Abstract
This research contributes to the field of Human-Robot Collaboration (HRC) within dynamic and unstructured environments by extending the previously proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) architecture to handle the uncertainty and irregularity inherent in real-world sensor data. Recognising the challenges posed by low-cost sensors, which are highly susceptible to environmental conditions and often fail to provide regular periodic readings, this paper introduces additional pre-processing blocks. These include two indirect Kalman filters and an additional LSTM network, which together enhance the input variables for the fuzzification process. The enhanced FS-LSTM approach is evaluated using real-world data, demonstrating its effectiveness in extracting meaningful information and accurately recognising human activities. This work underscores the potential of robotics in addressing global challenges, particularly in labour-intensive and hazardous tasks. By improving the integration of humans and robots in unstructured environments, this research contributes to the broader exploration of robotics in new societal applications, fostering connections and collaborations across diverse fields.

Abstract
O pensamento computacional é considerado uma aptidão fundamental no século XXI, pois permite aumentar a capacidade analítica das crianças nas diversas áreas do conhecimento (WING, 2006; RESNICK, 2012). Ao ser desenvolvido o pensamento computacional, as crianças ficam mais literadas tecnologicamente, deixando de ser meros utilizadores e passando a ter a aptidão de desenvolver (BARRADAS; LENCASTRE; SOARES; VALENTE, 2019). O desenvolvido do pensamento computacional promove competências como o ‘pensamento abstrato’ - utilização de diferentes níveis de abstração para compreender os problemas e, passo a passo, os solucionar -, o ‘pensamento algorítmico’ - expressão de soluções em diferentes etapas de forma a encontrar a forma mais eficaz de resolver um problema -, o ‘pensamento lógico’ - formulação e exclusão de hipóteses - e o ‘pensamento dimensionável’ - decomposição de um grande problema em pequenas partes ou composição de pequenas partes para formular uma solução mais complexa (PHILLIPS, 2009; RESNICK, 2012; MIT, 2011). Tais competências, associadas às ciências da computação, transpõem-se para outras áreas de saber e, consequentemente, para o dia a dia dos jovens, tornando-os mais reflexivos e críticos, logo, mais preparados para o mundo (BRENNAN; RESNICK, 2012). O presente capítulo relata uma experiência pedagógica desenvolvida do âmbito de um programa de Doutoramento em Engenharia Eletrotécnica e de Computadores com aplicação à Educação. Sendo o pensamento computacional a capacidade de formular um problema e de encontrar uma solução (CUNY; SNYDER; WING, 2010; COSTERMANS, 2001), este estudo pretendeu desenvolver o pensamento computacional em idades precoces (COELHO; ALMEIDA; ALMEIDA; LEDESMA; BOTELHO; ABRANTES, 2016) usando a plataforma CODE.org de forma a resolver problemas reais através do estímulo à capacidade de abstração com recurso à prática simulada e experimentada. A plataforma CODE.org. é um ambiente de aprendizagem online que tem como objetivo disponibilizar exercícios sobre computação para crianças de várias idades. O grupo de estudo foi constituído por 133 alunos pertencentes a cinco turmas do 4.º ano de escolaridade (9 e 10 anos de idade) ao longo de dois anos letivos, assim divididos: 2017/2018, 28 alunos 4.º A e 28 alunos 4.º B; 2018/2019, 27 alunos 4.º C, 26 alunos 4.º D, 24 alunos 4.º ano E. Inicialmente foram trabalhados com todos os alunos os conceitos básicos da plataforma CODE.org. – i.e., a forma de encaixe dos blocos, localização das áreas de trabalho, detalhes da interface como o palco da ação e os botões de execução, bem como as credenciais de acesso. Posteriormente, foi desenvolvido o pensamento computacional através de exercícios práticos laboratoriais orientadas à resolução de problemas (JONASSEN, 2004) envolvendo (1) sequências, (2) ciclos, (3) execução em paralelo, (4) eventos, (5) condições, (6) operadores e (7) dados, o que permitiu aos alunos criar o seu primeiro jogo FlappyBird (também na plataforma CODE.org.). Os dados foram recolhidos através dos registos automáticos da plataforma CODE.org para tratamento estatístico. A principal conclusão retirada a partir desta experiência pedagógica é que a plataforma CODE.org. é uma opção válida para desenvolver o pensamento computacional em idades precoces e uma boa forma dos alunos começarem a resolver problemas reais através do estímulo à capacidade de abstração com recurso à prática simulada e experimentada. A nossa convicção é que esta experiência pedagógica dotará estas crianças de competências essenciais para a vida cada vez mais complexa no século XXI, das quais fazem parte a criatividade e a inovação, o pensamento crítico e a resolução de problemas, a comunicação e a colaboração (PARTNERSHIP FOR 21ST CENTURY SKILLS, 2009).

