A selection of recent scientific publications from the members of the laboratory
61 entries « ‹ 1 of 2
› » 2021
Bacciu, Davide; Sarli, Daniele Di; Faraji, Pouria; Gallicchio, Claudio; Micheli, Alessio
Federated Reservoir Computing Neural Networks Conference Forthcoming
Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE, Forthcoming.
@conference{federatedRCIJCNN2021,
title = {Federated Reservoir Computing Neural Networks},
author = {Davide Bacciu and Daniele Di Sarli and Pouria Faraji and Claudio Gallicchio and Alessio Micheli},
year = {2021},
date = {2021-07-18},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},
publisher = {IEEE},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
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Bacciu, Davide; Bertoncini, Gioele; Morelli, Davide
Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data Journal Article
In: Neural Computing Applications, 2021.
@article{somHR2021b,
title = {Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data},
author = {Davide Bacciu and Gioele Bertoncini and Davide Morelli},
doi = {10.1007/s00521-020-05600-4},
year = {2021},
date = {2021-01-04},
journal = {Neural Computing Applications},
publisher = {Springer-Nature},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Yussupov, Vladimir; Soldani, Jacopo; Breitenbücher, Uwe; Brogi, Antonio; Leymann, Frank
FaaSten your decisions: A classification framework and technology review of function-as-a-Service platforms Journal Article
In: Journal of Systems and Software, 175 , pp. 110906, 2021, ISSN: 0164-1212.
@article{Yussupov2021_FaaSReview,
title = {FaaSten your decisions: A classification framework and technology review of function-as-a-Service platforms},
author = {Vladimir Yussupov and Jacopo Soldani and Uwe Breitenbücher and Antonio Brogi and Frank Leymann},
url = {http://www.sciencedirect.com/science/article/pii/S0164121221000030},
doi = {https://doi.org/10.1016/j.jss.2021.110906},
issn = {0164-1212},
year = {2021},
date = {2021-01-01},
journal = {Journal of Systems and Software},
volume = {175},
pages = {110906},
keywords = {},
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tppubtype = {article}
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Forti, S; Paganelli, F; Brogi, A
Probabilistic QoS-aware Placement of VNF Chains at the Edge Journal Article
In: Theory Pract. Log. Program., 2021, (In Press.).
@article{DBLP:journals/tplp/Forti21,
title = {Probabilistic QoS-aware Placement of VNF Chains at the Edge},
author = {S Forti and F Paganelli and A Brogi},
year = {2021},
date = {2021-01-01},
journal = {Theory Pract. Log. Program.},
note = {In Press.},
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Paganelli, Federica; Cappanera, Paola; Cuffaro, Giovanni
Tenant-defined service function chaining in a multi-site
network slice Journal Article
In: Future Generation Computer Systems, 2021.
@article{paganelli2021tenant,
title = {Tenant-defined service function chaining in a multi-site
network slice},
author = {Federica Paganelli and Paola Cappanera and Giovanni Cuffaro},
year = {2021},
date = {2021-01-01},
journal = {Future Generation Computer Systems},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
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2020
Numeroso, Danilo; Bacciu, Davide
Explaining Deep Graph Networks with Molecular Counterfactuals. Workshop
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Machine Learning for Molecules - Accepted as Contributed Talk (Oral), 2020.
@workshop{Numeroso2020,
title = {Explaining Deep Graph Networks with Molecular Counterfactuals.},
author = {Danilo Numeroso and Davide Bacciu},
url = {https://arxiv.org/pdf/2011.05134.pdf, Arxiv},
year = {2020},
date = {2020-12-12},
booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Machine Learning for Molecules - Accepted as Contributed Talk (Oral)},
keywords = {},
pubstate = {published},
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Bacciu, Davide; Conte, Alessio; Grossi, Roberto; Landolfi, Francesco; Marino, Andrea
K-plex Cover Pooling for Graph Neural Networks Workshop
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Learning Meets Combinatorial Algorithms, 2020.
@workshop{kplexWS2020,
title = {K-plex Cover Pooling for Graph Neural Networks},
author = {Davide Bacciu and Alessio Conte and Roberto Grossi and Francesco Landolfi and Andrea Marino},
year = {2020},
date = {2020-12-11},
booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Learning Meets Combinatorial Algorithms},
abstract = {We introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity pattern. Our pooling method, named KPlexPool, builds on the concepts of graph covers and $k$-plexes, i.e. pseudo-cliques where each node can miss up to $k$ links. The experimental evaluation on molecular and social graph classification shows that KPlexPool achieves state of the art performances, supporting the intuition that well-founded graph-theoretic approaches can be effectively integrated in learning models for graphs.},
keywords = {},
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We introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity pattern. Our pooling method, named KPlexPool, builds on the concepts of graph covers and $k$-plexes, i.e. pseudo-cliques where each node can miss up to $k$ links. The experimental evaluation on molecular and social graph classification shows that KPlexPool achieves state of the art performances, supporting the intuition that well-founded graph-theoretic approaches can be effectively integrated in learning models for graphs.
Cossu, Andrea; Carta, Antonio; Bacciu, Davide
Continual Learning with Gated Incremental Memories for Sequential Data Processing Conference
Proceedings of the 2020 IEEE World Congress on Computational Intelligence, 2020.
@conference{Cossu2020,
title = {Continual Learning with Gated Incremental Memories for Sequential Data Processing},
author = {Andrea Cossu and Antonio Carta and Davide Bacciu},
url = {https://arxiv.org/pdf/2004.04077.pdf, Arxiv},
doi = {10.1109/IJCNN48605.2020.9207550},
year = {2020},
date = {2020-07-19},
booktitle = {Proceedings of the 2020 IEEE World Congress on Computational Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Mazzei, Daniele; Baldi, Giacomo; Fantoni, Gualtiero; Montelisciani, Gabriele; Pitasi, Antonio; Ricci, Laura; Rizzello, Lorenzo
A Blockchain Tokenizer for Industrial IOT trustless applications Journal Article
In: Future Generation Computer Systems, 105 , pp. 432–445, 2020.
