L’enseignement secondaire en Algérie : Inefficience technique, inefficience d’échelle et impact du milieu socioéconomique

Les disparités régionales relatives à la réussite au baccalauréat sont considérées importantes en Algérie. La présente étude vise à mesurer l'inefficience relative des 50 Directions d'Education de Wilaya (DEW) en Algérie au niveau de l'enseignement secondaire en utilisant l'approche Data Envelopment Analysis (DEA). Bien que les résultats empiriques montrent que le fonctionnement de ces institutions se caractérise par un niveau moyen élevé d'inefficience relative globale de l'ordre de 24,5%, l'inefficience technique pure ne représente que 8% en moyenne alors que les 16,5% restants sont attribuables aux dés-économies d'échelle et aux facteurs socioéconomiques et socioculturels défavorables**. Mots clés: Inégalités éducatives, Fonction de production éducative, Data Envelopment Analysis, Efficience technique, Rendement d'échelle, milieu socioéconomique. English Title: Secondary education in Algeria: Technical inefficiency, scale inefficiency and the impact of the socioeconomic environment Regional disparities related to the success in the baccalauréat exam are considered important in Algeria. The present study aims to measure the relative inefficiency of the 50 Educational districts "Directions d'Education de Wilaya" (DEW) at the secondary education level using Data Envelopment Analysis approach (DEA). Although the empirical results show that the operation of these institutions is characterized by a high level of overall inefficiency of the order of 24.5% on average, pure technical inefficiency represents only 8% on average while the remaining 16.5% are attributable to diseconomies of scale and to adverse socioeconomic and sociocultural factors. Key words: Educational inequalities, Educational production function, Data Envelopment Analysis, Technical efficiency, Return to scale,  Socioeconomic environment &nbsp

H(infini)-feedback design for linear systems subject to input saturation

In this paper, we study gain attenuation of linear systems subject to input saturation \dot x=Ax+b\sg(u). In case there exists a family $\cal{K}$ of stabilizing feedback, we address the issue of estimating the infimum of $\gamma_k$, the gain of the output map $v\mapsto x_v$, defined for $k\in {\cal{K}}$, and where $x_v$ is the solution of \dot x=Ax+b\sg(k(x)+v) starting at zero. For the 2D oscillator and double integrator, we explicitely determine an appropriate family $\cal{K}$ of stabilizing feedback and prove that the infimum over the gains is equal to zero

An Event-Controlled Online Trajectory Generator Based on the Human-Robot Interaction Force Processing

International audienceThe problem of robotic co-manipulation is often addressed using impedance control based methods where we seek to establish a mathematical relation between the velocity of the human-robot interaction point and the force applied by the human operator at this point. This paper addresses the problem of co-manipulation for handling tasks seen as a constrained optimal control problem. The proposed point of view relies on the implementation of a specific online trajectory generator (OTG) associated to a kinematic feedback loop. This OTG is designed so as to translate the human operator intentions to ideal trajectories that the robot must follow. It works as an automaton with two states of motion whose transitions are controlled by comparing the magnitude of the force to an adjustable threshold, in order to enable the operator to keep authority over the robot's states of motion. To ensure the smoothness of the interaction, we propose to generate a velocity profile collinear to the force applied at the interaction point. The feedback control loop is then used to satisfy the requirements of stability and of trajectory tracking to guarantee assistance and operator security. The overall strategy is applied to the penducobot problem

PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment

Following the advent of immersive technologies and the increasing interest in representing interactive geometrical format, 3D Point Clouds (PC) have emerged as a promising solution and effective means to display 3D visual information. In addition to other challenges in immersive applications, objective and subjective quality assessments of compressed 3D content remain open problems and an area of research interest. Yet most of the efforts in the research area ignore the local geometrical structures between points representation. In this paper, we overcome this limitation by introducing a novel and efficient objective metric for Point Clouds Quality Assessment, by learning local intrinsic dependencies using Graph Neural Network (GNN). To evaluate the performance of our method, two well-known datasets have been used. The results demonstrate the effectiveness and reliability of our solution compared to state-of-the-art metrics

