Ionel Eduard Stan

JBHI Journal 2026 Journal Article

A Systematic Literature Review of Innovations, Challenges, and Future Directions in Telemonitoring and Wearable Health Technologies

• Ionel Eduard Stan
• Daniela D’Auria
• Paolo Napoletano

Telemonitoring and wearable devices are transforming healthcare by enabling continuous patient monitoring and personalized interventions. However, their integration remains fragmented due to technical, ethical, and regulatory barriers. This systematic review, following PRISMA 2020 guidelines, synthesizes insights from 165 studies (2020–2024) to assess key advancements and persistent challenges. AI-driven diagnostics show potential but require clinical validation, while energy-efficient communication protocols lack standardization. Data security improvements, such as hybrid encryption, enhance protection but introduce computational overhead. Regulatory misalignment and clinician skepticism hinder interoperability and large-scale adoption. Despite usability advancements, disparities in patient accessibility and clinician engagement highlight the need for human-centered design. To convert promises into practice, we outline a blueprint that pairs harmonized regulatory pathways with explainable, edge-deployed AI, leverages low-latency data exchange, and promotes cross-sector interoperability convergence. Realizing this agenda through longitudinal, multicenter trials and inclusive, community-anchored implementation will unlock equitable and scalable telemonitoring ecosystems.

TIME Conference 2025 Conference Paper

Assessing the (In)Ability of LLMs to Reason in Interval Temporal Logic

• Pietro Bellodi
• Pietro Casavecchia
• Alberto Paparella
• Guido Sciavicco
• Ionel Eduard Stan

The logical reasoning skills of Large Language Models (LLMs) is poorly understood and often overstated. Current evaluation suites rely on algebraic or commonsense puzzles that mix reasoning with symbolic manipulation and/or provide static datasets that quickly saturate or leak into pretraining corpora. In purely logical terms, the most relevant reasoning skill is the meta-mathematical task of valid formula recognition, which is at the foundation of higher-level reasoning tasks (including deduction and minimization of assertions, to name just a few). In the current landscape of LLMs benchmarking, puzzles are most often stated in propositional or first-order logic, with a few exceptions for point-based temporal logic, such as LTL; yet, in the real world, event-based temporal statements are prevalent, and they are more naturally expressed in interval-based temporal logic. Interval temporal logic offers a much richer (w. r. t. point-based temporal logic, for example) variety of problems, and not only do different languages present different expressive powers, but also the computational complexity of the validity problem can vary widely. In this paper, we tackle the problem of assessing the ability of LLMs to reason about interval-based statements in the form of validity recognition. We explore whether their accuracy is sensible to the underlying language, the computational complexity of the associated validity problem, and the intrinsic hardness of the problem in terms of formula length and modal depth of the problem. We benchmark several frontier LLMs (Gemma 3 27b It, Llama 4 Maverick, DeepSeek Chat V3 release 0324, Qwen 3 32b, and Qwen 3 235b) and show that, despite apparently impressive performance on algebraic or commonsense benchmarks, they falter on logically rigorous tasks.

TIME Conference 2025 Conference Paper

Temporal Association Rules from Motifs (Short Paper)

• Mauro Milella
• Giovanni Pagliarini
• Guido Sciavicco
• Ionel Eduard Stan

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.

TIME Conference 2024 Conference Paper

Fitting's Style Many-Valued Interval Temporal Logic Tableau System: Theory and Implementation

• Guillermo Badia
• Carles Noguera
• Alberto Paparella
• Guido Sciavicco
• Ionel Eduard Stan

Many-valued logics, often referred to as fuzzy logics, are a fundamental tool for reasoning about uncertainty, and are based on truth value algebras that generalize the Boolean one; the same logic can be interpreted on algebras from different varieties, for different purposes and pose different challenges. Although temporal many-valued logics, that is, the many-valued counterpart of popular temporal logics, have received little attention in the literature, the many-valued generalization of Halpern and Shoham’s interval temporal logic has been recently introduced and studied, and a sound and complete tableau system for it has been presented for the case in which it is interpreted on some finite Heyting algebra. In this paper, we take a step further in this inquiry by exploring a tableau system for Halpern and Shoham’s interval temporal logic interpreted on some finite {FL_{ew}}-algebra, therefore generalizing the Heyting case, and by providing its open-source implementation.

I&C Journal 2024 Journal Article

Neural-symbolic temporal decision trees for multivariate time series classification

• Giovanni Pagliarini
• Simone Scaboro
• Giuseppe Serra
• Guido Sciavicco
• Ionel Eduard Stan

Multivariate time series classification is an ubiquitous and widely studied problem. Due to their strong generalization capability, neural networks are suitable for this problem, but their intrinsic black-box nature often limits their applicability. Temporal decision trees are a relevant alternative to neural networks for the same task regarding classification performances while attaining higher levels of transparency and interpretability. In this work, we approach the problem of hybridizing these two techniques, and present three independent, natural hybridization solutions to study if, and in what measure, both the ability of neural networks to capture complex temporal patterns and the transparency and flexibility of temporal decision trees can be leveraged. To this end, we provide initial experimental results for several tasks in a binary classification setting, showing that our proposed neural-symbolic hybridization schemata may be a step towards accurate and interpretable models.

