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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.

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.

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; 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

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.

International audience

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.

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; 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.

A new hyperspectral texture descriptor, Relative Spectral Difference Occurrence Matrix (RSDOM) is proposed. Developed in a metrological framework, it simultaneously considers the distribution of spectra and their spatial arrangement in the hyperspectral image. It is generic and adapted for any number of spectral band or range. As validation, a texture classification scheme is applied on HyTexiLa dataset using RSDOM. The obtained accuracy is excellent (95.6%), comparable to Opponent Band Local Binary Pattern (OBLBP) but at a much-reduced feature size (0.1% of OBLBP's). © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

[Context] Product line engineering is a conception and production paradigm in which the purpose is no longer to develop a single product but to develop a collection of products. They present a strategy that provides to the organizations a more competitive edge by improving productivity and decreasing costs. However, these benefits can be missed if users find difficulties when they are configuring a product from the line. This situation is widespread mainly when they deal with large product lines that contain a plethora of characteristics. [Problem] In these cases, the configuration process becomes monotonous and an error prone task. Furthermore, it is crucial to guide stakeholders by providing assistance during such complex process. Consequently, the configuration process envisages recommending the best configuration alternatives to users until leading them to a satisfying experience. [Contribution] Our proposal aims to enhance future configuration processes and maximize the user satisfaction based on previous executions. Therefore, we mine and analyze previous product line configurations by logging user's actions. User guidance is orchestrated by a goal-question-metric approach. [Results] Our research has shown that guidance solutions can be proposed according to the trace mining. Integrating these solutions throughout the configuration process can assist potential users that they are configuring complex lines. The usability of analyzing traces in order to guide future configurations is demonstrated using an e-shop case study from the software industry.

International audience

International audience; Robust calibration of next-generation radio-interferometers, as the square kilometer array (SKA) for instance, is a crucial prepro-cessing step for sky imaging. Recently, several robust calibration es-timators based on the use of well known strong sources in the field of view (FOV) have been proposed in the literature. For that, usually a compound-Gaussian (CG) noise is considered since it can take into account the presence of outliers, such that unknown weak sources in the FOV, and remains mathematically tractable. Specifically, the CG model is a zero-mean Gaussian process with a random variance, usually called texture. Performance bounds provide the lowest mean squared error (MSE) that an unbiased estimator can hope to reach. To the best of our knowledge, there is a lack of practical bounds dedicated to the robust calibration of future radio interferometers as far as they can be used as design tools for a given FOV. In this work, the hybrid Cramér-Rao bound (HCRB) is derived for a propagation model based on the Jones-matrix formalism corrupted by a CG noise for several texture distributions (K-distribution, Student's t, Cauchy, and Inverse-Gaussian compound Gaussian distribution (IG-CG)). In this standard physical model, the waveform propagation model is parametrized by a set of deterministic physical parameters of interest (complex gains, phases, DOA, etc.). In this work, we show that the HCRB w.r.t. the physical deterministic parameters is in fact given by the modified CRB (MCRB). The MCRB is easy to derive since only the first-order moment of the inverse of the texture variable is needed.

With the increase in measurement/sensing technologies, the collected data are in intrinsically multidimensional in a large number of applications. This can be interpreted as a growth of the dimensionality/order of the associated tensor. There exists therefore a crucial need to derive equivalent and alternative models of a high-order tensor as a graph of low-order tensors. In this work we consider a " train " graph, i.e., a Q-order tensor will be represented as a Tensor Train (TT) composed of Q − 2 3-order core tensors and two core matrices. In this context, it has been shown that a canonical rank-R CPD / PARAFAC model can always be represented exactly by a TT model whose cores are canonical rank-R CPD/PARAFAC. This model is called TT-CPD. We generalize this equivalence to the PARALIND model in order to take into account potential linear dependencies in factors. We derive and discuss here uniqueness conditions for the case of the TT-PARALIND model., Avec l'accroissement des moyens d'acquisition et de mesures, les données d'intérêt sont par essence de nature multidimensionnelle. Ceci peut s'interpréter comme un accroissement de la dimension/ordre des modèles tensoriels associés. Il y a donc un besoin crucial d'avoir à notre disposition des représentations équivalentes d'un tenseur d'ordre élevé en un graphe de tenseurs d'ordre réduit. Dans ce travail nous considérons un graphe de type "train", c'est-à-dire, qu'un tenseur d'ordre Q sera représenté par un train de tenseur (TT) composé de Q − 2 coeurs tensoriels d'ordre 3 et deux coeurs matriciels. Dans ce cadre, il a été démontré qu'un modèle CPD/PARAFAC de rang canonique R peut être toujours représenté de manière exacte par un modèle TT dont les coeurs sont eux-même CPD/PARAFAC de rang canonique R. Ce modèle est nommé TT-CPD. Nous généralisons cette équivalence au modèle PARALIND afin de prendre en compte des potentielles dépendances linéaires dans les facteurs. Nous donnons et discutons ici les conditions d'unicité dans le cas du modèle TT-PARALIND.

