<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">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></records></xml>