Ciências e Tecnologia | Artigos em revistas internacionais / Papers in international journals
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Percorrer Ciências e Tecnologia | Artigos em revistas internacionais / Papers in international journals por Domínios Científicos e Tecnológicos (FOS) "Ciências Naturais::Ciências da Computação e da Informação"
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- Economic impact of healthcare cyber risksPublication . Brilhante, Maria de Fátima; Mendonça, Sandra; Pestana, Pedro Duarte; Rocha, Maria Luísa; Santos, RuiPurpose: The healthcare sector is a primary target for cybercriminals, with health data breaches ranking among the most critical threats. Despite stringent penalties imposed by the U.S. Department of Health and Human Services Office for Civil Rights (OCR), vulnerabilities still persist due to slow detection and ineffective data protection measures. On the other hand, as organizations are often reluctant to disclose security breaches for fear of reputational and market share losses, penalties can serve as a useful proxy for quantifying losses and insurance claims. Methods: This study analyzes fines and settlements (2008–2024) using the traditional lognormal, general extreme value (GEV) and other heavy-tailed statistical models, including the geo-max-stable loglogistic law, and also the mixture models hyperexponential and hyperloglogistic. Results: Mixture models, either the hyperexponential or the hyperloglogistic, deliver the best fit for OCR penalties, and for yearly maxima, the best fit is achieved with the GEV distribution. Regarding Attorneys General fines, the hyperexponential model is optimal, with the GEV model excelling again for their yearly maxima. Hence, mixture models effectively capture the dual nature of penalty data, comprising clusters of moderate and extreme values. However, yearly maxima align better with the GEV model. Conclusions: The findings suggest that while Panjer’s theory for aggregate claims suffices for moderate claims, it must be supplemented with strategies to address extreme cybercrime scenarios, ensuring insurers and reinsurers can manage severe losses effectively.
- A framework for adaptive recommendation in online environmentsPublication . Azambuja, Rogério Xavier de; Morais, A. Jorge; Filipe, Vítor Manuel JesusRecent advancements in deep learning and large language models (LLMs) have led to the development of innovative technologies that enhance recommender systems. Different heuristics, architectures, and techniques for filtering information have been proposed to obtain successful computational models for the recommendation problem; however, several issues must be addressed in online environments. This research focuses on a specific type of recommendation, which combines sequential recommendation with session-based recommendation. The goal is to solve the complex next-item recommendation problem in Web applications, using the wine domain as a case study. This paper describes a framework developed to provide adaptive recommendations by rethinking the initial data modeling to better understand users’ dynamic taste profiles. Three main contributions are presented: (a) a novel dataset of wines called X-Wines; (b) an updated recommendation model named X-Model4Rec – eXtensible Model for Recommendation, which utilizes attention and transformer mechanisms central to LLMs; and (c) a collaborative Web platform designed to support adaptive wine recommendations for users in an online environment. The results indicate that the proposed framework can enhance recommendations in online environments and encourage further scientific exploration of this topic.
- Guiding evacuees to Improve fire building evacuation efficiency: hazard and congestion models to support decision making by a context-aware recommender systemPublication . Coelho, António; Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Ramos MoreiraFires in large buildings can have tragic consequences, including the loss of human lives. Despite the advancements in building construction and fire safety technologies, the unpredictable nature of fires, particularly in large buildings, remains an enormous challenge. Acknowledging the paramount importance of prioritising human safety, the academic community has been focusing consistently on enhancing the efficiency of building evacuation. While previous studies have integrated evacuation simulation models, aiding in aspects such as the design of evacuation routes and emergency signalling, modelling human behaviour during a fire emergency remains challenging due to cognitive complexities. Moreover, behavioural differences from country to country add another layer of complexity, hindering the creation of a universal behaviour model. Instead of centring on modelling the occupant behaviour, this paper proposes an innovative approach aimed at enhancing the occupants’ behaviour predictability by providing real-time information to the occupants regarding the most suitable evacuation routes. The proposed models use a building’s environmental conditions to generate contextual information, aiding in developing solutions to make the occupants’ behaviour more predictable by providing them with real-time information on the most appropriate and efficient evacuation routes at each moment, guiding the occupants to safety during a fire emergency. The models were incorporated into a context-aware recommender system for testing purposes. The simulation results indicate that such a system, coupled with hazard and congestion models, positively influences the occupants’ behaviour, fostering faster adaptation to the environmental conditions and ultimately enhancing the efficiency of building evacuations.
