{"id":920,"date":"2022-01-23T17:24:43","date_gmt":"2022-01-23T16:24:43","guid":{"rendered":"https:\/\/roveri.faculty.polimi.it\/?page_id=920"},"modified":"2024-03-01T16:11:23","modified_gmt":"2024-03-01T15:11:23","slug":"bio-2","status":"publish","type":"page","link":"https:\/\/roveri.faculty.polimi.it\/?page_id=920","title":{"rendered":"Publications"},"content":{"rendered":"\t\t
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  • Scientific Productivity (updated 10\/2023): more than 131 publications with 116 co-authors according to Scopus.\u00a0<\/span><\/li>
  • Publication Impact (updated 10\/2023)<\/span>: h-index 30 and more than <\/span><\/span>4350\u00a0citations according to Google Scholar.<\/span><\/span><\/span><\/li>
  • Link to the Google Scholar profile: https:\/\/scholar.google.it\/citations?user=j2NbakMAAAAJ&hl=en<\/span><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    INTERNATIONAL JOURNALS<\/strong><\/span><\/p>\n

      \n
    1. A. Falcetta, M. Roveri, \u201cEVAD: encrypted vibrational anomaly detection with homomorphic encryption\u201d, Neural Computing and Applications, Springer 2024 [Q1 SCIMAGO].
      <\/li>\n
    2. G. Casale, M. Roveri, “Scheduling Inputs in Early Exit Neural Networks\u201d, IEEE Transactions on Computers, 2023 [Q1 SCIMAGO]<\/li>\n
    3. L. Barbieri, M. Brambilla, M. Stefanutti, C. Romano, N. De Carlo, M. Roveri \u201cA Tiny Transformer-Based Anomaly Detection Framework for IoT Solutions\u201d, IEEE Open Journal of Signal Processing, 2023. [Q2 SCIMAGO \u2013 Signal Processing]<\/li>\n
    4. M. Pavan, E. Ostrovan, A. Caltabiano, M. Roveri, \u201cTyBox: an automatic design and code-generation toolbox for TinyML incremental on-device learning\u201d, ACM Transactions on Embedded Computing Systems, 2023. [Q2 SCIMAGO \u2013 Artificial Intelligence]<\/li>\n
    5. O. Cartagena, F. Trov\u00f2, M. Roveri, D. Saez, \u201cEvolving Fuzzy Prediction Intervals in Nonstationary Environments\u201d, IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), 2023. [Q1 SCIMAGO \u2013 Artificial Intelligence]<\/li>\n
    6. S. Disabato and M. Roveri, “Tiny Machine Learning for Concept Drift,” in IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2022. [Q1 SCIMAGO \u2013 Artificial Intelligence]<\/span><\/li>\n
    7. A. Falcetta, M. Roveri, \u201cPrivacy-preserving machine learning with homomorphic encryption: an introduction\u201d, IEEE Computational Intelligence Magazine, August, 2022 [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    8. G. Canonaco, M. Roveri, C. Alippi, F. Podenzani, A. Bennardo, M. Conti, N. Mancini, \u201cA transfer-learning approach for corrosion prediction in pipeline infrastructures\u201d. Applied Intelligence, Vol. 52, N. 7, pp. 7622-7637, 2022. [Q2 SCIMAGO \u2013 Computer Science]<\/span><\/li>\n
    9. A. Soldevila,G. Boracchi, M. Roveri, S. Tornil-Sin, V. Puig, \u201cLeak detection and localization in water distribution networks by combining expert knowledge and data-driven models\u201d, Neural Computing and Applications, Vol. 34, pp. 4759\u20134779, 2022 [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    10. S. Disabato, M. Roveri, C. Alippi. “Distributed deep convolutional neural networks for the internet-of-things.” IEEE Transactions on Computers, vol. 70, no. 8, pp. 1239-1252, 1 Aug. 2021, doi: 10.1109\/TC.2021.3062227 [Q1 SCIMAGO – Computer Science]. <\/span><\/li>\n
    11. A. Bozorgchenani, S. Disabato, D. Tarchi, M. Roveri, \u201cAn Energy Harvesting Solution for Computation Offloading in Fog Computing Networks\u201d, in Computer Communications, Elsevier, 2020, 160, pp.577-587 [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    12. M. Roveri, “Learning Discrete-Time Markov Chains Under Concept Drift,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2570-2582, Sept. 2019 [Q1 SCIMAGO – Computer Science]. <\/span><\/li>\n
    13. R. Fantacci, F. Nizzi, T. Pecorella, L. Pierucci, M. Roveri, \u201cFalse Data Detection for Fog and Internet of Things Networks\u201d, Sensors 2019, 19, 4235. [Q2 SCIMAGO – Computer Science]. <\/span><\/li>\n
    14. Giuzio, Antonio, Giansalvatore Mecca, Elisa Quintarelli, Manuel Roveri, Donatello Santoro, and Letizia Tanca. “INDIANA: An interactive system for assisting database exploration.” Information Systems, Vol. 83, Pages 40-56, 2019. [Q2 SCIMAGO – Computer Science].<\/span><\/li>\n
    15. C. Alippi and M. Roveri, “The (Not) Far-Away Path to Smart Cyber-Physical Systems: An Information-Centric Framework,” in IEEE Computer, vol. 50, no. 4, pp. 38-47, April 2017. [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    16. C. Alippi, S. Ntalampiras and M. Roveri, “Model-Free Fault Detection and Isolation in Large-Scale Cyber-Physical Systems,” in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 1, no. 1, pp. 61-71, Feb. 2017. [Q1 SCIMAGO – Computer Science]<\/span><\/li>\n