@article{mazzei2020blockchain,
title = {A Blockchain Tokenizer for Industrial IOT trustless applications},
author = {Daniele Mazzei and Giacomo Baldi and Gualtiero Fantoni and Gabriele Montelisciani and Antonio Pitasi and Laura Ricci and Lorenzo Rizzello},
year = {2020},
date = {2020-01-01},
journal = {Future Generation Computer Systems},
volume = {105},
pages = {432--445},
publisher = {North-Holland},
keywords = {},
pubstate = {published},
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Karvelis, Petros; Mazzei, Daniele; Biviano, Matteo; Stylios, Chrysostomos
PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction Journal Article
In: Sensors, 20 (11), pp. 3181, 2020.
@article{karvelis2020portweather,
title = {PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction},
author = {Petros Karvelis and Daniele Mazzei and Matteo Biviano and Chrysostomos Stylios},
year = {2020},
date = {2020-01-01},
journal = {Sensors},
volume = {20},
number = {11},
pages = {3181},
publisher = {Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
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Cerina, L; Santambrogio, MD; Franco, G; Gallicchio, C; Micheli, A
EchoBay: Design and Optimization of Echo State Networks under Memory and Time Constraints Journal Article
In: ACM Transactions on Architecture and Code Optimization (TACO), 17 (3), pp. 1–24, 2020.
@article{cerina2020echobay,
title = {EchoBay: Design and Optimization of Echo State Networks under Memory and Time Constraints},
author = {L Cerina and MD Santambrogio and G Franco and C Gallicchio and A Micheli},
year = {2020},
date = {2020-01-01},
journal = {ACM Transactions on Architecture and Code Optimization (TACO)},
volume = {17},
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Belli, Dimitri; Chessa, Stefano; Foschini, Luca; Girolami, Michele
The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection Journal Article
In: Pervasive and Mobile Computing, 67 , pp. 101231, 2020, ISSN: 1574-1192.
@article{BELLI2020101231,
title = {The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection},
author = {Dimitri Belli and Stefano Chessa and Luca Foschini and Michele Girolami},
url = {http://www.sciencedirect.com/science/article/pii/S1574119220300845},
doi = {https://doi.org/10.1016/j.pmcj.2020.101231},
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year = {2020},
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Belli, D; Chessa, S; Foschini, L; Girolami, M
A Probabilistic Model for the Deployment of Human-Enabled Edge Computing in Massive Sensing Scenarios Journal Article
In: IEEE Internet of Things Journal, 7 (3), pp. 2421-2431, 2020.
@article{Belli2020JIOT,
title = {A Probabilistic Model for the Deployment of Human-Enabled Edge Computing in Massive Sensing Scenarios},
author = {D Belli and S Chessa and L Foschini and M Girolami},
doi = {10.1109/JIOT.2019.2957835},
year = {2020},
date = {2020-01-01},
journal = {IEEE Internet of Things Journal},
volume = {7},
number = {3},
pages = {2421-2431},
keywords = {},
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Dynamic Bayesian network for crop growth prediction in greenhouses Journal Article
In: Computers and Electronics in Agriculture, 169 , pp. 105167, 2020, ISSN: 0168-1699.
@article{KOCIAN2020105167,
title = {Dynamic Bayesian network for crop growth prediction in greenhouses},
url = {http://www.sciencedirect.com/science/article/pii/S0168169919321131},
doi = {https://doi.org/10.1016/j.compag.2019.105167},
issn = {0168-1699},
year = {2020},
date = {2020-01-01},
journal = {Computers and Electronics in Agriculture},
volume = {169},
pages = {105167},
keywords = {},
pubstate = {published},
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Castellana, Daniele; Bacciu, Davide
Learning from Non-Binary Constituency Trees via Tensor Decomposition Inproceedings
In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 3899–3910, International Committee on Computational Linguistics, Barcelona, Spain (Online), 2020.
@inproceedings{castellana-bacciu-2020-learning,
title = {Learning from Non-Binary Constituency Trees via Tensor Decomposition},
author = {Daniele Castellana and Davide Bacciu},
url = {https://www.aclweb.org/anthology/2020.coling-main.346},
doi = {10.18653/v1/2020.coling-main.346},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
pages = {3899--3910},
publisher = {International Committee on Computational Linguistics},
address = {Barcelona, Spain (Online)},
keywords = {},
pubstate = {published},
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Bacciu, Davide; Errica, Federico; Micheli, Alessio; Podda, Marco
A gentle introduction to deep learning for graphs Journal Article
In: Neural Networks, 129 , pp. 203 - 221, 2020, ISSN: 0893-6080.
@article{BACCIU2020203,
title = {A gentle introduction to deep learning for graphs},
author = {Davide Bacciu and Federico Errica and Alessio Micheli and Marco Podda},
doi = {https://doi.org/10.1016/j.neunet.2020.06.006},
issn = {0893-6080},
year = {2020},
date = {2020-01-01},
journal = {Neural Networks},
volume = {129},
pages = {203 - 221},
keywords = {},
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Sarli, D D; Gallicchio, C; Micheli, A
Gated Echo State Networks: a preliminary study Inproceedings
In: 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1-5, 2020.
@inproceedings{gatedESN2020,
title = {Gated Echo State Networks: a preliminary study},
author = {D D Sarli and C Gallicchio and A Micheli},
doi = {10.1109/INISTA49547.2020.9194681},
year = {2020},
date = {2020-01-01},
booktitle = {2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)},
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Gallicchio, Claudio; Micheli, Alessio
Fast and Deep Graph Neural Networks Inproceedings
In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI
2020, The Thirty-Second Innovative Applications of Artificial Intelligence
Conference, IAAI 2020, The Tenth AAAI Symposium on Educational
Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA,
February 7-12, 2020, pp. 3898–3905, AAAI Press, 2020.