Organometallic Perovskite Solar Cell

Halide organometallic perovskite has an important role in the efficiency increase of the solar cell. Thus in this work, we formed the basic nucleus of the organic perovskite, and we studied its morphological properties. The X-ray diffraction result shows that this compound is consistent, homogeneous, and has preferential orientation growth be at (100) plane, which means that the experimental conditions which we worked on were optimal. After adding both tin iodide and methylamine chloride in organic solvents (DMF and DMSO). Deposited this mixture by spray pyrolysis method at Specific temperature 120C°, on the glass substrate, a thin black layer formed; the result of X-ray diffraction on this latter layer showed that it is a spectrum of perovskite compound, which has preferential orientation growth be at (110) plane. Via optical proprieties, it found that has low gap energy of 1.78 eV, and transmittance of 1,6% furthermore it has a high absorption coefficient of 8.104 cm-1, in the visible domain. But it has a relatively high value of Auerbach energy 0.6 eV due to the crystal defects. So this compound could be an active layer in the solar cell

A domain adaptive deep learning solution for scanpath prediction of paintings

Cultural heritage understanding and preservation is an important issue for society as it represents a fundamental aspect of its identity. Paintings represent a significant part of cultural heritage, and are the subject of study continuously. However, the way viewers perceive paintings is strictly related to the so-called HVS (Human Vision System) behaviour. This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. In further details, we introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans, including the fundamental understanding of a scene, and then extend it to painting images. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention. We use an FCNN (Fully Convolutional Neural Network), in which we exploit a differentiable channel-wise selection and Soft-Argmax modules. We also incorporate learnable Gaussian distributions onto the network bottleneck to simulate visual attention process bias in natural scene images. Furthermore, to reduce the effect of shifts between different domains (i.e. natural images, painting), we urge the model to learn unsupervised general features from other domains using a gradient reversal classifier. The results obtained by our model outperform existing state-of-the-art ones in terms of accuracy and efficiency.Comment: Accepted at CBMI2022 graz, austri

Telemedicine and e-health in Algeria facing challenges in medical practice

The Algerian health system operates in a demographic, economic, environmental, and societal specific context to meet the challenges of epidemiological transition. Development of information and communication technologies in this atmosphere allow telemedicine and e-health emergence in the vast country of Algeria. Thus, the country may face these challenges by including them in a national telemedicine plan and making a major focus of global action for the prevention and control of prevalent diseases. The interest to adopt this tool in daily practice stems from an improvement in the quality of communication between practitioners and in the doctor–patient relationship with a possibility of quick access to care and more efficient care pathways. Thus, national goals in fight plans against diseases will be achieved. Telemedicine and e-health projects that methodologically well-defined, respecting regulations and using all means and all available resources, including WHO mobile health, are to be designed and implemented in all areas, especially in the national plan against not-communicated diseases, maternal and child health, old aging health, and mental health. This approach will integrate telemedicine in the health care system whose inevitable implementation can be done on solid foundations and will be actively supported by SATeS

An Inter-observer consistent deep adversarial training for visual scanpath prediction

The visual scanpath is a sequence of points through which the human gaze moves while exploring a scene. It represents the fundamental concepts upon which visual attention research is based. As a result, the ability to predict them has emerged as an important task in recent years. In this paper, we propose an inter-observer consistent adversarial training approach for scanpath prediction through a lightweight deep neural network. The adversarial method employs a discriminative neural network as a dynamic loss that is better suited to model the natural stochastic phenomenon while maintaining consistency between the distributions related to the subjective nature of scanpaths traversed by different observers. Through extensive testing, we show the competitiveness of our approach in regard to state-of-the-art methods.Comment: ICIP202