Papers by Fabrizio Maturo
Fuzzy Centrality Measures in Social Network Analysis: Theory and Application in a University Department Collaboration Network
International Journal of Approximate Reasoning, 2025
The motivation behind this research stems from the inherent complexity and vagueness in human soc... more The motivation behind this research stems from the inherent complexity and vagueness in human social interactions, which traditional Social Network Analysis (SNA) approaches often fail to capture adequately. Conventional SNA methods typically represent relationships as binary or weighted ties, thereby losing the subtle nuances and inherent uncertainty in real-world social connections. The need to preserve the vagueness of social relations and provide a more accurate representation of these relationships motivates the introduction of a fuzzy-based approach to SNA. This paper proposes a novel framework for Fuzzy Social Network Analysis (FSNA), which extends traditional SNA to accommodate the vagueness of relationships. The proposed method redefines the ties between nodes as fuzzy numbers rather than crisp values and introduces a comprehensive set of fuzzy centrality indices, including fuzzy degree centrality, fuzzy betweenness centrality, and fuzzy closeness centrality, among others. These indices are designed to measure the importance and influence of nodes within a network while preserving the uncertainty in the relationships between them. The applicability of the proposed framework is demonstrated through a case study involving a university department's collaboration network, where relationships between faculty members are analyzed using data collected via a fascinating mouse-tracking technique.
The Risks of Artificial Intelligence in Research: Ethical and Methodological Challenges in the Peer Review Process
AI and Ethics, 2025
Artificial intelligence (AI) is transforming scientific research, but its application in peer rev... more Artificial intelligence (AI) is transforming scientific research, but its application in peer review raises ethical and methodological concerns. This paper examines the risks of AI-driven peer review, focusing on the lack of domain expertise among reviewers, AI’s ability to challenge any article when stress-tested, and the potential compromise of research integrity. These issues compound systemic problems such as self-citations, citation markets, lenient reviews, and the proliferation of special issues that grant guest editors, especially in high-impact journals, undue influence over academic careers. Predatory journals publishing thousands of papers with questionable review processes further threaten the credibility of scientific databases, creating conflicts of interest and financial incentives within academic publishing. An overlooked factor worsening these challenges is that peer reviewers are unpaid, making the process unsustainable. As scholars struggle to balance reviewing with their own research, many rely on AI tools to expedite evaluations, potentially sacrificing depth and rigor. While AI can assist, its overuse risks diminishing the reliability of scholarly publishing. If high-quality peer review is to be preserved, the academic community must reconsider incentives and structural reforms to ensure fair and thorough assessments. This paper illustrates how AI can fabricate pretextual negative reviews, enabling the rejection of even high-quality, previously published papers. Through a concrete example, we show how AI-driven review systems, when manipulated, can selectively generate biased assessments to serve editorial or institutional agendas. Addressing these vulnerabilities requires a fundamental reevaluation of peer review practices to maintain research integrity and safeguard the future of scientific publishing.
AnnuityRIR: Annuity Random Interest Rates
Research Square (Research Square), May 16, 2024
Scholars have taken a keen interest in predicting corporate crises in the past decades. However, ... more Scholars have taken a keen interest in predicting corporate crises in the past decades. However, most studies focused on classical parametric models that, by their nature, can consider few predictors and interactions and must respect numerous assumptions. Over the past few years, the economy has faced a severe structural crisis that has resulted in significantly lower income, cash, and capital levels than in the past. This crisis has led to insolvency and bankruptcy in many cases. Hence, there is a renewed interest in research for new models for forecasting business crises using novel advanced machine learning techniques. This study aims to develop a model that achieves state-of-the-art accuracy while being fully interpretable, overcoming the limitations of previous research. The model demonstrates excellent predictive performance on par with black-box approaches while maintaining complete transparency by leveraging Explainable Boosting Machines, an intrinsically interpretable tree-based ensemble method, and hyperparameter optimization. The approach automatically considers all possible interactions and uncovers relevant aspects not considered in past studies. This line of research provides compelling results that can bring new insights to the literature on corporate crisis prediction. The interpretable nature of the model is a key advancement, enabling practical application and a deeper understanding of the factors driving corporate financial distress.
