Abstract
The rapid production of mobile devices along with the wireless applications boom is continuing to evolve daily. This motivates the exploitation of wireless spectrum using multiple Radio Access Technologies (multi-RAT) and developing innovative network selection techniques to cope with such intensive demand while improving Quality of Service (QoS). Thus, we propose a distributed framework for dynamic network selection at the edge level, and resource allocation at the Radio Access Network (RAN) level, while taking into consideration diverse applications' characteristics. In particular, our framework employs a deep Multi-Agent Reinforcement Learning (DMARL) algorithm, that aims to maximize the edge nodes' quality of experience while extending the battery lifetime of the nodes and leveraging adaptive compression schemes. Indeed, our framework enables data transfer from the network's edge nodes, with multi-RAT capabilities, to the cloud in a cost and energy-efficient manner, while maintaining QoS requirements of different supported applications. Our results depict that our solution outperforms state-of-the-art techniques of network selection in terms of energy consumption, latency, and cost.

Abstract
A 2D numerical investigation of the power absorption of a constrained wave energy hyperbaric converter (WEHC) under full-scale sea wave conditions is presented. A fully non-linear numerical model DualSPHysics, based on the coupling of a smoothed particle hydrodynamics (SPH) fluid solver with a multibody dynamics solver, is used to model the interaction between wave and WEHC sub-systems. The numerical model was first validated against experimental data for a similar device, with a good accordance between PTO position and velocity. The model is then employed to study the hydrodynamics of a constrained WEHC considering several sea states, different hydraulic power take-off (PTO) damping and breakwater geometries. It is observed that the capture width ratio (CWR) is particularly sensitive to variations in the PTO damping, although the CWR absolute maximum is less sensitive considering mild variations applied to the PTO damping. Both wave height and wave period have an important effect on the CWR. The breakwater geometry is also essential for the performance of the WEHC, with a decrease in maximum CWR of about 15% for porous breakwater. These results are necessary to understand the full-scale behaviour of WEHC.

Abstract
Autonomous driving is one of the fastest developing fields of robotics. With the ever-growing interest in autonomous driving, the ability to provide robots with both efficient and safe navigation capabilities is of paramount significance. With the continuous development of automation technology, higher levels of autonomous driving can be achieved with vision-based methodologies. Moreover, materials handling in industrial assembly lines can be performed efficiently using automated guided vehicles (AGVs). However, the visual perception of industrial environments is complex due to the existence of many obstacles in pre-defined routes. With the INDTECH 4.0 project, we aim to develop an autonomous navigation system, allowing the AGV to detect and avoid obstacles based in the processing of depth data acquired with a frontal depth camera mounted on the AGV. Applying the RANSAC (random sample consensus) and Euclidean clustering algorithms to the 3D point clouds captured by the camera, we can isolate obstacles from the ground plane and separate them into clusters. The clusters give information about the location of obstacles with respect to the AGV position. In experiments conducted outdoors and indoors, the results revealed that the method is very effective, returning high percentages of detection for most tests.

Abstract
Socioeconomic changes have caused profound transformations in forest landscapes and increased abandonment of rural areas, leading to fuel accumulation and higher landscape homogeneity, and consequently, raising the rural fires risk. Rural fires risk is also fueled by climate change, due to heat waves and lack of precipitation. In this context, rural communities inhabiting forest areas are those who suffer the most, because rural fires, land degradation and climate change can disturb their food and economic strategy. These communities already suffer from underdeveloped rural infrastructure, and services, lack of labor and education opportunities, that trigger poverty and migration. Given this accelerating pace of change and increasing uncertainty, many fields of knowledge have been dedicated to contributing towards a more sustainable and inclusive future. In service research, transformative service research (TSR) literature plays a central role on understanding problems and finding solutions that improve well-being and create uplifting change through services. Similarly, the fire research field highlights the need for an integrated perspective to analyze all the aspects involved in rural fires occurrence, whether they are of an environmental or economic nature, or a sociological or demographic nature. This study aims to explore new services to cocreate value with forest-related rural communities, thus helping to manage forest areas and mitigate rural fires risks. A qualitative methodology was employed involving 28 participants related to fire management and forest areas and communities, including actors from industries, public entities, academics, the third sector. The data collected through individual interviews were transcribed, coded, and analyzed following a thematic analysis approach, with NVivo software support. Overall, the study emphasizes the need for an endogenous and adapted set of services to cocreate value with vulnerable communities in forest areas, which consequently enable rural fires mitigation. Given the high level of land abandonment and accumulation of residual materials that increases the risk of rural fires, the development of valuing and recovery solutions is a priority. Finally, this research can also help decision-makers and stakeholders to generate and support services that cocreate value with rural communities to a sustainable, safe and inclusive future.

Abstract
Typically, classification algorithms use correlation analysis to make decisions. However, these decisions and the models they learn are not easily understandable for the typical user. Causal discovery is the field that studies the means to find causal relationships in observational data. Although highly interpretable, causal discovery algorithms tend to not perform so well in classification problems. This paper aims to propose a hybrid decision tree approach (SC tree) that mixes causal discovery with correlation analysis through the implementation of a custom metric to split the data in the tree's construction (Semi-causal gain ratio). In the results, the proposed methodology obtained a significant performance improvement (11.26% mean error rate) when compared to several causal baselines CDT-PS (23.67% ) and CDT-SPS (25.14%), matching closely the performance of J48 (10.20%), used as a correlation baseline, in ten binary data sets. Besides, when compared with PC in discrete data sets, the proposed approach obtained substantial improvement (16.17% against 28.07% in terms of mean error rate).

Abstract
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes of real-time data streams. Designing accurate models using such data streams, to revolutionize the decision-taking process, inaugurates pervasive computing as a worthy paradigm for a better quality-of-life (e.g., smart homes and self-driving cars.). The confluence of pervasive computing and artificial intelligence, namely Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy and latency requirements. In this context, an intelligent resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g., edge nodes and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and reinforcement learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed training and inference across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.