Abstract The exponential growth of unstructured documents generated daily underscores the urgent need to develop technologies to structure information effectively. Traditional Information Extraction (IE) models enable the transformation of textual data into structured formats (e.g., semantic triplets), facilitating efficient searches and uncovering hidden data insights. However, they require predefined ontologies and, often, extensive human efforts. On the other hand, Open IE tools extract information without any input knowledge, but they are limited in capturing entire and in-depth contexts. Furthermore, the state of the art presents a substantial discrepancy between the efforts carried out in English-centric methods and those in low-resource languages, such as Italian. Our study aims to address the aforementioned key challenges. To this end, we first define Open Named Information Extraction (ONIE), an approach that generalizes IE across diverse domains without requiring input ontologies and captures complex relationships. Then, we develop LLIMONIIE (Large Language Instructed Model for Open Named Italian Information Extraction), a novel end-to-end generative information extraction framework that leverages the capabilities of Large Language Models (LLMs) to perform ONIE from Italian documents, able to extract Named Entities and Open Relations uniformly. Furthermore, we devise an innovative dataset generation methodology to support our research. Finally, we release the code and dataset, contributing to the scientific community and the development of low-resource languages. Experiments demonstrate the potential of our proposal, achieving competitive results compared to the actual state of the art of Italian IE.

In the recent digitization era, capturing, representing, and understanding knowledge is essential in countless real-world scenarios. Knowledge graphs emerged as a powerful tool for representing information through an adequately interconnected and interpretable structure in such a context. Nevertheless, generating proper knowledge graphs usually requires significant manual effort and domain expertise, resulting in graphs often affected by human subjectivity, limited scalability, or inability to capture implicit knowledge or handle heterogeneity. This paper proposes an innovative zero-shot strategy tailored to uncover reliable knowledge from text leveraging the recent highly effective generative large language models, with a particular focus on the GPT-3.5 model. Our proposal aims to create a suitable knowledge graph or improve existing ones by discovering missing qualitative triples. To assess the effectiveness of our methodology, we performed experiments on domain-specific datasets, confirming its potential for scalable and versatile knowledge discovery.

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), one straightforward method for enhancing workplace security involves monitoring their usage This paper introduces an Operator Area Network (OAN) system which improves the existing solutions by increasing portability across different users and environments, non-intrusiveness and privacy. To enhance robustness in detecting the situations in which PPEs are not used correctly, we take advantage of Machine Learning to analyse the received signal strength indicator (RSSI) between PPEs in the same OAN The novelty of this work is that it does not exploit RSSI as a proxy of the distance but instead recognises a signature of the correct wearing of the PPE By employing this system, employers can effectively ensure the proper usage of PPE devices at their worksites while also minimising any adverse effects on workers’ comfort and reducing the setup burden for employers. The system runs a Support Vector Machine (SVM) model several times per second and employs a post-processing algorithm to enhance its initial accuracy further As a result, the system effectively reduces false positives by about 80% and swiftly detects instances of improper usage of the worker’s PPE, raising the alarm in less than seven seconds. Moreover, the post-processing algorithm can be customised to meet the specific needs of different use cases, allowing for a flexible trade-off between the detection time interval and the overall accuracy of the detection system.

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 systems, due to the enormous number of requests to process. It follows that such systems play a crucial role for financial operators, as their effectiveness is directly related to gains and losses of money. Despite the huge investments in terms of financial and human resources devoted to the development of such systems, the state-of-the-art solutions are transversally affected by some well-known problems that make the development of credit scoring systems a challenging task, mainly related to the unbalance and heterogeneity of the involved data, problems to which it adds the scarcity of public datasets. The Region-based Training Data Segmentation (RTDS) strategy proposed in this work revolves around a divide-and-conquer approach, where the user classification depends on the results of several sub-classifications. In more detail, the training data is divided into regions that bound different users and features, which are used to train several classification models that will lead toward the final classification through a majority voting rule. Such a strategy relies on the consideration that the independent analysis of different users and features can lead to a more accurate classification than that offered by a single evaluation model trained on the entire dataset. The validation process carried out using three public real-world datasets with a different number of features. samples, and degree of data imbalance demonstrates the effectiveness of the proposed strategy. which outperforms the canonical training one in the context of all the datasets.

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 such devices in the context of biometric systems, where the EEG data are exploited for user identification purposes. Based on the current literature, which reports that many of these systems are designed by combining the EEG data with a series of external stimuli (Evoked Potentials) to improve the reliability and stability over time of the EEG patterns, this work is aimed to formalize a biometric identification system based on low-cost EEG devices and simple stimulation instruments, such as images and sounds generated by a computer. In other words, our objective is to design a low-cost EEG-based biometric approach exploitable on a large number of real-world scenarios.