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International audience; Interest and need for Wireless Body Area Networks (WBANs) havesignicantly increased recently. WBAN consists of miniaturized sensors de-signed to collect and transmit data through wireless network, enabling medicalspecialists to monitor patients during their normal daily life and providing realtime opinions for medical diagnosis. Many wireless technologies have provedthemselves in WBAN applications, while others are still under investigations.The choice of the technology to adopt may depend on the disease to moni-tor and the performance requirements, i.e. reliability, latency and data rate.In addition, the suitable sensor is essential when seeking to extract the datarelated to a medical measure.This paper aims at surveying the wireless technologies used in WBAN sys-tems. In addition to a detailed survey on the existing technologies, the use ofthe emerging Low Power Wide Area Network (LPWAN) technologies, and thefuture 5G, B5G and 6G is investigated, where the suitability of these technolo-gies to WBAN applications is studied from several perspectives. Furthermore,medical applications of WBAN are discussed by presenting their methodolo-gies, the adopted wireless technologies and the used sensors. Given that eachmedical application requires the appropriate sensor to extract the data, wehighlight a wide range of the sensors used in the market for medical systems.Recent and future challenges in WBAN systems are given related to the powerconsumption, the emergence of the Internet of Things (IoT) technologies inWBAN and others.

International audience; Automation throughout history has caused profound changes in employment dynamics. With the advent of the fourth industrial revolution, a new threat may affect employability, asrobots and AI-based processes can now assume tasks considered exclusive to humans. This position paperaims to motivate the study of the effects of AI and automation on employability, extending it into a collaborative network perspective. The problem is firstly observed from a historical perspective. The collaboration aspects are considered through the analysis of two casestudies. Results suggestthata latent element of collaborative networks, complexity, may have effects in terms of employability.

International audience; The Digital Twin has the ability to store historical and current data about the physical object. In IoT, the concept of general-purpose sensing is aiming at determining a set of basic sensing capabilities from which deriving, by means of Artificial Intelligence algorithms and techniques (termed Synthetic Sensing), relevant information for describing an environment. This paper explores the possible relationships and the feasibility of integration of Synthetic Sensing within a Digital Twin framework. The relevant concepts and associated technologies, the challenges, and some of the enabled scenarios that this integration can bring are presented. This approach is in its infancy and there is a strong need to validate and assess its viability, feasibility, and benefits. The paper identifies some challenges and validation steps that can lead to consolidation and broad adoption of this approach. Finally, the paper presents some future work that aiming at proving the approach.

International audience; We reviewed research papers related to pandemics/epidemics (disease outbreaks of a global/regional scope) published in major operations management, operations research, and management science journals through the end of 2019. We evaluate and categorize these papers. We study research trends, explore research gaps, and provide directions for more efficient and effective research in the future. In addition, our recommendations include the lessons learned from the ongoing pandemic, COVID-19. We discuss papers in the following categories: (a) warning signals/surveillance, (b) disease propagation leading to pandemic conditions, (c) mitigation, (d) vaccines and therapeutics development, (e) resource management, (f) supply chain configuration, (g) decision support systems for managing pandemics/epidemics, and (h) risk assessment.

International audience; The co-utilization of torrefied biomass and coal in thermochemical conversion technologies is an attractive process for the transition to green energy and chemicals. The main advantage of this process is the production of high quality oil products (closer resembling crude-oil) via synergistic interaction of primary products from the two feedstocks. These synergistic reactions often involve secondary reactions, which are promoted at high pressures. This paper reports quantitative results on the extent of synergistic reactions and the role of pressure on these reactions during co-pyrolysis of torrefied biomass and coal. Torrefied biomass was produced at 280 • C in a pilot rotary kiln and subsequently both pyrolysis and co-pyrolysis of torrefied biomass and coal was investigated in a fixed bed reactor (heating rate 7 • C/min) at temperatures of 400-600 • C and pressures of 1, 15 and 30 bar. The results show that the prior removal of hemicellulose during torrefaction maximized the potential of hydrogen transfer from cellulose/lignin-derived products to depolymerized coal fragments. Furthermore, the dehydration and condensation reactions of depolymerized fragments were suppressed during co-pyrolysis in favour of synergistic reactions between the fragments. These reactions occurred predominantly in the molten/ liquid phase and their rates could be indirectly controlled by pressure (through changing the evaporation rate), resulting in substantial changes in product distribution. In the presence of coal and its released vapours, the methoxyphenols (guaiacols) and furanics yields were significantly enhanced (positive deviation >54% and >40%, respectively, compared to additive predictions), whereas the phenol yields were inhibited (negative deviation >20%); suggesting the inhibition of demethoxylation reactions in the presence of coal depolymerized fragments. To date no studies have reported the molten phase synergistic reaction pathways occurring during copyrolysis of torrefied biomass and coal. Based on the findings in this paper, reaction pathways were proposed for these molten phase synergistic reactions.

Most public blockchain protocols, including the popular Bitcoin and Ethereum blockchains, do not formally specify the order in which miners should select transactions from the pool of pending (or uncommitted) transactions for inclusion in the blockchain. Over the years, informal conventions or "norms" for transaction ordering have, however, emerged via the use of shared software by miners, e.g., the GetBlockTemplate (GBT) mining protocol in Bitcoin Core. Today, a widely held view is that Bitcoin miners prioritize transactions based on their offered "transaction fee-per-byte." Bitcoin users are, consequently, encouraged to increase the fees to accelerate the commitment of their transactions, particularly during periods of congestion. In this paper, we audit the Bitcoin blockchain and present statistically significant evidence of mining pools deviating from the norms to accelerate the commitment of transactions for which they have (i) a selfish or vested interest, or (ii) received dark-fee payments via opaque (non-public) side-channels. As blockchains are increasingly being used as a record-keeping substrate for a variety of decentralized (financial technology) systems, our findings call for an urgent discussion on defining neutrality norms that miners must adhere to when ordering transactions in the chains. Finally, we make our data sets and scripts publicly available., This is a pre-print of our paper accepted to appear to ACM IMC 2021

International audience; Systems engineering, or engineering in general, has long been relying on document-centric approaches. Switching to model-based systems engineering, or MBSE for short, has extensively been discussed over the past three decades. Since about two decades, MBSE has been commonly associated with the modeling language SysML (Systems Modeling Language), which offers a standardized notation, not a methodology of using it. SysML needs therefore to be associated with a methodology supported by tools. In this paper, a methodology supported by the free and open-source software TTool is associated with SysML. This paper focuses discussion on methodological issues, leading the authors to share their experience in real-time systems modeling. Modeling with SysML is more than just drawing the different diagrams. Associated tools offer possibilities to analyze SysML models for specific properties. In this paper, verification addresses both safety and security properties. The TTool model checker inputs the SysML model enriched with safety properties to be verified and outputs an yes/no answer for each property. Security verification checks SysML models against confidentiality, integrity, and authenticity properties. As an illustration of the proposed approach, an aircraft cockpit door control system is modeled in SysML and verified against safety and security properties.

