• Congratulations to Iacopo Colonelli: one of his papers (from his PhD thesis) has been selected by the journal Future Generation Computer Systems as Editor's Choice. These papers are promotionally Open Access for 6 months. 
• Congratulations to Gianluca Mittone: the presentation Efficiently Distributed Machine Learning has been awarded as “Best PhD Symposium Award” at 29-th International European Conference on Parallel and Distributed Computing. 

• Alessandro Pansa, I. Butera, J.J. Gómez-Hernández, B. Vigna, B. (2023). Predicting discharge from a complex karst system using the ensemble smoother with multiple data assimilation. Stochastic Environmental Research and Risk Assessment, 37(1), 185-201, 10.1007/s00477-022-02287-y
• E. Di Nardo, G. D'Onofrio, Tommaso Martini (2023) Approximating the first passage time density from data using generalized Laguerre polynomials.   Commun. Nonlinear Sci. Numer. Simul. 118, 106991, 10.1016/j.cnsns.2022.106991
• Chalachew Muluken Liyew, R. Meo, E. Di Nardo, S. Ferraris (2023) Multivariate Time Series Evapotranspiration Forecasting using Machine Learning Techniques.   In Artificial Intelligence and Agents, Proceedings of the the 38th ACM/SIGAPP Symposium on Applied Computing, ACM editor, 377-380, 10.1145/3555776.3577838
• C. Agnese, G. Baiamonte, E. Di Nardo, S. Ferraris, Tommaso Martini (2022)  Modelling the Frequency of Interarrival Times and Rainfall Depths with the Poisson Hurwitz-Lerch Zeta Distribution. Fractal Fract. 6(9), 509,  10.3390/fractalfract6090509
• Chalachew Muluken Liyew, H.A. Melese (2021). Machine learning techniques to predict daily rainfall amount. Journal of Big Data, 8(1), 1-11, 10.21203/rs.3.rs-801241/v1

• M. Asadullah, Md Murad Hossain, S. Rahaman, M. S. Amin, M. S. A. Sumy, M. Y. A. Parh, M. A. Hossain (2023) Evaluation of machine learning techniques for hypertension risk prediction based on medical data in Bangladesh. Indonesian Journal of Electrical Engineering and Computer Science, 31(3), 1794-1802, 10.11591/ijeecs.v31.i3.pp1794-1802
• Gianluca Mittone, F. Svoboda, M. Aldinucci, N. D. Lane, and P. Lio, (2023) A federated learning benchmark for drug-target interaction, in Companion proceedings of the acm web conference 2023 (www ’23 companion), Austin, Texas, 10.1145/3543873.3587687
• Gianluca Mittone, N. Tonci, R. Birke, I. Colonnelli, D. Medić, A. Bartolini, R. Esposito, E. Parisi, F. Beneventi, M. Polato, M. Torquati, L. Benini, and M. Aldinucci, (2023) Experimenting with emerging RISC-V systems for decentralised machine learning, in 20th ACM international conference on computing frontiers (cf ’23), Bologna, Italy,  10.1145/3587135.3592211 
• Gianluca Mittone, W. Riviera, I. Colonnelli, R. Birke, and M. Aldinucci, (2023) Model-agnostic federated learning, in Euro-par 2023: parallel processing, Limassol, Cyprus, 10.48550/arXiv.2303.04906 
  D. Caldo, Bologna S., Conte L., Saad Amin M., Anselma L., Basile V., Md. Murad Hossain, Mazzei A., Heritier P. , Ferracini R., Kon E., De Nunzio G. (2023) Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain. Scientific Reports, 13(1), 4654, 10.1038/s41598-023-31741-2
• M. S. Khan, T. D. Nath, Md Murad Hossain, A. Mukherjee, H. B. Hasnath, T. M. Meem, U. Khan, (2023) Comparison of Multiclass Classification Techniques Using Dry Bean Dataset, International Journal of Cognitive Computing in Engineering, 4, 6-20, 10.1016/j.ijcce.2023.01.002
• Y. Arfat, M. Aldinucci, Gianluca Mittone et al.  (2022) Towards extreme scale technologies and accelerators for eurohpc hw/sw supercomputing applications for exascale: the textarossa approach, Microprocessors and microsystems, 95, 104679, 10.1016/j.micpro.2022.104679
• Alice Battiston, E. Massaro, C.R.  Binder and R. Schifanella High spatial resolution dataset of La Mobilière insurance customers. (2022) Scientific Data, 9, 81, 10.1038/s41597-022-01174-z
