@conference{nokey,

title = {Deep Reinforcement Learning for Network Slice Placement and the DeepNetSlice Toolkit},

author = {Alex Pasquali and Vincenzo Lomonaco and Davide Bacciu and Federica Paganelli},

year  = {2024},

date = {2024-05-05},

urldate = {2024-05-05},

booktitle = {Proceedings of the IEEE International Conference on Machine Learning for Communication and Networking 2024 (IEEE ICMLCN 2024)},

publisher = {IEEE},

keywords = {},

pubstate = {forthcoming},

tppubtype = {conference}

}

@conference{Lanciano2021,

title = { Predictive Auto-scaling with OpenStack Monasca},

author = {Giacomo Lanciano and Filippo Galli and Tommaso Cucinotta and Davide Bacciu and Andrea Passarella},

url = {https://arxiv.org/abs/2111.02133, Arxiv},

doi = {10.1145/3468737.3494104},

year  = {2021},

date = {2021-12-06},

urldate = {2021-12-06},

booktitle = {Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2021)},

pages = {1-10},

abstract = {Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services.

To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future. Our approach leverages on time-series forecasting techniques, like those based on machine learning and artificial neural networks, to predict the future dynamics of key metrics, e.g., resource consumption metrics, and apply a threshold-based scaling policy on them. The result is a predictive automation policy that is able, for instance, to automatically anticipate peaks in the load of a cloud application and trigger ahead of time appropriate scaling actions to accommodate the expected increase in traffic.

We prototyped our approach as an open-source OpenStack component, which relies on, and extends, the monitoring capabilities offered by Monasca, resulting in the addition of predictive metrics that can be leveraged by orchestration components like Heat or Senlin. We show experimental results using a recurrent neural network and a multi-layer perceptron as predictor, which are compared with a simple linear regression and a traditional non-predictive auto-scaling policy. However, the proposed framework allows for the easy customization of the prediction policy as needed. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{BacciuIJCNN2021,

title = {Federated Reservoir Computing Neural Networks},

author = {Davide Bacciu and Daniele Di Sarli and Pouria Faraji and Claudio Gallicchio and Alessio Micheli},

doi = {10.1109/IJCNN52387.2021.9534035},

year  = {2021},

date = {2021-07-18},

urldate = {2021-07-18},

booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},

publisher = {IEEE},

abstract = {A critical aspect in Federated Learning is the aggregation strategy for the combination of multiple models, trained on the edge, into a single model that incorporates all the knowledge in the federation. Common Federated Learning approaches for Recurrent Neural Networks (RNNs) do not provide guarantees on the predictive performance of the aggregated model. In this paper we show how the use of Echo State Networks (ESNs), which are efficient state-of-the-art RNN models for time-series processing, enables a form of federation that is optimal in the sense that it produces models mathematically equivalent to the corresponding centralized model. Furthermore, the proposed method is compliant with privacy constraints. The proposed method, which we denote as Incremental Federated Learning, is experimentally evaluated against an averaging strategy on two datasets for human state and activity recognition.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{iml2017,

title = {On the Need of Machine Learning as a Service for the Internet of Things},

author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio},

isbn = {978-1-4503-5243-7},

year  = {2017},

date = {2017-10-18},

booktitle = {To appear in the Proc. of the International Conference on Internet of Things and Machine Learning (IML 2017)},

journal = {Proc},

publisher = {ACM},

series = {International Conference Proceedings Series (ICPS)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ie2016,

title = { Detecting socialization events in ageing people: the experienze of the DOREMI project},

author = {Bacciu Davide and Chessa Stefano and Ferro Erina and Fortunati Luigi and Gallicchio Claudio and La Rosa Davide and Llorente Miguel and Micheli Alessio and Palumbo Filippo and Parodi Oberdan and Valenti Andrea and Vozzi Federico},

doi = {10.1109/IE.2016.28},

issn = {2472-7571 },

year  = {2016},

date = {2016-10-27},

urldate = {2016-10-27},

booktitle = {Proceedings of the IEEE 12th International Conference on Intelligent Environments (IE 2016), },

pages = {132-135},

publisher = {IEEE},

address = {UK, London},

abstract = {The detection of socialization events is useful to build indicators about social isolation of people, which is an important indicator in e-health applications. On the other hand, it is rather difficult to achieve with non-invasive solutions. This paper reports about the currently work-in-progress on the technological solution for the detection of socialization events adopted in the DOREMI project.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_774434,

title = {ESNigma: efficient feature selection for Echo State Networks},

author = {Bacciu Davide and Benedetti Filippo and Micheli Alessio},

url = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-104.pdf},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15)},

pages = {189--194},

publisher = {i6doc.com publ.},

abstract = {The paper introduces a feature selection wrapper designed specifically for Echo State Networks. It defines a feature scoring heuristics, applicable to generic subset search algorithms, which allows to reduce the need for model retraining with respect to wrappers in literature. The experimental assessment on real-word noisy sequential data shows that the proposed method can identify a compact set of relevant, highly predictive features with as little as $60%$ of the time required by the original wrapper.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_775269,

