Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Caro, Valerio De; Mauro, Antonio Di; Bacciu, Davide; Gallicchio, Claudio Communication-Efficient Ridge Regression in Federated Echo State Networks Conference Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , 2023. Caro, Valerio De; Bacciu, Davide; Gallicchio, Claudio Decentralized Plasticity in Reservoir Dynamical Networks for Pervasive Environments Workshop Proceedings of the 2023 ICML Workshop on Localized Learning: Decentralized Model Updates via Non-Global Objectives
, 2023. Caro, Valerio De; Danzinger, Herbert; Gallicchio, Claudio; Könczöl, Clemens; Lomonaco, Vincenzo; Marmpena, Mina; Marpena, Mina; Politi, Sevasti; Veledar, Omar; Bacciu, Davide Prediction of Driver's Stress Affection in Simulated Autonomous Driving Scenarios Conference Proceedings of 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, 2023. Caro, Valerio De; Gallicchio, Claudio; Bacciu, Davide Continual adaptation of federated reservoirs in pervasive environments Journal Article In: Neurocomputing, pp. 126638, 2023, ISSN: 0925-2312. Caro, Valerio De; Gallicchio, Claudio; Bacciu, Davide Federated Adaptation of Reservoirs via Intrinsic Plasticity Conference Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022), 2022. Cossu, Andrea; Bacciu, Davide; Carta, Antonio; Gallicchio, Claudio; Lomonaco, Vincenzo Continual Learning with Echo State Networks Conference Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)
, vol. 275-280, 2021. Bacciu, Davide; Sarli, Daniele Di; Faraji, Pouria; Gallicchio, Claudio; Micheli, Alessio Federated Reservoir Computing Neural Networks Conference Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE, 2021. Bacciu, Davide; Sarli, Daniele Di; Gallicchio, Claudio; Micheli, Alessio; Puccinelli, Niccolo Benchmarking Reservoir and Recurrent Neural Networks for Human State and Activity Recognition Conference Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021), vol. 12862, Springer, 2021. Davide, Bacciu; Maurizio, Di Rocco; Mauro, Dragone; Claudio, Gallicchio; Alessio, Micheli; Alessandro, Saffiotti An Ambient Intelligence Approach for Learning in Smart Robotic Environments Journal Article In: Computational Intelligence, 2019, (Early View (Online Version of Record before inclusion in an issue)
). Bacciu, Davide; Crecchi, Francesco Augmenting Recurrent Neural Networks Resilience by Dropout Journal Article In: IEEE Transactions on Neural Networs and Learning Systems, 2019. Davide, Bacciu; Michele, Colombo; Davide, Morelli; David, Plans Randomized neural networks for preference learning with physiological data Journal Article In: Neurocomputing, vol. 298, pp. 9-20, 2018. Davide, Bacciu; Andrea, Bongiorno Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs Conference Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN 2018) , IEEE, 2018. Davide, Bacciu; Stefano, Chessa; Claudio, Gallicchio; Alessio, Micheli; Luca, Pedrelli; Erina, Ferro; Luigi, Fortunati; Davide, La Rosa; Filippo, Palumbo; Federico, Vozzi; Oberdan, Parodi A Learning System for Automatic Berg Balance Scale Score Estimation Journal Article In: Engineering Applications of Artificial Intelligence journal, vol. 66, pp. 60-74, 2017. Davide, Bacciu; Francesco, Crecchi; Davide, Morelli DropIn: Making Neural Networks Robust to Missing Inputs by Dropout Conference Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN 2017) , IEEE, 2017, ISBN: 978-1-5090-6182-2. Davide, Bacciu Unsupervised feature selection for sensor time-series in pervasive computing applications Journal Article In: Neural Computing and Applications, vol. 27, no. 5, pp. 1077-1091, 2016, ISSN: 1433-3058. Giuseppe, Amato; Davide, Bacciu; Stefano, Chessa; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Alessio, Micheli; Arantxa, Renteria; Claudio, Vairo A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living Conference Proceedings of the 7th International Conference on Ambient Intelligence (ISAMI'16), vol. 476, Advances in Intelligent Systems and Computing Springer, 2016, ISBN: 978-3-319-40113-3. Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli A reservoir activation kernel for trees Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'16), i6doc.com, 2016, ISBN: 978-287587027-. Mauro, Dragone; Giuseppe, Amato; Davide, Bacciu; Stefano, Chessa; Sonya, Coleman; Maurizio, Di Rocco; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Liam, Maguire; Martin, McGinnity; Alessio, Micheli; M.P., O'Hare Gregory; Arantxa, Renteria; Alessandro, Saffiotti; Claudio, Vairo; Philip, Vance A Cognitive Robotic Ecology Approach to Self-configuring and Evolving AAL Systems Journal Article In: Engineering Applications of Artificial Intelligence, vol. 45, no. C, pp. 269–280, 2015, ISSN: 0952-1976. Davide, Bacciu; Filippo, Benedetti; Alessio, Micheli ESNigma: efficient feature selection for Echo State Networks Conference Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15), i6doc.com publ., 2015. Giuseppe, Amato; Davide, Bacciu; Mathias, Broxvall; Stefano, Chessa; Sonya, Coleman; Maurizio, Di Rocco; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Martin, McGinnity T; Alessio, Micheli; AK, Ray; Arantxa, Renteria; Alessandro, Saffiotti; David, Swords; Claudio, Vairo; Philip, Vance Robotic Ubiquitous Cognitive Ecology for Smart Homes Journal Article In: Journal of Intelligent & Robotic Systems, vol. 80, no. 1, pp. 57-81, 2015, ISSN: 0921-0296. Davide, Bacciu; Paolo, Barsocchi; Stefano, Chessa; Claudio, Gallicchio; Alessio, Micheli An experimental characterization of reservoir computing in ambient assisted living applications Journal Article In: Neural Computing and Applications, vol. 24, no. 6, pp. 1451-1464, 2014, ISSN: 0941-0643. Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli; Maurizio, Di Rocco; Alessandro, Saffiotti Learning context-aware mobile robot navigation in home environments Conference Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014), IEEE, 2014, ISBN: 9781479961702. Davide, Bacciu; Stefano, CHESSA; Claudio, Gallicchio; Alessio, MICHELI; Paolo, Barsocchi An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living Conference Neural Nets and Surroundings - 22nd Italian Workshop on Neural Nets, vol. 19, Springer, 2013. Davide, Bacciu; Claudio, Gallicchio; Alessandro, Lenzi; Stefano, Chessa; Alessio, Micheli; Susanna, Pelagatti; Claudio, Vairo Distributed Neural Computation over WSN in Ambient Intelligence Conference Advances in Intelligent Systems and Computing - Ambient Intelligence - Software and Applications, vol. 219, Springer Verlag, 2013. Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli; Paolo, Barsocchi; Stefano, Chessa Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing Conference Proceedings of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI), 2011.
@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}
}
@workshop{nokey,
title = {Decentralized Plasticity in Reservoir Dynamical Networks for Pervasive Environments},
author = {Valerio De Caro and Davide Bacciu and Claudio Gallicchio
},
url = {https://openreview.net/forum?id=5hScPOeDaR, PDF},
year = {2023},
date = {2023-07-29},
urldate = {2023-07-29},
booktitle = {Proceedings of the 2023 ICML Workshop on Localized Learning: Decentralized Model Updates via Non-Global Objectives
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@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}
}
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.