TIME Conference 2023 Conference Paper

A Sound and Complete Tableau System for Fuzzy Halpern and Shoham's Interval Temporal Logic

• Willem Conradie
• Riccardo Monego
• Emilio Muñoz-Velasco
• Guido Sciavicco
• Ionel Eduard Stan

Interval temporal logic plays a critical role in various applications, including planning, scheduling, and formal verification; recently, interval temporal logic has also been successfully applied to learning from temporal data. Halpern and Shoham’s interval temporal logic, in particular, stands out as a very intuitive, yet expressive, interval-based formalism. To address real-world scenarios involving uncertainty and imprecision, Halpern and Shoham’s logic has been recently generalized to the fuzzy (many-valued) case. The resulting language capitalizes on many-valued modal logics, allowing for a range of truth values that reflect multiple expert perspectives, but inherits the bad computational behaviour of its crisp counterpart. In this work, we investigate a sound and complete tableau system for fuzzy Halpern and Shoham’s logic, which, although possibly non-terminating, offers a semi-decision procedure for the finite case.

ECAI Conference 2023 Conference Paper

Evolutionary Explainable Rule Extraction from (Modal) Random Forests

• Michele Ghiotti
• Federico Manzella
• Giovanni Pagliarini
• Guido Sciavicco
• Ionel Eduard Stan

Symbolic learning is the subfield of machine learning concerned with learning predictive models with knowledge represented in logical form, such as decision tree and decision list models. Ensemble learning methods, such as random forests, are usually deployed to improve the performance of decision trees; unfortunately, interpreting tree ensembles is challenging. In order to deal with unstructured (e. g. , temporal or spatial) data, moreover, decision trees and random forests have been recently generalized to the use of modal logics, which are harder to interpret than their propositional counterpart. Recently, a methodology for extracting simple rules from propositional random forests, based on a sequence of optimization steps, was proposed. In this work, we generalize this approach along two directions: from propositional to modal logic and from a sequence of optimization steps to a single multi-objective optimization problem. Even if confined to the temporal domain, our experimental results, based on open-source implementations and public data, show that our method is robust and able to extract small, accurate, and informative decision lists even for complex classification problems.

TIME Conference 2022 Conference Paper

Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification

• Giovanni Pagliarini
• Simone Scaboro
• Giuseppe Serra 0001
• Guido Sciavicco
• Ionel Eduard Stan

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.

TIME Conference 2021 Conference Paper

Interval Temporal Random Forests with an Application to COVID-19 Diagnosis

• Federico Manzella
• Giovanni Pagliarini
• Guido Sciavicco
• Ionel Eduard Stan

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.

TIME Conference 2020 Conference Paper

Knowledge Extraction with Interval Temporal Logic Decision Trees

• Guido Sciavicco
• Ionel Eduard Stan

Multivariate temporal, or time, series classification is, in a way, the temporal generalization of (numeric) classification, as every instance is described by multiple time series instead of multiple values. Symbolic classification is the machine learning strategy to extract explicit knowledge from a data set, and the problem of symbolic classification of multivariate temporal series requires the design, implementation, and test of ad-hoc machine learning algorithms, such as, for example, algorithms for the extraction of temporal versions of decision trees. One of the most well-known algorithms for decision tree extraction from categorical data is Quinlan’s ID3, which was later extended to deal with numerical attributes, resulting in an algorithm known as C4. 5, and implemented in many open-sources data mining libraries, including the so-called Weka, which features an implementation of C4. 5 called J48. ID3 was recently generalized to deal with temporal data in form of timelines, which can be seen as discrete (categorical) versions of multivariate time series, and such a generalization, based on the interval temporal logic HS, is known as Temporal ID3. In this paper we introduce Temporal C4. 5, that allows the extraction of temporal decision trees from undiscretized multivariate time series, describe its implementation, called Temporal J48, and discuss the outcome of a set of experiments with the latter on a collection of public data sets, comparing the results with those obtained by other, classical, multivariate time series classification methods.

JELIA Conference 2019 Conference Paper

Interval Temporal Logic Decision Tree Learning

• Andrea Brunello
• Guido Sciavicco
• Ionel Eduard Stan

Abstract Decision trees are simple, yet powerful, classification models used to classify categorical and numerical data, and, despite their simplicity, they are commonly used in operations research and management, as well as in knowledge mining. From a logical point of view, a decision tree can be seen as a structured set of logical rules written in propositional logic. Since knowledge mining is rapidly evolving towards temporal knowledge mining, and since in many cases temporal information is best described by interval temporal logics, propositional logic decision trees may evolve towards interval temporal logic decision trees. In this paper, we define the problem of interval temporal logic decision tree learning, and propose a solution that generalizes classical decision tree learning.

AIJ Journal 2019 Journal Article

On coarser interval temporal logics

• Emilio Muñoz-Velasco
• Mercedes Pelegrín
• Pietro Sala
• Guido Sciavicco
• Ionel Eduard Stan

The primary characteristic of interval temporal logic is that intervals, rather than points, are taken as the primitive ontological entities. Given their generally bad computational behavior of interval temporal logics, several techniques exist to produce decidable and computationally affordable temporal logics based on intervals. In this paper we take inspiration from Golumbic and Shamir's coarser interval algebras, which generalize the classical Allen's Interval Algebra, in order to define two previously unknown variants of Halpern and Shoham's logic (HS) based on coarser relations. We prove that, perhaps surprisingly, the satisfiability problem for the coarsest of the two variants, namely HS 3, not only is decidable, but PSpace-complete in the finite/discrete case, and PSpace-hard in any other case; besides proving its complexity bounds, we implement a tableau-based satisfiability checker for it and test it against a systematically generated benchmark. Our results are strengthened by showing that not all coarser-than-Allen's relations are a guarantee of decidability, as we prove that the second variant, namely HS 7, remains undecidable in all interesting cases.

OVERLAY Workshop 2019 Workshop Paper

Towards Interval Temporal Logic Rule-Based Classification

• Estrella Lucena-Sanchez
• Emilio Muñ oz-Velasco
• Guido Sciavicco
• Ionel Eduard Stan
• Alessandro Vaccari