@article{SchVDBRig23-TPLP-IJ,
title = {Automatic Differentiation in Prolog},
doi = {10.1017/S1471068423000145},
journal = {Theory and Practice of Logic Programming},
publisher = {Cambridge University Press},
author = {Schrijvers, Tom and Van Den Berg, Birthe and Riguzzi, Fabrizio},
year = {2023},
pages = {900–917},
volume = {23},
number = {4},
pdf = {https://arxiv.org/pdf/2305.07878.pdf}
}
@inproceedings{AzzGenRigPLP2023-IW,
author = {Azzolini, Damiano and Gentili, Elisabetta and Riguzzi, Fabrizio},
title = {Link Prediction in Knowledge Graphs with Probabilistic Logic Programming: Work in Progress},
series = {{CEUR} Workshop Proceedings},
booktitle = {Proceedings of the International Conference on Logic Programming 2023 Workshops co-located with the 39th International Conference on Logic Programming ({ICLP} 2023)},
editor = {Arias, Joaquín and Batsakis, Sotiris and Faber, Wolfgang and Gupta, Gopal and Pacenza, Francesco and Papadakis, Emmanuel and Robaldo, Livio and Ruckschloss, Kilian and Salazar, Elmer and Saribatur, Zeynep G. and Tachmazidis, Ilias and Weitkamper, Felix and Wyner, Adam},
volume = {3437},
pages = {1--4},
publisher = {CEUR-WS.org},
year = {2023},
url = {https://ceur-ws.org/Vol-3437/short5PLP.pdf}
}
@inproceedings{AzzBelRigMAP-AIXIA-IC,
author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
editor = {Dovier, Agostino and Montanari, Angelo and Orlandini, Andrea},
title = {{MAP} Inference in Probabilistic Answer Set Programs},
booktitle = {AIxIA 2022 -- Advances in Artificial Intelligence},
year = {2023},
publisher = {Springer International Publishing},
address = {Cham},
pages = {413--426},
abstract = {Reasoning with uncertain data is a central task in artificial intelligence. In some cases, the goal is to find the most likely assignment to a subset of random variables, named query variables, while some other variables are observed. This task is called Maximum a Posteriori (MAP). When the set of query variables is the complement of the observed variables, the task goes under the name of Most Probable Explanation (MPE). In this paper, we introduce the definitions of cautious and brave MAP and MPE tasks in the context of Probabilistic Answer Set Programming under the credal semantics and provide an algorithm to solve them. Empirical results show that the brave version of both tasks is usually faster to compute. On the brave MPE task, the adoption of a state-of-the-art ASP solver makes the computation much faster than a naive approach based on the enumeration of all the worlds.},
isbn = {978-3-031-27181-6},
url = {https://link.springer.com/chapter/10.1007/978-3-031-27181-6_29},
doi = {10.1007/978-3-031-27181-6_29}
}
@inproceedings{AzzBelRigApprox-AIXIA-IC,
author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
editor = {Dovier, Agostino and Montanari, Angelo and Orlandini, Andrea},
title = {Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities},
booktitle = {AIxIA 2022 -- Advances in Artificial Intelligence},
year = {2023},
publisher = {Springer International Publishing},
address = {Cham},
pages = {33--46},
abstract = {``Type 1'' statements were introduced by Halpern in 1990 with the goal to represent statistical information about a domain of interest. These are of the form ``x{\%} of the elements share the same property''. The recently proposed language PASTA (Probabilistic Answer set programming for STAtistical probabilities) extends Probabilistic Logic Programs under the Distribution Semantics and allows the definition of this type of statements. To perform exact inference, PASTA programs are converted into probabilistic answer set programs under the Credal Semantics. However, this algorithm is infeasible for scenarios when more than a few random variables are involved. Here, we propose several algorithms to perform both conditional and unconditional approximate inference in PASTA programs and test them on different benchmarks. The results show that approximate algorithms scale to hundreds of variables and thus can manage real world domains.},
isbn = {978-3-031-27181-6},
url = {https://link.springer.com/chapter/10.1007/978-3-031-27181-6_3},
doi = {10.1007/978-3-031-27181-6_3}
}