    17. M. Roveri, F. Trov\u00f2, “An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks”, International Journal of Neural Systems, vol. 27, no. 3, pp. 16, 2017.  [Q1 SCIMAGO – Computer Science]<\/span><\/li>\n
    18. C. Alippi, R. Fantacci, D. Marabissi, M. Roveri, “A Cloud to the Ground: The New Frontier of Intelligent and Autonomous Networks of Things”, IEEE Communication Magazine, Vol.54, No 12, pp. 14-20, December 2016. [Q1 SCIMAGO – Computer Science]<\/span><\/li>\n
    19. G. Boracchi, M. Michaelides, M. Roveri, “A cognitive monitoring system for detecting and isolating contaminants and faults in intelligent buildings.” IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 48, No.3, pp. 433-447, 2016. [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    20. S. Brienza, M. Roveri, D. De Guglielmo, G. Anastasi, “Just-in-Time Adaptive Algorithm for Optimal Parameter Setting in 802.15.4 WSNs”, ACM Transactions on Autonomous and Adaptive Systems (TAAS), vol. 10, no. 4, 2016. [Q2 SCIMAGO – Computer Science]<\/span><\/li>\n
    21. C. Alippi; G. Boracchi; M. Roveri, “Hierarchical Change-Detection Tests”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 2, pp.246-258, 2016. [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    22. C. Alippi, M. Bocca, G. Boracchi, N. Patwari, M. Roveri, RTI Goes Wild: Radio Tomographic Imaging for Outdoor People Detection and Localization, IEEE Transactions on Mobile Computing, vol. 15, no. 10, pp. 2585-2598, 2016. [Q1 SCIMAGO – Computer Science]<\/span><\/li>\n
    23. Alippi, G. Boracchi and M. Roveri, “A Reprogrammable and Intelligent Monitoring System for Rock-Collapse Forecasting,” in IEEE Systems Journal, vol. 10, no. 2, pp. 733-744, June 2016. [Q1 SCIMAGO – Computer Science]<\/span><\/li>\n
    24. M. \u00c0. Cuguer\u00f3-Escofet, J. Quevedo, C. Alippi, M. Roveri, V. Puig, D. Garc\u00eda and F. Trov\u00f2, “Model- vs. data-based approaches applied to fault diagnosis in potable water supply networks”, Journal of Hydroinformatics, vol. 18, no. 6, 2016<\/span><\/li>\n
    25. Buoncristiano, G. Mecca, E. Quintarelli, M. Roveri, D. Santoro, and L. Tanca, \u201cDatabase Challenges for Exploratory Computing\u201d, SIGMOD Rec. Vol. 44, No. 2, pp. 17-22, August 2015 [Q2 SCIMAGO – Computer Science]<\/span><\/li>\n
    26. G. Ditzler, M. Roveri, C. Alippi and R. Polikar, “Learning in Nonstationary Environments: A Survey,” in IEEE Computational Intelligence Magazine, vol. 10, no. 4, pp. 12-25, Nov. 2015. [Q1 SCIMAGO – Computer Science]. This paper received the Outstanding Computational Intelligence Magazine Paper Award from the IEEE Computational Intelligence Society in 2018.<\/span><\/li>\n
    27. C. Alippi, M. Roveri, F. Trov\u00f2, \u201cA Self-building and Cluster-based Cognitive Fault Diagnosis System for Sensor Networks\u201d, IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 6, pp. 1021-1032, June 2014. [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    28. C. Alippi, G. Boracchi, M. Roveri, \u201cEnsembles of Change-Point Methods to Estimate the Change Point in Residual Sequences\u201d, Soft Computing, Springer, Vol. 17, No. 11, pp.  1971-1981, Nov. 2013. [Q2 SCIMAGO – Computer Science]<\/span><\/li>\n
    29. C. Alippi, S. Ntalampiras, M. Roveri, \u201dA Cognitive Fault Diagnosis System for Distributed Sensor Networks\u201d, IEEE Transactions on Neural Networks and Learning Systems, Vol. 24, No. 8, pp. 1213-1226, Aug. 2013. [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    30. C. AIippi, R. Camplani, C. Galperti, A. Marullo, M. Roveri, \u201cA high frequency sampling monitoring system for environmental and structural applications\u201d, ACM Transactions on Sensor Networks, Vol. 9, No. 4, Art. 41, 32 pages, July 2013. [Q2 SCIMAGO – Computer Science]<\/span><\/li>\n
    31. C. Alippi, G. Boracchi, M. Roveri, \u201cJust In Time Classifiers for Recurrent Concepts\u201d, IEEE Transactions on Neural Networks and Learning Systems, Vol. 24, No. 4, pp. 620 \u2013 634, April 2013. [Q1 SCIMAGO – Computer Science]. This paper received the IEEE CIS Outstanding TNNLS Paper Award from the IEEE Computational Intelligence Society in 2016.<\/span><\/li>\n
    32. C. Alippi, G. Boracchi, M. Roveri, \u201cA just-in-time adaptive classification system based on the intersection of confidence intervals rule\u201d, Neural Networks, Elsevier, Vol. 24, No. 8, pp. 791-800, Oct. 2011. [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    33. C. Alippi, R. Camplani, C. Galperti, M. Roveri, “A robust, adaptive, solar powered WSN framework for aquatic environmental monitoring”, IEEE Sensors Journal, Vol. 11, No. 1, pp. 45-55, Jan. 2011. [Q1 SCIMAGO – Electrical and Electronic Engineering].<\/span><\/li>\n
