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A
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.
M. Atzmueller, Becker, M., Molino, A., Mueller, J., Peters, J., and Sirbu, A., “Applications for Environmental Sensing in EveryAware”, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 135–155.
M. Becker, Caminiti, S., Fiorella, D., Francis, L., Gravino, P., Haklay, M. Muki, Hotho, A., Loreto, V., Mueller, J., Ricchiuti, F., Servedio, V. D. P., Sirbu, A., and Tria, F., “Awareness and learning in participatory noise sensing.”, PLoS One, vol. 8, p. e81638, 2013.
B
A. Balliu, Olivetti, D., Babaoglu, O., Marzolla, M., and Sirbu, A., “BiDAl: Big Data Analyzer for Cluster Traces”, in Informatika (BigSys workshop), 2014.
A. Balliu, Olivetti, D., Babaoglu, O., Marzolla, M., and Sirbu, A., “A Big Data Analyzer for Large Trace Logs”, Computing, 2015.
C
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., “Comparison of evolutionary algorithms in gene regulatory network model inference.”, BMC Bioinformatics, vol. 11, p. 59, 2010.
A. Sirbu, Ruskin, H. J., and Crane, M., “Cross-platform microarray data normalisation for regulatory network inference.”, PLoS One, vol. 5, p. e13822, 2010.
D
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.
J. Kim, Sirbu, A., Giannotti, F., and Gabrielli, L., “Digital Footprints of International Migration on Twitter”, in International Symposium on Intelligent Data Analysis, 2020.
E
P. Contucci, Panizzi, E., Ricci-Tersenghi, F., and Sirbu, A., “Egalitarianism in the rank aggregation problem: a new dimension for democracy”, Quality & Quantity, pp. 1–16, 2015.
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.
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.
H
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, 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.
I
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.
L
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.
N
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: a python library to model and analyze diffusion processes over complex networks”, International Journal of Data Science and Analytics, pp. 1–19, 2017.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: a python library to model and analyze diffusion processes over complex networks”, International Journal of Data Science and Analytics, vol. 5, pp. 61–79, 2018.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: Studying Network Diffusion Dynamics”, in IEEE International Conference on Data Science and Advanced Analytics, DSA, Tokyo, 2017.
O
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, Loreto, V., Servedio, V. D. P., and Tria, F., “Opinion dynamics with disagreement and modulated information”, Journal of Statistical Physics, pp. 1–20, 2013.
P
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., “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 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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