Abstract
The combination of class imbalance and overlap is currently one of the most challenging issues in machine learning. While seminal work focused on establishing class overlap as a complicating factor for classification tasks in imbalanced domains, ongoing research mostly concerns the study of their synergy over real-word applications. However, given the lack of a well-formulated definition and measurement of class overlap in real-world domains, especially in the presence of class imbalance, the research community has not yet reached a consensus on the characterisation of both problems. This naturally complicates the evaluation of existing approaches to address these issues simultaneously and prevents future research from moving towards the devise of specialised solutions. In this work, we advocate for a unified view of the problem of class overlap in imbalanced domains. Acknowledging class overlap as the overarching problem - since it has proven to be more harmful for classification tasks than class imbalance - we start by discussing the key concepts associated to its definition, identification, and measurement in real-world domains, while advocating for a characterisation of the problem that attends to multiple sources of complexity. We then provide an overview of existing data complexity measures and establish the link to what specific types of class overlap problems these measures cover, proposing a novel taxonomy of class overlap complexity measures. Additionally, we characterise the relationship between measures, the insights they provide, and discuss to what extent they account for class imbalance. Finally, we systematise the current body of knowledge on the topic across several branches of Machine Learning (Data Analysis, Data Preprocessing, Algorithm Design, and Meta-learning), identifying existing limitations and discussing possible lines for future research.

Abstract
The consumption of plant-based beverages has seen a substantial increase in Portugal, which reflects the consumers changing eating habits, due to their growing ethical and environmental concerns. This study takes into account a specific Portuguese brand of plant-based beverages and empirically tested, using the PLS-SEM technique, a conceptual model to analyze the impact of perceived value, brand equity and satisfaction on loyalty intentions of Portuguese plant-based beverages' consumers. Based on 216 responses to a survey questionnaire, both the perceived value and the brand equity of this plant-based beverage brand explain satisfaction and loyalty intention. Moreover, brand equity mediates the relationship between perceived value and satisfaction and loyalty intention being this indirect effect more relevant than the direct one.

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Abstract
The development of accurate methods for OCT image analysis is highly dependent on the availability of large annotated datasets. As such datasets are usually expensive and hard to obtain, novel approaches based on deep generative models have been proposed for data augmentation. In this work, a flow-based network (SRFlow) and a generative adversarial network (ESRGAN) are used for synthesizing high-resolution OCT B-scans from low-resolution versions of real OCT images. The quality of the images generated by the two models is assessed using two standard fidelity-oriented metrics and a learned perceptual quality metric. The performance of two classification models trained on real and synthetic images is also evaluated. The obtained results show that the images generated by SRFlow preserve higher fidelity to the ground truth, while the outputs of ESRGAN present, on average, better perceptual quality. Independently of the architecture of the network chosen to classify the OCT B-scans, the model's performance always improves when images generated by SRFlow are included in the training set.

Abstract
The environmental protection and energy conservation concerns have spurred the development of new solutions in the automotive industry. This has led to the popularity of electric vehicles (EV) and Plugin hybrid electric vehicles (PHEV). On the other hand, this surge in popularity has created a challenge for the development of various new technologies and services, such as charging technology systems and stations. However, unidirectional charging offers hardware simplicity and easier interconnection and enable a G2V model, while bidirectional charging solutions enables G2V and V2G solutions, which can help stabilize AC power by utilizing the energy stored in the battery. This paper presents an EV battery charging system that uses a compact and straightforward bidirectional converter. The system can draw power from either traditional electrical sources or sustainable energy sources like photovoltaic modules, with the option of using lithium rechargeable batteries and supercapacitors as an Energy Storage System (ESS). Several Simulink simulations were conducted to investigate battery behavior under different power sources, and the results show the good effectiveness of the developed system, allowing it to be used in more comprehensive studies in the field of EV charging. © 2023 IEEE.