    34. C. Alippi, G. Boracchi, R. Camplani, M. Roveri, “Detecting External Disturbances on Camera Lens in Wireless Multimedia Sensor Networks”, IEEE Transactions on Instrumentation and Measurement, Vol. 59, No. 11, pp. 2982-2990, Nov. 2010. [Q1 SCIMAGO – Electrical and Electronic Engineering].<\/span><\/li>\n
    35. C. Alippi, G. Anastasi, M. Di Francesco, M. Roveri, “An Adaptive Sampling Algorithm for Effective Energy Management in Wireless Sensor Networks with Energy-hungry Sensors”, IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 11, pp. 335 \u2013 344, Nov. 2009. [Q1 SCIMAGO – Electrical and Electronic Engineering].<\/span><\/li>\n
    36. C. Alippi, R. Camplani, M. Roveri, \u201cAn Adaptive, LLC-based and Hierarchical Power-aware Routing Algorithm”, IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 9, pp. 3347-3357, Sept. 2009.  [Q1 SCIMAGO – Electrical and Electronic Engineering].<\/span><\/li>\n
    37. C. Alippi, G. Anastasi, M. Di Francesco, M. Roveri, “Energy Management in Wireless Sensor Networks with Energy-Hungry Sensors”, IEEE Instrumentation and Measurement Magazine, Vol. 12, No. 2, pp. 16-23, April 2009. [Q2 SCIMAGO – Electrical and Electronic Engineering].<\/span><\/li>\n
    38. C. Alippi, M. Roveri, \u201cJust-in-time Adaptive Classifiers. Part II. Designing the classifier\u201d, IEEE Transactions on Neural Networks, Vol. 19, No. 12, pp. 2053 \u2013 2064, Dec. 2008. [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n
    39. C. Alippi, M. Roveri, \u201cJust-in-time Adaptive Classifiers. Part I. Detecting non-stationary Changes\u201d, IEEE Transactions on Neural Networks, Vol. 19, No. 7, pp. 1145 \u2013 1153, July 2008. [Q1 SCIMAGO – Computer Science]<\/span><\/li>\n
    40. F. Amigoni, N. Gatti, C. Pinciroli, M. Roveri, \u201cWhat Planner for Ambient Intelligence Applications?\u201d, IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 35, No. 1, pp. 7\u201321, Jan. 2005. [Q1 SCIMAGO – Computer Science].<\/span><\/li>\n<\/ol>\n

      ITALIAN JOURNALS<\/span><\/span><\/strong><\/p>\n

        \n
      1. A. Capone, M. Cesana, M. Roveri, \u201cGli oggetti nel futuro di Internet\u201d, Rivista AEIT, n. 11\/12, Nov.\/Dec. 2012.<\/li>\n<\/ol>\n

        BOOK CHAPTERS<\/strong><\/span><\/p>\n

          \n
        1. F. A. Schreiber and M. Roveri, \u201cSensors and Wireless Sensor Networks as data sources: models and languages\u201d, Data Management in Pervasive Systems – Springer Cham, Heidelberg, pp.69-92, 2015.<\/li>\n
        2. C. Alippi, R. Camplani, A. Marullo,  M. Roveri, \u201cAlgorithms and Tools for Intelligent Monitoring of Critical Infrastructure Systems\u201d, Intelligent Monitoring, Control, and Security of Critical Infrastructure Systems, Studies in Computational Intelligence, Springer Berlin Heidelberg, Vol. 565, pp. 167-184, 2015.<\/span><\/li>\n
        3. C. Alippi, G. Boracchi, G. Ditzler, R. Polikar, M. Roveri, \u201cAdaptive Classifiers for Nonstationary Environments\u201d, Contemporary Issues in Systems Science and Engineering, IEEE\/Wiley Press Book Series, 2015.<\/span><\/li>\n
        4. C. Alippi, G. Boracchi, M. Roveri, \u201cAbove and below the ocean surface: a WSN framework for monitoring the Great Barrier Reef\u201d, Building Sensor Networks: From Design to Applications, CRC Press, pp. 271- 290, Sept. 2013.<\/span><\/li>\n
        5. C. Alippi, G. Boracchi, R. Camplani, M. Roveri, \u201cWireless Sensor Networks for Monitoring Vineyards\u201d, Methodologies and Technologies for Networked Enterprises, Lecture Notes in Computer Science, LNCS 7200, pp. 295-310, July 2012.<\/span><\/li>\n
        6. C. Alippi, R. Camplani, A. Marullo,  M. Roveri, \u201cA Real-Time Monitoring Framework for Landslide and Rock-Collapse Forecasting\u201d, Smart Sensing Technology for Agriculture and Environmental Monitoring, Lecture Notes in Electrical Engineering, Springer, Vol. 146, pp. 285-302, 2012.<\/span><\/li>\n
        7. C. Alippi, A. Marullo, M. Roveri, \u201cNuovi Sistemi di Monitoraggio: Infrastrutture di Acquisizione e di Trasmissione Dati\u201d, MIARIA Tecnologia e Conoscenza al Servizio della Sicurezza, Bellavite Ed., Vol. 1, 2011.<\/span><\/li>\n
        8. C. Alippi, R. Camplani, C. Galperti, M. Roveri, \u201cFrom labs to real environments: the dark side of WSNs\u201d, Recent Advances in Sensing Technology Series: Lecture Notes in Electrical Engineering, Springer Verlag, Vol. 49, pp. 143-168, 2009.<\/span><\/li>\n
        9. C. Alippi, M. Roveri, G. Vanini, \u201cRobustness in Neural Networks\u201d, Encyclopedia of Information Science and Technology, 2nd ed., Ed. Information Science Reference, Hershey – New York, Vol. 7, pp. 3314-3321, 2008.<\/span><\/li>\n<\/ol>\n

