is a new paradigm of the Internet of Things (IoT) where devices are inherently mobile. While mobility enables innovative applications and allows new services, it remains a challenging issue as it causes disconnection of nodes and intermittent connectivity, which negatively impacts the network operation and performance. In addition, energy saving presents a real challenge for networks with limited resources. In this context, a new energy efficient and mobility aware routing protocol called EMA-RPL is proposed based on the well-known Routing Protocol for Low power and Lossy Networks (the RPL standard). Unlike the RPL which is basically designed for static devices, EMA-RPL enables better sustaining of connectivity of mobile nodes and conserving energy. The proposed protocol integrates an enhanced mobility detection method through a continuous control of the distance between the mobile node and its attachment, a novel point of attachment prediction based on the new location of the mobile node, and an efficient replacement strategy preserving the mobile node energy. EMA-RPL overcomes and mitigates problems caused by the mobility of nodes. Simulations using Cooja/Contiki show that EMA-RPL outperforms both the RPL and its mobility aware variant (MRPL) in terms of handover delay, data loss rate, signaling cost and energy consumption.

International audience; Internet of Things (IoT) is increasingly deployed in different domains and environments including smart homes, smart cities, healthcare, industry 4.0, and smart agriculture, by connecting a large number of physical objects to deliver a new class of applications. The rising number of these connected objects and their heterogeneity have raised new research directions and challenges regarding their communications, scalability and the large amount of data that generate. As a result, new communication technologies have been proposed to be applied in these environments and applications, mainly to consider their main inherent properties which are the information that generate and handle, and the content that disseminate. Among the adopted techniques and recently integrated into the IoT is the ICN (Information Centric Networking) paradigm. The choice of integration of ICN in the context of IoT is mainly motivated by all the advantages it represents, in particular content caching and decoupling senders and receivers. In this paper, we provide a detailed analysis of the motivations behind using ICN in IoT environments and a survey of existing research work that have already applied ICN as a communication support for IoT applications.

International audience; Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods.

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Drought disasters significantly affected human life and water resources. Therefore, forecasting methods like statistical models, machine learning, and deep learning architectures help scientists to take effective decisions to decrease the effects of natural disasters by providing decision-making plans. Droughts can be forecasted using meteoro-logical indices like the standardized precipitation evapotranspiration index (SPEI), which aid governments in taking drought-prevention steps. In this paper, we present a big drought architecture for drought modeling and forecasting. The proposed architecture is composed of 5 layers: Data collection, data preprocessing, data storage, data processing and interpretation, and decision making. Besides, we present a comparative study between three different methods ARIMA, PROPHET, and LSTM for drought forecasting. Three different metrics are used for the performance evaluation Root Mean Squared Error (RMSE), coefficient of determination (R2), and Mean Squared Error (MAE). Experiments are carried out using data from the province of Jiangsu. Results revealed that LSTM outperformed the other models, and ARIMA outperformed the PROPHET model.

International audience; Physical-layer network coding (PNC) can effectively improve wireless networks by extracting superimposed signals and transforming collision’s dilemma into an advantage. The proposition of higher layer methods (such as Data Link and Network Layer) explicitly designed for PNC can better enhance wireless network performances. Since the field of PNC is very vast and has several applications, the researchers of the higher layers find it very difficult to propose a suitable PNC system for their research, which has led to the scarcity of research works of this type. This article proposes a new classification method designed to help researchers choose the optimal method that suits their system. This paper is a complete guide for researchers of higher layers to discover the PNC field and improve wireless network performances.

International audience; This paper deals with the problem of prescribedtime stabilization of controllable linear systems with input delay. The problem is reformulated under a cascade PDE-ODE setting from which a prescribed-time predictor feedback is designed based on the backstepping approach, and whose transformation makes use of time-varying kernels. The bounded invertibility of the transformation is guaranteed. It is proved that the solution converges to the equilibrium in a prescribed-time. A simulation example is presented to illustrate the results.

