@conference{nokey,

title = {ECGAN: generative adversarial network for electrocardiography},

author = {Lorenzo Simone and Davide Bacciu },

year  = {2023},

date = {2023-06-12},

urldate = {2023-06-12},

booktitle = {Proceedings of Artificial Intelligence In Medicine 2023 (AIME 2023)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Matteoni2022,

title = {Continual Learning for Human State Monitoring},

author = {Federico Matteoni and Andrea Cossu and Claudio Gallicchio and Vincenzo Lomonaco and Davide Bacciu},

editor = {Michel Verleysen},

url = {https://arxiv.org/pdf/2207.00010, 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 = {Continual Learning (CL) on time series data represents a promising but under-studied avenue for real-world applications. We propose two new CL benchmarks for Human State Monitoring. We carefully designed the benchmarks to mirror real-world environments in which new subjects are continuously added. We conducted an empirical evaluation to assess the ability of popular CL strategies to mitigate forgetting in our benchmarks. Our results show that, possibly due to the domain-incremental properties of our benchmarks, forgetting can be easily tackled even with a simple finetuning and that existing strategies struggle in accumulating knowledge over a fixed, held-out, test subject.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@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{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_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}

}