@proceedings {1509,
	title = {GET-Viz: a library for automatic generation of visual dashboard for geographical time series},
	year = {2022},
	address = {Chicago, USA},
	author = {Fadda, Daniele and Michela Natilli and S Rinzivillo}
}
@article {1488,
	title = {The long-tail effect of the COVID-19 lockdown on Italians{\textquoteright} quality of life, sleep and physical activity},
	journal = {Scientific Data},
	volume = {9},
	number = {1},
	year = {2022},
	pages = {1{\textendash}10},
	abstract = {From March 2020 to May 2021, several lockdown periods caused by the COVID-19 pandemic have limited people{\textquoteright}s usual activities and mobility in Italy, as well as around the world. These unprecedented confinement measures dramatically modified citizens{\textquoteright} daily lifestyles and behaviours. However, with the advent of summer 2021 and thanks to the vaccination campaign that significantly prevents serious illness and death, and reduces the risk of contagion, all the Italian regions finally returned to regular behaviours and routines. Anyhow, it is unclear if there is a long-tail effect on people{\textquoteright}s quality of life, sleep- and physical activity-related behaviours. Thanks to the dataset described in this paper, it will be possible to obtain accurate insights of the changes induced by the lockdown period in the Italians{\textquoteright} health that will permit to provide practical suggestions at local, regional, and state institutions and companies to improve infrastructures and services that could be beneficial to Italians{\textquoteright} well being.},
	url = {https://www.nature.com/articles/s41597-022-01376-5},
	author = {Michela Natilli and Alessio Rossi and Trecroci, Athos and Cavaggioni, Luca and Merati, Giampiero and Formenti, Damiano}
}
@proceedings {1498,
	title = {Semantic Enrichment of XAI Explanations for Healthcare},
	year = {2022},
	abstract = {Explaining black-box models decisions is crucial to increase doctors{\textquoteright} 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{\textquoteright}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.},
	author = {Luca Corbucci and Anna Monreale and Cecilia Panigutti and Michela Natilli and Smiraglio, Simona and Dino Pedreschi}
}
@proceedings {1500,
	title = {SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics.},
	year = {2022},
	address = {Tirrenia, Pisa},
	abstract = {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{\textquoteright} 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.},
	author = {Trasarti, Roberto and Grossi, Valerio and Michela Natilli and Rapisarda, Beatrice}
}
@article {1470,
	title = {Explaining the difference between men{\textquoteright}s and women{\textquoteright}s football},
	journal = {PLOS ONE},
	volume = {16},
	year = {2021},
	month = {Apr-08-2021},
	pages = {e0255407},
	abstract = {Women{\textquoteright}s football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men{\textquoteright}s football. While the two sports are often compared based on the players{\textquoteright} physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match{\textquoteright}s playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men{\textquoteright}s and women{\textquoteright}s football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier{\textquoteright}s decisions. The differences between men{\textquoteright}s and women{\textquoteright}s football are rooted in play accuracy, the recovery time of ball possession, and the players{\textquoteright} performance quality. Our methodology may help journalists and fans understand what makes women{\textquoteright}s football a distinct sport and coaches design tactics tailored to female teams.},
	doi = {https://doi.org/10.1371/journal.pone.0255407},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255407},
	author = {Luca Pappalardo and Alessio Rossi and Michela Natilli and Paolo Cintia},
	editor = {Constantinou, Anthony C.}
}
@article {1501,
	title = {Understanding eating choices among university students: A study using data from cafeteria cashiers{\textquoteright} transactions},
	journal = {Health Policy},
	volume = {125},
	number = {5},
	year = {2021},
	pages = {665{\textendash}673},
	author = {Lorenzoni, Valentina and Triulzi, Isotta and Martinucci, Irene and Toncelli, Letizia and Michela Natilli and Barale, Roberto and Turchetti, Giuseppe}
}
@conference {1408,
	title = {Analysis and Visualization of Performance Indicators in University Admission Tests},
	booktitle = {Formal Methods. FM 2019 International Workshops},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the question or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the composition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.},
	isbn = {978-3-030-54994-7},
	doi = {https://doi.org/10.1007/978-3-030-54994-7_14},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-54994-7_14},
	author = {Michela Natilli and Daniele Fadda and S Rinzivillo and Dino Pedreschi and Licari, Federica},
	editor = {Sekerinski, Emil and Moreira, Nelma and Oliveira, Jos{\'e} N. and Ratiu, Daniel and Riccardo Guidotti and Farrell, Marie and Luckcuck, Matt and Marmsoler, Diego and Campos, Jos{\'e} and Astarte, Troy and Gonnord, Laure and Cerone, Antonio and Couto, Luis and Dongol, Brijesh and Kutrib, Martin and Monteiro, Pedro and Delmas, David}
}
@article {1272,
	title = {A Visual Analytics Platform to Measure Performance on University Entrance Tests (Discussion Paper)},
	year = {2019},
	author = {Boncoraglio, Daniele and Deri, Francesca and Distefano, Francesco and Daniele Fadda and Filippi, Giorgio and Forte, Giuseppe and Licari, Federica and Michela Natilli and Dino Pedreschi and S Rinzivillo}
}
@conference {1269,
	title = {Exploring Students Eating Habits Through Individual Profiling and Clustering Analysis},
	booktitle = {ECML PKDD 2018 Workshops},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	author = {Michela Natilli and Anna Monreale and Riccardo Guidotti and Luca Pappalardo}
}
@article {1194,
	title = {Gastroesophageal reflux symptoms among Italian university students: epidemiology and dietary correlates using automatically recorded transactions},
	journal = {BMC gastroenterology},
	volume = {18},
	number = {1},
	year = {2018},
	pages = {116},
	abstract = {Background: Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal disorders
worldwide, with relevant impact on the quality of life and health care costs.The aim of our study is to assess the
prevalence of GERD based on self-reported symptoms among university students in central Italy. The secondary aim is
to evaluate lifestyle correlates, particularly eating habits, in GERD students using automatically recorded transactions
through cashiers at university canteen.