@inproceedings{DBLP:conf/aaai/GallicchioM20,
title = {Fast and Deep Graph Neural Networks},
author = {Claudio Gallicchio and Alessio Micheli},
year = {2020},
date = {2020-01-01},
booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI
2020, The Thirty-Second Innovative Applications of Artificial Intelligence
Conference, IAAI 2020, The Tenth AAAI Symposium on Educational
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February 7-12, 2020},
pages = {3898--3905},
publisher = {AAAI Press},
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Carta, Antonio; Sperduti, Alessandro; Bacciu, Davide
Incremental training of a recurrent neural network exploiting a multi-scale dynamic memory Conference
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML-PKDD 2020), Springer International Publishing, 2020.
@conference{ecml2020LMN,
title = {Incremental training of a recurrent neural network exploiting a multi-scale dynamic memory},
author = {Antonio Carta and Alessandro Sperduti and Davide Bacciu},
url = {https://arxiv.org/pdf/2006.16800.pdf, Arxiv},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML-PKDD 2020)},
publisher = {Springer International Publishing},
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tppubtype = {conference}
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Bogo, Matteo; Soldani, Jacopo; Neri, Davide; Brogi, Antonio
Component-aware orchestration of cloud-based enterprise applications, from TOSCA to Docker and Kubernetes Journal Article
In: Software: Practice and Experience, 50 (9), pp. 1793-1821, 2020.
@article{toskose,
title = {Component-aware orchestration of cloud-based enterprise applications, from TOSCA to Docker and Kubernetes},
author = {Matteo Bogo and Jacopo Soldani and Davide Neri and Antonio Brogi},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.2848},
doi = {10.1002/spe.2848},
year = {2020},
date = {2020-01-01},
journal = {Software: Practice and Experience},
volume = {50},
number = {9},
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Wurster, Michael; Breitenbücher, Uwe; Falkenthal, Michael; Krieger, Christoph; Leymann, Frank; Saatkamp, Karoline; Soldani, Jacopo
The essential deployment metamodel: a systematic review of deployment automation technologies Journal Article
In: SICS Software-Intensive Cyber-Physical Systems, 35 (1), pp. 63-75, 2020.
@article{Wurster2020_EDMM,
title = {The essential deployment metamodel: a systematic review of deployment automation technologies},
author = {Wurster, Michael and Breitenbücher, Uwe and Falkenthal, Michael and Krieger, Christoph and Leymann, Frank and Saatkamp, Karoline and Soldani, Jacopo},
doi = {10.1007/s00450-019-00412-x},
year = {2020},
date = {2020-01-01},
journal = {SICS Software-Intensive Cyber-Physical Systems},
volume = {35},
number = {1},
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Forti, Stefano; Ferrari, Gian Luigi; Brogi, Antonio
Secure Cloud-Edge Deployments, with Trust Journal Article
In: Future Gener. Comput. Syst., 102 , pp. 775–788, 2020.
@article{DBLP:journals/fgcs/FortiFB20,
title = {Secure Cloud-Edge Deployments, with Trust},
author = {Stefano Forti and Gian Luigi Ferrari and Antonio Brogi},
url = {https://doi.org/10.1016/j.future.2019.08.020},
doi = {10.1016/j.future.2019.08.020},
year = {2020},
date = {2020-01-01},
journal = {Future Gener. Comput. Syst.},
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pages = {775--788},
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Forti, Stefano; Brogi, Antonio
Continuous Reasoning for Managing Next-Gen Distributed Applications Inproceedings
In: Proceedings 36th International Conference on Logic Programming (ICLP - Technical
Communications), pp. 164–177, 2020.
@inproceedings{DBLP:journals/corr/abs-2009-10245,
title = {Continuous Reasoning for Managing Next-Gen Distributed Applications},
author = {Stefano Forti and Antonio Brogi},
url = {https://doi.org/10.4204/EPTCS.325.22},
doi = {10.4204/EPTCS.325.22},
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Lesort, Timothée; Lomonaco, Vincenzo; Stoian, Andrei; Maltoni, Davide; Filliat, David; Díaz-Rodríguez, Natalia
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges Journal Article
In: Information Fusion, 58 , pp. 52-68, 2020, ISSN: 1566-2535.
@article{LESORT202052,
title = {Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges},
author = {Timothée Lesort and Vincenzo Lomonaco and Andrei Stoian and Davide Maltoni and David Filliat and Natalia Díaz-Rodríguez},
url = {https://www.sciencedirect.com/science/article/pii/S1566253519307377},
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She, Q; Feng, F; Hao, X; Yang, Q; Lan, C; Lomonaco, V; Shi, X; Wang, Z; Guo, Y; Zhang, Y; Qiao, F; Chan, R H M
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning Inproceedings
In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4767-4773, 2020.
@inproceedings{9196887,
title = {OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning},
author = {Q She and F Feng and X Hao and Q Yang and C Lan and V Lomonaco and X Shi and Z Wang and Y Guo and Y Zhang and F Qiao and R H M Chan},
doi = {10.1109/ICRA40945.2020.9196887},
year = {2020},
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Bruni, Roberto; á, Hern; Montanari, Ugo
Bayesian network semantics for Petri nets Journal Article
In: Theor. Comput. Sci., 807 , pp. 95–113, 2020.
@article{DBLP:journals/tcs/BruniMM20,
title = {Bayesian network semantics for Petri nets},
author = {Roberto Bruni and Hern á and Ugo Montanari},
year = {2020},
date = {2020-01-01},
journal = {Theor. Comput. Sci.},
volume = {807},
pages = {95--113},
keywords = {},
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Bruni, Roberto; Montanari, Ugo; Sammartino, Matteo
Algebras for Tree Decomposable Graphs Inproceedings
In: Gadducci, Fabio; Kehrer, Timo (Ed.): Graph Transformation - 13th International Conference, ICGT 2020,
Held as Part of STAF 2020, Bergen, Norway, June 25-26, 2020, Proceedings, pp. 203–220, Springer, 2020.
