CIIC - Artigos em Revistas com Peer Review
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- Security Mechanisms of a Mobile Health Application for Promoting Physical Activity among Older AdultsPublication . Bastos, David; Ribeiro, José; Silva, Fernando; Rodrigues, Mário; Rabadão, Carlos; Fernández-Caballero, Antonio; Barraca, João Paulo; Rocha, Nelson Pacheco; Pereira, AntónioPhysical activity contributes to the maintenance of health conditions and functioning. However, the percentage of older adults who comply with the recommendations for physical activity levels is low when compared to the same percentages on younger groups. The SmartWalk system aims to encourage older adults to perform physical activity (i.e., walking in the city), which is monitored and adjusted by healthcare providers for best results. The study reported in this article focused on the implementation of SmartWalk security services to keep personal data safe during communications and while at rest, which were validated considering a comprehensive use case. The security framework offers various mechanisms, including an authentication system that was designed to complement the pairs of usernames and passwords with trusted execution environments and token-based features, authorization with different access levels, symmetric and asymmetric key cryptography, critical transactions review, and logging supported by blockchain technology. The resulting implementation contributes for a common understanding of the security features of trustful smart cities’ applications, which conforms with existing legislation and regulations.
- Solving large-scale SONET network design problems using bee-inspired algorithmsPublication . Bernardino, Eugénia Moreira; Bernardino, Anabela Moreira; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelIn the past years, the number of users of Internet-based applications has exponentially increased and consequently the request for transmission capacity or bandwidth has significantly augmented. When managed properly, the ring networks are uniquely suited to deliver a large amount of bandwidth in a reliable and inexpensive way. In this paper, we consider two problems that arise in the design of optical telecommunication networks, namely the SONET Ring Assignment Problem (SRAP) and the Intraring Synchronous Optical Network Design Problem (IDP), known to be NP-hard. In SRAP, the objective is to minimise the number of rings (i.e., DXCs). In IDP, the objective is to minimise the number of ADMs. Both problems are subject to a ring capacity constraint. To solve these problems, we propose two bee-inspired algorithms: Hybrid Artificial Bee Colony and Hybrid Bees Algorithm. We hybridise the basic form of these algorithms with local search, in order to refine newly constructed solutions. We also perform comparisons with other algorithms from the literature and use larger instances. The simulation results verify the effectiveness and robustness of the proposed algorithms.
- Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural NetworksPublication . Miragaia, Rolando; Chávez, Francisco; Díaz, Josefa; Vivas, Antonio; Prieto, Maria Henar; Moñino, Maria JoséDigitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness, three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.
- Parallel Niche Pareto AlineaGA--an evolutionary multiobjective approach on multiple sequence alignmentPublication . Silva, Fernando José Mateus da; Sánchez Pérez, Juan Manuel; Gómez Pulido, Juan Antonio; Vega Rodríguez, Miguel A.Multiple sequence alignment is one of the most recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although exact methods exist for solving this problem, their use is limited by the computing demands which are necessary for exploring such a large and complex search space. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, presenting results that are better than those provided by the sum of several sequential Genetic Algorithms. Although these methods are often used to optimize a single objective, they can also be used in multidimensional domains, finding all possible tradeoffs among multiple conflicting objectives. Parallel AlineaGA is an Evolutionary Algorithm which uses a Parallel Genetic Algorithm for performing multiple sequence alignment. We now present the Parallel Niche Pareto AlineaGA, a multiobjective version of Parallel AlineaGA. We compare the performance of both versions using eight BAliBASE datasets. We also measure up the quality of the obtained solutions with the ones achieved by T-Coffee and ClustalW2, allowing us to observe that our algorithm reaches for better solutions in the majority of the datasets.
- Log pseudonymization: Privacy maintenance in practicePublication . Varanda, Artur; Santos, Leonel; Costa, Rogério Luís de C.; Oliveira, Adail; Rabadão, CarlosMobile phones, social media, and Internet of Things (IoT) devices are examples of day-to-day technologies that collect large amounts of data, including people's location, habits, and preferences. The first regulations on digital data collection and processing privacy were created decades ago, but such an increased amount of collected digital data and the risks associated with the illegal processing and exposure of personal information led to several new regulations, including the European General Data Protection Regulation. Recent regulations require that personal data controllers implement several technical and organizational measures to protect data privacy. Much attention was given to data gathering, storage, and processing at system and database levels. But at the system administration level, log files usually store data that can lead to the identification of an individual, which means they must be processed to guarantee personal data privacy. In this work, we deal with pseudonymization. We discuss log sources, formats and data, log management architectures, and the log processing pipeline, considering pseudonymization and security requirements. We describe an architecture for log pseudonymization during the ingestion phase and present its implementation using Elasticsearch, Logstash, and Kibana, providing conclusions and helpful insights on log pseudonymization for privacy protection.
