02085nas a2200265   4500008004100000022001400041245010300055210006900158260001600227490000700243520114900250100002001399700002001419700001801439700002701457700001701484700002501501700001701526700002001543700001901563700002301582700002001605700001901625856017501644       2023                        eng d  a1388-195700aGenerative AI models should include detection mechanisms as a condition for public releaseAbstract0 aGenerative AI models should include detection mechanisms as a co  cJan-12-20230 v253 aThe new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required.1 aKnott, Alistair1 aPedreschi, Dino1 aChatila, Raja1 aChakraborti, Tapabrata1 aLeavy, Susan1 aBaeza-Yates, Ricardo1 aEyers, David1 aTrotman, Andrew1 aTeal, Paul, D.1 aBiecek, Przemyslaw1 aRussell, Stuart1 aBengio, Yoshua  uhttps://link.springer.com/article/10.1007/s10676-023-09728-4?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231028&utm_content=10.1007/s10676-023-09728-401517nas a2200169   4500008004100000245007000041210006900111300000700180490000700187520095100194100002001145700002301165700001901188700001701207700002101224856010201245       2022                        eng d00aGenerating mobility networks with generative adversarial networks0 aGenerating mobility networks with generative adversarial network  a580 v113 aThe increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city’s entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people’s movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.1 aMauro, Giovanni1 aLuca, Massimiliano1 aLonga, Antonio1 aLepri, Bruno1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/generating-mobility-networks-generative-adversarial-networks00526nas a2200121   4500008004100000245009700041210006900138260001700207100001900224700002100243700001800264856012200282       2022                        eng d00aGET-Viz: a library for automatic generation of visual dashboard for geographical time series0 aGETViz a library for automatic generation of visual dashboard fo  aChicago, USA1 aFadda, Daniele1 aNatilli, Michela1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/get-viz-library-automatic-generation-visual-dashboard-geographical-time-series-003888nas a2200589   4500008004100000020001400041245010600055210006900161260001500230520215400245100001702399700002302416700002802439700002102467700002202488700001802510700002702528700002302555700001702578700001602595700001802611700002102629700002602650700002102676700002102697700002302718700001802741700001702759700002102776700001802797700001702815700001902832700001702851700002902868700001902897700002102916700001802937700002302955700002202978700002003000700001903020700001903039700002303058700001803081700002403099700001703123700001803140700002203158700002603180700002703206856006503233       2021                        eng d  a1572-843900aGive more data, awareness and control to individual citizens, and they will help COVID-19 containment0 aGive more data awareness and control to individual citizens and   c2021/02/023 aThe rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.1 aNanni, Mirco1 aAndrienko, Gennady1 aBarabasi, Albert-Laszlo1 aBoldrini, Chiara1 aBonchi, Francesco1 aCattuto, Ciro1 aChiaromonte, Francesca1 aComandé, Giovanni1 aConti, Marco1 aCoté, Mark1 aDignum, Frank1 aDignum, Virginia1 aDomingo-Ferrer, Josep1 aFerragina, Paolo1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aHelbing, Dirk1 aKaski, Kimmo1 aKertész, János1 aLehmann, Sune1 aLepri, Bruno1 aLukowicz, Paul1 aMatwin, Stan1 aJiménez, David, Megías1 aMonreale, Anna1 aMorik, Katharina1 aOliver, Nuria1 aPassarella, Andrea1 aPasserini, Andrea1 aPedreschi, Dino1 aPentland, Alex1 aPianesi, Fabio1 aPratesi, Francesca1 aRinzivillo, S1 aRuggieri, Salvatore1 aSiebes, Arno1 aTorra, Vicenc1 aTrasarti, Roberto1 avan den Hoven, Jeroen1 aVespignani, Alessandro  uhttps://link.springer.com/article/10.1007/s10676-020-09572-w02525nas a2200205   4500008004100000020001400041245007100055210006900126260001600195300001100211490000800222520189700230100001802127700002302145700001902168700001902187700002002206700002102226856007202247       2021                        eng d  a0004-370200aGLocalX - From Local to Global Explanations of Black Box AI Models0 aGLocalX From Local to Global Explanations of Black Box AI Models  c2021/05/01/  a1034570 v2943 aArtificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLocalX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLocalX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLocalX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLocalX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S000437022100008401510nas a2200181   4500008004100000020002200041245004300063210004300106260005200149520094000201100001801141700002301159700001901182700001901201700001901220700002001239856006901259       2020                        eng d  a978-3-030-43823-400aGlobal Explanations with Local Scoring0 aGlobal Explanations with Local Scoring  aChambSpringer International Publishingc2020//3 aArtificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these “black box” models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aCellier, Peggy1 aDriessens, Kurt  uhttps://link.springer.com/chapter/10.1007%2F978-3-030-43823-4_1403273nas a2200229   