International audience; The nowadays CPPS (Cyber Physical Production System) depends essentially on Operational Technologies (OTs) that have been acquired from a wide range of Information Technologies (ITs). In order to guarantee the CPPSs requirements, several of ITs have been adjusted or adapted. As a result, new issues appeared, in the design of this production system, such as the integration of safety, security and vulnerability risks. The greater part of CPPSs vulnerabilities have generally not been considered in the design phase. However, this dependence on ITs makes CPPSs increasingly vulnerable to cyber-attacks and security threats, which affect their global performance. The focus of this research is to address the cyber security challenges that these systems have to cope. It discusses the features of the CPPSs threat environment and align safety and security risk analysis. In fact, the first problem in security risk analyzes is determining the likelihood of the cyber-attacks. The use likelihood in security analysis is not practical and sometimes does not make sense. Therefore, this prevents the research community from using a panoply of methods that exist in the field of safety because they are based on the use of probabilities. In this paper it is introduced a new vulnerability scoring system for industrial systems, called ICVSS. The proposed approach based on the calculation of variety of metrics distributed into two groups of metrics inherited from the classical CVSS metrics. The ICVSS not only make possible to assess the vulnerability and to ease the communication between the safety and security teams, but also it is a good alternative to replace the likelihood in order to be able to use these safety methods. Next, a new approach to co-analysis and co-assessment of safety and security called FVMEARA (Failure and Vulnerability Modes, and Effect Analysis and Risk Assessment) is presented. Actually, FVMEARA is a use case that shows the effectiveness of our methodology in which we have been able to reuse the FMEA method, known as the most widely used in the field of safety, to carry out a co-analysis of hazards and threats and to assess their risks. Even more, the proposed solution complies with the standards IEC 60812 for safety and the standard IEC 62443 for security.

International audience; Low-power Internet of Things (IoT) networks are widely deployed in various environments with resource constrained devices, making their states monitoring particularly challenging. In this paper, we propose an adaptive monitoring mechanism for low-power IoT devices, by using a reinforcement learning (RL) method to automatically adapt the polling frequencies of the collected attributes. Our goal is to minimize the number of monitoring packets while keeping accurate and timely detection of threshold crossings associated to supervised attributes. We study the various RL parameter settings under different monitoring attribute behaviors using OpenAi Gym simulator. We implement the RL based adaptive polling in Contiki OS and we evaluate its performance using Cooja simulator. Our results show that our approach converges to optimal polling frequencies and outperforms static periodic notification-based methods by reducing the number of monitoring packets, with a percentage of correctly detected threshold crossings exceeding 80%.

International audience; Surrogate modeling is an increasingly popular tool for engineering design as it enables to model the performance of very complex systems with a limited computational cost. A large number of techniques exists for the surrogate modeling of continuous functions, however, only a very few methods for the surrogate modeling of mixed continuous/discrete functions have been developed. In this chapter, existing adaptations and variants of Gaussian process-based surrogate modeling techniques for mixed continuous/discrete variables are described, discussed and compared on several analytical test-cases and aerospace design problems.

International audience; In just a few years, social networking sites have become the most popular landmarks on the Internet. They revolutionized the way we communicate, and socialized the Web. However, while it is now impossible to deny their impact, it can take a variety of forms, not all of them are positive. As a result, the detection of anomalies on social networks is a topic of current research that has attracted researchers since the 2000s. This problem is of crucial importance to prevent abnormal activities. So far, all existing works have been devoted to one-dimensional networks. Our approach attempts to provide a new anomaly detection method based on examining relationships between OSN users using multidimensional networks.

Monitoring the epidemiological situation of public health remains a challenge, given the exponential growth of health data. Indeed, these latter come from various sources such as, health professionals, biologists, experts in health organizations, etc. In this context, reconciling the search for the greatest amount of appropriate signals and the way of treating them, is the ultimate challenge.

International audience; Business Process (BP) development can be defined as the process of constructing a workflow application by composing a set of services performing BP's activities. In this respect, Cloud Services (CSs) are being increasingly used in BP development to ensure a high level of performance with a low operating cost. Although large companies may benefit from CSs' advantages, Small and Medium-sized Enterprises (SMEs) and startups are falling behind in cloud usage due to missing Information Technology competence, (IT-competence). The crucial challenge facing SMEs and startups in cloud-based BP development is to effectively address the so-called business and IT alignment issue. It represents the alignment between two different domains; one that entails technical cloud resource requirements and another comprising business-level. Formerly, we present this issue as a discovery challenge of suitable CSs performing abstract BP's activities. To address this challenge, firstly, we introduce the concept of cloud-aware BP by proposing a Domain-Specific Language (DSL) named "BP4Cloud" to enrich BP modeling and cover both business and technical requirements. Secondly, we propose an Activity-Services Matching algorithm that automates the discovery of CSs performing BP's activities. As a part of the evaluation, we set up by clarifying the specification of BP4Cloud elements through a proof of concept implementation applied on a real BP. Then, we proceed by evaluating the precision and recall of our Activity-Service Matching algorithm.