Quality and quantity, Apr 4, 2024
Intertemporal preferences are closely related to essential aspects of an individual's emotional a... more Intertemporal preferences are closely related to essential aspects of an individual's emotional and cognitive domains. Discount functions are used to quantify these preferences, which can help us understand conditions such as addiction, depression, and Attention Deficit Hyperactivity Disorder (ADHD). However, traditional parametric models are limited when dealing with intertemporal preferences, mainly when behavioural biases are involved. This study exploits Functional Data Analysis (FDA) to investigate the properties of discount functions in intertemporal choices comparing people suffering from the Hikikomori pathology (a condition that involves social withdrawal) and normal people. Notably, the goal of this research is to look for statistically significant differences in the dynamics of intertemporal decision-making according to different gravity of the Hikikomori condition through the magnified FDA lens on different functional dimensions; the distinctive curves of discount functions categorised by Hikikomori scores prompted a more profound investigation via the so-called augmented functional analysis of variance. The original curves and their derivatives, and the discount rates and their first derivatives provide the different functional dimensions explored. This original approach of analyzing differences between subgroups according to decision-making behaviours is exciting from a methodological and practical perspective.
The purpose of this paper is the study of rough hyperlattice. In this regards we introduce rough ... more The purpose of this paper is the study of rough hyperlattice. In this regards we introduce rough sublattice and rough ideals of lattices. We will proceed by obtaining lower and upper approximations in these lattices.
Quality and quantity, Apr 15, 2024
Diversity is fundamental in many disciplines, such as ecology, business, biology, and medicine. F... more Diversity is fundamental in many disciplines, such as ecology, business, biology, and medicine. From a statistical perspective, calculating a measure of diversity, whatever the context of reference, always poses the same methodological challenges. For example, in the ecological field, although biodiversity is widely recognised as a positive element of an ecosystem, and there are decades of studies in this regard, there is no consensus measure to evaluate it. The problem is that diversity is a complex, multidimensional, and multivariate concept. Limiting to the idea of diversity as variety, recent studies have presented functional data analysis to deal with diversity profiles and their inherently high-dimensional nature. A limitation of this recent research is that the identification of anomalies currently still focuses on univariate measures of biodiversity. This study proposes an original approach to identifying anomalous patterns in environmental communities' biodiversity by leveraging functional boxplots and functional clustering. The latter approaches are implemented to standardised and normalised Hill's numbers treating them as functional data and Hill's numbers integral functions. Each of these functional transformations offers a peculiar and exciting point of view and interpretation. This research is valuable for identifying warning signs that precede pathological situations of biodiversity loss and the presence of possible pollutants.
ArXiV, 2024
Many conventional statistical and machine learning methods face challenges when applied directly ... more Many conventional statistical and machine learning methods face challenges when applied directly to high dimensional temporal observations. In recent decades, Functional Data Analysis (FDA) has gained widespread popularity as a framework for modeling and analyzing data that are, by their nature, functions in the domain of time. Although supervised classification has been extensively explored in recent decades within the FDA literature, ensemble learning of functional classifiers has only recently emerged as a topic of significant interest. Thus, the latter subject presents unexplored facets and challenges from various statistical perspectives. The focal point of this paper lies in the realm of ensemble learning for functional data and aims to show how different functional data representations can be used to train ensemble members and how base model predictions can be combined through majority voting. The so-called Functional Voting Classifier (FVC) is proposed to demonstrate how different functional representations leading to augmented diversity can increase predictive accuracy. Many real-world datasets from several 1
Research Square, 2024
Scholars have taken a keen interest in predicting corporate crises in the past decades. However, ... more Scholars have taken a keen interest in predicting corporate crises in the past decades. However, most studies focused on classical parametric models that, by their nature, can consider few predictors and interactions and must respect numerous assumptions. Over the past few years, the economy has faced a severe structural crisis that has resulted in significantly lower income, cash, and capital levels than in the past. This crisis has led to insolvency and bankruptcy in many cases. Hence, there is a renewed interest in research for new models for forecasting business crises using novel advanced machine learning techniques. This study aims to develop a model that achieves state-of-the-art accuracy while being fully interpretable, overcoming the limitations of previous research. The model demonstrates excellent predictive performance on par with black-box approaches while maintaining complete transparency by leveraging Explainable
Annuity Random Interest Rates [R package AnnuityRIR version 1.0-0]
Computational Statistics, Jul 25, 2022
This paper offers a supervised classification strategy that combines functional data analysis wit... more This paper offers a supervised classification strategy that combines functional data analysis with unsupervised and supervised classification methods. Specifically, a two-steps classification technique for high-dimensional time series treated as functional data is suggested. The first stage is based on extracting additional knowledge from the data using unsupervised classification employing suitable metrics. The second phase applies functional supervised classification of the new patterns learned via appropriate basis representations. The experiments on ECG data and comparison with the classical approaches show the effectiveness of the proposed technique and exciting refinement in terms of accuracy. A simulation study with six scenarios is also offered to demonstrate the efficacy of the suggested strategy. The results reveal that this line of investigation is compelling and worthy of further development.
