<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mila Andreani</style></author><author><style face="normal" font="default" size="100%">Candila, Vincenzo</style></author><author><style face="normal" font="default" size="100%">Petrella, Lea</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical and Statistical Methods for Actuarial Sciences and Finance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer, Cham</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mila Andreani</style></author><author><style face="normal" font="default" size="100%">Candila, Vincenzo</style></author><author><style face="normal" font="default" size="100%">Morelli, Giacomo</style></author><author><style face="normal" font="default" size="100%">Petrella, Lea</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach</style></title><secondary-title><style face="normal" font="default" size="100%">Risks</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2227-9091/9/8/144</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">144</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mila Andreani</style></author><author><style face="normal" font="default" size="100%">Vincenzo Candila</style></author><author><style face="normal" font="default" size="100%">Lea Petrella</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantile Regression Forest with mixed-frequency data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://it.pearson.com//docenti/universita/partnership/sis.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Pearson</style></publisher><volume><style face="normal" font="default" size="100%">Book of Short Papers SIS 2021</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">V. Macchiati</style></author><author><style face="normal" font="default" size="100%">G. Brandi</style></author><author><style face="normal" font="default" size="100%">T. Di Matteo</style></author><author><style face="normal" font="default" size="100%">D. Paolotti</style></author><author><style face="normal" font="default" size="100%">G. Caldarelli</style></author><author><style face="normal" font="default" size="100%">G. Cimini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Systemic liquidity contagion in the European interbank market</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Economic Interaction and Coordination</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Epidemic model</style></keyword><keyword><style  face="normal" font="default" size="100%">European Interbank market</style></keyword><keyword><style  face="normal" font="default" size="100%">Financial contagion</style></keyword><keyword><style  face="normal" font="default" size="100%">Liquidity shocks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jisu Kim</style></author><author><style face="normal" font="default" size="100%">Alina Sirbu</style></author><author><style face="normal" font="default" size="100%">Giannotti, Fosca</style></author><author><style face="normal" font="default" size="100%">Gabrielli, Lorenzo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digital Footprints of International Migration on Twitter</style></title><secondary-title><style face="normal" font="default" size="100%">International Symposium on Intelligent Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Studying migration using traditional data has some limitations. To date, there have been several studies proposing innovative methodologies to measure migration stocks and flows from social big data. Nevertheless, a uniform definition of a migrant is difficult to find as it varies from one work to another depending on the purpose of the study and nature of the dataset used. In this work, a generic methodology is developed to identify migrants within the Twitter population. This describes a migrant as a person who has the current residence different from the nationality. The residence is defined as the location where a user spends most of his/her time in a certain year. The nationality is inferred from linguistic and social connections to a migrant’s country of origin. This methodology is validated first with an internal gold standard dataset and second with two official statistics, and shows strong performance scores and correlation coefficients. Our method has the advantage that it can identify both immigrants and emigrants, regardless of the origin/destination countries. The new methodology can be used to study various aspects of migration, including opinions, integration, attachment, stocks and flows, motivations for migration, etc. Here, we exemplify how trending topics across and throughout different migrant communities can be observed.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cecilia Panigutti</style></author><author><style face="normal" font="default" size="100%">Perotti, Alan</style></author><author><style face="normal" font="default" size="100%">Pedreschi, Dino</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Doctor XAI: an ontology-based approach to black-box sequential data classification explanations</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://dl.acm.org/doi/abs/10.1145/3351095.3372855</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Several recent advancements in Machine Learning involve black box models: algorithms that do not provide human-understandable explanations in support of their decisions. This limitation hampers the fairness, accountability and transparency of these models; the field of eXplainable Artificial Intelligence (XAI) tries to solve this problem providing human-understandable explanations for black-box models. However, healthcare datasets (and the related learning tasks) often present peculiar features, such as sequential data, multi-label predictions, and links to structured background knowledge. In this paper, we introduce Doctor XAI, a model-agnostic explainability technique able to deal with multi-labeled, sequential, ontology-linked data. We focus on explaining Doctor AI, a multilabel classifier which takes as input the clinical history of a patient in order to predict the next visit. Furthermore, we show how exploiting the temporal dimension in the data and the domain knowledge encoded in the medical ontology improves the quality of the mined explanations.