@inproceedings{DBLP:conf/gg/BruniMS20,
title = {Algebras for Tree Decomposable Graphs},
author = {Roberto Bruni and Ugo Montanari and Matteo Sammartino},
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year = {2020},
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booktitle = {Graph Transformation - 13th International Conference, ICGT 2020,
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volume = {12150},
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publisher = {Springer},
series = {Lecture Notes in Computer Science},
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2019
Bacciu, Davide; Crecchi, Francesco
Augmenting Recurrent Neural Networks Resilience by Dropout Journal Article
In: IEEE Transactions on Neural Networs and Learning Systems, 2019.
@article{Bacciu2019,
title = {Augmenting Recurrent Neural Networks Resilience by Dropout},
author = {Davide Bacciu and Francesco Crecchi},
doi = {10.1109/TNNLS.2019.2899744},
year = {2019},
date = {2019-03-31},
journal = {IEEE Transactions on Neural Networs and Learning Systems},
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Matteis, T De; Mencagli, G; Sensi, D De; Torquati, M; Danelutto, M
GASSER: An Auto-Tunable System for General Sliding-Window Streaming Operators on GPUs Journal Article
In: IEEE Access, 7 , pp. 48753-48769, 2019, ISSN: 2169-3536.
@article{8688411,
title = {GASSER: An Auto-Tunable System for General Sliding-Window Streaming Operators on GPUs},
author = {T De Matteis and G Mencagli and D De Sensi and M Torquati and M Danelutto},
doi = {10.1109/ACCESS.2019.2910312},
issn = {2169-3536},
year = {2019},
date = {2019-01-01},
journal = {IEEE Access},
volume = {7},
pages = {48753-48769},
abstract = {Today's stream processing systems handle high-volume data streams in an efficient manner. To achieve this goal, they are designed to scale out on large clusters of commodity machines. However, despite the efficient use of distributed architectures, they lack support to co-processors like graphical processing units (GPUs) ready to accelerate data-parallel tasks. The main reason for this lack of integration is that GPU processing and the streaming paradigm have different processing models, with GPUs needing a bulk of data present at once while the streaming paradigm advocates a tuple-at-a-time processing model. This paper contributes to fill this gap by proposing Gasser, a system for offloading the execution of sliding-window operators on GPUs. The system focuses on completely general functions by targeting the parallel processing of non-incremental queries that are not supported by the few existing GPU-based streaming prototypes. Furthermore, Gasser provides an auto-tuning approach able to automatically find the optimal value of the configuration parameters (i.e., batch length and the degree of parallelism) needed to optimize throughput and latency with the given query and data stream. The experimental part assesses the performance efficiency of Gasser by comparing its peak throughput and latency against Apache Flink, a popular and scalable streaming system. Furthermore, we evaluate the penalty induced by supporting completely general queries against the performance achieved by the state-of-the-art solution specifically optimized for incremental queries. Finally, we show the speed and accuracy of the auto-tuning approach adopted by Gasser, which is able to self-configure the system by finding the right configuration parameters without manual tuning by the users.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Today's stream processing systems handle high-volume data streams in an efficient manner. To achieve this goal, they are designed to scale out on large clusters of commodity machines. However, despite the efficient use of distributed architectures, they lack support to co-processors like graphical processing units (GPUs) ready to accelerate data-parallel tasks. The main reason for this lack of integration is that GPU processing and the streaming paradigm have different processing models, with GPUs needing a bulk of data present at once while the streaming paradigm advocates a tuple-at-a-time processing model. This paper contributes to fill this gap by proposing Gasser, a system for offloading the execution of sliding-window operators on GPUs. The system focuses on completely general functions by targeting the parallel processing of non-incremental queries that are not supported by the few existing GPU-based streaming prototypes. Furthermore, Gasser provides an auto-tuning approach able to automatically find the optimal value of the configuration parameters (i.e., batch length and the degree of parallelism) needed to optimize throughput and latency with the given query and data stream. The experimental part assesses the performance efficiency of Gasser by comparing its peak throughput and latency against Apache Flink, a popular and scalable streaming system. Furthermore, we evaluate the penalty induced by supporting completely general queries against the performance achieved by the state-of-the-art solution specifically optimized for incremental queries. Finally, we show the speed and accuracy of the auto-tuning approach adopted by Gasser, which is able to self-configure the system by finding the right configuration parameters without manual tuning by the users.
Mencagli, G; Torquati, M; Griebler, D; Danelutto, M; Fernandes, L G L
Raising the Parallel Abstraction Level for Streaming Analytics Applications Journal Article
In: IEEE Access, 7 , pp. 131944-131961, 2019, ISSN: 2169-3536.
@article{8834783,
title = {Raising the Parallel Abstraction Level for Streaming Analytics Applications},
author = {G Mencagli and M Torquati and D Griebler and M Danelutto and L G L Fernandes},
doi = {10.1109/ACCESS.2019.2941183},
issn = {2169-3536},
year = {2019},
date = {2019-01-01},
journal = {IEEE Access},
volume = {7},
pages = {131944-131961},
abstract = {In the stream processing domain, applications are represented by graphs of operators arbitrarily connected and filled with their business logic code. The APIs of existing Stream Processing Systems (SPSs) ease the development of transformations that recur in the streaming practice (e.g., filtering, aggregation and joins). In contrast, their parallelism abstractions are quite limited since they provide support to stateless operators only, or when the state is organized in a set of key-value pairs. This paper presents how the parallel patterns methodology can be revisited for sliding-window streaming analytics. Our vision fosters a design process of the application as composition and nesting of ready-to-use patterns provided through a C++17 fluent interface. Our prototype implements the run-time system of the patterns in the FastFlow parallel library expressing thread-based parallelism. The experimental analysis shows interesting outcomes. First, our pattern-based approach allows easy prototyping of different versions of the application, and the programmer can leverage nesting of patterns to increase performance (up to 37% in one of the two considered test-bed cases). Second, our FastFlow implementation outperforms (three times faster) the handmade porting of our patterns in popular JVM-based SPSs. Finally, in the concluding part of this paper, we explore the use of a task-based run-time system, by deriving interesting insights into how to make our patterns library suitable for multi backends.},
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In the stream processing domain, applications are represented by graphs of operators arbitrarily connected and filled with their business logic code. The APIs of existing Stream Processing Systems (SPSs) ease the development of transformations that recur in the streaming practice (e.g., filtering, aggregation and joins). In contrast, their parallelism abstractions are quite limited since they provide support to stateless operators only, or when the state is organized in a set of key-value pairs. This paper presents how the parallel patterns methodology can be revisited for sliding-window streaming analytics. Our vision fosters a design process of the application as composition and nesting of ready-to-use patterns provided through a C++17 fluent interface. Our prototype implements the run-time system of the patterns in the FastFlow parallel library expressing thread-based parallelism. The experimental analysis shows interesting outcomes. First, our pattern-based approach allows easy prototyping of different versions of the application, and the programmer can leverage nesting of patterns to increase performance (up to 37% in one of the two considered test-bed cases). Second, our FastFlow implementation outperforms (three times faster) the handmade porting of our patterns in popular JVM-based SPSs. Finally, in the concluding part of this paper, we explore the use of a task-based run-time system, by deriving interesting insights into how to make our patterns library suitable for multi backends.
