Could Multimedia approaches help Remote Sensing Analysis?

International audienceThe paper explores how multimedia approaches used in image understanding tasks could be adapted and used in remote sensing image analysis. Two approaches are investigated: the classical Bag of Visual Words (BoVW) approach and the Deep Learning approach. Tests are performed for the classification of the UC Merced Land Use Dataset which provide better results than the state of the art

Gravity-induced ischemia in the brain and prone positioning for COVID-19 patients breathing spontaneously: still far from the truth!

International audienceGlobal mindset is usually considered as a positive skill or resource that helps individuals and companies succeed internationally. We argue that it is also a collective scheme of thought that brings some actors together and sets others apart. We investigate this perspective through a qualitative study of French MNC managers, internationalisation support providers, and SME owners and managers attempting to create or grow their business in China. We reveal that global mindset is a double‐edged concept: it is not solely an instrument for integration, but also a doxa, a particular viewpoint imposed to identify and reject outsiders through symbolic struggles. This alternative conceptualisation is necessary to rethink the social forces at work in the field of international business. It is also necessary to encourage educators and practitioners to acknowledge the struggles that result from the imposition of certain views and behaviours and to adapt education, support and training programs accordingly.L’objectif de cet article est de comprendre la dynamique des compétences interculturelles individuelles et collectives des prestataires dans l’expérience de service du client. Les résultats de l’étude de cas d’une business unit française prestataire de services linguistiques qui excelle en la matière montrent qu’une articulation eff icace des deux niveaux de compétence assure la satisfaction des clients et contribue à la compétitivité de l’entreprise

Clinical characteristics and outcomes of critically ill COVID-19 patients in Sfax, Tunisia

Background Africa, like the rest of the world, has been impacted by the coronavirus disease 2019 (COVID-19) pandemic. However, only a few studies covering this subject in Africa have been published. Methods We conducted a retrospective study of critically ill adult COVID-19 patients—all of whom had a confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection—admitted to the intensive care unit (ICU) of Habib Bourguiba University Hospital (Sfax, Tunisia). Results A total of 96 patients were admitted into our ICU for respiratory distress due to COVID-19 infection. Mean age was 62.4±12.8 years and median age was 64 years. Mean arterial oxygen tension (PaO2)/fractional inspired oxygen (FiO2) ratio was 105±60 and ≤300 in all cases but one. Oxygen support was required for all patients (100%) and invasive mechanical ventilation for 38 (40%). Prone positioning was applied in 67 patients (70%). Within the study period, 47 of the 96 patients died (49%). Multivariate analysis showed that the factors associated with poor outcome were the development of acute renal failure (odds ratio [OR], 6.7; 95% confidence interval [CI], 1.75–25.9), the use of mechanical ventilation (OR, 5.8; 95% CI, 1.54–22.0), and serum cholinesterase (SChE) activity lower than 5,000 UI/L (OR, 5.0; 95% CI, 1.34–19). Conclusions In this retrospective cohort study of critically ill patients admitted to the ICU in Sfax, Tunisia, for acute respiratory failure following COVID-19 infection, the mortality rate was high. The development of acute renal failure, the use of mechanical ventilation, and SChE activity lower than 5,000 UI/L were associated with a poor outcome

Pacifica: Poetry International: Revolutions in Tunisian Poetry

A collection of contemporary poetry from Tunisia, including poems in translation with their French and Arabic original texts

Automatic video fire detection approach based on PJF color modeling and spatio-temporal analysis

Recently, due to the huge damage caused by fires in many countries in the world, fire detection is getting more and more interest as an increasing important issue.Nowadays, the early fire detection in video surveillance scenes is emerging as an alternative solution to overcome the shortcomings of the current inefficient sensors. In this paper, we propose a new video based-fire detection method exploiting color and motion information of fire. Our approach consists in detecting all moving regions in the scene to select then areas likely to be fire. Further, motion analysis is required to identify the accurate fire regions. The proposed method is evaluated on different video datasets containing diverse fire and non-fire videos. Experimental results demonstrate the effectiveness of our proposed method by achieving high fire detection and low false alarms rates. Moreover, it greatly outperforms the related works with 98.81 % accuracy and only 2% of false positive rate

Three dimensional Deep Learning approach for remote sensing image classification

International audienceRecently, a variety of approaches has been enriching the field of Remote Sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited faced to the rich spatio-spectral content of today's large datasets. It would seem intriguing to resort to Deep Learning (DL) based approaches at this stage with regards to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS datasets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is firstly to explore the performance of DL architectures for the RS hyperspectral dataset classification and secondly to introduce a new three-dimensional DL approach that enables a joint spectral and spatial information process. A set of three-dimensional schemes is proposed and evaluated. Experimental results based on well knownhyperspectral datasets demonstrate that the proposed method is able to achieve a better classification rate than state of the art methods with lower computational costs

Generative Adversarial Network (GAN) for Remote Sensing Images unsupervised Learning

National audienc

Regions Based Semi-fragile Watermarking Scheme

In this paper, we propose a new semi-fragile watermarking scheme in the frequency domain for surveillance videos authentication. Our system starts operating by generating a binary watermark based on a novel watermark construction process. This latter combines Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) detectors to extract frames relevant features that can resist common attacks while being fragile to intentional manipulations. Furthermore, the watermark security is improved using torus automorphism mapping. For the embedding process, Regions of Interest (ROI) are detected and then used as watermark holders. These regions are decomposed into different frequency sub-bands using Singular Value Decomposition (SVD) as well as Discrete Wavelet Transform (DWT). Then, the watermark is embedded in selected bands following an additive method. A blind detection is conducted to extract the hidden signature from the watermarked video. Evaluation results show that the proposed scheme is suitable for authentication purpose since it efficiently discriminates malicious manipulations from non-malicious ones. Besides, it preserves a high level of perceptual quality