@conference {1610,
	title = {Exploiting Vehicular Data for Exposure-Aware Pedestrian Routing},
	booktitle = {2025 26th IEEE International Conference on Mobile Data Management (MDM)},
	year = {2025},
	abstract = {Vehicular traffic is one of the major sources of air pollution in urban settings, making it essential to clearly understand how much and where vehicle emissions impact residents. Recent approaches manage to yield pollution maps at the microscopic level by processing GPS trajectories of vehicles. That is achieved by applying mathematical models to estimate instantaneous emissions from GPS data, extending estimates to areas without data through missing data imputation, and further considering air dispersion factors. In this work, we leverage such inferred knowledge to implement an emission-aware pedestrian routing strategy and to study its impact on the reduction of exposure to vehicular pollutants and walking time. The study is realized through simulations of large masses of pedestrians over a medium-sized city in Italy, analyzing the interplay between the two factors - exposure versus walking time - in terms of time efficiency of paths and changes over existing habits both at a global and at an individual level. Experiments suggest that exposure-aware routing can yield a significant margin of improvement in health over most paths with minor effects on mobility, making it feasible and effective.},
	doi = {10.1109/MDM65600.2025.00022},
	author = {Aliyev, Gurban and Nanni, Mirco}
}
@conference {1597,
	title = {Enhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection},
	booktitle = {Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT},
	year = {2024},
	publisher = {INSTICC},
	organization = {INSTICC},
	isbn = {978-989-758-709-2},
	doi = {10.5220/0012863600003767},
	author = {Luca Corbucci and Anna Monreale and Roberto Pellungrini}
}
@conference {1548,
	title = {Evaluating the Privacy Exposure of Interpretable Global and Local Explainers},
	booktitle = {Submitted at Journal of Artificial Intelligence and Law},
	year = {2023},
	author = {Francesca Naretto and Anna Monreale and Fosca Giannotti}
}
@conference {1546,
	title = {EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories},
	booktitle = {Discovery Science },
	year = {2023},
	author = {Francesca Naretto and Roberto Pellungrini and Daniele Fadda and Salvo Rinzivillo}
}
@conference {1568,
	title = {Explain and Interpret Few-Shot Learning},
	booktitle = {Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lisbon, Portugal, July 26-28, 2023},
	year = {2023},
	publisher = {CEUR-WS.org},
	organization = {CEUR-WS.org},
	abstract = {Recent advancements in Artificial Intelligence have been fueled by vast datasets, powerful computing resources, and sophisticated algorithms. However, traditional Machine Learning models face limitations in handling scarce data. Few-Shot Learning (FSL) offers a promising solution by training models on a small number of examples per class. This manuscript introduces FXI-FSL, a framework for eXplainability and Interpretability in FSL, which aims to develop post-hoc explainability algorithms and interpretableby-design alternatives. A noteworthy contribution is the SIamese Network EXplainer (SINEX), a post-hoc approach shedding light on Siamese Network behavior. The proposed framework seeks to unveil the rationale behind FSL models, instilling trust in their real-world applications. Moreover, it emerges as a safeguard for developers, facilitating models fine-tuning prior to deployment, and as a guide for end users navigating the decisions of these models},
	url = {https://ceur-ws.org/Vol-3554/paper38.pdf},
	author = {Andrea Fedele}
}
@conference {1595,
	title = {Explaining Black-Boxes in Federated Learning},
	booktitle = {Explainable Artificial Intelligence},
	year = {2023},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	address = {Cham},
	abstract = {Federated Learning has witnessed increasing popularity in the past few years for its ability to train Machine Learning models in critical contexts, using private data without moving them. Most of the work in the literature proposes algorithms and architectures for training neural networks, which although they present high performance in different predicting tasks and are easy to be learned with a cooperative mechanism, their predictive reasoning is obscure. Therefore, in this paper, we propose a variant of SHAP, one of the most widely used explanation methods, tailored to Horizontal server-based Federated Learning. The basic idea is having the possibility to explain an instance{\textquoteright}s prediction performed by the trained Machine Leaning model as an aggregation of the explanations provided by the clients participating in the cooperation. We empirically test our proposal on two different tabular datasets, and we observe interesting and encouraging preliminary results.},
	isbn = {978-3-031-44067-0},
	author = {Luca Corbucci and Guidotti, Riccardo and Monreale, Anna}
}
@article {1540,
	title = {Explaining Black Box with visual exploration of Latent Space},
	journal = {EuroVis{\textendash}Short Papers},
	year = {2022},
