@conference{Ceni2023,

title = { Improving Fairness via Intrinsic Plasticity in Echo State Networks },

author = {Andrea Ceni and Davide Bacciu and Valerio De Caro and Claudio Gallicchio and Luca Oneto

},

editor = {Michel Verleysen},

year  = {2023},

date = {2023-10-04},

urldate = {2023-10-04},

booktitle = {Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Cossu2023,

title = { A Protocol for Continual Explanation of SHAP },

author = {Andrea Cossu and Francesco Spinnato and Riccardo Guidotti and Davide Bacciu},

editor = {Michel Verleysen},

year  = {2023},

date = {2023-10-04},

urldate = {2023-10-04},

booktitle = {Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Caro2023,

title = { Communication-Efficient Ridge Regression in Federated Echo State Networks },

author = {Valerio De Caro and Antonio Di Mauro and Davide Bacciu and Claudio Gallicchio



},

editor = {Michel Verleysen},

year  = {2023},

date = {2023-10-04},

urldate = {2023-10-04},

booktitle = {Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{DeCaro2023,

title = {Prediction of Driver's Stress Affection in Simulated Autonomous Driving Scenarios},

author = {Valerio De Caro and Herbert Danzinger and Claudio Gallicchio and Clemens Könczöl and Vincenzo Lomonaco and Mina Marmpena and Mina Marpena and Sevasti Politi and Omar Veledar and Davide Bacciu},

year  = {2023},

date = {2023-06-04},

urldate = {2023-06-04},

booktitle = {Proceedings of 2023 IEEE International Conference on Acoustics, Speech and Signal Processing},

abstract = {We investigate the task of predicting stress affection from physiological data of users experiencing simulations of autonomous driving. We approach this task on two levels of granularity, depending on whether the prediction is performed at end of the simulation, or along the simulation. In the former, denoted as coarse-grained prediction, we employed Decision Trees. In the latter, denoted as fine-grained prediction, we employed Echo State Networks, a Recurrent Neural Network

that allows efficient learning from temporal data and hence is

suitable for pervasive environments. We conduct experiments on a private dataset of physiological data from people participating in multiple driving scenarios simulating different stressful events. The results show that the proposed model is capable of detecting conditions of event-related cognitive stress proving, the existence of a correlation between stressful events and the physiological data.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Bacciu2021d,

title = {TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence},

editor = {Davide Bacciu and Siranush Akarmazyan and Eric Armengaud and Manlio Bacco and George Bravos and Calogero Calandra and Emanuele Carlini and Antonio Carta and Pietro Cassara and Massimo Coppola and Charalampos Davalas and Patrizio Dazzi and Maria Carmela Degennaro and Daniele Di Sarli and Jürgen Dobaj and Claudio Gallicchio and Sylvain Girbal and Alberto Gotta and Riccardo Groppo and Vincenzo Lomonaco and Georg Macher and Daniele Mazzei and Gabriele Mencagli and Dimitrios Michail and Alessio Micheli and Roberta Peroglio and Salvatore Petroni and Rosaria Potenza and Farank Pourdanesh and Christos Sardianos and Konstantinos Tserpes and Fulvio Tagliabò and Jakob Valtl and Iraklis Varlamis and Omar Veledar},

doi = {10.1109/COINS51742.2021.9524099},

year  = {2021},

date = {2021-08-23},

urldate = {2021-08-23},

booktitle = {Proceedings of the 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS) },

abstract = {This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges},

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{Bacciu2021,

title = {Benchmarking Reservoir and Recurrent Neural Networks for Human State and Activity Recognition},

author = {Davide Bacciu and Daniele Di Sarli and Claudio Gallicchio and Alessio Micheli and Niccolo Puccinelli},

doi = {10.1007/978-3-030-85099-9_14},

year  = {2021},

date = {2021-06-16},

urldate = {2021-06-16},

booktitle = {Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021)},

volume = {12862},

pages = {168-179},

publisher = {Springer},

abstract = {Monitoring of human states from streams of sensor data is an appealing applicative area for Recurrent Neural Network (RNN) models. In such a scenario, Echo State Network (ESN) models from the Reservoir Computing paradigm can represent good candidates due to the efficient training algorithms, which, compared to fully trainable RNNs, definitely ease embedding on edge devices.



In this paper, we provide an experimental analysis aimed at assessing the performance of ESNs on tasks of human state and activity recognition, in both shallow and deep setups. Our analysis is conducted in comparison with vanilla RNNs, Long Short-Term Memory, Gated Recurrent Units, and their deep variations. Our empirical results on several datasets clearly indicate that, despite their simplicity, ESNs are able to achieve a level of accuracy that is competitive with those models that require full adaptation of the parameters. From a broader perspective, our analysis also points out that recurrent networks can be a first choice for the class of tasks under consideration, in particular in their deep and gated variants.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ijcnn2018,

title = {Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs},

author = {Bacciu Davide and Bongiorno Andrea},

url = {https://arxiv.org/abs/1805.09244},

year  = {2018},

date = {2018-07-09},

urldate = {2018-07-09},

booktitle = {Proceedings  of the 2018 International Joint Conference on Neural Networks (IJCNN 2018) },

pages = {1-9},

publisher = {IEEE},

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{ijcnn2017,

title = {DropIn: Making Neural Networks Robust to Missing Inputs by Dropout},

author = {Bacciu Davide and Crecchi Francesco and Morelli Davide},

url = {https://arxiv.org/abs/1705.02643},

doi = {10.1109/IJCNN.2017.7966106},

isbn = {978-1-5090-6182-2},

year  = {2017},

date = {2017-05-19},

urldate = {2017-05-19},

booktitle = {Proceedings  of the 2017 International Joint Conference on Neural Networks (IJCNN 2017) },

pages = {2080-2087},

publisher = {IEEE},

abstract = {The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20%–50% of the inputs are not available.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{esann2016,

title = {A reservoir activation kernel for trees},

author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio

},

editor = {M. Verleysen},

url = {https://www.researchgate.net/profile/Claudio_Gallicchio/publication/313236954_A_Reservoir_Activation_Kernel_for_Trees/links/58a9db0892851cf0e3c6b8df/A-Reservoir-Activation-Kernel-for-Trees.pdf},

isbn = {978-287587027-},

year  = {2016},

date = {2016-04-29},

urldate = {2016-04-29},

booktitle = {Proceedings  of the European Symposium on Artificial Neural Networks,  Computational Intelligence and Machine Learning (ESANN'16)},

pages = {29-34},

publisher = { i6doc.com},

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_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_466867,

title = {Self-Sustaining Learning for Robotic Ecologies},

author = {BACCIU Davide and Broxvall Mathias and Coleman Sonya and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Guzman Roberto and Lopez Raul and Lozano-Peiteado Hector and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Vairo Claudio},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceedings of the 1st International Conference on Sensor Networks, SENSORNETS 2012},

pages = {99--103},

abstract = {The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specific area, and to channel it to a central processing node for on-line or off-line analysis. The WSN technology, however, can be used for much more ambitious goals. We claim that merging the concepts and technology of WSN with the concepts and technology of distributed robotics and multi-agent systems can open new ways to design systems able to provide intelligent services in our homes and working places. We also claim that endowing these systems with learning capabilities can greatly increase their viability and acceptability, by simplifying design, customization and adaptation to changing user needs. To support these claims, we illustrate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors, effectors and mobile robots.},

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}

}