@article {260,
	title = {Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry},
	journal = {International Journal of Advances in Intelligent Informatics},
	volume = {6},
	year = {2020},
	pages = {13{\textendash}22},
	abstract = {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.},
	keywords = {CNN, Deep learning, Self-supervision, Steelworks},
	issn = {2548-3161},
	doi = {10.26555/ijain.v6i1.410},
	url = {http://ijain.org/index.php/IJAIN/article/view/410},
	author = {Filippo Galli and Antonio Ritacco and Giacomo Lanciano and Marco Vannocci and Valentina Colla and Marco Vannucci}
}
@conference {259,
	title = {SOM-Based Behavioral Analysis for Virtualized Network Functions},
	booktitle = {Proceedings of the 35th Annual ACM Symposium on Applied Computing},
	year = {2020},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {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.},
	keywords = {machine learning, network function virtualization, self-organizing maps},
	isbn = {9781450368667},
	doi = {10.1145/3341105.3374110},
	url = {https://doi.org/10.1145/3341105.3374110},
	author = {Giacomo Lanciano and Antonio Ritacco and Tommaso Cucinotta and Marco Vannucci and Antonino Artale and Luca Basili and Enrica Sposato and Joao Barata}
}
@conference {257,
	title = {Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning},
	booktitle = {28th International Conference on Automated Planning and Scheduling (ICAPS 2018)},
	year = {2018},
	abstract = {Conformance 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{\textquoteright} 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{\textquoteright} 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{\textquoteright}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.},
	keywords = {Automated Planning, Conformance Checking, PDDL, Process Mining, Trace Alignment},
	url = {https://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17739/16951},
	author = {Massimiliano de Leoni and Giacomo Lanciano and Andrea Marrella}
}
@article {258,
	title = {Models and Logs used in the paper {\textquoteleft}Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning{\textquoteright} accepted for ICAPS 2018},
	year = {2018},
	publisher = {TU Eindhoven},
	keywords = {Computation Theory and Mathematics, Event Logs, Petri nets},
	doi = {10.4121/UUID:A02AFEC8-B7C7-42B7-8DFF-36D3DE3032BE},
	url = {https://data.4tu.nl/repository/uuid:a02afec8-b7c7-42b7-8dff-36d3de3032be},
	author = {Giacomo Lanciano and Massimiliano de Leoni}
}
@conference {256,
	title = {A Tool for Aligning Event Logs and Prescriptive Process Models through Automated Planning},
	booktitle = {Proceedings of the BPM Demo Track and BPM Dissertation Award co-located with 15th International Conference on Business Process Modeling (BPM 2017)},
	year = {2017},
	abstract = {In 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.},
	url = {http://ceur-ws.org/Vol-1920/BPM_2017_paper_187.pdf},
	author = {Massimiliano de Leoni and Giacomo Lanciano and Andrea Marrella}
}