With serious concerns on environmental problems related to CO\(_{2}\) emissions and energy crisis, special attention has recently been paid to Electric Vehicles (EVs). Specifically, an accurate estimation of energy consumption for EV is still a big issue. Therefore, quantifying the impact of various external factors on EV energy consumption, such as road topology, driver profile and traffic conditions is a necessity. In this paper, we review the energy consumption models for EVs. Then, we analyze the impact of the road gradient on EV energy consumption through a set of real world experimentations. The obtained results, allow us to better understand the impact of upward and downward gradient on both energy consumption and energy recuperation processes.

International audience; Airborne laser scanning (ALS) data with regression models measure height, diameter and crown tree at an individual tree level to estimate inventory data, including volume, biomass, basal area, and diameter at breast height. The optical images provide high spatial resolution and multispectral reflectance, which improves the accuracy of tree identification. Fusion of multispectral and light detection and ranging data decrease segmentation errors. Many researches demonstrate that fusion reduced RMSE (root mean squared error) by 7% to 8%. In this paper, we study the feasibility of fusing of LIDAR and Multispectral data sets for forest structure parameter assessment. This fusion provides many features such as identifying individual trees, species, measuring tree height and crown diameter. To assess the benefit of fusing ALS and Sentinel images, 4 regression algorithms (Linear Model, Partial least Squares Regression, Random Forest and Support Vector Machine) were compared to model basel area (G) and Gini index (GINI) as a function of ALS variables alone and both ALS and Sentinel 2 variables. Resulting models were compared based on both RMSE (Root Mean Square Error) and R 2 values.

A robust nonlinear control is designed for stabilizing linear multi-input–multi-output systems. The presented control law homogenizes a linear system (without its transformation to a canonical form) with a specified degree and stabilizes it in a finite time (or with a fixed-time attraction to any compact set containing the origin) if the degree of homogeneity is negative (positive). The tuning procedure is formalized in an linear matrix inequalities (LMI) form. Performance of the approach is illustrated by numerical and experimental examples.

International audience; High accuracy air pollution monitoring in a smart city requires the deployment of a huge number of sensors in this city. One of the most appropriate wireless technologies expected to support high density deployment is LoRaWAN which belongs to the Low Power Wide Area Network (LPWAN) family and offers long communication range, multi-year battery lifetime and low cost end devices. It has been designed for End Devices (EDs) and applications that need to send small amounts of data a few times per hour. However, a high number of end devices breaks the orthogonality of LoRaWAN transmissions, which was one of the main advantages of LoRaWAN. Hence, network performances are strongly impacted. To solve this problem, we propose a solution called OAPM (Orthogonal Air Pollution Monitoring) which ensures the orthogonality of LoRaWAN transmissions and provides accurate air pollution monitoring. In this paper, we show how to organize EDs into clusters and sub-clusters, assign transmission times to EDs, configurate and synchronize them, taking into account the specificities of LoRaWAN and the features of the air pollution monitoring application. Simulation results corroborate the very good behavior of OAPM.

The ultimate goal of this project is to develop a recommender system for the SmartVideo platform. The platform streams different content of local channels for the Grand Est Region of France to a large public. So, we aim to propose a solution to alleviate the data representation and data collection issue of recommender systems by adopting and adjusting the xAPI standard to fit our case of study and to be able to represent our usage data in a formal and consistent format. Then, we will propose and implement a bunch of recommendation algorithms that we are going to test in order to evaluate our developed recommender system., Le but ultime de ce projet est de développer un système de recommandation dédié à la plateforme SmartVideo de diffusion de vidéo en ligne. En effet, la plateforme met à disposition diverses contenus des chaînes locales de la région Grand Est du France. Alors, nous allons présenter une solution pour alléger le problème de représentation et de collecte de données d’usages par adopter et ajuster le standard xAPI pour représenter et collecter les données de façon simple et formelle. Ensuite, nous allons proposer et implanter des algorithmes de recommandation que nous allons les tester pour évaluer notre système de recommandation.

