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A. Sirbu, Pedreschi, D., Giannotti, F., and Kertész, J., Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model, PloS one, vol. 14, p. e0213246, 2019.
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, Giannotti, F., Pedreschi, D., and Kertész, J., Public opinion and Algorithmic bias, ERCIM News, 2019.
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., Predicting System-level Power for a Hybrid Supercomputer, in 2016 International Conference on High Performance Computing Simulation (HPCS), Innsbruck, Austria, 2016.
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., Towards Data-Driven Autonomics in Data Centers, in IEEE International Conference on Cloud and Autonomic Computing, 2015.
A. Sirbu, Crane, M., and Ruskin, H. J., Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks, Microarrays, vol. 4, pp. 255–269, 2015.
A. Sirbu and Babaoglu, O., Power Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer, 22nd International European Conference on Parallel and Distributed Computing, Euro-Par 2016, vol. LNCS 9833. Springer LNCS, Grenoble, France, 2016.
A. Sirbu, Crane, M., and Ruskin, H. J., EGIA–Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach, in Complex Networks V, Springer International Publishing, 2014, pp. 217–229.
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.
M. Setzu and Atzori, M., SPARQL Queries over Source Code, in 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), 2016.
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.
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 and Mesnard, F., Typing Linear Constraints for Moding CLP() Programs, in SAS, 2008, pp. 128-143.
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.
A. Rossi, Pappalardo, L., Cintia, P., F Iaia, M., Fernàndez, J., and Medina, D., Effective injury forecasting in soccer with GPS training data and machine learning, PloS one, vol. 13, p. e0201264, 2018.