Salvatore Carta

University of Cagliari | UNICA · Department of Mathematics and Information Technology

Today's computer system security is critical at every operational level and device, as the compromise of a single element can propagate through connected other network elements, causing unpredictable and dangerous effects. To face unauthorized access and evolving malicious strategies, researchers have intensified efforts to develop effective Intrus...

Biometric systems leveraging ElectroEncephaloGram (EEG) data for user authentication present significant potential in diverse contexts, especially in Smart City ecosystems where secure access to sensitive data is crucial (e.g., healthcare systems, intelligent transportation, smart grids, public safety, and citizen services). However, the complexity...

The exponential growth of unstructured documents generated daily underscores the urgent need to develop technologies to structure information effectively. Traditional Information Extraction (IE) models permit transforming textual data to structured formats (e.g., semantic triplets), enabling efficient searches and uncovering hidden data insights. H...

The development of user identity verification approaches using biometric systems based on EEG data holds significant promise across various domains. However, the inherent complexity and variability of this data make designing reliable solutions challenging. In response to these challenges, this work introduces a Normalized Neural Network Ensemble (...

Ultrasound is a readily available, non-invasive and low-cost screening for the identification of endometriosis lesions, but its diagnostic specificity strongly depends on the experience of the operator. For this reason, computer-aided diagnosis tools based on Artificial Intelligence techniques can provide significant help to the clinical staff, bot...

In recent years, the concept of smart cities has garnered increasing attention as urban areas grapple with the challenges of population growth, resource management, and infrastructure optimization [...]

Workplace safety is a prominent concern, motivating researchers across diverse disciplines to investigate valuable ways to address its challenges. However, creating an efficient system to address this issue remains a significant challenge. Since many accidents happen due to improper usage or complete removal of Personal Protective Equipment (PPE),...

The growing availability of low-cost devices able of performing an Electroencephalography (EEG) has opened stimulating scenarios in the security field, where such data could be exploited as a biometric approach for user identification. However, a series of problems, first of all, the difficulty of obtaining unique and stable EEG patterns over time,...

The ever-increasing use of services based on computer networks, even in crucial areas unthinkable until a few years ago, has made the security of these networks a crucial element for anyone, also in consideration of the increasingly sophisticated techniques and strategies available to attackers. In this context, Intrusion Detection Systems (IDSs) p...

The ever-increasing use of services based on computer networks, even in crucial areas unthinkable until a few years ago, has made the security of these networks a crucial element for anyone, also in consideration of the increasingly sophisticated techniques and strategies available to attackers. In this context, Intrusion Detection Systems (IDSs) p...

In the current digitalization era, capturing and effectively representing knowledge is crucial in most real-world scenarios. In this context, knowledge graphs represent a potent tool for retrieving and organizing a vast amount of information in a properly interconnected and interpretable structure. However, their generation is still challenging and...

Ultrasound is a readily available, non-invasive and low cost screening for the identification of endometriosis lesions, but its diagnostic specificity strongly depends on the experience of the operator. For this reason, computer-aided diagnosis tools based on Artificial Intelligence techniques can provide significant help to the clinical staff, bot...

The scientific advances of recent years have made available to anyone affordable hardware devices capable of doing something unthinkable until a few years ago, the reading of brain waves. It means that through small wearable devices it is possible to perform an Electroencephalography (EEG), albeit with less potential than those offered by high-cost...

The last few decades have witnessed the increasing deployment of digital technologies in the urban environment with the goal of creating improved services to citizens especially related to their safety. This motivation, enabled by the widespread evolution of cutting edge technologies within the Artificial Intelligence, Internet of Things, and Compu...

In this paper, we propose an innovative tool able to enrich cultural and creative spots (gems , hereinafter) extracted from the European Commission Cultural Gems portal, by suggesting relevant keywords ( tags ) and YouTube videos (represented with proper thumbnails ). On the one hand, the system queries the YouTube search portal, selects the videos...

Machine learning techniques have recently become the norm for detecting patterns in financial markets. However, relying solely on machine learning algorithms for decision-making can have negative consequences, especially in a critical domain such as the financial one. On the other hand, it is well-known that transforming data into actionable insigh...

