Abstract
The increasing relevance of remote sensing and artificial intelligence (AI) for archaeological research and cultural heritage management is undeniable. However, there is a critical gap in this field. Many studies conclude with identifying hundreds or even thousands of potential sites, but very few follow through with crucial fieldwork validation to confirm their existence. This research addresses this gap by proposing and implementing a fieldwork validation pipeline. In northern Portugal's Alto Minho region, we employed this pipeline to verify 237 potential burial mounds identified by an AI-powered algorithm. Fieldwork provided valuable information on the optimal conditions for burial mounds and the specific factors that led the algorithm to err. Based on these insights, we implemented two key improvements to the algorithm. First, we incorporated a slope map derived from LiDAR-generated terrain models to eliminate potential burial mound inferences in areas with high slopes. Second, we trained a Vision Transformer model using digital orthophotos of both confirmed burial mounds and previously identified False Positives. This further refines the algorithm's ability to distinguish genuine sites. The improved algorithm was then tested in two areas: the original Alto Minho validation region and the Barbanza region in Spain, where the location of burial mounds was well established through prior field work.

Abstract
The increasing number of IoT deployment scenarios and applications fostered the development of a multitude of specially crafted communication solutions, several proprietary, which are erecting barriers to IoT interoperability, impairing their pervasiveness. To address such problems, several middleware solutions exist to standardize IoT communications, hence promoting and facilitating interoperability. Although being increasingly adopted in most IoT systems, it became clear that there was no one size fits all solution that could address the multiple Quality-of-Service heterogeneous IoT systems may impose. Consequently, we witness new interoperability challenges regarding the usage of diverse middleware. In this work, we address this issue by proposing a novel architecture - the PolyglIoT, that can effectively interconnect diverse middleware solutions while considering the delivery QoS requirements alongside the proposed translation. We analyze the performance and robustness of the solution and show that such Multiprotocol Translator is feasible and can achieve a high performance, thus becoming a fundamental piece to enable future highly heterogeneous IoT systems of systems.

Abstract
O ensino de conteúdos de matemática na educação básica apresenta alguns desafios. Muitos desses vêm sendo superados com a utilização de tecnologias da informação e comunicação. Nesse contexto, a robótica educacional vem ganhando espaço, estando cada vez mais presente em ambientes escolares. Porém, há escassez de materiais que auxiliem os professores no uso dessa tecnologia em sala de aula. Para começar a suplantar esse problema, neste artigo, apresenta-se o desenvolvimento de uma ontologia capaz de auxiliar o ensino e aprendizagem da disciplina de matemática utilizando robótica educacional. A ontologia denominada Ontologia de Conteúdo de Matemática Combinada com Robótica Educacional (Onto-ENSINARE) foi construída com base na metodologia Ontology Development 101 com os aspectos de completude, consistência e concisão. Para validar a ontologia foram utilizadas consultas SPARQL para obtenção de respostas úteis aos professores de matemática da educação básica.

Abstract
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm's performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80% and, according to our results, good performance in terms of accuracy and speed, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected an important event type in rink hockey games, namely, the occurrence of penalties.

Abstract
Identifying the influential spreaders in complex networks has emerged as an important research challenge to control the spread of (mis)information or infectious diseases. Researchers have proposed many centrality measures to identify the influential nodes (spreaders) in the past few years. Still, most of them have not considered the importance of the edges in unweighted networks. To address this issue, we propose a novel centrality measure to identify the spreading ability of the Influential Spreaders using the Potential Edge Weight method (IS-PEW). Considering the connectivity structure, the ability of information exchange, and the importance of neighbouring nodes, we measure the potential edge weight. The ranking similarity of spreaders identified by IS-PEW and the baseline centrality methods are compared with the Susceptible-Infectious-Recovered (SIR) epidemic simulator using Kendall's rank correlation. The spreading ability of the top-ranking spreaders is also compared for five different percentages of top-ranking node sets using six different real networks.

Abstract
Archaeological discoveries can benefit enormously from linked open data (LOD) technologies since, as new objects are discovered, data about them can be placed in the LOD cloud and instantly accessible to third parties. This article presents a framework developed to publish LOD on arrowheads from the Chalcolithic and Early/Middle Bronze Age chronologies (2800/2900 BC to 1500 BC) found in the last 25 years of excavations on an archaeological site in Portugal. These arrowheads were kept in boxes, hidden from the possibility of being studied and viewed by interested parties. The framework encompasses a metadata application profile (MAP) and tools to be used with this MAP, such as a namespace, two metadata schemas and eight vocabulary coding schemes. The MAP domain model was developed with the support of the scientific literature about this type of arrowheads, and the team integrated two archaeologists. This framework was created with the design philosophy of maximising data interoperability, so terms from the CIDOC CRM conceptual models and other vocabularies widely used in the LOD cloud were used. The MAP was tested using a set of seven arrowheads, which proved, in the first instance, the viability of the developed MAP. The team plans to test the model in future work with arrowheads of other excavations.