Abstract

Abstract
The fast development and adoption of IoT technologies has been enabling their application into increasingly sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices increases annually, they still present severe cyber-security vulnerabilities, becoming potential targets and entry points for further attacks. As these nodes become compromised, attackers aim to set up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes in a cloaked fashion, and network timing covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim to analyse the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol, particularly upon the DSME behaviour, one of the most promising for large-scale time critical communications. This enables us to better understand the involved risk of such threats and help support the development of active cyber-security mechanisms to mitigate these threats, which, for now, we provide in the form of practical network setup recommendations.

Abstract
An immersive narrative means to promote immersion of its target audience, considering three dimensions of narrative immersion: temporal, spatial and emotional. We present and demonstrate the use of a method for evaluating and classifying these dimensions in narratives, enabling reflections for their reformulation according to the pedagogical objectives of the teacher. The method was developed as an artifact of Design Science Research (DSR). It was applied empirically in Portuguese higher education, on an asynchronous e-learning course at Universidade Aberta, which uses narratives for narrative immersion and promoting self-regulation and co-regulation of learning: Software Development Laboratory. The results show that the method enables detection of differences in the level of use of the various dimensions of narrative immersion, as shown for a sample case. This analysis, beyond its usefulness for this evaluation and classification upon narratives, enables their creators (e.g. teachers and non-specialist professionals) to become aware of these situations. Therefore, it provides an overview and supports grounded reflection, inspiring interventions to reformulate the dimensions of narrative immersion that one wants to provide to the target audience.;Uma narrativa imersiva deve prover a imersão do público-alvo considerando três dimensões de imersão narrativa: temporal, espacial e emocional. Apresentamos e exemplificamos o uso de um método que avalia e classifica estas dimensões em narrativas, apoiando reflexões para a sua alteração, de acordo com os objetivos pedagógicos almejados pelo docente. O método foi desenvolvido enquanto artefacto de Design Science Research (DSR). Foi aplicado empiricamente numa unidade curricular do ensino superior português em regime de e-learning assíncrono na Universidade Aberta, que utiliza narrativas para imersão narrativa e promover a autorregulação e corregulação das aprendizagemns: Laboratório de Desenvolvimento de Software. Os resultados indicam que o método permite detectar diferenças de nível de recurso às várias dimensões de imersão narrativa, como ocorreu no caso apresentado. Esta análise, além de permitir esta avaliação e classificação em narrativas, dá aos seus criadores (e.g., professores e profissionais não especialistas) consciência destas situações ao longo da narrativa. Possibilita, desta forma, uma visão global e uma reflexão fundamentada, que pode inspirar intervenções para reformular as dimensões de imersão narrativa a proporcionar ao público-alvo.

Abstract
Real Estate Agents perform the tedious job of selecting and filtering pictures of houses manually on a daily basis, in order to choose the most suitable ones for their websites and provide a better description of the properties they are selling. However, this process consumes a lot of time, causing delays in the advertisement of homes and reception of proposals. In order to expedite and automate this task, Computer Vision solutions can be employed. Deep Learning, which is a subfield of Machine Learning, has been highly successful in solving image recognition problems, making it a promising solution for this particular context. Therefore, this paper proposes the application of Vision Transformers to indoor room classification. The study compares various image classification architectures, ranging from traditional Convolutional Neural Networks to the latest Vision Transformer architecture. Using a dataset based on well-known scene classification datasets, their performance is analyzed. The results demonstrate that Vision Transformers are one of the most effective architectures for indoor classification, with highly favorable outcomes in automating image recognition and selection in the Real Estate industry.

Abstract
Digital Twin (DT) is recognized as a key enabling technology of Industry 4.0 and 5.0 and can be used in collaborative networks formed to fulfillment of complex tasks of the manufacturing industry. In the last years, the variety and complexity of DTs have been significantly increasing with new technologies and smarter solutions. The current definition of DT, such as cognitive, hybrid, and others, embraces a wide range of solutions with different aspects. In this sense, this article discusses DT definitions and presents a five-dimensional analytical framework to classify the different proposals. Finally, to better understand the proposal, we analyzed 12 articles using the analytical framework. We argue this research may help researchers and practitioners to better understand digital twins and compare different solutions.

Abstract
The accurate prediction of biological age can bring important benefits in promoting therapeutic and behavioural strategies for healthy aging. We propose the development of age prediction models using multi-modal datasets, including transcriptomics, methylation and histological images from lung tissue samples of 793 human donors. From a technical point of view this is a challenging problem since not all donors are covered by the same data modalities and the datasets have a very high feature dimensionality with a relatively smaller number of samples. To fairly compare performance across different data types, we’ve created a test set including donors represented in each modality. Given the unique characteristics of the data distribution, we developed gradient boosting tree and convolutional neural network models for each dataset. The performance of the models can be affected by several covariates, including smoking history, and, most importantly, by a skewed distribution of age. Data-centric approaches, including feature engineering, feature selection, data stratification and resampling, proved fundamental in building models that were optimally adapted for each data modality, resulting in significant improvements in model performance for imbalanced regression. The models were then applied to the test set independently, and later combined into a multi-modal ensemble through a voting strategy, predicting age with a median absolute error of 4 years. Even if prediction accuracy remains a challenge, in this work we provide insights to address the difficulties of multi-modal data integration and imbalanced data prediction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.