Franco, Giuseppe; Cerina, Luca; Gallicchio, Claudio; Micheli, Alessio; Santambrogio, Marco Domenico
Continuous Blood Pressure Estimation Through Optimized Echo State Networks Inproceedings
In: International Conference on Artificial Neural Networks, pp. 48–61, Springer 2019.
@inproceedings{franco2019continuous,
title = {Continuous Blood Pressure Estimation Through Optimized Echo State Networks},
author = {Giuseppe Franco and Luca Cerina and Claudio Gallicchio and Alessio Micheli and Marco Domenico Santambrogio},
year = {2019},
date = {2019-01-01},
booktitle = {International Conference on Artificial Neural Networks},
pages = {48--61},
organization = {Springer},
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pubstate = {published},
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Parvin, Parvaneh; Chessa, Stefano; Kaptein, Maurits; `o, Fabio Patern
Personalized real-time anomaly detection and health feedback for older adults Journal Article
In: Journal of Ambient Intelligence and Smart Environments, 11 (5), pp. 453–469, 2019, ISSN: 1876-1364.
@article{Parvin2019,
title = {Personalized real-time anomaly detection and health feedback for older adults},
author = {Parvaneh Parvin and Stefano Chessa and Maurits Kaptein and Fabio Patern `o},
doi = {10.3233/AIS-190536},
issn = {1876-1364},
year = {2019},
date = {2019-01-01},
journal = {Journal of Ambient Intelligence and Smart Environments},
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number = {5},
pages = {453--469},
publisher = {IOS Press},
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Bacciu, Davide; Rocco, Maurizio Di; Dragone, Mauro; Gallicchio, Claudio; Micheli, Alessio; Saffiotti, Alessandro
An ambient intelligence approach for learning in smart robotic environments Journal Article
In: Computational Intelligence, 35 (4), pp. 1060–1087, 2019.
@article{DBLP:journals/ci/BacciuRDGMS19,
title = {An ambient intelligence approach for learning in smart robotic environments},
author = {Davide Bacciu and Maurizio Di Rocco and Mauro Dragone and Claudio Gallicchio and Alessio Micheli and Alessandro Saffiotti},
doi = {10.1111/coin.12233},
year = {2019},
date = {2019-01-01},
journal = {Computational Intelligence},
volume = {35},
number = {4},
pages = {1060--1087},
keywords = {},
pubstate = {published},
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}
Brogi, Antonio; Carrasco, Jose; Durán, Francisco; Pimentel, Ernesto; Soldani, Jacopo
Robust Management of Trans-Cloud Applications Inproceedings
In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pp. 219-223, 2019, ISSN: 2159-6190.
@inproceedings{robust-trans-cloud,
title = {Robust Management of Trans-Cloud Applications},
author = {Antonio Brogi and Jose Carrasco and Francisco Durán and Ernesto Pimentel and Jacopo Soldani},
url = {http://dx.doi.org/10.1109/CLOUD.2019.00046},
doi = {10.1109/CLOUD.2019.00046},
issn = {2159-6190},
year = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE 12th International Conference on Cloud Computing (CLOUD)},
pages = {219-223},
keywords = {},
pubstate = {published},
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}
Maltoni, Davide; Lomonaco, Vincenzo
Continuous learning in single-incremental-task scenarios Journal Article
In: Neural Networks, 116 , pp. 56-73, 2019, ISSN: 0893-6080.
@article{MALTONI201956,
title = {Continuous learning in single-incremental-task scenarios},
author = {Davide Maltoni and Vincenzo Lomonaco},
url = {https://www.sciencedirect.com/science/article/pii/S0893608019300838},
doi = {https://doi.org/10.1016/j.neunet.2019.03.010},
issn = {0893-6080},
year = {2019},
date = {2019-01-01},
journal = {Neural Networks},
volume = {116},
pages = {56-73},
abstract = {It was recently shown that architectural, regularization and rehearsal strategies can be used to train deep models sequentially on a number of disjoint tasks without forgetting previously acquired knowledge. However, these strategies are still unsatisfactory if the tasks are not disjoint but constitute a single incremental task (e.g., class-incremental learning). In this paper we point out the differences between multi-task and single-incremental-task scenarios and show that well-known approaches such as LWF, EWC and SI are not ideal for incremental task scenarios. A new approach, denoted as AR1, combining architectural and regularization strategies is then specifically proposed. AR1 overhead (in terms of memory and computation) is very small thus making it suitable for online learning. When tested on CORe50 and iCIFAR-100, AR1 outperformed existing regularization strategies by a good margin.},
keywords = {},
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}
It was recently shown that architectural, regularization and rehearsal strategies can be used to train deep models sequentially on a number of disjoint tasks without forgetting previously acquired knowledge. However, these strategies are still unsatisfactory if the tasks are not disjoint but constitute a single incremental task (e.g., class-incremental learning). In this paper we point out the differences between multi-task and single-incremental-task scenarios and show that well-known approaches such as LWF, EWC and SI are not ideal for incremental task scenarios. A new approach, denoted as AR1, combining architectural and regularization strategies is then specifically proposed. AR1 overhead (in terms of memory and computation) is very small thus making it suitable for online learning. When tested on CORe50 and iCIFAR-100, AR1 outperformed existing regularization strategies by a good margin.