• Md Murad Hossain, M. Asadullah, M. A. Hossain, M. S. Amin  (2022). Prediction of depression using machine learning tools taking consideration of oversampling. Malaysian Journal of Public Health Medicine, 22(2), 244-253, 10.37268/mjphm/vol.22/no.2/art.1564
• A. Mazzei, L. Anselma, M. Sanguinetti, A. Rapp, D. Mana, Md Murad Hossain, V. Patti,R. Simeoni, L. Longo (2022) Anticipating User Intentions in Customer Care Dialogue Systems, IEEE Transactions on human-machine systems, 1-11, 10.1109/THMS.2022.3184400
• G. Gallone,  J. Kang, F. Bruno, J.K. Han, O. De Filippo, H. Yang, M. Doronzo, K. Park, Gianluca Mittone, H. Kang et al. (2022) Impact of left ventricular ejection fraction on procedural and long-term outcomes of bifurcation percutaneous coronary intervention, The American Journal of Cardiology,172, 18-25, 10.1016/j.amjcard.2022.02.015
• Gianluca Mittone et al., TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale, (2021) in 24th Euromicro Conference on Digital System Design (DSD), 286-294, 10.1109/DSD53832.2021.00051
• M. Aldinucci, V. Cesare, I. Colonnelli, A. R. Martinelli, Gianluca Mittone, B. Cantalupo, C. Cavazzoni, M. Drocco (2021) Practical parallelization of scientific applications with OpenMP, OpenACC and MPI,  Journal of parallel and distributed computing, 157, 13-29, 10.1016/j.jpdc.2021.05.017
• Y. Arfat, Gianluca Mittone, R. Esposito, B. Cantalupo, G. De Ferrari, M. Aldinucci (2021) Machine learning for cardiology Minerva Cardiology and Angiology,1-23, 10.23736/S2724-5683.21.05709-4
• F. D’Ascenzo, O.De Filippo, G.Gallone, Gianluca Mittone, I. Colonnelli, Y. Arfat et al. (2021) Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets, The Lancet,397, (10270), 199-207, 10.1016/S0140-6736(20)32519-8

• Yasir Arfat, M. Aldinucci, G. Mittone et al.  (2022) Towards extreme scale technologies and accelerators for eurohpc hw/sw supercomputing applications for exascale: the textarossa approach, Microprocessors and microsystems,  95, 104679, 10.1016/j.micpro.2022.104679
• Davide Tricarico, M. Calandri, M. Barba, C. Piatti, C. Geninatti, D. Basile, M. Gatti, M. Melis, A. Veltri (2022) Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary "Real Life" Results. Diagnostics, 12(3), 570,  10.3390/diagnostics12030570
• D. Giordano, F. Giobergia, E. Pastor, A. La Macchia, T. Cerquitelli, E. Baralis, M. Mellia, Davide Tricarico (2022) Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case, Computers in Industry, 134, 103554, 10.1016/j.compind.2021.103554.
• Yasir Arfat, G. Mittone, R. Esposito, B. Cantalupo, G. De Ferrari, M. Aldinucci (2021) Machine learning for cardiology Minerva Cardiology and Angiology,1-23, 10.23736/S2724-5683.21.05709-4
• F.D’Ascenzo, O.De Filippo, G.Gallone, G. Mittone, M. A. Deriu, M. Iannaccone, I. Colonnelli, Yasir Arfat et al. (2021) Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets, The Lancet, 397, (10270), 199-207, 10.1016/S0140-6736(20)32519-8
• J.Ito, Emanuele Lucrezia, G.Palm, S.Gruen (2019) Detection and evaluation of bursts in terms of novelty and surprise. Mathematical Biosciences and Engineering, 16, 6990-7008, 10.3934/mbe.2019351
• Daniele Rama, T. Piccardi, M. Redi, R. Schifanella (2022) A Large Scale Study of Reader Interactions with Images on Wikipedia. EPJ Data Science, 11(1),1, 10.1140/epjds/s13688-021-00312-8
• Shuyi Yang, D.Ienco, R.Esposito, R.G.Pensa (2021) ESA*: A Generic Framework for Semi-supervised Inductive Learning, Neurocomputing, 447, 102-117, 10.1016/j.neucom.2021.03.051
• C. Berloco, G. De Francisci Morales, D. Frassineti, G. Greco, H. Kumarasinghe, M. Lamieri, E. Massaro, A. Miola, Shuyi Yang, (2021) Predicting corporate credit risk: Network contagion via trade credit. PLoS ONE 16(4), e0250115, 10.1371/journal.pone.0250115
• Shuyi Yang, D. Ienco, R. Esposito, R. G. Pensa (2021) ESA☆: A generic framework for semi-supervised inductive learning, Neurocomputing, 447, 102-117, 10.1016/j.neucom.2021.03.051
• Shyi Yang (2020) Data Scientist: from zero to hero. Towards Data Science. A Medium publication sharing concepts, ideas and codes. (On line contribution).  

