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Facebook reviews contain reviews and reviewers' information and include a set of likes, comments, sharing, and reactions called Facebook Behaviors (FBs). We extend existing research on review helpfulness to fit Facebook reviews by demonstrating that Facebook behaviors can impact review helpfulness. This study proposes a theoretical model that explains reviews' helpfulness based on FBs and baseline features. The model is empirically validated using a real Facebook data set and different feature selection methods (FS) to determine the importance level of such features to maximize the helpfulness prediction. Consequently, a combination of the impactful features is identified based on a robust and effective model. In this context, the like and love behaviors deliver the best predictive performance. Furthermore, we employ different classification techniques and a set of influencer features. The results showed the performance of the proposed model by 0.925 of accuracy.The outcomes of the current study can be applied to develop a smart review ranking system for Facebook product pages.

In the context of Industry 4.0, IoRT-aware BPs represent an attractive paradigm that aims to automate the classic business process (BP) using the internet of robotics things (IoRT). Nonetheless, the execution of these processes within the enterprises may be costly due to the consumed resources, recruitment cost, etc. To bridge these gaps, the business process outsourcing (BPO) strategy can be applied to outsource partially or totally a process to external service suppliers. Despite the various advantages of BPO, it is not a trivial task for enterprises to determine which part of the process should be outsourced and which environment would be selected to deploy it. This paper deals with the decision-making outsourcing of an IoRT-aware BP to the fog and/or cloud environments. The fog environment includes devices at the edge of the network which will ensure the latency requirements of some latency-sensitive applications. However, relying on cloud, the availability and computational requirements of applications can be met. Toward these objectives, we realized an in-depth analysis of the enterprise requirements, where we identified a set of relevant criteria that may impact the outsourcing decision. Then, we applied the method based on the removal effects of criteria (MEREC) to automatically generate the weights of the identified criteria. Using these weights, we performed the selection of the suitable execution environment by using the ELECTRE IS method. As an approach evaluation, we sought help from an expert to estimate the precision, recall, and F-score of our approach. The obtained results show that our approach is the most similar to the expert result, and it has acceptable values.

Horizontal fracture-simulated completions remain the most reliable method of producing hydrocarbons from shale formations. The vast majority of unconventional wells are completed using the “Plug and Perf” method. This method involves using either a coiled tubing (CT) with a positive displacement motor or a jointed pipe to mill out composite plugs after fracturing operations are completed. An estimated average of 120,000 composite plugs is installed in the US alone each year. Bridge plug drillouts from milling operations tend to accumulate in horizontal wells and can cause stuck pipe incidents and loss of well control. Efficient removal of composite plugs’ debris is crucial in achieving operational efficacies and full production potential. This paper provides an overview of the various bridge plug drillouts cleaning practices adopted in horizontal wells. It discusses several case histories, showcasing how operators solved cleanout challenges. Developed mechanistic models to better understand hole cleaning are also reviewed. As more unconventional wells are being set at more extensive depths, an economical and optimized coiled tubing process becomes increasingly important. This paper focuses on delivering a more conclusive set of recommendations to increase efficiency and improve current composite plug coiled tubing cleaning-milling practices, increase operational efficiency and reduce cost.

A new approach is demonstrated in which soft experimentation can be performed for MMP measurements, thus replacing the common practice of slim tube displacement laboratory experiments. Recovery potential from oil reservoirs by miscible flue gas injection was studied by slim tube and field-scale numerical simulation using two flue gases and seven crude oils sampled at different depths in three candidate reservoirs. The soft experimentations were conducted using Eclipse300TM, a three-phase compositional simulator. This study investigates minimum miscibility pressure (MMP), a significant miscible gas injection project screening tool. Successful design of the project is contingent to the accurate determination of the MMP. This study evaluates effects of important factors such as injection pressure, oil component composition, and injection gas composition on the MMP and recovery efficiency for slim tube and field-scale displacements. Two applicable MMP correlations were used for comparison and validation purposes.

The goal of the study was to determine whether the semantic variant of primary progressive aphasia (svPPA) affects the intrinsic connectivity network anchored to left and right anterior hippocampus, but spares the posterior hippocampus. A resting-state functional connectivity MRI (rs-fcMRI) study was conducted in a group of patients with svPPA and in controls, using a seed-to-voxel approach. In comparison to controls, massively reduced connectivity was found in the anterior hippocampus, mainly the left one, for svPPA patients but not in the left or right posterior hippocampus. In svPPA, the anterior hippocampus showed reduced functional connectivity with regions implicated in the semantic memory network. Significant correlation was also found between the functional connectivity strength of the left anterior hippocampus and the ventromedial cortex, and performance in semantic tasks. These findings indicate that the functional disconnection of the anterior hippocampus may be a promising in vivo biomarker of svPPA and illustrate the role of this hippocampal subregion in the semantic memory system.