Pellegrini, Lorenzo; Graffieti, Gabriele; Lomonaco, Vincenzo; Maltoni, Davide
Latent Replay for Real-Time Continual Learning Journal Article
In: CoRR, abs/1912.01100 , 2019.
@article{DBLP:journals/corr/abs-1912-01100,
title = {Latent Replay for Real-Time Continual Learning},
author = {Lorenzo Pellegrini and Gabriele Graffieti and Vincenzo Lomonaco and Davide Maltoni},
url = {http://arxiv.org/abs/1912.01100},
year = {2019},
date = {2019-01-01},
journal = {CoRR},
volume = {abs/1912.01100},
keywords = {},
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}
Chiti, Francesco; Fantacci, Romano; Paganelli, Federica; Picano, Benedetta
Virtual functions placement with time constraints in fog
computing: A matching theory perspective Journal Article
In: IEEE Transactions on Network and Service Management, 16 (3), pp. 980–989, 2019.
@article{chiti2019virtual,
title = {Virtual functions placement with time constraints in fog
computing: A matching theory perspective},
author = {Francesco Chiti and Romano Fantacci and Federica Paganelli and Benedetta Picano},
year = {2019},
date = {2019-01-01},
journal = {IEEE Transactions on Network and Service Management},
volume = {16},
number = {3},
pages = {980--989},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cappanera, Paola; Paganelli, Federica; Paradiso, Francesca
VNF placement for service chaining in a distributed cloud
environment with multiple stakeholders Journal Article
In: Computer Communications, 133 , pp. 24-40, 2019, ISSN: 0140-3664.
@article{CAPPANERA201924,
title = {VNF placement for service chaining in a distributed cloud
environment with multiple stakeholders},
author = {Paola Cappanera and Federica Paganelli and Francesca Paradiso},
url = {https://www.sciencedirect.com/science/article/pii/S0140366418303104},
doi = {https://doi.org/10.1016/j.comcom.2018.10.008},
issn = {0140-3664},
year = {2019},
date = {2019-01-01},
journal = {Computer Communications},
volume = {133},
pages = {24-40},
keywords = {},
pubstate = {published},
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2018
Bacciu, Davide; Colombo, Michele; Morelli, Davide; Plans, David
Randomized neural networks for preference learning with physiological data Journal Article
In: Neurocomputing, 298 , pp. 9-20, 2018.
@article{bacciu2018,
title = {Randomized neural networks for preference learning with physiological data},
author = {Davide Bacciu and Michele Colombo and Davide Morelli and David Plans},
doi = {10.1016/j.neucom.2017.11.070},
year = {2018},
date = {2018-07-12},
journal = {Neurocomputing},
volume = {298},
pages = {9-20},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mazzei, Daniele; Baldi, Giacomo; Montelisciani, Gabriele; Fantoni, Gualtiero
A full stack for quick prototyping of IoT solutions Journal Article
In: Annals of Telecommunications, 73 (7-8), pp. 439–449, 2018.
@article{mazzei2018full,
title = {A full stack for quick prototyping of IoT solutions},
author = {Daniele Mazzei and Giacomo Baldi and Gabriele Montelisciani and Gualtiero Fantoni},
year = {2018},
date = {2018-01-01},
journal = {Annals of Telecommunications},
volume = {73},
number = {7-8},
pages = {439--449},
publisher = {Springer International Publishing},
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tppubtype = {article}
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Cominelli, Lorenzo; Mazzei, Daniele; Rossi, Danilo Emilio De
SEAI: Social emotional artificial intelligence based on Damasio’s Theory of Mind Journal Article
In: Frontiers in Robotics and AI, 5 , pp. 6, 2018.
@article{cominelli2018seai,
title = {SEAI: Social emotional artificial intelligence based on Damasio’s Theory of Mind},
author = {Lorenzo Cominelli and Daniele Mazzei and Danilo Emilio De Rossi},
year = {2018},
date = {2018-01-01},
journal = {Frontiers in Robotics and AI},
volume = {5},
pages = {6},
publisher = {Frontiers},
keywords = {},
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Lazzeri, Nicole; Mazzei, Daniele; Cominelli, Lorenzo; Cisternino, Antonio; Rossi, Danilo Emilio De
Designing the mind of a social robot Journal Article
In: Applied Sciences, 8 (2), pp. 302, 2018.
@article{lazzeri2018designing,
title = {Designing the mind of a social robot},
author = {Nicole Lazzeri and Daniele Mazzei and Lorenzo Cominelli and Antonio Cisternino and Danilo Emilio De Rossi},
year = {2018},
date = {2018-01-01},
journal = {Applied Sciences},
volume = {8},
number = {2},
pages = {302},
publisher = {Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Mencagli, Gabriele; Torquati, Massimo; Danelutto, Marco
Elastic-PPQ: A two-level autonomic system for spatial preference query processing over dynamic data streams Journal Article
In: Future Generation Computer Systems, 79 , pp. 862 - 877, 2018, ISSN: 0167-739X.