Papers

• Duilio Balsamo, P.Bajardi, A.Salomone, R.Schifanella (2021) Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions J. Med. Internet Res. 23(1), e21212, 10.2196/21212
• Duilio Balsamo, P.Bajardi, A.Panisson (2019) First hand Opiates Abuse on Social Media: Monitoring Geospatial Patterns of Interest Through a Digital Cohort. In the World Wide Web Conference 2019  (WWW'19) Association for Computing Machinery, New York, NY, USA, 2572-2579, 10.1145/3308558.3313634
• B.Gobbo, Duilio Balsamo, M.Mauri, P.Bajardi, A.Panisson, P.Ciuccarelli (2019) Topic Tomographies (TopTom): a visual approach to distill information from media streams. Computer Graphics Forum, Vol.38, 609-621. 10.1111/cgf.13714
• M. Aldinucci, D. Atienza, F. Bolelli, M. Caballero, I. Colonnelli, J. Flich, J. A. Gómez, D. González, C. Grana, M. Grangetto, S. Leo, P. López, D. Oniga, R. Paredes, L. Pireddu, E. Quiñones, T. Silva, E. Tartaglione, and M. Zapater (2022) The DeepHealth toolkit: a key european free and open-source software for deep learning and computer vision ready to exploit heterogeneous HPC and Cloud architectures, in Technologies and applications for big data value, E. Curry, S. Auer, A. J. Berre, A. Metzger, M. S. Perez, and S. Zillner, Eds., Cham: Springer international publishing, 183–202, 10.1007/978-3-030-78307-5_9
• Iacopo Colonnelli, M. Aldinucci, B. Cantalupo, L. Padovani, S. Rabellino, C. Spampinato, R. Morelli, R. Di Carlo, N. Magini, C. Cavazzoni, (2022) Distributed workflows with Jupyter, Future Generation Computer Systems, 128,  282–298, 10.1016/j.future.2021.10.007
• M. Aldinucci, V. Cesare, I. Colonnelli, A. R. Martinelli, G.Mittone, B. Cantalupo, C. Cavazzoni, M. Drocco (2021) Practical parallelization of scientific applications with OpenMP, OpenACC and MPI.  Journal of parallel and distributed computing, 157, 13-29, 10.1016/j.jpdc.2021.05.017
• O. D. Filippo, J. Kang, F. Bruno, J. Han, A. Saglietto, H. Yang, G. Patti, K. Park, R. Parma, H. Kim, L. D. Luca, H. Gwon, M. Iannaccone, W. J. Chun, G. Smolka, S. Hur, E. Cerrato, S. H. Han, C. di Mario, Y. B. Song, J. Escaned, K. H. Choi, G. Helft, J. Doh, A. T. Giachet, S. Hong, S. Muscoli, C. Nam, G. Gallone, D. Capodanno, D. Trabattoni, Y. Imori, V. Dusi, B. Cortese, A. Montefusco, F. Conrotto, I. Colonnelli, I. Sheiban, G. M. de Ferrari, B. Koo, F. D’Ascenzo (2021), Benefit of extended dual antiplatelet therapy duration in acute coronary syndrome patients treated with drug eluting stents for coronary bifurcation lesions (from the BIFURCAT registry), The american journal of cardiology, 156, 16-23, 10.1016/j.amjcard.2021.07.005
• Iacopo Colonnelli et al., "TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale," (2021) 24th Euromicro Conference on Digital System Design (DSD), 286-294, 10.1109/DSD53832.2021.00051
• Iacopo Colonnelli (2021) Towards Cloud-HPC Continuum: Container-native workflow manager for hybrid infrastructures
• Iacopo Colonnelli, B.Cantalupo, R.Esposito, M.Pennisi, C.Spampinato, M. Aldinucci (2021) HPC Application Cloudification: The StreamFlow Toolkit, In 12th workshop on parallel programming and run-time management techniques for many-core architectures and 10th workshop on design tools and architectures for multicore embedded computing platforms (Ditam 2021), Dagstuhl, Germany, 5, 1–13,  10.4230/OASIcs.PARMA-DITAM.2021.5
