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class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.14027">arXiv:2406.14027</a> <span> [<a href="https://arxiv.org/pdf/2406.14027">pdf</a>, <a href="https://arxiv.org/format/2406.14027">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> How to design a dataset compliant with an ML-based system ODD? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cappi%2C+C">Cyril Cappi</a>, <a href="/search/?searchtype=author&query=Cohen%2C+N">No茅mie Cohen</a>, <a href="/search/?searchtype=author&query=Ducoffe%2C+M">M茅lanie Ducoffe</a>, <a href="/search/?searchtype=author&query=Gabreau%2C+C">Christophe Gabreau</a>, <a href="/search/?searchtype=author&query=Gardes%2C+L">Laurent Gardes</a>, <a href="/search/?searchtype=author&query=Gauffriau%2C+A">Adrien Gauffriau</a>, <a href="/search/?searchtype=author&query=Ginestet%2C+J">Jean-Brice Ginestet</a>, <a href="/search/?searchtype=author&query=Mamalet%2C+F">Franck Mamalet</a>, <a href="/search/?searchtype=author&query=Mussot%2C+V">Vincent Mussot</a>, <a href="/search/?searchtype=author&query=Pagetti%2C+C">Claire Pagetti</a>, <a href="/search/?searchtype=author&query=Vigouroux%2C+D">David Vigouroux</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.14027v1-abstract-short" style="display: inline;"> This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Learning (ML) system. Relying on emerging certification standards, we describe the process for establishing ODDs at both the system and image levels. In the process, we present the translation of high-level system constrai… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14027v1-abstract-full').style.display = 'inline'; document.getElementById('2406.14027v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.14027v1-abstract-full" style="display: none;"> This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Learning (ML) system. Relying on emerging certification standards, we describe the process for establishing ODDs at both the system and image levels. In the process, we present the translation of high-level system constraints into actionable image-level properties, allowing for the definition of verifiable Data Quality Requirements (DQRs). To illustrate this approach, we use the Landing Approach Runway Detection (LARD) dataset which combines synthetic imagery and real footage, and we focus on the steps required to verify the DQRs. The replicable framework presented in this paper addresses the challenges of designing a dataset compliant with the stringent needs of ML-based systems certification in safety-critical applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14027v1-abstract-full').style.display = 'none'; document.getElementById('2406.14027v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12th European Congress on Embedded Real Time Software and Systems, Jun 2024, Toulouse, France. arXiv admin note: text overlap with arXiv:2304.09938</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.08788">arXiv:2403.08788</a> <span> [<a href="https://arxiv.org/pdf/2403.08788">pdf</a>, <a href="https://arxiv.org/format/2403.08788">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Verification for Object Detection -- IBP IoU </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cohen%2C+N">No茅mie Cohen</a>, <a href="/search/?searchtype=author&query=Ducoffe%2C+M">M茅lanie Ducoffe</a>, <a href="/search/?searchtype=author&query=Boumazouza%2C+R">Ryma Boumazouza</a>, <a href="/search/?searchtype=author&query=Gabreau%2C+C">Christophe Gabreau</a>, <a href="/search/?searchtype=author&query=Pagetti%2C+C">Claire Pagetti</a>, <a href="/search/?searchtype=author&query=Pucel%2C+X">Xavier Pucel</a>, <a href="/search/?searchtype=author&query=Galametz%2C+A">Audrey Galametz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.08788v1-abstract-short" style="display: inline;"> We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric. The approach has been implemented in an open source code, named IBP IoU, compatible with popular abstract interpretation based verification tools. The resulting verifier is evaluated on landing approach runway detect… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.08788v1-abstract-full').style.display = 'inline'; document.getElementById('2403.08788v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.08788v1-abstract-full" style="display: none;"> We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric. The approach has been implemented in an open source code, named IBP IoU, compatible with popular abstract interpretation based verification tools. The resulting verifier is evaluated on landing approach runway detection and handwritten digit recognition case studies. Comparisons against a baseline (Vanilla IBP IoU) highlight the superior performance of IBP IoU in ensuring accuracy and stability, contributing to more secure and robust machine learning applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.08788v1-abstract-full').style.display = 'none'; document.getElementById('2403.08788v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.07221">arXiv:2106.07221</a> <span> [<a