<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Luca Corbucci</style></author><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Cecilia Panigutti</style></author><author><style face="normal" font="default" size="100%">Michela Natilli</style></author><author><style face="normal" font="default" size="100%">Smiraglio, Simona</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Enrichment of XAI Explanations for Healthcare</style></title><secondary-title><style face="normal" font="default" size="100%">24th International Conference on Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Explaining black-box models decisions is crucial to increase doctors' trust in AI-based clinical decision support systems. However, current eXplainable Artificial Intelligence (XAI) techniques usually provide explanations that are not easily understandable by experts outside of AI. Furthermore, most of the them produce explanations that consider only the input features of the algorithm. However, broader information about the clinical context of a patient is usually available even if not processed by the AI-based clinical decision support system for its decision. Enriching the explanations with relevant clinical information concerning the health status of a patient would increase the ability of human experts to assess the reliability of the AI decision. Therefore, in this paper we present a methodology that aims to enable clinical reasoning by semantically enriching AI explanations. Starting from a medical AI explanation based only on the input features provided to the algorithm, our methodology leverages medical ontologies and NLP embedding techniques to link relevant information present in the patient's clinical notes to the original explanation. We validate our methodology with two experiments involving a human expert. Our results highlight promising performance in correctly identifying relevant information about the diseases of the patients, in particular about the associated morphology. This suggests that the presented methodology could be a first step toward developing a natural language explanation of AI decision support systems.</style></abstract></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%">Trasarti, Roberto</style></author><author><style face="normal" font="default" size="100%">Grossi, Valerio</style></author><author><style face="normal" font="default" size="100%">Michela Natilli</style></author><author><style face="normal" font="default" size="100%">Rapisarda, Beatrice</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics.</style></title><secondary-title><style face="normal" font="default" size="100%">30th Italian Symposium on Advanced Database Systems (SEBD – Sistemi Evoluti per Basi di Dati)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><pub-location><style face="normal" font="default" size="100%">Tirrenia, Pisa</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">SoBigData RI has the ambition to support the rising demand for cross-disciplinary research and innovation on the multiple aspects of social complexity from combined data and model-driven perspectives and the increasing importance of ethics and data scientists’ responsibility as pillars of trustworthy use of Big Data and analytical technology. Digital traces of human activities offer a considerable opportunity to scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and on a global scale. This increasing wealth of data is a chance to understand social complexity, provided we can rely on social mining, i.e., adequate means for accessing big social data and models for extracting knowledge from them. SoBigData RI, with its tools and services, empowers researchers and innovators through a platform for the design and execution of large-scale social mining experiments, open to users with diverse backgrounds, accessible on the cloud (aligned with EOSC), and also exploiting supercomputing facilities. Pushing the FAIR (Findable, Accessible, Interoperable) and FACT (Fair, Accountable, Confidential, and Transparent) principles will render social mining experiments more efficiently designed, adjusted, and repeatable by domain experts that are not data scientists. SoBigData RI moves forward from the simple awareness of ethical and legal challenges in social mining to the development of concrete tools that operationalize ethics with value-sensitive design, incorporating values and norms for privacy protection, fairness, transparency, and pluralism. SoBigData RI is the result of two H2020 grants (g.a. n.654024 and 871042), and it is part of the ESFRI 2021 Roadmap.</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%">Guidotti, Riccardo</style></author><author><style face="normal" font="default" size="100%">Monreale, Anna</style></author><author><style face="normal" font="default" size="100%">Ruggieri, Salvatore</style></author><author><style face="normal" font="default" size="100%">Naretto, Francesca</style></author><author><style face="normal" font="default" size="100%">Turini, Franco</style></author><author><style face="normal" font="default" size="100%">Pedreschi, Dino</style></author><author><style face="normal" font="default" size="100%">Giannotti, Fosca</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stable and actionable explanations of black-box models through factual and counterfactual rules</style></title><secondary-title><style face="normal" font="default" size="100%">Data Mining and Knowledge Discovery</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><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%">Cornacchia, Giuliano</style></author><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05</style></date></pub-dates></dates><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%">Andrienko, Gennady</style></author><author><style face="normal" font="default" size="100%">Andrienko, Natalia</style></author><author><style face="normal" font="default" size="100%">Boldrini, Chiara</style></author><author><style face="normal" font="default" size="100%">Caldarelli, Guido</style></author><author><style face="normal" font="default" size="100%">Paolo Cintia</style></author><author><style face="normal" font="default" size="100%">Cresci, Stefano</style></author><author><style face="normal" font="default" size="100%">Facchini, Angelo</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Gionis, Aristides</style></author><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">others</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">(So) Big Data and the transformation of the city</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Data Science and Analytics</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://link.springer.com/article/10.1007/s41060-020-00207-3</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.