	abstract = {Autoencoders are a powerful yet opaque feature reduction technique, on top of which we propose a novel way for the joint visual exploration of both latent and real space. By interactively exploiting the mapping between latent and real features, it is possible to unveil the meaning of latent features while providing deeper insight into the original variables. To achieve this goal, we exploit and re-adapt existing approaches from eXplainable Artificial Intelligence (XAI) to understand the relationships between the input and latent features. The uncovered relationships between input features and latent ones allow the user to understand the data structure concerning external variables such as the predictions of a classification model. We developed an interactive framework that visually explores the latent space and allows the user to understand the relationships of the input features with model prediction.},
	url = {https://diglib.eg.org/xmlui/bitstream/handle/10.2312/evs20221098/085-089.pdf?sequence=1},
	author = {Bodria, Francesco and Rinzivillo, Salvatore and Fadda, Daniele and Guidotti, Riccardo and Giannotti, Fosca and Pedreschi, Dino}
}
@conference {1567,
	title = {Explaining Siamese Networks in Few-Shot Learning for Audio Data},
	booktitle = {Discovery Science - 25th International Conference, DS 2022, Montpellier, France, October 10-12, 2022, Proceedings},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Machine learning models are not able to generalize correctly when queried on samples belonging to class distributions that were never seen during training. This is a critical issue, since real world applications might need to quickly adapt without the necessity of re-training. To overcome these limitations, few-shot learning frameworks have been proposed and their applicability has been studied widely for computer vision tasks. Siamese Networks learn pairs similarity in form of a metric that can be easily extended on new unseen classes. Unfortunately, the downside of such systems is the lack of explainability. We propose a method to explain the outcomes of Siamese Networks in the context of few-shot learning for audio data. This objective is pursued through a local perturbation-based approach that evaluates segments-weighted-average contributions to the final outcome considering the interplay between different areas of the audio spectrogram. Qualitative and quantitative results demonstrate that our method is able to show common intra-class characteristics and erroneous reliance on silent sections.},
	doi = {10.1007/978-3-031-18840-4_36},
	url = {https://doi.org/10.1007/978-3-031-18840-4_36},
	author = {Andrea Fedele and Riccardo Guidotti and Dino Pedreschi}
}
@article {1402,
	title = {Estimating the Total Volume of Queries to a Search Engine},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	year = {2021},
	pages = {1-1},
	abstract = {We study the problem of estimating the total number of searches (volume) of queries in a specific domain, which were submitted to a search engine in a given time period. Our statistical model assumes that the distribution of searches follows a Zipf{\textquoteright}s law, and that the observed sample volumes are biased accordingly to three possible scenarios. These assumptions are consistent with empirical data, with keyword research practices, and with approximate algorithms used to take counts of query frequencies. A few estimators of the parameters of the distribution are devised and experimented, based on the nature of the empirical/simulated data. We apply the methods on the domain of recipes and cooking queries searched in Italian in 2017. The observed volumes of sample queries are collected from Google Trends (continuous data) and SearchVolume (binned data). The estimated total number of queries and total volume are computed for the two cases, and the results are compared and discussed.},
	doi = {10.1109/TKDE.2021.3054668},
	url = {https://ieeexplore.ieee.org/abstract/document/9336245},
	author = {F. Lillo and Salvatore Ruggieri}
}
@conference {1551,
	title = {Explainable for Trustworthy AI},
	booktitle = {Human-Centered Artificial Intelligence - Advanced Lectures, 18th European Advanced Course on AI, ACAI 2021, Berlin, Germany, October 11-15, 2021, extended and improved lecture notes},
	year = {2021},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-031-24349-3_10},
	url = {https://doi.org/10.1007/978-3-031-24349-3_10},
	author = {Fosca Giannotti and Francesca Naretto and Francesco Bodria}
}
@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 {1403,
	title = {Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts},
	journal = {Sensors},
	volume = {20},
	number = {24},
	year = {2020},
	pages = {7122},
	abstract = {Application of ultra\&ndash;short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people\&rsquo;s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70\%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.},
	issn = {1424-8220},
	doi = {10.3390/s20247122},
	url = {https://www.mdpi.com/1424-8220/20/24/7122},
	author = {Alessio Rossi and Dino Pedreschi and Clifton, David A. and Morelli, Davide}
}
@conference {1420,
	title = {Estimating countries{\textquoteright} peace index through the lens of the world news as monitored by GDELT},
	booktitle = {2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)},
	year = {2020},
	month = {2020},
	abstract = {Peacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying machine learning techniques, we demonstrate that news media attention, sentiment, and social stability from GDELT can be used as proxies for measuring GPI at a monthly level. Additionally, through the variable importance analysis, we show that each country{\textquoteright}s socio-economic, political, and military profile emerges. This could bring added value to researchers interested in "Data Science for Social Good", to policy-makers, and peacekeeping organizations since they could monitor peacefulness almost real-time, and therefore facilitate timely and more efficient policy-making.