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Viviane Dib</style></author><author><style face="normal" font="default" size="100%">Marco Aurélio Nalon</style></author><author><style face="normal" font="default" size="100%">Nino Tavares Amazonas</style></author><author><style face="normal" font="default" size="100%">Cristina Yuri Vidal</style></author><author><style face="normal" font="default" size="100%">Iván A. Ortiz-Rodríguez</style></author><author><style face="normal" font="default" size="100%">Jan Daněk</style></author><author><style face="normal" font="default" size="100%">Maíra Formis de Oliveira</style></author><author><style face="normal" font="default" size="100%">Paola Alberti</style></author><author><style face="normal" font="default" size="100%">Rafaela Aparecida da Silva</style></author><author><style face="normal" font="default" size="100%">Raíza Salomão Precinoto</style></author><author><style face="normal" font="default" size="100%">Taciana Figueiredo Gomes</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Drivers of change in biodiversity and ecosystem services in the Cantareira System Protected Area : A prospective analysis of the implementation of public policies</style></title><secondary-title><style face="normal" font="default" size="100%">Biota Neotropica</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biodiversity</style></keyword><keyword><style  face="normal" font="default" size="100%">Cantareira System Protected Area</style></keyword><keyword><style  face="normal" font="default" size="100%">Ecosystem services</style></keyword><keyword><style  face="normal" font="default" size="100%">GLOBIO</style></keyword><keyword><style  face="normal" font="default" size="100%">InVEST</style></keyword><keyword><style  face="normal" font="default" size="100%">Modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Scenarios</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.scielo.br/scielo.php?script=sci_arttext&amp;pid=S1676-06032020000500201</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">20</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The lack of implementation of well-designed public policies aimed at the conservation of natural ecosystems has resulted, at a global level, in the decline of ecosystem functioning and, consequently, of the contributions they make to people. The poor enforcement of important environmental legislation in Brazil - for instance, the “Atlantic Forest Law” (Law n.11.428/2006) and the “Forest Code” (Law n.12.651/2012) - could compromise the overall maintenance of ecosystems and the services they provide. To explore the implications of different levels of federal laws’ enforcement within the Cantareira System Protected Area (PA) - a PA in southeastern Brazil that provides fresh water for 47% of the Sao Paulo Metropolitan Area -, we developed a conceptual framework to identify indirect and direct drives of biodiversity and ecosystem changes. We also projected four land-use scenarios to 2050 to test the effects of deforestation control and forest restoration practices on biodiversity and ecosystem services maintenance: the “business-as-usual” scenario (BAU), which assumes that all trends in land-use cover changes observed in the past will continue in the future, and three alternative exploratory scenarios considering the Atlantic Forest Law implementation, the partial implementation of the Forest Code and the full implementation of the Forest Code. Using the land-use maps generated for each scenario, we assessed the impacts of land-use changes on biodiversity conservation and soil retention. Our results revealed all alternative scenarios could increase biodiversity conservation (by 7%; 12%; and 12%, respectively), reduce soil loss (by 24.70%; 34.70%; and 38.12%, respectively) and sediment exportation to water (by 27.47%; 55.06%; and 59.28%, respectively), when compared to the BAU scenario. Our findings highlight the importance of restoring and conserving native vegetation for the maintenance and improvement of biodiversity conservation and for the provision of ecosystem services.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mila Andreani</style></author><author><style face="normal" font="default" size="100%">Lea Petrella</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic Quantile Regression Forest</style></title><secondary-title><style face="normal" font="default" size="100%">SIS 2020 - 50th Conference of the Italian Statistical Society</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Docenti/Universit%C3%A0/Pearson-SIS-2020-atti-convegno.