@article {,
	title = {A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities},
	journal = {Sensors},
	volume = {21},
	year = {2021},
	month = {04/2021},
	abstract = {<p>Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight {\textquotedblleft}suspicious{\textquotedblright} AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel{\textemdash}and the gear it adopts{\textemdash}is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.</p>
},
	keywords = {Automatic Identification System, Machine Learning, maritime surveillance, Synthetic Aperture Radar data integration},
	issn = {1424-8220},
	doi = {10.3390/s21082756},
	url = {https://www.mdpi.com/1424-8220/21/8/2756},
	author = {Alessandro Galdelli and Adriano Mancini and Carmen Ferra Vega and Anna Nora Tassetti}
}
@conference {,
	title = {A Novel Spatio-Temporal Multi-Task Approach for the Prediction of Diabetes-Related Complication: a Cardiopathy Case of Study},
	booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}},
	year = {2020},
	month = {7},
	publisher = {International Joint Conferences on Artificial Intelligence Organization},
	organization = {International Joint Conferences on Artificial Intelligence Organization},
	url = {https://doi.org/10.24963/ijcai.2020/593},
	author = {Romeo, Luca and Armentano, Giuseppe and Nicolucci, Antonio and Vespasiani, Marco and Vespasiani, Giacomo and Frontoni, Emanuele},
	editor = {Christian Bessiere}
}
@conference {,
	title = {A Cloud Computing Architecture to Map Trawling Activities Using Positioning Data},
	year = {2019},
	abstract = {<p>Descriptive and spatially-explicit information on fisheries plays a key role for an efficient integrated management of the maritime activities and the sustainable use of marine resources. However, this information is today still hard to obtain and, consequently, is a major issue for implementing Marine Spatial Planning (MSP). Since 2002, the Automatic Identification System (AIS) has been undergoing a major development allowing now for a real time geo-tracking and identification of equipped vessels of more than 15m in length overall (LOA) and, if properly processed, for the production of adequate information for MSP. Such monitoring systems or other low-cost and low-burden solutions are still missing for small vessels (LOA \&lt; 12m), whose catches and fishing effort remain spatially unassessed and, hence, unregulated. In this context, we propose an architecture to process vessel tracking data, understand the behaviour of trawling fleets and map related fishing activities. It could be used to process not only AIS data but also positioning data from other low cost systems as IoT sensors that share their position over LoRa and 2G/3G/4G links. Analysis gives back important and verified data (overall accuracy of 92\% for trawlers) and opens up development perspectives for monitoring small scale fisheries, helping hence to fill fishery data gaps and obtain a clearer picture of the fishing grounds as a whole.</p>
},
	doi = {10.1115/DETC2019-97779},
	url = {https://doi.org/10.1115/DETC2019-97779},
	author = {Alessandro Galdelli and Adriano Mancini and Anna Nora Tassetti and Carmen Ferra Vega and Enrico Armelloni and Giuseppe Scarcella and Gianna Fabi and Primo Zingaretti}
}
@article {,
	title = {The KIMORE dataset: KInematic assessment of MOvement and clinical scores for remote monitoring of physical REhabilitation},
	journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
	volume = {27},
	year = {2019},
	pages = {1436{\textendash}1448},
	author = {Capecci, Marianna and Ceravolo, Maria Gabriella and Ferracuti, Francesco and Iarlori, Sabrina and Monteri{\`u}, Andrea and Romeo, Luca and Verdini, Federica}
}
@article {,
	title = {Prospective motor control obeys to idiosyncratic strategies in autism},
	journal = {Scientific reports},
	volume = {8},
	year = {2018},
	pages = {1{\textendash}9},
	author = {Cavallo, Andrea and Romeo, Luca and Ansuini, Caterina and Podda, Jessica and Battaglia, Francesca and Veneselli, Edvige and Pontil, Massimiliano and Becchio, Cristina}
}
@article {De Giovanni2013627,
	title = {A heuristic and an exact method for the gate matrix connection cost minimization problem},
	journal = {International Transactions in Operational Research},
	volume = {20},
	number = {5},
	year = {2013},
	note = {cited By 1},
	pages = {627-643},
	abstract = {In many applications, a sequencing of patterns (electronic circuit nodes, cutting patterns, product orders, etc.) has to be found in order to optimize some given objective function, giving rise to the so-called open stack problems. We focus on a problem related to the optimization of gate matrix layouts: electronic circuits are obtained by connecting gates and one seeks a gate layout permutation that minimizes connection costs under restrictions on the circuit area. In the literature, the connection costs and circuit area are also known as time of open stacks and maximum number of open stacks, respectively. We propose a genetic algorithm providing heuristic solutions and a branch-and-cut algorithm based on a new linear integer programming formulation that represents, to the best of our knowledge, the first exact method proposed in the literature. The algorithms have been tested on real instances and on data sets from the literature. The computational results give evidence that the proposed methods provide solutions that improve the ones found by the approaches presented in the literature. {\textcopyright} 2013 International Federation of Operational Research Societies.},