},
	doi = {https://doi.org/10.1109/DSAA49011.2020.00034},
	url = {https://ieeexplore.ieee.org/abstract/document/9260052},
	author = {V. Voukelatou and Luca Pappalardo and Lorenzo Gabrielli and Fosca Giannotti}
}
@article {1405,
	title = {An ethico-legal framework for social data science},
	year = {2020},
	month = {2020/03/31},
	abstract = {This paper presents a framework for research infrastructures enabling ethically sensitive and legally compliant data science in Europe. Our goal is to describe how to design and implement an open platform for big data social science, including, in particular, personal data. To this end, we discuss a number of infrastructural, organizational and methodological principles to be developed for a concrete implementation. These include not only systematically tools and methodologies that effectively enable both the empirical evaluation of the privacy risk and data transformations by using privacy-preserving approaches, but also the development of training materials (a massive open online course) and organizational instruments based on legal and ethical principles. This paper provides, by way of example, the implementation that was adopted within the context of the SoBigData Research Infrastructure.},
	isbn = {2364-4168},
	doi = {https://doi.org/10.1007/s41060-020-00211-7},
	url = {https://link.springer.com/article/10.1007/s41060-020-00211-7},
	author = {Forg{\'o}, Nikolaus and H{\"a}nold, Stefanie and van~den Hoven, Jeroen and Kr{\"u}gel, Tina and Lishchuk, Iryna and Mahieu, Ren{\'e} and Anna Monreale and Dino Pedreschi and Francesca Pratesi and van Putten, David}
}
@article {1369,
	title = {Evaluating community detection algorithms for progressively evolving graphs},
	journal = {arXiv preprint arXiv:2007.08635},
	year = {2020},
	author = {Cazabet, R{\'e}my and Boudebza, Souaad and Giulio Rossetti}
}
@conference {1480,
	title = {Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis.},
	booktitle = {SEBD},
	year = {2020},
	author = {Francesco Bodria and Panisson, Andr{\'e} and Perotti, Alan and Piaggesi, Simone}
}
@conference {1412,
	title = {Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars},
	booktitle = {Discovery Science},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {We present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences {\textendash} albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability.},
	isbn = {978-3-030-61527-7},
	doi = {https://doi.org/10.1007/978-3-030-61527-7_24},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-61527-7_24},
	author = {Lampridis, Orestis and Riccardo Guidotti and Salvatore Ruggieri},
	editor = {Appice, Annalisa and Tsoumakas, Grigorios and Manolopoulos, Yannis and Matwin, Stan}
}
@conference {1357,
	title = {Eva: Attribute-Aware Network Segmentation},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@conference {1373,
	title = {Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {Giulio Rossetti}
}
@conference {1262,
	title = {Explaining multi-label black-box classifiers for health applications},
	booktitle = {International Workshop on Health Intelligence},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Today the state-of-the-art performance in classification is achieved by the so-called {\textquotedblleft}black boxes{\textquotedblright}, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.},
	doi = {10.1007/978-3-030-24409-5_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-24409-5_9},
	author = {Cecilia Panigutti and Riccardo Guidotti and Anna Monreale and Dino Pedreschi}
}
@article {1086,
	title = {Effective injury forecasting in soccer with GPS training data and machine learning},
	journal = {PloS one},
	volume = {13},
	number = {7},
	year = {2018},
	pages = {e0201264},
	abstract = {Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.},
	doi = {https://doi.org/10.1371/journal.pone.0201264},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201264},
	author = {Alessio Rossi and Luca Pappalardo and Paolo Cintia and Iaia, F Marcello and Fern{\`a}ndez, Javier and Medina, Daniel}
}
@conference {1136,
	title = {Explaining successful docker images using pattern mining analysis},
	booktitle = {Federation of International Conferences on Software Technologies: Applications and Foundations},
