00428nas a2200121   4500008004100000245005200041210005100093260001300144100001900157700002300176700001800199856008900217       2023                        eng d00aAgnostic Label-Only Membership Inference Attack0 aAgnostic LabelOnly Membership Inference Attack  bSpringer1 aMonreale, Anna1 aNaretto, Francesca1 aRizzo, Simone  uhttps://kdd.isti.cnr.it/publications/agnostic-label-only-membership-inference-attack01696nas a2200097   4500008004100000245011300041210006900154520131100223100001901534856004501553       2023                        eng d00aAnalysis, Prediction and Mitigation of Exposure to Vehicular Air Pollution Based on Multi-Source Urban Data.0 aAnalysis Prediction and Mitigation of Exposure to Vehicular Air 3 aAn increasing amount of vehicular emissions in urban air pollution create a health risk for urban residents. Meanwhile, calculation and analysis of vehicular pollution using GPS trajectories and microscopic models is getting more popular as this method proves to be more useful and reliable in comparison to other methods. However, GPS-trajectory-based estimations suffer from the lack of GPS data and absence of validation/calibration of estimated emission amounts. Another problem is in the assessment of pollution levels using GPS trajectories as previous studies only consider changes in total vehicular emissions and ignore air quality guideline levels. In this paper, the methodology and preliminary results of experiments conducted for imputation of missing emission data are reported. An existing graph convolutional network model which is designed to predict traffic flows is adopted to estimate vehicular emissions in Pisa. This approach is based on the assumption that the same model can predict traffic emissions as a traffic flow and resulting emission are correlated. In the end of the paper, there is a discussion of future research directions planned to be taken during my PhD period to address issues in the estimation, analysis and mitigation of exposure to vehicular emissions in cities.1 aAliyev, Gurban  uhttps://ceur-ws.org/Vol-3478/paper13.pdf00549nas a2200133   4500008004100000245009600041210006900137300001100206490000600217100001900223700002300242700002100265856012900286       2023                        eng d00aAttributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions0 aAttributed Stream Hypergraphs temporal modeling of nodeattribute  a1–190 v81 aFailla, Andrea1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/attributed-stream-hypergraphs-temporal-modeling-node-attributed-high-order-interactions01277nas a2200121   4500008004100000245003900041210003800080260000900118520092900127100002001056700002401076856005501100       2023                        eng d00aAUC-based Selective Classification0 aAUCbased Selective Classification  bPMLR3 aSelective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The approach is specifically targeted at optimizing the AUC. We provide both theoretical justifications and a novel algorithm, called AUCROSS, to achieve such a goal. Experiments show that our method succeeds in trading-off coverage for AUC, improving over existing selective classification methods targeted at optimizing accuracy.1 aPugnana, Andrea1 aRuggieri, Salvatore  uhttps://proceedings.mlr.press/v206/pugnana23a.html00482nas a2200121   4500008004100000245007000041210006800111260001300179100001900192700002200211700002300233856010400256       2022                        eng d00aAttribute-aware Community Events in Feature-rich Dynamic Networks0 aAttributeaware Community Events in Featurerich Dynamic Networks  bSpringer1 aFailla, Andrea1 aMazzoni, Federico1 aCitraro, Salvatore  uhttps://kdd.isti.cnr.it/publications/attribute-aware-community-events-feature-rich-dynamic-networks00549nas a2200121   4500008004100000245012000041210006900161260001300230100001900243700002300262700002100285856012100306       2022                        eng d00aAttributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit0 aAttributed streamhypernetwork analysis homophilic behaviors in p  bSpringer1 aFailla, Andrea1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/attributed-stream-hypernetwork-analysis-homophilic-behaviors-pairwise-and-group02976nas a2200373   4500008004100000020002200041245008700063210006900150260005200219520183300271100002102104700001902125700001802144700002002162700002102182700002102203700001902224700002402243700001802267700002302285700001902308700001902327700002102346700001802367700001802385700001902403700002002422700001602442700002002458700001902478700002002497700001802517856006702535       2020                        eng d  a978-3-030-54994-700aAnalysis and Visualization of Performance Indicators in University Admission Tests0 aAnalysis and Visualization of Performance Indicators in Universi  aChambSpringer International Publishingc2020//3 aThis 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.1 aNatilli, Michela1 aFadda, Daniele1 aRinzivillo, S1 aPedreschi, Dino1 aLicari, Federica1 aSekerinski, Emil1 aMoreira, Nelma1 aOliveira, José, N.1 aRatiu, Daniel1 aGuidotti, Riccardo1 aFarrell, Marie1 aLuckcuck, Matt1 aMarmsoler, Diego1 aCampos, José1 aAstarte, Troy1 aGonnord, Laure1 aCerone, Antonio1 aCouto, Luis1 aDongol, Brijesh1 aKutrib, Martin1 aMonteiro, Pedro1 aDelmas, David  uhttps://link.springer.com/chapter/10.1007/978-3-030-54994-7_1401480nas a2200121   