International audience; Business process optimization is a strategic activity in organizations because of its potential to increase profit margins and reduce operational costs. One of the main challenges in this activity is concerned with the problem of optimizing allocation and sharing of resources. Companies are continuously adjusting their resources to their needs following different strategies. However, the dynamic provisioning strategies are hard to compare. This paper proposes an automatic analysis technique to evaluate and compare the execution time and resource occupancy of a business process relative to a workload and a provisioning strategy. Four different strategies are presented, which are guided-respectively-by recent resource usage, recent resource request, predicted behavior, and a combination of available strategies. Such analysis is performed on models conforming to an extension of BPMN with quantitative information, including resource availability and constraints. Within this framework, the approach is fully mechanized using a formal and executable specification in the Maude rewriting logic framework, which relies on existing techniques and tools for simulating probabilistic and real-time specifications. The paper includes results on the extensive experimentation that has been carried out for validation purposes.

The use of two-phase nozzles for low-grade heat valorization by electricity production increases the energy recovery rate using Trilateral Flash or Wet to Dry cycles. A model benchmark for nozzle flow rate and efficiency estimation was conducted on experimental data from the literature. These data come from a series of tests made on geothermal energy production water two-phase nozzles. A new transitional bubble-to-droplet interfacial area density formulation (TA-BD model) is presented by the paper. It is compared to three models from literature. Two nozzles operating with different inlet and outlet conditions were modeled. The calibration flexibility and the robustness of the models are discussed in association with physical analysis. The paper shows how the models using single or redundant adaptation parameters fail to provide good results simultaneously on flow rate and efficiency. It appeared that the TA-BD model is the more flexible and robust. The Homogeneous Relaxation Model (HRM) model gives also good results. Furthermore, TA-BD model gives the lowest average discrepancies. Especially at the best efficiency point of the first test case, TA-BD model shows less than 1% discrepancy where the HRM model has 18% discrepancy in efficiency. The TA-BD model appeared to be easier to calibrate than the HRM model. Finally, regarding the proposed TA-BD model, the sensitivity to the geometry and operating conditions shows that the interfacial area density formulation could be completed to include the effect of nozzle's section profile, the effect of the inlet temperature on bubbles number density, and the effect of outlet pressure on the droplets number density.

International audience; Dwindling fossil fuel reserves and global climate change drive researchers to discover and develop new strategies to derive energy from renewable sources, such as biomass, including wood. However, when poorly controlled, wood burning can be a source of atmospheric pollution. With the ultimate purpose of better controlling pollutant emissions from domestic small combustion installations, this paper follows two objectives. First, temperature and pollutant measurements obtained at the chimney outlet of a domestic inset fed with wood logs and working under nominal operating conditions are presented. Measured pollutants include CO, CO 2 , nitrogen oxides (NOx), monoaromatic, carbonyl, and phenolic compounds, as well as polycyclic-aromatic hydrocarbons (PAHs) and sugars with measurement made using different types of wood. Second, this paper describes a first attempt of modeling based on a detailed chemistry and tests it for simulating the measured pollutants. The model includes a previously developed detailed chemical kinetic mechanism and a simplified model of thermal transfer in the wood log. A network of ideal reactors (equivalent reactor network (ERN)), for which simulations using the detailed kinetic model are feasible, is proposed to represent both the primary pyrolysis and the combustion of the emitted gaseous species. With only adjusting the parameters used in the model in order to simulate the smoke temperature and the CO 2 mole fraction for a single batch well, simulations give a reasonably good order of magnitude for all of the measured pollutants for the seven used wood batches. A sensitivity analysis of the used parameters and of the structure of the ERN model is also presented.

Time series are ubiquitous in data mining applications. Similar to other types of data, annotations can be challenging to acquire, thus preventing from training time series classification models. In this context, clustering methods can be an appropriate alternative as they create homogeneous groups allowing a better analysis of the data structure. Time series clustering has been investigated for many years and multiple approaches have already been proposed. Following the advent of deep learning in computer vision, researchers recently started to study the use of deep clustering to cluster time series data. The existing approaches mostly rely on representation learning (imported from computer vision), which consists of learning a representation of the data and performing the clustering task using this new representation. The goal of this paper is to provide a careful study and an experimental comparison of the existing literature on time series representation learning for deep clustering. In this paper, we went beyond the sole comparison of existing approaches and proposed to decompose deep clustering methods into three main components: (1) network architecture, (2) pretext loss, and (3) clustering loss. We evaluated all combinations of these components (totaling 300 different models) with the objective to study their relative influence on the clustering performance. We also experimentally compared the most efficient combinations we identified with existing non-deep clustering methods. Experiments were performed using the largest repository of time series datasets (the UCR/UEA archive) composed of 128 univariate and 30 multivariate datasets. Finally, we proposed an extension of the class activation maps method to the unsupervised case which allows to identify patterns providing highlights on how the network clustered the time series.

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.

In this paper, we analyze the effect of an enhanced voltage flip technique on the power performance of a piezoelectric energy harvester. The enhanced voltage flip principle is based on a synchronized-switch-based architecture, and is referred to as FAR (Full Active Rectifier). It uses a tiny amount of the stored charge to boost the voltage flip. This work aims to demonstrate that, beside the enhanced flip efficiency, the FAR also contributes to improve the power efficiency of the harvester, especially under changing load constraint. Therefore, the paper proposes a thorough comparison between the FAR and its conventional counterpart, the Switch-only technique. The FAR is easy to implement and does not require any external inductor or capacitor. It only needs a reduced set of switches, an active diode and a simple control sequence, and can thus be implemented on a fully integrated circuit. The FAR can be used as a standalone voltage flip solution or in addition to further boost the flip efficiency in a state-of-the-art architecture such as SSHC for example. Tests were performed on a 0.35-µm process CMOS prototype IC. Experimental results revealed that the FAR extracts 19.1μW from an off-the-shelf piezoelectric transducer when the output voltage is regulated at 1 V with 1 V open-circuit voltage and delivers up to 20% more power than the conventional Switch-only technique under load constraint. It also shows over 11× power efficiency improvement compared to a conventional diode-based full bridge rectifier.