With the rapid growth of image collections, image classification and annotation has been active areas of research with notable recent progress. Bag-of-Visual-Words (BoVW) model, which relies on building visual vocabulary, has been widely used in this area. Recently, attention has been shifted to the use of advanced architectures which are characterized by multi-level processing. Hierarchical Max-Pooling (HMAX) model has attracted a great deal of attention in image classification. To improve image classification and annotation, several approaches based on ontologies have been proposed. However, image classification and annotation remain a challenging problem due to many related issues like the problem of ambiguity between classes. This problem can affect the quality of both classification and annotation results. In this paper, we propose an ontology-based image classification and annotation approach. Our contributions consist of the following: (1) exploiting ontological relationships between classes during both image classification and annotation processes; (2) combining the outputs of hypernym–hyponym classifiers to lead to a better discrimination between classes; and (3) annotating images by combining hypernym and hyponym classification results in order to improve image annotation and to reduce the ambiguous and inconsistent annotations. The aim is to improve image classification and annotation by using ontologies. Several strategies have been experimented, and the obtained results have shown that our proposal improves image classification and annotation.

Today, the existence of several air pollution sources, such as household, transportation and industrial activities, increases the levels of air criteria pollutants in the urban areas and thus causes premature deaths, cardiovascular diseases and acute respiratory infections. To control the pollution levels, wireless technologies, like Wireless Sensor Networks (WSNs), Vehicular Sensor Networks (VSNs) and cellular networks, have become intensively adopted to collect real world data and to transmit decisions to people and interested governments in real time. Within this framework, we developed a new dynamic air pollution monitoring protocol using a communication model dedicated to enable wireless access for mobile objects. Based on Network Simulator 3 (NS-3), the obtained experimental results show that our solution can be applied to control the air data with better performance either in low or high density of vehicles in the regions of interest.

International audience; The Internet of Things (IoT) ecosystem includes a plethora of devices equipped with heterogeneous communication interfaces. They exhibit different mobility patterns and hardware constraints as memory, battery and processing power. On the other hand, IoT applications are overlayed on top of these constrained-devices imposing stringent requirements in term of data availability, data coherence, and response latency. To cope with these challenges, Named Data Networking (NDN) architecture is positioned in the middle layer to act as the networking layer. By providing easy data access thanks to the unique and location-independent content names, in-network caching and name-based routing, NDN expects to hide from IoT applications the complexity and diversity of the underlying Things by adapting the network operation to their features. In this paper, we focus on data availability requirements threatened by high IoT network dynamics related to sensors mobility. We address the producer mobility issue in NDN/IoT networks using the routing-based approach. We propose a novel and efficient forwarding algorithm named AFIRM in order to support producer mobility, and compare its performances to those of other relevant solutions based on the routing approach.

BackgroundAn important but rarely addressed question in nano-therapy is to know whether bio-degraded nanoparticles with reduced sizes and weakened heating power are able to maintain sufficient anti-tumor activity to fully eradicate a tumor, hence preventing tumor re-growth. To answer it, we studied magnetosomes, which are nanoparticles synthesized by magnetotactic bacteria with sufficiently large sizes (~ 30 nm on average) to enable a follow-up of nanoparticle sizes/heating power variations under two different altering conditions that do not prevent anti-tumor activity, i.e. in vitro cellular internalization and in vivo intra-tumor stay for more than 30 days.ResultsWhen magnetosomes are internalized in U87-Luc cells by being incubated with these cells during 24 h in vitro, the dominant magnetosome sizes within the magnetosome size distribution (DMS) and specific absorption rate (SAR) strongly decrease from DMS ~ 40 nm and SAR ~ 1234 W/gFebefore internalization to DMS ~ 11 nm and SAR ~ 57 W/gFe after internalization, a behavior that does not prevent internalized magnetosomes to efficiently destroy U87-Luc cell, i.e. the percentage of U87-Luc living cells incubated with magnetosomes decreases by 25% between before and after alternating magnetic field (AMF) application. When 2 µl of a suspension containing 40 µg of magnetosomes are administered to intracranial U87-Luc tumors of 2 mm3and exposed (or not) to 15 magnetic sessions (MS), each one consisting in 30 min application of an AMF of 27 mT and 198 kHz, DMS and SAR decrease between before and after the 15 MS from ~ 40 nm and ~ 4 W/gFedown to ~ 29 nm and ~ 0 W/gFe. Although the magnetosome heating power is weakened in vivo, i.e. no measurable tumor temperature increase is observed after the sixth MS, anti-tumor activity remains persistent up to the 15th MS, resulting in full tumor disappearance among 50% of treated mice.ConclusionHere, we report sustained magnetosome anti-tumor activity under conditions of significant magnetosome size reduction and complete loss of magnetosome heating power.