          PUBLICATIONS AT INTERNATIONAL CONFERENCES<\/strong><\/span><\/p>\n

            \n
          1. L. Colombo, A. Falcetta, M. Roveri, \u201cTIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignment\u201d, 2023 International Conference on AI-ML Systems, Bangalore, India, 2023.<\/li>\n
          2. M. Pavan, L. Navarro, A. Caltabiano, and M. Roveri, \u201cUnveiling the Potential of Tiny Machine Learning for Enhanced People Counting in UWB Radar Data\u201d, Workshop on Simplification, Compression, Efficiency and Frugality for Artificial Intelligence at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECML PKDD, Torino Italy, 2023.<\/li>\n
          3. A. Falcetta, M. Pavan, S. Canali, V. Schiaffonati, M. Roveri, \u201cTo Personalize or Not To Personalize? Soft Personalization and the Ethics of ML for Health\u201d, The 10th IEEE International Conference on Data Science and Advanced Analytics (DSAA), Thessaloniki, Greece, 2023.<\/li>\n
          4. A. Falcetta, M. Roveri, \u201cT4C: A Framework for Time-Series Clustering-as-a-Service\u201d, CEUR Workshop Proceedings vol. 3252 \u2013 Workshop proceedings, 2023.<\/li>\n
          5. F. Puoti, A. Falcetta, M. Roveri, D. Riva, D. Chiggiato, \u201cGreenTea: Time-series Exploration as-a-Service for environmental science\u201d, IEEE Conference on Artificial Intelligence (CAI), Santa Clara, CA, USA, 2023.<\/li>\n
          6. M. Gambella, A. M. Roveri, \u201cEDANAS: Adaptive Neural Architecture Search for Early Exit Neural Networks\u201d, the International Joint Conference on Neural Networks (IEEE IJCNN), Gold Coast, Queensland, Australia, 2023.<\/li>\n
          7. Y. Chen, M. Roveri, S. Tuli, G. Casale, \u201cCoupling QoS Co-Simulation with Online Adaptive Arrival Forecasting\u201d, IEEE 19th International Conference on Network and Service Management (CNSM), Niagara Falls, Canada, 2023.<\/li>\n
          8. K. Malialis, M. Roveri, C. Alippi, C. G. Panayiotou and M. M. Polycarpou, “A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification,” 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 2022.<\/li>\n
          9. M. Roveri, \u201dIs tiny deep learning the new deep learning?.” Computational Intelligence and Data Analytics: Proceedings of ICCIDA 2022. Singapore: Springer Nature Singapore, 2022.<\/li>\n
          10. M. Gambella, A. Falcetta, M. Roveri, \u201cCNAS: Constrained Neural Architecture Search\u201d, 2022 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2022), Prague, 2022.<\/li>\n
          11. A. Falcetta, M. Roveri, \u201cTIMEX: an Automatic Framework for Time-Series Forecasting-as-a-Service\u201d, Accepted at the 6th International Workshop on Automation in Machine Learning, held in conjunction with the SIGKDD Conference on Knowledge Discovery and Data Mining conference (KDD2022), Washington DC, 2022.<\/span><\/li>\n
          12. A. Falcetta, M. Roveri, \u201cPrivacy-preserving time series prediction with temporal convolutional neural networks\u201d, in Proc. 2022 International Joint Conference on Neural Network (IJCNN2022), Padova, Italy, 2022.<\/span><\/li>\n
          13. M. Pavan, A. Caltabiano, M. Roveri, \u201cTinyML for UWB-radar based presence detection\u201d, in Proc. 2022 International Joint Conference on Neural Network (IJCNN2022), Padova, Italy, 2022.<\/span><\/li>\n
          14. D. Christofidellis, A. Berrios Torres, A. Dave, M. Roveri, K. Schmidt, S. Swaminathan, H. Vandierendonck, D. Zubarev, M. Manica, \u201cPGT: a prompt based generative transformer for the patent domain\u201d, In Proc. Workshop on Knowledge Retrieval and Language Models, held in conjunction with the 39th International Conference on Machine Learning (ICML 2022), Baltimore, 2022.<\/span><\/li>\n
          15. S. Disabato, G. Canonaco, P. Flikkema, M. Roveri, C. Alippi, “Birdsong Detection at the Edge with Deep Learning\u201d, in Proc. 7th IEEE International Conference on Smart Computing (SMARTCOMP 2021), pp. 9-16, 2021.<\/span><\/li>\n
          16. G. Canonaco, A. Soprani, M. Roveri, M. Restelli, “Time-Variant Variational Transfer for Value Functions”,  in Proc. 37th Conference on Uncertainty in Artificial Intelligence (UAI2021), Online, July 27-30, 2021. [A++\/A+ Class 1 conference]<\/span><\/li>\n
          17. G. Canonaco, A. Bergamasco, A. Mongelluzzo, M. Roveri, \u201cAdaptive Federated Learning in Presence of Concept Drift\u201d, in Proc. 2021 International Joint Conference on Neural Network (IJCNN2021), 2021.<\/span><\/li>\n
          18. S. Disabato, M. Roveri. \u201cIncremental On-Device Tiny Machine Learning.\u201d in Proc. 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things. 2020.<\/span><\/li>\n
