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A. Baroni, Conte, A., Patrignani, M., and Ruggieri, S., Efficiently Clustering Very Large Attributed Graphs, arXiv preprint arXiv:1703.08590, 2017.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., Evolving networks: Eras and turning points, Intell. Data Anal., vol. 17, pp. 27–48, 2013.
F. Bodria, Panisson, A., Perotti, A., and Piaggesi, S., Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis., in SEBD, 2020.
F. Bodria, Rinzivillo, S., Fadda, D., Guidotti, R., Giannotti, F., and Pedreschi, D., Explaining Black Box with visual exploration of Latent Space, EuroVis–Short Papers, 2022.
K. S. Boeg, Ira Assent,, and Magnani, M., Efficient GPU-based skyline computation, in DAMON@SIGMOD 2013, 2013.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., ExAnte: Anticipated Data Reduction in Constrained Pattern Mining, in PKDD, 2003, pp. 59-70.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint, in ICDM, 2003, pp. 11-18.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., Efficient breadth-first mining of frequent pattern with monotone constraints, Knowl. Inf. Syst., vol. 8, pp. 131-153, 2005.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., Exante: A Preprocessing Method for Frequent-Pattern Mining, IEEE Intelligent Systems, vol. 20, pp. 25-31, 2005.
A. Fedele, Guidotti, R., and Pedreschi, D., Explaining Siamese Networks in Few-Shot Learning for Audio Data, in Discovery Science - 25th International Conference, DS 2022, Montpellier, France, October 10-12, 2022, Proceedings, 2022.
A. Fedele, Explain and Interpret Few-Shot Learning, in 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, 2023.
N. Forgó, Hänold, S., van den Hoven, J., Krügel, T., Lishchuk, I., Mahieu, R., Monreale, A., Pedreschi, D., Pratesi, F., and van Putten, D., An ethico-legal framework for social data science, 2020.
B. Furletti, Fornasari, F., and Montanari, C., AN EXTENSIBLE AND INTERACTIVE SOFTWARE AGENT FOR MOBILE DEVICES BASED ON GPS DATA, in IADIS International Conference Applied Computing, 2008.
F. Giannotti, Naretto, F., and Bodria, F., Explainable for Trustworthy AI, in 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, 2021.
F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases, in CSL, 1998, pp. 58-72.
F. Giannotti, Manco, G., Pedreschi, D., and Turini, F., Experiences with a Logic-Based Knowledge Discovery Support Environment, in AI*IA, 1999, pp. 202-213.
F. Giannotti, Manco, G., Pedreschi, D., and Turini, F., Experiences with a Logic-based knowledge discovery Support Environment, in 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 1999.
F. Giannotti, Nanni, M., and Pedreschi, D., Efficient Mining of Temporally Annotated Sequences, in SDM, 2006.
P. Gravino, Sirbu, A., Becker, M., Servedio, V. D. P., and Loreto, V., Experimental Assessment of the Emergence of Awareness and Its Influence on Behavioral Changes: The Everyaware Lesson, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 337–362.
R. Guidotti and Coscia, M., On the Equivalence Between Community Discovery and Clustering, in International Conference on Smart Objects and Technologies for Social Good, 2017.