03175nas a2200337   4500008004100000022001400041245016700055210006900222260001200291490000700303520202400310653001702334653003702351653002302388653001102411653001102422653001302433653001402446100001702460700002702477700002802504700002602532700003202558700001602590700003202606700001902638700003302657700003202690700003102722856008402753       2020                        eng d  a1676-061100aDrivers of change in biodiversity and ecosystem services in the Cantareira System Protected Area : A prospective analysis of the implementation of public policies0 aDrivers of change in biodiversity and ecosystem services in the   c05/20200 v203 aThe 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.10aBiodiversity10aCantareira System Protected Area10aEcosystem services10aGLOBIO10aInVEST10aModeling10aScenarios1 aDib, Viviane1 aNalon, Marco, Aurélio1 aAmazonas, Nino, Tavares1 aVidal, Cristina, Yuri1 aOrtiz-Rodríguez, Iván, A.1 aDaněk, Jan1 ade Oliveira, Maíra, Formis1 aAlberti, Paola1 ada Silva, Rafaela, Aparecida1 aPrecinoto, Raíza, Salomão1 aGomes, Taciana, Figueiredo  uhttps://www.scielo.br/scielo.php?script=sci_arttext&pid=S1676-0603202000050020101497nas a2200169   4500008004100000245007900041210006900120520087200189100002501061700002301086700001901109700002001128700001901148700002201167700002101189856011701210       2020                        eng d00aMeasuring objective and subjective well-being: dimensions and data sources0 aMeasuring objective and subjective wellbeing dimensions and data3 aWell-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.1 aVoukelatou, Vasiliki1 aGabrielli, Lorenzo1 aMiliou, Ioanna1 aCresci, Stefano1 aSharma, Rajesh1 aTesconi, Maurizio1 aPappalardo, Luca  uhttps://datasciencephd.eu/publications/measuring-objective-and-subjective-well-being-dimensions-and-data-sources01940nas a2200241   4500008004100000022001400041245010300055210006900158300001200227490000600239520121400245653000801459653001801467653002101485653001501506100001901521700002101540700002201561700002001583700002101603700002001624856005401644       2020                        eng d  a2548-316100aSelf-supervised pre-training of CNNs for flatness defect classification in the steelworks industry0 aSelfsupervised pretraining of CNNs for flatness defect classific  a13–220 v63 aClassification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization.10aCNN10aDeep learning10aSelf-supervision10aSteelworks1 aGalli, Filippo1 aRitacco, Antonio1 aLanciano, Giacomo1 aVannocci, Marco1 aColla, Valentina1 aVannucci, Marco  uhttp://ijain.org/index.php/IJAIN/article/view/41001581nas a2200241   4500008004100000020001800041245006800059210006700127260005900194520079900253653002101052653003601073653002501109100002201134700002101156700002301177700002001200700002101220700001701241700002001258700001701278856004401295       2020                        eng d  a978145036866700aSOM-Based Behavioral Analysis for Virtualized Network Functions0 aSOMBased Behavioral Analysis for Virtualized Network Functions  aNew York, NY, USAbAssociation for Computing Machinery3 aIn this paper, we propose a mechanism based on Self-Organizing Maps for analyzing the resource consumption behaviors and detecting possible anomalies in data centers for Network Function Virtualization (NFV). Our approach is based on a joint analysis of two historical data sets available through two separate monitoring systems: system-level metrics for the physical and virtual machines obtained from the monitoring infrastructure, and application-level metrics available from the individual virtualized network functions. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, highlight some of the capabilities of our system to identify interesting points in space and time of the evolution of the monitored infrastructure.10amachine learning10anetwork function virtualization10aself-organizing maps1 aLanciano, Giacomo1 aRitacco, Antonio1 aCucinotta, Tommaso1 aVannucci, Marco1 aArtale, Antonino1 aBasili, Luca1 aSposato, Enrica1 aBarata, Joao  uhttps://doi.org/10.1145/3341105.337411002440nas a2200313   4500008004100000022001400041245009900055210006900154260001200223490001300235520142800248653001401676653001301690653003101703653002601734653003001760653002701790653002301817653002401840100003201864700002001896700002101916700003301937700002401970700002701994700001802021700001602039856007102055       2020                        eng d  a1755-087400aSpatiotemporal effects of Hurricane Ivan on an endemic epiphytic orchid: 10 years of follow-up0 aSpatiotemporal effects of Hurricane Ivan on an endemic epiphytic  c10/20190 v13, 20203 aBackground: 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.
10aCaribbean10acyclones10aintegral projection models10amanagement strategies10aplant population dynamics10astochastic growth rate10atransfer functions10atransient behaviour1 aOrtiz-Rodríguez, Iván, A.1 aRaventós, Jose1 aMújica, Ernesto1 aGonzález-Hernández, Elaine1 aVega-Peña, Ernesto1 aOrtega-Larrocea, Pilar1 aBonet, Andreu1 aMerow, Cory  uhttps://www.tandfonline.com/doi/full/10.1080/17550874.2019.167349500454nas a2200133   4500008004100000245004600041210004100087100002300128700002300151700002700174700002400201700002000225856007500245       2018                        eng d00aIs this Sentence Difficult? Do you Agree?0 athis Sentence Difficult Do you Agree1 aBrunato, Dominique1 aDe Mattei, Lorenzo1 aDell’Orletta, Felice1 aIavarone, Benedetta1 aVenturi, Giulia  uhttps://datasciencephd.eu/publications/sentence-difficult-do-you-agree