Social business processes support enterprises tap into the opportunities of Web 2.0. To ensure an efficient support, this paper presents an approach for identifying an enterprise’s business processes that could be converted into social. Indeed, not all processes are eligible for this conversion. The approach is built upon the concept of enterprise architecture that sheds the light on the necessary contextual elements for guiding the socialization of business processes. Due to the fuzzy nature of socialization, two multi-criteria decision making techniques, fuzzy logic and fuzzy-decision tree, enhance this guidance. A set of experiments for evaluating the proposed approach are presented in the paper, as well.

International audience; The insurance industry is heavily dependent on several processes executed among multiple entities, such as insurer, insured, and third-party services. The increasingly competitive environment is pushing insurance companies to use advanced technologies to address multiple challenges, namely lack of trust, lack of transparency, and economic instability. To this end, blockchain is used as an emerging technology that enables transparent and secure data storage and transmission. In this paper, we propose CioSy, a collaborative blockchain-based insurance system for monitoring and processing the insurance transactions. To the best of our knowledge, the existing approaches do not consider collaborative insurance to achieve an automated, transparent, and tamper-proof solution. CioSy aims at automating the insurance policy processing, claim handling, and payment using smart contracts. For validation purposes, an experimental prototype is developed on Ethereum blockchain. Our experimental results show that the proposed approach is both feasible and economical in terms of time and cost.

Wildfire prediction from Earth Observation (EO) data has gained much attention in the past years, through the development of connected sensors and weather satellites. Nowadays, it is possible to extract knowledge from collected EO data and to learn from this knowledge without human intervention to trigger wildfire alerts. However, exploiting knowledge extracted from multiple EO data sources at run-time and predicting wildfire raise multiple challenges. One major challenge is to provide dynamic construction of service composition plans, according to the data obtained from sensors. In this paper, we present a knowledge-driven Machine Learning approach that relies on historical data related to wildfire observations to guide the collection of EO data and to automatically and dynamically compose services for triggering wildfire alerts.

International audience; The rapid development of blockchain technology has influenced several fields, including financial, healthcare, and supply chain systems. Recently, this technology has also been applied in education thanks to its unique features, including decentralization, trustworthiness, and security. Despite the bright side of blockchain, several concerns continue to undermine its adoption in education, such as legal, immutability, and scalability issues. Thus, a comprehensive survey about blockchain adoption in education deems necessary. This paper is a systematic literature review of blockchain applications in the education field. To do so, and after retrieving scientific publications on the topic, we classify the outcomes according to several categories. In this paper, we focus on two major themes: educational applications and benefits of blockchain integration. We combine the themes, then express and analyze the results as bubble charts. We analyze the proposed solutions in terms of the blockchain-based applications that have been developed for educational purposes and the benefits that the blockchain technology could bring to the education field. Finally, we describe some research gaps that need to be addressed and educational areas that could be explored for future research of blockchain adoption in education.