          19. S. Disabato, A. Falcetta, A. Mongelluzzo, M. Roveri, \u201cA privacy-preserving distributed architecture for deep-learning-as-a-service\u201d, in Proc. 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, 2020.<\/span><\/li>\n
          20. J. Valdes, L. Nikolic, S. Disabato and M. Roveri, \u201cA Computational Intelligence Characterization of Solar Magnetograms\u201d, in Proc. 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, 2020.<\/span><\/li>\n
          21. G. Canonaco, M. Roveri, C. Alippi, F. Podenzani, A. Bennardo, M. Conti, N. Mancini, \u201cCorrosion Prediction in Oil and Gas Pipelines: a Machine Learning Approach\u201d, in Proc. IEEE International Instrumentation and Measurement technology Conference (I2MTC), 2020.<\/span><\/li>\n
          22. G. Canonaco, M. Restelli, M. Roveri, \u201cModel-Free Non-Stationarity Detection and Adaptation in Reinforcement Learning\u201d, in Proc. 24th European Conference on Artificial Intelligence (ECAI 2020), 2020.<\/span><\/li>\n
          23. F. Conti, G. Lorenzini, G. Parenti, D. Scaccabarozzi, M. Roveri, M. Tarabini, \u201cPrototyping and Metrological Characterization of a Data Acquisition and Processing System Based on Edge Computing\u201d,  in Proc. 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), 2020.<\/span><\/li>\n
          24. S. Disabato, M. Roveri, \u201cLearning Convolutional Neural Networks in presence of Concept Drift\u201d, in Proc. 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 2019, pp. 1-8.<\/span><\/li>\n
          25. S. Disabato and M. Roveri, “Reducing the Computation Load of Convolutional Neural Networks through Gate Classification,” in Proc. 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, 2018, pp. 1-8.<\/span><\/li>\n
          26. D. Cogliati, M. Falchetto, D. Pau, M. Roveri and G. Viscardi, “Intelligent Cyber-Physical Systems for Industry 4.0,” in Proc. 2018 International Conference on Artificial Intelligence for Industries (AI4I), Laguna Hills, CA, USA, 2018, pp. 19-22.<\/span><\/li>\n
          27. C. Alippi, S. Disabato, M. Roveri, \u201cMoving Convolutional Neural Networks to Embedded Systems: the AlexNet and VGG-16 case\u201d, in Proc. 2018 ACM\/IEEE International Conference on Information Processing in Sensor Networks Porto (IPSN 2018), Portugal, April 11-13, 2018. [A++\/A+ Class 1 conference]<\/span><\/li>\n
          28. C. Alippi, M. Roveri, I. Scarabottolo, \u201cA spectrum-based adaptive sampling algorithm for smart sensing\u201d, in Proc. IEEE 3rd International Conference on Smart World Congress (SmartWorld 2017), San Francisco, USA, 4-8 August 2017.<\/span><\/li>\n
          29. C. Alippi, V. D’Alto, M. Falchetto, D. Pau and M. Roveri, “Detecting changes at the sensor level in cyber-physical systems: Methodology and technological implementation,” in Proc. 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp. 1780-1786.<\/span><\/li>\n
          30. C. Alippi, W. Qi, M. Roveri, \u201cLearning in Nonstationary Environments: A Hybrid Approach\u201d, in Proc.  International Conference on Artificial Intelligence and Soft Computing (ICAISC), 2017, pp. 703-714.<\/span><\/li>\n
          31. C. Alippi, R. Ambrosini, D. Cogliati, V. Longoni, M. Roveri, “A lightweight and energy-efficient Internet-of-Birds Tracking System”, in Proc. IEEE International Conference on Pervasive Computing and Communications (PerCom 2017), Hawaii, USA, 13-17 March 2017. [A++\/A+ Class 1 conference]<\/span><\/li>\n
          32. Pinciroli R., Gribaudo M., Roveri M., Serazzi G., \u201cCapacity Planning of Fog Computing Infrastructures for Smart Monitoring\u201d. In: Balsamo S., Marin A., Vicario E. (eds) New Frontiers in Quantitative Methods in Informatics. InfQ 2017. Communications in Computer and Information Science, vol 825. Springer, 2017.<\/span><\/li>\n
          33. C. Alippi, S. Ntalampiras and M. Roveri, \u201cDesigning HMM Models in the Age of Big Data\u201d, in Proc. INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece, 2016. This paper won the Best Regular Paper Award.<\/span><\/li>\n
          34. G. Tacconelli, M. Roveri, \u201cA CPM-based Change Detection Test for Big Data\u201d, in Proc. INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece, 2016. <\/span><\/li>\n
          35. C. Alippi, G. Boracchi, D. Carrera, M. Roveri, \u201cChange Detection in Multivariate Datastreams: Likelihood and Detectability Loss\u201d, in Proc. International Joint Conference of Artificial Intelligence (IJCAI), New York, USA, July 9 \u2013 13, 2016 [A++\/A+ Class 1 conference]<\/span><\/li>\n
          36. C. Alippi, S. Ntalampiras and M. Roveri, \u201cOnline Model-free Sensor Fault Identification and Dictionary Learning in Cyber-Physical Systems\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2016), July 24 \u2013 29, 2016, Vancouver, Canada.<\/span><\/li>\n
