01940nas a2200241   4500008004100000022001400041245010300055210006900158300001200227490000600239520121400245653000801459653001801467653002101485653001501506100001901521700002101540700002201561700002001583700002101603700002001624856005401644       2020                        eng d  a2548-316100aSelf-supervised pre-training of CNNs for flatness defect classification in the steelworks industry0 aSelfsupervised pretraining of CNNs for flatness defect classific  a13–220 v63 aClassification 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.10aCNN10aDeep learning10aSelf-supervision10aSteelworks1 aGalli, Filippo1 aRitacco, Antonio1 aLanciano, Giacomo1 aVannocci, Marco1 aColla, Valentina1 aVannucci, Marco  uhttp://ijain.org/index.php/IJAIN/article/view/41001581nas a2200241   4500008004100000020001800041245006800059210006700127260005900194520079900253653002101052653003601073653002501109100002201134700002101156700002301177700002001200700002101220700001701241700002001258700001701278856004401295       2020                        eng d  a978145036866700aSOM-Based Behavioral Analysis for Virtualized Network Functions0 aSOMBased Behavioral Analysis for Virtualized Network Functions  aNew York, NY, USAbAssociation for Computing Machinery3 aIn 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.10amachine learning10anetwork function virtualization10aself-organizing maps1 aLanciano, Giacomo1 aRitacco, Antonio1 aCucinotta, Tommaso1 aVannucci, Marco1 aArtale, Antonino1 aBasili, Luca1 aSposato, Enrica1 aBarata, Joao  uhttps://doi.org/10.1145/3341105.3374110