Data as a Service (DaaS) is seen as a promising cloud offering for wrangling the overload of information and making it available across cloud platforms anytime and anywhere. While there exist a large number of DaaS providers in the market, each one has a different way to describe its provided services as well as supplied datasets. The lack of a well-defined machine-readable model strongly hinders the automatic selection and composition of DaaSs. This paper presents MoDaaS, a model-driven framework for the modeling and the description of DaaS services. MoDaaS enables DaaS providers to describe their services capabilities and concerns according to a shared ontology, thereafter it enables them to automatically generate service views in order to assist the integration and data exchange between heterogeneous services.

International audience; Unmanned Aerial Vehicles (UAVs) have known much popularity for dangerous missions for human operators or for applications which do not need human intervention (such as monitoring and surveillance of physical infrastructures and interest areas). They operate in frequency bands (IEEE L-Band, IEEE S-Band, and ISM band) shared with other users. Accordingly, these frequency bands have become overcrowded and UAVs may face the issue of spectrum scarcity. Furthermore, there are particular difficulties associated with aeronautical communication links. Cognitive radio (CR) has emerged as a promising strategy for resolving the problems caused by scarce spectrum. It checks the spectrum availability and allows the adjustment of the transmission parameters. The aim is to opportunistically use spectral bands with minimum interference to applications or other users. In this paper, we present a centralized CR based frequency allocation scheme for UAV-Ground Control Station (GCS) communication in surveillance applications within an urban environment. In the proposed model, the GCS monitors and allocates available WiMAX frequencies using CR and Software Defined Radio (SDR). If no WiMAX frequency is available at a given time, the Wi-Fi will be used. Therefore in the worst case, our approach will have the same performance as when the Wi-Fi is only used for UAV-GCS communication.

International audience; In recent years, a large number of researchers investigate the conception of systems that use a unique Unmanned Ariel Vehicles (UAV) or multiple independent UAVs to conduct civil or military missions, with minimal human intervention. In this paper we focus on using multiple UAVs to cooperatively monitor a crowded area. Communication in such UAVs network is an ongoing project. Due to the lack of proper communication standards and rules, designing a reliable communication model is essential for: (i) multi-UAV coordination, (ii) efficient bandwidth sharing according to data priority and urgency and (iii) avoiding useless transmission of the same data by multiple UAVs. To address the above challenges, we propose a centralized data-oriented communication architecture for crowd surveillance allocations using an UAV fleet. The Ground Control Station (GCS) is used as a central coordinator to manage bandwidth usage for the UAV fleet in its coverage area. To allow UAVs to send priority messages urgently to the GCS, we define two classes of urgent messages: critical state and important result. The class of the data as well as other relevant information about the detected event will be used by the GCS to authorize or not UAV data transmission and hence to optimize the bandwidth usage efficiency.

