Abstract
In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal. We evaluate our approach in a quantitative manner in two ways, first by measuring its note prediction accuracy, and second via a regression task in the valence-arousal plane. Our results demonstrate that our proposed approaches outperform conditioning using control tokens which is representative of the current state of the art.

Abstract
Cardiac auscultation is still the most cost-effective screening procedure for cardiovascular diseases. The development of computer assisted methods can empower a large variety of health professionals and thus enable mass cardiac health low-cost screening. The procedure for correct cardiac auscultation includes listening to the heart sounds of the four main auscultation spots. Until recently, attempts to develop automatic heart sound analysis methods that explore the multi-channel richness of a real auscultation, were very difficult due to the lack of adequate public datasets. In this work, we use the CirCor Dataset which is characterized by the existence of more than one heart sound per patient (each patient has heart sounds collected at different auscultation spots). Using this dataset, we evaluate and quantify the comparative impact of using a single or a multichannel approach. A single channel approach uses the sound from a single auscultation spot, whereas a multi-channel approach uses four auscultation spots in an asynchronous way. From the different classifiers tested, models that use four auscultation spots achieved a higher overall performance than those that search for abnormalities in a single heart sound spot. Our best result is a multi-channel SVM that analyzes four auscultation spots, with an overall performance of 87,4 %. This opens the path to future research using a multi-channel approach.

Abstract
Cyber-Physical Systems (CPS) transform traditional systems into a network of connected and heterogeneous systems, integrating computational and physical elements, that works as a complex system whose overall properties are greater than the sum of its parts. However, CPS is not free from faulty episodes and their consequences such as malfunctions, breakdowns, and service interruption. Traditional centralized models for fault-tolerance do not meet the complexity of the current industrial scenarios and particularly the industrial CPS requirements. Having this in mind, this work presents a holonic-based architecture to address the fault-tolerance in CPS by distributing the detection, diagnosis, and recovery in the local individual entities and also considers the emergent behaviour resulting from the collaboration of these entities. An experimental case study is used to illustrate the potential application of the fault-tolerant approach. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
Although innovation capabilities are important drivers of export performance, few studies address how they influence export performance in the context of emerging economies. This paper evaluates the moderating effects of government institutional support and firms' active and reactive internationalization behaviors on the relationship between innovation capabilities and export performance. The sample analyzed is based on 250 Mozambican small and medium enterprises (SMEs). The results indicate that although innovation capabilities positively influence the export performance of Mozambican SMEs, the moderating effects of government institutional support and firms' active and reactive internationalization behaviors were not found to be statistically significant.

Abstract
An early diagnosis of cancer is essential for a good prognosis, and the identification of differentially expressed genes can enable a better personalization of the treatment plan that can target those genes in therapy. This work proposes a pipeline that predicts the presence of lung cancer and the subtype allowing the identification of differentially expressed genes for lung cancer adenocarcinoma and squamous cell carcinoma subtypes. A gradient boosted tree model is used for the classification tasks based on RNA-seq data. The analysis of gene expressions that better differentiate cancerous from normal tissue, and features that distinguish between lung subtypes is the main focus of the present work. Differential expressed genes are analyzed by performing hierarchical clustering in order to identify gene signatures that are commonly regulated and biological signatures associated with a specific subtype. This analysis highlighted patterns of commonly regulated genes already known in the literature as cancer or subtype-specific genes, and others that are not yet documented in the literature.