@article{DECARO2023126638,
title = {Continual adaptation of federated reservoirs in pervasive environments},
author = {Valerio De Caro and Claudio Gallicchio and Davide Bacciu},
url = {https://www.sciencedirect.com/science/article/pii/S0925231223007610},
doi = {https://doi.org/10.1016/j.neucom.2023.126638},
issn = {0925-2312},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Neurocomputing},
pages = {126638},
abstract = {When performing learning tasks in pervasive environments, the main challenge arises from the need of combining federated and continual settings. The former comes from the massive distribution of devices with privacy-regulated data. The latter is required by the low resources of the participating devices, which may retain data for short periods of time. In this paper, we propose a setup for learning with Echo State Networks (ESNs) in pervasive environments. Our proposal focuses on the use of Intrinsic Plasticity (IP), a gradient-based method for adapting the reservoir’s non-linearity. First, we extend the objective function of IP to include the uncertainty arising from the distribution of the data over space and time. Then, we propose Federated Intrinsic Plasticity (FedIP), which is intended for client–server federated topologies with stationary data, and adapts the learning scheme provided by Federated Averaging (FedAvg) to include the learning rule of IP. Finally, we further extend this algorithm for learning to Federated Continual Intrinsic Plasticity (FedCLIP) to equip clients with CL strategies for dealing with continuous data streams. We evaluate our approach on an incremental setup built upon real-world datasets from human monitoring, where we tune the complexity of the scenario in terms of the distribution of the data over space and time. Results show that both our algorithms improve the representation capabilities and the performance of the ESN, while being robust to catastrophic forgetting.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{Caro2022,
title = {Federated Adaptation of Reservoirs via Intrinsic Plasticity},
author = {Valerio {De Caro} and Claudio Gallicchio and Davide Bacciu},
editor = {Michel Verleysen},
url = {https://arxiv.org/abs/2206.11087, Arxiv},
year = {2022},
date = {2022-10-05},
urldate = {2022-10-05},
booktitle = {Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022)},
abstract = {We propose a novel algorithm for performing federated learning with Echo State Networks (ESNs) in a client-server scenario. In particular, our proposal focuses on the adaptation of reservoirs by combining Intrinsic Plasticity with Federated Averaging. The former is a gradient-based method for adapting the reservoir's non-linearity in a local and unsupervised manner, while the latter provides the framework for learning in the federated scenario. We evaluate our approach on real-world datasets from human monitoring, in comparison with the previous approach for federated ESNs existing in literature. Results show that adapting the reservoir with our algorithm provides a significant improvement on the performance of the global model. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{Cossu2021,
title = { Continual Learning with Echo State Networks },
author = {Andrea Cossu and Davide Bacciu and Antonio Carta and Claudio Gallicchio and Vincenzo Lomonaco},
editor = {Michel Verleysen},
url = {https://arxiv.org/abs/2105.07674, Arxiv},
doi = {10.14428/esann/2021.ES2021-80},
year = {2021},
date = {2021-10-06},
urldate = {2021-10-06},
booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)
},
volume = {275-280},
abstract = { Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge. The study of CL for sequential patterns revolves around trained recurrent networks. In this work, instead, we introduce CL in the context of Echo State Networks (ESNs), where the recurrent component is kept fixed. We provide the first evaluation of catastrophic forgetting in ESNs and we highlight the benefits in using CL strategies which are not applicable to trained recurrent models. Our results confirm the ESN as a promising model for CL and open to its use in streaming scenarios. },
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}
}
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.@article{rubicon2019CI,
title = {An Ambient Intelligence Approach for Learning in Smart Robotic Environments},
author = {Bacciu Davide and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Micheli Alessio and Saffiotti Alessandro},
doi = {10.1111/coin.12233},
year = {2019},
date = {2019-07-31},
journal = {Computational Intelligence},
abstract = {Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. In order to reduce the amount of preparation and pre-programming required for their deployment in real world applications, it is important to make these systems self-learning, self-configuring, and self-adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre-existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to pro-actively and smoothly adapt to subtle changes in the environment and in the habits and preferences
of their user(s).},
note = {Early View (Online Version of Record before inclusion in an issue)
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
of their user(s).
