@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 {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}
}
@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}
}
@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 {267,
	title = {Is this Sentence Difficult? Do you Agree?},
	booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
	year = {2018},
	author = {Brunato, Dominique and De Mattei, Lorenzo and Dell{\textquoteright}Orletta, Felice and Iavarone, Benedetta and Venturi, Giulia}
}
