Abstract
Abstract The article focuses on the consumption of goods used by consumers of different generations from 3 different countries: Albania, Polish and Portugal. The aim of the analysis was to identify respondents‘ indications concerning: (1) knowledge of the definition of the circular economy, (2) declared by respondents places of purchase of used products and (3) type of purchased products used by respondents. The analysis was conducted among 495 respondents from Albania, Polish and Portugal representing three generations (X, Y, Z). Correspondence analysis was used for statistical data analysis. Statistically significant differences in knowledge of the definition of the circular economy were shown between respondents from Albania, Polish and Portugal. It was also found that respondents‘ preferences regarding the place of purchase of second-hand goods are differentiated (at a statistically significant level) by nationality and year of birth (generation). The obtained results open the possibility of further research aimed at identifying different behaviors among these groups of consumers. The presented work, both in the cognitive and application part, can be a source of knowledge and popularization of research, as well as a source of inspiration for in-depth reflection and scientific discussion. The analyses presented in the publication may complement the existing research in the field of circular economy. Extending the survey to other EU countries can help define a strategy for policymakers, manufacturers and retailers to make greater use of circular economy solutions, while maintaining the viability of their operations.

Abstract
As the field of Machine Learning evolves, the number of available learning algorithms and their parameters continues to grow. On the one hand, this is positive as it allows for the finding of potentially more accurate models. On the other hand, however, it also makes the process of finding the right model more complex, given the number of possible configurations. Traditionally, data scientists rely on trial-and-error or brute force procedures, which are costly, or on their own intuition or expertise, which is hard to acquire. In this paper we propose an approach for algorithm recommendation based on meta-learning. The approach can be used in real-time to predict the best n algorithms (based on a selected performance metric) and their configuration, for a given ML problem. We evaluate it through cross-validation, and by comparing it against an Auto ML approach, in terms of accuracy and time. Results show that the proposed approach recommends algorithms that are similar to those of traditional approaches, in terms of performance, in just a fraction of the time.

Abstract
In the last years, the number of machine learning algorithms and their parameters has increased significantly. On the one hand, this increases the chances of finding better models. On the other hand, it increases the complexity of the task of training a model, as the search space expands significantly. As the size of datasets also grows, traditional approaches based on extensive search start to become prohibitively expensive in terms of computational resources and time, especially in data streaming scenarios. This paper describes an approach based on meta-learning that tackles two main challenges. The first is to predict key performance indicators of machine learning models. The second is to recommend the best algorithm/configuration for training a model for a given machine learning problem. When compared to a state-of-the-art method (AutoML), the proposed approach is up to 130x faster and only 4% worse in terms of average model quality. Hence, it is especially suited for scenarios in which models need to be updated regularly, such as in streaming scenarios with big data, in which some accuracy can be traded for a much shorter model training time.

Abstract
Emerging wearable technology able to monitor electrocardiogram (ECG) continuously for long periods of time without disrupting the patient's daily life represents a great opportunity to improve suboptimal current diagnosis of paroxysmal atrial fibrillation (AF). However, its integration into clinical practice is still limited because the acquired ECG recording is often strongly contaminated by transient noise, thus leading to numerous false alarms of AF and requiring manual interpretation of extensive amounts of ECG data. To improve this situation, automated selection of ECG segments with sufficient quality for precise diagnosis has been widely proposed, and numerous algorithms for such ECG quality assessment can be found. Although most have reported successful performance on ECG signals acquired from healthy subjects, only a recent algorithm based on a well-known pre-trained convolutional neural network (CNN), such as AlexNet, has maintained a similar efficiency in the context of paroxysmal AF. Hence, having in mind the latest major advances in the development of neural networks, the main goal of this work was to compare the most recent pre-trained CNN models in terms of classification performance between high- and low-quality ECG excerpts and computational time. In global values, all reported a similar classification performance, which was significantly superior than the one provided by previous methods based on combining hand-crafted ECG features with conventional machine learning classifiers. Nonetheless, shallow networks (such as AlexNet) trended to detect better high-quality ECG excerpts and deep CNN models to identify better noisy ECG segments. The networks with a moderate depth of about 20 layers presented the best balanced performance on both groups of ECG excerpts. Indeed, GoogLeNet (with a depth of 22 layers) obtained very close values of sensitivity and specificity about 87%. It also maintained a misclassification rate of AF episodes similar to AlexNet and an acceptable computation time, thus constituting the best alternative for quality assessment of wearable, long-term ECG recordings acquired from patients with paroxysmal AF.

Abstract
Distributed Machine Learning, in which data and learning tasks are scattered across a cluster of computers, is one of the answers of the field to the challenges posed by Big Data. Still, in an era in which data abounds, decisions must still be made regarding which specific data to use on the training of the model, either because the amount of available data is simply too large, or because the training time or complexity of the model must be kept low. Typical approaches include, for example, selection based on data freshness. However, old data are not necessarily outdated and might still contain relevant patterns. Likewise, relying only on recent data may significantly decrease data diversity and representativity, and decrease model quality. The goal of this paper is to compare different heuristics for selecting data in a distributed Machine Learning scenario. Specifically, we ascertain whether selecting data based on their characteristics (meta-features), and optimizing for maximum diversity, improves model quality while, eventually, allowing to reduce model complexity. This will allow to develop more informed data selection strategies in distributed settings, in which the criteria are not only the location of the data or the state of each node in the cluster, but also include intrinsic and relevant characteristics of the data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Artificial intelligence and machine learning have been widely applied in several areas with the twofold goal of improving people’s well-being and accelerating computational processes. This may be seen in medical assistance (e.g., automatic verification of MRI images) and in personal assistants that adapt the content to the user based on his/her preferences, to optimize query response times in relational databases and accelerate the information retrieval process. Most of machine learning algorithms used need a dataset to train on, so that the resulting models can be used, for example, to predict a value or enable user-specific results. Considering predictive methods, when new data arrives, a new training of the model may be needed. Speculative computation is a machine learning subfield that seeks to enable computation to be one step ahead of the user by speculating the value that will be received to be computed. A change in the environment may affect the execution, but the adjustments are rapidly performed. This paper intends to provide an overview of the field of speculative computation, describing its main characteristics and advantages, and different scenarios of the medical field in which it is applied. It also provides a critical and comparative analysis with other machine learning methods and a description of how to apply different algorithms to create better systems. © Springer Nature Switzerland AG 2023.