</style></abstract></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%">F Morandin</style></author><author><style face="normal" font="default" size="100%">G Amato</style></author><author><style face="normal" font="default" size="100%">R Gini</style></author><author><style face="normal" font="default" size="100%">C Metta</style></author><author><style face="normal" font="default" size="100%">M Parton</style></author><author><style face="normal" font="default" size="100%">G.C. Pascutto</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SAI a Sensible Artificial Intelligence that plays Go</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><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>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marco Malvaldi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sarò Franco - Vita di Franco Turini, executive chef dell’Università di Pisa</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://store.streetlib.com/it/marco-malvaldi/saro-franco-9788833392523/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Pisa University Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Pisa, Italy</style></pub-location><pages><style face="normal" font="default" size="100%">20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Chi è Franco Turini? Come molti sanno, uno dei pionieri dell’informatica italiana. Ma non è questa la domanda che ci interessa. Quella a cui questo breve saggio si propone di rispondere è una questione molto più importante: chi avrebbe voluto essere Franco Turini?
In questo scritto, la vita e la carriera di Turini vengono ripercorse alla luce della sua vera, unica e irredimibile passione: la cucina. In un intreccio romanzesco, denso di colpi di scena e assolutamente falso e tendenzioso, il contributo di Franco Turini all’informatica e all’intelligenza artiﬁciale si dipana, indissolubilmente intrecciato alla sua passione per i fornelli, attraverso le molte intuizioni geniali che lo hanno colpito mentre cucinava.</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%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On The Stability of Interpretable Models</style></title><secondary-title><style face="normal" font="default" size="100%">2019 International Joint Conference on Neural Networks (IJCNN)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/abstract/document/8852158</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process. Bias in data collection and preparation, or in model's construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models.</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%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Bontcheva, Kalina</style></author><author><style face="normal" font="default" size="100%">Valerio Grossi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SoBigData: Social Mining &amp; Big Data Ecosystem</style></title><secondary-title><style face="normal" font="default" size="100%">Companion of the The Web Conference 2018 on The Web Conference 2018</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sobigdata.eu/sites/default/files/www%202018.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">International World Wide Web Conferences Steering Committee</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by &quot;big data&quot;. The research community uses the SoBigData facilities as a &quot;secure digital wind-tunnel&quot; for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.</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%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A survey of methods for explaining black box models</style></title><secondary-title><style face="normal" font="default" size="100%">ACM computing surveys (CSUR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://dl.acm.org/doi/abs/10.1145/3236009</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">51</style></volume><pages><style face="normal" font="default" size="100%">93</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.</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%">Alessandro Lulli</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Patrizio Dazzi</style></author><author><style face="normal" font="default" size="100%">Matteo Dell'Amico</style></author><author><style face="normal" font="default" size="100%">Pietro Michiardi</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Laura Ricci</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Scalable and flexible clustering solutions for mobile phone-based population indicators</style></title><secondary-title><style face="normal" font="default" size="100%">I. J. Data Science and Analytics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/s41060-017-0065-y</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">285–299</style></pages><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%">Alessandro Baroni</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Segregation discovery in a social network of companies</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Intelligent Information Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sep</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/s10844-017-0485-0</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce a framework for the data-driven analysis of social segregation of minority groups, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem is introduced, which consists of searching sub-groups of population and minorities for which a segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem by computing a multi-dimensional data cube that can be explored by the analyst. The machinery underlying the search algorithm relies on frequent itemset mining concepts and tools. The framework is challenged on a cases study in the context of company networks. We analyse segregation on the grounds of sex and age for directors in the boards of the Italian companies. The network includes 2.15M companies and 3.63M directors.