4500008004100000245010100041210006900142300001100211490000600222520104400228100002101272856006501293       2020                        eng d00aANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks0 aANGEL efficient and effective nodecentric community discovery in  a1–230 v53 aCommunity discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.1 aRossetti, Giulio  uhttps://link.springer.com/article/10.1007/s41109-020-00270-601223nas a2200121   4500008004100000245008900041210006900130260009200199520067800291100002000969700001900989856009301008       2020                        eng d00aArtificial Intelligence (AI): new developments and innovations applied to e-commerce0 aArtificial Intelligence AI new developments and innovations appl  bEuropean Parliament's committee on the Internal Market and Consumer Protectionc05/20203 aThis in-depth analysis discusses the opportunities and challenges brought by the recent and the foreseeable developments of Artificial Intelligence into online platforms and marketplaces. The paper advocates the importance to support tustworthy, explainable AI (in order to fight discrimination and manipulation, and empower citizens), and societal-aware AI (in order to fight polarisation, monopolistic concentration and excessive inequality, and pursue diversity and openness).
This document was provided by the Policy Department for Economic, Scientific and Quality of Life Policies at the request of the committee on the Internal Market and Consumer Protection (IMCO).1 aPedreschi, Dino1 aMiliou, Ioanna  uhttps://www.europarl.europa.eu/thinktank/en/document.html?reference=IPOL_IDA(2020)64879102309nas a2200205   4500008004100000022001400041245005800055210005800113260001600171300001400187490000700201520153700208100001801745700001401763700001901777700002001796700002101816700001501837856025101852       2020                        eng d  a1545-597100aAuthenticated Outlier Mining for Outsourced Databases0 aAuthenticated Outlier Mining for Outsourced Databases  cJan-03-2020  a222 - 2350 v173 aThe Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records (ARs) into the dataset, from which it constructs a set of artificial outliers (AOs) and artificial non-outliers (ANOs). The AOs and ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify ANOs and AOs without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.1 aDong, Boxiang1 aWang, Hui1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, Wenge  uhttps://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8858https://ieeexplore.ieee.org/document/8048342/http://xplorestaging.ieee.org/ielx7/8858/9034462/08048342.pdf?arnumber=8048342https://ieeexplore.ieee.org/ielam/8858/9034462/8048342-aam.pdf00389nas a2200121   4500008004100000245004100041210003700082300001200119100002300131700001900154700002000173856007400193       2019                        eng d00aThe AI black box Explanation Problem0 aAI black box Explanation Problem  a12–131 aGuidotti, Riccardo1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/ai-black-box-explanation-problem01975nas a2200157   4500008004100000245009800041210006900139300001300208490000700221520143200228100001701660700002001677700002101697700002101718856007801739       2019                        eng d00aAlgorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model0 aAlgorithmic bias amplifies opinion fragmentation and polarizatio  ae02132460 v143 aThe flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population.1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca1 aKertész, János  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.021324602991nas a2200157   4500008004100000245013100041210006900172300000900241490000700250520245000257100002002707700001902727700001902746700002202765856004602787       2019                        eng d00aAnalysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations0 aAnalysis of the Impact of Interpolation Methods of Missing RRint  a31630 v193 aWearable physiological monitors have become increasingly popular, often worn during people’s daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.1 aMorelli, Davide1 aRossi, Alessio1 aCairo, Massimo1 aClifton, David, A  uhttps://www.mdpi.com/1424-8220/19/14/316301882nas a2200157   4500008004100000245006300041210006300104300000700167490000600174520139900180100001901579700002101598700002001619700002101639856006401660       2018                        eng d00aActive and passive diffusion processes in complex networks0 aActive and passive diffusion processes in complex networks  a420 v33 aIdeas, information, viruses: all of them, with their mechanisms, spread over the complex social information, viruses: all tissues described by our interpersonal relations. Usually, to simulate and understand the unfolding of such complex phenomena are used general mathematical models; these models act agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such degree of abstraction makes it easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, incorrect, simulation outcomes. In this work we introduce the concepts of active and passive diffusion to discriminate the degree in which individuals choice affect the overall spreading of content over a social graph. Moving from the analysis of a well-known passive diffusion schema, the Threshold model (that can be used to model peer-pressure related processes), we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation.