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Pearson</style></publisher><volume><style face="normal" font="default" size="100%">Book of Short Papers SIS 2020</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alina Sirbu</style></author><author><style face="normal" font="default" size="100%">Andrienko, Gennady</style></author><author><style face="normal" font="default" size="100%">Andrienko, Natalia</style></author><author><style face="normal" font="default" size="100%">Boldrini, Chiara</style></author><author><style face="normal" font="default" size="100%">Conti, Marco</style></author><author><style face="normal" font="default" size="100%">Giannotti, Fosca</style></author><author><style face="normal" font="default" size="100%">Guidotti, Riccardo</style></author><author><style face="normal" font="default" size="100%">Bertoli, Simone</style></author><author><style face="normal" font="default" size="100%">Jisu Kim</style></author><author><style face="normal" font="default" size="100%">Muntean, Cristina Ioana</style></author><author><style face="normal" font="default" size="100%">others</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Human migration: the big data perspective</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Data Science and Analytics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><pages><style face="normal" font="default" size="100%">1–20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tripodi, Giorgio</style></author><author><style face="normal" font="default" size="100%">Chiaromonte, Francesca</style></author><author><style face="normal" font="default" size="100%">Lillo, Fabrizio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge and Social Relatedness Shape Research Portfolio Diversification</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific Reports</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">10</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">14232</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Silvia Corbara</style></author><author><style face="normal" font="default" size="100%">Alejandro Moreo</style></author><author><style face="normal" font="default" size="100%">Fabrizio Sebastiani</style></author><author><style face="normal" font="default" size="100%">Mirko Tavoni</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Alberto Casadei</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">L’Epistola a Cangrande al vaglio della Computational Authorship Verification: risultati preliminari (con una postilla sulla cosiddetta “XIV Epistola di Dante Alighieri”)</style></title><secondary-title><style face="normal" font="default" size="100%">Nuove inchieste sull’epistola a Cangrande: atti della giornata di studi, Pisa 18 dicembre 2018</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><publisher><style face="normal" font="default" size="100%">Pisa University Press</style></publisher><isbn><style face="normal" font="default" size="100%">978-88-3339-333-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work we apply techniques from computational Authorship Verification (AV) to the problem of detecting whether the “Epistle to Cangrande” is an authentic work by Dante Alighieri or is instead the work of a forger. The AV algorithm we use is based on “machine learning”: the algorithm “trains” an automatic system (a “classifier”) to detect whether a certain Latin text is Dante’s or not Dante’s, by exposing it to a corpus of example Latin texts by Dante and example Latin texts by authors coeval to Dante. The detection is based on the analysis of a  set of stylometric features, i.e., style-related linguistic traits whose us-age frequencies tend to represent an author’s unconscious “signature”. 
The analysis carried out in this work suggests that, of the two parts into which the Epistle is traditionally subdivided, neither is Dante’s. Experiments in which we have applied our AV system to each text in the corpus  suggest that the system has a fairly high degree of accuracy, thus lending credibility to its hypothesis about the authorship of the Epistle. In the last  section of this paper we apply our system to what has been hypothesized to be “Dante’s 14th Epistle”; the system rejects, with very high confidence, the hypothesis that this epistle might be Dante’s.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vasiliki Voukelatou</style></author><author><style face="normal" font="default" size="100%">Gabrielli, Lorenzo</style></author><author><style face="normal" font="default" size="100%">Miliou, Ioanna</style></author><author><style face="normal" font="default" size="100%">Cresci, Stefano</style></author><author><style face="normal" font="default" size="100%">Sharma, Rajesh</style></author><author><style face="normal" font="default" size="100%">Tesconi, Maurizio</style></author><author><style face="normal" font="default" size="100%">Pappalardo, Luca</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring objective and subjective well-being: dimensions and data sources</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Data Science and Analytics (JDSA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Well-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Agnese Bonavita</style></author><author><style face="normal" font="default" size="100%">Guidotti, Riccardo</style></author><author><style face="normal" font="default" size="100%">Nanni,Mirco</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-Adapting Trajectory Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">3rd International Workshop on Big Mobility Data Analytics (BMDA)  2020</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Mobility Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">User Modeling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Filippo Galli</style></author><author><style