</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%">Pollacci, Laura</style></author><author><style face="normal" font="default" size="100%">Alina Sirbu</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Claudio Lucchese</style></author><author><style face="normal" font="default" size="100%">Muntean, Cristina Ioana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter</style></title><secondary-title><style face="normal" font="default" size="100%">Conference of the Italian Association for Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-319-70169-1_9</style></url></web-urls></urls><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%">While sentiment analysis has received significant attention in the last years, problems still exist when tools need to be applied to microblogging content. This because, typically, the text to be analysed consists of very short messages lacking in structure and semantic context. At the same time, the amount of text produced by online platforms is enormous. So, one needs simple, fast and effective methods in order to be able to efficiently study sentiment in these data. Lexicon-based methods, which use a predefined dictionary of terms tagged with sentiment valences to evaluate sentiment in longer sentences, can be a valid approach. Here we present a method based on epidemic spreading to automatically extend the dictionary used in lexicon-based sentiment analysis, starting from a reduced dictionary and large amounts of Twitter data. The resulting dictionary is shown to contain valences that correlate well with human-annotated sentiment, and to produce tweet sentiment classifications comparable to the original dictionary, with the advantage of being able to tag more tweets than the original. The method is easily extensible to various languages and applicable to large amounts of data.</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%">Valerio Grossi</style></author><author><style face="normal" font="default" size="100%">Andrea Romei</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Survey on using constraints in data mining</style></title><secondary-title><style face="normal" font="default" size="100%">Data Mining and Knowledge Discovery</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">424–464</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints in a data mining task requires specific definition and satisfaction tools during knowledge extraction. This survey proposes three groups of studies based on classification, clustering and pattern mining, whether the constraints are on the data, the models or the measures, respectively. We consider the distinctions between hard and soft constraint satisfaction, and between the knowledge extraction phases where constraints are considered. In addition to discussing how constraints can be used in data mining, we show how constraint-based languages can be used throughout the data mining process.</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%">Mattia Setzu</style></author><author><style face="normal" font="default" size="100%">Atzori, Maurizio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SPARQL Queries over Source Code</style></title><secondary-title><style face="normal" font="default" size="100%">2016 IEEE Tenth International Conference on Semantic Computing (ICSC)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><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%">Marco Fiore</style></author><author><style face="normal" font="default" size="100%">Zubair Shafiq</style></author><author><style face="normal" font="default" size="100%">Zbigniew Smoreda</style></author><author><style face="normal" font="default" size="100%">Razvan Stanica</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Special Issue on Mobile Traffic Analytics</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Communications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1016/j.comcom.2016.10.009</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">95</style></volume><pages><style face="normal" font="default" size="100%">1–2</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This Special Issue of Computer Communications is dedicated to mobile traffic data analysis. This is an emerging field of research that stems from the increasing pervasiveness in our lives of always-connected mobile devices. These devices continuously collect, generate, receive or communicate data; in doing so, they leave trails of digital crumbs that can be followed, recorded and analysed in many and varied ways, and for a number of different purposes.
From a data collection perspective, applications running on smartphones allow tracking user activities with extreme accuracy, in terms of mobility, context, and service usage. Yet, having individuals informedly install and run software that monitors their actions is not obvious; finding adequate incentives is equivalently complex. The other option is gathering mobile traffic data in the mobile network. This is an increasingly common practice for telecommunication operators: the collection of minimum information required for billing is giving way to in-depth inspection and recording of mobile service usages in space and time, and of traffic flows at the network edge and core. In this case, data access remains the major impediment, due to privacy and industrial secrecy reasons.

