01861nas a2200145   4500008004100000245006100041210006100102260001300163520137000176100001401546700001701560700002101577700002301598856009401621       2020                        eng d00aDigital Footprints of International Migration on Twitter0 aDigital Footprints of International Migration on Twitter  bSpringer3 aStudying 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.1 aKim, Jisu1 aSirbu, Alina1 aGiannotti, Fosca1 aGabrielli, Lorenzo  uhttps://datasciencephd.eu/publications/digital-footprints-international-migration-twitter01523nas a2200121   4500008004100000245010000041210006900141520107500210100002301285700001801308700002001326856005501346       2020                        eng d00aDoctor XAI: an ontology-based approach to black-box sequential data classification explanations0 aDoctor XAI an ontologybased approach to blackbox sequential data3 aSeveral 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.1 aPanigutti, Cecilia1 aPerotti, Alan1 aPedreschi, Dino  uhttps://dl.acm.org/doi/abs/10.1145/3351095.337285503175nas 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=S1676-0603202000050020100497nas a2200133   4500008004100000022001800041245003900059210003900098260002100137490003400158100001900192700001800211856013400229       2020                        eng d  a978889191077600aDynamic Quantile Regression Forest0 aDynamic Quantile Regression Forest  bPearsonc10/20200 vBook of Short Papers SIS 20201 aAndreani, Mila1 aPetrella, Lea  uhttps://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Docenti/Universit%C3%A0/Pearson-SIS-2020-atti-convegno.pdf00582nas a2200133   4500008004100000245011600041210006900157300001100226100002400237700002000261700002100281700002100302856012500323       2019                        eng d00aDoes the position in the inter-sectoral knowledge space affect the international competitiveness of industries?0 aDoes the position in the intersectoral knowledge space affect th  a1–481 aLamperti, Francesco1 aMalerba, Franco1 aMavilia, Roberto1 aTripodi, Giorgio  uhttps://datasciencephd.eu/publications/does-position-inter-sectoral-knowledge-space-affect-international-competitiveness