face="normal" font="default" size="100%">Antonio Ritacco</style></author><author><style face="normal" font="default" size="100%">Giacomo Lanciano</style></author><author><style face="normal" font="default" size="100%">Marco Vannocci</style></author><author><style face="normal" font="default" size="100%">Valentina Colla</style></author><author><style face="normal" font="default" size="100%">Marco Vannucci</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Advances in Intelligent Informatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CNN</style></keyword><keyword><style  face="normal" font="default" size="100%">Deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Self-supervision</style></keyword><keyword><style  face="normal" font="default" size="100%">Steelworks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ijain.org/index.php/IJAIN/article/view/410</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">13–22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giacomo Lanciano</style></author><author><style face="normal" font="default" size="100%">Antonio Ritacco</style></author><author><style face="normal" font="default" size="100%">Tommaso Cucinotta</style></author><author><style face="normal" font="default" size="100%">Marco Vannucci</style></author><author><style face="normal" font="default" size="100%">Antonino Artale</style></author><author><style face="normal" font="default" size="100%">Luca Basili</style></author><author><style face="normal" font="default" size="100%">Enrica Sposato</style></author><author><style face="normal" font="default" size="100%">Joao Barata</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SOM-Based Behavioral Analysis for Virtualized Network Functions</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 35th Annual ACM Symposium on Applied Computing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">network function virtualization</style></keyword><keyword><style  face="normal" font="default" size="100%">self-organizing maps</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1145/3341105.3374110</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Association for Computing Machinery</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><isbn><style face="normal" font="default" size="100%">9781450368667</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Iván A. Ortiz-Rodríguez</style></author><author><style face="normal" font="default" size="100%">Jose Raventós</style></author><author><style face="normal" font="default" size="100%">Ernesto Mújica</style></author><author><style face="normal" font="default" size="100%">Elaine González-Hernández</style></author><author><style face="normal" font="default" size="100%">Ernesto Vega-Peña</style></author><author><style face="normal" font="default" size="100%">Pilar Ortega-Larrocea</style></author><author><style face="normal" font="default" size="100%">Andreu Bonet</style></author><author><style face="normal" font="default" size="100%">Cory Merow</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatiotemporal effects of Hurricane Ivan on an endemic epiphytic orchid: 10 years of follow-up</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Ecology &amp; Diversity </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Caribbean</style></keyword><keyword><style  face="normal" font="default" size="100%">cyclones</style></keyword><keyword><style  face="normal" font="default" size="100%">integral projection models</style></keyword><keyword><style  face="normal" font="default" size="100%">management strategies</style></keyword><keyword><style  face="normal" font="default" size="100%">plant population dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">stochastic growth rate</style></keyword><keyword><style  face="normal" font="default" size="100%">transfer functions</style></keyword><keyword><style  face="normal" font="default" size="100%">transient behaviour</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.tandfonline.com/doi/full/10.1080/17550874.2019.1673495</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">13, 2020</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Background: Hurricanes have a strong influence on the ecological dynamics and structure of tropical forests. Orchid populations are especially vulnerable to these perturbations due to their canopy exposure and lack of underground storage organs and seed banks.

Aims: We evaluated the effects of Hurricane Ivan on the population of the endemic epiphytic orchid Encyclia bocourtii to propose a management strategy.

Methods: Using a pre- and post-hurricane dataset (2003–2013), we assessed the population asymptotic and transient dynamics. We also identified the individual size-stages that maximise population inertia and E. bocourtii’s spatial arrangement relative to phorophytes and other epiphytes.

Results: Hurricane Ivan severely affected the survival and growth of individuals of E. bocourtii, and caused an immediate decline of the population growth rate from λ = 1.05 to λ = 0.32, which was buffered by a population reactivity of ρ1 = 1.42. Our stochastic model predicted an annual population decrease of 14%. We found an aggregated spatial pattern between E. bocourtii and its host trees, and a random pattern relative to other epiphytes.

Conclusion: Our findings suggest that E. bocourtii is not safe from local extinction. We propose the propagation and reintroduction of reproductive specimens, the relocation of surviving individuals, and the establishment of new plantations of phorophytes.