Our analysis, performed both in synthetic and real-world data, underline that the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches to capture the real complexity of the simulated system better.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/article/10.1007/s41109-018-0100-501262nas a2200133   4500008004100000245005000041210005000091520085000141100002500991700002101016700002301037700001901060856004901079       2018                        eng d00aAnalyzing Privacy Risk in Human Mobility Data0 aAnalyzing Privacy Risk in Human Mobility Data3 aMobility data are of fundamental importance for understanding the patterns of human movements, developing analytical services and modeling human dynamics. Unfortunately, mobility data also contain individual sensitive information, making it necessary an accurate privacy risk assessment for the individuals involved. In this paper, we propose a methodology for assessing privacy risk in human mobility data. Given a set of individual and collective mobility features, we define the minimum data format necessary for the computation of each feature and we define a set of possible attacks on these data formats. We perform experiments computing the empirical risk in a real-world mobility dataset, and show how the distributions of the considered mobility features are affected by the removal of individuals with different levels of privacy risk.1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttps://doi.org/10.1007/978-3-030-04771-9_1001264nas a2200109   4500008004100000245005200041210005200093520088000145100002301025700002401048856008201072       2018                        eng d00aAssessing the Stability of Interpretable Models0 aAssessing the Stability of Interpretable Models3 aInterpretable 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, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.1 aGuidotti, Riccardo1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/assessing-stability-interpretable-models01007nas a2200181   4500008004100000245005700041210005700098260001300155300001400168520046300182100002300645700001900668700001900687700002100706700001600727700001700743856006500760       2017                        eng d00aApplications for Environmental Sensing in EveryAware0 aApplications for Environmental Sensing in EveryAware  bSpringer  a135–1553 aThis chapter provides a technical description of the EveryAware applications for air quality and noise monitoring. Specifically, we introduce AirProbe, for measuring air quality, and WideNoise Plus for estimating environmental noise. We also include an overview on hardware components and smartphone-based measurement technology, and we present the according web backend, e.g., providing for real-time tracking, data storage, analysis and visualizations.

1 aAtzmueller, Martin1 aBecker, Martin1 aMolino, Andrea1 aMueller, Juergen1 aPeters, Jan1 aSirbu, Alina  uhttp://link.springer.com/chapter/10.1007/978-3-319-25658-0_700999nas a2200121   4500008004100000245004200041210004200083520063500125100002500760700002300785700002100808856004800829       2017                        eng d00aAssessing Privacy Risk in Retail Data0 aAssessing Privacy Risk in Retail Data3 aRetail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.1 aPellungrini, Roberto1 aPratesi, Francesca1 aPappalardo, Luca  uhttps://doi.org/10.1007/978-3-319-71970-2_302036nas a2200169   4500008004100000245011000041210006900151300001100220490000600231520139500237100002301632700002101655700001901676700002201695700002201717856012701739       2017                        eng d00aAssessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models0 aAssessing the use of mobile phone data to describe recurrent mob  a1609500 v43 aThe recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.1 aPanigutti, Cecilia1 aTizzoni, Michele1 aBajardi, Paolo1 aSmoreda, Zbigniew1 aColizza, Vittoria  uhttps://kdd.isti.cnr.it/publications/assessing-use-mobile-phone-data-describe-recurrent-mobility-patterns-spatial-epidemic02012nas a2200157   4500008004100000245005800041210005800099520153700157100001801694700002101712700001901733700002001752700002101772700001101793856005001804       2017                        eng d00aAuthenticated Outlier Mining for Outsourced Databases0 aAuthenticated Outlier Mining for Outsourced Databases3 aThe Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records (ARs) into the dataset, from which it constructs a set of artificial outliers (AOs) and artificial non-outliers (ANOs). The AOs and ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify ANOs and AOs without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.1 aDong, Boxiang1 aWang, Hui, Wendy1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, W  uhttps://ieeexplore.ieee.org/document/8048342/01069nas a2200133   4500008004100000245014000041210006900181520047400250100002100724700001900745700002200764700002000786856012900806       2016                        eng d00aAdvances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings0 aAdvances in Network Science 12th International Conference and Sc3 aThis book