@article{GreSalFab23-Biomed-IJ,
author = {Greco, Salvatore and Salatiello, Alessandro and Fabbri, Nicolò and Riguzzi, Fabrizio and Locorotondo, Emanuele and Spaggiari, Riccardo and De Giorgi, Alfredo and Passaro, Angelina},
title = {Rapid Assessment of {COVID-19} Mortality Risk with {GASS} Classifiers},
journal = {Biomedicines},
volume = {11},
year = {2023},
number = {3},
article-number = {831},
url = {https://www.mdpi.com/2227-9059/11/3/831},
issn = {2227-9059},
abstract = {Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.},
doi = {10.3390/biomedicines11030831}
}
@unpublished{RigMyk22-SSRN-IJ,
author = {Riguzzi, Fabrizio and Mykhailova, Mariia},
title = {Quantum Algorithms for {WMC}, {MPE} and {MAP}},
note = {Available at SSRN},
url = {https://ssrn.com/abstract=4169880}
}
@inproceedings{AzzBelRig22-abdPASP-NC,
title = {Abduction in (Probabilistic) Answer Set Programming},
author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
year = {2022},
editor = {Roberta Calegari and Giovanni Ciatto and Andrea Omicini},
booktitle = {Proceedings of the 36th Italian Conference on Computational Logic},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Bologna, Italy},
volume = {3204},
pages = {90--103},
pdf = {http://ceur-ws.org/Vol-3204/paper_9.pdf}
}
@inproceedings{AzzRigLam22-hybridSummary-IW,
title = {Semantics for Hybrid Probabilistic Logic Programs with Function Symbols: Technical Summary},
author = {Azzolini, Damiano and Riguzzi, Fabrizio and Lamma, Evelina},
year = {2022},
editor = {Joaquín Arias and Roberta Calegari and Luke Dickens and Wolfgang Faber and Jorge Fandinno and Gopal Gupta and Markus Hecher and Daniela Inclezan and Emily LeBlanc and Michael Morak and Elmer Salazar and Jessica Zangari},
booktitle = {Proceedings of the International Conference on Logic Programming 2022 Workshops co-located with the 38th International Conference on Logic Programming (ICLP 2022)},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Haifa, Israel},
volume = {3193},
pages = {1--5},
pdf = {http://ceur-ws.org/Vol-3193/short1PLP.pdf}
}
@inproceedings{AzzBellRig2022-PASTA-IC,
author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
editor = {Gottlob, Georg and Inclezan, Daniela and Maratea, Marco},
title = {Statistical Statements in Probabilistic Logic Programming},
booktitle = {Logic Programming and Nonmonotonic Reasoning},
year = {2022},
publisher = {Springer International Publishing},
address = {Cham},
pages = {43--55},
isbn = {978-3-031-15707-3},
doi = {10.1007/978-3-031-15707-3_4},
url = {https://link.springer.com/chapter/10.1007/978-3-031-15707-3_4},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/AzzBellRig2022PASTA.pdf}
}
@inproceedings{AlbZesRig2022-Iterative-IC,
author = {Alberti, Marco and Zese, Riccardo and Riguzzi, Fabrizio and Lamma, Evelina},
year = {2022},
title = {{An Iterative Fixpoint Semantics for MKNF Hybrid Knowledge Bases with Function Symbols}},
editor = {Lierler, Yuliya and Morales, Jose F. and Dodaro, Carmine and Dahl, Veronica and Gebser, Martin and Tekle, Tuncay},
booktitle = {Proceedings of the 38th International Conference on
Logic Programming (Technical Communications)},
series = {Electronic Proceedings in Theoretical Computer Science},
issn = {2075-2180},
volume = {364},
publisher = {Open Publishing Association},
address = {Waterloo, Australia},
pages = {65-78},
doi = {10.4204/EPTCS.364.7},
url = {https://eptcs.web.cse.unsw.edu.au/paper.cgi?ICLP2022.7},
pdf = {https://eptcs.web.cse.unsw.edu.au/paper.cgi?ICLP2022.7.pdf}
}
@inproceedings{AzzBelFerRigZes22-recently-IC,
title = {Abduction in Probabilistic Logic Programs},
booktitle = {Proceedings of the 38th International Conference on Logic Programming (Technical Communications), Recently Published Research track},
issn = {2075-2180},
doi = {10.4204/EPTCS.364},
volume = {364},
pages = {174--176},
series = {Electronic Proceedings in Theoretical Computer Science},
publisher = {Open Publishing Association},
address = {Waterloo, Australia},
editor = {Yuliya Lierler and Jose F. Morales and Carmine Dodaro and Veronica Dahl and Martin Gebser and Tuncay Tekle},
year = {2022},
author = {Damiano Azzolini and Elena Bellodi and Stefano Ferilli and Fabrizio Riguzzi and Riccardo Zese},