          37. M. Roveri, C. Alippi and W. Qi, \u201cAn improved Hilbert-Huang Transform for non-linear and time-variant signals\u201d, in Proc. 26th Italian Workshop on Neural Networks (WIRN 2016), May 18-20, 2016, Vietri Sul Mare, Italy<\/span><\/li>\n
          38. M. Roveri, G. Boracchi, M. P Michaelides, \u201cDetecting Contaminants in Smart Buildings by Exploiting Temporal and Spatial Correlation\u201d in Proc. 2015 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2015), Dec. 7-10, 2015, Cape Town, South Africa.<\/span><\/li>\n
          39. M. Roveri and F. Trov\u00f2, “Making Intelligent the Embedded Systems through Cognitive Outlier and Fault Detection”, in Proc. 25th Italian Workshop on Neural Networks (WIRN 2015), May 20-22, 2015, Vietri Sul Mare, Italy<\/span><\/li>\n
          40. M. Buoncristiano, G. Mecca, E. Quintarelli, M. Roveri, D. Santoro and L. Tanca, \u201cExploratory Computing: What is there for the Database Researcher?\u201d in Proc. 23rd Italian Symposium on Advanced Database Systems (SEBD 2015), June 14-17, Gaeta, Italy.<\/span><\/li>\n
          41. C. Alippi, M. Roveri, F. Trov\u00f2, \u201cLearning Causal Dependencies to Detect and Diagnose Faults in Sensor Networks\u201d, in Proc. 2014 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014), Dec. 9-12, 2014, Orlando, Florida.  <\/span><\/li>\n
          42. A. Antola, L. Mezzalira, M. Roveri, \u201cGINGER: a minimizing-effects reprogramming paradigm for distributed sensor networks\u201d, in Proc. 2014 IEEE International Symposium on Robotic and Sensors Environments (IEEE ROSE 2014), Oct. 16-18, Timisoara, Romania, 2014.<\/span><\/li>\n
          43. M. Roveri, F. Trov\u00f2, \u201cAn Ensemble of HMMs for Cognitive Fault Detection in Distributed Sensor Networks\u201d, in Proc. 10th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2014), Sep. 19-21, Rhodes, Greece, 2014.<\/span><\/li>\n
          44. G. Boracchi, M. Michaelides, M. Roveri, \u201cA Cognitive Monitoring System for Contaminant Detection in Intelligent Buildings\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2014), July 6 \u2013 11, 2014, Beijing, China.<\/span><\/li>\n
          45. G. Boracchi, M. Roveri, \u201cExploiting Self-Similarity for Change Detection\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2014), July 6 \u2013 11, 2014, Beijing, China.<\/span><\/li>\n
          46. G. Boracchi, M. Roveri, \u201cA Reconfigurable and Element-wise ICI-based Change-Detection Test for Streaming Data\u201d, in Proc. IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (IEEE CIVEMSA 2014), May 5-7 2014, Ottawa, Ontario, Canada<\/span><\/li>\n
          47. S. Ntalampiras, M. Roveri, “Rock Collapse Forecasting: a Novel Approach Based on the Classification of Micro-Acoustic Signals in the Wavelet Domain”, in Proc. IEEE SENSORS Conference (IEEE SENSORS 2013), Baltimore, USA, Nov. 3-6, 2013.<\/span><\/li>\n
          48. S. Brienza, D. De Guglielmo, C. Alippi, G. Anastasi, M. Roveri, “A Learning-based Algorithm for Optimal MAC Parameters Setting in IEEE 802.15.4 Wireless Sensor Networks”, in Proc. ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (ACM PE-WASUN 2013), Barcelona, Spain, Nov. 3-7, 2013.<\/span><\/li>\n
          49. G. Boracchi, V. Puig, M. Roveri, \u201cA Hierarchy of Change-Point Methods for Estimating the Time Instant of Leakages in Water Distribution Networks\u201d, in Proc. Workshop on Learning strategies and data processing in nonstationary environments (LEAPS 2013), Paphos, Cyprus, Sept. 30 – Oct. 2, 2013. <\/span><\/li>\n
          50. C. Alippi, S. Ntalampiras, M. Roveri, “Model ensemble for an effective on-line reconstruction of missing data in sensor networks”, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2013), Dallas, USA, Aug. 4-9, 2013.<\/span><\/li>\n
          51. J. Quevedo, C. Alippi, M.A. Cuguero, S. Ntalampiras, V. Puig, M. Roveri, D. Garc\u0131a, \u201cTemporal\/Spatial Model-Based Fault Diagnosis vs. Hidden Markov Models Change Detection Method: Application to the Barcelona Water Network\u201d, in Proc. Mediterranean Conference on Control and Automation (MED\u201913), Crete, Greece, June 25-28, 2013.<\/span><\/li>\n
          52. C. Alippi, G. Boracchi, V. Puig, M. Roveri, \u201cAn Ensemble Approach to Estimate the Fault-Time Instant\u201d, in Proc. IEEE International Conference on Intelligent Control and Information Processing (IEEE ICICIP 2013), Beijing, China, June 9 – 11, 2013.<\/span><\/li>\n
          53. C. Alippi, R. Camplani, M. Roveri, G. Viscardi, “NetBrick: a high-performance, low-power hardware platform for wireless and hybrid sensor network”, in Proc. IEEE International Conference on Mobile Ad hoc and Sensor Systems (IEEE MASS 2012), Las Vegas, USA, Oct. 8-11, 2012<\/span><\/li>\n