@article{MENCAGLI2018862,
title = {Elastic-PPQ: A two-level autonomic system for spatial preference query processing over dynamic data streams},
author = {Gabriele Mencagli and Massimo Torquati and Marco Danelutto},
url = {http://www.sciencedirect.com/science/article/pii/S0167739X1730938X},
doi = {https://doi.org/10.1016/j.future.2017.09.004},
issn = {0167-739X},
year = {2018},
date = {2018-01-01},
journal = {Future Generation Computer Systems},
volume = {79},
pages = {862 - 877},
abstract = {Paradigms like Internet of Things and the most recent Internet of Everything are shifting the attention towards systems able to process unbounded sequences of items in the form of data streams. In the real world, data streams may be highly variable, exhibiting burstiness in the arrival rate and non-stationarities such as trends and cyclic behaviors. Furthermore, input items may be not ordered according to timestamps. This raises the complexity of stream processing systems, which must support elastic resource management and autonomic QoS control through sophisticated strategies and run-time mechanisms. In this paper we present Elastic-PPQ, a system for processing spatial preference queries over dynamic data streams. The key aspect of the system design is the existence of two adaptation levels handling workload variations at different time-scales. To address fast time-scale variations we design a fine regulatory mechanism of load balancing supported by a control-theoretic approach. The logic of the second adaptation level, targeting slower time-scale variations, is incorporated in a Fuzzy Logic Controller that makes scale in/out decisions of the system parallelism degree. The approach has been successfully evaluated under synthetic and real-world datasets.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Paradigms like Internet of Things and the most recent Internet of Everything are shifting the attention towards systems able to process unbounded sequences of items in the form of data streams. In the real world, data streams may be highly variable, exhibiting burstiness in the arrival rate and non-stationarities such as trends and cyclic behaviors. Furthermore, input items may be not ordered according to timestamps. This raises the complexity of stream processing systems, which must support elastic resource management and autonomic QoS control through sophisticated strategies and run-time mechanisms. In this paper we present Elastic-PPQ, a system for processing spatial preference queries over dynamic data streams. The key aspect of the system design is the existence of two adaptation levels handling workload variations at different time-scales. To address fast time-scale variations we design a fine regulatory mechanism of load balancing supported by a control-theoretic approach. The logic of the second adaptation level, targeting slower time-scale variations, is incorporated in a Fuzzy Logic Controller that makes scale in/out decisions of the system parallelism degree. The approach has been successfully evaluated under synthetic and real-world datasets.
Mencagli, Gabriele; Torquati, Massimo; Lucattini, Fabio; Cuomo, Salvatore; Aldinucci, Marco
Harnessing sliding-window execution semantics for parallel stream processing Journal Article
In: Journal of Parallel and Distributed Computing, 116 , pp. 74 - 88, 2018, ISSN: 0743-7315, (Towards the Internet of Data: Applications, Opportunities and Future Challenges).
@article{MENCAGLI201874,
title = {Harnessing sliding-window execution semantics for parallel stream processing},
author = {Gabriele Mencagli and Massimo Torquati and Fabio Lucattini and Salvatore Cuomo and Marco Aldinucci},
url = {http://www.sciencedirect.com/science/article/pii/S0743731517302976},
doi = {https://doi.org/10.1016/j.jpdc.2017.10.021},
issn = {0743-7315},
year = {2018},
date = {2018-01-01},
journal = {Journal of Parallel and Distributed Computing},
volume = {116},
pages = {74 - 88},
abstract = {According to the recent trend in data acquisition and processing technology, big data are increasingly available in the form of unbounded streams of elementary data items to be processed in real-time. In this paper we study in detail the paradigm of sliding windows, a well-known technique for approximated queries that update their results continuously as new fresh data arrive from the stream. In this work we focus on the relationship between the various existing sliding window semantics and the way the query processing is performed from the parallelism perspective. From this study two alternative parallel models are identified, each covering semantics with very precise properties. Each model is described in terms of its pros and cons, and parallel implementations in the FastFlow framework are analyzed by discussing the layout of the concurrent data structures used for the efficient windows representation in each model.},
note = {Towards the Internet of Data: Applications, Opportunities and Future Challenges},
keywords = {},
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According to the recent trend in data acquisition and processing technology, big data are increasingly available in the form of unbounded streams of elementary data items to be processed in real-time. In this paper we study in detail the paradigm of sliding windows, a well-known technique for approximated queries that update their results continuously as new fresh data arrive from the stream. In this work we focus on the relationship between the various existing sliding window semantics and the way the query processing is performed from the parallelism perspective. From this study two alternative parallel models are identified, each covering semantics with very precise properties. Each model is described in terms of its pros and cons, and parallel implementations in the FastFlow framework are analyzed by discussing the layout of the concurrent data structures used for the efficient windows representation in each model.
Brogi, Antonio; Canciani, Andrea; Soldani, Jacopo
Fault-aware management protocols for multi-component applications Journal Article
In: Journal of Systems and Software, 139 , pp. 189 - 210, 2018, ISSN: 0164-1212.
@article{fault-aware-management-protocols,
title = {Fault-aware management protocols for multi-component applications},
author = {Antonio Brogi and Andrea Canciani and Jacopo Soldani},
doi = {10.1016/j.jss.2018.02.005},
issn = {0164-1212},
year = {2018},
date = {2018-01-01},
journal = {Journal of Systems and Software},
volume = {139},
pages = {189 - 210},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2017
Lomonaco, Vincenzo; Maltoni, Davide
CORe50: a New Dataset and Benchmark for Continuous Object Recognition Inproceedings
In: Levine, Sergey; Vanhoucke, Vincent; Goldberg, Ken (Ed.): Proceedings of the 1st Annual Conference on Robot Learning, pp. 17–26, PMLR, 2017.
@inproceedings{pmlr-v78-lomonaco17a,
title = {CORe50: a New Dataset and Benchmark for Continuous Object Recognition},
author = {Vincenzo Lomonaco and Davide Maltoni},
editor = {Sergey Levine and Vincent Vanhoucke and Ken Goldberg},
url = {http://proceedings.mlr.press/v78/lomonaco17a.html},
year = {2017},
date = {2017-11-01},
booktitle = {Proceedings of the 1st Annual Conference on Robot Learning},
volume = {78},
pages = {17--26},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
abstract = {Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem. In fact, fully retraining models each time new data become available is infeasible, due to computational and storage issues, while naïve incremental strategies have been shown to suffer from catastrophic forgetting. In the context of real-world object recognition applications (e.g., robotic vision), where continuous learning is crucial, very few datasets and benchmarks are available to evaluate and compare emerging techniques. In this work we propose a new dataset and benchmark CORe50, specifically designed for continuous object recognition, and introduce baseline approaches for different continuous learning scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem. In fact, fully retraining models each time new data become available is infeasible, due to computational and storage issues, while naïve incremental strategies have been shown to suffer from catastrophic forgetting. In the context of real-world object recognition applications (e.g., robotic vision), where continuous learning is crucial, very few datasets and benchmarks are available to evaluate and compare emerging techniques. In this work we propose a new dataset and benchmark CORe50, specifically designed for continuous object recognition, and introduce baseline approaches for different continuous learning scenarios.
