%0 Journal Article %J International Journal of Advances in Intelligent Informatics %D 2020 %T Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry %A Filippo Galli %A Antonio Ritacco %A Giacomo Lanciano %A Marco Vannocci %A Valentina Colla %A Marco Vannucci %K CNN %K Deep learning %K Self-supervision %K Steelworks %X Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization. %B International Journal of Advances in Intelligent Informatics %V 6 %P 13–22 %G eng %U http://ijain.org/index.php/IJAIN/article/view/410 %R 10.26555/ijain.v6i1.410 %0 Conference Paper %B Proceedings of the 35th Annual ACM Symposium on Applied Computing %D 2020 %T SOM-Based Behavioral Analysis for Virtualized Network Functions %A Giacomo Lanciano %A Antonio Ritacco %A Tommaso Cucinotta %A Marco Vannucci %A Antonino Artale %A Luca Basili %A Enrica Sposato %A Joao Barata %K machine learning %K network function virtualization %K self-organizing maps %X In this paper, we propose a mechanism based on Self-Organizing Maps for analyzing the resource consumption behaviors and detecting possible anomalies in data centers for Network Function Virtualization (NFV). Our approach is based on a joint analysis of two historical data sets available through two separate monitoring systems: system-level metrics for the physical and virtual machines obtained from the monitoring infrastructure, and application-level metrics available from the individual virtualized network functions. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, highlight some of the capabilities of our system to identify interesting points in space and time of the evolution of the monitored infrastructure. %B Proceedings of the 35th Annual ACM Symposium on Applied Computing %I Association for Computing Machinery %C New York, NY, USA %@ 9781450368667 %G eng %U https://doi.org/10.1145/3341105.3374110 %R 10.1145/3341105.3374110