          54. C. Alippi, M. Roveri, F. Trov\u00f2, “A ”learning from models” cognitive fault diagnosis system”, in Proc. ENNS International Conference on Artificial Neural Networks (ICANN 2012), Lausanne, Swizerland, Sept. 11-14, 2013.<\/span><\/li>\n
          55. C. Alippi, G. Boracchi, M. Roveri, “On-line reconstruction of missing data in sensor\/actuator networks by exploiting temporal and spatial redundancy”, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2012), Brisbane, Australia, June 10-15, 2012. <\/span><\/li>\n
          56. C. Alippi, S. Ntalampiras, M. Roveri, “An HMM-based change detection method for intelligent embedded sensors”, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2012), Brisbane, Australia, June 10-15, 2012.<\/span><\/li>\n
          57. C. Alippi, G. Boracchi, M. Roveri, “Just-In-Time Ensemble of Classifiers”, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2012), Brisbane, Australia, June 10-15, 2012.<\/span><\/li>\n
          58. T. Mahmoodi, M. Roveri, \u201cIdentifying network failure via detecting changes in power profile”, in Proc. IEEE Pervasive Computing and Communications Workshops (IEEE PERCOM Workshops), pp. 758-763, Lugano, Switzerland, March 23, 2012.<\/span><\/li>\n
          59. C. Alippi, R. Camplani, M. Roveri, L. Vaccaro, \u201cREEL: a real-time, computationally-efficient, reprogrammable framework for Wireless Sensor Networks\u201c, in Proc. IEEE Sensors Conference (IEEE SENSORS 2011), Limerik, Ireland, Oct. 28-31, 2011.<\/span><\/li>\n
          60. C. Alippi, G. Boracchi, A. Marullo, M. Roveri, \u201cA Step Towards the Prediction of a Rock Collapse: Analysis of Micro-Acoustic Bursts\u201c, in Proc. IEEE Sensors Conference (IEEE SENSORS 2011), Limerik, Ireland, Oct. 28-31, 2011.<\/span><\/li>\n
          61. C. Alippi, G. Boracchi, M. Roveri, \u201cAn Effective Just-in-Time Adaptive Classifier for Gradual Concept Drifts\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2011), San Jose, USA, July 31 \u2013 Aug. 5, 2011.<\/span><\/li>\n
          62. C. Alippi, G. Boracchi, M. Roveri, \u201cA Hierarchical, Nonparametric Sequential Change-Detection Test\u201c, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2011), San Jose, USA, July 31 \u2013 Aug. 5, 2011.<\/span><\/li>\n
          63. C. Alippi, G. Boracchi, M. Roveri, \u201cA distributed Self-adaptive Nonparametric Change-Detection Test for Sensor\/Actuator Networks\u201d, in Proc. ENNS International Conference on Artificial Neural Networks (ICANN 2011), Espoo, Finland, June 14-17, 2011. Lecture Notes in Computer Science, Vol. 6792\/2011, pp. 173-180, 2011.<\/span><\/li>\n
          64. C. Alippi, R. Camplani, C. Galperti, A. Marullo, M. Roveri, \u201cAn hybrid wireless-wired monitoring system for real-time rock collapse forecasting\u201d, in Proc. IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2010), San Francisco, USA, Nov. 8-12, 2010.<\/span><\/li>\n
          65. C. Alippi, G. Boracchi, M. Roveri, \u201cAdaptive Classifiers with ICI-based Adaptive Knowledge Base Management\u201d, in Proc. ENNS International Conference on Artificial Neural Networks (ICANN 2010), Thessaloniki, Greece, Sept. 15-18, 2010.<\/span><\/li>\n
          66. C. Alippi, G. Boracchi, M. Roveri, \u201cChange Detection Tests Using the ICI rule\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2010), Barcelona, Spain, July 18-23, 2010.<\/span><\/li>\n
          67. C. Alippi, M. Roveri, \u201cVirtual k-fold cross validation: an effective method for accuracy assessment\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2010), Barcelona, Spain, July 18-23, 2010.<\/span><\/li>\n
          68. C. Alippi, G. Boracchi, M. Roveri, “Detecting Drops on Lens in Wireless Multimedia Sensor Network Nodes”, in Proc. IEEE International Workshop on RObotic and Sensors Environments (IEEE ROSE 2009), pp. 128-133, Lecco, Italy, Nov. 6-7, 2009.<\/span><\/li>\n
          69. C. Alippi, R. Camplani, M. Roveri, “A Virtual Machine for energy management in WSNs”, in Proc. IEEE International Workshop on RObotic and Sensors Environments (IEEE ROSE 2009), pp. 173-177, Lecco, Italy, Nov. 6-7, 2009.<\/span><\/li>\n
          70. C. Alippi, G. Boracchi, M. Roveri, “Just in time classifiers: managing the slow drift case”, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2009), Atlanta, USA, June 14-16, 2009.<\/span><\/li>\n
          71. C. Alippi, G. Baroni, A. Bersani, M. Roveri, “Unsupervised feature selection algorithms for Wireless Sensor Network”, in Proc. IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (IEEE CIMSA 2009), Hong Kong, China, May 11-13, 2009.<\/span><\/li>\n