</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lamperti, Francesco</style></author><author><style face="normal" font="default" size="100%">Malerba, Franco</style></author><author><style face="normal" font="default" size="100%">Mavilia, Roberto</style></author><author><style face="normal" font="default" size="100%">Tripodi, Giorgio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Does the position in the inter-sectoral knowledge space affect the international competitiveness of industries?</style></title><secondary-title><style face="normal" font="default" size="100%">Economics of Innovation and New Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><pages><style face="normal" font="default" size="100%">1–48</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Silvia Corbara</style></author><author><style face="normal" font="default" size="100%">Moreo, Alejandro</style></author><author><style face="normal" font="default" size="100%">Sebastiani, Fabrizio</style></author><author><style face="normal" font="default" size="100%">Tavoni, Mirko</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Cristani, Marco</style></author><author><style face="normal" font="default" size="100%">Prati, Andrea</style></author><author><style face="normal" font="default" size="100%">Lanz, Oswald</style></author><author><style face="normal" font="default" size="100%">Messelodi, Stefano</style></author><author><style face="normal" font="default" size="100%">Sebe, Nicu</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The Epistle to Cangrande Through the Lens of Computational Authorship Verification</style></title><secondary-title><style face="normal" font="default" size="100%">New Trends in Image Analysis and Processing – ICIAP 2019</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><isbn><style face="normal" font="default" size="100%">978-3-030-30754-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cecilia Panigutti</style></author><author><style face="normal" font="default" size="100%">Guidotti, Riccardo</style></author><author><style face="normal" font="default" size="100%">Monreale, Anna</style></author><author><style face="normal" font="default" size="100%">Pedreschi, Dino</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Explaining multi-label black-box classifiers for health applications</style></title><secondary-title><style face="normal" font="default" size="100%">International Workshop on Health Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Explainable Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Healthcare</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-24409-5_9</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Today the state-of-the-art performance in classification is achieved by the so-called “black boxes”, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Di Gangi</style></author><author><style face="normal" font="default" size="100%">D. R. Lo Sardo</style></author><author><style face="normal" font="default" size="100%">V. Macchiati</style></author><author><style face="normal" font="default" size="100%">T. P. Minh</style></author><author><style face="normal" font="default" size="100%">F. Pinotti</style></author><author><style face="normal" font="default" size="100%">A. Ramadiah</style></author><author><style face="normal" font="default" size="100%">M. Wilinski</style></author><author><style face="normal" font="default" size="100%">P. Barucca</style></author><author><style face="normal" font="default" size="100%">G. Cimini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Network Sensitivity of Systemic Risk</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Network Theory in Finance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gorrell, Genevieve</style></author><author><style face="normal" font="default" size="100%">Bakir, Mehmet E</style></author><author><style face="normal" font="default" size="100%">Roberts, Ian</style></author><author><style face="normal" font="default" size="100%">Greenwood, Mark A</style></author><author><style face="normal" font="default" size="100%">Iavarone, Benedetta</style></author><author><style face="normal" font="default" size="100%">Bontcheva, Kalina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Partisanship, propaganda and post-truth politics: Quantifying impact in online</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:1902.01752</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Massimiliano de Leoni</style></author><author><style face="normal" font="default" size="100%">Giacomo Lanciano</style></author><author><style face="normal" font="default" size="100%">Andrea Marrella</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning</style></title><secondary-title><style face="normal" font="default" size="100%">28th International Conference on Automated Planning and Scheduling (ICAPS 2018)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automated Planning</style></keyword><keyword><style  face="normal" font="default" size="100%">Conformance Checking</style></keyword><keyword><style  face="normal" font="default" size="100%">PDDL</style></keyword><keyword><style  face="normal" font="default" size="100%">Process Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Trace Alignment</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17739/16951</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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' 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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Costantino Carugno</style></author><author><style face="normal" font="default" size="100%">Tommaso Radicioni</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ECHO CHAMBERS E POLARIZZAZIONE Uno sguardo critico sulla diffusione dell’informazione nei social network</style></title><secondary-title><style face="normal" font="default" size="100%">The Lab’s Quarterly</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://thelabsquarterly.files.wordpress.com/2019/04/2018.4-the-labs-quarterly-7.