Supporting a Multi-formalism Model Driven Development Process with Model Transformation, a TOPCASED implementation

International audienceThe ASSERT (Automated proof based System and Software Engineering for Real-Time Applications) European Integrated Project (IST-FP6-004033, http://www.assert-project.net/) defined and experimented a multi formalism Model Driven Engineering (MDE) process, enforcing an approach with separated specification and refinement of functional and non-functional properties.• Functional specification, design and development is based on UML profiles to support AADL concepts [2] and behavioural specification.• Real time Architecture properties are based on extensions targeting Ravenscar Computing execution Model (RCM see [6]) constraints upon component interface and ports.• Model transformation is supporting correctness preserving rules towards a Virtual Machine execution environment or a verification dedicated environment.A tool chain called IDEA (Integrated Development Environment for ASSERT) supporting the process was developed by the CS ASSERT team on top of the Eclipse/TOPCASED environment allowing:• Integrated use of several formalisms in a development life-cycle (UML, AADL, IF[4]) .• Model transformation from UML to IF, AADL to RCM and RCM to Ada• Automated code generationThe approach experimented allows combined use of best suited formalisms and features for MDE developments. The TOPCASED tool proved to be a unique integrated toolset for prototyping UML and meta models supporting tools.The main feedback gained from applying the notations and approach on small to medium case studies is that UML profiling is not scalable, and that use of several Domain Specific Languages (DSL) seems far more suitable. Semantic clashes can be limited by raising the abstraction level, and by partitioning properties for verification

The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning

Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state–of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient’s ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician’s experience, motivation and tiredness. Here, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, a multi-head convolutional neural network was trained on 20225 ROCF drawings. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. The neural network outperforms both online raters and clinicians. Our AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images

Toward Polychronous Analysis and Validation for Timed Software Architectures in AADL

International audienceHigh-level architecture modeling languages, such as Architecture Analysis & Design Language (AADL), are gradually adopted in the design of embedded systems so that design choice verification, architecture exploration, and system property check- ing are carried out as early as possible. This paper presents our recent contributions to cope with clock-based timing analysis and validation of software architectures specified in AADL. In order to avoid semantics ambiguities of AADL, we mainly consider the AADL features related to real-time and logical time properties. We endue them with a semantics in the polychronous model of computation; this semantics is quickly reviewed. The semantics enables timing analysis, formal verification and simulation. In addition, thread-level scheduling, based on affine clock relations is also briefly presented here. A tutorial avionic case study is finally adopted to illustrate our overall contribution

Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning

AMS subject classifications. 33F05, 49M99, 65D99, 90C08International audienceThis article introduces a new non-linear dictionary learning method for histograms in the probability simplex. The method leverages optimal transport theory, in the sense that our aim is to reconstruct histograms using so called displacement interpolations (a.k.a. Wasserstein barycenters) between dictionary atoms; such atoms are themselves synthetic histograms in the probability simplex. Our method simultaneously estimates such atoms, and, for each datapoint, the vector of weights that can optimally reconstruct it as an optimal transport barycenter of such atoms. Our method is computationally tractable thanks to the addition of an entropic regularization to the usual optimal transportation problem, leading to an approximation scheme that is efficient, parallel and simple to differentiate. Both atoms and weights are learned using a gradient-based descent method. Gradients are obtained by automatic differentiation of the generalized Sinkhorn iterations that yield barycenters with entropic smoothing. Because of its formulation relying on Wasserstein barycenters instead of the usual matrix product between dictionary and codes, our method allows for non-linear relationships between atoms and the reconstruction of input data. We illustrate its application in several different image processing settings

Gestion alternative des eaux pluviales urbainesà Strasbourg (2010-2020) : retour d'expérience sur 10 ans de recherche et une démarche d'écologie urbaine en devenir

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