Qualitative and quantitative models in socio-economics systems and social work
Clustering Data Streams via Functional Data Analysis: a Comparison between Hierarchical Clustering and K-means Approaches
49th Scientific meeting of the Italian Statistical Society, May 25, 2018
Recently, the analysis of web data, has become essential in many researchfields. For example, for... more Recently, the analysis of web data, has become essential in many researchfields. For example, for a large number of companies, corporate strategies shouldbe based on the analysis of customer behaviour in surfing the world wide web. Themain issues in analysing web traffic and web data are that they often flow continuouslyfrom a source and are potentially unbounded in size, and these circumstancesinhibit to store the whole dataset. In this paper, we propose an alternative clusteringfunctional data stream method to implement existing techniques, and we addressphenomena in which web data are expressed by a curve or a function. In particular,we deal with a specific type of web data, i.e. trends of google queries. Specifically,focusing on top football players data, we compare the functional k-meansapproach to the functional Hierarchical Clustering for detecting specific pattern ofsearch trends over time.
Frontiers in Public Health, Mar 17, 2023
Alternative Fuzzy Operations: A Critical Approach and Applications in Social and Economic Sciences
The applications of fuzzy numbers to Social Sciences, Economy, and Natural Sciences request, in v... more The applications of fuzzy numbers to Social Sciences, Economy, and Natural Sciences request, in various cases that the spreads, i.e. the region of indeterminateness, of the results of the operations between fuzzy numbers be less than the ones expected by the Zadeh’s extension principle. Moreover, it appears to be necessary to consider operations that save the shapes of fuzzy numbers. To this aim fuzzy operations are dealt with, alternatives to the operations induced by the extension principle. Critical analyses of logical principles which support the various operations are carried out. Some application to Social Sciences and Economy are considered
The price of risk based on multilinear measures
International Review of Economics & Finance, Sep 1, 2022
A behavioral approach to inconsistencies in intertemporal choices with the Analytic Hierarchy Process methodology
Annals of Finance
Monitoring the Spatial Correlation Among Functional Data Streams Through Moran’s Index
New Statistical Developments in Data Science, 2019
This paper focuses on measuring the spatial correlation among functional data streams recorded by... more This paper focuses on measuring the spatial correlation among functional data streams recorded by sensor networks. In many real world applications, spatially located sensors are used for performing at a very high frequency, repeated measurements of some variable. Due to the spatial correlation, sensed data are more likely to be similar when measured at nearby locations rather than in distant places. In order to monitor such correlation over time and to deal with huge amount of data, we propose a strategy based on computing the well known Moran’s index and Geary’s index on summaries of the data.
The purpose of this paper is the study of rough hyperlattice. In this regards we introduce rough ... more The purpose of this paper is the study of rough hyperlattice. In this regards we introduce rough sublattice and rough ideals of lattices. We will proceed by obtaining lower and upper approximations in these lattices.
On the Use of Propensity Score Matching in Biomedicine and Pulmonology
Archivos de Bronconeumología, 2022
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Papers by Fabrizio Maturo