constitutes the refereed proceedings of the 12th International Conference and  School of Network Science, NetSci-X 2016, held in Wroclaw, Poland, in January 2016. The 12 full and 6 short papers were carefully reviewed and selected from 59 submissions. The papers deal with the study of network models in domains ranging from biology and physics to computer science, from financial markets to cultural integration, and from social media to infectious diseases.1 aWierzbicki, Adam1 aBrandes, Ulrik1 aSchweitzer, Frank1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/advances-network-science-12th-international-conference-and-school-netsci-x-2016-wroclaw01717nas a2200181   4500008004100000245007400041210006900115300001200184490000600196520110000202100002101302700002101323700002301344700002201367700002001389700002101409856010501430       2016                        eng d00aAn analytical framework to nowcast well-being using mobile phone data0 aanalytical framework to nowcast wellbeing using mobile phone dat  a75–920 v23 aAn intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users’ trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly “nowcast” the well-being and the socio-economic development of a territory.1 aPappalardo, Luca1 aVanhoof, Maarten1 aGabrielli, Lorenzo1 aSmoreda, Zbigniew1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/analytical-framework-nowcast-well-being-using-mobile-phone-data01172nas a2200133   4500008004100000245009500041210006900136260002800205520061000233100002000843700002300863700002100886856013100907       2016                        eng d00a“Are we playing like Music-Stars?” Placing Emerging Artists on the Italian Music Scene0 aAre we playing like MusicStars Placing Emerging Artists on the I  aRiva del Gardac09/20163 aThe Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a deep study of the Italian music scene and how the new generation ofmusicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional mu- sic and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results ob- tained are a first attempt to outline some rules suggesting how to reach the success in the musical Italian scene.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/%E2%80%9Care-we-playing-music-stars%E2%80%9D-placing-emerging-artists-italian-music-scene01416nas a2200133   4500008004100000245001900041210001900060260001300079520107000092100002301162700002101185700002001206856005601226       2016                        eng d00aAudio Ergo Sum0 aAudio Ergo Sum  bSpringer3 aNobody can state “Rock is my favorite genre” or “David Bowie is my favorite artist”. We defined a Personal Listening Data Model able to capture musical preferences through indicators and patterns, and we discovered that we are all characterized by a limited set of musical preferences, but not by a unique predilection. The empowered capacity of mobile devices and their growing adoption in our everyday life is generating an enormous increment in the production of personal data such as calls, positioning, online purchases and even music listening. Musical listening is a type of data that has started receiving more attention from the scientific community as consequence of the increasing availability of rich and punctual online data sources. Starting from the listening of 30k Last.Fm users, we show how the employment of the Personal Listening Data Models can provide higher levels of self-awareness. In addition, the proposed model will enable the development of a wide range of analysis and musical services both at personal and at collective level.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/audio-ergo-sum01426nas a2200133   4500008004100000245009200041210006900133260001900202520089400221100002001115700002001135700001901155856011801174       2014                        eng d00aAn abstract state machine (ASM) representation of learning process in FLOSS communities0 aabstract state machine ASM representation of learning process in  bSpringer, Cham3 aFree/Libre Open Source Software (FLOSS) communities as collaborative environments enable the occurrence of learning between participants in these groups. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes. One successful way of doing this is through specification methods. In this paper, we describe the adoption of Abstract States Machines (ASMs) as a specification methodology for the description of learning processes in FLOSS. The goal of this endeavor is to represent the many possible steps and/or activities FLOSS participants go through during interactions that can be categorized as learning processes. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/abstract-state-machine-asm-representation-learning-process-floss-communities01995nas a2200157   4500008003900000245005100039210005100090300001400141490000700155520154700162100001901709700002001728700002201748700001901770856004801789       2014                          d00aAnonymity preserving sequential pattern mining0 aAnonymity preserving sequential pattern mining  a141–1730 v223 aThe increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility.1 aMonreale, Anna1 aPedreschi, Dino1 aPensa, Ruggero, G1 aPinelli, Fabio  uhttp://dx.doi.org/10.1007/s10506-014-9154-601456nas a2200145   4500008004100000245006500041210006400106260003300170520092200203100002401125700001701149700002001166700002201186856010201208       2014                        eng d00aAnti-discrimination analysis using privacy attack strategies0 aAntidiscrimination analysis using privacy attack strategies  bSpringer, Berlin, Heidelberg3 aSocial discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.1 aRuggieri, Salvatore1 aHajian, Sara1 aKamiran, Faisal1 aZhang, Xiangliang  uhttps://kdd.isti.cnr.it/publications/anti-discrimination-analysis-using-privacy-attack-strategies00525nas a2200133   4500008003900000245007200039210006900111260002800180100002200208700002300230700001800253700001800271856010200289       2013                          d00aAnalysis of GSM Calls Data for Understanding User Mobility Behavior0 aAnalysis of GSM Calls Data for Understanding User Mobility Behav  aSanta Clara, California1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aRenso, Chiara1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/analysis-gsm-calls-data-understanding-user-mobility-behavior00465nas a2200133   4500008003900000245006300039210006200102260000900164490000600173100002800179700001900207700001800226856008700244       2013                          d00aAssessing the Attractiveness of Places with Movement Data 0 aAssessing the Attractiveness of Places with Movement Data  c20130 v41 aFurtado, André Salvaro1 aFileto, Renato1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/assessing-attractiveness-places-movement-data00515nas a2200121   4500008003900000245007700039210006900116100002200185700002300207700001800230700003800248856010700286       2013                          d00aAverage Speed Estimation For Road Networks Based On GPS Raw Trajectories0 aAverage Speed Estimation For Road Networks Based On GPS Raw Traj1 aBarbosa, Ivanildo1 aCasanova, Marco, A1 aRenso, Chiara1 aMacêdo, José Antônio Fernandes  uhttps://kdd.isti.cnr.it/publications/average-speed-estimation-road-networks-based-gps-raw-trajectories02195nas a2200289   4500008004100000022001400041245005900055210005800114260000900172300001100181490000600192520133700198100001901535700002201554700002101576700002001597700002001617700002801637700001901665700002101684700002101705700002601726700002401752700001701776700002001793856009201813       2013                        eng d  a1932-620300aAwareness and learning in participatory noise sensing.0 aAwareness and learning in participatory noise sensing  c2013  ae816380 v83 a<p>The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.</p>1 aBecker, Martin1 aCaminiti, Saverio1 aFiorella, Donato1 aFrancis, Louise1 aGravino, Pietro1 aHaklay, Mordechai, Muki1 aHotho, Andreas1 aLoreto, Vittorio1 aMueller, Juergen1 aRicchiuti, Ferdinando1 aServedio, Vito, D P1 aSirbu, Alina1 aTria, Francesca  uhttps://kdd.isti.cnr.it/publications/awareness-and-learning-participatory-noise-sensing00754nas a2200241   4500008004100000245007800041210006900119100001900188700002100207700001700228700002100245700001900266700001600285700002300301700001700324700001700341700002000358700002000378700002200398700002600420700001600446856005000462       2012                        eng d00aAn Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants0 aAgentBased Model to Evaluate Carpooling at Large Manufacturing P1 aBellemans, Tom1 aBothe, Sebastian1 aCho, Sungjin1 aGiannotti, Fosca1 aJanssens, Davy1 aKnapen, Luk1 aKörner, Christine1 aMay, Michael1 aNanni, Mirco1 aPedreschi, Dino1 aStange, Hendrik1 aTrasarti, Roberto1 aYasar, Ansar-Ul-Haque1 aWets, Geert  uhttp://dx.doi.org/10.1016/j.procs.2012.08.00100515nas a2200157   4500008003900000245004500039210004400084260002100128100002200149700002200171700002300193700001800216700002100234700002100255856008100276       2012                          d00aAnalisi di Mobilita' con dati eterogenei0 aAnalisi di Mobilita con dati eterogenei  aPisabISTI - CNR1 aFurletti, Barbara1 aTrasarti, Roberto1 aGabrielli, Lorenzo1 aRinzivillo, S1 aPappalardo, Luca1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/analisi-di-mobilita-con-dati-eterogenei01580nas a2200145   4500008003900000245008100039210006900120260000900189520106400198100001601262700001901278700001301297700001401310856011001324       2012                          d00aAnonymity: a Comparison between the Legal and Computer Science Perspectives.0 aAnonymity a Comparison between the Legal and Computer Science Pe  c20123 aPrivacy preservation has emerged as a major challenge in ICT. One possible solution for enforcing privacy is to guarantee anonymity. Indeed, according to international regulations, no restriction is applied to the handling of anonymous data. Consequently, in the past years the notion of anonymity has been extensively studied by two different communities: Law researchers and professionals that propose definitions of privacy regulations, and Computer Scientists attempting to provide technical solutions for enforcing the legal requirements.