Despite the issues inherent to the data collection, the richness of knowledge that can be extracted from the aforementioned sources is such that actors in both academia and industry are putting significant effort in gathering, analysing and possibly making available mobile traffic data. Indeed, mobile traffic data typically contain information on large populations of individuals (from thousands to millions users) with high spatio-temporal granularity. The combination of accuracy and coverage is unprecedented, and it has proven key in validating theories and scaling up experimental studies in a number of research fields across many disciplines, including physics, sociology, epidemiology, transportation systems, and, of course, mobile networking.

As a result, we witness today a rapid growth of the literature that proposes or exploits mobile traffic analytics. Included in this Special Issue are eight papers that cover a significant portion of the different research topics in this area, ranging from data collection to the characterization of land use and mobile service consumption, from the inference and prediction of user mobility to the detection of malicious traffic. These papers were selected from 30 high-quality submissions after at least two rounds of reviews by experts and guest editors. The original submissions were received from five continents and a variety of countries, including Austria, Argentina, Belgium, Brazil, Chile, China, France, Germany, Italy, South Korea, Luxembourg, Pakistan, Saudi Arabia, Spain, Sweden, Tunisia, Turkey, USA. The accepted papers reflect this geographical heterogeneity, and are authored by researchers based in Europe, North and South America.</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%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Ioanna Miliou</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A supervised approach for intra-/inter-community interaction prediction in dynamic social networks</style></title><secondary-title><style face="normal" font="default" size="100%">Social Network Analysis and Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s13278-016-0397-y</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intra-community and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.</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%">Alessandro Baroni</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Segregation Discovery in a Social Network of Companies</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%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer, Cham</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce a framework for a data-driven analysis of segregation of minority groups in social networks, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem consists of searching sub-graphs and sub-groups for which a reference segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem. The framework is challenged on the analysis of segregation of social groups in the boards of directors of the real and large network of Italian companies connected through shared directors.</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%">Stefano Marchetti</style></author><author><style face="normal" font="default" size="100%">Caterina Giusti</style></author><author><style face="normal" font="default" size="100%">Monica Pratesi</style></author><author><style face="normal" font="default" size="100%">Nicola Salvati</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Small Area Model-Based Estimators Using Big Data Sources</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">263–281</style></pages><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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Sassi, Andrea</style></author><author><style face="normal" font="default" size="100%">Michele Berlingerio</style></author><author><style face="normal" font="default" size="100%">Pascale, Alessandra</style></author><author><style face="normal" font="default" size="100%">Ghaddar, Bissan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Social or green? A data-driven approach for more enjoyable carpooling</style></title><secondary-title><style face="normal" font="default" size="100%">Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><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%">Gennady Andrienko</style></author><author><style face="normal" font="default" size="100%">Natalia Andrienko</style></author><author><style face="normal" font="default" size="100%">C. Hunter</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Stefan Wrobel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Scalable Analysis of Movement Data for Extracting and Exploring Significant Places</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Visualization and Computer Graphics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">19</style></volume><section><style face="normal" font="default" size="100%">49</style></section></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%">Christine Parent</style></author><author><style face="normal" font="default" size="100%">Stefano Spaccapietra</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Gennady Andrienko</style></author><author><style face="normal" font="default" size="100%">Natalia Andrienko</style></author><author><style face="normal" font="default" size="100%">Vania Bogorny</style></author><author><style face="normal" font="default" size="100%">Damiani M L,</style></author><author><style face="normal" font="default" size="100%">Gkoulalas-Divanis A,</style></author><author><style face="normal" font="default" size="100%">de José Antônio Fernandes Macêdo</style></author><author><style face="normal" font="default" size="100%">Nikos Pelekis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Trajectories Modeling and Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Computing Surveys</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">August 2013</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">45</style></volume></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%">Michele