An integrated dynamic model of natural gas pipeline networks is developed in this paper. Components for gas supply, e.g., pipelines, junctions, compressor stations, LNG terminals, regulation stations and gas storage facilities are included in the model. These components are firstly modeled with respect to their properties and functions and, then, integrated at the system level by Graph Theory. The model can be used for simulating the system response in different scenarios of operation, and evaluate the consequences from the perspectives of supply security and resilience. A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio. Finally, the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view. The main contributions of the paper are: firstly, a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level; a method, based on the dynamic model, is proposed to analyze the security and resilience of supply from a system perspective.

PurposeThe article provides a conceptual replication and enrichment of the status quo bias theory in the specific context of understanding IT department user resistance and user adoption. The findings can assist technology renewals and associated change management professionals to assess and plan the adoption and active usage of human resource systems.Design/methodology/approachThe authors used survey method to gather data. All items were based on prior literature. They administrated the survey to employees of GOODTECH (name changed), information systems (IS) department members, situated in France. They obtained 103 valid responses along with usage data from the system to run their path model, in order to validate the proposed research model.FindingsThe study offers an enriched user resistance model (URM) to understand why IT-savvy employees would resist or adopt new human resource tools. Apart from providing partial validity to status quo bias theory in the French context, the enriched model uses behavioral intention to use as an intermediate variable to explain the influence of two key constructs of the original theory: switching cost and switching benefits. This research provides a better explanatory power to understand the cause of user resistance and new IT use.Research limitations/implicationsThe sample size used in the study can be considered as a limitation, although power analysis reveals that the results are significant and valid. The context of the study is also limited to one country and to a specific type of IS implementation scenario. Since the purpose of the paper was to offer contextual theory enhancement, the findings are valid for this purpose.Practical implicationsDigital project managers are offered a framework to increase technology adoption of new human resource tools and evaluate how to reduce user resistance at times of technology renewals. Self-efficacy for change and colleagues’ opinion can indirectly impact behavioral intention to use via switching cost and switching benefit perceptions and thus reducing resistance perceptions as well as increasing adoption of new IT tools in post-implementation phases.Originality/valueThe paper enriches the well-established user resistance theory in IS domain in a context of human resource post-implementation phase by studying IT-savvy end user's perceptions. The paper demonstrates the need to integrate user adoption and user resistance variables in one parsimonious framework and extends support to emerging research on dual focus perspective.

In this paper, we propose a method for coarse camera pose computation which is robust to viewing conditions and does not require a detailed model of the scene. This method meets the growing need of easy deployment of robotics or augmented reality applications in any environments, especially those for which no accurate 3D model nor huge amount of ground truth data are available. It exploits the ability of deep learning techniques to reliably detect objects regardless of viewing conditions. Previous works have also shown that abstracting the geometry of a scene of objects by an ellipsoid cloud allows to compute the camera pose accurately enough for various application needs. Though promising, these approaches use the ellipses fitted to the detection bounding boxes as an approximation of the imaged objects. In this paper, we go one step further and propose a learning-based method which detects improved elliptic approximations of objects which are coherent with the 3D ellipsoid in terms of perspective projection. Experiments prove that the accuracy of the computed pose significantly increases thanks to our method and is more robust to the variability of the boundaries of the detection boxes. This is achieved with very little effort in terms of training data acquisition -- a few hundred calibrated images of which only three need manual object annotation. Code and models are released at https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization., Comment: Presented at 3DV 2020. Code and models released at https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization

International audience; Electricity plays a significant role in daily life and is the main component of countless applications. Thus, ongoing research is necessary to improve the existing approaches, or find new approaches, to enhancing power generation. The thermoelectric generator (TEG) is among the notable and widespread technologies used to produce electricity, and converts waste energy into electrical energy using the Seebeck effect. Due to the Seebeck effect, temperature change can be turned into electrical energy; hence, a TEG can be applied whenever there is a temperature difference. The present paper presents the theoretical background of the TEG, in addition to a comprehensive review of the TEG and its implementation in various fields. This paper also sheds light on the new technologies of the TEG and their related challenges. Notably, it was found that the TEG is efficient in hybrid heat recovery systems, such as the phase change material (PCM), heat pipe (HP), and proton exchange membrane (PEM), and the efficiency of the TEG has increased due to a set of improvements in the TEG’s materials. Moreover, results show that the TEG technology has been frequently applied in recent years, and all of the investigated papers agree that the TEG is a promising technology in power generation and heat recovery systems.

In this work, microwave planar resonators are printed with silver nanoparticle inks using two printing technologies, inkjet printing and aerosol jet printing, on polyimide substrates. The microwave resonators used in this paper operate in the frequency band 5–21 GHz. The printing parameters, such as the number of printed layers of silver nanoparticle inks, drop spacing, and sintering time, were optimized to ensure repeatable and conductive test patterns. To improve the electrical conductivity of silver deposits, which are first dried using a hot plate or an oven, two complementary sintering methods are used: intense pulsed light (IPL) and laser sintering. This paper presents the results of different strategies for increasing the final quality factor of printed planar resonators and the trade-offs (sintering time versus final conductivity/unloaded Q) that can be reached. Improvement of the resonator unloaded quality factor (up to +55%) and of the equivalent electrical conductivity (up to 14.94 S/μm) at 14 GHz have been obtained thanks to these nonconventional sintering techniques. The total sintering durations of different combinations of sintering techniques (hot plate, oven, IPL, and laser) range from 960 to 90 min with a final conductivity from 14.94 to 7.1 S/μm at 14 GHz, respectively.

The Two-Dimensional Fast Fourier Transform (2D-FFT) algorithm is used for the study of many modern systems applied for security and biometrics. The adoption of this algorithm, which is a compute intensive task, is limited due to its hardware design complexity. The first objective of this paper is to underline the effect of the hardware/software co-design (Hw/Sw co-design) for the reduction of the processing time and power consumption. Secondly, we propose an innovative architecture for the 2D-FFT algorithm tested on Zynq Soc, which requires less processing time and memory compared to the traditional algorithm. Three implementations (one software and two Hw/Sw co-designs) of the 2D-FFT algorithm using the Zynq SoC are presented in this paper. The first is based on ARM processor. A speedup of 29x is obtained compared to the original implementation thanks to many optimizations. The second is a Hw/Sw co-design solution of the traditional 2D-FFT algorithm introduced on a hybrid platform combining an ARM Cortex-A9 processor with an FPGA. The third is also a Hw/Sw co-design solution using our optimized 2D-FFT algorithm to reach the real-time contraints for high-resolution images (1920 × 1080). It provides a speedup of 1.13x, 3.31x and 96.21x faster than the Hw/Sw co-design implementation of the traditional RC algorithm, the pure software implementations with and without optimizations, respectively.