	doi = {10.1111/itor.12025},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84881022755\&partnerID=40\&md5=243ec854395721c11c14eec4036e7a7b},
	author = {De Giovanni, L. and Gionata Massi and F. Pezzella and Pfetsch, M.E. and Rinaldi, G. and Ventura, P.d}
}
@conference {Dragoni2011541,
	title = {A continuos learning for a face recognition system},
	booktitle = {ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence},
	volume = {1},
	year = {2011},
	note = {cited By 0},
	pages = {541-544},
	abstract = {A system of Multiple Neural Networks has been proposed to solve the face recognition problem. Our idea is that a set of expert networks specialized to recognize specific parts of face are better than a single network. This is because a single network could no longer be able to correctly recognize the subject when some characteristics partially change. For this purpose we assume that each network has a reliability factor defined as the probability that the network is giving the desired output. In case of conflicts between the outputs of the networks the reliability factor can be dynamically re-evaluated on the base of the Bayes Rule. The new reliabilities will be used to establish who is the subject. Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960150495\&partnerID=40\&md5=4a12ff2305013f4969b4fc0242f2b7ec},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@article {Dragoni201179,
	title = {A continuous learning in a changing environment},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6979 LNCS},
	number = {PART 2},
	year = {2011},
	note = {cited By 0},
	pages = {79-88},
	abstract = {We propose a Hybrid System for dynamic environments, where a "Multiple Neural Networks" system works with Bayes Rule to solve a face recognition problem. One or more neural nets may no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. For this purpose, we assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used to establish who is the conflict winner, making the final choice (the name of subject). Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning. {\textcopyright} 2011 Springer-Verlag.},
	doi = {10.1007/978-3-642-24088-1_9},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80052998017\&partnerID=40\&md5=5004a58b7f29764e0e8129d5c456e8b9},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@article {Dragoni2011121,
	title = {Face recognition system in a dynamical environment},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6692 LNCS},
	number = {PART 2},
	year = {2011},
	note = {cited By 0},
	pages = {121-128},
	abstract = {We propose a Hybrid System for dynamic environments, where a "Multiple Neural Networks" system works with Bayes Rule to solve the face recognition problem. One or more neural nets may no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. For this purpose, we assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used to establish who is the conflict winner, making the final choice. Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning. {\textcopyright} 2011 Springer-Verlag.},
	doi = {10.1007/978-3-642-21498-1_16},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79957967237\&partnerID=40\&md5=ef902ad97692ff2d6ae546f1a63764bb},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@conference {Dragoni2010185,
	title = {Hybrid System for a never-ending unsupervised learning},
	booktitle = {2010 10th International Conference on Hybrid Intelligent Systems, HIS 2010},
	year = {2010},
	note = {cited By 0},
	pages = {185-190},
	abstract = {We propose a Hybrid System for dynamic environments, where a "Multiple Neural Networks" system works with Bayes Rule. One or more neural nets may no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying two algorithms, the "Inclusion based" and the "Weighted" one over all the maximally consistent subsets of the global outcome. {\textcopyright} 2010 IEEE.},
	doi = {10.1109/HIS.2010.5601070},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78650122885\&partnerID=40\&md5=c1f15dbf9914a0b618932ba197795fa9},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@article {Dragoni2010296,
	title = {An hybrid system for continuous learning},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6077 LNAI},
	number = {PART 2},
	year = {2010},
	note = {cited By 0},
	pages = {296-303},
	abstract = {We propose a Multiple Neural Networks system for dynamic environments, where one or more neural nets could no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying two algorithms, the Inclusion based and the Weighted one over all the maximally consistent subsets of the global outcome. {\textcopyright} 2010 Springer-Verlag.},
	doi = {10.1007/978-3-642-13803-4_37},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77954587081\&partnerID=40\&md5=623ab72ce6e0ef83d2087d387550b607},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri and Mazzieri, M.}
}
@conference {Dragoni2010421,
	title = {Multiple Neural Networks and Bayesian belief revision for a never-ending unsupervised learning},
	booktitle = {Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA{\textquoteright}10},