Abstract
Low Power Wide Area Networks (LP-WAN) are receiving a lot of attention because of their ability to communicate using radio frequency in long distances, with low-power consumption and low-cost devices. In this paper, we provide a comparison between the two LP-WAN platforms that are leading the market, the Sigfox and the LoRaWAN, based on the literature. Both platforms are analyzed considering the context of the forest fire detection and verification systems. Many aspects are being considered to identify which LP-WAN is more adequate to be used in this kind of systems, such as battery lifetime, coverage range, business model and costs. The comparison shows that both platforms are very similar in most of the aspects, although LoRaWAN is more flexible than Sigfox on the deployment and management of the network infrastructure. LoRaWAN allows customers to implement and manage their own infrastructure network, which is essential in systems which monitor vast forest areas.

Abstract
This article describes part of the development cycle of an educational robotic platform to be used as an interdisciplinary teaching tool integrated in the curriculum. We focus on the creation of the alpha and beta versions of our prototype and it’s evaluation by representative users. The SUS score of 92.5 points, Best Imaginable, show a very stable and satisfactory robotic platform, with almost no usability problems detected.

Abstract
STEM areas (Science, Technology, Engineering and Math) are continuously growing but the number of technical workers do not accompany that growth. As the 21st century brings new challenges, students should be prepared for an increasingly complex life and work environments that will privilege proficiency in Learning and Innovation Skills that include Creativity and Innovation, Critical Thinking and Problem Solving, Communication and Collaboration. Also, the need to continuously explore new pedagogical practices in teaching and learning creates an opportunity to build new contents by balancing a stable and tested curriculum with new tools that stimulate creativity, allowing students to better understand the world they live in. This article describes the development of an educational robotics kit, aimed at children and teens from 8 to 18 years old, meant to work as an interdisciplinary teaching tool that can be applied directly in a curriculum.

Abstract
Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people's daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This connectivity allows for a better integration of the pervasive computing, making devices smart and capable of interacting with each other and with the corresponding users in a sublime way. However, the widespread adoption of IoT devices has introduced some security challenges, because these devices usually run in environments that have limited resources. As IoT technology becomes more integrated into critical infrastructure and daily life, the need for stronger security measures will increase. These devices are exposed to a variety of cyber-attacks. This literature review synthesizes the current research of artificial intelligence (AI) technologies to improve IoT security. This review addresses key research questions, including: (1) What are the primary challenges and threats that IoT devices face?; (2) How can AI be used to improve IoT security?; (3) What AI techniques are currently being used for this purpose?; and (4) How does applying AI to IoT security differ from traditional methods? Methods: We included a total of 33 peer-reviewed studies published between 2020 and 2024, specifically in journal and conference papers written in English. Studies irrelevant to the use of AI for IoT security, duplicate studies, and articles without full-text access were excluded. The literature search was conducted using scientific databases, including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. Results were synthesized through a narrative synthesis approach, with the help of the Parsifal tool to organize and visualize key themes and trends. Results: We focus on the use of machine learning, deep learning, and federated learning, which are used for anomaly detection to identify and mitigate the security threats inherent to these devices. AI-driven technologies offer promising solutions for attack detection and predictive analysis, reducing the need for human intervention more significantly. This review acknowledges limitations such as the rapidly evolving nature of IoT technologies, the early-stage development or proprietary nature of many AI techniques, the variable performance of AI models in real-world applications, and potential biases in the search and selection of articles. The risk of bias in this systematic review is moderate. While the study selection and data collection processes are robust, the reliance on narrative synthesis and the limited exploration of potential biases in the selection process introduce some risk. Transparency in funding and conflict of interest reporting reduces bias in those areas. Discussion: The effectiveness of these AI-based approaches can vary depending on the performance of the model and the computational efficiency. In this article, we provide a comprehensive overview of existing AI models applied to IoT security, including machine learning (ML), deep learning (DL), and hybrid approaches. We also examine their role in enhancing the detection accuracy. Despite all the advances, challenges still remain in terms of data privacy and the scalability of AI solutions in IoT security. Conclusion: This review provides a comprehensive overview of ML applications to enhance IoT security. We also discuss and outline future directions, emphasizing the need for collaboration between interested parties and ongoing innovation to address the evolving threat landscape in IoT security.