The Last decade saw significant growth in the digital terrestrial television (DTT) subscribers. The High peak-to-average power ratio (PAPR) of the transmitted signal is implied by the large fluctuations in its amplitude, which is a serious issue in the contemporary and next generation DTT broadcasting systems. These large fluctuations prevent feeding the high power amplifier (HPA) at an operating point near its non-linear (NL) saturation region, thereby lowering its power efficiency. For a better future, energy-efficient green communications is the need of hour. This paper focuses on digital predistortion (DPD), which is among the widely used techniques for HPA linearization in order to minimize the NL effects. DPD technique requires a feedback signal. This paper proposes a novel adding signal-based digital predistortion, where the output of the HPA is gradually linearized by adding a signal at the input in an iterative manner. By Taylor series approximation, the DPD problem can be posed as a convex one. We prove that a tighter upper bound exists for the convergence factor and instead of directly solving the convex problem, we pick a convergence factor value within its bounds that offers a good trade-off between convergence speed and residual error value. The simulation results show that the proposed technique can quickly linearize the HPA with good convergence.

International audience; The Internet of Things (IoT) is currently used in many wireless sensor network (WSN) applications. Conventionally, the WSN’s energy consumption is considered as one of the prime concerns. The energy consumption is mostly due to the sensing, data processing, communications and other wasted energies such as idle listening, collisions and overhearing. These sensor nodes are regularly powered by an external power source and consequently have a shorter lifetime. Fortunately, different approaches for wireless energy harvesting (WEH) have been improved. Thus, wake-up radio (WuR) becomes a remedy for WEH which provides active, passive and semi-passive circuits consumption and other protocols. For instance, the most used, convincing and productive go to passive WuR which can significantly increase the network lifetime in a sensor network (SN) by decreasing unnecessary idle listening. Eventually, this paper proposes a state of the art in active, semi-passive, centering more on passive WuR and covers the applications areas. Then, an overview of WuR related to physical, medium access control (MAC) and routing layers. Lastly, the paper accentuates the potential research opportunities for wake-up technology for future IoT applications.

International audience; Copy-move is a very popular image falsification where a semantically coherent part of the image, the source area, is copied and pasted at another position within the same image as the so-called target area. The majority of existing copy-move detectors search for matching areas and thus identify the source and target zones indifferently, while only the target really represents a tampered area. To the best of our knowledge, at the moment of preparing this paper there has been only one published method called BusterNet that is capable of performing source and target disambiguation by using a specifically designed deep neural network. Different from the deep-learning-based BusterNet method, we propose in this paper a source and target disentangling approach based on local statistical model of image patches. Our proposed method acts as a secondstage detector after a first stage of copy-move detection of duplicated areas. We had the following intuition: even if no manipulation (e.g., scaling and rotation) is added on target area, its boundaries should expose a statistical deviation from the pristine area and the source area; further, if the target area is manipulated, the deviation should appear not only on the boundaries but on the full zone. Our method relies on machine learning tool with Gaussian Mixture Model to describe likelihood of image patches. Likelihoods are then compared between the pristine region and the candidate source/target areas as identified by the first-stage detector. Experiments and comparisons demonstrate the effectiveness of the proposed method.

The current evolution of technical and environmental regulations and the recent development of hydrogen mobility require technological breakthroughs in the industrial areas, such as in harbours. Thus, multi-energy microgrids integrating low-carbon energy sources, electrical and hydrogen loads and storage solutions have to be designed. To improve their economic viability, the microgrid components must be carefully sized and the energy must be distributed in the most cost-effective way at any time. So as to foster the expansion of multi-energy microgrids in industrial areas, we propose in this paper a two-level optimization for the energy management and the sizing, applied to an original multi-energy scenario considering electricity and hydrogen as energy vectors. The designed energy management optimization takes into account an objective of economic profitability and constraints related to the availability of the sources and storage solutions, their reliability and the costs. The sizing optimization aims to propose different solutions allowing the benefits to be maximized and the expenses to be minimized. One of the main contributions of the paper is to compare the possible ways of valorisation of the energy available in an industrial microgrid. The results show that the sale of hydrogen allows income to be increased, in addition to self-consumption and sale to the electrical grid, but the electrolyzer involves high investments costs. Finally, a sensitivity analysis is presented and shows that the investment costs of electrolyzer and battery and the hydrogen selling price are key points in the optimal design.

A major drawback of nonlinear dimensionality reduction (DR) techniques is their inability to preserve some authentic information from the source domain, leading to projections that are often hard to interpret when it comes to observing anything other than the topological structure of the data. In this paper, we propose a nonlinear DR approach enforcing projection constraints resulting from an a priori knowledge about the structure of the data in multivariate periodical time series. We then propose several ways of exploiting this constrained projection to extract user-relevant information, such as the nominal behavior of a periodical dynamical system or the deviant behaviors which may occur at different time scales. The techniques are demonstrated on both a synthetic dataset composed of simulated multivariate data exhibiting a periodical behavior, and a real dataset corresponding to six months of sensor data acquisitions and recordings inside experimental buildings. 1 We would like to thank the Institut National de l'Energie Solaire (INES) and the CEA, LITEN, Laboratoire Energetique du Bâtiment for providing the data.

International audience; Licensed Shared Access (LSA) is a new concept proposed by the radio spectrum policy group in order to optimize spectrum usage: a Mobile Network Operator (MNO) can access temporarily to other incumbent's spectrum after obtaining a license. The licensing process is made via an auction mechanism. The mechanisms proposed in the literature for the LSA context are oneshot auction mechanisms which allocate all the available spectrum as a unique block. In this paper, we first show how to increase the performance of those auctions (in terms of revenue, efficiency and fairness of the allocation) while preserving truthful bidding, by splitting spectrum and converting single block auctions into multi-block auctions. Simulation results illustrate how appropriately choosing the number of blocks allows to increase the aforementioned metrics. Second, we show how to convert one-shot mechanisms to equivalent ascending mechanisms (in terms of allocations and payments) so that we add transparency and privacy to the auction.