This thesis deals with the formal description and development of an electronic grammar of Arabic language. This work is a prerequisite for the creation of automatic Arabic processing tools. This language presents many challenges for automatic processing. Indeed the order of words in Arabic is relatively free,the morphology is rich and the diacritics are omitted in written texts. Although several research studies have addressed some of these issues, electronic resources useful for the processing of Arabic remain relatively rare or not widely available. In this thesis work, we are interested in the representation of syntax (word order) and the meaning of modern standard Arabic. As a formal system of language representation, we chose the formalism of Tree Adjoining Grammar. Thus we proposed an electronic adjoint tree grammar of Arabic named"ArabTAGV2.0". This resource partially reuses the pre-existing modeling in the manually defined grammar "ArabTAG" and integrates it into an abstract representation called meta-grammar. The linguistic expert canthus describe the syntax and semantics of the language with abstraction tools facilitating the maintenance and extension of the grammar. The new described grammar has 1074 syntactical rules (not lexicalized) and27 semantic frameworks (predicative relations). This resource was evaluated by analyzing a corpus from excerpts of an Arabic textbook.; Cette thèse traite de la description formelle et du développement d’une grammaire électronique de la langue arabe. Ce travail est un prérequis à la création d’outils de traitement automatique de l’arabe.Cette langue présente de nombreux défis pour un traitement automatique. En effet l’ordre de mots en arabe est relativement libre, la morphologie y est riche et les diacritiques sont omis dans les textes écrits. Bien que plusieurs travaux de recherche aient abordé certaines de ces problématiques, les ressources électroniques utiles pour le traitement de l’arabe demeurent relativement rares ou encore peu disponibles. Dans ce travail de thèse, nous nous sommes intéressés à la représentation de la syntaxe (ordre des mots) et du sens de l’arabe standard moderne. Comme système formel de représentation de la langue, nous avons choisi le formalisme des grammaires d’arbres adjoints (Tree Adjoining Grammar). Nous avons ainsi proposé une grammaire d’arbres adjoints électronique de l’arabe nommée « ArabTAG V2.0 ». Cette ressource réutilise en partie la modélisation préexistante dans la grammaire définie manuellement «ArabTAG » et l’intègre à une représentation abstraite appelée méta-grammaire. L’expert linguiste peut ainsi décrire la syntaxe et sémantique de la langue avec des outils d’abstraction facilitant la maintenance et l’extension de la grammaire. La grammaire ainsi décrite compte 1074 règles syntaxiques (non lexicalisées) et 27 cadres sémantiques (relations prédicatives). Cette ressource a été évaluée en analysant un corpus issu d’extraits d’un manuel scolaire d’apprentissage de l’arabe.

This thesis deals with the formal description and development of an electronic grammar of Arabic language. This work is a prerequisite for the creation of automatic Arabic processing tools. This language presents many challenges for automatic processing. Indeed the order of words in Arabic is relatively free,the morphology is rich and the diacritics are omitted in written texts. Although several research studies have addressed some of these issues, electronic resources useful for the processing of Arabic remain relatively rare or not widely available. In this thesis work, we are interested in the representation of syntax (word order) and the meaning of modern standard Arabic. As a formal system of language representation, we chose the formalism of Tree Adjoining Grammar. Thus we proposed an electronic adjoint tree grammar of Arabic named"ArabTAGV2.0". This resource partially reuses the pre-existing modeling in the manually defined grammar "ArabTAG" and integrates it into an abstract representation called meta-grammar. The linguistic expert canthus describe the syntax and semantics of the language with abstraction tools facilitating the maintenance and extension of the grammar. The new described grammar has 1074 syntactical rules (not lexicalized) and27 semantic frameworks (predicative relations). This resource was evaluated by analyzing a corpus from excerpts of an Arabic textbook.; Cette thèse traite de la description formelle et du développement d’une grammaire électronique de la langue arabe. Ce travail est un prérequis à la création d’outils de traitement automatique de l’arabe.Cette langue présente de nombreux défis pour un traitement automatique. En effet l’ordre de mots en arabe est relativement libre, la morphologie y est riche et les diacritiques sont omis dans les textes écrits. Bien que plusieurs travaux de recherche aient abordé certaines de ces problématiques, les ressources électroniques utiles pour le traitement de l’arabe demeurent relativement rares ou encore peu disponibles. Dans ce travail de thèse, nous nous sommes intéressés à la représentation de la syntaxe (ordre des mots) et du sens de l’arabe standard moderne. Comme système formel de représentation de la langue, nous avons choisi le formalisme des grammaires d’arbres adjoints (Tree Adjoining Grammar). Nous avons ainsi proposé une grammaire d’arbres adjoints électronique de l’arabe nommée « ArabTAG V2.0 ». Cette ressource réutilise en partie la modélisation préexistante dans la grammaire définie manuellement «ArabTAG » et l’intègre à une représentation abstraite appelée méta-grammaire. L’expert linguiste peut ainsi décrire la syntaxe et sémantique de la langue avec des outils d’abstraction facilitant la maintenance et l’extension de la grammaire. La grammaire ainsi décrite compte 1074 règles syntaxiques (non lexicalisées) et 27 cadres sémantiques (relations prédicatives). Cette ressource a été évaluée en analysant un corpus issu d’extraits d’un manuel scolaire d’apprentissage de l’arabe.

