In this conceptual essay, we integrate broader insights into the state of research on management, organizations, and environmental sustainability, enabling a clearer view of where the field stands and the directions in which it should best grow. To this end, we first review the findings and insights of the articles published within this special rev...
Roughly speaking, an ALF metric of real dimension 2n should bea complete metric such that its asymptotic cone is 2n−1 dimensional, the volume growth of this metric is of order 2n − 1 and its sectional curvature tends to 0 at infinity. More precisely, in this thesis, an ALF metric of real dimension 2n has a 2n−1 dimensional metric cone as its asympt...
With the continuous development of AutonomousVehicles (AVs), Intrusion Detection Systems (IDSs) became es-sential to ensure the security of in-vehicle (IV) networks. In theliterature, classic machine learning (ML) metrics used to evaluateAI-based IV-IDSs present significant limitations and fail to assesstheir robustness fully. To address this, our ...
Les systèmes d’information ont pour rôle de contribuer à l’efficacité des organisations. L’alignement opérationnel des applications avec le métier est un élément clé de la cohérence de ces systèmes. Dans cet article, nous nous intéressons à la détection de potentielles incohérences dans l’alignement entre le métier, plus précisément des processus m...
In predictive maintenance, the complexity of the data often requires the use of Deep Learning models. These models, called “black boxes”, have proved their worth in predicting the Remaining Useful Life (RUL) of industrial machines. However, the inherent opacity of these models requires the incorporation of post-hoc explanation methods to enhance tr...
Published in ArXiv
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. ...
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. ...
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into prac...
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. ...
Model-based System Engineering (MBSE) has been advocated as a promising approach to reduce the complexity of AI-based systems development. However, given the uncertainties and risks associated with Artificial Intelligence (AI), the successful application of MBSE requires the assessment of AI trustworthiness. To deal with this issue, this paper prov...