International audience; Historically, the study of mountain hydrology and the water cycle has been largely observational, with meteorological forcing and hydrological variables extrapolated from a few infrequent manual measurements. Recent developments in Internet of Things (IoT) technology are revolutionizing the field of mountain hydrology. Low-power wireless sensor networks can now generate denser data in real-time and for a fraction of the cost of labor-intensive manual measurement campaigns.The American River Hydrological Observatory (ARHO) project has deployed thirteen low-power wireless IoT networks throughout the American River basin to monitor California’s snowpack. The networks feature a total of 945 environmental sensors, each reporting a reading every 15 minutes. The data reported is made available to the scientific community minutes after it is generated.This paper provides an in-depth technical description of the ARHO project. It details the requirements and different technical options, describes the technology deployed today, and discusses the challenges associated with large-scale environmental monitoring in extreme conditions.

International audience; Robust multimodal fusion is one of the challenging research problems in semantic scene understanding. In real-world applications, the fusion system can overcome the drawbacks of individual sensors by taking different feature representations and statistical properties of multiple modalities (e.g., RGB-depth cameras, multispectral cameras). In this paper, we propose a novel central multimodal fusion framework for semantic image segmentation of road scenes, aiming to effectively learn joint feature representations and optimally combine deep neural networks with statistical priors. More specifically, the proposed fusion framework can automatically generate a central branch by sequentially mapping multimodal features into a common space, including both low-level and high-level features. Besides, in order to reduce the model uncertainty, we employ statistical fusion to compute the final prediction, which leads to significant performance improvement. We conduct extensive experiments on various outdoor scene datasets. Both qualitative and quantitative experiments demonstrate that our central fusion framework achieves competitive performance against existing multimodal fusion methods.

Purpose The combination of desalination technology with renewable energy sources (RES) provides a sustainable approach for increasing potable water availability without imposing negative environmental effects. This paper aims to present the development of a platform, which is an internet-based tool integrating the design optimization of desalination systems with spatial modeling based on a geographic information system (GIS). Design/methodology/approach The proposed platform assists decision-makers to select the optimal location and configuration of both the energy- and water-related subsystems of desalination plants that are power-supplied by RES, such that the lifetime cost of the overall desalination plant is minimized. It enables to optimize the desalination plant site selection and sizing with various hybrid power supply (solar, wind, wave and electrical grid power systems) and desalination technologies combinations, while simultaneously exploiting spatial technologies in an internet-based GIS platform. Findings A pilot study for the optimal design of stand-alone and grid-connected desalination plants powered by RES is presented, which demonstrates the functionality and features of the proposed platform. It is also shown that a grid-connected desalination plant designed by the proposed software design tool exhibits significantly lower lifetime installation and maintenance costs compared to its stand-alone counterpart. Originality/value The proposed platform combines technological, scientific and industrial knowledge with information about societal/political conditions and geo-spatial technologies in a user-friendly graphical interface. Therefore, it provides a design tool enabling its users to secure water supply in a sustainable and economically viable manner.

International audience; In order to ensure reliability of systems early in the design phase, it is becoming crucial to have models able to predict the behavior of systems exposed to ElectroStatic Discharge (ESD). This is an increasing necessity since the number of embedded electronic products is growing and since they are employed in applications where people's safety is a requirement, such as automotive and aeronautic applications. Until now, quasi-static models of protection devices have succeeded in providing fairly good results in failure predictions (mainly hard failures). Today, the increased frequency range of such devices requires dynamic models able to reproduce their transient behavior. In this paper, we investigate if conventional methods for modeling linear devices, generally used in the frequency domain, could be used to obtain an equivalent frequency model for ESD protection devices, which exhibit non-linear behavior. A methodology to extract an ESD protection SPICE model from Transmission Line Pulse (TLP) measurements to address transient and frequency simulation is proposed and detailed. We demonstrate that, in well-defined conditions, such frequency models can give accurate results to predict overshoots related to protection device triggering delays. Validation of the models is performed under TLP and Human Metal Model (HMM) conditions on three off-the-shelf devices.

International audience; Live video analytics have become a key technology to support surveillance, security, traffic control, and even consumer multimedia applications in real time. The continuous growth in number of networked video cameras will further increase their widespread adoption. Yet, until now, developments in video analytics have largely focused on using fixed cameras, omitting the ever-growing presence of mobile cameras such as car dash-cams, drones, and smartphones. Edge computing, coupled with centralized clouds, has helped alleviate the network traffic and processing load, reducing latency and data transmissions. However, the current approach of processing video feeds through a hierarchy of clusters across a somewhat predictable path in the network will not be sufficient to support the integration of mobile feeds into the video analytics architecture. In this paper, we argue that a crucial step towards supporting heterogeneous camera sources is the adoption of a flat edge computing architecture. Such architecture should enable the dynamic distribution of processing loads through distributed computing points of presence, rapidly adapting to sudden changes in traffic conditions. In support of this hypothesis, we present exploratory results that show that smartly distributing and processing vision modules in parallel across available edge compute nodes can ultimately lead to better resource utilization and improved performance. CCS CONCEPTS • Computer systems organization → Distributed architectures; Pipeline computing; • Computing methodologies → Computer vision problems; Object detection.

Making co-existent and convergent the need for efficiency of relational query processing over Clouds and the security of data themselves is figuring-out how one of the most challenging research problems in the Big Data era. Indeed, in actual analytics-oriented engines, such as Google Analytics and Amazon S3, where key–value storage-representation and efficient-management models are employed as to cope with the simultaneous processing of billions of transactions, querying encrypted data is becoming one of the most annoying problem, which has also attracted a great deal of attention from the research community. While this issue has been applied to a large variety of data formats, e.g. relational, RDF and multidimensional data, very few initiatives have pointed-out skyline query processing over encrypted data, which is, indeed, relevant for database analytics. In order to fulfill this methodological and technological gap, in this paper we introduce an innovative algorithm for effectively and efficiently supporting skyline query processing over encrypted data in Cloud-enabled databases, named as Attribute-Order-Preserving-Free-SFS ( AOPF-SFS ), a suitable extension of the well-known Sort-Filter-Skyline ( SFS ) algorithm. The proposed algorithm enables the processing of skyline queries over encrypted data, even without preserving the order on each attribute as order-preserving encryption would do. We also present eSkyline, a prototype system that embeds AOPF-SFS equipped with a suitable query interface comprising an encryption scheme that facilitates the evaluation of domination relationships, hence allows for state-of-the-art skyline processing algorithms to be used. In order to prove the effectiveness and the reliability of our system, we also provide the details of the underlying encryption scheme, plus a suitable GUI that allows a user to interact with a server, and showcases the efficiency of computing skyline queries and decrypting the results.