@article{tnnnls_dropin2019,
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},
urldate = {2019-03-31},
journal = {IEEE Transactions on Neural Networs and Learning Systems},
abstract = {The paper discusses the simple idea that dropout regularization can be used to efficiently induce resiliency to missing inputs at prediction time in a generic neural network. We show how the approach can be effective on tasks where imputation strategies often fail, namely involving recurrent neural networks and scenarios where whole sequences of input observations are missing. The experimental analysis provides an assessment of the accuracy-resiliency tradeoff in multiple recurrent models, including reservoir computing methods, and comprising real-world ambient intelligence and biomedical time series.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{neurocomp2017,
title = {Randomized neural networks for preference learning with physiological data},
author = {Bacciu Davide and Colombo Michele and Morelli Davide and Plans David},
editor = {Fabio Aiolli and Luca Oneto and Michael Biehl },
url = {https://authors.elsevier.com/a/1Wxbz_L2Otpsb3},
doi = {10.1016/j.neucom.2017.11.070},
year = {2018},
date = {2018-07-12},
journal = {Neurocomputing},
volume = {298},
pages = {9-20},
abstract = {The paper discusses the use of randomized neural networks to learn a complete ordering between samples of heart-rate variability data by relying solely on partial and subject-dependent information concerning pairwise relations between samples. We confront two approaches, i.e. Extreme Learning Machines and Echo State Networks, assessing the effectiveness in exploiting hand-engineered heart-rate variability features versus using raw beat-to-beat sequential data. Additionally, we introduce a weight sharing architecture and a preference learning error function whose performance is compared with a standard architecture realizing pairwise ranking as a binary-classification task. The models are evaluated on real-world data from a mobile application realizing a guided breathing exercise, using a dataset of over 54K exercising sessions. Results show how a randomized neural model processing information in its raw sequential form can outperform its vectorial counterpart, increasing accuracy in predicting the correct sample ordering by about 20%. Further, the experiments highlight the importance of using weight sharing architectures to learn smooth and generalizable complete orders induced by the preference relation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@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}
}
@article{eaai2017,
title = {A Learning System for Automatic Berg Balance Scale Score Estimation},
author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio and Pedrelli Luca and Ferro Erina and Fortunati Luigi and La Rosa Davide and Palumbo Filippo and Vozzi Federico and Parodi Oberdan},
url = {http://www.sciencedirect.com/science/article/pii/S0952197617302026},
doi = {https://doi.org/10.1016/j.engappai.2017.08.018},
year = {2017},
date = {2017-08-24},
urldate = {2017-08-24},
journal = {Engineering Applications of Artificial Intelligence journal},
volume = {66},
pages = {60-74},
abstract = {The objective of this work is the development of a learning system for the automatic assessment of balance abilities in elderly people. The system is based on estimating the Berg Balance Scale (BBS) score from the stream of sensor data gathered by a Wii Balance Board. The scientific challenge tackled by our investigation is to assess the feasibility of exploiting the richness of the temporal signals gathered by the balance board for inferring the complete BBS score based on data from a single BBS exercise.
The relation between the data collected by the balance board and the BBS score is inferred by neural networks for temporal data, modeled in particular as Echo State Networks within the Reservoir Computing (RC) paradigm, as a result of a comprehensive comparison among different learning models. The proposed system results to be able to estimate the complete BBS score directly from temporal data on exercise #10 of the BBS test, with ≈≈10 s of duration. Experimental results on real-world data show an absolute error below 4 BBS score points (i.e. below the 7% of the whole BBS range), resulting in a favorable trade-off between predictive performance and user’s required time with respect to previous works in literature. Results achieved by RC models compare well also with respect to different related learning models.
Overall, the proposed system puts forward as an effective tool for an accurate automated assessment of balance abilities in the elderly and it is characterized by being unobtrusive, easy to use and suitable for autonomous usage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The relation between the data collected by the balance board and the BBS score is inferred by neural networks for temporal data, modeled in particular as Echo State Networks within the Reservoir Computing (RC) paradigm, as a result of a comprehensive comparison among different learning models. The proposed system results to be able to estimate the complete BBS score directly from temporal data on exercise #10 of the BBS test, with ≈≈10 s of duration. Experimental results on real-world data show an absolute error below 4 BBS score points (i.e. below the 7% of the whole BBS range), resulting in a favorable trade-off between predictive performance and user’s required time with respect to previous works in literature. Results achieved by RC models compare well also with respect to different related learning models.
