latest.bib

@article{AzzRigLam21-AIJ-IJ,
  title = {A Semantics for Hybrid Probabilistic Logic Programs with Function Symbols},
  author = {Azzolini, Damiano and Riguzzi, Fabrizio and Lamma, Evelina},
  journal = {Artificial Intelligence},
  year = {2021},
  copyright = {Elsevier},
  issn = {0004-3702},
  url = {http://ml.unife.it/wp-content/uploads/Papers/AzzRigLam21-AIJ-IJ.pdf},
  doi = {10.1016/j.artint.2021.103452},
  note = {The final publication is available at Elsevier via \url{https://doi.org/10.1016/j.artint.2021.103452} }
}
@article{LosVen21-JEGTP-IJ,
  author = {Losi, Enzo and Venturini, Mauro and Manservigi, Lucrezia and Ceschini, Giuseppe Fabio and Bechini, Giovanni and Cota, Giuseppe and Riguzzi, Fabrizio},
  title = {Structured Methodology for Clustering Gas Turbine Transients by means of Multi-variate Time Series},
  year = {2021},
  publisher = {ASME},
  journal = {Journal of Engineering for Gas Turbines and Power},
  volume = {143},
  number = {3},
  pages = {031014-1 (13 pages)},
  doi = {10.1115/1.4049503}
}
@inproceedings{FraLamRig20-LOD-IC,
  author = {Fraccaroli, Michele
and Lamma, Evelina
and Riguzzi, Fabrizio},
  editor = {Nicosia, Giuseppe
and Ojha, Varun
and La Malfa, Emanuele
and Jansen, Giorgio
and Sciacca, Vincenzo
and Pardalos, Panos
and Giuffrida, Giovanni
and Umeton, Renato},
  title = {Automatic Setting of {DNN} Hyper-Parameters by Mixing {Bayesian Optimization} and Tuning Rules},
  booktitle = {Machine Learning, Optimization, and Data Science, 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I},
  year = {2020},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {477--488},
  abstract = {Deep learning techniques play an increasingly important role in industrial and research environments due to their outstanding results. However, the large number of hyper-parameters to be set may lead to errors if they are set manually. The state-of-the-art hyper-parameters tuning methods are grid search, random search, and Bayesian Optimization. The first two methods are expensive because they try, respectively, all possible combinations and random combinations of hyper-parameters. Bayesian Optimization, instead, builds a surrogate model of the objective function, quantifies the uncertainty in the surrogate using Gaussian Process Regression and uses an acquisition function to decide where to sample the new set of hyper-parameters. This work faces the field of Hyper-Parameters Optimization (HPO). The aim is to improve Bayesian Optimization applied to Deep Neural Networks. For this goal, we build a new algorithm for evaluating and analyzing the results of the network on the training and validation sets and use a set of tuning rules to add new hyper-parameters and/or to reduce the hyper-parameter search space to select a better combination.},
  isbn = {978-3-030-64583-0},
  doi = {10.1007/978-3-030-64583-0_43},
  note = {The final publication is available at Springer via \url{https://link.springer.com/chapter/10.1007/978-3-030-64583-0_43}},
  copyright = {Springer},
  pdf = {http://ml.unife.it/wp-content/uploads/Papers/FraLamRig-LOD20.pdf},
  series = {Lecture Notes in Computer Science},
  volume = {12565}
}
@article{AlbGavLam20-FI-IJ,
  author = {Marco Alberti and
               Marco Gavanelli and
               Evelina Lamma and
               Fabrizio Riguzzi and
               Ken Satoh and
               Riccardo Zese},
  title = {Dischargeable Obligations in the {SCIFF} Framework},
  journal = {Fundamenta Informaticae},
  volume = {176},
  number = {3-4},
  pages = {321--348},
  year = {2020},
  doi = {10.3233/FI-2020-1976},
  publisher = {IOS Press}
}
@article{CheCotGavLamMelRig20-EAAI-IJ,
  author = {Federico Chesani and
               Giuseppe Cota and
               Marco Gavanelli and
               Evelina Lamma and
               Paola Mello and
               Fabrizio Riguzzi},
  title = {Declarative and Mathematical Programming approaches to Decision Support
               Systems for food recycling},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {95},
  pages = {103861},
  year = {2020},
  doi = {10.1016/j.engappai.2020.103861},
  scopus = {2-s2.0-85089188550}
}
@inproceedings{LosVen20-TurboExpo-IC,
  author = {Losi, Enzo and Venturini, Mauro and Manservigi, Lucrezia and Ceschini, Giusppe Fabio and Bechini, Giovanni. and Cota, Giuseppe and Riguzzi, Fabrizio},
  title = {Structured Methodology for Clustering Gas Turbine Transients by means of Multi-variate Time Series},
  year = {2020},
  publisher = {ASME},
  booktitle = {Proc. ASME Turbo Expo 2020},
  abstract = {The challenges related to current energy market force gas turbine owners to improve the reliability and availability of gas turbine engines, especially in the ever competitive market of the Oil & Gas sector. Gas turbine trip leads to business interruption and also reduces equipment remaining useful life. Thus, the identification of symptoms of trips is a key factor to predict their occurrence and avoid further damages and costs. Gas turbine transients are tracked by gas turbine operators while they occur, but a database including the complete details of past events for many fleets of engines is not always available. Therefore, a methodology aimed at classifying transients into clusters that identify the type of event (e.g., normal shutdown or trip) is required. Clustering is a data mining technique that addresses the scope of partitioning multi-variate time series into a given number of homogeneous and separated groups. In such a manner, the multi-variate time series belonging to the same cluster are very similar to each other and dissimilar to those of the other clusters.