• F.D’Ascenzo, O.De Filippo, G.Gallone, G. Mittone, M. A. Deriu, M. Iannaccone, Iacopo Colonnelli, Y.Arfat et al. (2021) Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets, The Lancet 397, 10270, 199-207. 10.1016/S0140-6736(20)32519-8
• V.Cesare, Iacopo Colonnelli, M.Aldinucci (2020) Practical parallelization of scientific applications, In Proc. of 28th euromicro intl. conference on parallel distributed and network-based processing, Västerås, Sweden, 376-384, 10.1016/j.jpdc.2021.05.017
• Iacopo Colonnelli, B.Cantalupo, I.Merelli, M.Aldinucci (2020) Streamflow: cross-breeding cloud with HPC, IEEE Transactions on Emerging Topics in Computing. 9(4), 1723-1737, 10.1109/TETC.2020.3019202
• M.Drocco, P.Viviani, Iacopo Colonnelli, M.Aldinucci, M.Grangetto,(2019) Accelerating spectral graph analysis through wavefronts of linear algebra operations In Proc. of 27th euromicro intl. conference on parallel distributed and network-based processing, Pavia, Italy, 9-16, 10.1109/EMPDP.2019.8671640
• P.Viviani, M.Drocco, D.Baccega, Iacopo Colonnelli, M.Aldinucci (2019) Deep learning at scale In Proc. of 27th euromicro intl. conference on parallel distributed and network-based processing, Pavia, Italy, 124-131, 10.1109/EMPDP.2019.8671552
• Elena Travaglia, V.L. Morgia, E.T. Venturino (2020) Poxvirus, red and grey squirrel dynamics: Is the recovery of a common predator affecting system equilibria? Insights from a predator-prey ecoepidemic model. Discrete and continuous dynamical systems. Series B. 25(6), 2023-2040, 10.3934/dcdsb.2019200
• M. Mazza, M. Semplice, S. Serra-Capizzano, Elena Travaglia (2021) A matrix-theoretic spectral analysis of incompressible Navier–Stokes staggered DG approximations and a related spectrally based preconditioning approach. Numerische Mathematik, 149(4), 933-971, 10.1007/s00211-021-01247-y
• M. Semplice, Elena Travaglia, G. Puppo (2021) One- and Multi-dimensional CWENOZ Reconstructions for Implementing Boundary Conditions Without Ghost Cells. Communications on Applied Mathematics and Computation. 1-27, 10.1007/s42967-021-00151-4

• C. Rubina Nava, Luigi Riso , M.G. Zoia (2022) Automatic variable selection for MIDAS regressions: an application. Book of Short Papers SIS 2022, Pearson editor, 1841-1846, ISBN: 9788891932310
• Luigi Riso, M. Guerzoni (2022) Concept Drift Estimation with Graphical Models. Information Sciences, 606, 786-804 10.1016/j.ins.2022.05.056
• Claudia Berloco, G. De Francisci Morales, D. Frassineti, G. Greco, H. Kumarasinghe, M. Lamieri, E. Massaro, A. Miola, S. Yang (2021) Predicting corporate credit risk: Network contagion via trade credit. PLoS ONE 16(4), e0250115, 10.1371/journal.pone.0250115
• Adane Nega Tarekegn, M. Giacobini, K. Michalak (2021) A review of methods for imbalanced multi-label classification. Pattern recognition,118, 107965, 10.1016/j.patcog.2021.107965
• Adane Nega Tarekegn, K. Michalak, M. Giacobini (2020) Cross Validation Approach to Evaluate Clustering Algorithms: An Experimental Study using Multi-label Datasets. SN Computer Science, 1, 1-9, 10.1007/s42979-020-00283-z
• Adane Nega Tarekegn, F.Ricceri , G.Costa, E.Ferracin, M. Giacobini (2020) Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. JMIR Medical Informatics, 8(6), e16678,10.2196/16678