Bacciu, Davide; Chessa, Stefano; Gallicchio, Claudio; Micheli, Alessio
On the Need of Machine Learning as a Service for the Internet of Things Conference
Proceedings of the International Conference on Internet of Things and Machine Learning (IML 2017), ACM, 2017, ISBN: 978-1-4503-5243-7.
@conference{mlAaS18,
title = {On the Need of Machine Learning as a Service for the Internet of Things},
author = {Davide Bacciu and Stefano Chessa and Claudio Gallicchio and Alessio Micheli},
isbn = {978-1-4503-5243-7},
year = {2017},
date = {2017-10-18},
booktitle = {Proceedings of the International Conference on Internet of Things and Machine Learning (IML 2017)},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {conference}
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Mencagli, G; Torquati, M; Danelutto, M; Matteis, T De
Parallel Continuous Preference Queries over Out-of-Order and Bursty Data Streams Journal Article
In: IEEE Transactions on Parallel and Distributed Systems, 28 (9), pp. 2608-2624, 2017, ISSN: 1558-2183.
@article{7873332,
title = {Parallel Continuous Preference Queries over Out-of-Order and Bursty Data Streams},
author = {G Mencagli and M Torquati and M Danelutto and T De Matteis},
doi = {10.1109/TPDS.2017.2679197},
issn = {1558-2183},
year = {2017},
date = {2017-09-01},
journal = {IEEE Transactions on Parallel and Distributed Systems},
volume = {28},
number = {9},
pages = {2608-2624},
abstract = {Techniques to handle traffic bursts and out-of-order arrivals are of paramount importance to provide real-time sensor data analytics in domains like traffic surveillance, transportation management, healthcare and security applications. In these systems the amount of raw data coming from sensors must be analyzed by continuous queries that extract value-added information used to make informed decisions in real-time. To perform this task with timing constraints, parallelism must be exploited in the query execution in order to enable the real-time processing on parallel architectures. In this paper we focus on continuous preference queries, a representative class of continuous queries for decision making, and we propose a parallel query model targeting the efficient processing over out-of-order and bursty data streams. We study how to integrate punctuation mechanisms in order to enable out-of-order processing. Then, we present advanced scheduling strategies targeting scenarios with different burstiness levels, parameterized using the index of dispersion quantity. Extensive experiments have been performed using synthetic datasets and real-world data streams obtained from an existing real-time locating system. The experimental evaluation demonstrates the efficiency of our parallel solution and its effectiveness in handling the out-of-orderness degrees and burstiness levels of real-world applications.},
keywords = {},
pubstate = {published},
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Techniques to handle traffic bursts and out-of-order arrivals are of paramount importance to provide real-time sensor data analytics in domains like traffic surveillance, transportation management, healthcare and security applications. In these systems the amount of raw data coming from sensors must be analyzed by continuous queries that extract value-added information used to make informed decisions in real-time. To perform this task with timing constraints, parallelism must be exploited in the query execution in order to enable the real-time processing on parallel architectures. In this paper we focus on continuous preference queries, a representative class of continuous queries for decision making, and we propose a parallel query model targeting the efficient processing over out-of-order and bursty data streams. We study how to integrate punctuation mechanisms in order to enable out-of-order processing. Then, we present advanced scheduling strategies targeting scenarios with different burstiness levels, parameterized using the index of dispersion quantity. Extensive experiments have been performed using synthetic datasets and real-world data streams obtained from an existing real-time locating system. The experimental evaluation demonstrates the efficiency of our parallel solution and its effectiveness in handling the out-of-orderness degrees and burstiness levels of real-world applications.
Bacciu, Davide; Chessa, Stefano; Gallicchio, Claudio; Micheli, Alessio; Pedrelli, Luca; Ferro, Erina; Fortunati, Luigi; Rosa, Davide La; Palumbo, Filippo; Vozzi, Federico; others,
A learning system for automatic Berg Balance Scale score estimation Journal Article
In: Engineering Applications of Artificial Intelligence, 66 , pp. 60–74, 2017.
@article{bacciu2017learning,
title = {A learning system for automatic Berg Balance Scale score estimation},
author = {Davide Bacciu and Stefano Chessa and Claudio Gallicchio and Alessio Micheli and Luca Pedrelli and Erina Ferro and Luigi Fortunati and Davide La Rosa and Filippo Palumbo and Federico Vozzi and others},
year = {2017},
date = {2017-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {66},
pages = {60--74},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Chessa, Stefano; Girolami, Michele; Foschini, Luca; Ianniello, Raffaele; Corradi, Antonio; Bellavista, Paolo
Mobile crowd sensing management with the ParticipAct living lab Journal Article
In: Pervasive and Mobile Computing, 38 , pp. 200 - 214, 2017, ISSN: 1574-1192.
@article{CHESSA2017200,
title = {Mobile crowd sensing management with the ParticipAct living lab},
author = {Stefano Chessa and Michele Girolami and Luca Foschini and Raffaele Ianniello and Antonio Corradi and Paolo Bellavista},
url = {http://www.sciencedirect.com/science/article/pii/S1574119216302127},
doi = {https://doi.org/10.1016/j.pmcj.2016.09.005},
issn = {1574-1192},
year = {2017},
date = {2017-01-01},
journal = {Pervasive and Mobile Computing},
volume = {38},
pages = {200 - 214},
keywords = {},
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}
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