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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.04917">arXiv:2312.04917</a> <span> [<a href="https://arxiv.org/pdf/2312.04917">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</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="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-031-49266-2_10">10.1007/978-3-031-49266-2_10 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=J%C3%B6ckel%2C+L">Lisa J枚ckel</a>, <a href="/search/cs?searchtype=author&query=Kl%C3%A4s%2C+M">Michael Kl盲s</a>, <a href="/search/cs?searchtype=author&query=Gro%C3%9F%2C+J">Janek Gro脽</a>, <a href="/search/cs?searchtype=author&query=Gerber%2C+P">Pascal Gerber</a>, <a href="/search/cs?searchtype=author&query=Scholz%2C+M">Markus Scholz</a>, <a href="/search/cs?searchtype=author&query=Eberle%2C+J">Jonathan Eberle</a>, <a href="/search/cs?searchtype=author&query=Teschner%2C+M">Marc Teschner</a>, <a href="/search/cs?searchtype=author&query=Seifert%2C+D">Daniel Seifert</a>, <a href="/search/cs?searchtype=author&query=Hawkins%2C+R">Richard Hawkins</a>, <a href="/search/cs?searchtype=author&query=Molloy%2C+J">John Molloy</a>, <a href="/search/cs?searchtype=author&query=Ottnad%2C+J">Jens Ottnad</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="2312.04917v1-abstract-short" style="display: inline;"> Assurance Cases (ACs) are an established approach in safety engineering to argue quality claims in a structured way. In the context of quality assurance for Machine Learning (ML)-based software components, ACs are also being discussed and appear promising. Tools for operationalizing ACs do exist, yet mainly focus on supporting safety engineers on the system level. However, assuring the quality of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.04917v1-abstract-full').style.display = 'inline'; document.getElementById('2312.04917v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.04917v1-abstract-full" style="display: none;"> Assurance Cases (ACs) are an established approach in safety engineering to argue quality claims in a structured way. In the context of quality assurance for Machine Learning (ML)-based software components, ACs are also being discussed and appear promising. Tools for operationalizing ACs do exist, yet mainly focus on supporting safety engineers on the system level. However, assuring the quality of an ML component within the system is commonly the responsibility of data scientists, who are usually less familiar with these tools. To address this gap, we propose a framework to support the operationalization of ACs for ML components based on technologies that data scientists use on a daily basis: Python and Jupyter Notebook. Our aim is to make the process of creating ML-related evidence in ACs more effective. Results from the application of the framework, documented through notebooks, can be integrated into existing AC tools. We illustrate the application of the framework on an example excerpt concerned with the quality of the test data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.04917v1-abstract-full').style.display = 'none'; document.getElementById('2312.04917v1-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> 8 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </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">Accepted for publication at International Conference on Product-Focused Software Process Improvement (Profes 2023), https://conf.researchr.org/home/profes-2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.09009">arXiv:2010.09009</a> <span> [<a href="https://arxiv.org/pdf/2010.09009">pdf</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> </div> </div> <p class="title is-5 mathjax"> Image-based Automated Species Identification: Can Virtual Data Augmentation Overcome Problems of Insufficient Sampling? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Klasen%2C+M">Morris Klasen</a>, <a href="/search/cs?searchtype=author&query=Ahrens%2C+D">Dirk Ahrens</a>, <a href="/search/cs?searchtype=author&query=Eberle%2C+J">Jonas Eberle</a>, <a href="/search/cs?searchtype=author&query=Steinhage%2C+V">Volker Steinhage</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="2010.09009v1-abstract-short" style="display: inline;"> Automated species identification and delimitation is challenging, particularly in rare and thus often scarcely sampled species, which do not allow sufficient discrimination of infraspecific versus interspecific variation. Typical problems arising from either low or exaggerated interspecific morphological differentiation are best met by automated methods of machine learning that learn efficient and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.09009v1-abstract-full').style.display = 'inline'; document.getElementById('2010.09009v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.09009v1-abstract-full" style="display: none;"> Automated species identification and delimitation is challenging, particularly in rare and thus often scarcely sampled species, which do not allow sufficient discrimination of infraspecific versus interspecific variation. Typical problems arising from either low or exaggerated interspecific morphological differentiation are best met by automated methods of machine learning that learn efficient and effective species identification from training samples. However, limited infraspecific sampling remains a key challenge also in machine learning. 1In this study, we assessed whether a two-level data augmentation approach may help to overcome the problem of scarce training data in automated visual species identification. The first level of visual data augmentation applies classic approaches of data augmentation and generation of faked images using a GAN approach. Descriptive feature vectors are derived from bottleneck features of a VGG-16 convolutional neural network (CNN) that are then stepwise reduced in dimensionality using Global Average Pooling and PCA to prevent overfitting. The second level of data augmentation employs synthetic additional sampling in feature space by an oversampling algorithm in vector space (SMOTE). Applied on two challenging datasets of scarab beetles (Coleoptera), our augmentation approach outperformed a non-augmented deep learning baseline approach as well as a traditional 2D morphometric approach (Procrustes analysis). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.09009v1-abstract-full').style.display = 'none'; document.getElementById('2010.09009v1-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 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div 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