The Intentional Analytics Model (IAM) has been recently envisioned as a new paradigm to couple OLAP and analytics. It relies on two basic ideas: (i) letting the user explore data by expressing her analysis intentions rather than the data she needs, and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of interesting model components (e.g., clusters). In this paper we contribute to give a proof-of-concept for the IAM vision by delivering an end-to-end implementation of , one of the five intention operators introduced by IAM. Among the research challenges left open in IAM, those we address are (i) automatically tuning the size of models (e.g., the number of clusters), (ii) devising a measure to estimate the interestingness of model components, (iii) selecting the most effective chart or graph for visualizing each enhanced cube depending on its features, and (iv) devising a visual metaphor to display enhanced cubes and interact with them. We assess the validity of our approach in terms of user effort for formulating intentions, effectiveness, efficiency, and scalability.

Fuel cell (FC) is a promising alternative energy source in a wide range of applications. Due to the unsatisfactory durability performance, FC has not yet been widely used. Prognostics and health management (PHM) has been demonstrated to be an effective solution to enhance the FC durability performance by predicting FC degradation characteristics and adopting health condition based control and maintenance. As the primary task of PHM, prognostics seeks to estimate the remaining useful life (RUL) of FC as early and accurately as possible. However, when FC faces dynamic operating conditions, its degradation characteristics are often hidden in the complex system dynamic behaviors, which makes prognostics challenging. To address this issue, a hybrid prognostics approach is proposed in this paper. Specifically, the health indicator of FC is extracted using a degradation behavior model and sliding-window model identification method. Subsequently, a symbolic-based long short-term memory networks (LSTM) is used to predict the health indicator degradation trend and estimate the RUL. The experimental and simulation results show that the proposed model is able to describe the dynamic behavior of the FC stack voltage and the extracted health indicator show a significant degradation trend. Moreover, health indicator prediction and RUL estimation performance can be improved by deploying the proposed symbolic-based LSTM prognostics model. The proposed approach provides a prognostic horizon approaching 50% of the FC life-cycle, and the average relative accuracy of estimated RUL is close to 90%.

Optical flow is a crucial component of the feature space for early visual processing of dynamic scenes especially in new applications such as self-driving vehicles, drones and autonomous robots. The dynamic vision sensors are well suited for such applications because of their asynchronous, sparse and temporally precise representation of the visual dynamics. Many algorithms proposed for computing visual flow for these sensors suffer from the aperture problem as the direction of the estimated flow is governed by the curvature of the object rather than the true motion direction. Some methods that do overcome this problem by temporal windowing under-utilize the true precise temporal nature of the dynamic sensors. In this paper, we propose a novel multi-scale plane fitting based visual flow algorithm that is robust to the aperture problem and also computationally fast and efficient. Our algorithm performs well in many scenarios ranging from fixed camera recording simple geometric shapes to real world scenarios such as camera mounted on a moving car and can successfully perform event-by-event motion estimation of objects in the scene to allow for predictions of upto 500 ms i.e. equivalent to 10 to 25 frames with traditional cameras., Pre-print version, 12 pages, 12 figures. Accepted in IEEE tPAMI July 2020

International audience; For better data availability and accessibility while ensuring data secrecy, end-users often tend to outsource their data to the cloud servers in an encrypted form. However, this brings a major challenge to perform the search for some keywords over encrypted content without disclosing any information to unintended entities. This paper proposes a novel expressive authorized keyword search scheme relying on the concept of ciphertext-policy attribute-based encryption. The originality of the proposed scheme is multifold. First, it supports the generic and convenient multi-owner and multi-user scenario, where the encrypted data are outsourced by several data owners and searchable by multiple users. Second, the formal security analysis proves that the proposed scheme is semantically secure against chosen keyword and outsider's keyword guessing attacks. Third, an interactive protocol is introduced which avoids the need of any secure channels between users and service provider. Fourth, due to the concept of bilinear-map accumulator, the system can efficiently revoke users and/or their attributes, and authenticate them prior to launching any expensive search operations. Fifth, conjunctive keyword search is provided thus enabling to search for multiple keywords simultaneously, with minimal cost. Sixth, the performance analysis shows that the proposed scheme outperforms closely-related works.

International audience; The rapidly-growing business of ride-on-demand (RoD) service such as Uber, Lyft and Didi proves the effectiveness of their new service model-using mobile apps and dynamic pricing to coordinate between drivers, passengers and the service provider, to manipulate the supply and demand, and to improve service responsiveness as well as quality. Despite its success, dynamic pricing creates a new problem for drivers: how to seek for passengers to maximize revenue under dynamic prices. Seeking route recommendation has already been studied extensively in traditional taxi service, but most studies do not consider the effects of taxis and passengers on the seeking taxi simultaneously. Further, in RoD service it is necessary to consider more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we employ a force-directed approach to model, by analogy, the relationship between vacant cars and passengers as that between positive and negative charges in electrostatic field. We extract features from multi-source urban data to describe dynamic prices, the status of RoD, taxi and public transportation services, and incorporate them into our model. The model is then used in route recommendation in every intersection so that a driver in a vacant RoD car knows which road segment to take next. We conduct extensive experiments based on our multi-source urban data, including RoD service operational data, taxi GPS trajectory data and public transportation distribution data, and results not only show that our approach outperforms existing baselines, but also justify the need to incorporate multi-source urban data and dynamic prices.

Collective self-consumption (CSC) is often presented as an approach for better energy management. This paper proposes a multi-agent and multi-level model to analyze the impact of CSC in the residential sector. It considers human activity as the core of electricity consumption in the household. In addition to a co-simulation between a multi-agent model of human activity and a thermal model, we added a layer to simulate interactions within an association of households, together with collective means of production and storage. This model was used to perform various studies. First, CSC configurations with individual and collective storage were simulated. The results highlight the impact of individual and collective means of storage on the way energy is consumed, exchanged, charged or discharged in the group of households and within each household. Finally, a large structure including energy exchanges between a set of households (neighborhood) forming a CSC was modeled. The results clearly show the impact of energy exchange between neighborhoods on the overall CSC.