In this contribution we address the problem with an interdisciplinary approach, in the aim to encourage the reciprocal understanding and collaboration between researchers in the two areas. To achieve this, we compare the different notions of anonymity provided in the European data protection Law with the formal models proposed in Computer Science. This analysis allows us to identify the main similarities and differences between the two points of view, hence highlighting the need for a joint research effort.1 aMascetti, S1 aMonreale, Anna1 aRicci, A1 aGerino, A  uhttps://kdd.isti.cnr.it/publications/anonymity-comparison-between-legal-and-computer-science-perspectives01748nas a2200169   4500008003900000245008300039210006900122260000900191520116900200100001001369700002101379700001901400700002001419700002101439700001101460856010701471       2012                          d00aAUDIO: An Integrity Auditing Framework of Outlier-Mining-as-a-Service Systems.0 aAUDIO An Integrity Auditing Framework of OutlierMiningasaService  c20123 aSpurred by developments such as cloud computing, there has been considerable recent interest in the data-mining-as-a-service paradigm. Users lacking in expertise or computational resources can outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises issues about result integrity: how can the data owner verify that the mining results returned by the server are correct? In this paper, we present AUDIO, an integrity auditing framework for the specific task of distance-based outlier mining outsourcing. It provides efficient and practical verification approaches to check both completeness and correctness of the mining results. The key idea of our approach is to insert a small amount of artificial tuples into the outsourced data; the artificial tuples will produce artificial outliers and non-outliers that do not exist in the original dataset. The server’s answer is verified by analyzing the presence of artificial outliers/non-outliers, obtaining a probabilistic guarantee of correctness and completeness of the mining result. Our empirical results show the effectiveness and efficiency of our method.1 aR.Liu1 aWang, Hui, Wendy1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, W  uhttps://kdd.isti.cnr.it/publications/audio-integrity-auditing-framework-outlier-mining-service-systems00539nas a2200145   4500008003900000245007400039210006900113300001200182100001700194700002200211700001800233700002100251700002000272856010100292       2010                          d00aAdvanced knowledge discovery on movement data with the GeoPKDD system0 aAdvanced knowledge discovery on movement data with the GeoPKDD s  a693-6961 aNanni, Mirco1 aTrasarti, Roberto1 aRenso, Chiara1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/advanced-knowledge-discovery-movement-data-geopkdd-system-000537nas a2200145   4500008003900000245007400039210006900113300001200182100001700194700002200211700001800233700002100251700002000272856009900292       2010                          d00aAdvanced knowledge discovery on movement data with the GeoPKDD system0 aAdvanced knowledge discovery on movement data with the GeoPKDD s  a693-6961 aNanni, Mirco1 aTrasarti, Roberto1 aRenso, Chiara1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/advanced-knowledge-discovery-movement-data-geopkdd-system01803nas a2200157   4500008003900000245006600039210006500105300001000170520126700180100002501447700002001472700002101492700001901513700002001532856009301552       2010                          d00aAs Time Goes by: Discovering Eras in Evolving Social Networks0 aAs Time Goes by Discovering Eras in Evolving Social Networks  a81-903 aWithin the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus instead on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset; we illustrate how the discovered temporal clustering highlights the crucial moments when the network had profound changes in its structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/time-goes-discovering-eras-evolving-social-networks00453nas a2200145   4500008003900000245004500039210004500084250000700129260001900136100002200155700001900177700001900196700002000215856007200235       2009                          d00aAnonymous Sequences from Trajectory Data0 aAnonymous Sequences from Trajectory Data  a17  aCamogli, Italy1 aPensa, Ruggero, G1 aMonreale, Anna1 aPinelli, Fabio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/anonymous-sequences-trajectory-data00451nas a2200145   4500008004100000245004300041210004300084300001200127490000700139100002100146700002200167700002100189700002000210856007500230       2008                        eng d00aAnonymity preserving pattern discovery0 aAnonymity preserving pattern discovery  a703-7270 v171 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/anonymity-preserving-pattern-discovery00659nas a2200181   