@conference {263,
	title = {Digital Footprints of International Migration on Twitter},
	booktitle = {International Symposium on Intelligent Data Analysis},
	year = {2020},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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{\textquoteright}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.},
	author = {Jisu Kim and Alina Sirbu and Giannotti, Fosca and Gabrielli, Lorenzo}
}
@article {271,
	title = {Drivers of change in biodiversity and ecosystem services in the Cantareira System Protected Area : A prospective analysis of the implementation of public policies},
	journal = {Biota Neotropica},
	volume = {20},
	year = {2020},
	month = {05/2020},
	type = {Article},
	abstract = {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 {\textquotedblleft}Atlantic Forest Law{\textquotedblright} (Law n.11.428/2006) and the {\textquotedblleft}Forest Code{\textquotedblright} (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{\textquoteright} 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 {\textquotedblleft}business-as-usual{\textquotedblright} 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.},
	keywords = {Biodiversity, Cantareira System Protected Area, Ecosystem services, GLOBIO, InVEST, Modeling, Scenarios},
	issn = {1676-0611},
	doi = {https://doi.org/10.1590/1676-0611-bn-2019-0915 },
	url = {https://www.scielo.br/scielo.php?script=sci_arttext\&pid=S1676-06032020000500201},
	author = {Viviane Dib and Marco Aur{\'e}lio Nalon and Nino Tavares Amazonas and Cristina Yuri Vidal and Iv{\'a}n A. Ortiz-Rodr{\'\i}guez and Jan Dan{\v e}k and Ma{\'\i}ra Formis de Oliveira and Paola Alberti and Rafaela Aparecida da Silva and Ra{\'\i}za Salom{\~a}o Precinoto and Taciana Figueiredo Gomes}
}
@article {264,
	title = {Human migration: the big data perspective},
	journal = {International Journal of Data Science and Analytics},
	year = {2020},
	pages = {1{\textendash}20},
	abstract = {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.},
	author = {Alina Sirbu and Andrienko, Gennady and Andrienko, Natalia and Boldrini, Chiara and Conti, Marco and Giannotti, Fosca and Guidotti, Riccardo and Bertoli, Simone and Jisu Kim and Muntean, Cristina Ioana and others}
}
@article {276,
	title = {Measuring objective and subjective well-being: dimensions and data sources},
	journal = {International Journal of Data Science and Analytics (JDSA)},
	year = {2020},
	abstract = {Well-being is an important value for people{\textquoteright}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.},
	doi = {https://doi.org/10.1007/s41060-020-00224-2},
	author = {Vasiliki Voukelatou and Gabrielli, Lorenzo and Miliou, Ioanna and Cresci, Stefano and Sharma, Rajesh and Tesconi, Maurizio and Pappalardo, Luca}
}
@conference {254,
	title = {Self-Adapting Trajectory Segmentation},
	booktitle = {3rd International Workshop on Big Mobility Data Analytics (BMDA)  2020},
	year = {2020},
	abstract = {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.},
	keywords = {Mobility Data Mining, Segmentation, User Modeling},
	author = {Agnese Bonavita and Guidotti, Riccardo and Nanni,Mirco}
}
@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}
}
@article {272,
	title = {Spatiotemporal effects of Hurricane Ivan on an endemic epiphytic orchid: 10 years of follow-up},
	journal = {Plant Ecology \& Diversity },
	volume = {13, 2020},
	year = {2020},
	month = {10/2019},
	type = {Article},
	abstract = {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{\textendash}2013), we assessed the population asymptotic and transient dynamics. We also identified the individual size-stages that maximise population inertia and E. bocourtii{\textquoteright}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.
},
	keywords = {Caribbean, cyclones, integral projection models, management strategies, plant population dynamics, stochastic growth rate, transfer functions, transient behaviour},
	issn = {1755-0874},
	doi = {https://doi.org/10.1080/17550874.2019.1673495},
	url = {https://www.tandfonline.com/doi/full/10.1080/17550874.2019.1673495},
	author = {Iv{\'a}n A. Ortiz-Rodr{\'\i}guez and Jose Ravent{\'o}s and Ernesto M{\'u}jica and Elaine Gonz{\'a}lez-Hern{\'a}ndez and Ernesto Vega-Pe{\~n}a and Pilar Ortega-Larrocea and Andreu Bonet and Cory Merow}
}
@conference {241,
	title = {Explaining multi-label black-box classifiers for health applications},
	booktitle = {International Workshop on Health Intelligence},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Today the state-of-the-art performance in classification is achieved by the so-called {\textquotedblleft}black boxes{\textquotedblright}, 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.},
	keywords = {Explainable Machine Learning, Healthcare},
	doi = {10.1007/978-3-030-24409-5_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-24409-5_9},
	author = {Cecilia Panigutti and Guidotti, Riccardo and Monreale, Anna and Pedreschi, Dino}
}
@article {268,
	title = {Partisanship, propaganda and post-truth politics: Quantifying impact in online},
	journal = {arXiv preprint arXiv:1902.01752},
	year = {2019},
	author = {Gorrell, Genevieve and Bakir, Mehmet E and Roberts, Ian and Greenwood, Mark A and Iavarone, Benedetta and Bontcheva, Kalina}
}
@conference {269,
	title = {Quantifying media influence and partisan attention on Twitter during the UK EU referendum},
	booktitle = {International Conference on Social Informatics},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	author = {Gorrell, Genevieve and Roberts, Ian and Greenwood, Mark A and Bakir, Mehmet E and Iavarone, Benedetta and Bontcheva, Kalina}
}
@article {251,
	title = {The calorimeter of the Mu2e experiment at Fermilab},
	journal = {Journal of Instrumentation},
	volume = {12},
	year = {2017},
	abstract = {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.},
	doi = {10.1088/1748-0221/12/01/c01061},
	url = {https://doi.org/10.1088\%2F1748-0221\%2F12\%2F01\%2Fc01061},
	author = {N. Atanov and V. Baranov and J. Budagov and F. Cervelli and F. Colao and M. Cordelli and G. Corradi and E. Dan{\'e} and Y.I. Davydov and S. Di Falco and E. Diociaiuti and S. Donati and R. Donghia and B. Echenard and K. Flood and S. Giovannella and V. Glagolev and F. Grancagnolo and F. Happacher and D.G. Hitlin and M. Martini and S. Miscetti and T. Miyashita and L. Morescalchi and P. Murat and G. Pezzullo and F. Porter and F. Raffaelli and Tommaso Radicioni and M. Ricci and A. Saputi and I. Sarra and F. Spinella and G. Tassielli and V. Tereshchenko and Z. Usubov and R.Y. Zhu}
}
