Abstract
The present chapter scrutinises invariable question tags in British English and European Portuguese from a contrastive point of view. Most previous research focuses on variable question tags and, if contrastive, compares other languages. This study shows that invariable question tags are more frequent and varied in European Portuguese than in British English. Furthermore, the results reveal that, while some invariable question tags appear across the two languages, others are registered only in one of them, despite the existence of equivalent expressions in the other. Additional asymmetries emerge concerning linguistic features of invariable question tags across the two languages. Based on the significant correlations between invariable question tags and their features, a model of predicted associations is put forward. © The Editor(s) (ifapplicable) and The Author (s),under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Previous literature on the typology of gerund clauses in Portuguese has overlooked a peculiar type of clauses which are always introduced by como ( 'as' ) and display an array of characteristics that set them apart from all other gerund clauses (and from other, somehow similar, constructions in different languages). In this paper, we provide an in-depth syntactic and semantic characterisation of these como-gerund clauses and the contexts in which they arise, highlighting their similarities and differences regarding other constructions, namely resultative and depictive secondary predicates. We put forward a proposal to deal with their syntactic configurations and the restrictions they exhibit. We also propose that como is obligatory in these clauses because it marks a type-shift operation, which gives como gerund clauses a predicative interpretation, usually found in the nominal domain.

Abstract
Aquaculture presents itself as one of the most rapidly developing means of sustainable production of animal protein to feed ever-growing populations. Recirculating aquaculture systems offer higher control and fewer inconveniences than traditional systems, making them an attractive option for fish production. Although the sector's digitalization is in its early stages, its application should increase its rentability while conserving the environment. This paper aims to promote the sector's evolution by assessing parameter importance in mortality with tree-based machine learning models, verifying the method's natural robustness and how it compares to a specially devised one, and at the same time evaluating the concept's relevance in predicting categorical mortality values. In particular, to better understand the aquaculture production process through a systematic data evaluation, an exploration based on real-time data acquisition is fully needed. Moreover, algorithm robustness is a key ingredient in this application since measurements are greatly affected by errors. This invalidates the application of traditional machine learning methods, where models are sensitive to production data variations and sensor noise. The study found the parameters that play relevant roles in the production phases, such as pH and nitrate concentration. While the obtained predictive metrics are still sub-optimal, further enhancements could be achieved through rigorous analysis of feature engineering, fine-tuning model hyperparameters, and exploring more advanced algorithms. Additionally, incorporating larger and more diverse datasets, refining data pre-processing techniques, and iteratively optimizing the model architecture may contribute to significant improvements in predictive performance. Despite that, the impact costs of using adjusted machine learning metrics are clear, as are the importance of data rounding in pre-processing and directions for improvement regarding data acquisition and transformation.

Abstract

Abstract
Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. In Computer Numerical Control machining, challenges involve identifying and verifying specific patterns of interest or trends in a time-series dataset. However, this can be a challenge due to the extensive diversity. Therefore, this work aims to develop a methodology capable of verifying the presence of a specific pattern of interest in a given collection of time-series. This study mainly focuses on evaluating One-Class Classification techniques using Linear Frequency Cepstral Coefficients to describe the patterns on the time-series. A real-world dataset produced by turning machines was used, where a time-series with a certain pattern needed to be verified to monitor the wear offset. The initial findings reveal that the classifiers can accurately distinguish between the time-series' target pattern and the remaining data. Specifically, the One-Class Support Vector Machine achieves a classification accuracy of 95.6 % +/- 1.2 and an F1-score of 95.4 % +/- 1.3.

Abstract
Keeping high service levels of a fast-growing number of servers is crucial and challenging for IT operations teams. Online monitoring systems trigger many occurrences that experts find hard to keep up with. In addition, most of the triggered warnings do not correspond to real, critical problems, making it difficult for technicians to know which to focus on and address in a timely manner. Outlier and concept drift detection techniques can be applied to multiple streams of readings related to server monitoring metrics, but they also generate many False Positives. Ranking algorithms can already prioritize relevant results in information retrieval and recommender systems. However, these approaches are supervised, making them inapplicable in event detection on data streams. We propose a framework that combines event aggregations and uses a customized clustering algorithm to score and rank alarms in the context of IT operations. To the best of our knowledge, this is the first unsupervised, online, high-dimensional approach to rank IT ops events and contributes to advancing knowledge about associated key concepts and challenges of this problem.