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## Publications

Publications (125)

Neuroaesthetics, as defined by Zeki in 1999, is the scientific approach to the study of aesthetic perceptions of art, music, or any other experience that can give rise to aesthetic judgments. One way to understand the processes of neuroaesthetics is studying the electroencephalogram (EEG) signals that are recorded from subjects while they are expos...

The most representative interval temporal logic, called HS, was introduced by Halpern and Shoham in the nineties. Recently, HS has been proposed as a suitable formalism for modern artificial intelligence applications; however, when dealing with real-life data one is not always able to express temporal relations and propositional labels in a definit...

The ever more accurate search for deep analysis in customer data is a really strong technological trend nowadays, quite appealing to both private and public companies. This is particularly true in the contact center domain, where speech analytics is an extremely powerful methodology for gaining insights from unstructured data, coming from customer...

The large amount of data that is produced today with new technologies is an impediment for machine learning algorithms to work correctly, both due to the memory requirements and the necessary execution times. That is why the processes of reducing both the quantity and the size of the data are increasingly important. One of these processes is the so...

Materials research in archaeological ceramic artifacts is a consolidated practice that helps architectural heritage preservation. Ancient buildings located within the historic centres, in particular, mark the image and history of each city at different periods, and when damaged historical masonry needs restoration actions, a good characterization o...

Branching Algebra is the natural branching-time generalization of Allen's Interval Algebra. As in the linear case, the consistency problem for Branching Algebra is NP-hard. Branching Algebra has many potential applications in different areas of Artificial Intelligence; therefore, being able to efficiently solve classical problems expressed in Branc...

When investigating the causes of contamination in specific contexts, such as in underground water wells, multivariate regression is commonly used to establish possible links between the chemical-physical values of the samples and the levels of contaminant. Two issues often arise from such a statistical analysis: selecting the best predicting variab...

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,...

Symbolic learning represents the most straightforward approach to interpretable modeling, but its applications have been hampered by a single structural design choice: the adoption of propositional logic as the underlying language. Recently, more-than-propositional symbolic learning methods have started to appear, in particular for time-dependent d...

There is a very extensive literature on the design and test of models of environmental pollution, especially in the atmosphere. Current and recent models, however, are focused on explaining the causes and their temporal relationships, but do not explore, in full detail, the performances of pure forecasting models. We consider here three years of da...

Geochemical fingerprinting is a rapidly expanding discipline in the earth and environmental sciences, anchored in the recognition that geological processes leave behind physical, chemical and sometimes also isotopic patterns in the samples. Furthermore, the geochemical fingerprinting of natural cycles (water, carbon, soil and biota fingerprinting)...

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the p...

Model checking is a very well-known problem, with many practical applications. A possible declination of such a problem in the interval logic setting is the so-called finite model checking, that consists of verifying an interval temporal logic formula, typically of Halpern and Shoham's logic of Allen's relations HS, on a fully represented finite in...

Due to the unwavering interest of both residents and authorities in the air quality of urban agglomerations, we pose the following question in this paper: What impact do current and past meteorological factors and traffic flow intensity have on air quality? What is the impact of lagged variables on the fit of an explanation model, and how do they a...

Geochemical fingerprinting is a rapidly expanding discipline in the earth and environmental sciences, based on the idea that geological processes leave behind physical and chemical patterns in the samples. In recent years, computational statistics and artificial intelligence methods have started to be used to help the process of geochemical fingerp...

Chlorophyll-a is a specific form of chlorophyll used in oxygenic photosynthesis which has been linked to nutrient presence in sea waters, and being able to correctly determine its concentrations may turn out to be a key step in helping preventing and controlling illegal fishing activities in certain areas. In this work, we consider open access data...

Regression analysis is the statistical process used to estimate the relationship between a dependent variable and one or more independent variables. In machine learning, typical statistical approaches to regression such as linear regression are often replaced with symbolic learning, such as decision tree regression, to capture non-linear behaviour...

In this paper, we deal with the ultimately-periodic finite interval temporal logic model checking problem. The problem has been shown to be in PTIME for full Halpern and Shoham's interval temporal logic (HS for short) over finite models, as well as for the HS fragment featuring a modality for Allen relation meets and metric constraints over non-spa...

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 classifica...

Association rule extraction is a very well-known and important problem in machine learning, and especially in the sub-field of explainable machine learning. Association rules are naturally extracted from data sets with Boolean (or at least categorical) attributes. In order for rule extraction algorithms to be applicable to data sets with numerical...

In order to refine the research on the impact of environmental factors on the concentration of pollutants in the air, in this paper, we present a mathematical model that allows the possibility of taking into account the past values of factors (explanatory variables) when modeling the current concentration of pollution. We conducted numerical analyz...

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 proposi...

Anthropogenic environmental pollution is a known and indisputable issue, and the importance of searching for reliable mathematical models that help understanding the underlying process is witnessed by the extensive literature on the topic. In this article, we focus on the temporal aspects of the processes that govern the concentration of pollutants...

Supervised classification is one of the main computational tasks of modern artificial intelligence, and it is used to automatically extract an underlying theory from a set of already classified instances. The available learning schemata are mostly limited to static instances, in which the temporal component of the information is absent, neglected,...

The temporal aspects often play an important role in information extraction. Given the peculiarities of temporal data, their management typically requires the use of dedicated algorithms, that make the overall data mining process complex, especially in those cases in which a dataset is characterised by both temporal and atemporal information. In su...

It is well-known that in some regression problems the e↵ect of an independent variables on the dependent one(s) may be delayed; this phenomenon is known as lag. Lag regression is one of the standard techniques for time series explanation and prediction. However, using lagged variables to transform a multivariate time series so that a propo-sitional...

In this work, a data set describing phone interactions arising in a multichannel and multiskill contact centre is considered with the aim of classifying inbound sessions into those that will be eventually managed by an agent and those that, instead, will be abandoned before. More precisely, the goal of the work is to extract interpretable pieces of...

Extracting rules from temporal series is a well-established temporal data mining technique. The current literature contains a number of different algorithms and experiments that allow one to abstract temporal series and, later, extract meaningful rules from them. In this paper, we approach this problem in a rather general way, without resorting, as...

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 proposi...

In the European academic systems, the public funding to single universities depends on many factors, which are periodically evaluated. One of such factors is the rate of success, that is, the rate of students that do complete their course of study. At many levels, therefore, there is an increasing interest in being able to predict the risk that a s...

Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time serie...

Interval temporal logics provide a natural framework for temporal reasoning about interval structures over linearly ordered domains, where intervals are taken as first-class citizens. Their expressive power and computational behavior mainly depend on two parameters: the set of modalities they feature and the linear orders over which they are interp...

The interpretability of classification systems refers to the ability of these to express their behaviour in a way that is easily understandable by a user. Interpretable classification models allow for external validation by an expert and, in certain disciplines such as medicine or business, providing information about decision making is essential f...

Time series play a major role in many analysis tasks. As an example, in the stock market, they can be used to model price histories and to make predictions about future trends. Sometimes, information contained in a time series is complemented by other kinds of data, which may be encoded by static attributes, e.g., categorical or numeric ones, or by...

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...

Although there exist several decidable fragments of Halpern and Shoham's interval temporal logic HS, the computational complexity of their satisfiability problem tend to be generally high. Recently, the fragment HS3 of HS, based on coarser-than-Allen's relations, has been introduced, and it has been proven to be not only decidable, but also relativ...

There are two natural and well-studied approaches to temporal ontology and reasoning: point-based and interval-based. Usually, interval-based temporal reasoning deals with points as a particular case of duration-less intervals. A recent result by Balbiani, Goranko, and Sciavicco presented an explicit two-sorted point-interval temporal framework in...

Sequences play a major role in the extraction of information from data. As an example, in business intelligence, they can be used to track the evolution of customer behaviors over time or to model relevant relationships. In this paper, we focus our attention on the domain of contact centers, where sequential data typically take the form of oral or...

Evaluating in a correct, fair, systematic and reliable way the quality of the work is a central problem in modern business. Both from the psychological and the social point of view, this problem is very far away from being solved, let alone from being managed by a (semi-) automatic decision support system. In this paper we consider the case study o...

There are two natural and well-studied approaches to temporal ontology and reasoning: point-based and interval-based. Usually, interval-based temporal reasoning deals with points as a particular case of duration-less intervals. A recent result by Balbiani, Goranko, and Sciavicco presented an explicit two-sorted point-interval temporal framework in...

Within the timeline-based framework, planning problems are modeled as sets of independent, but interacting, components whose behavior over time is described by a set of temporal constraints. Timeline-based planning is being used successfully in a number of complex tasks, but its theoretical properties are not so well studied. In particular, while i...

Interval temporal logics provide a natural framework for reasoning about interval structures over linearly ordered domains. Despite being relevant for a broad spectrum of application domains, ranging from temporal databases to artificial intelligence and verification of reactive systems, interval temporal logics still miss tools capable of efficien...

Sales forecasting uses historical sales figures, in association with products characteristics and peculiarities, to predict short-term or long-term future performance in a business, and it can be used to derive sound financial and business plans. By using publicly available data, we build an accurate regression model for online sales forecasting ob...

In this paper, we consider the well-known modal logics K,T,K4, and S4, and we study some of their sub-propositional fragments, namely the classical Horn fragment, the Krom fragment, the so-called core fragment, defined as the intersection of the Horn and the Krom fragments, plus their sub-fragments obtained by limiting the use of boxes and diamonds...