Monitoring applications are increasingly important to enable predictive maintenance and real-time anomaly detection in industrial and civil safety-critical infrastructures. Typical monitoring pipelines consist of a sensor network that collects and streams IoT data toward a cloud infrastructure that provides storage, visualisation and data analytic...

Offering timely support to users in eCoaching systems is a key factor to keep them engaged. However, coaches usually follow a lot of users, so it is hard for them to prioritize those with whom they should interact first. Timeliness is especially needed when health implications might be the consequence of a lack of support. In this paper, we focus o...

Breast cancer is the most prevalent type of cancer among the female world population. Its early detection has a crucial role in enhancing the effectiveness of treatments, as well as reducing serious complications and deaths. Ultrasound imaging represents a standard diagnostic technique for this purpose, due to its low invasiveness and cost. However...

The rating of users requesting financial services is a growing task, especially in this historical period of the COVID-19 pandemic characterized by a dramatic increase in online activities, mainly related to e-commerce. This kind of assessment is a task manually performed in the past that today needs to be carried out by automatic credit scoring sy...

The relatively recent introduction on the market of low-cost devices able to perform an Electroencephalography (EEG) has opened a stimulating research scenario that involves a large number of researchers previously excluded due to the high costs of such hardware. In this regard, one of the most stimulating research fields is focused on the use of s...

In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches rel...

This open-source tool, written in Python, referred to as XAI StatArb, implements a machine learning approach (ML) powered by eXplainable Artificial Intelligence techniques integrated into a statistical arbitrage trading pipeline. Specifically, given a set of stocks and their raw financial information, the tool aims at forecasting the next day’s ret...

The relatively recent introduction on the market of low-cost devices able to perform an Electroencephalography (EEG) has opened a stimulating research scenario that involves a large number of researchers previously excluded due to the high costs of such hardware. In this regard, one of the most stimulating research fields is focused on the use of s...

The rating of users requesting financial services is a growing task, especially in this historical period of the COVID-19 pandemic characterized by a dramatic increase in online activities, mainly related to e-commerce. This kind of assessment is a task manually performed in the past that today needs to be carried out by automatic credit scoring sy...

Nowadays, video-sharing portals’ popularity has entailed massive growth in data uploads over the Internet. For several applications (e.g., browsing, retrieval, or recommendation of videos), dealing with vast data volumes has become a critical issue. In a video-sharing scenario, the devising of tools and infrastructures able to completely satisfy us...

One of the most important steps when employing machine learning approaches is the feature engineering process. It plays a key role in the identification that features that can effectively help modeling the given classification or regression task. This process is usually not trivial and it might lead to the development of handcrafted features. Withi...

Breast cancer is the most prevalent type of cancer among the female world population. Its early detection has a crucial role in enhancing the effectiveness of treatments, as well as reducing serious complications and deaths. Ultrasound imaging represents a standard diagnostic technique for this purpose, due to its low invasiveness and cost. However...

The Payments Systems Directive 2 (PSD2), recently issued by the European Union, allows the banks to share their customer data if they authorize the operation. On the one hand, this opportunity offers interesting perspectives to the financial operators, allowing them to evaluate the customers reliability (Credit Scoring) even in the absence of the c...

The Payments Systems Directive 2 (PSD2), recently issued by the European Union, allows the banks to share their customer data if they authorize the operation. On the one hand, this opportunity offers interesting perspectives to the financial operators, allowing them to evaluate the customers reliability (Credit Scoring) even in the absence of the c...

The potential offered by the Internet, combined with the enormous number of connectable devices, offers benefits in many areas of our modern societies, both public and private. The possibility of making heterogeneous devices communicate with each other through the Internet has given rise to a constantly growing scenario, which was unthinkable not l...

Nowadays, Smart Cities applications are becoming steadily popular, thanks to their main objective of improving people daily habits. The services provided by the aforementioned applications may be either addressed to the entire digital population or narrowed towards a specific kind of audience, like drivers and pedestrians. In this sense, the propos...

The credit scoring models are aimed to assess the capability of refunding a loan by assessing user reliability in several financial contexts, representing a crucial instrument for a large number of financial operators such as banks. Literature solutions offer many approaches designed to evaluate users' reliability on the basis of information about...