-costantino-carugno-tommaso-radicioni-1.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">20</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Understanding the algorithms that contribute to the formation of our daily reality requires an in-depth look at how information is disseminated in online social networks (OSN). In this article, we will observe how news propagation is restricted by the presence of virtual borders that limit the interaction between users. This phenomenon, known as &quot;echo chamber&quot; formation, has the effect of polarizing the public debate on conflicting positions. Inside an echo chamber, information is not conveyed through a horizontal exchange between users, but due to the presence of like or follower aggregators, called hubs. This analysis will be carried out considering a casestudy in two of the main OSNs: Facebook and Twitter. From the study of user interaction networks we will observe how the algorithmic choices made are crucial to the polarization of the debate around a topic of discussion.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><section><style face="normal" font="default" size="100%">173</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Baltakiene, M</style></author><author><style face="normal" font="default" size="100%">Baltakys, K</style></author><author><style face="normal" font="default" size="100%">Cardamone, D</style></author><author><style face="normal" font="default" size="100%">Parisi, F</style></author><author><style face="normal" font="default" size="100%">Tommaso Radicioni</style></author><author><style face="normal" font="default" size="100%">Torricelli, M</style></author><author><style face="normal" font="default" size="100%">de Jeude, JA</style></author><author><style face="normal" font="default" size="100%">Saracco, F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Maximum entropy approach to link prediction in bipartite networks</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:1805.04307</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>45</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giacomo Lanciano</style></author><author><style face="normal" font="default" size="100%">Massimiliano de Leoni</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Models and Logs used in the paper ‘Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning’ accepted for ICAPS 2018</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computation Theory and Mathematics</style></keyword><keyword><style  face="normal" font="default" size="100%">Event Logs</style></keyword><keyword><style  face="normal" font="default" size="100%">Petri nets</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://data.4tu.nl/repository/uuid:a02afec8-b7c7-42b7-8dff-36d3de3032be</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">TU Eindhoven</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gorrell, Genevieve</style></author><author><style face="normal" font="default" size="100%">Roberts, Ian</style></author><author><style face="normal" font="default" size="100%">Greenwood, Mark A</style></author><author><style face="normal" font="default" size="100%">Bakir, Mehmet E</style></author><author><style face="normal" font="default" size="100%">Iavarone, Benedetta</style></author><author><style face="normal" font="default" size="100%">Bontcheva, Kalina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantifying media influence and partisan attention on Twitter during the UK EU referendum</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Social Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Agnese Bonavita</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Search for H-&gt; mu mu in the VBF production channel with the CMS experiment at LHC</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brunato, Dominique</style></author><author><style face="normal" font="default" size="100%">De Mattei, Lorenzo</style></author><author><style face="normal" font="default" size="100%">Dell’Orletta, Felice</style></author><author><style face="normal" font="default" size="100%">Iavarone, Benedetta</style></author><author><style face="normal" font="default" size="100%">Venturi, Giulia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Is this Sentence Difficult? Do you Agree?</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cecilia Panigutti</style></author><author><style face="normal" font="default" size="100%">Tizzoni, Michele</style></author><author><style face="normal" font="default" size="100%">Bajardi, Paolo</style></author><author><style face="normal" font="default" size="100%">Smoreda, Zbigniew</style></author><author><style face="normal" font="default" size="100%">Colizza, Vittoria</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models</style></title><secondary-title><style face="normal" font="default" size="100%">Royal Society open science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://royalsocietypublishing.org/doi/full/10.1098/rsos.160950</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">160950</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">N. Atanov</style></author><author><style