International audience; To fulfill its complex business demands, an organization may require the integration of many SaaS applications. A main challenge in this regard is to select the suitable SaaS offerings that satisfy all the business requirements. Due to the diversity of QoS needs and the large number of SaaS providers, selecting the suitable set of SaaS applications to be integrated may result in QoS degradation and higher costs. In this paper, we propose to use a System-of-Systems (SoS)-based approach for the selection and integration of SaaS. We believe that the use of SoS and its dynamic properties could improve the service selection at SaaS level. To do so, we propose three Mixed Integer Programming models that optimize the global SaaS behavior by minimizing the overall delivery costs, and maximizing the global functional and QoS utilities. The implementation of the approach and the experimental results highlight the efficiency of our proposed solution.

The explosion of web 2.0 and social networks has created an enormous and rewarding source of information that has motivated researchers in different fields to exploit it. Our work revolves around the issue of access and identification of social information and their use in building a user profile enriched with a social dimension, and operating in a process of personalization and recommendation. We study several approaches of Social IR (Information Retrieval), distinguished by the type of incorporated social information. We also study various social recommendation approaches classified by the type of recommendation. We then present a study of techniques for modeling the social user profile dimension, followed by a critical discussion. Thus, we propose our social recommendation approach integrating an advanced social user profile model. L’explosion du web 2.0 et des réseaux sociaux a crée une source d’information énorme et enrichissante qui a motivé les chercheurs dans différents domaines à l’exploiter. Notre travail s’articule autour de la problématique d’accès et d’identification des informations sociales et leur exploitation dans la construction d’un profil utilisateur enrichi d’une dimension sociale, et son exploitation dans un processus de personnalisation et de recommandation. Nous étudions différentes approches sociales de RI (Recherche d’Information), distinguées par le type d’informations sociales incorporées. Nous étudions également diverses approches de recommandation sociale classées par le type de recommandation. Nous exposons ensuite une étude des techniques de modélisation de la dimension sociale du profil utilisateur, suivie par une discussion critique. Ainsi, nous présentons notre approche de recommandation sociale proposée intégrant un modèle avancé de profil utilisateur social.

Nowadays, Wireless Body Area Networks (WBANs) are revolutionizing the field of healthcare applications which is deeply related to human life and requires a noteworthy attention. In this medical context, reliability, energy consumption and real time monitoring are the main Quality of Service (QoS) challenges to satisfy. This kind of networks relies on wireless channel medium which is sensitive to several factors such as body shadowing, interference, human postures and body signal absorption. In this paper, our main focus is to exploit the knowledge related to the link quality to select the most appropriate Body Node Coordinator (BNC) in order to improve the overall network performance. Our experimental study which is based on real testbeds revealed that the position of the BNC is sensitive to human morphology and practiced activity.

International audience; The increasing amount of remote sensing data has opened the door to new challenging research topics. Nowadays, significant efforts are devoted to pixel and object based classification in case of massive data. This paper addresses the problem of semantic segmentation of big remote sensing images. To do this, we proposed a top-down approach based on two main steps. The first step aims to compute features at the object-level. These features constitute the input of a multi-layer feed-forward network to generate a structure for classifying remote sensing objects. The goal of the second step is to use this structure to label every pixel in new images. Several experiments are conducted based on real datasets and results show good classification accuracy of the proposed approach. In addition, the comparison with existing classification techniques proves the effectiveness of the proposed approach especially for big remote sensing data.

International audience; In-network caching in Named Data Networking (NDN) based Internet of Things (IoT) plays a central role for efficient data dissemination. Data cached throughout the network may quickly become obsolete as they are transient and frequently updated by their producers. As such, NDN-based IoT networks impose stringent requirement in terms of data freshness. While various cache replacement policies were proposed, none has considered the cache freshness requirement. In this paper, we introduce a novel cache replacement policy called Least Fresh First (LFF) integrating the cache freshness requirement. LFF evicts invalid cached contents based on time series forecasting of sensors future events. Extensive simulations are performed to evaluate the performance of LFF and to compare it to the different well-known cache replacement policies in ICN-based IoT networks. The obtained results show that LFF significantly improves data freshness compared to other policies, while enhancing the server hit reduction ratio, the hop reduction ratio and the response latency.