In the current age of overwhelming information and massive production of textual data on the Web, Event Detection has become an increasingly important task in various application domains. Several research branches have been developed to tackle the problem from different perspectives, including Natural Language Processing and Big Data analysis, with...

In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches rel...

Since their appearance, Smart Cities have aimed at improving the daily life of people, helping to make public services smarter and more efficient. Several of these services are often intended to provide better security conditions for citizens and drivers. In this vein, we present Heimdall, an AI-based video surveillance system for traffic monitorin...

Since their appearance, Smart Cities have aimed at improving the daily life of people, helping to make public services smarter and more efficient. Several of these services are often intended to provide better security conditions for citizens and drivers. In this vein, we present HEIMDALL, an AI-based video surveillance system for traffic monitorin...

In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial market behavior and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two...

In this manuscript, we propose a Machine Learning approach to tackle a binary classification problem whose goal is to predict the magnitude (high or low) of future stock price variations for individual companies of the S&P 500 index. Sets of lexicons are generated from globally published articles with the goal of identifying the most impactful word...

The stock market forecasting is one of the most challenging application of machine learning, as its historical data are naturally noisy and unstable. Most of the successful approaches act in a supervised manner, labeling training data as being of positive or negative moments of the market. However, training machine learning classifiers in such a wa...

The adoption of computer-aided stock trading methods is gaining popularity in recent years, mainly because of their ability to process efficiently past information through machine learning to predict future market behavior. Several approaches have been proposed to this task, with the most effective ones using fusion of a pile of classifiers decisio...

Financial forecasting represents a challenging task, mainly due to the irregularity of the market, high fluctuations and noise of the involved data, as well as collateral phenomena including investor mood and mass psychology. In recent years, many researchers focused their work on predicting the performance of the market by exploiting novel Machine...

The anomaly-based Intrusion Detection Systems (IDSs) represent one of the most efficient methods in countering the intrusion attempts against the ever growing number of network-based services. Despite the central role they play, their effectiveness is jeopardized by a series of problems that reduce the IDS effectiveness in a real-world context, mai...

The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based Intrusion Detection Systems, whic...

Manual event tagging may be a very long and stressful activity, due the monotonous operations involved. This is particularly true when dealing with online video tagging, as for football matches, in which the burden of events to tag can consist of many thousands of actions, according to the desired level of granularity. In this work we describe an a...

Press releases represent a valuable resource for financial trading and have long been exploited by researchers for the development of automatic stock price pre-dictors. We hereby propose an NLP-based approach to generate industry-specific lexicons from news documents, with the goal of dynamically capturing, on a daily basis, the correlation between...

Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction,...

Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction,...

The anomaly-based Intrusion Detection Systems (IDSs) represent one of the most efficient methods in countering the intrusion attempts against the ever growing number of network-based services. Despite the central role they play, their effectiveness is jeopardized by a series of problems that reduce the IDS effectiveness in a real-world context, mai...

eCoaching platforms have become powerful tools to support users in their day-to-day physical routines. More and more research works show that motivational factors are strictly linked with the user inclination to share her fitness achievements on social media platforms. In this paper, we tackle the problem of analyzing and modeling users’ contextual...

In the last decade, market financial forecasting has attracted high interests amongst the researchers in pattern recognition. Usually, the data used for analysing the market, and then gamble on its future trend, are provided as time series; this aspect , along with the high fluctuation of this kind of data, cuts out the use of very efficient classi...

Nowadays, machine learning usage has gained significant interest in financial time series prediction, hence being a promise land for financial applications such as algorithmic trading. In this setting, this paper proposes a general framework based on an ensemble of regression algorithms and dynamic asset selection applied to the well known statisti...

Nowadays, machine learning usage has gained significant interest in financial time series prediction, hence being a promise land for financial applications such as algorithmic trading. In this setting, this paper proposes a general approach based on an ensemble of regression algorithms and dynamic asset selection applied to the well-known statistic...

The increasing amount of credit offered by financial institutions has required intelligent and efficient methodologies of credit scoring. Therefore, the use of different machine learning solutions to that task has been growing during the past recent years. Such procedures have been used in order to identify customers who are reliable or unreliable,...