face="normal" font="default" size="100%">V. Baranov</style></author><author><style face="normal" font="default" size="100%">J. Budagov</style></author><author><style face="normal" font="default" size="100%">F. Cervelli</style></author><author><style face="normal" font="default" size="100%">F. Colao</style></author><author><style face="normal" font="default" size="100%">M. Cordelli</style></author><author><style face="normal" font="default" size="100%">G. Corradi</style></author><author><style face="normal" font="default" size="100%">E. Dané</style></author><author><style face="normal" font="default" size="100%">Y.I. Davydov</style></author><author><style face="normal" font="default" size="100%">S. Di Falco</style></author><author><style face="normal" font="default" size="100%">E. Diociaiuti</style></author><author><style face="normal" font="default" size="100%">S. Donati</style></author><author><style face="normal" font="default" size="100%">R. Donghia</style></author><author><style face="normal" font="default" size="100%">B. Echenard</style></author><author><style face="normal" font="default" size="100%">K. Flood</style></author><author><style face="normal" font="default" size="100%">S. Giovannella</style></author><author><style face="normal" font="default" size="100%">V. Glagolev</style></author><author><style face="normal" font="default" size="100%">F. Grancagnolo</style></author><author><style face="normal" font="default" size="100%">F. Happacher</style></author><author><style face="normal" font="default" size="100%">D.G. Hitlin</style></author><author><style face="normal" font="default" size="100%">M. Martini</style></author><author><style face="normal" font="default" size="100%">S. Miscetti</style></author><author><style face="normal" font="default" size="100%">T. Miyashita</style></author><author><style face="normal" font="default" size="100%">L. Morescalchi</style></author><author><style face="normal" font="default" size="100%">P. Murat</style></author><author><style face="normal" font="default" size="100%">G. Pezzullo</style></author><author><style face="normal" font="default" size="100%">F. Porter</style></author><author><style face="normal" font="default" size="100%">F. Raffaelli</style></author><author><style face="normal" font="default" size="100%">Tommaso Radicioni</style></author><author><style face="normal" font="default" size="100%">M. Ricci</style></author><author><style face="normal" font="default" size="100%">A. Saputi</style></author><author><style face="normal" font="default" size="100%">I. Sarra</style></author><author><style face="normal" font="default" size="100%">F. Spinella</style></author><author><style face="normal" font="default" size="100%">G. Tassielli</style></author><author><style face="normal" font="default" size="100%">V. Tereshchenko</style></author><author><style face="normal" font="default" size="100%">Z. Usubov</style></author><author><style face="normal" font="default" size="100%">R.Y. Zhu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The calorimeter of the Mu2e experiment at Fermilab</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Instrumentation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1088%2F1748-0221%2F12%2F01%2Fc01061</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Mu2e experiment at Fermilab looks for Charged Lepton Flavor Violation (CLFV) improving by 4 orders of magnitude the current experimental sensitivity for the muon to electron conversion in a muonic atom. A positive signal could not be explained in the framework of the current Standard Model of particle interactions and therefore would be a clear indication of new physics. In 3 years of data taking, Mu2e is expected to observe less than one background event mimicking the electron coming from muon conversion. Achieving such a level of background suppression requires a deep knowledge of the experimental apparatus: a straw tube tracker, measuring the electron momentum and time, a cosmic ray veto system rejecting most of cosmic ray background and a pure CsI crystal calorimeter, that will measure time of flight, energy and impact position of the converted electron. The calorimeter has to operate in a harsh radiation environment, in a 10−4 Torr vacuum and inside a 1 T magnetic field. The results of the first qualification tests of the calorimeter components are reported together with the energy and time performances expected from the simulation and measured in beam tests of a small scale prototype.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Massimiliano de Leoni</style></author><author><style face="normal" font="default" size="100%">Giacomo Lanciano</style></author><author><style face="normal" font="default" size="100%">Andrea Marrella</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Tool for Aligning Event Logs and Prescriptive Process Models through Automated Planning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the BPM Demo Track and BPM Dissertation Award co-located with 15th International Conference on Business Process Modeling (BPM 2017)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1920/BPM_2017_paper_187.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tommaso Radicioni</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Calibration and performance optimization of the Electromagnetic Calorimeter in Mu2e</style></title><secondary-title><style face="normal" font="default" size="100%">University of Pisa</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://etd.adm.unipi.it/theses/available/etd-09252016-171400/unrestricted/MasterThesis.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>