	year = {2018},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Docker is on the rise in today{\textquoteright}s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image directly impacts on its usage, and hence on the potential revenues of its developers. In this paper, we present a frequent pattern mining-based approach for understanding how to improve an image to increase its popularity. The results in this work can provide valuable insights to Docker image providers, helping them to design more competitive software products.},
	doi = {10.1007/978-3-030-04771-9_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-04771-9_9},
	author = {Riccardo Guidotti and Soldani, Jacopo and Neri, Davide and Antonio Brogi}
}
@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 {971,
	title = {Efficiently Clustering Very Large Attributed Graphs},
	journal = {arXiv preprint arXiv:1703.08590},
	year = {2017},
	abstract = {Attributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.},
	doi = {10.1145/3110025.3110030},
	author = {Alessandro Baroni and Conte, Alessio and Patrignani, Maurizio and Salvatore Ruggieri}
}
@article {1052,
	title = {An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities},
	journal = {Education and Information Technologies},
	volume = {22},
	number = {6},
	year = {2017},
	pages = {3207{\textendash}3229},
	abstract = {Free\Libre Open Source Software (FLOSS) environments are increasingly dubbed as learning environments where practical software engineering skills can be acquired. Numerous studies have extensively investigated how knowledge is acquired in these environments through a collaborative learning model that define a learning process. Such a learning process, identified either as a result of surveys or by means of questionnaires, can be depicted through a series of graphical representations indicating the steps FLOSS community members go through as they acquire and exchange skills. These representations are referred to as a-priori learning models. They are Petri net-like workflow nets (WF-net) that provide a visual representation of the learning process as it is expected to occur. These models are representations of a learning framework or paradigm in FLOSS communities. As such, the credibility of any models is estimated through a process of model verification and validation. Therefore in this paper, we analyze these models in comparison with the real behavior captured in FLOSS repositories by means of conformance verification in process mining. The purpose of our study is twofold. Firstly, the results of our analysis provide insights on the possible discrepancies that are observed between the initial theoretical representations of learning processes and the real behavior captured in FLOSS event logs, constructed from mailing archives. Secondly, this comparison helps foster the understanding on how learning actually takes place in FLOSS environments based on empirical evidence directly from the data.},
	doi = {10.1007/s10639-017-9573-6},
	url = {https://link.springer.com/article/10.1007/s10639-017-9573-6},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {970,
	title = {Enumerating Distinct Decision Trees},
	booktitle = {International Conference on Machine Learning},
	year = {2017},
	abstract = {The search space for the feature selection problem in decision tree learning is the lattice of subsets of the available features. We provide an exact enumeration procedure of the subsets that lead to all and only the distinct decision trees. The procedure can be adopted to prune the search space of complete and heuristics search methods in wrapper models for feature selection. Based on this, we design a computational optimization of the sequential backward elimination heuristics with a performance improvement of up to 100X.},
	url = {http://proceedings.mlr.press/v70/ruggieri17a.html},
	author = {Salvatore Ruggieri}
}
@conference {1127,
	title = {On the Equivalence Between Community Discovery and Clustering},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	author = {Riccardo Guidotti and Michele Coscia}
}
@inbook {850,
	title = {Experimental Assessment of the Emergence of Awareness and Its Influence on Behavioral Changes: The Everyaware Lesson},
	booktitle = {Participatory Sensing, Opinions and Collective Awareness},
	year = {2017},
	pages = {337{\textendash}362},
	publisher = {Springer},
	organization = {Springer},