International audience; Most of the negative group delay (NGD) circuit recent investigation were focused on the classes of lowpass and bandpass categories. Few research works are currently available in the literature on the typical high-pass (HP) NGD function. This paper introduces an original HP-NGD circuit theory of four-port passive topology constituted by a single lumped capacitor. The S-matrix equivalent model of the innovative topology is established from the admittance matrix. Then, the basic frequency analytical responses are expressed. Then, the GD model is derived in function of the capacitor. It was found analytically that the four-port mono-capacitor passive circuit is susceptible to behave originally as an unfamiliar HP-NGD function. An innovative HP-NGD analysis is explored in addition to the expression of synthesis design equation in function of the expected NGD cutoff frequency which is linked to NGD optimal value. The validity of the established HP-NGD circuit theory is verified with a proof of concept (POC). As expected, the HP-NGD behavior showing a good correlation between analytical model and simulation is obtained.

This paper presents a new dataset of measured multichannel room impulse responses (RIRs) named dEchorate. It includes annotations of early echo timings and 3D positions of microphones, real sources, and image sources under different wall configurations in a cuboid room. These data provide a tool for benchmarking recent methods in echo-aware speech enhancement, room geometry estimation, RIR estimation, acoustic echo retrieval, microphone calibration, echo labeling, and reflector position estimation. The dataset is provided with software utilities to easily access, manipulate, and visualize the data as well as baseline methods for echo-related tasks.

International audience; Unlike previous mobile networks, 5G New Radio (5G-NR) provides unprecedented exibility in the radio access network (RAN) to support diverse use cases in a multi-tenant environment. In this context, the need for programmability and control through software-de ned radio access networking (SD-RAN) is well established. While the underlying RAN is designed to be ultra exible and lean, existing SD-RAN controllers are either not exible to address all use cases or use a one-size-ts-all approach. In this paper, we present FlexRIC, a exible and e cient software development kit (SDK) that enables to build specialized serviceoriented controllers. FlexRIC has a modular architecture with minimal footprint and is designed with extensibility in mind. We validate the SDK building concrete implementations of two specialized controllers for state-of-the-art 5G use cases: (1) a recursive RAN controller that virtualizes the network to allow multiple tenants to concurrently control and operate their services in a shared infrastructure over the heterogeneous landscape of 5G networks, and (2) an SD-RAN controller providing programmability for multi-radio access technology (RAT) RAN slicing, and ow-based tra c control targeting low-latency communications. The results reveal that FlexRIC reduces the round-trip time by two while incurring 83 % less CPU compared with O-RAN's reference implementation, and uses 10x less CPU and one third of the memory when compared to FlexRAN. Such performance is required to unleash the potential of emerging 5G use cases. CCS CONCEPTS • Networks → Mobile networks; Programmable networks.

International audience; In this paper, we propose a method to both extract and classify symbols in floorplan images. This method relies on the very recent developments of Graph Neural Networks (GNN). In the proposed approach, floorplan images are first converted into Region Adjacency Graphs (RAGs). In order to achieve both classification and extraction, two different GNNs are used. The first one aims at classifying each node of the graph while the second targets the extraction of clusters corresponding to symbols. In both cases, the model is able to take into account edge features. Each model is firstly evaluated independently before combining both tasks simultaneously, increasing the quickness of the results.

International audience; The growing intelligence of the nowadays distribution grids is due to the integration of Information and Communication Technologies (ICTs). Such a development is converting the traditional grid into the smart grid concept. Communicating over a reliable ICT infrastructure, distributed Intelligent Electronic Devices (IEDs) perform supervision, protection, and control of the smart grid. The communication network is not errorless, and any error of the underlying communication network can degrade the power grid performance or even cause instability in it. Hence, there is a necessity to obtain a modeling method to analyze its Quality of Service (QoS). One of the most important quality measures of the Substation Communication Network (SCN) traffic is the message transmission delay. Thus, this paper proposes a methodology to evaluate the message transmission delay through an analytical model. To estimate the transmission delay, a port connection model represents the message distribution model. Then a traffic-flow-source model, and a traffic-flow-service model are applied to characterize each flow while it is served through different ports. Finally, delay is calculated based on the corresponding flow and service information. The estimation model is applied on an IEC-61850-MMS (Manufacturing Message Specification)-based communication scenario for a load-shedding goal that is considered over a real reduced-scale HV/MV smart substation test bench. The estimated results are compared to the measured values by the Wireshark network analyzer that indicates an estimation error of less than 10%.

In order to contribute to the decarbonization of heating consumption, France has set some objectives to develop District heating and/or cooling networks (DHCN). In this context, it is useful to identify the most appropriate district areas for implementing DHCN, and to assess their potential of development. After introducing the situation of DHCN in France, this paper presents a typology to assess the potential of development of those networks in France, based on an original method that involves the calculation of 3 variables: the linear heating/cooling density (LHD/LCD), the Proportion of Buildings anterior to 1990 and the share of energy consumption from the residential sector, to assess respectively the energy density, the supply temperatures and the energy mix between the different sectors of activities. LHD and LCD are calculated using a Delaunay triangulation and Kruskal's algorithm applied to buildings whose heating and cooling demands are calculated at a local scale. 18 different types of districts are defined for both DHN and DCN, among which 9 have a sufficient LHD/LCD. Then, the potential of development for DHN and DCN is calculated on the French territory. Thus, the DHN potential is assessed to 132 TWh/y while the DCN potential is assessed to 7.8 TWh/y.

Power-to-Gas–Oxyfuel, or Electrolysis-Methanation-Oxyfuel, is an advanced concept that addresses some concerns of conventional Power-to-Gas: supply of high-purity CO 2 , release of greenhouse gases to the atmosphere and fate of the O 2 from electrolysis. Due to the intermittent nature of several renewable energy sources, massive storage is needed to balance supply and demand. This paper focuses on the storage phase required for Electrolysis-Methanation-Oxyfuel. Synthetic CH 4 , O 2 and CO 2 have to be stored at different times. Due to the high fluid rates and volumes required, salt caverns are potential candidates. While salt caverns have been used for decades to store CH 4 , storage of CO 2 and O 2 has not been implemented to date. A generic seasonal scenario with a 200 MW oxyfuel unit is investigated. Numerical modelling that couples cavern thermodynamics with the thermomechanical response of the surrounding rock salt has been performed. The results, although exploratory, show that the caverns would be stable as they respect the criteria commonly used for cavern design. Moreover, combined storage of CO 2 and O 2 in the same cavern, rather than independent storage, would reduce the number of caverns needed and the likelihood of phase changes, but it would require separation of the two substances at the ground surface.

