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A. Rossi, Perri, E., Pappalardo, L., Cintia, P., and F Iaia, M., “Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load”, Applied Sciences, vol. 9, p. 5174, 2019.
S. Ruggieri, “Using t-closeness anonymity to control for non-discrimination.”, Trans. Data Privacy, vol. 7, pp. 99–129, 2014.
S. Ruggieri, Eirinakis, P., Subramani, K., and Wojciechowski, P., “On the complexity of quantified linear systems”, Theoretical Computer Science, vol. 518, pp. 128–134, 2014.
S. Ruggieri and Mesnard, F., “Typing Linear Constraints for Moding CLP() Programs”, in SAS, 2008, pp. 128-143.
S. Ruggieri, “Data Anonymity Meets Non-discrimination”, in Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on, 2013.
S. Ruggieri, “Learning from polyhedral sets”, in Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013.
S. Ruggieri and Turini, F., “A KDD process for discrimination discovery”, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2016.
S. Ruggieri, “Enumerating Distinct Decision Trees”, in International Conference on Machine Learning, 2017.
S. Ruggieri, “Introduction to the special issue on Artificial Intelligence for Society and Economy”, Intelligenza Artificiale, vol. 9, pp. 23–23, 2015.
S. Ruggieri, Hajian, S., Kamiran, F., and Zhang, X., “Anti-discrimination analysis using privacy attack strategies”, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2014.
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R. Portocarre Sarmento, Lemos, L., Cordeiro, M., Rossetti, G., and Cardoso, D., “DynComm R Package–Dynamic Community Detection for Evolving Networks”, arXiv preprint arXiv:1905.01498, 2019.
V. D. P. Servedio, Caminiti, S., Gravino, P., Loreto, V., Sirbu, A., and Tria, F., “Large Scale Engagement Through Web-Gaming and Social Computations”, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 237–254.
M. Setzu, Guidotti, R., Monreale, A., and Turini, F., “Global Explanations with Local Scoring”, in Machine Learning and Knowledge Discovery in Databases, Cham, 2020.
M. Setzu and Atzori, M., “SPARQL Queries over Source Code”, in 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), 2016.
M. Setzu, Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., and Giannotti, F., “GLocalX - From Local to Global Explanations of Black Box AI Models”, vol. 294, p. 103457, 2021.
A. Sirbu, Loreto, V., Servedio, V. D. P., and Tria, F., “Opinion dynamics with disagreement and modulated information”, Journal of Statistical Physics, pp. 1–20, 2013.
A. Sirbu, Becker, M., Caminiti, S., De Baets, B., Elen, B., Francis, L., Gravino, P., Hotho, A., Ingarra, S., Loreto, V., Molino, A., Mueller, J., Peters, J., Ricchiuti, F., Saracino, F., Servedio, V. D. P., Stumme, G., Theunis, J., Tria, F., and Van den Bossche, J., “Participatory Patterns in an International Air Quality Monitoring Initiative.”, PLoS One, vol. 10, p. e0136763, 2015.
A. Sirbu and Babaoglu, O., “Towards operator-less data centers through data-driven, predictive, proactive autonomics”, Cluster Computing, pp. 1–14, 2016.
A. Sirbu, Kerr, G., Crane, M., and Ruskin, H. J., “RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.”, PLoS One, vol. 7, p. e50986, 2012.
A. Sirbu, Loreto, V., Servedio, V. D. P., and Tria, F., “Opinion dynamics: models, extensions and external effects”, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 363–401.
A. Sirbu, Ruskin, H. J., and Crane, M., “Integrating heterogeneous gene expression data for gene regulatory network modelling.”, Theory Biosci, vol. 131, pp. 95-102, 2012.
A. Sirbu, Ruskin, H. J., and Crane, M., “Cross-platform microarray data normalisation for regulatory network inference.”, PLoS One, vol. 5, p. e13822, 2010.
A. Sirbu, Ruskin, H. J., and Crane, M., “Comparison of evolutionary algorithms in gene regulatory network model inference.”, BMC Bioinformatics, vol. 11, p. 59, 2010.
A. Sirbu and Babaoglu, O., “A Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q”, in Euro-Par 2015: parallel Processing Workshops, LNCS 9523, 2015.
A. Sirbu and Babaoglu, O., “Predicting System-level Power for a Hybrid Supercomputer”, in 2016 International Conference on High Performance Computing Simulation (HPCS), Innsbruck, Austria, 2016.

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