La recherche de prédicteurs d’habilités langagières en aphasie post-accident vasculaire cérébral (AVC) basés sur la matière blanche a récemment vu un élan. Cela a été motivé par l’émergence du modèle à double-voie où des faisceaux de matière blanche dorsaux et ventraux jouent un rôle important dans le langage, ainsi que par l’avènement de la tractographie basée sur l’imagerie par résonance magnétique (IRM) de diffusion permettant l’étude in-vivo des faisceaux de matière blanche et de leurs propriétés structurelles. Les caractéristiques structurelles et la charge lésionnelle des faisceaux de matière blanche ont permis de prédire les troubles langagiers dans la phase chronique dans quelques études. Cependant, les prédicteurs aigus de matière blanche des habilités syntaxiques en aphasie post-AVC chronique sont méconnus. L’exploitation de la tractographie dans l’étude des faisceaux langagiers de matière blanche a été limitée par plusieurs défis méthodologiques, dont la difficulté de reconstruire des faisceaux ayant une architecture complexe. Des progrès méthodologiques ont été récemment introduits afin d’adresser ces limites, dont le plus important est la tractographie basée sur l’imagerie à haute résolution angulaire (« HARDI »). Cependant, la fiabilité test-retest de la reconstruction et des propriétés structurelles d’une approche de tractographie HARDI de pointe n’a pas encore été évaluée. Le premier article de cette thèse visait à évaluer la fiabilité test-retest de la reconstruction et des propriétés structurelles (anisotropie fractionnelle, FA; diffusivité moyenne, axiale et radiale, MD, AD, RD; nombre d’orientations de fibres, NuFO; volume du faisceau; longueur moyenne des « streamlines ») de faisceaux langagiers majeurs (arqué, inférieur fronto-occipital, inférieur longitudinal, unciné, AF, IFOF, ILF, UF) obtenus avec une approche de tractographie HARDI de pointe. La majorité des mesures de propriétés structurelles ont montré une bonne ou excellente fiabilité. Ces résultats ont des implications importantes pour l’utilisation d’une telle approche pour l’étude des faisceaux langagiers de matière blanche, car ils renforcent la confiance dans la stabilité des reconstructions et les propriétés structurelles obtenus avec la tractographie HARDI. Le second article de cette thèse visait à déterminer si et quelles propriétés structurelles (FA, AD, volume du faisceau), et la charge lésionnelle, de l’AF et l’UF gauches dans la phase aigüe (≤ 3 jours), obtenus avec l’approche de tractographie HARDI utilisée dans le premier article, prédisent les habilités syntaxiques dans le discours spontané en aphasie post-AVC chronique (≥ 6 mois). Des régressions multiples ascendantes ont révélé que le volume de l’AF prédit la production des verbes, la complexité des phrases et la complexité de la structure argumentale du verbe. Le volume de l’UF a amélioré la prédiction de cette dernière. Ces résultats indiquent que le volume semble être un bon prédicteur précoce des habilités syntaxiques dans le discours spontané en aphasie post-AVC chronique. Mis ensemble, les résultats de cette thèse soulignent l’utilité d’une approche de tractographie HARDI de pointe et son potentiel pour le développement futur de biomarqueurs précoces pouvant améliorer le pronostic de patients ayant une aphasie post-AVC chronique. Cela pourrait promouvoir l’optimisation des soins et le développement de thérapies pour le bienfait des patients et leurs familles., The search for white matter predictors of language abilities in post-stroke aphasia has gained momentum in recent years. This growing interest has been driven by the emergence of the dual-stream framework where dorsal and ventral white matter bundles play an important functional role in language, as well as the advent of diffusion magnetic resonance imaging (MRI)-based tractography which allows the in-vivo investigation of white matter bundles and their structural properties. Structural characteristics, as well as the lesion load, of white matter bundles have been previously found to predict language impairments in the chronic phase. However, little is known about acute white matter predictors of syntactic abilities in chronic post-stroke aphasia. Leveraging tractography to study white matter language bundles has been limited by several methodological challenges, such as the difficulty of reconstructing white matter bundles with a complex fiber architecture. A number of methodological advances have been introduced fairly recently to address these limitations, the most important of which is the advent of tractography based on High Angular Resolution Imaging (HARDI). However, the test-retest reliability of the reconstruction and structural properties of a state-of-the-art HARDI-based tractography pipeline has not been previously assessed. The first article of the present thesis aimed to assess the test-retest reliability of the reconstruction and structural properties (fractional anisotropy, FA; mean, axial, radial diffusivity, MD, AD, RD; number of fiber orientations, NuFO; bundle volume; mean length of streamlines) of major white matter language bundles (arcuate, inferior fronto-occipital, inferior longitudinal, and uncinate fasciculi, AF, IFOF, ILF, UF) obtained using a state-of-the-art HARDI-based tractography pipeline. Most measures of structural properties showed good to excellent test-retest reliability. These findings have important implications for the use of such a pipeline for the study of white matter language bundles, as they increase our confidence that the reconstructions and structural properties obtained from the tractography pipeline are stable and not due to random variations in measurement. The second article of the thesis aimed to determine whether and which structural properties (FA, AD, bundle volume), as well as the lesion load, of the left AF and UF in the acute phase post-stroke (≤ 3 days), obtained with the same state-of-the-art HARDI-based tractography pipeline used in the first article, predict syntactic abilities in connected speech in chronic post-stroke aphasia (≥ 6 months). Forward multiple regressions revealed that the left AF’s volume predicted the percentage of verbs produced, the structural complexity of sentences, as well as verb-argument structure complexity. The left UF’s volume improved the prediction of verbs with a complex argument structure. These findings indicate that the bundle volume may be a good early predictor of syntactic ability in connected speech in chronic post-stroke aphasia. Overall, the findings of this thesis highlight the usefulness of a state-of-the-art HARDI-based tractography approach and its potential for the future development of early biomarkers that could improve the prognosis and personalized care of patients with chronic post-stroke aphasia. This would promote the optimization of patient care and the development of therapies for the benefit of patients and their families.

