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A. Rossi, Pedreschi, D., Clifton, D. A., and Morelli, D., “Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts”, Sensors, vol. 20, p. 7122, 2020.
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.
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, “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 Mesnard, F., “Typing Linear Constraints for Moding CLP() Programs”, in SAS, 2008, pp. 128-143.
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.
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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., “Cohesion, consensus and extreme information in opinion dynamics”, Advances in Complex Systems, vol. 16, p. 1350035, 2013.
A. Sirbu, Ruskin, H. J., and Crane, M., “Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role”, in Evolutionary Algorithms, InTech, 2011.
A. Sirbu, Ruskin, H. J., and Crane, M., “Regulatory network modelling: Correlation for structure and parameter optimisation”, Proceedings of The IASTED Technology Conferences (International Conference on Computational Bioscience), Cambridge, Massachusetts, pp. 3473–3481, 2010.
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.

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