          72. C. Alippi, R. Camplani, C. Galperti, M. Roveri, \u201cEffective design of WSNs: from the lab to the real world\u201d, in Proc. IEEE International Conference on Sensing Technology (IEEE ICST 2008), Tainan, Taiwan, Nov 30 – Dec 3, 2008.<\/span><\/li>\n
          73. C. Alippi, M. Fuhrman, M. Roveri, \u201ck-NN classifiers: investigating the k = k(n) relationship\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2008), Hong Kong, June 1-6, 2008.<\/span><\/li>\n
          74. C. Alippi, R. Camplani, C. Galperti, M. Roveri, L. Sportiello, \u201cTowards a credible WSNs deployment: a monitoring framework based on an adaptive communication protocol and energy-harvesting availability\u201d, in Proc. IEEE International Instrumentation and Measurement Technology Conference (IEEE I2MTC 2008), pp. 66-71, Victoria, Canada, May 12-15, 2008.<\/span><\/li>\n
          75. C. Alippi, R. Camplani, M. Roveri, \u201cA Hierarchical LLC Routing Algorithm for WSNs\u201d, in Proc. IEEE International Workshop on RObotic and Sensors Environments (IEEE ROSE 2007), pp. 1-6, Ontario, Canada, Oct. 12-13, 2007.<\/span><\/li>\n
          76. C. Alippi, G. Anastasi, C. Galperti, F. Mancini, M. Roveri, \u201cAdaptive Sampling for Energy Conservation in Wireless Sensor Networks for Snow Monitoring Applications\u201d, in Proc. IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2007), pp. 1 \u2013 6, Pisa, Italy, Oct. 8-11, 2007.<\/span><\/li>\n
          77. C. Alippi, M. Roveri, \u201cAdaptive Classifiers in Stationary Conditions\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2007), pp. 1008 \u2013 1013, Orlando, USA, Aug. 12-17, 2007.<\/span><\/li>\n
          78. C. Alippi, M. Roveri, \u201cJust-in-Time Adaptive Classifiers in Non-Stationary Conditions\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2007), pp. 1014 – 1019, Orlando, USA, Aug. 12-17, 2007. <\/span><\/li>\n
          79. C. Alippi, M. Roveri, \u201cReducing Computational Complexity in k-NN based Adaptive Classifiers\u201d, in Proc. International Conference on Computational Intelligence for Measurement Systems and Applications (IEEE CIMSA 2007), pp. 68 \u2013 71, Ostuni, Italia, June 27-29, 2007.<\/span><\/li>\n
          80. C. Alippi, G. Pelosi, M. Roveri, \u201cComputational intelligence techniques to detect toxic gas presence\u201d, in Proc. International Conference on Computational Intelligence for Measurement Systems and Applications (IEEE CIMSA 2006), pp. 40 \u2013 44, La Coruna, Spain, July 12-14, 2006.<\/span><\/li>\n
          81. M. Gamassi, M. Roveri, F. Scotti, V. Piuri, \u201cGenetic Techniques for Pattern Extraction in Particle Boards Images\u201d, in Proc. International Conference on Computational Intelligence for Measurement Systems and Applications (IEEE CIMSA 2006), pp. 129 – 134, La Coruna, Spain, July 12-14, 2006. <\/span><\/li>\n
          82. C. Alippi, M. Roveri, \u201cA computational intelligence-based criterion to detect non-stationarity trends\u201d, in Proc. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2006), pp. 5040-5044, Vancouver, Canada, July 16-21, 2006.<\/span><\/li>\n
          83. C. Alippi, M. Roveri, \u201cAn adaptive cusum-based test for signal change detection\u201d, in Proc. IEEE International Symposium on Circuits and Systems (IEEE ISCAS 2006), pp. 5752 \u2013 5755, Kos, Greece, May 21-24, 2006.<\/span><\/li>\n
          84. V. Piuri, M. Roveri, F. Scotti, \u201cVisual Inspection of Particle Boards for Quality Assessment\u201d, in Proc. IEEE International Conference on Image Processing (IEEE ICIP 2005), Vol. 3, pp. 521 \u2013 524, Genova, Italy, Sept. 11-14, 2005.<\/span><\/li>\n
          85. C. Alippi, F. Pessina, M. Roveri, \u201cAn Adaptive System for Automatic Invoice-Documents Classification\u201d, in Proc. IEEE International Conference on Image Processing (IEEE ICIP 2005), Vol. 2, pp. 526 – 529, Genova, Italy, Sept. 11-14, 2005.<\/span><\/li>\n
          86. V. Piuri, M. Roveri, F. Scotti, \u201cComputational Intelligence in Industrial Quality Control\u201d, in Proc. IEEE International Workshop on Intelligent Signal Processing (IEEE WISP 2005), pp. 4 \u2013 9, Faro, Portugal, Sept. 1-3, 2005.<\/span><\/li>\n
          87. N. Bianchessi, V. Piuri, G. Righini, M. Roveri, G. Laneve, A. Zigrino, \u201cAn optimization approach to the planning of Earth observing satellites \u201d, in Proc. International Workshop on Planning and Scheduling for Space, pp. 207-212, Darmstadt, Germany, June 23-25, 2004.<\/span><\/li>\n
          88. V. Piuri, M. Roveri, \u201cA Simulation Environment for Concatenated and Turbo Codes Analysis and Optimization\u201d, in Proc. IEEE Midwest Symposium On Circuits and Systems (IEEE MWCAS 2003), Vol. 3, pp. 1210-1212, Cairo, Egypt, Dec. 27-30, 2003.<\/span><\/li>\n<\/ol>\n