- An Integrated Cybernetic Awareness Strategy to Assess Cybersecurity Attitudes and Behaviours in School ContextPublication . Antunes, Mário; Silva, Carina; Marques, FredericoDigital exposure to the Internet among the younger generations, notwithstanding their digital abilities, has increased and raised the alarm regarding the need to intensify the education on cybersecurity in schools. Understanding of the human factor and its influence on children, namely their attitudes and behaviors online, is pivotal to reinforce their awareness towards cyberattacks, and to promote their digital citizenship. This paper aims to present an integrated cybersecurity and cyberawareness strategy composed of three major steps: (1) Cybersecurity attitude and behavior assessment, (2) self-diagnosis, and (3) teaching/learning activities. The following contributions are made: Two questionnaires to assess risky attitudes and behaviors regarding cybersecurity; a self-diagnosis to measure students’ skills on cybersecurity; a lesson plan addressing cyberawareness to be applied on Information and Communications Technology (ICT) and citizenship education curricular units. Cybersecurity risky attitudes and behaviors were evaluated in a junior high school population of 164 students attending the sixth and ninth grades. The assessment focused on two main subjects: To identify the attitudes and behaviors that raise the risk on cybersecurity among the participating students; to characterize the acquired students’ cybersecurity and cyberawareness skills. Global and individual scores and the histograms for attitudes and behaviors are presented. The items in which we have observed significant differences between sixth and ninth grades are depicted and quantified by their corresponding p-values obtained through the Mann–Whitney non-parametric test. Regarding the results obtained on the assessment of attitudes and behaviors, although positive, we observed that the attitudes and behaviors in ninth grade students are globally inferior compared to those attained by sixth grade students. The deployed strategy for cyberawareness was applied in a school context; however, the same approach is suitable to be applied in other types of organizations, namely enterprises, healthcare institutions and public sector.
- Explainable prototype-based image classification using adaptive feature extractors in medical imagesPublication . Vasconcellos, Nicolas; Tavora, Luis M. N.; Miragaia, Rolando; Grilo, Carlos; Thomaz, LucasPrototype-based classifiers are a category of Explainable Artificial Intelligence methods that use representative samples from the data, called prototypes, to classify new inputs based on a similarity criterion. However, these methods often rely on pre-trained Convolutional Neural Networks as feature extractors, which may not be adapted for the specific type of data being used, thus not suited for identifying the most representative prototypes. In this paper, we propose a method named Explainable Prototype-based Image Classification, a cluster-oriented training strategy that enhances the performance and explainability of prototype-based classifiers. Our method uses a novel loss function, called Cluster Density Error, to fine-tune the feature extractor and preserve the most representative feature vectors in the latent space. We also use Principal Component Analysis-based approach to reduce the dimensionality and complexity of the feature vectors. We conduct experiments on four medical image datasets and compare the results with those from different prototype-based classifiers and state-of-the-art non-explainable learning methods. The proposed method demonstrated superior explainable capabilities and comparable classification performance to the compared methods. Specifically, the proposed method achieved up to 95.01% accuracy and 0.992 AUC using only 43 prototypes. This translated to an improvement in accuracy and AUC score of 21.54% and 9.06%, respectively, and a substantial reduction in the number of prototypes by 98,38%
- Wireless Body Area Networks for Healthcare Applications: Protocol Stack ReviewPublication . Filipe, Luis; Fdez-Riverola, Florentino; Costa, Nuno; Pereira, AntónioWireless Body Area Networks (WBANs) supporting healthcare applications are in early development stage but offer valuable contributions at monitoring, diagnostic, or therapeutic levels. They cover real-time medical information gathering obtained from different sensors with secure data communication and low power consumption. As a consequence of the increasing interest in the application of this type of networks, several articles dealing with different aspects of such systems have been published recently. In this paper, we compile and compare technologies and protocols published in the most recent researches, seeking WBAN issues for medical monitoring purposes to select the most useful solutions for this area of networking. The most important features under consideration in our analysis include wireless communication protocols, frequency bands, data bandwidth, transmission distance, encryption, authentication methods, power consumption, and mobility. Our study demonstrates that some characteristics of surveyed protocols are very useful to medical appliances and patients in a WBAN domain.
- Illuminating the past: state of the artPublication . Happa, Jassim; Mudge, Mark; Debattista, Kurt; Artusi, Alessandro; Gonçalves, Alexandrino; Chalmers, AlanVirtual reconstruction and representation of historical environments and objects have been of research interest for nearly two decades. Physically based and historically accurate illumination allows archaeologists and historians to authentically visualise a past environment to deduce new knowledge. This report reviews the current state of illuminating cultural heritage sites and objects using computer graphics for scientific, preservation and research purposes. We present the most noteworthy and up-to-date examples of reconstructions employing appropriate illumination models in object and image space, and in the visual perception domain. Finally, we also discuss the difficulties in rendering, documentation, validation and identify probable research challenges for the future. The report is aimed for researchers new to cultural heritage reconstruction who wish to learn about methods to illuminate the past.
- On the Use of Perfect Sequences and Genetic Algorithms for Estimating the Indoor Location of Wireless SensorsPublication . Ferreira, Marco; Bagarić, J.; Lanza-Gutierrez, Jose M.; Mendes, Silvio; Pereira, João; Gomez-Pulido, Juan A.Determining the indoor location is usually performed by using several sensors. Some of these sensors are fixed to a known location and either transmit or receive information that allows other sensors to estimate their own locations. The estimation of the location can use information such as the time-of-arrival of the transmitted signals, or the received signal strength, among others. Major problems of indoor location include the interferences caused by the many obstacles in such cases, causing among others the signal multipath problem and the variation of the signal strength due to the many transmission media in the path from the emitter to the receiver. In this paper, the creation and usage of perfect sequences that eliminate the signal multipath problem are presented. It also shows the influence of the positioning of the fixed sensors to the precision of the location estimation. Finally, genetic algorithms were used for searching the optimal location of these fixed sensors, therefore minimizing the location estimation error.