	abstract = {The emergence of awareness is deeply connected to the process of learning. In fact, by learning that high sound levels may harm one{\textquoteright}s health, that noise levels that we estimate as innocuous may be dangerous, that there exist an alternative path we can walk to go to work and minimize our exposure to air pollution, etc., citizens will be able to understand the environment around them and act consequently to go toward a more sustainable world.},
	doi = {10.1007/978-3-319-25658-0_16},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-25658-0_16},
	author = {Pietro Gravino and Alina Sirbu and Becker, Martin and Vito D P Servedio and Vittorio Loreto}
}
@article {805,
	title = {Egalitarianism in the rank aggregation problem: a new dimension for democracy},
	journal = {Quality \& Quantity},
	year = {2015},
	pages = {1{\textendash}16},
	doi = {10.1007/s11135-015-0197-x},
	url = {http://link.springer.com/article/10.1007/s11135-015-0197-x},
	author = {Contucci, Pierluigi and Panizzi, Emanuele and Ricci-Tersenghi, Federico and Alina Sirbu}
}
@booklet {974,
	title = {An exploration of learning processes as process maps in FLOSS repositories},
	year = {2015},
	abstract = {Evidence suggests that Free/Libre Open Source Software (FLOSS) environ-ments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while mak-ing use of the tools available in these environments. A number of studies docu-ment the existence of learning processes in FLOSS through the analysis of sur-veys and questionnaires filled by FLOSS project participants. At the same time, the interest in understanding the dynamics of the FLOSS phenomenon, its popu-larity and success resulted in the development of tools and techniques for extract-ing and analyzing data from different FLOSS data sources. This new field is called Mining Software Repositories (MSR). In spite of these efforts, there is limited work aiming to provide empirical evidence of learning processes directly from FLOSS repositories. In this paper, we seek to trigger such an initiative by proposing an approach based on Process Mining to trace learning behaviors from FLOSS participants{\textquoteright} trails of activities, as recorded in FLOSS repositories, and visualize them as pro-cess maps. Process maps provide a pictorial representation of real behavior as it is recorded in FLOSS data. Our aim is to provide critical evidence that boosts the understanding of learning behavior in FLOSS communities by analyzing the rel-evant repositories. In order to accomplish this, we propose an effective approach that comprises first the mining of FLOSS repositories in order to generate Event logs, and then the generation of process maps, equipped with relevant statistical data interpreting and indicating the value of process discovery from these reposi-tories.},
	url = {http://eprints.adm.unipi.it/id/eprint/2344},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@inbook {808,
	title = {EGIA{\textendash}Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach},
	booktitle = {Complex Networks V},
	year = {2014},
	pages = {217{\textendash}229},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	doi = {10.1007/978-3-319-05401-8_21},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-05401-8_21},
	author = {Alina Sirbu and Martin Crane and Heather J Ruskin}
}
@conference {508,
	title = {Efficient GPU-based skyline computation},
	booktitle = {DAMON@SIGMOD 2013},
	year = {2013},
	month = {2013},
	author = {Kenneth S. Boeg and Ira Assent, and Matteo Magnani}
}
@conference {729,
	title = {"Engine Matters": {A} First Large Scale Data Driven Study on Cyclists{\textquoteright} Performance},
	booktitle = {13th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, TX, USA, December 7-10, 2013},
	year = {2013},
	doi = {10.1109/ICDMW.2013.41},
	url = {http://dx.doi.org/10.1109/ICDMW.2013.41},
	author = {Paolo Cintia and Luca Pappalardo and Dino Pedreschi}
}
@article {567,
	title = {Evolving networks: Eras and turning points},
	journal = {Intell. Data Anal.},
	volume = {17},
	number = {1},
	year = {2013},
	pages = {27{\textendash}48},
	abstract = {Within 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 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, able to detect the turning points at the beginning of the eras. 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 and null models, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset, a collaboration graph extracted from a cinema database, and a network extracted from a database of terrorist attacks; we illustrate how the discovered temporal clustering highlights the crucial moments when the networks witnessed profound changes in their 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.},