Coscia</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial and Temporal Evaluation of Network-based Analysis of Human Mobility</style></title><secondary-title><style face="normal" font="default" size="100%">Social Network Analysis and Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><volume><style face="normal" font="default" size="100%">to appear</style></volume></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monica Wachowicz</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio and Spatio-temporal Reasoning and Decision Support Tools</style></title><secondary-title><style face="normal" font="default" size="100%">Entry at Encyclopedia of Social Network Analysis and Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><edition><style face="normal" font="default" size="100%">springer</style></edition></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%">Vittoria Cozza</style></author><author><style face="normal" font="default" size="100%">Antonio Messina</style></author><author><style face="normal" font="default" size="100%">Danilo Montesi</style></author><author><style face="normal" font="default" size="100%">Luca Arietta</style></author><author><style face="normal" font="default" size="100%">Matteo Magnani</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio temporal keyword-queries in Social Networs</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates></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%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Alessandra Raffaetà</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-Temporal Data</style></title><secondary-title><style face="normal" font="default" size="100%">Spatio-Temporal Databases: Flexible Querying and Reasoning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pages><style face="normal" font="default" size="100%">75</style></pages><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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rebecca Ong</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Monica Wachowicz</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study on Parameter Estimation for a Mining Flock Algorithm </style></title><secondary-title><style face="normal" font="default" size="100%">Mining Complex Patterns Workshop, ECML PKDD 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates></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%">Batty, Michael</style></author><author><style face="normal" font="default" size="100%">Axhausen, Kay W</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Pozdnoukhov, Alexei</style></author><author><style face="normal" font="default" size="100%">Bazzani, Armando</style></author><author><style face="normal" font="default" size="100%">Monica Wachowicz</style></author><author><style face="normal" font="default" size="100%">Ouzounis, Georgios</style></author><author><style face="normal" font="default" size="100%">Portugali, Yuval</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Smart cities of the future</style></title><secondary-title><style face="normal" font="default" size="100%">European Physical Journal-Special Topics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">214</style></volume><pages><style face="normal" font="default" size="100%">481</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Here we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for urban populations. We begin by defining the state of the art, explaining the science of smart cities. We define six scenarios based on new cities badging themselves as smart, older cities regenerating themselves as smart, the development of science parks, tech cities, and technopoles focused on high technologies, the development of urban services using contemporary ICT, the use of ICT to develop new urban intelligence functions, and the development of online and mobile forms of participation. Seven project areas are then proposed: Integrated Databases for the Smart City, Sensing, Networking and the Impact of New Social Media, Modelling Network Performance, Mobility and Travel Behaviour, Modelling Urban Land Use, Transport and Economic Interactions, Modelling Urban Transactional Activities in Labour and Housing Markets, Decision Support as Urban Intelligence, Participatory Governance and Planning Structures for the Smart City. Finally we anticipate the paradigm shifts that will occur in this research and define a series of key demonstrators which we believe are important to progressing a science of smart cities.</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%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Michele Berlingerio</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Scalable Link Prediction on Multidimensional Networks</style></title><secondary-title><style face="normal" font="default" size="100%">ICDM Workshops</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pub-location><style face="normal" font="default" size="100%">Vancouver</style></pub-location><pages><style face="normal" font="default" size="100%">979-986</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alina Sirbu</style></author><author><style face="normal" font="default" size="100%">Heather J Ruskin</style></author><author><style face="normal" font="default" size="100%">Martin Crane</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role</style></title><secondary-title><style face="normal" font="default" size="100%">Evolutionary Algorithms</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.intechopen.com/articles/show/title/stages-of-gene-regulatory-network-inference-the-evolutionary-algorithm-role</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">InTech</style></publisher><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%">Tomei, Gabriele</style></author><author><style face="normal" font="default" size="100%">Paletti, F</style></author><author><style face="normal" font="default" size="100%">Michela Natilli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stiramenti identitari. 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