	year = {2010},
	note = {cited By 0},
	pages = {421-426},
	abstract = {A system of Multiple Neural Networks has been proposed to solve the face recognition problem. Our idea is that a set of expert networks specialized to recognize specific parts of face are better than a single network. This is because a single network could no longer be able to correctly recognize the subject when some characteristics partially change. For this purpose we assume that each network has a reliability factor defined as the probability that the network is giving the desired output. In case of conflicts between the outputs of the networks the reliability factor can be dynamically re-evaluated on the base of the Bayes Rrule. The new reliabilities will be used to establish who is the subject. Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning. {\textcopyright} 2010 IEEE.},
	doi = {10.1109/ISDA.2010.5687229},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79851492270\&partnerID=40\&md5=51abe7b48e1259d0bbdbd2e8c498e0b4},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@conference {Dragoni2009164,
	title = {Multiple neural networks system for dynamic environments},
	booktitle = {ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications},
	year = {2009},
	note = {cited By 0},
	pages = {164-168},
	abstract = {We propose a "Multiple Neural Networks" system for dynamic environments, where one or more neural nets may no longer be able to properly operate, due to sensible partial changes in the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s "degree of reliability" is defined as "the probability that the net is giving the desired output", in case of conflicts between the outputs of the various nets the re-evaluation of their "degrees of reliability" can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying the "Inclusion based" algorithm over all the maximally consistent subsets of the global outcome. Finally, the nets recognized as responsible for the conflicts will be automatically forced to learn about the changes in the individuals{\textquoteright} characteristics and avoid to make the same error in the immediate future. {\textcopyright} 2009 IEEE.},
	doi = {10.1109/ISDA.2009.85},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77949498622\&partnerID=40\&md5=1b4b7bdf4786549e90ad4ae17f59a011},
	author = {Dragoni, A.F. and Paola Baldassarri and Vallesi, G. and Mazzieri, M.}
}
@conference {Montesanto2008356,
	title = {Capturing the human action semantics using a query-by-example},
	booktitle = {SIGMAP 2008 - Proceedings of the International Conference on Signal Processing and Multimedia Applications},
	year = {2008},
	note = {cited By 0},
	pages = {356-363},
	abstract = {The paper describes a method for extracting human action semantics in video{\textquoteright}s using queries-by-example.b Here we consider the indexing and the matching problems of content-based human motion data retrieval. The query formulation is based on trajectories that may be easily built or extracted by following relevant points on a video, by a novice user too. The so realized trajectories contain high value of action semantics. The semantic schema is built by splitting a trajectory in time ordered sub-sequences that contain the features of extracted points. This kind of semantic representation allows reducing the search space dimensionality and, being human-oriented, allows a selective recognition of actions that are very similar among them. A neural network system analyzes the video semantic similarity, using a two-layer architecture of multilayer perceptrons, which is able to learn the semantic schema of the actions and to recognize them.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-55849129652\&partnerID=40\&md5=a8587b1b9833a9ccf64510cc75e426ab},
	author = {A. Montesanto and Paola Baldassarri and Dragoni, A.F. and Vallesi, G. and Paolo Puliti}
}
@conference {Montesanto2007229,
	title = {Fingerprints recognition using minutiae extraction: A fuzzy approach},
	booktitle = {Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007},
	year = {2007},
	note = {cited By 0},
	pages = {229-234},
	abstract = {The aim of this paper is to study the fingerprint verification based on local ridge discontinuities features (minutiae) only using grey scale images. We extract minutiae using two algorithms those following ridge lines and then recording ridge endings and bifurcations. Moreover we use a third algorithm able to develop a minutiae verification processing a local area using a neural network ( multilayer perceptron). Fingerprint distortion is filtered using a minutiae whole representation based on regular invariant moments. The results of the three minutiae extraction algorithms are joined during the minutiae pattern matching phase for fingerprint verification. Here we propose a new method of matching that use fuzzy operator to bypass the problem of different numbers of minutiae extracted from the algorithms. Experimental evidences show fingerprint recognition up to 95\%. {\textcopyright} 2007 IEEE.},
	doi = {10.1109/ICIAP.2007.4362784},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-48149113403\&partnerID=40\&md5=01f07886247083f3db66c7477c334984},
	author = {A. Montesanto and Paola Baldassarri and Vallesi, G. and Guido Tascini}
}