	doi = {10.3233/IDA-120566},
	url = {http://dx.doi.org/10.3233/IDA-120566},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@proceedings {529,
	title = {Explaining the PRoduct Range Effect in Purchase Data},
	year = {2013},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@conference {TrasartiRPNM10,
	title = {Exploring Real Mobility Data with M-Atlas},
	booktitle = {ECML/PKDD (3)},
	year = {2010},
	pages = {624-627},
	abstract = {Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applications.},
	doi = {10.1007/978-3-642-15939-8_48},
	author = {Roberto Trasarti and S Rinzivillo and Fabio Pinelli and Mirco Nanni and Anna Monreale and Chiara Renso and Dino Pedreschi and Fosca Giannotti}
}
@conference {470,
	title = {AN EXTENSIBLE AND INTERACTIVE SOFTWARE AGENT FOR MOBILE DEVICES BASED ON GPS DATA},
	booktitle = {IADIS International Conference Applied Computing},
	year = {2008},
	month = {2008},
	isbn = {978-972-8924-56-0},
	url = {http://www.iadisportal.org/digital-library/mdownload/an-extensible-and-interactive-software-agent-for-mobile-devices-based-on-gps-data},
	author = {Barbara Furletti and Francesco Fornasari and Claudio Montanari}
}
@conference {DBLP:conf/sdm/GiannottiNP06,
	title = {Efficient Mining of Temporally Annotated Sequences},
	booktitle = {SDM},
	year = {2006},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi}
}
@inbook {466,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	series = {LNAI},
	volume = {4155},
	year = {2006},
	pages = {307-326},
	doi = {10.1007/11829263_17},
	url = {http://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@conference {TuriniBFR06,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	year = {2006},
	pages = {307-326},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@article {DBLP:journals/kais/BonchiGMP05,
	title = {Efficient breadth-first mining of frequent pattern with monotone constraints},
	journal = {Knowl. Inf. Syst.},
	volume = {8},
	number = {2},
	year = {2005},
	pages = {131-153},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@article {DBLP:journals/expert/BonchiGMP05,
	title = {Exante: A Preprocessing Method for Frequent-Pattern Mining},
	journal = {IEEE Intelligent Systems},
	volume = {20},
	number = {3},
	year = {2005},
	pages = {25-31},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@conference {RinzivilloT05,
	title = {Extracting spatial association rules from spatial transactions},
	booktitle = {ACM GIS},
	year = {2005},
	pages = {79-86},
	author = {S Rinzivillo and Franco Turini}
}
@conference {DBLP:conf/icdm/BonchiGMP03,
	title = {ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint},
	booktitle = {ICDM},
	year = {2003},
	pages = {11-18},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@conference {DBLP:conf/pkdd/BonchiGMP03a,
	title = {ExAnte: Anticipated Data Reduction in Constrained Pattern Mining},
	booktitle = {PKDD},
	year = {2003},
	pages = {59-70},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@conference {DBLP:conf/gis/RaffaetaTR02,
	title = {Enhancing GISs for spatio-temporal reasoning},
	booktitle = {ACM-GIS},
	year = {2002},
	pages = {42-48},
	author = {Alessandra Raffaet{\`a} and Franco Turini and Chiara Renso}
}
@conference {DBLP:conf/dmkd/GiannottiMPT99,
	title = {Experiences with a Logic-based knowledge discovery Support Environment},
	booktitle = {1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery},
	year = {1999},
	author = {Fosca Giannotti and Giuseppe Manco and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/aiia/GiannottiMPT99,
	title = {Experiences with a Logic-Based Knowledge Discovery Support Environment},
	booktitle = {AI*IA},
	year = {1999},
	pages = {202-213},
	author = {Fosca Giannotti and Giuseppe Manco and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/csl/GiannottiMNP98,
	title = {On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases},
	booktitle = {CSL},
	year = {1998},
	pages = {58-72},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/deductive/CorciuloGPZ94,
	title = {Expressive Power of Non-Deterministic Operators for Logic-based Languages},
	booktitle = {Workshop on Deductive Databases and Logic Programming},
	year = {1994},
	pages = {27-40},
	author = {Luca Corciulo and Fosca Giannotti and Dino Pedreschi and Carlo Zaniolo}
}
