Effective energy management in households is critical to achieving overall energy efficiency and sustainability goals. This study introduces a novel approach to predicting short-term energy consumption for households using federated learning (FL) models. The approach achieves short-term energy consumption predictions (i.e. for the next 10 minutes) by analyzing local data, such as the current watt consumption and activated devices. The key innovation in this approach is the use of privacy-preserving machine learning techniques, ensuring that personal data is never shared during the training process. The models are used to predict potential spikes in energy demand, allowing for proactive management. In addition, a local Large Language Model (LLM) is integrated to generate personalized recommendations for users, aimed at avoiding predicted consumption spikes and promoting energy-efficient behavior. This approach not only preserves user privacy but also enhances user engagement by providing actionable insights based on local consumption patterns.

The rise of financial crime that has been observed in recent years has created an increasing concern around the topic and many people, organizations and governments are more and more frequently trying to combat it. Despite the increase of interest in this area, there is a lack of specialized datasets that can be used to train and evaluate works that try to tackle those problems. This article proposes a new micro-benchmark dataset for algorithms and models that identify individuals and organizations, and their multiple writings, in news articles, and presents an approach that assists in its creation. Experimental efforts are also reported, using this dataset, to identify individuals and organizations in financial-crime-related articles using various low-billion parameter Large Language Models (LLMs). For these experiments, standard metrics (Accuracy, Precision, Recall, F1 Score) are reported and various prompt variants comprising the best practices of prompt engineering are tested. In addition, to address the problem of ambiguous entity mentions, a simple, yet effective LLM-based disambiguation method is proposed, ensuring that the evaluation aligns with reality. Finally, the proposed approach is compared against a widely used stateof-the-art open-source baseline, showing the superiority of the proposed method.

The TEACHING project enhances AI applications in pervasive environments via Humanistic Intelligence, fostering synergy between humans and Cyber-Physical Systems of Systems (CPSoS). Here, we present the TEACHING Platform, a microservice-based framework providing the technological advancements to represent humans and CPSoS as containerized software models that interact to mutually empower each other.

The comprehension of the underwater environment is recently being accelerated by technological advances in sensors, robotics and Artificial Intelligence (AI). At the forefront of this evolution, lies the Autonomous Underwater Vehicle (AUV), a sophisticated ocean exploration tool that is capable of performing underwater mapping, leveraging data obtained by onboard sensors. AUVs can navigate autonomously in unknown environments without any human interaction, while their level of autonomy is tightly linked to their path planning strategy. In this study, we perform a comparative analysis of a Deep Reinforcement Learning (DRL) method utilising two neural network models, a Linear Model (LM) that consists only of linear layers, and a Convolutional Model (CM) that consists of convolution layers for feature extraction that are merged with linear layers. Our evaluation focuses on assessing the performance of the proposed models for generating optimal paths in 3D underwater environments based on path length and obstacle avoidance. Through comprehensive simulations, we showcase the efficiency of our solution and present a comprehensive framework tailored for solving path planning problems in 3D complex underwater settings.

The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable to increase user trust and improve the acceptance of recommendations. In this study, we focus on a context-aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior.