Scientific Publications of the University of Toulouse II Le Mirail
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Unified access to interdisciplinary open data platforms: Open Science Data Network
No full textInternational audienceOpen Science is based on a collaborative network to develop transparent, accessible, and shared knowledge. Open Research Data Platforms (ORDPs) are deployed to fulfill the needs for data sharing of a specific community and/or scientific discipline. The high variety of research areas creates a barrier to data sharing between research entities. To enable this research data to be found by the research entities that need it, it is necessary to establish access to different ORDPs that are unknown to these research entities. The goal of this article is to provide a quantitative analysis showing the current limitations of data sharing between ORDPs in Open Science. We then propose a solution to improve data access and sharing based on theoretical foundations and an experimental approach.We propose to extend our theoretical interoperability model, which helps us to define the necessary steps to interoperate ORDPs. We present and discuss a quantitative evaluation of ORDPs’ interoperability. Based on this exploratory study, we propose a solution that enables research entities to discover unknown ORDPs, thereby facilitating access to relevant data. This solution is the Open Science Data Network (OSDN), a decentralized, distributed, and federated network of ORDPs that integrates a query propagation process and robustness features. To enable the deployment of OSDN at an Open Science scale, we designed our solution by considering its adoption cost relative to a non-organized interoperability approach. With two ORDPs integrated into the OSDN, the adoption cost is estimated to be reduced by at least 17%. This reduction approaches 100% as the number of integrated ORDPs increases.To demonstrate the feasibility of the solution, we developed a Proof of Concept (POC) and applied it to two research projects from different domains and involving distinct research communities. For the first research project, we measured a 7% increase in the volume of accessed data and an 80% reduction in the time needed to find this data. In addition, researcher from this experiment was able to formulate new intra- and interdisciplinary research questions thanks to the newly accessed data. In the second research project, we observed an increase in data volume of up to a factor of 3968. More importantly, this process led to the discovery of new essential data that was previously missing
Narrative, visual, and didactic choices in the Supertroupers Project: designing a digital comic on energy
No full textInternational audienceThis paper reports on the design of a digital comic episode for teaching the concept of energy in secondary education. Developed through a collaboration between physics education researchers, a scriptwriter, and an illustrator, the episode introduces energy-related key ideas—such as physical state, transfer, conservation, and sources—through a fictional narrative centred on the construction of an hipotetical energy-generating device. The design was guided by a combined framework of Design-Based Research and the Model of Educational Reconstruction, allowing for alignment between scientific content, student conceptions, and didactic structuring. Classroom testing of a prototype in France and Spain informed several revisions, including clearer sequencing of environmental issues and enhanced narrative coherence. The final resource remains to be tested. It is part of a multilingual series and is accompanied by teacher support materials. This case illustrates the affordances and boundaries of DBR and MER for designing multimodal educational artefacts in international contexts
The Palaeolithic origin of eyed needles
No full textInternational audienceVarious Palaeolithic tools made of osseous materials can be attributed to the processing of skin and plant fibres for clothing production, such as smoothers, awls, and possibly double points and épingles. However, the eyed needle is the technological innovation that unequivocally marks the beginning of intricate sewing in the Palaeolithic. It is a tool whose shape and mode of use have persisted until today, although its manufacturing techniques have changed over time. This tool is perfectly suited to manual sewing, which is inherently complex, and has various technical, economic, and cultural implications for hunter-gatherer societies. We present a concise overview of the published research on the origin of eyed needles in the Palaeolithic, in different parts of the world and at different periods, from Siberia and the Caucasus to China and North America, and their spread across different territories with their evolution and diversity. We will address the importance of morphometric analyses that allow us to assess this evolution and diversity within a techno-complex and between geographic areas and chrono-cultural contexts, presenting different hypotheses proposed about their origin and dispersion. Additionally, we discuss technological issues regarding the raw materials used (generally bone, although some specimens have been recovered in antler and ivory), and their transformation into needles. Studies in this regard are scarce, and most have focused on the Late Glacial period of Western Europe, although they are gradually extending to other geographic areas and periods. Thus, we examine the different techniques employed in producing the same object type at different times and places in the Northern Hemisphere. Finally, we address the question of needle use, the threads that may have been used, fractures, and repairs
Low-latency online estimation of human upper-limb pose and kinematics from a single 360 camera
No full textInternational audienceWe present a fully online framework for streaminghuman upper-limb kinematics estimation from a single 360camera. Incoming frames are processed sequentially throughvertical-boundary-aware tracking, pseudo-perspective rendering,and Neural Localizer Fields to estimate a sparse set of 3Danatomical landmarks in real time. These landmarks are mappedto an OpenSim-compatible biomechanical model, with jointangles computed on the fly via an online inverse kinematicssolver. The system achieves end-to-end latencies as low as22.9 ms on a high-performance setup. Evaluated in a single-participant scenario involving an initial T-pose calibration andrepeated object displacement toward the camera, it demonstratesrobust performance under moderate self-occlusion and sphericaldistortion. While tested in a constrained setting, its modular, real-time design makes it a promising candidate for human–robotinteraction and other motion analysis applications, enablingminimal, markerless, and anatomically interpretable upper-limbtracking from omnidirectional vision
"Pour un art-monde : Carnaval et carnavalesque dans l’œuvre de The Old Boys’ Club"
No full textInternational audienc
Efficiency of Parallel and Restart Exploration Strategies in Model Free Stochastic Simulations
No full textICT for Sustainability: Barriers and Opportunities in Spreading Awareness of the Sustainable Development Goals Among Young Rural Farmers
No full textInternational audienceThis study analyzes the potential of Information and Communication Technology (ICT) in spreading awareness of the Sustainable Development Goals (SDGs) among young rural farmers in the Ecuadorian Amazon, considering structural barriers such as the digital divide and gender inequalities. A mixed approach (surveys of 110 youth and interviews with 7 community communicators) was used to identify living conditions, access to information, and understanding of the SDGs in the Llanganates Sangay Ecological Corridor. The results reveal that although 95.39% of the young people have access to the Internet, gender gaps persist in education, land tenure, and access to credit, with significant disadvantages for women. Only 44.68% are aware of the SDGs, and their implementation is limited by decontextualized global messages and low Internet access in rural areas. Community media, especially radio, are reliable broadcasting channels, yet they face operational challenges. ICT offers opportunities to make local realities visible and promote the SDGs but requires digital inclusion policies, critical training, and territorialized strategies. This study highlights the need to address structural inequalities and strengthen youth participation in sustainability agendas
An analytic theory of convolutional neural network inverse problems solvers
No full textSupervised convolutional neural networks (CNNs) are widely used to solve imaging inverse problems, achieving state-of-the-art performance in numerous applications. However, despite their empirical success, these methods are poorly understood from a theoretical perspective and often treated as black boxes. To bridge this gap, we analyze trained neural networks through the lens of the Minimum Mean Square Error (MMSE) estimator, incorporating functional constraints that capture two fundamental inductive biases of CNNs: translation equivariance and locality via finite receptive fields. Under the empirical training distribution, we derive an analytic, interpretable, and tractable formula for this constrained variant, termed Local-Equivariant MMSE (LE-MMSE). Through extensive numerical experiments across various inverse problems (denoising, inpainting, deconvolution), datasets (FFHQ, CIFAR-10, FashionMNIST), and architectures (U-Net, ResNet, PatchMLP), we demonstrate that our theory matches the neural networks outputs (PSNR ≳ 25dB). Furthermore, we provide insights into the differences between physics-aware and physics-agnostic estimators, the impact of high-density regions in the training (patch) distribution, and the influence of other factors (dataset size, patch size, etc. ).</div
Explainable quantum convolutional neural network for attack detection in healthcare IoMT systems using SHAP and Grid5000 computing
No full textInternational audienceThe growth of the Internet of Medical Things (IoMT) improves patient care and data analysis, but it alsomakes healthcare systems newly vulnerable to cyberattacks.Current intrusion detection systems (IDS) strug-gle to work with the many different IoMT protocols. More importantly, they are ”black boxes” that can’texplain their decisions, which is a major problem in healthcare where safety is critical. This paper pro-poses a hybrid explainable quantum convolutional neural network (QCNN–SHAP) framework for accurateand transparent attack detection in IoMT environments. The QCNN exploits quantum feature encoding andentanglement to capture complex correlations in high-dimensional IoMT traffic data, while SHAP (SHapleyAdditive exPlanations) provides feature-level interpretability for each detection decision. We tested our modelon the CICIoMT2024 dataset, a benchmark for multi-protocol IoMT security assessment, using the Grid5000distributed computing platform. The proposed QCNN–SHAP model achieved a detection accuracy of 98.05%,outperforming current models like CNN, LSTM–Autoencoder, and Transformer–XAI models while maintain-ing explainability and moderate computational cost. SHAP analysis showed that key network features suchas packet size variance, flow duration, and protocol type were the most influential in identifying attacks. Theresults validate the model’s robustness, interpretability, and suitability for real-time IoMT intrusion detectionin healthcare contexts