This paper presents a structured methodology composed of a subsequent matching algorithm, a featured-based clustering approach exploiting the unsupervised fuzzy C-means algorithm and a procedure that assigns a label to each cluster for classification purposes. The methodology is applied to a real-word case-study, by investigating transients acquired from a fleet of Siemens gas turbines in operation during three years.
The results obtained by using heterogeneous datasets including six measured variables allowed values of Precision, Recall and Accuracy higher than 90 % in almost all cases.},
  volume = {Volume 9: Oil and Gas Applications; Organic Rankine Cycle Power Systems; Steam Turbine},
  series = {Turbo Expo: Power for Land, Sea, and Air},
  pages = {1--16},
  note = {V009T21A005},
  doi = {10.1115/GT2020-14751}
}
@incollection{CotZesBelLamRig20-SSWS-BC,
  title = {A Framework for Reasoning on Probabilistic Description Logics},
  author = {Cota, Giuseppe and Zese, Riccardo and Bellodi, Elena and Lamma, Evelina and Riguzzi, Fabrizio},
  booktitle = {Applications and Practices in Ontology Design, Extraction, and Reasoning},
  series = {Studies on the Semantic Web},
  volume = {49},
  editor = {Cota, Giuseppe and Daquino, Marilena and Pozzato, Gian Luca},
  isbn = {978-1-64368-142-9},
  doi = {10.3233/SSW200040},
  language = {English},
  pages = {127-144},
  year = {2020},
  publisher = {{IOS} Press},
  abstract = {While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics.
	In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics.
	The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.
	},
  copyright = {Akademische Verlagsgesellschaft AKA GmbH, Berlin}
}
@article{RigBelZesAlbLam20-ML-IJ,
  author = {Riguzzi, Fabrizio and Bellodi, Elena and Zese, Riccardo and Alberti, Marco and Lamma, Evelina},
  title = {Probabilistic inductive constraint logic},
  journal = {Machine Learning},
  year = {2020},
  pages = {1-32},
  doi = {10.1007/s10994-020-05911-6},
  pdf = {https://link.springer.com/content/pdf/10.1007/s10994-020-05911-6.pdf},
  publisher = {Springer},
  issn = {08856125}
}
@inproceedings{Rig20-ECAI-IC,
  title = {Quantum Weighted Model Counting},
  author = {Fabrizio Riguzzi},
  year = {2020},
  booktitle = {24th European Conference on Artificial Intelligence (ECAI 2020)},
  editor = {Giuseppe {De Giacomo} and Alejandro Catala and Bistra Dilkina and Michela Milano and Sen\'en Barro and Alberto Bugar\'in and J\'er\^ome Lang},
  http = {http://ebooks.iospress.nl/volumearticle/55196},
  publisher = {IOS Press},
  address = {Amsterdam, Berlin, Washington DC},
  pages = {2640-2647},
  doi = {10.3233/FAIA200401},
  copyright = {CC BY-NC 4.0},
  pdf = {http://ml.unife.it/wp-content/uploads/Papers/Rig-ECAI20.pdf}
}
@article{Rig20-MNa-RE,
  author = {Fabrizio Riguzzi},
  title = {Review of
  {Kahl, Patrick Thor; Leclerc, Anthony P.; Tran, Son Cao A parallel memory-efficient epistemic logic program solver: harder, better, faster. Ann. Math. Artif. Intell. 86 (2019), no. 1-3, 61–85}},
  journal = {Mathematical Reviews},
  publisher = {American Mathematical Society},
  copyright = {American Mathematical Society},
  year = {2020},
  month = {January},
  issn = {2167-5163},
  mrnumber = {MR3977565},
  mrreviewer = {Fabrizio Riguzzi},
  url = {http://www.ams.org/mathscinet-getitem?mr=3977565  }
}
@article{Rig20-MNb-RE,
  author = {Fabrizio Riguzzi},
  title = {Review of
  {Costantini, Stefania. About epistemic negation and world views in epistemic logic programs.