4500008004100000245015000041210006900191300000800260490000700268520247100275100002202746700002102768700002502789700002102814700001902835700002402854700002002878700002002898700002002918700002302938856008202961       2018                        eng d00aGastroesophageal reflux symptoms among Italian university students: epidemiology and dietary correlates using automatically recorded transactions0 aGastroesophageal reflux symptoms among Italian university studen  a1160 v183 aBackground: 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–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 students1 aMartinucci, Irene1 aNatilli, Michela1 aLorenzoni, Valentina1 aPappalardo, Luca1 aMonreale, Anna1 aTurchetti, Giuseppe1 aPedreschi, Dino1 aMarchi, Santino1 aBarale, Roberto1 ade Bortoli, Nicola  uhttps://bmcgastroenterol.biomedcentral.com/articles/10.1186/s12876-018-0832-901833nas a2200169   4500008004100000245005500041210005500096260000700151300000900158490000700167520134100174100002701515700002101542700002301563700002801586856004901614       2018                        eng d00aGravity and scaling laws of city to city migration0 aGravity and scaling laws of city to city migration  c07  a1-190 v133 aModels of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.1 aCuriel, Rafael, Prieto1 aPappalardo, Luca1 aGabrielli, Lorenzo1 aBishop, Steven, Richard  uhttps://doi.org/10.1371/journal.pone.019989201694nas a2200157   4500008004100000245006800041210006700109260003800176300001200214520112100226100002301347700002001370700002001390700002401410856010201434       2016                        eng d00aGoing Beyond GDP to Nowcast Well-Being Using Retail Market Data0 aGoing Beyond GDP to Nowcast WellBeing Using Retail Market Data  bSpringer International Publishing  a29–423 aOne of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory.1 aGuidotti, Riccardo1 aCoscia, Michele1 aPedreschi, Dino1 aPennacchioli, Diego  uhttps://kdd.isti.cnr.it/publications/going-beyond-gdp-nowcast-well-being-using-retail-market-data00445nas a2200133   4500008004100000245005900041210005700100100001800157700002200175700003400197700001800231700002000249856004200269       2013                        eng d00aA Gravity Model for Speed Estimation over Road Network0 aGravity Model for Speed Estimation over Road Network1 aCintia, Paolo1 aTrasarti, Roberto1 ade Macêdo, José, Antônio F1 aAlmada, Livia1 aFereira, Camila  uhttp://dx.doi.org/10.1109/MDM.2013.8301250nas a2200169   4500008003900000022002200039245006500061210006200126520069900188100002300887700002300910700002100933700001900954700002000973700001800993856006901011       2010                          d  a978-989-20-1953-600aA Generalisation-based Approach to Anonymising Movement Data0 aGeneralisationbased Approach to Anonymising Movement Data3 aThe possibility to collect, store, disseminate, and analyze data about movements of people raises
very serious privacy concerns, given the sensitivity of the information about personal positions. In
particular, sensitive information about individuals can be uncovered with the use of data mining and
visual analytics methods. In this paper we present a method for the generalization of trajectory data
that can be adopted as the first step of a process to obtain k-anonymity in spatio-temporal datasets.
We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this
method of generalization of trajectories preserves the clustering analysis results. 1 aAndrienko, Gennady1 aAndrienko, Natalia1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino1 aRinzivillo, S  uhttp://agile2010.dsi.uminho.pt/pen/ShortPapers_PDF%5C122_DOC.pdf00457nas a2200121   4500008003900000245006200039210006100101300001400162100002100176700002000197700002400217856009400241       2009                          d00aGeographic privacy-aware knowledge discovery and delivery0 aGeographic privacyaware knowledge discovery and delivery  a1157-11581 aGiannotti, Fosca1 aPedreschi, Dino1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/geographic-privacy-aware-knowledge-discovery-and-delivery00517nas a2200145   4500008003900000245006100039210005600100100002100156700001700177700002000194700001800214700001800232700002200250856009900272       2009                          d00aGeoPKDD – Geographic Privacy-aware Knowledge Discovery0 aGeoPKDD Geographic Privacyaware Knowledge Discovery1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRinzivillo, S1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/geopkdd-%E2%80%93-geographic-privacy-aware-knowledge-discovery00468nas a2200121   4500008004100000245007300041210006900114300001200183490000700195100002100202700001900223856010400242       1994                        eng d00aGate Splitting in LOTOS Specifications Using Abstract Interpretation0 aGate Splitting in LOTOS Specifications Using Abstract Interpreta  a127-1490 v231 aGiannotti, Fosca1 aLatella, Diego  uhttps://kdd.isti.cnr.it/content/gate-splitting-lotos-specifications-using-abstract-interpretation-000447nas a2200109   4500008004100000245007300041210006900114300001200183100002100195700001900216856010200235       1993                        eng d00aGate Splitting in LOTOS Specifications Using Abstract Interpretation0 aGate Splitting in LOTOS Specifications Using Abstract Interpreta  a437-4521 aGiannotti, Fosca1 aLatella, Diego  uhttps://kdd.isti.cnr.it/content/gate-splitting-lotos-specifications-using-abstract-interpretation