International audience; In the context of software product line engineering, collaborative configuration is a decision-making process where multiple stakeholders contribute in building a single product specification. Several approaches addressing collaboration during configuration have already been proposed, but we still have little hard evidence about their effectiveness and little understanding about how collaborative configuration process should be carried out. This paper presents a classification framework to help understand existing collaborative configuration approaches. To elaborate it, a systematic mapping study was conducted guided by three research questions and 41 primary studies was selected out of 238 identified ones. The proposed framework is composed of four dimensions capturing main aspects related to configuration approaches: purpose, collaboration, process and tool. Each dimension is itself multi-faceted and a set of attributes is associated to each facet. Using this framework, we position and classify existing approaches, structure the representation of each approach characteristics, highlight their strengths and weaknesses, compare them to each other, and identify open issues. This study gives a solid foundation for classifying existing and fu-* Corresponding author ture approaches for product lines collaborative configuration. Researchers and practitioners can use our framework for identifying existing research/technical gaps to attack, better scoping their own contributions, or understanding existing ones.

International audience; In the context of software product line engineering, collaborative configuration is a decision-making process where multiple stakeholders contribute in building a single product specification. Several approaches addressing collaboration during configuration have already been proposed, but we still have little hard evidence about their effectiveness and little understanding about how collaborative configuration process should be carried out. This paper presents a classification framework to help understand existing collaborative configuration approaches. To elaborate it, a systematic mapping study was conducted guided by three research questions and 41 primary studies was selected out of 238 identified ones. The proposed framework is composed of four dimensions capturing main aspects related to configuration approaches: purpose, collaboration, process and tool. Each dimension is itself multi-faceted and a set of attributes is associated to each facet. Using this framework, we position and classify existing approaches, structure the representation of each approach characteristics, highlight their strengths and weaknesses, compare them to each other, and identify open issues. This study gives a solid foundation for classifying existing and fu-* Corresponding author ture approaches for product lines collaborative configuration. Researchers and practitioners can use our framework for identifying existing research/technical gaps to attack, better scoping their own contributions, or understanding existing ones.

International audience; The Information-Centric Networking (ICN) paradigm is shaping the foreseen future Internet architecture by focusing on the data itself rather than its hosting location. It is a shift from a host-centric communication model to a content-centric model supporting among others unique and location-independent content names, in-network caching and name-based routing. By leveraging the easy data access, and reducing both the retrieval delay and the load on the data producer, the ICN can be a viable framework to support the Internet of Things (IoT), interconnecting billions of heterogeneous constrained objects. Among several ICN architectures, the Named Data Networking (NDN) is considered as a suitable ICN architecture for IoT systems. However, its default caching approach lacks a data freshness mechanism, while IoT data are transient and frequently updated by the producer which imposes stringent requirements in terms of information freshness. Furthermore, IoT devices are usually resource-constrained with harsh limitations on energy, memory and processing power. We propose in this paper a caching strategy and a novel cache freshness mechanism to monitor the validity of cached contents in an IoT environment while minimizing the caching process cost. We compared our solution to several relevant schemes using the ccnSim simulator. Our solution exhibits the best system performances in terms of hop reduction ratio, server hit reduction ratio and response latency, yet it provides the lowest cache cost and significantly improves the content validity.

International audience; An objective of satellite remote sensing is to predict or characterize the land cover change (LCC) over time. Sometimes we are capable of describing the changes of land cover with a probability distribution. However, we need sufficient knowledge about the natural variability of these changes, which is not always possible. In general, uncertainties can be subdivided into aleatory and epistemic. The main problem is that classical probability theory does not make a clear distinction between aleatory and epistemic uncertainties in the way they are represented, i.e., both of them are described with a probability distribution. The aim of this paper is to propagate the aleatory and epistemic uncertainty associated with both input parameters (features extracted from satellite image object) and model structure of LCC prediction process using belief function theory. This will help reducing in a significant way the uncertainty about future changes of land cover. In this study, the changes prediction of land cover in Cairo region, Egypt for next 16 years (2030) is anticipated using multi-temporal Landsat TM5 satellite images in 1987 and 2014. The LCC prediction model results indicated that 15% of the agriculture and 6.5% of the desert will be urbanized in 2030. We conclude that our method based on belief function theory has a potential to reduce uncertainty and improve the prediction accuracy and is applicable in LCC analysis.

