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Percorrer Ciências e Tecnologia / Sciences and Technology por Domínios Científicos e Tecnológicos (FOS) "Ciências Naturais::Ciências da Computação e da Informação"
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- Cyber-vulnerabilities life cycle and risk assessmentPublication . Pestana, Pedro Duarte; Rocha, Maria Luísa; Sequeira, Fernando; Lovric, MiodragCyber-Vulnerabilities Life Cycle and Risk Assessment - Dictionary Entry
- 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).
- Metrologia e a transição digital: medição da severidade de vulnerabilidades e risco de exploraçãoPublication . Brilhante, Maria de Fátima; Pestana, Dinis; Pestana, Pedro Duarte; Rocha, Maria Luísa; Sequeira, FernandoA transição digital torna desejável normalizar a medição do risco associado às vulnerabilidades, fundamental para a priorização das necessidades de remediação ou mitigação, seja patch ou workaround, e é um desafio para a evolução da Metrologia no que se refere a meios auxiliares de medições virtuais. Torna também desejável aperfeiçoar as métricas usadas e sua utilização, nomeadamente no que se re- fere a reavaliação, se possível automática, da remediação do risco ao longo do tempo após descoberta e divulgação da vulnerabilidade. O CVSS — Common Vulnerability Scoring System usa métricas base, métricas temporais e métricas ambientais para calcular scores com o objetivo de priorizar as necessidades de correção das vulnerabilidades. Porém é estático, as métricas temporais, facultativas e pouco usadas, não estão preparadas para lhe conferir potencialidades dinâmicas, que são o ponto forte do EPSS — Exploit Prediction Scoring System, que surgiu em 2021. Fazemos uma avaliação crítica da evolução da versão 2 para a versão 3.1 do CVSS e de propostas de alteração das suas métricas temporais no desiderato de tornar o sistema dinâmico. O enquadramento de variáveis do ciclo de vida de vulnerabilidades na teoria dos valores extremos, eventualmente sujeitos a filtragem geométrica, sugere modelações alternativas (Geral de Valores Extremos, Pareto Generalizada, Log-logística) ao tradicional ajustamento com Pareto ou com Lognormal na procura de metodologias racionais de alteração do cálculo de modificações da pontuação do CVSS. Por outro lado abordamos a possibilidade de usar aprendizagem de máquina para reavaliação simples da medição da severidade ao longo do tempo
- Pest detection in olive groves using YOLOv7 and YOLOv8 modelsPublication . Alves, Adília; Pereira, José Alberto Cardoso; Khanal, Salik; Morais, A. Jorge; Filipe, Vítor Manuel Jesus; Pereira, Ana I.; Mendes, Armando; Fernandes, Florbela P.; Pacheco, Maria F.; Coelho, João P.; Lima, JoséModern agriculture faces important challenges for feeding a fast-growing planet’s population in a sustainable way. One of the most important challenges faced by agriculture is the increasing destruction caused by pests to important crops. It is very important to control and manage pests in order to reduce the losses they cause. However, pest detection and monitoring are very resources consuming tasks. The recent development of computer vision-based technology has made it possible to automatize pest detection efficiently. In Mediterranean olive groves, the olive fly (Bactrocera oleae Rossi) is considered the key-pest of the crop. This paper presents olive fly detection using the lightweight YOLO-based model for versions 7 and 8, respectively, YOLOv7-tiny and YOLOv8n. The proposed object detection models were trained, validated, and tested using two different image datasets collected in various locations of Portugal and Greece. The images are constituted by sticky yellow trap photos and by McPhail trap photos with olive fly exemplars. The performance of the models was evaluated using precision, recall, and mAP.95. The YOLOV7-tiny model best performance is 88.3% of precision, 85% of Recall, 90% of mAP.50, and 53% of mAP.95. The YOLOV8n model best performance is 85% of precision, 85% of Recall, 90% mAP.50, and 55% of mAP.50 YOLO8n model achieved worst results than YOLOv7-tiny for a dataset without negative images (images without olive fly exemplars). Aiming at installing an experimental prototype in the olive grove, the YOLOv8n model was implemented in a Ubuntu Server 23.04 Raspberry PI 3 microcomputer.
- Risk assessment of vulnerabilities exploitationPublication . Brilhante, Maria de Fátima; Pestana, Pedro Duarte; Rocha, Maria Luísa; Sequeira, Fernando; Henriques-Rodrigues, L.; Menezes, R.; Faria, S.Using the Kolmogorov–Smirnov, Cramér–von Mises and Anderson– Darling tests, and the not so commonly applied Vuong’s test, it is shown that a two components hyperlog-logistic distribution, i.e., a mixture of two geo-max-stable log-logistic distributions, provides a good fit for the time from disclosure to update of vulnerabilities sampled from the CVEdetails.com database. It is also shown that the hyperlog-logistic distribution provides a better fit than a heavy-tailed distribution of maxima, or a log-logistic distribution, or even a heavy-tailed two components hyperexponential distribution. Moreover, ways of incorporating uncertainty and of modeling vulnerabilities lifecycle into the Common Vulnerabilities Scoring System (CVSS), the most widely used score to assess severity of vulnerabilities, are discussed, in order to obtain an improved CVSS calculator and the evolution of a score over time.
- 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.