4500008004100000245008100041210006900122100001800191700001500209700002100224700001500245700002200260700002600282700001800308700002600326700001900352856010600371       2008                        eng d00aAn Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology0 aApplication of Advanced SpatioTemporal Formalisms to Behavioural1 aCeccarelli, T1 aCenteno, D1 aGiannotti, Fosca1 aMassolo, A1 aParent, Christine1 aRaffaetà, Alessandra1 aRenso, Chiara1 aSpaccapietra, Stefano1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/application-advanced-spatio-temporal-formalisms-behavioural-ecology-000698nas a2200205   4500008004100000245008100041210006900122300001000191490000700201100002600208700001800234700001500252700002100267700001500288700002200303700001800325700002600343700001900369856010400388       2008                        eng d00aAn Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology0 aApplication of Advanced SpatioTemporal Formalisms to Behavioural  a37-720 v121 aRaffaetà, Alessandra1 aCeccarelli, T1 aCenteno, D1 aGiannotti, Fosca1 aMassolo, A1 aParent, Christine1 aRenso, Chiara1 aSpaccapietra, Stefano1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/application-advanced-spatio-temporal-formalisms-behavioural-ecology00388nas a2200133   4500008004100000245003000041210003000071490000700101100002100108700002200129700002100151700002000172856006200192       2005                        eng d00aAnonymity and data mining0 aAnonymity and data mining0 v201 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/anonymity-and-data-mining00476nas a2200133   4500008004100000245005900041210005900100300001000159100002200169700002100191700002200212700002000234856008800254       2003                        eng d00aAdaptive Constraint Pushing in Frequent Pattern Mining0 aAdaptive Constraint Pushing in Frequent Pattern Mining  a47-581 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/adaptive-constraint-pushing-frequent-pattern-mining00475nas a2200133   4500008004100000245005900041210005900100300001100159100002300170700002300193700001800216700001900234856008800253       1997                        eng d00aApplying Restriction Constraint to Deductive Databases0 aApplying Restriction Constraint to Deductive Databases  a3–251 aAquilino, Domenico1 aAsirelli, Patrizia1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/applying-restriction-constraint-deductive-databases00495nas a2200145   4500008004100000245006000041210006000101300000900161490000700170100002300177700002300200700001800223700001900241856008900260       1997                        eng d00aApplying Restriction Constraints to Deductive Databases0 aApplying Restriction Constraints to Deductive Databases  a3-250 v191 aAquilino, Domenico1 aAsirelli, Patrizia1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/applying-restriction-constraints-deductive-databases00415nas a2200109   4500008004100000245006500041210006200106300001200168100001800180700002100198856008600219       1994                        eng d00aAn abstract interpreter for the specification language LOTOS0 aabstract interpreter for the specification language LOTOS  a309-3231 aFiore, Franco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/abstract-interpreter-specification-language-lotos00474nas a2200121   4500008004100000245007300041210006900114300001200183100001900195700001800214700001900232856010100251       1994                        eng d00aAmalgamating Language and Meta-language for Composing Logic Programs0 aAmalgamating Language and Metalanguage for Composing Logic Progr  a408-4221 aBrogi, Antonio1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/amalgamating-language-and-meta-language-composing-logic-programs00502nas a2200121   4500008004100000245008600041210006900127300001200196100002000208700002100228700002100249856011000270       1992                        eng d00aAnalysis of Concurrent Transactions in a Functional Database Programming Language0 aAnalysis of Concurrent Transactions in a Functional Database Pro  a174-1841 aAmato, Giuseppe1 aGiannotti, Fosca1 aMainetto, Gianni  uhttps://kdd.isti.cnr.it/content/analysis-concurrent-transactions-functional-database-programming-language00457nas a2200133   4500008004100000245005400041210005400095300001000149100002200159700002000181700002300201700002100224856007800245       1990                        eng d00aAlgebraic Properties of a Class of Logic Programs0 aAlgebraic Properties of a Class of Logic Programs  a23-391 aMancarella, Paolo1 aPedreschi, Dino1 aRondinelli, Marina1 aTagliatti, Marco  uhttps://kdd.isti.cnr.it/content/algebraic-properties-class-logic-programs00329nas a2200109   4500008004100000245003300041210003000074300001400104100002200118700002000140856005900160       1988                        eng d00aAn Algebra of Logic Programs0 aAlgebra of Logic Programs  a1006-10231 aMancarella, Paolo1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/algebra-logic-programs