In the context of an Internet-of-Things (IoT) ecosystem, this paper discusses two necessary stages for managing federations of things. The first stage defines things in terms of duties and non-functional properties that define the quality of these duties. And, the second stage uses these properties to assign appropriate things to future federations. Specialized into ad hoc and planned, federations are expected to satisfy needs and requirements of real-life situations like traffic control that arise at run-time. A set of experiments using a mix of real and simulated datasets, demonstrate the technical doability of thing assignment to federations and are presented in the paper, as well.

The design of an optimum spacing between oil wells entails both reservoir characterization and economics considerations. High hydrocarbon recovery requires short distances between wells. However, higher well density leads to a greater development cost. Accordingly, determination of an optimum well spacing is primordial in the development of oil fields. As a matter of fact, the identification of optimum well spacing for heterogeneous sandstone reservoirs undergoing waterflooding requires extensive analytical and numerical studies. The intent of this work is therefore to develop type curves as a quick tool in estimating ultimate recovery and reduce excessive reservoir simulation cost in analog reservoirs. These type curves utilize reservoir heterogeneity and well spacing in the estimating of oil recovery. In this work, we investigated numerically the effects of heterogeneity and well spacing on ultimate recovery using Eclipse black oil simulation and PEEP economic software 2015 and 2009 versions, respectively. The study involved a 50-ft thick Middle Eastern reservoir with porosity variability ranging from 0.2 to 0.9. Corresponding average matrix permeabilities of 1, 10 and 100 md were considered. Type curves relating well spacing and heterogeneity to ultimate oil recovery were developed. Type curves and net present value calculations indicated that there is exists an ultimate well spacing for each of the considered matrix permeabilities.

The objective and the main contribution of this issue are dedicated to using subsurface data to delineate a basin beneath the Gulf of Tunis and its neighbouring areas, and to investigate the potential of this area in terms of hydrocarbon resources. Available well data provided information about the subsurface geology beneath the Gulf of Tunis. 2D seismic data allowed delineation of the basin shape, strata geometries, and some potential promising subsurface structures in terms of hydrocarbon accumulation. Together with lithostratigraphic data obtained from drilled wells, seismic data permitted the construction of isochron and isobath maps of Upper Cretaceous-Neogene strata. Structural and lithostratigraphic interpretations indicate that the area is tectonically complex, and they highlight the tectonic control of strata deposition during the Cretaceous and Neogene. Tectonic activity related to the geodynamic evolution of the northern African margin appears to have been responsible for several thickness and facies variations, and to have played a significant role in the establishment and evolution of petroleum systems in northeastern Tunisia. As for petroleum systems in the basin, the Cretaceous series of the Bahloul, Mouelha and Fahdene formations are acknowledged to be the main source rocks. In addition, potential reservoirs (Fractured Abiod and Bou Dabbous carbonated formations) sealed by shaly and marly formations (Haria and Souar formations respectively) show favourable geometries of trap structures (anticlines, tilted blocks, unconformities, etc.) which make this area adequate for hydrocarbon accumulations.

International audience; There are no doubts that the Internet-of-Things (IoT) has conquered the ICT industry to the extent that many governments and organizations are already rolling out many anywhere,anytime online services that IoT sustains. However, like any emerging and disruptive technology, multiple obstacles are slowing down IoT practical adoption including the passive nature and privacy invasion of things. This paper examines how to empower things with necessary capabilities that would make them proactive and responsive. This means things can, for instance reach out to collaborative peers, (un)form dynamic communities when necessary, avoid malicious peers, and be “questioned” for their actions. To achieve such empowerment, this paper presents an approach for agentifying things using norms along with commitments that operationalize these norms. Both norms and commitments are specialized into social (i.e., application independent) and business (i.e., application dependent), respectively. Being proactive, things could violate commitments at run-time, which needs to be detected through monitoring. In this paper, thing agentification is illustrated with a case study about missing children and demonstrated with a testbed that uses different IoT-related technologies such as Eclipse Mosquitto broker and Message Queuing Telemetry Transport protocol. Some experiments conducted upon this testbed are also discussed

Pressure transient analysis has been used to evaluate performance of a well located between one sealing fault and one constant pressure boundary. Type curves were generated by determining 1) dimensionless pressure and 2) rate of change of dimensionless pressure drop with respect to dimensionless time. When the well is located closer to the no flow boundary, both sets of type curves have three distinct slopes. These slopes characterize: 1) flow in an infinite reservoir, 2) presence of the no flow, and 3) the constant-pressure boundaries. When the well is closer to the constant pressure boundary, the type curves show two distinct slopes. These correspond to: 1) flow in an infinite reservoir, and 2) the presence of a constant pressure boundary. The type curves can be used to match actual pressure drawdown data and determine the drainage area and relative well location with respect to physical boundaries.