 Theory Pract. Log. Program.  19  (2019),  no. 5-6, 790--807.}},
  journal = {Mathematical Reviews},
  publisher = {American Mathematical Society},
  copyright = {American Mathematical Society},
  year = {2020},
  month = {May},
  issn = {2167-5163},
  mrnumber = {MR4010537},
  mrreviewer = {Fabrizio Riguzzi},
  url = {http://www.ams.org/mathscinet-getitem?mr=4010537  }
}
@article{Rig20-MNc-RE,
  author = {Fabrizio Riguzzi},
  title = {Review of
  {Arieli, Ofer; Borg, AnneMarie; Heyninck, Jesse A review of the relations between logical argumentation and reasoning with maximal consistency. Ann. Math. Artif. Intell. 87 (2019), no. 3, 187--226.}},
  journal = {Mathematical Reviews},
  publisher = {American Mathematical Society},
  copyright = {American Mathematical Society},
  year = {2020},
  issn = {2167-5163},
  mrnumber = {MR4038076},
  mrreviewer = {Fabrizio Riguzzi},
  url = {http://www.ams.org/mathscinet-getitem?mr=4038076  }
}
@article{BelAlbRig20-TPLP-IJ,
  author = {Elena Bellodi and Marco Alberti and Fabrizio Riguzzi and Riccardo Zese},
  title = {{MAP} Inference for Probabilistic Logic Programming},
  journal = {Theory and Practice of Logic Programming},
  publisher = {Cambridge University Press},
  copyright = {Cambridge University Press},
  year = {2020},
  url = {https://arxiv.org/abs/2008.01394},
  volume = {20},
  doi = {10.1017/S1471068420000174},
  pdf = {https://arxiv.org/pdf/2008.01394.pdf},
  number = {5},
  pages = {641–655}
}
@inproceedings{AzzRigLam20-PLP-IW,
  title = {An Analysis of {Gibbs} Sampling for Probabilistic Logic Programs},
  booktitle = {Workshop on Probabilistic Logic Programming (PLP 2020)},
  year = 2020,
  author = {Azzolini, Damiano and Fabrizio Riguzzi and Evelina Lamma},
  editor = {Carmine Dodaro and George Aristidis Elder and Wolfgang Faber and Jorge Fandinno and Martin Gebser and Markus Hecher and Emily LeBlanc and Michael Morak and Jessica Zangari},
  volume = {2678},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  address = {Aachen, Germany},
  issn = {1613-0073},
  venue = {Rende, Italy},
  eventdate = {September 19, 2020},
  copyright = {by the authors},
  url = {http://ceur-ws.org/Vol-2678/paper12.pdf},
  pages = {1-13},
  scopus = {2-s2.0-85092328818}
}
@proceedings{ICLP2020TC-EB,
  editor = {Francesco Ricca and
               Alessandra Russo and
               Sergio Greco and
               Nicola Leone and
               Alexander Artikis and
               Gerhard Friedrich and
               Paul Fodor and
               Angelika Kimmig and
               Francesca A. Lisi and
               Marco Maratea and
               Alessandra Mileo and
               Fabrizio Riguzzi},
  title = {Proceedings 36th International Conference on Logic Programming (Technical
               Communications)},
  year = {2020},
  url = {https://arxiv.org/abs/2009.09158},
  doi = {10.4204/EPTCS.325},
  series = {Electronic Proceedings in Theoretical Computer Science},
  number = {325}
}
@inproceedings{AzzBelBraRigLam20-ICLP-IC,
  title = {Modeling Bitcoin Lightning Network by Logic Programming},
  booktitle = {Proceedings of the 36th International Conference on Logic Programming (Technical Communications)},
  year = 2020,
  author = {Damiano Azzolini and Elena Bellodi and Alessandro Brancaleoni and Fabrizio Riguzzi and Evelina Lamma},
  editor = {Francesco Ricca and Alessandra Russo and Sergio Greco and Nicola Leone and Alexander Artikis and Gerhard Friedrich and Paul Fodor and Angelika Kimmig and Francesca Lisi and Marco Maratea and Alessandra Mileo and Fabrizio Riguzzi},
  publisher = {Open Publishing Association},
  address = {Waterloo, Australia},
  issn = {2075-2180},
  venue = {Rende, Italy},
  eventdate = {September 18-25, 2020},
  copyright = {by the authors},
  url = {https://cgi.cse.unsw.edu.au/~eptcs/content.cgi?ICLP2020#EPTCS325.30},
  http = {https://arxiv.org/html/2009.09158v1/#EPTCS325.30},
  doi = {10.4204/EPTCS.325},
  scopus = {2-s2.0-85092656765},
  pages = {258-260}
}
@inproceedings{AzzRigLam20-BSCT-IW,
  author = {Azzolini, Damiano and Riguzzi, Fabrizio and Lamma, Evelina},
  editor = {Abramowicz, Witold and Klein, Gary},
  title = {Modeling Smart Contracts with Probabilistic Logic Programming},
  booktitle = {Business Information Systems Workshops},
  year = {2020},
  publisher = {Springer International Publishing},
  series = {Lecture Notes in Business Information Processing},
  volume = {394},
  address = {Cham},
  pages = {86--98},
  isbn = {978-3-030-61146-0},
  url = {http://ml.unife.it/wp-content/uploads/Papers/AzzRigLam-BSCT20.pdf},
  copyright = {Springer},
  doi = {10.1007/978-3-030-61146-0_7},
  note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-030-61146-0_7}}
}

This file was generated by bibtex2html 1.98.