title = {Smart environments and context-awareness for lifestyle management in a healthy active ageing framework},

author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio and Ferro Erina and Fortunati Luigi and Palumbo Filippo and Parodi Oberdan and Vozzi Federico and Hanke Sten and Kropf Johannes and Kreiner Karl},

url = {http://springerlink.com/content/0302-9743/copyright/2005/},

doi = {10.1007/978-3-319-23485-4_6},

year  = {2015},

date = {2015-01-01},

booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9273},

pages = {54--66},

publisher = {Springer Verlag},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{icfEann14,

title = {An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications},

author = {Bacciu Davide},

doi = {10.1007/978-3-319-11071-4_4},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

booktitle = {Communications in Computer and Information Science - Engineering Applications of Neural Networks},

journal = {COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE},

volume = {459},

pages = {39--48},

publisher = {Springer International Publishing},

abstract = {The paper discusses an efficient feature selection approach for multivariate timeseries of heterogeneous sensor data within a pervasive computing scenario.  An iterative filtering procedure is devised

to reduce information redundancy measured in terms of timeseries cross-correlation. The algorithm is capable of identifying non-redundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. A comparative experimental analysis on real-world data

from pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in literature when dealing with sensor timeseries.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_588269,

title = {Learning context-aware mobile robot navigation in home environments},

author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio and Di Rocco Maurizio and Saffiotti Alessandro},

doi = {10.1109/IISA.2014.6878733},

isbn = {9781479961702},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014)},

pages = {57--62},

publisher = {IEEE},

abstract = {We present an approach to make planning adaptive in order to enable context-aware mobile robot navigation. We integrate a model-based planner with a distributed learning system based on reservoir computing, to yield personalized planning and resource allocations that account for user preferences and environmental changes. We demonstrate our approach in a real robot ecology, and show that the learning system can effectively exploit historical data about navigation performance to modify the models in the planner, without any prior information oncerning the phenomenon being modeled. The plans produced by the adapted CL fail more rarely than the ones generated by a non-adaptive planner. The distributed learning system handles the new learning task autonomously, and is able to automatically identify the sensorial information most relevant for the task, thus reducing the communication and computational overhead of the predictive task},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_159900,

title = {An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living},

author = {Bacciu Davide and CHESSA Stefano and Gallicchio Claudio and MICHELI Alessio and Barsocchi Paolo},

doi = {10.1007/978-3-642-35467-0_5},

year  = {2013},

date = {2013-01-01},

booktitle = {Neural Nets and Surroundings - 22nd Italian Workshop on Neural Nets},

journal = {SMART INNOVATION, SYSTEMS AND TECHNOLOGIES},

volume = {19},

pages = {41--50},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_238038,

title = {Distributed Neural Computation over WSN in Ambient Intelligence},

author = {Bacciu Davide and Gallicchio Claudio and Lenzi Alessandro and Chessa Stefano and Micheli Alessio and Pelagatti Susanna and Vairo Claudio },

doi = {10.1007/978-3-319-00566-9_19},

year  = {2013},

date = {2013-01-01},

booktitle = {Advances in Intelligent Systems and Computing - Ambient Intelligence - Software and Applications},

journal = {ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING},

volume = {219},

pages = {147--154},

publisher = {Springer Verlag},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_193770,

title = {A General Purpose Distributed Learning Model for Robotic Ecologies},

author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Lenzi Alessandro and Micheli Alessio and Pelagatti Susanna},

url = {http://www.ifac-papersonline.net/Detailed/55807.html},

doi = {10.3182/20120905-3-HR-2030.00178},

year  = {2012},

date = {2012-01-01},

booktitle = {Robot Control - 10th IFAC Symposium on Robot Control},

journal = {IFAC PROCEEDINGS VOLUMES},

volume = {10},

pages = {435--440},

publisher = {ELSEVIER SCIENCE BV},

abstract = {The design of a learning system for robotic ecologies need to account for some key aspects of the ecology model such as distributivity, heterogeneity of the computational, sensory and actuator capabilities, as well as self-configurability. The paper proposes general guiding principles for learning systems' design that ensue from key ecology properties, and presents a distributed learning system for the Rubicon ecology that draws inspiration from such guidelines. The proposed learning system provides the Rubicon ecology with a set of general-purpose learning services which can be used to learn generic computational tasks that involve predicting information of interest based on dynamic sensorial input streams.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_202140,

title = {Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing},

author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio and Barsocchi Paolo and Chessa Stefano},

url = {http://ijcai-11.iiia.csic.es/files/proceedings/Space,%20Time%20and%20Ambient%20Intelligence%20Proceeding.pdf},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proceedings of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI)},

pages = {1--6},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}