href="https://arxiv.org/pdf/2106.07221">pdf</a>, <a href="https://arxiv.org/format/2106.07221">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Certification of embedded systems based on Machine Learning: A survey </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Vidot%2C+G">Guillaume Vidot</a>, <a href="/search/?searchtype=author&query=Gabreau%2C+C">Christophe Gabreau</a>, <a href="/search/?searchtype=author&query=Ober%2C+I">Ileana Ober</a>, <a href="/search/?searchtype=author&query=Ober%2C+I">Iulian Ober</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.07221v2-abstract-short" style="display: inline;"> Advances in machine learning (ML) open the way to innovating functions in the avionic domain, such as navigation/surveillance assistance (e.g. vision-based navigation, obstacle sensing, virtual sensing), speechto-text applications, autonomous flight, predictive maintenance or cockpit assistance. Current certification standards and practices, which were defined and refined decades over decades with… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.07221v2-abstract-full').style.display = 'inline'; document.getElementById('2106.07221v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.07221v2-abstract-full" style="display: none;"> Advances in machine learning (ML) open the way to innovating functions in the avionic domain, such as navigation/surveillance assistance (e.g. vision-based navigation, obstacle sensing, virtual sensing), speechto-text applications, autonomous flight, predictive maintenance or cockpit assistance. Current certification standards and practices, which were defined and refined decades over decades with classical programming in mind, do not however support this new development paradigm. This article provides an overview of the main challenges raised by the use ML in the demonstration of compliance with regulation requirements, and a survey of literature relevant to these challenges, with particular focus on the issues of robustness and explainability of ML results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.07221v2-abstract-full').style.display = 'none'; document.getElementById('2106.07221v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.10529">arXiv:2103.10529</a> <span> [<a href="https://arxiv.org/pdf/2103.10529">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> White Paper Machine Learning in Certified Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Delseny%2C+H">Herv茅 Delseny</a>, <a href="/search/?searchtype=author&query=Gabreau%2C+C">Christophe Gabreau</a>, <a href="/search/?searchtype=author&query=Gauffriau%2C+A">Adrien Gauffriau</a>, <a href="/search/?searchtype=author&query=Beaudouin%2C+B">Bernard Beaudouin</a>, <a href="/search/?searchtype=author&query=Ponsolle%2C+L">Ludovic Ponsolle</a>, <a href="/search/?searchtype=author&query=Alecu%2C+L">Lucian Alecu</a>, <a href="/search/?searchtype=author&query=Bonnin%2C+H">Hugues Bonnin</a>, <a href="/search/?searchtype=author&query=Beltran%2C+B">Brice Beltran</a>, <a href="/search/?searchtype=author&query=Duchel%2C+D">Didier Duchel</a>, <a href="/search/?searchtype=author&query=Ginestet%2C+J">Jean-Brice Ginestet</a>, <a href="/search/?searchtype=author&query=Hervieu%2C+A">Alexandre Hervieu</a>, <a href="/search/?searchtype=author&query=Martinez%2C+G">Ghilaine Martinez</a>, <a href="/search/?searchtype=author&query=Pasquet%2C+S">Sylvain Pasquet</a>, <a href="/search/?searchtype=author&query=Delmas%2C+K">Kevin Delmas</a>, <a href="/search/?searchtype=author&query=Pagetti%2C+C">Claire Pagetti</a>, <a href="/search/?searchtype=author&query=Gabriel%2C+J">Jean-Marc Gabriel</a>, <a href="/search/?searchtype=author&query=Chapdelaine%2C+C">Camille Chapdelaine</a>, <a href="/search/?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/?searchtype=author&query=Damour%2C+M">Mathieu Damour</a>, <a href="/search/?searchtype=author&query=Cappi%2C+C">Cyril Cappi</a>, <a href="/search/?searchtype=author&query=Gard%C3%A8s%2C+L">Laurent Gard猫s</a>, <a href="/search/?searchtype=author&query=De+Grancey%2C+F">Florence De Grancey</a>, <a href="/search/?searchtype=author&query=Jenn%2C+E">Eric Jenn</a>, <a href="/search/?searchtype=author&query=Lefevre%2C+B">Baptiste Lefevre</a>, <a href="/search/?searchtype=author&query=Flandin%2C+G">Gregory Flandin</a> , et al. (3 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.10529v1-abstract-short" style="display: inline;"> Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.10529v1-abstract-full').style.display = 'inline'; document.getElementById('2103.10529v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.10529v1-abstract-full" style="display: none;"> Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup茅ry de Toulouse (IRT), as part of the DEEL Project. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.10529v1-abstract-full').style.display = 'none'; document.getElementById('2103.10529v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">113 pages, White paper</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> S079L03T00-005 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2; 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