Methods: A web-survey was created and launched through an app, ad-hoc developed for an interactive exchange of
information with students, including anthropometric data and lifestyle habits. Moreover, the web-survey allowed
users a self-diagnosis of GERD through a simple questionnaire. As regard eating habits, detailed collection of meals
consumed, including number and type of dishes, were automatically recorded through cashiers at the university
canteen equipped with an automatic registration system.
Results: We collected 3012 questionnaires. A total of 792 students (26.2\% of the respondents) reported typical GERD
symptoms occurring at least weekly. Female sex was more prevalent than male sex. In the set of students with GERD,
the percentage of smokers was higher, and our results showed that when BMI tends to higher values the percentage
of students with GERD tends to increase. When evaluating correlates with diet, we found, among all users, a lower
frequency of legumes choice in GERD students and, among frequent users, a lower frequency of choice of pasta and
rice in GERD students.
Discussion: The results of our study are in line with the values reported in the literature. Nowadays, GERD is a common
problem in our communities, and can potentially lead to serious medical complications; the economic burden
involved in the diagnostic and therapeutic management of the disease has a relevant impact on healthcare costs.
Conclusions: To our knowledge, this is the first study evaluating the prevalence of typical GERD{\textendash}related symptoms
in a young population of University students in Italy. Considering the young age of enrolled subjects, our prevalence
rate, relatively high compared to the usual estimates, could represent a further negative factor for the future
economic sustainability of the healthcare system.
Keywords: Gastroesophageal reflux disease, GERD, Heartburn, Regurgitation, Diet, Prevalence, University students},
	doi = {10.1186/s12876-018-0832-9},
	url = {https://bmcgastroenterol.biomedcentral.com/articles/10.1186/s12876-018-0832-9},
	author = {Martinucci, Irene and Michela Natilli and Lorenzoni, Valentina and Luca Pappalardo and Anna Monreale and Turchetti, Giuseppe and Dino Pedreschi and Marchi, Santino and Barale, Roberto and de Bortoli, Nicola}
}
@article {1273,
	title = {Wine and Food Tourism First European Conference},
	journal = {Edizioni ETS Pisa},
	year = {2012},
	author = {Romano, Maria Francesca and Michela Natilli}
}
@book {1275,
	title = {Dinamiche di impoverimento. Meccanismi, traiettorie ed effetti in un contesto locale},
	year = {2011},
	publisher = {Carocci Editore},
	organization = {Carocci Editore},
	author = {Tomei, Gabriele and Michela Natilli}
}
@conference {1277,
	title = {The impact of wine and food tourism in Italy: an analysis of official statistical data at province level},
	booktitle = {First European Conference on Wine and Food Tourism},
	year = {2011},
	author = {Michela Natilli and Romano, Maria Francesca}
}
@conference {1276,
	title = {The language of tourists in a wine and food blog},
	booktitle = {First European Conference on Wine and Food Tourism},
	year = {2011},
	author = {Pavone, Pasquale and Michela Natilli and Romano, Maria Francesca}
}
@article {1274,
	title = {Measuring the effectiveness of homeopathic care through objective and shared indicators},
	journal = {Homeopathy},
	volume = {100},
	number = {04},
	year = {2011},
	pages = {212{\textendash}219},
	author = {Leone, Laura and Marchitiello, Maria and Michela Natilli and Romano, Maria Francesca}
}
@article {1278,
	title = {Stiramenti identitari. Strategie di integrazione degli strannieri nella provincia di Massa Carrara tra appartenenza etnica ed esperienza transnazionale},
	year = {2011},
	author = {Tomei, Gabriele and Paletti, F and Michela Natilli}
}
@conference {1279,
	title = {Poverty as a Social Condition: a Case Study on a Small Municipality in Tuscany},
	booktitle = {Global Recession: Regional Impacts on Housing, Jobs, Health and Wellbeing},
	year = {2009},
	publisher = {SEAFORD},
	organization = {SEAFORD},
	author = {Tomei, Gabriele and Michela Natilli}
}
@article {1270,
	title = {Wine tourism in Italy: New profiles, styles of consumption, ways of touring},
	journal = {Turizam: me{\dj}unarodni znanstveno-stru{\v c}ni {\v c}asopis},
	volume = {57},
	number = {4},
	year = {2009},
	pages = {463{\textendash}475},
	author = {Romano, Maria Francesca and Michela Natilli}
}
@conference {1280,
	title = {Comparative indicators of regional poverty and deprivation: Poland versus EU-15 Member States},
	booktitle = {conference Comparative Economic Analysis of Households" Behaviour (CEAHB): Old and New EU Members, Warsaw University},
	year = {2005},
	author = {Betti, Gianni and Mulas, Anna and Michela Natilli and Neri, Laura and Verma, Vijay}
}
@booklet {1271,
	title = {Indicators of social exclusion and poverty in Europe{\textquoteright}s regions},
	year = {2005},
	author = {Verma, Vijay and Betti, Gianni and Michela Natilli and Lemmi, Achille}
}
@conference {1281,
	title = {Personal income in the gross and net forms: applications of the Siena Micro-Simulation Model (SM2)},
	booktitle = {conference of the Societ{\`a} Italiana di Economia, Demografia e Statistica (SIEDS), Campobasso},
	year = {2003},
	author = {Verma, V and Betti, G and Ballini, F and Michela Natilli and Galgani, S}
}