url = {https://arxiv.org/html/2208.02685v1/#EPTCS364.27}
}
@inproceedings{FraLamRig22-recently-IC,
title = {Exploiting Parameters Learning for Hyper-parameters Optimization in Deep Neural Networks},
booktitle = {Proceedings of the 38th International Conference on Logic Programming (Technical Communications), Recently Published Research track},
issn = {2075-2180},
doi = {10.4204/EPTCS.364},
volume = {364},
pages = {142--144},
series = {Electronic Proceedings in Theoretical Computer Science},
publisher = {Open Publishing Association},
address = {Waterloo, Australia},
editor = {Yuliya Lierler and Jose F. Morales and Carmine Dodaro and Veronica Dahl and Martin Gebser and Tuncay Tekle},
year = {2022},
author = {Michele Fraccaroli and Fabrizio Riguzzi and Evelina Lamma},
url = {https://arxiv.org/html/2208.02685v1/#EPTCS364.17}
}
@article{RocChiNal2022-APPSCI-IJ,
author = {Rocchi, Alessandro and Chiozzi, Andrea and Nale, Marco and Nikolic, Zeljana and Riguzzi, Fabrizio and Mantovan, Luana and Gilli, Alessandro and Benvenuti, Elena},
title = {A Machine Learning Framework for Multi-Hazard Risk Assessment at the Regional Scale in Earthquake and Flood-Prone Areas},
journal = {Applied Sciences},
volume = {12},
year = {2022},
number = {2},
article-number = {583},
url = {https://www.mdpi.com/2076-3417/12/2/583},
issn = {2076-3417},
abstract = {Communities are confronted with the rapidly growing impact of disasters, due to many factors that cause an increase in the vulnerability of society combined with an increase in hazardous events such as earthquakes and floods. The possible impacts of such events are large, also in developed countries, and governments and stakeholders must adopt risk reduction strategies at different levels of management stages of the communities. This study is aimed at proposing a sound qualitative multi-hazard risk analysis methodology for the assessment of combined seismic and hydraulic risk at the regional scale, which can assist governments and stakeholders in decision making and prioritization of interventions. The method is based on the use of machine learning techniques to aggregate large datasets made of many variables different in nature each of which carries information related to specific risk components and clusterize observations. The framework is applied to the case study of the Emilia Romagna region, for which the different municipalities are grouped into four homogeneous clusters ranked in terms of relative levels of combined risk. The proposed approach proves to be robust and delivers a very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling at the regional scale.},
doi = {10.3390/app12020583}
}
@article{AzzBellFer2022-IJAR-IJ,
title = {Abduction with probabilistic logic programming under the distribution semantics},
journal = {International Journal of Approximate Reasoning},
volume = {142},
pages = {41-63},
year = {2022},
issn = {0888-613X},
doi = {10.1016/j.ijar.2021.11.003},
url = {https://www.sciencedirect.com/science/article/pii/S0888613X2100181X},
author = {Damiano Azzolini and Elena Bellodi and Stefano Ferilli and Fabrizio Riguzzi and Riccardo Zese},
keywords = {Abduction, Distribution semantics, Probabilistic logic programming, Statistical relational artificial intelligence},
abstract = {In Probabilistic Abductive Logic Programming we are given a probabilistic logic program, a set of abducible facts, and a set of constraints. Inference in probabilistic abductive logic programs aims to find a subset of the abducible facts that is compatible with the constraints and that maximizes the joint probability of the query and the constraints. In this paper, we extend the PITA reasoner with an algorithm to perform abduction on probabilistic abductive logic programs exploiting Binary Decision Diagrams. Tests on several synthetic datasets show the effectiveness of our approach.},
scopus = {2-s2.0-85119493622}
}
@article{FraLamRig2022-SwX-IJ,
title = {Symbolic {DNN-Tuner}: A {Python} and {ProbLog}-based system for optimizing Deep Neural Networks hyperparameters},
journal = {SoftwareX},
volume = {17},
pages = {100957},
year = {2022},
issn = {2352-7110},