- Measuring the risk of vulnerabilities exploitationPublication . Brilhante, Maria de Fátima; Pestana, Dinis; Pestana, Pedro Duarte; Rocha, Maria LuísaModeling the vulnerabilities lifecycle and exploitation frequency are at the core of security of networks evaluation. Pareto, Weibull, and log-normal models have been widely used to model the exploit and patch availability dates, the time to compromise a system, the time between compromises, and the exploitation volumes. Random samples (systematic and simple random sampling) of the time from publication to update of cybervulnerabilities disclosed in 2021 and in 2022 are analyzed to evaluate the goodness-of-fit of the traditional Pareto and log-normal laws. As censoring and thinning almost surely occur, other heavy-tailed distributions in the domain of attraction of extreme value or geo-extreme value laws are investigated as suitable alternatives. Goodness-of-fit tests, the Akaike information criterion (AIC), and the Vuong test, support the statistical choice of log-logistic, a geomax stable law in the domain of attraction of the Fréchet model of maxima, with hyperexponential and general extreme value fittings as runners-up. Evidence that the data come from a mixture of differently stretched populations affects vulnerabilities scoring systems, specifically the common vulnerabilities scoring system (CVSS).
- The state of the art in procedural audioPublication . Menexopoulos, Dimitris; Pestana, Pedro Duarte; Reiss, Joshua D.Procedural audio may be defined as real-time sound generation according to programmatic rules and live input. It is often considered a subset of sound synthesis and is especially applicable to nonlinear media, such as video games, virtual reality experiences and interactive audiovisual installations. However, there is resistance to widespread adoption of procedural audio because there is little awareness of the state of the art, including the diversity of sounds that may be generated, the controllability of procedural audio models, and the quality of the sounds that it produces. The authors address all of these aspects in this review paper, while attempting a large-scale categorization of sounds that have been approached through procedural audio techniques. The role of recent advancements in neural audio synthesis, its current implementations, and potential future applications in the field are also discussed. Review materials are available
- X-Model4Rec: an extensible recommender model based on the user’s dynamic taste profilePublication . Azambuja, Rogério Xavier de; Morais, A. Jorge; Filipe, Vítor Manuel JesusSeveral approaches have been proposed to obtain successful models to solve complex next-item recommendation problem in non-prohibitive computational time, such as by using heuristics, designing architectures, and applying information filtering techniques. In the current technological scenario of artificial intelligence, sequential recommender systems have been gaining attention and they are a highly demanding research area, especially using deep learning in their development. Our research focuses on an efficient and practical model for managing sequential session-based recommendations of specific products for users using the wine and movie domains as case studies. Through an innovative recommender model called X-Model4Rec – eXtensible Model for Recommendation, we explore the user's dynamic taste profile using architectures with transformer and multi-head attention mechanisms to solve the next-item recommendation problem. The performance of the proposed model is compared to that of classical and baseline recommender models on two real-world datasets of wines and movies, and the results are better for most of the evaluation metrics.
- X-Wines: dados sobre vinhos para ampla utilizaçãoPublication . Filipe, Vítor Manuel Jesus; Azambuja, Rogério Xavier de; Morais, A. JorgeNo atual cenário de crescimento tecnológico, à semelhança da maioria dos produtos agrícolas, o vinho apresenta um volume de dados disponibilizado muito reduzido ou com poucos elementos, o que limita a exploração científica, como é o caso nos sistemas de recomendação. Este artigo apresenta e avalia uma nova base de dados denominada X-Wines no seu primeiro ano de publicação. Ela é constituída por 100.646 rótulos de vinhos produzidos em 62 países e 21 milhões de classificações reais dos consumidores encontrados na Web aberta em 2022. X-Wines é disponibilizada para ser livremente utilizada em sistemas de recomendação, aprendizado de máquina e uso geral, como uma contribuição à ciência de dados.