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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 and Babaoglu, O., Towards operator-less data centers through data-driven, predictive, proactive autonomics, Cluster Computing, pp. 1–14, 2016.
A. Sirbu, Andrienko, G., Andrienko, N., Boldrini, C., Conti, M., Giannotti, F., Guidotti, R., Bertoli, S., Kim, J., Muntean, C. Ioana, Pappalardo, L., Passarella, A., Pedreschi, D., Pollacci, L., Pratesi, F., and Sharma, R., Human migration: the big data perspective, International Journal of Data Science and Analytics, pp. 1–20, 2020.
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., 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, 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 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, Giannotti, F., Pedreschi, D., and Kertész, J., Public opinion and Algorithmic bias, ERCIM News, 2019.
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.
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.