doi = {10.1016/j.softx.2021.100957},
url = {https://www.sciencedirect.com/science/article/pii/S2352711021001825},
author = {Michele Fraccaroli and Evelina Lamma and Fabrizio Riguzzi},
keywords = {Deep learning, Probabilistic Logic Programming, Hyper-parameters tuning, Neural-symbolic integration},
abstract = {The application of deep learning models to increasingly complex contexts has led to a rise in the complexity of the models themselves. Due to this, there is an increase in the number of hyper-parameters (HPs) to be set and Hyper-Parameter Optimization (HPO) algorithms occupy a fundamental role in deep learning. Bayesian Optimization (BO) is the state-of-the-art of HPO for deep learning models. BO keeps track of past results and uses them to build a probabilistic model, building a probability density of HPs. This work aims to improve BO applied to Deep Neural Networks (DNNs) by an analysis of the results of the network on training and validation sets. This analysis is obtained by applying symbolic tuning rules, implemented in Probabilistic Logic Programming (PLP). The resulting system, called Symbolic DNN-Tuner, logically evaluates the results obtained from the training and the validation phase and, by applying symbolic tuning rules, fixes the network architecture, and its HPs, leading to improved performance. In this paper, we present the general system and its implementation. We also show its graphical interface and a simple example of execution.}
}
@article{LosVen22-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 = {Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models},
journal = {Journal of Engineering for Gas Turbines and Power},
volume = {144},
number = {3},
year = {2022},
issn = {0742-4795},
doi = {10.1115/1.4053194},
publisher = asme_p,
note = {{GTP-21-1324}}
}
@inproceedings{AzzRigBelLam22-BSCT-IW,
title = {A Probabilistic Logic Model of Lightning Network},
author = {Azzolini, Damiano and Riguzzi, Fabrizio and Bellodi, Elena and Lamma, Evelina},
booktitle = {Business Information Systems Workshops},
year = {2022},
editor = {Abramowicz, Witold and Auer, S{\"o}ren and Str{\'o}{\.{z}}yna, Milena},
pages = {321--333},
series = {Lecture Notes in Business Information Processing (LNBIP)},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
eventdate = {June 14-17, 2021},
doi = {10.1007/978-3-031-04216-4_28},
url = {https://link.springer.com/chapter/10.1007/978-3-031-04216-4_28},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/AzzRigBelLam22-BSCT-IW.pdf}
}
@incollection{ZesBelFraRigLam22-MLNVM-BC,
author = {Zese, Riccardo and Bellodi, Elena and Fraccaroli, Michele and Riguzzi, Fabrizio and Lamma, Evelina},
editor = {Micheloni, Rino and Zambelli, Cristian},
title = {Neural Networks and Deep Learning Fundamentals},
booktitle = {Machine Learning and Non-volatile Memories},
year = {2022},
publisher = {Springer International Publishing},
address = {Cham},
pages = {23--42},
abstract = {In the last decade, Neural Networks (NNs) have come to the fore as one of the most powerful and versatile approaches to many machine learning tasks. Deep Learning (DL)Deep Learning (DL), the latest incarnation of NNs, is nowadays applied in every scenario that needs models able to predict or classify data. From computer vision to speech-to-text, DLDeep Learning (DL) techniques are able to achieve super-human performance in many cases. This chapter is devoted to give a (not comprehensive) introduction to the field, describing the main branches and model architectures, in order to try to give a roadmap of this area to the reader.},
isbn = {978-3-031-03841-9},
doi = {10.1007/978-3-031-03841-9_2},
url = {https://doi.org/10.1007/978-3-031-03841-9_2}
}
@article{AzzRig2022-CRYPT-IJ,
author = {Azzolini, Damiano and Riguzzi, Fabrizio},
title = {Probabilistic Logic Models for the Lightning Network},
journal = {Cryptography},
volume = {6},
year = {2022},
number = {2},
article-number = {29},
url = {https://www.mdpi.com/2410-387X/6/2/29},
pdf = {https://www.mdpi.com/2410-387X/6/2/29/pdf?version=1655360685},
issn = {2410-387X},
doi = {10.3390/cryptography6020029}
}
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