This study explores teachers' beliefs about self-observation on practice as one alternative for the appraisal process. In this study, teachers were interviewed about how they viewed self-observation and how well it served them in developing their teaching on the one hand, and enhancing reflective practice on the other one. The findings in this paper are based on a qualitative research that employed an interpretivist/constructivist theory, in which qualitative data was gathered primarily through interviews with a view to clarifying teachers' perceptions and understanding the beliefs and practices behind them. The study attempted to answer mainly the following question. "What do teachers understand by self-observation and how do they perceive its role in professional development?" The findings of the study revolve around two main themes; the benefits and the drawbacks of self-observation theory. While, the first one describes self-observation as an opportunity for reflection, awareness and professional growth, the second view claims that such practices depend on personality and attitudes, and is limited by various constraints.

International audience; Internet of Things-based environments collect and generate huge amounts of data about users, their activities, and their surroundings , which can disclose some sensitive information and threaten their privacy. Hence, the user's collected and handled data by IoT-based applications need to be exploited and secured in an appropriate way to protect personal data and user's privacy. Therefore, we aim at improving the data ownership, transparency, and auditability for users. To this end, we propose an end-to-end privacy-preserving framework for the IoT data using blockchain technology. The smart contract use in our framework will hence enforce the privacy requirement compliance according to the user's (i.e., data owner) privacy preferences and end-user's (i.e., data consumer) requests. To do so, we detail the design of the system architecture by introducing its core components and functionalities and highlight through an example of how it operates in a real-world use-case.

International audience; The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service-oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS).

International audience; The Internet of Things (IoT) provides the opportunity to collect, process and analyze data. This opportunity helps to understand preferences and life patterns of individuals in order to offer them customized services. However, privacy has become a significant issue due to the personal nature of the knowledge derived from these data and the involved potential risks. Despite the increasing legislation pressure, few proposed solutions havedealt with the privacy requirements, such as consent and choice, purpose specification, and collection limitation. In this paper, we propose a privacy ontology in order to incorporate privacy legislation into privacy policies while considering several privacy requirements. Our proposed ontology aims both at making the smart devices more autonomous and able to infer data access rights and enforcing the privacy policy compliance atthe execution level. We implemented and evaluated our privacy ontology based on a healthcare scenario.

Patients with Alzheimer's disease (AD) and semantic variant primary progressive aphasia (svPPA) can present with similar language impairments, mainly in naming. It has been hypothesized that these deficits are associated with different brain mechanisms in each disease, but no previous study has used a network approach to explore this hypothesis. The aim of this study was to compare resting-state functional magnetic resonance imaging (rs-fMRI) language network in AD, svPPA patients, and cognitively unimpaired elderly adults (CTRL). Therefore, 10 AD patients, 12 svPPA patients and 11 CTRL underwent rs-fMRI. Seed-based functional connectivity analyses were conducted using regions of interest in the left anterior temporal lobe (ATL), left posterior middle temporal gyrus (pMTG) and left inferior frontal gyrus (IFG), applying a voxelwise correction for gray matter volume. In AD patients, the left pMTG was the only key language region showing functional connectivity changes, mainly a reduced interhemispheric functional connectivity with its right-hemisphere counterpart, in comparison to CTRL. In svPPA patients, we observed a functional isolation of the left ATL, both decreases and increases in functional connectivity from the left pMTG and increased functional connectivity form the left IFG. Post-hoc analyses showed that naming impairments were overall associated with the functional disconnections observed across the language network. In conclusion, AD and svPPA patients present distinct language network functional connectivity profiles. In AD patients, functional connectivity changes were restricted to the left pMTG and were overall less severe in comparison to svPPA patients. Results in svPPA patients suggest decreased functional connectivity along the ventral language pathway and increased functional connectivity along the dorsal language pathway. Finally, the observed connectivity patterns are overall consistent with previously reported structural connectivity and language profiles in these patients.