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- A distributed multiagent system architecture for body area networks applied to healthcare monitoringPublication . Felisberto, Filipe; Laza, Rosalía; Fdez-Riverola, Florentino; Pereira, AntónioIn the last years the area of health monitoring has grown significantly, attracting the attention of both academia and commercial sectors. At the same time, the availability of new biomedical sensors and suitable network protocols has led to the appearance of a new generation of wireless sensor networks, the so-called wireless body area networks. Nowadays, these networks are routinely used for continuous monitoring of vital parameters, movement, and the surrounding environment of people, but the large volume of data generated in different locations represents a major obstacle for the appropriate design, development, and deployment of more elaborated intelligent systems. In this context, we present an open and distributed architecture based on a multiagent system for recognizing human movements, identifying human postures, and detecting harmful activities. The proposed system evolved from a single node for fall detection to a multisensor hardware solution capable of identifying unhampered falls and analyzing the users’ movement. The experiments carried out contemplate two different scenarios and demonstrate the accuracy of our proposal as a real distributed movement monitoring and accident detection system. Moreover, we also characterize its performance, enabling future analyses and comparisons with similar approaches.
- Digital forensic artifacts of the Your Phone application in Windows 10Publication . Domingues, Patrício; Frade, Miguel; Andrade, Luis Miguel; Silva, João VictorYour Phone is a Microsoft system that comprises two applications: a smartphone app for Android 7 + smartphones and a desktop application for Windows 10/18.03+. It allows users to access their most recent smartphone-stored photos/screenshots and send/receive short message service (SMS) and multimedia messaging service (MMS) within their Your Phone-linked Windows 10 personal computers. In this paper, we analyze the digital forensic artifacts created at Windows 10 personal computers whose users have the Your Phone system installed and activated. Our results show that besides the most recent 25 photos/screenshots and the content of the last 30-day of sent/received SMS/MMS, the contact database of the linked smartphone(s) is available in a accessible SQLite3 database kept at the Windows 10 system. This way, when the linked smartphone cannot be forensically analyzed, data gathered through the Your Phone artifacts may constitute a valuable digital forensic asset. Furthermore, to explore and export the main data of the Your Phone database as well as recoverable deleted data, a set of python scripts – Your Phone Analyzer (YPA) – is presented. YPA is available wrapped within an Autopsy module to assist digital practitioners to extract the main artifacts from the Your Phone system.
- Using Secure Multi-Party Computation to Create Clinical Trial CohortsPublication . Borges, Rafael; Ferreira, Bruno; Antunes, Carlos Machado; Maximiano, Marisa; Gomes, Ricardo; Távora, Vitor; Dias, Manuel; Bezerra, Ricardo Correia; Domingues, Patrício; Antunes, Carlos MachadoThe increasing volume of digital medical data offers substantial research opportunities, though its complete utilization is hindered by ongoing privacy and security obstacles. This proof-of-concept study explores and confirms the viability of using Secure Multi-Party Computation (SMPC) to ensure protection and integrity of sensitive patient data, allowing the construction of clinical trial cohorts. Our findings reveal that SMPC facilitates collaborative data analysis on distributed, private datasets with negligible computational costs and optimized data partition sizes. The established architecture incorporates patient information via a blockchain-based decentralized healthcare platform and employs the MPyC library in Python for secure computations on Fast Healthcare Interoperability Resources (FHIR)-format data. The outcomes affirm SMPC’s capacity to maintain patient privacy during cohort formation, with minimal overhead. It illustrates the potential of SMPC-based methodologies to expand access to medical research data. A key contribution of this work is eliminating the need for complex cryptographic key management while maintaining patient privacy, illustrating the potential of SMPC-based methodologies to expand access to medical research data by reducing implementation barriers.
- Polymer Melt Stability Monitoring in Injection Moulding Using LSTM-Based Time-Series ModelsPublication . Costa, Pedro; Mendes, Sílvio Priem; Loureiro, PauloThis work presents a data-driven framework for early detection of polymer melt instability in industrial injection moulding using Long Short-Term Memory (LSTM) time-series models. The study uses six months of continuous production data comprising approximately 280,000 injection cycles collected from a fully operational thermoplastic injection line. Because melt behaviour evolves gradually and conventional threshold-based monitoring often fails to capture these transitions, the proposed approach models temporal patterns in torque, pressure, temperature, and rheology to identify drift conditions that precede quality degradation. A physically informed labelling strategy enables supervised learning even with sparse defect annotations by defining volatile zones as short time windows preceding operator-identified non-conforming parts, allowing the model to recognise instability windows minutes before defects emerge. The framework is designed for deployment on standard machine signals without requiring additional sensors, supporting proactive process adjustments, improved stability, and reduced scrap in injection moulding environments. These findings demonstrate the potential of temporal deep-learning models to enhance real-time monitoring and contribute to more robust and adaptive manufacturing operations.
- Indicator-based multi objective evolutionary algorithms and an application in filament winding processPublication . Yevseyeva, Iryna; de Melo, Francisco Queirós; Grácio, José; Basto-Fernandes, VitorThis work presents recent developments on multi objective evolutionary algorithms, so-called set-based evolutionary algorithms. These techniques are shown to approximate a Pareto front of efficient solutions taking into account both quality of the approximation and its diversity, both important in the design of these methods. Set-based evolutionary algorithms outperform their predecessors on a variety of benchmark problems and are suggested as tools to be used for solving complex mechanical engineering problems, such as filament winding process discussed in this work.
- 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.