Abstract
Music-based therapies have been yielding favorable clinical outcomes in children with Autism Spectrum Disorder (ASD). However, there is a lack of guidelines for content selection in music-based interventions. In this context, we propose a methodology for conducting experimental studies on musical preferences in children diagnosed with ASD. It consists of a generative music system with seven manipulable musical parameters where participants are encouraged to create music content according to their preferences. We conducted a preliminary transversal study with 24 children in the state of Pará, Brazil. The results suggest preferences for fast tempo, higher pitch, consonance, high event density, and timbres with smooth attacks. Intriguingly, the results revealed inconsistency in the identified preferences across therapy sessions. The critical need for personalized regulation in music-based interventions for children with ASD highlights the unique nature of individual responses, emphasizing the imperative of tailoring therapeutic approaches accordingly. © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.

Abstract
Micro-companies play an extremely important role in the economy being the main driver of economic growth. They contribute decisively for employability, business innovation and in reducing social asymmetries. This role of micro-companies in particular and, small and medium enterprises in general, is widely recognised in the literature. Nevertheless, the number of longitudinal studies that explicitly address the contribution of micro-companies to the European economy is reduced, and most of them are essentially reports produced by European and national agencies that analyse the importance of this phenomenon in their economies. This study intends to characterise the birth, death and survival rate of micro-companies in the European Union. The study adopts a quantitative and statistical approach in data analysis between 2008 and 2016, which allows us to characterise the evolution of these indicators and to understand which countries have the best and the worst performances.

Abstract
Concept-based models naturally lend themselves to the development of inherently interpretable skin lesion diagnosis, as medical experts make decisions based on a set of visual patterns of the lesion. Nevertheless, the development of these models depends on the existence of concept-annotated datasets, whose availability is scarce due to the specialized knowledge and expertise required in the annotation process. In this work, we show that vision-language models can be used to alleviate the dependence on a large number of concept-annotated samples. In particular, we propose an embedding learning strategy to adapt CLIP to the downstream task of skin lesion classification using concept-based descriptions as textual embeddings. Our experiments reveal that vision-language models not only attain better accuracy when using concepts as textual embeddings, but also require a smaller number of concept-annotated samples to attain comparable performance to approaches specifically devised for automatic concept generation.

Abstract
This work addresses the contemporary challenges in computing, caused by the stagnation of Moore's Law and Dennard scaling. The shift towards heterogeneous architectures necessitates innovative compilation strategies, prompting initiatives like the Multi-Level Intermediate Representation (MLIR) project, where progressive code lowering can be achieved through the use of dialects. Our work focuses on developing an MLIR dialect capable of representing streaming data accesses to memory, and Single Instruction Multiple Data (SIMD) vector operations. We also propose our own Structured Representation Language (SRL), a Design Specific Language (DSL) to serve as a precursor into the MLIR layer and subsequent inter-operation between new and existing dialects. The SRL exposes the streaming and vector computational concepts to a higher-level, and serves as intermediate step to supporting code generation containing our proposed dialect from arbitrary input code, which we leave as future work. This paper presents the syntaxes of the SRL DSL and of the dialect, and illustrates how we aim to employ them to target both General-Purpose Processors (GPPs) with SIMD co-processors and custom hardware options such as Field-Programmable Gate Arrayss (FPGAs) and Coarse-Grained Re-configurable Arrays (CGRAs).

Abstract
In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal. It consists of a countrywide network of collection bin units, available in public areas. Two metrics are considered to evaluate the system's success: (i) user engagement, and (ii) used cooking oil collection efficiency. The presented system should (i) perform under scenarios of temporary communication network failures, and (ii) be scalable to accommodate an ever-growing number of installed collection units. Thus, we choose a disruptive approach from the traditional cloud computing paradigm. It relies on edge node infrastructure to process, store, and act upon the locally collected data. The communication appears as a delay-tolerant task, i.e., an edge computing solution. We conduct a comparative analysis revealing the benefits of the edge computing enabled collection bin vs. a cloud computing solution. The studied period considers four years of collected data. An exponential increase in the amount of used cooking oil collected is identified, with the developed solution being responsible for surpassing the national collection totals of previous years. During the same period, we also improved the collection process as we were able to more accurately estimate the optimal collection and system's maintenance intervals.

Abstract
Stand-level forest tree species perception and identification are needed for monitoring-related operations, being crucial for better biodiversity and inventory management in forested areas. This paper contributes to this knowledge domain by researching tree trunk types multispectral perception at stand-level. YOLOv5 and YOLOv8 - Convolutional Neural Networks specialized at object detection and segmentation - were trained to detect and segment two tree trunk genus (pine and eucalyptus) using datasets collected in a forest region in Portugal. The dataset comprises only two categories, which correspond to the two tree genus. The datasets were manually annotated for object detection and segmentation with RGB and RGB-NIR images, and are publicly available. The Small variant of YOLOv8 was the best model at detection and segmentation tasks, achieving an F1 measure above 87% and 62%, respectively. The findings of this study suggest that the use of extended spectra, including Visible and Near Infrared, produces superior results. The trained models can be integrated into forest tractors and robots to monitor forest genus across different spectra. This can assist forest managers in controlling their forest stands.