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ФРАНЦУЗСКИЙ ПАРАДОКС: ИСЛАМ И ЛАИЦИЗМ
No full textФРАНЦУЗСКИЙ ПАРАДОКС: ИСЛАМ И ЛАИЦИЗМ. (2025). European Journal of Interdisciplinary Research and Development , 40, 316-325.Russian translation of J. Tolan, "A French Paradox?: Islam and Laïcité." Georgetown Journal of International Affairs 18 (2017), 41-50.Совместим ли Ислам с лаицизмом, являющимся одной из разновидностей секуляризма во Франции? И напротив, позволяет ли лаицизм свободному вероисповеданию мусульман в рамках нейтрального режима, или же это всего лишь кодовое слово, маскирующее исламофобию? Если бы я сформулировал эти вопросы в духе манихейского мировоззрения, то именно такие формулировки часто звучат во французском политическом дискурсе, в СМИ (как французских, так и зарубежных) и в других источниках. К счастью, более тонкие подходы к этим вопросам также существуют: лаицизм стал предметом активных исследований среди учёных -как французских, так и иностранных -в таких областях, как социология, история и политология. Было опубликовано множество статей и книг политиками, военными и религиозными деятелями (большинство из них, конечно, полемичны, но некоторые заслуживают внимания).</div
Les diplomaties du nord en Algérie (1962-1968) : concurrences et complémentarités
No full textInternational audienceEven before it gained independence, the two northern blocs paid close attention to Algeria, one the key-countries of anti-imperialist struggles. Powers as diverse as the United States, the USSR, France, West Germany, and Bulgaria developed strategies of influence and defense of economic interests during the 1960s, relying on the tool of civil and military cooperation. These policies also dealt with Algeria's third-worldist positions and its international influence, which they tried to accommodate or associate with. Between and within the blocs, the strategies of each government coordinated or clashed, and were also used with some success by the Algerian side.Avant même l’accession à l’indépendance de l’Algérie, les deux blocs du Nord ont porté une forte attention à ce pays-phare des luttes anti-impérialistes. Des puissances aussi diverses que les États-Unis, l’URSS, la France, la RFA ou la Bulgarie y développent, dans le courant des années 1960, des stratégies d’influence et de défense des intérêts économiques, qui s’appuient sur l’outil des coopérations civile et militaire. Ces politiques composent aussi avec les positions tiers-mondistes et l’influence internationale de l’Algérie, que l’on essaie de s’en accommoder ou bien de s’y associer. Entre les blocs et en leur sein, les stratégies de chaque gouvernement se coordonnent ou s’affrontent et sont aussi utilisées avec un certain succès par la partie algérienne
Les espaces publics et collectifs dans les villes d’Hispanie méridionale et du nord de l’Afrique entre les IIIe et VIIIe siècles
No full textInternational audienc
Toward a Complete System for Pupil Monitoring Using Deep Learning for Digital Twins
No full textInternational audienceSchool dropout is a significant issue with profound economic and social implications. Research suggests that addressing key factors – such as repetitive absenteeism, parental disengagement, and a negative school climate – can effectively mitigate the problem. This chapter presents a comprehensive system designed to prevent school dropout by targeting these underlying causes. The system consists of four subsystems implemented on an embedded platform. The first is an attendance detection system with GUI facilitating the use and the update. The second is an access control system that safeguards students by preventing unauthorized access to restricted areas like laboratories. Both systems use face detection and recognition techniques. The third subsystem focuses on behavior recognition to detect and prevent bullying and assaults. Lastly, the system integrates IoT technology for data logging and real-time parent notifications. With an accuracy rate of 92.12%, further enhanced by a multi-test confirmation mechanism, the system demonstrates a promising solution for reducing school dropout rates
A Transductive and Inductive GNNs for Physical Moving Objects Detection in Surface Scenes for Digital Twins
No full textInternational audienceComputer vision applications using static or moving cameras are often required in digital twins generation. More specifically, the detection of moving objects is essential to provide a virtual representation of an environment in order to reflect physical moving objects accurately. To this end, background subtraction (BGS) is then applied to separate the background (BG) and the foreground (FG) from videos. Numerous publications employ mathematical, machine learning, and signal processing models to be more robust to the open challenges presented in videos. Recently, many methods using graph neural networks for BGS have been reported, with very promising outcomes. This chapter provides a survey of transductive and inductive Graph Neural Networks (GNNs) for moving objects detection (MOD) comparing their architectures. After analysis of their strategies and limitations, a comparative evaluation of the large-scale CDnet2014 dataset is provided. Finally, we conclude with some potential future research directions
Fusion of GNN and GBDT Models for Graph and Node Classification
No full textInternational audienceThe discipline of graph-based machine learning, which focuses on learning from structured graph data, is expanding rapidly. Numerous applications in recommendation systems, bioinformatics, and social network analysis fall within this domain. However, traditional Graph Neural Networks (GNNs) face difficulties when dealing with datasets that frequently contain structured and graph data. Our approach addresses this challenge by creating a proposed fusion model of GNN and Gradient Boosting Decision Trees (GBDTs). We investigated the effectiveness of using the GNN and GBDT based fusion model using logistic regression by combining the embeddings of GNN and GBDT, stacking the predictions from GBDT variants for node classification and graph classification. The generality of the model is tested and validated on one heterogeneous and two homogeneous state-of-theart datasets with average accuracy of A:</div
Global Roadkill Data: a dataset on terrestrial vertebrate mortality caused by collision with vehicles
No full textInternational audienceRoadkill is widely recognized as one of the primary negative effects of roads on many wildlife species and also has socioeconomic impacts when they result in accidents. A comprehensive dataset of roadkill locations is essential to evaluate the factors contributing to roadkill risk and to enhance our comprehension of its impact on wildlife populations and socioeconomic dimensions. We undertook a compilation of roadkill records, encompassing both published and unpublished data gathered from road surveys or opportunistic sources. GLOBAL ROADKILL DATA includes 208,570 roadkill records of terrestrial vertebrates from 54 countries across six continents, encompassing data collected between 1971 and 2024. This dataset serves to minimise the collection of redundant data and acts as a valuable resource for local and macro scale analysis regarding rates of roadkill, road-and landscape-related features associated with risk of roadkill, vulnerability of species to road traffic, and populations at risk of local extinction. The objective of this dataset is to promote scientific progress in infrastructure ecology and terrestrial vertebrate conservation while limiting the socio-economic costs
Long-Term Spatio-Temporal Graph Attention Network for Traffic Forecasting
No full textInternational audienceccurate traffic flow prediction is a critical component of intelligent transportation systems and smart cities, playing an essential role in traffic control, transportation planning, and infrastructure development. Numerous recent research studies highlight the need to enhance prediction accuracy by addressing complex temporal and spatial dependencies. However, due to the complexity of these spatio-temporal patterns, achieving accurate traffic predictions is still a main challenge in long-term scenarios. In this context, we first provide a comprehensive overview of the traffic forecasting to locate where research is going on. Then, we develop a Long-Term Spatio-Temporal Graph Attention Network (LSTGAN) architecture designed to analyze long-term historical data to address the above issue. This architecture encodes several previous time steps and extracts temporal patterns using convolutional layers. These features are then combined with the spatial features captured by a spatial attention module and a graph convolution layer to be processed by a temporal attention decoder responsible for making predictions. Experiments on METR-LA and PEMS-BAY datasets show that our proposed architecture outperforms most existing state-of-the-art baselines