Image hash is an alternative to cryptographic hash functions for checking integrity of digital images. Compared to cryptographic hash functions, an image hash or a Perceptual Hash Function (PHF) is resilient to content preserving distortions and sensitive to malicious tampering. In this paper, a robust and secure image hashing technique using a Gaussian pyramid is proposed. A Gaussian pyramid decomposes an image into different resolution levels which can be utilized to obtain robust and compact hash features. These stable features have been utilized in the proposed work to construct a secure and robust image hash. The proposed scheme uses Laplacian of Gaussian (LOG) and disk filters to filter the low-resolution Gaussian decomposed image. The filtered images are then subtracted and their difference is used as a hash. To make the hash secure, a key is introduced before feature extraction, thus making the entire feature space random. The proposed hashing scheme has been evaluated through a number of experiments involving cases of non-malicious distortions and malicious tampering. Experimental results reveal that the proposed hashing scheme is robust against non-malicious distortions and is sensitive to detect minute malicious tampering. Moreover, False Positive Probability (FPP) and False Negative Probability (FNP) results demonstrate the effectiveness of the proposed scheme when compared to state-of-the-art image hashing algorithms proposed in the literature.

International audience; During the last two decades, architecture of traditional Data Warehouses (DW) have played a key role in decision support system. A typical architecture of traditional DW involves source systems, components implementing data collection process, stores (a central repository and data marts), as well as consumers: reporting and analytic applications. However, the emergence of several new Internet services, Mobile applications, Web applications, Social media (Facebook, Twitter, and Instagram, so on), Devices, and Sensors, requires new Data warehousing systems and suitable architectures: large volume and format of collected datasets, data source variety, streaming, integration of unstructured data and powerful analytical processing. In this paper, we propose a two level architecture for Data warehousing and OLAP over Big Data. The first level Platform Independent Architecture (PIA) allows identifying and specifying the main components of the architecture to collect, store, transform and process the different kind of data. This level is technology independent and focuses only on the requirements of the data features and the processes needed to design a Data warehousing and OLAP over Big Data. The second level Platform Specific Architecture (PSA) allows specifying the platforms and technologies that would be used to achieve the different steps from the collection of data until the reporting and analytic applications.

International audience; Product line engineering is a new production paradigm that provides organizations a competitive edge by improving productivity and decreasing costs. The purpose with this new production paradigm is no longer to develop a single product but to develop a product family and to generate the products of the line through configuration processes. However, the potential benefits of product line engineering can be missed when dealing with large product lines because the configuration processes become error prone tasks. Consequently, guiding stakeholders during such complex configuration processes and recommending the best configuration alternatives until leading to a satisfying experience becomes a challenge. This paper focuses on enhancing product line configuration processes through process mining techniques. Therefore, user's actions of previous product line configurations are logged, mined and analyzed. We conducted a preliminary research to motivate the advantages of process mining in product line configuration and to explore what can process mining bring for configuration processes and how to use it to enhance configuration processes. Thus, guidance questions are sketched in order to position process mining as a solving tool for the configuration difficulties. Furthermore, we propose a reference architecture that considers process mining for configuring product lines.

In recent years, the diffusion of electric vehicles(EVs) is of great importance in the road transport and automotive sectors. Reducing electricity consumption and increasing battery autonomy are actually motivating vehicle manufacturers to focus their attention on the EV concept. The evaluation of EV performance through realistic scenarios in traffic simulations is still a key issue. In fact, the road traffic Simulator Simulation of Urban Mobility (SUMO) integrates an energy model for calculating the EV energy consumption. However, this model underestimates the energy consumption values in the real world. In this paper, we intend to investigate the energy consumption models existing in the literature. Then, we assess the performance of the existing energy model in SUMO through a set of simulation scenarios. Finally, we present the main requirements for a realistic, accurate and scalable energy model to estimate the instantaneous energy consumption for EVs.