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 However, old data are not necessarily outdated and might still contain relevant patterns.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
Fires in large buildings can have tragic consequences, including the loss of human lives. Despite the advancements in building construction and fire safety technologies, the unpredictable nature of fires, particularly in large buildings, remains an enormous challenge. Acknowledging the paramount importance of prioritising human safety, the academic community has been focusing consistently on enhancing the efficiency of building evacuation. While previous studies have integrated evacuation simulation models, aiding in aspects such as the design of evacuation routes and emergency signalling, modelling human behaviour during a fire emergency remains challenging due to cognitive complexities. Moreover, behavioural differences from country to country add another layer of complexity, hindering the creation of a universal behaviour model. Instead of centring on modelling the occupant behaviour, this paper proposes an innovative approach aimed at enhancing the occupants' behaviour predictability by providing real-time information to the occupants regarding the most suitable evacuation routes. The proposed models use a building's environmental conditions to generate contextual information, aiding in developing solutions to make the occupants' behaviour more predictable by providing them with real-time information on the most appropriate and efficient evacuation routes at each moment, guiding the occupants to safety during a fire emergency. The models were incorporated into a context-aware recommender system for testing purposes. The simulation results indicate that such a system, coupled with hazard and congestion models, positively influences the occupants' behaviour, fostering faster adaptation to the environmental conditions and ultimately enhancing the efficiency of building evacuations.

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Abstract
Power transformers are one of the main elements of a power grid, and their downtime impacts the entire network. Repairing their failures can be very costly, so sophisticated maintenance techniques are necessary. To attempt to solve this problem, we developed a mixed-integer nonlinear optimization model that, focusing on a single power transformer, both schedules this maintenance and also decides how much of the hourly demand it will satisfy. A high level of load on a power transformer increases its temperature, which increases its degradation, and so these two decisions have to be carefully balanced. We also consider that power transformers have several components that degrade differently. Our model becomes very difficult to solve even in reasonably sized instances, so we also present an iterative refinement heuristic.

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.