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Documents authored by Pagliarini, Giovanni

Artifact
Software
DOI: 10.4230/artifacts.24782
Sole.jl

Authors: Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

Abstract
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Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, Ionel Eduard Stan. Sole.jl (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24782,
   title = {{Sole.jl}}, 
   author = {Milella, Mauro and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
   note = {Software, version 0.6.2., swhId: \href{https://archive.softwareheritage.org/swh:1:dir:dd723aee72578208606649ff12168e891cdae221;origin=https://github.com/aclai-lab/Sole.jl;visit=swh:1:snp:921c0e3817509d813e0ea03398f093fbd48ca539;anchor=swh:1:rev:8b79f0b7e41c91745a780262e11c7d07be660084}{\texttt{swh:1:dir:dd723aee72578208606649ff12168e891cdae221}} (visited on 2025-10-13)},
   url = {https://github.com/aclai-lab/Sole.jl},
   doi = {10.4230/artifacts.24782},
}
Artifact
Software
DOI: 10.4230/artifacts.24783
ModalAssociationRules.jl

Authors: Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

Abstract
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Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, Ionel Eduard Stan. ModalAssociationRules.jl (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24783,
   title = {{ModalAssociationRules.jl}}, 
   author = {Milella, Mauro and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
   note = {Software, version 0.1.0., swhId: \href{https://archive.softwareheritage.org/swh:1:dir:697da0b30a22cd23450ab445a887ebf1a602db8f;origin=https://github.com/aclai-lab/ModalAssociationRules.jl;visit=swh:1:snp:72b4fb9d69583cb16dd357b5c9de2a2359b80727;anchor=swh:1:rev:4c69384c9ff2e0cf401e27ae9874b1c728962829}{\texttt{swh:1:dir:697da0b30a22cd23450ab445a887ebf1a602db8f}} (visited on 2025-10-13)},
   url = {https://github.com/aclai-lab/ModalAssociationRules.jl},
   doi = {10.4230/artifacts.24783},
}
Document
Short Paper
DOI: 10.4230/LIPIcs.TIME.2025.19
Temporal Association Rules from Motifs (Short Paper)

Authors: Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
A motif is defined as a frequently occurring pattern within a (multivariate) time series. In recent years, various techniques have been developed to mine time series data. However, only a few studies have explored the idea of using motif discovery in temporal association rule mining. Interval-based temporal association rules have been recently defined and studied, along with the temporal version of the known frequent patterns, and therefore, association rule extraction algorithms (such as APRIORI and FP-Growth). In this work, we define a vocabulary of propositional letters wrapping motifs, and show how to extract temporal association rules starting from such a vocabulary. We apply our methodology to time series datasets in the fields of hand signs execution and gait recognition, and we discuss how they capture curious insights within data, keeping a high level of interpretability.
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Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan. Temporal Association Rules from Motifs (Short Paper). In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 19:1-19:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{milella_et_al:LIPIcs.TIME.2025.19,
  author =	{Milella, Mauro and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Temporal Association Rules from Motifs}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{19:1--19:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.19},
  URN =		{urn:nbn:de:0030-drops-244653},
  doi =		{10.4230/LIPIcs.TIME.2025.19},
  annote =	{Keywords: Motifs, Interval Temporal Logic, Association Rules}
}
Document
DOI: 10.4230/LIPIcs.TIME.2022.13
Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification

Authors: Giovanni Pagliarini, Simone Scaboro, Giuseppe Serra, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 247, 29th International Symposium on Temporal Representation and Reasoning (TIME 2022)

Abstract
Multivariate time series classification is a widely known problem, and its applications are ubiquitous. Due to their strong generalization capability, neural networks have been proven to be very powerful for the task, but their applicability is often limited by their intrinsic black-box nature. Recently, temporal decision trees have been shown to be a serious alternative to neural networks for the same task in terms of classification performances, while attaining higher levels of transparency and interpretability. In this work, we propose an initial approach to neural-symbolic temporal decision trees, that is, an hybrid method that leverages on both the ability of neural networks of capturing temporal patterns and the flexibility of temporal decision trees of taking decisions on intervals based on (possibly, externally computed) temporal features. While based on a proof-of-concept implementation, in our experiments on public datasets, neural-symbolic temporal decision trees show promising results.
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Giovanni Pagliarini, Simone Scaboro, Giuseppe Serra, Guido Sciavicco, and Ionel Eduard Stan. Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification. In 29th International Symposium on Temporal Representation and Reasoning (TIME 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 247, pp. 13:1-13:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{pagliarini_et_al:LIPIcs.TIME.2022.13,
  author =	{Pagliarini, Giovanni and Scaboro, Simone and Serra, Giuseppe and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification}},
  booktitle =	{29th International Symposium on Temporal Representation and Reasoning (TIME 2022)},
  pages =	{13:1--13:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-262-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{247},
  editor =	{Artikis, Alexander and Posenato, Roberto and Tonetta, Stefano},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2022.13},
  URN =		{urn:nbn:de:0030-drops-172607},
  doi =		{10.4230/LIPIcs.TIME.2022.13},
  annote =	{Keywords: Machine learning, neural-symbolic, temporal logic, hybrid temporal decision trees}
}
Document
DOI: 10.4230/LIPIcs.TIME.2021.7
Interval Temporal Random Forests with an Application to COVID-19 Diagnosis

Authors: Federico Manzella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
Symbolic learning is the logic-based approach to machine learning. The mission of symbolic learning is to provide algorithms and methodologies to extract logical information from data and express it in an interpretable way. In the context of temporal data, interval temporal logic has been recently proposed as a suitable tool for symbolic learning, specifically via the design of an interval temporal logic decision tree extraction algorithm. Building on it, we study here its natural generalization to interval temporal random forests, mimicking the corresponding schema at the propositional level. Interval temporal random forests turn out to be a very performing multivariate time series classification method, which, despite the introduction of a functional component, are still logically interpretable to some extent. We apply this method to the problem of diagnosing COVID-19 based on the time series that emerge from cough and breath recording of positive versus negative subjects. Our experiment show that our models achieve very high accuracies and sensitivities, often superior to those achieved by classical methods on the same data. Although other recent approaches to the same problem (based on different and more numerous data) show even better statistical results, our solution is the first logic-based, interpretable, and explainable one.
Cite as

Federico Manzella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan. Interval Temporal Random Forests with an Application to COVID-19 Diagnosis. In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{manzella_et_al:LIPIcs.TIME.2021.7,
  author =	{Manzella, Federico and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Interval Temporal Random Forests with an Application to COVID-19 Diagnosis}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{7:1--7:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.7},
  URN =		{urn:nbn:de:0030-drops-147837},
  doi =		{10.4230/LIPIcs.TIME.2021.7},
  annote =	{Keywords: Interval temporal logic, decision trees, random forests, sound-based diagnosis}
}
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