Davide Buoso

Autonomous drone technology increasingly enables their use in diverse applications, offering cost and time benefits in precision agriculture and surveillance. They are especially efficient in search and rescue and exploring hard-to-access areas. Navigating indoor settings and partially known environments poses significant challenges in autonomous robotics. This paper introduces a novel method that leverages depth image data to substantially improve performance in these contexts. We elucidate the method’s design, showcasing its dependability and advantages over conventional approaches. Furthermore, the paper delineates the critical procedures for effective autonomous robot guidance, tackling complex obstacles inherent to the field.

Legal Named Entity Recognition is a focal point in NLP systems in legal domain, due to its potential to streamline processes and enhance decision-making accuracy. This paper delves into the SpanMarker technique for span-level representation of entity, the LUKE model for enhanced entity recognition and LoRA for efficient fine-tuning of large models. The study evaluates these methodologies, individually and in synergy, to improve the accuracy and performance of legal NLP systems. Additionally, a new dataset (EDGAR-NER) has been explored. In-depth experimentation reveals the potential of these approaches. This research contributes to ongoing efforts in leveraging NLP to enhance legal text process, reducing the time needed to train models to achieve this goal.

This project explores the application of Federated Learning (FL) to the task of Semantic Segmentation (SS), with a focus on preserving client privacy while utilizing their data for model training. The proposed approach involves a centralized server pre-training phase on a labeled dataset, incorporating a style-transfer technique for domain adaptation. In the federated decentralized setting, our approach tackles the challenge of absent labels on client data. By leveraging pseudo-labels and self-training, the approach enables the utilization of unlabeled client images, effectively addressing the issue of limited ground truth availability. The report also provides additional insights into extending the applicability of this approach to domains beyond self-driving cars, such as satellite imagery. Additionally, an intriguing possibility explored in this project is the integration of a transformer model into the existing framework, presenting a promising alternative to the commonly employed CNN architectures.