Overall, the proposed system puts forward as an effective tool for an accurate automated assessment of balance abilities in the elderly and it is characterized by being unobtrusive, easy to use and suitable for autonomous usage.@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}
}
@article{icfNca15,
title = {Unsupervised feature selection for sensor time-series in pervasive computing applications},
author = {Bacciu Davide},
url = {https://pages.di.unipi.it/bacciu/wp-content/uploads/sites/12/2016/04/nca2015.pdf},
doi = {10.1007/s00521-015-1924-x},
issn = {1433-3058},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
journal = {Neural Computing and Applications},
volume = {27},
number = {5},
pages = {1077-1091},
publisher = {Springer London},
abstract = {The paper introduces an efficient feature selection approach for multivariate time-series of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of time-series cross-correlation. The algorithm is capable of identifying nonredundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. In particular, the proposed feature selection process does not require expert intervention to determine the number of selected features, which is a key advancement with respect to time-series filters in the literature. The characteristic of the prosed algorithm allows enriching learning systems, in pervasive computing applications, with a fully automatized feature selection mechanism which can be triggered and performed at run time during system operation. A comparative experimental analysis on real-world data from three pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in the literature when dealing with sensor time-series. Specifically, it is presented an assessment both in terms of reduction of time-series redundancy and in terms of preservation of informative features with respect to associated supervised learning tasks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{Amato2016,
title = {A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living},
author = {Amato Giuseppe and Bacciu Davide and Chessa Stefano and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and Micheli Alessio and Renteria Arantxa
and Vairo Claudio},
doi = {10.1007/978-3-319-40114-0_1},
isbn = {978-3-319-40113-3},
year = {2016},
date = {2016-06-03},
booktitle = {Proceedings of the 7th International Conference on Ambient Intelligence (ISAMI'16)},
volume = {476},
pages = {1-9},
publisher = {Springer},
series = {Advances in Intelligent Systems and Computing},
abstract = {We present a data benchmark for the assessment of human activity recognition solutions, collected as part of the EU FP7 RUBICON project, and available to the scientific community. The dataset provides fully annotated data pertaining to numerous user activities and comprises synchronized data streams collected from a highly sensor-rich home environment. A baseline activity recognition performance obtained through an Echo State Network approach is provided along with the dataset.},
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}
}
@article{Dragone:2015:CRE:2827370.2827596,
title = {A Cognitive Robotic Ecology Approach to Self-configuring and Evolving AAL Systems},
author = {Dragone Mauro and Amato Giuseppe and Bacciu Davide and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and Maguire Liam and McGinnity Martin and Micheli Alessio and O'Hare Gregory M.P. and Renteria Arantxa and Saffiotti Alessandro and Vairo Claudio and Vance Philip},
url = {http://dx.doi.org/10.1016/j.engappai.2015.07.004},
doi = {10.1016/j.engappai.2015.07.004},
issn = {0952-1976},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {45},
number = {C},
pages = {269--280},
publisher = {Pergamon Press, Inc.},
address = {Tarrytown, NY, USA},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@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}
}
@article{bacciuJirs15,
title = {Robotic Ubiquitous Cognitive Ecology for Smart Homes},
author = {Amato Giuseppe and Bacciu Davide and Broxvall Mathias and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and McGinnity T Martin and Micheli Alessio and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Swords David and Vairo Claudio and Vance Philip},
url = {http://dx.doi.org/10.1007/s10846-015-0178-2},
doi = {10.1007/s10846-015-0178-2},
issn = {0921-0296},
year = {2015},
date = {2015-01-01},
journal = {Journal of Intelligent & Robotic Systems},
volume = {80},
number = {1},
pages = {57-81},
publisher = {Springer Netherlands},
abstract = {Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nca2014,
title = {An experimental characterization of reservoir computing in ambient assisted living applications},
author = {Bacciu Davide and Barsocchi Paolo and Chessa Stefano and Gallicchio Claudio and Micheli Alessio},
url = {http://dx.doi.org/10.1007/s00521-013-1364-4, Publisher version
https://archive.ics.uci.edu/ml/datasets/Indoor+User+Movement+Prediction+from+RSS+data, Dataset @ UCI},
doi = {10.1007/s00521-013-1364-4},
issn = {0941-0643},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Neural Computing and Applications},
volume = {24},
number = {6},
pages = {1451-1464},
publisher = {Springer London},
abstract = {In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@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_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}
}