International audience; The Internet of Things (IoT) allows communication between devices, things, and any digital assets that send and receive data over a network without requiring interaction with a human. The main characteristic of IoT is the enormous quantity of data created by end-user’s devices that needs to be processed in a short time in the cloud. The current cloud-computing concept is not efficient to analyze very large data in a very short time and satisfy the users’ requirements. Analyzing the enormous quantity of data by the cloud will take a lot of time, which affects the quality of service (QoS) and negatively influences the IoT applications and the overall network performance. To overcome such challenges, a new architecture called edge computing — that allows to decentralize the process of data from the cloud to the network edge has been proposed to solve the problems occurred by using the cloud computing approach. Furthermore, edge computing supports IoT applications that require a short response time and consequently enhances the consumption of energy, resource utilization, etc. Motivated by the extensive research efforts in the edge computing and IoT applications, in this paper, we present a comprehensive review of edge and fog computing research in the IoT. We investigate the role of cloud, fog, and edge computing in the IoT environment. Subsequently, we cover in detail, different IoT use cases with edge and fog computing, the task scheduling in edge computing, the merger of software-defined networks (SDN) and network function virtualization (NFV) with edge computing, security and privacy efforts. Furthermore, the Blockchain in edge computing. Finally, we identify open research challenges and highlight future research directions.

International audience; Very few organizations can afford the necessary infrastructure for genomic data analytic, which requires a very large amount of storage and computational resources. In this context, cloud computing platforms can be adopted as a practical solution for storage and computations. However, sharing such sensitive information with cloud providers can lead to violating privacy preservation and public regulations. To resolve this problem, homomorphic encryption (HE) can be used. Homomorphic encryption enables computation over encrypted data, which helps tackling the problem of privacy preservation. Despite this advantage, existing HE schemes suffer from high computational complexity and storage overhead; designing a practical HE scheme that provides simultaneously the efficiency and the required level of security still remains an open question. In this paper, we propose a secure cloud based scheme for storing and analyzing classified genetic data using homomorphic encryption (HE). In our scheme, we adopted an optimization technique based on decomposing a homomorphic cipher text into a number of independent cipher texts with lower storage overhead using the Chinese remainder theorem (CRT). Optimization is then accomplished by applying parallel processing over the independent cipher texts. The presented technique is applied to improve the efficiency of two well-known HE schemes: the Domingo Ferrer (DF) and the Paillier cryptosystems. After examining the correctness of the proposed optimization, it is used to design a secure cloud-based application dedicated for genome data analysis. A virtual cloud environment was created for this purpose. Different performance and security analyses have shown the efficiency of such solution and its compatibility with real-world applications. The execution time is reduced to more than half while maintaining a high security level.

Video facial expression recognition is useful for many applications and received much interest lately. Although some solutions give really good results in a controlled environment (no occlusion), recognition in the presence of partial facial occlusion remains a challenging task. To handle occlusions, solutions based on the reconstruction of the occluded part of the face have been proposed. These solutions are mainly based on the texture or the geometry of the face. However, the similarity of the face movement between different persons doing the same expression seems to be a real asset for the reconstruction. In this paper we exploit this asset and propose a new solution based on an auto-encoder with skip connections to reconstruct the occluded part of the face in the optical flow domain. To the best of our knowledge, this is the first proposition to directly reconstruct the movement for facial expression recognition. We validated our approach in the controlled dataset CK+ on which different occlusions were generated. Our experiments show that the proposed method reduce significantly the gap, in terms of recognition accuracy, between occluded and non-occluded situations. We also compare our approach with existing state-of-the-art solutions. In order to lay the basis of a reproducible and fair comparison in the future, we also propose a new experimental protocol that includes occlusion generation and reconstruction evaluation.

International audience; Why do organizations develop and execute social media initiatives to create business value? This study addresses this prominent and critical research question for IS research on the business value of social media. Drawing on the institutional theory, we argue that mimetic, coercive, and normative pressure persuade organizations to use social media to improve marketing performance. We test our core proposition and theory-driven research model using data collected from a sample of leading Chinese agribusinesses. We find that coercive and mimetic pressure play pivotal roles in motivating organizations to use social media. We also discover that social media usage improves marketing performance and that the agribusiness market uncertainty plays a positive reinforcing role in the positive effect of social media usage on marketing performance. Thus, this paper contributes to IS research with an eloquent theoretical explanation and strong empirical evidence on why organizations deploy social media initiatives to improve their marketing activities and performance and the higher business value of social media under greater agribusiness market uncertainty.

The massive growth of the population with chronic diseases calls for a telecare system to enhance their quality of life and reduce their treatment costs. Most of the current solutions depend on reliable data, deterministic rules, or the similarity of patients, while studies have shown otherwise. To this end, in this paper, we have extended our previous work on the Hapicare framework to integrate probabilistic diagnosis and self-adaptive treatment. Our new framework enables sensors’ datastream analysis and online decision-making. Its ontology-based reasoning uses Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) ontology to add contextual information to the collected data. Moreover, probabilistic reasoning is applied for diagnosis and screening to manage the uncertainty and unreliability of data as well as the indeterministic medical rules. The treatment system is designed to be modifiable by the experts and automatically adaptable to patients’ needs. The probabilistic diagnosis performance has been evaluated based on two public datasets regarding symptoms and risk factors of two chronic diseases: chronic kidney disease and dermatologic disease. The results show that our solution outperforms a classical classifier specifically when more than 40% of the data are missing. The proposed framework is also validated using four scenarios. The evaluation results demonstrate the ability of the proposed framework to help patients and doctors diagnose and treat medical conditions and episodes.

Hyper-heuristics (HHs) are algorithms suitable for designing heuristics. HHs perform the search divided in two levels: they look for heuristic components in the high level and the heuristic is used, in the low level, to solve a set of instances of one or more problems. Different from offline HHs, hyper-heuristics with dynamic learning select or generate heuristics during the search. This paper proposes a hyper-heuristic associated with a dynamic learning strategy for selecting Iterated Greedy (IG) components. The proposal is capable of selecting appropriate values for six IG components: local search, perturbation, destruction size, neighborhood size, destruction position and local search focus. The proposed HH is tested with six dynamic adaptation strategies: random, \(\epsilon \)-greedy, probability matching, multi-armed bandit, LinUCB, and Thompson Sampling (TS). The hyper-parameters of each strategy are tuned by irace. As a testbed, we use several instances with four different sizes (20, 50, 100 and 200 jobs) of three different formulations of flowshop problems (permutation, no-wait, no-idle), two distinct objectives (makespan, flowtime), and four processing time distributions (uniform, exponential and job or machine correlated). The results show that different strategies are most suitable for adapting different IG components, TS performs quite well for all components and, except for local search, using adaptation is always beneficial when compared with the IG running with standard parameters.