Modal logic is a paradigm for several useful and applicable formal systems in computer science. It generally retains the low complexity of classical propositional logic, but notable exceptions exist in the domains of description, temporal, and spatial logic, where the most expressive formalisms have a very high complexity or are even undecidable. I...

We investigate the satisfiability problem for Horn fragments of the Halpern-Shoham interval temporal logic depending on the type (box or diamond) of the interval modal operators, the type of the underlying linear order (discrete or dense), and the type of semantics for the interval relations (reflexive or irreflexive). For example, we show that sat...

In this paper, we consider a data set taken from the administration of the Behavior Assessment System for Children test to 157 subjects, and we approach the problem of clustering and classify the subjects in an interpretable fashion. Because the Behavior Assessment System for Children test is originally composed of 149 questions (152 in the particu...

The primary characteristic of interval temporal logic is that intervals, rather than points, are taken as the primitive ontological entities. Their computational behaviour is generally bad, and several restrictions have been considered in order to define decidable and computationally affordable temporal logics based on intervals. In this paper we t...

The chop operator C is a binary modality that plays an important role in interval temporal logics. Such an operator, which is not definable in Halpern and Shoham's modal logic of time intervals HS, allows one to split an interval into two parts and to specify what is true over them. C appears both in Moszkowski's PITL (that pairs it with a modal co...

Interval temporal logics take time intervals, instead of time points, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham’s modal logic of time intervals HS, which associates a modal operator with each binary relation between intervals over a linear order (the so-called Allen’s interval relat...

Interval temporal logics provide a natural framework for temporal reasoning about interval structures over linearly ordered domains, where intervals are taken as the primitive ontological entities. Their computational behaviour and expressive power mainly depend on two parameters: the set of modalities they feature and the linear orders over which...

Interval temporal logics take time intervals, instead of time instants, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham’s modal logic of time intervals HS, which associates a modal operator with each binary relation between intervals over a linear order (the so-called Allen’s interval rel...

Interval temporal logics provide a natural framework for temporal reasoning about interval structures over linearly ordered domains, where intervals are taken as the primitive ontological entities. The most influential propositional interval-based logic is probably Halpern and Shoham’s Modal Logic of Time Intervals, a.k.a. HS. While most studies fo...

Interval temporal logics provide a natural framework for temporal reasoning about interval structures over linearly ordered do-mains, where intervals are taken as the primitive ontological entities. The most influential propositional interval-based logic is probably Halpern's and Shoham Modal Logic of Time Intervals, a.k.a. HS. While most stud-ies...

Interval temporal logics provide a natural framework for temporal reasoning about interval structures over linearly ordered domains, where intervals are taken as the primitive ontological entities. Their computational behavior mainly depends on two parameters: the set of modalities they feature and the linear orders over which they are interpreted....

Description logics of the DL-Lite family are widely used in knowledge representation because of their low computational complexity and rather good expressivity sufficient to capture important conceptual modelling constructs and the OWL2 QL profile of the Ontology Web Language (OWL). Recently, various point-based temporal extensions of DL-Lite have...

The fragment of propositional logic known as Horn theories plays a central role in automated reasoning. The problem of enumerating the maximal models of a Horn theory (MaxMod) has been proved to be computationally hard, unless P = NP. To the best of our knowledge, the only algorithm available for it is the one based on a brute-force approach. In th...

Interval temporal logics are quite expressive temporal logics, which turn out to be difficult to deal with in many respects. Even finite satisfiability of simple interval temporal logics presents non-trivial technical issues when it comes to the implementation of efficient tableau-based decision procedures. We focus our attention on the logic of Al...

Interval temporal logics are temporal logics that take time intervals, instead of time instants, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham's modal logic of time intervals (HS), which has a distinct modality for each binary relation between intervals over a linear order. As HS turns...

Interval temporal logics provide a natural framework for temporal reasoning about interval structures over linearly ordered domains, where intervals are taken as the primitive ontological entities. Despite being relevant for a broad spectrum of application domains, ranging from temporal databases to artificial intelligence and verification of react...

Qualitative spatial representation and reasoning plays a important role in various spatial applications. In this paper we introduce a new formalism, we name RCD calculus, for qualitative spatial reasoning with cardinal direction relations between regions of the plane approximated by rectangles. We believe this calculus leads to an attractive balanc...

In many real-world applications of knowledge representation and reasoning formalisms, one needs to cope with a number of spatial aspects in an integrated and efficient way. In this paper, we focus our attention on the so-called Rectangular Cardinal Direction calculus for qualitative spatial reasoning on cardinal relations between rectangles whose s...

The role of time in artificial intelligence is extremely important. Interval-based temporal reasoning can be seen as a generalization of the classical point-based one, and the first results in this field date back to Hamblin (1972) and Benhtem (1991) from the philosophical point of view, to Allen (1983) from the algebraic and first-order one, and t...