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/10.1145/3341105.337411001879nas a2200181   4500008004100000245009200041210006900133520125300202653002301455653002501478653000901503653001901512653002001531100002701551700002201578700002101600856007601621       2018                        eng d00aAligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning0 aAligning PartiallyOrdered ProcessExecution Traces and Models Usi3 aConformance checking is the problem of verifying if the actual executions of business processes, which are recorded by information systems in dedicated event logs, are compliant with a process model that encodes the process' constraints. Within conformance checking, alignment-based techniques can exactly pinpoint where deviations are observed. Existing alignment-based techniques rely on the assumption of a perfect knowledge of the order with which process' activities were executed in reality. However, experience shows that, due to logging errors and inaccuracies, it is not always possible to determine the exact order with which certain activities were executed. This paper illustrates an alignment-based technique where the perfect knowledge assumption of the execution's order is removed. The technique transforms the problem of alignment-based conformance checking into a planning problem encoded in PDDL, for which planners can find a correct solution in a finite amount of time. We implemented the technique as a software tool that is integrated with state-of-the-art planners. To showcase its practical relevance and scalability, we report on experiments with a real-life case study and several synthetic ones of increasing complexity.10aAutomated Planning10aConformance Checking10aPDDL10aProcess Mining10aTrace Alignment1 ade Leoni, Massimiliano1 aLanciano, Giacomo1 aMarrella, Andrea  uhttps://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17739/1695100624nas a2200145   4500008004100000245015600041210006900197260001700266653003900283653001500322653001500337100002200352700002700374856007700401       2018                        eng d00aModels and Logs used in the paper ‘Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning’ accepted for ICAPS 20180 aModels and Logs used in the paper Aligning PartiallyOrdered Proc  bTU Eindhoven10aComputation Theory and Mathematics10aEvent Logs10aPetri nets1 aLanciano, Giacomo1 ade Leoni, Massimiliano  uhttps://data.4tu.nl/repository/uuid:a02afec8-b7c7-42b7-8dff-36d3de3032be01713nas a2200121   4500008004100000245009400041210006900135520126200204100002701466700002201493700002101515856005501536       2017                        eng d00aA Tool for Aligning Event Logs and Prescriptive Process Models through Automated Planning0 aTool for Aligning Event Logs and Prescriptive Process Models thr3 aIn Conformance Checking, alignment is the problem of detecting and repairing nonconformity between the actual execution of a business process, as recorded in an event log, and the model of the same process. Literature proposes solutions for the alignment problem that are implementations of planning algorithms built ad-hoc for the specific problem. Unfortunately, in the era of big data, these ad-hoc implementations do not scale sufficiently compared with well-established planning systems. In this paper, we tackle the above issue by presenting a tool, also available in ProM, to represent instances of the alignment problem as automated planning problems in PDDL (Planning Domain Definition Language) for which state-of-the-art planners can find a correct solution in a finite amount of time. If alignment problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with advantages in term of versatility and customization. Furthermore, by employing several processes and event logs of different sizes, we show how our tool outperforms existing approaches of several order of magnitude and, in certain cases, carries out the task while existing approaches run out of memory.1 ade Leoni, Massimiliano1 aLanciano, Giacomo1 aMarrella, Andrea  uhttp://ceur-ws.org/Vol-1920/BPM_2017_paper_187.pdf