UML for Validation: Experimenting automatic test generation for flight software validation

International audienceUML for validation is a CNES study that aims at prototyping and experimenting automatic test generation technologies in the context of a model-based approach applied to on-board software development and tests. Starting from real test cases and test procedures taken from state-of-the-art onboard software, we first applied a reverse engineering methodology to obtain an augmented software specification model, i.e. ready to support automated test generation. In parallel, we defined and prototyped a test generation tool using innovative model-based technologies based on EMF (Eclipse Modeling Framework). Finally, a representative end-to-end experiment was performed to evaluate the benefit of such technologies

SelfClean: A Self-Supervised Data Cleaning Strategy

Most benchmark datasets for computer vision contain irrelevant images, near duplicates, and label errors. Consequently, model performance on these benchmarks may not be an accurate estimate of generalization capabilities. This is a particularly acute concern in computer vision for medicine where datasets are typically small, stakes are high, and annotation processes are expensive and error-prone. In this paper we propose SelfClean, a general procedure to clean up image datasets exploiting a latent space learned with self-supervision. By relying on self-supervised learning, our approach focuses on intrinsic properties of the data and avoids annotation biases. We formulate dataset cleaning as either a set of ranking problems, which significantly reduce human annotation effort, or a set of scoring problems, which enable fully automated decisions based on score distributions. We demonstrate that SelfClean achieves state-of-the-art performance in detecting irrelevant images, near duplicates, and label errors within popular computer vision benchmarks, retrieving both injected synthetic noise and natural contamination. In addition, we apply our method to multiple image datasets and confirm an improvement in evaluation reliability

Towards Reliable Dermatology Evaluation Benchmarks

Benchmark datasets for digital dermatology unwittingly contain inaccuracies that reduce trust in model performance estimates. We propose a resource-efficient data cleaning protocol to identify issues that escaped previous curation. The protocol leverages an existing algorithmic cleaning strategy and is followed by a confirmation process terminated by an intuitive stopping criterion. Based on confirmation by multiple dermatologists, we remove irrelevant samples and near duplicates and estimate the percentage of label errors in six dermatology image datasets for model evaluation promoted by the International Skin Imaging Collaboration. Along with this paper, we publish revised file lists for each dataset which should be used for model evaluation. Our work paves the way for more trustworthy performance assessment in digital dermatology.Comment: Link to the revised file lists: https://github.com/Digital-Dermatology/SelfClean-Revised-Benchmark

A Probabilistic Framework for Security Scenarios with Dependent Actions

This work addresses the growing need of performing meaningful probabilistic analysis of security. We propose a framework that integrates the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. This allows us to perform probabilistic evaluation of attack–defense scenarios involving dependent actions. To improve the efficiency of our computations, we make use of inference algorithms from Bayesian networks and encoding techniques from constraint reasoning. We discuss the algebraic theory underlying our framework and point out several generalizations which are possible thanks to the use of semiring theory

Computability of ordinary differential equations

In this paper we provide a brief review of several results about the computability of initial-value problems (IVPs) defined with ordinary differential equations (ODEs). We will consider a variety of settings and analyze how the computability of the IVP will be affected. Computational complexity results will also be presented, as well as computable versions of some classical theorems about the asymptotic behavior of ODEs.info:eu-repo/semantics/publishedVersio

An Algebraic Theory for Data Linkage

There are countless sources of data available to governments, companies, and citizens, which can be combined for good or evil. We analyse the concepts of combining data from common sources and linking data from different sources. We model the data and its information content to be found in a single source by an ordered partial monoid, and the transfer of information between sources by different types of morphisms. To capture the linkage between a family of sources, we use a form of Grothendieck construction to create an ordered partial monoid that brings together the global data of the family in a single structure. We apply our approach to database theory and axiomatic structures in approximate reasoning. Thus, ordered partial monoids provide a foundation for the algebraic study for information gathering in its most primitive form

Improved diagnosis by automated macro‐ and micro‐anatomical region mapping of skin photographs

Background: The exact location of skin lesions is key in clinical dermatology. On one hand, it supports differential diagnosis (DD) since most skin conditions have specific predilection sites. On the other hand, location matters for dermatosurgical interventions. In practice, lesion evaluation is not well standardized and anatomical descriptions vary or lack altogether. Automated determination of anatomical location could benefit both situations. Objective: Establish an automated method to determine anatomical regions in clinical patient pictures and evaluate the gain in DD performance of a deep learning model (DLM) when trained with lesion locations and images. Methods: Retrospective study based on three datasets: macro-anatomy for the main body regions with 6000 patient pictures partially labelled by a student, micro-anatomy for the ear region with 182 pictures labelled by a student and DD with 3347 pictures of 16 diseases determined by dermatologists in clinical settings. For each dataset, a DLM was trained and evaluated on an independent test set. The primary outcome measures were the precision and sensitivity with 95% CI. For DD, we compared the performance of a DLM trained with lesion pictures only with a DLM trained with both pictures and locations. Results: The average precision and sensitivity were 85% (CI 84-86), 84% (CI 83-85) for macro-anatomy, 81% (CI 80-83), 80% (CI 77-83) for micro-anatomy and 82% (CI 78-85), 81% (CI 77-84) for DD. We observed an improvement in DD performance of 6% (McNemar test P-value 0.0009) for both average precision and sensitivity when training with both lesion pictures and locations. Conclusion: Including location can be beneficial for DD DLM performance. The proposed method can generate body region maps from patient pictures and even reach surgery relevant anatomical precision, e.g. the ear region. Our method enables automated search of large clinical databases and make targeted anatomical image retrieval possible

Stratégie de réduction des micropolluants à l'échelle d'une agglomération : comparaison entre les effluents d’exutoires pluviaux et d’une station d'épuration

Colloque avec actes et comité de lecture. Internationale.International audienc

BMJ Open

INTRODUCTION: The prevalence of postnatal depression (PND) is significant: reaching up to 20% in the general population. In mechanistic terms, the risk of PND lies in an interaction between a maternal psychophysiological vulnerability and a chronic environmental context of stress. On the one hand, repetition of stressor during pregnancy mimics a chronic stress model that is relevant to the study of the allostatic load and the adaptive mechanisms. On the other hand, vulnerability factors reflect a psychological profile mirroring mindfulness functioning (psychological quality that involves bringing one's complete and non-judgemental attention to the present experience on a moment-to-moment basis). This psychological resource is linked to protective and resilient psychic functioning. Thus, PND appears to be a relevant model for studying the mechanisms of chronic stress and vulnerability to psychopathologies.In this article, we present the protocol of an ongoing study (started in May 2017). METHODS AND ANALYSIS: The study is being carried out in five maternities and will involve 260 women. We aim to determine the predictive psychobiological factors for PND emergence and to provide a better insight into the mechanisms involved in chronic stress during pregnancy. We use a multidisciplinary approach that encompasses psychological resources and biophysiological and genetic profiles in order to detect relevant vulnerability biomarkers for chronic stress and the development of PND. To do so, each woman will be involved in the study from her first trimester of pregnancy until 12 months postdelivery. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Ile de France III Ethics Committee, France (2016-A00887-44). We aim to disseminate the findings through international conferences and international peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT03088319; Pre-results