Abstract
Semantic measure (SM) algorithms allow software to mimic the human ability of assessing the strength of the semantic relations between elements such as concepts, entities, words, or sentences. SM algorithms are typically evaluated by comparison against gold standard datasets built by human annotators. These datasets are composed of pairs of elements and an averaged numeric rating. Building such datasets usually requires asking human annotators to assign a numeric value to their perception of the strength of the semantic relation between two elements. Large language models (LLMs) have recently been successfully used to perform tasks which previously required human intervention, such as text summarization, essay writing, image description, image synthesis, question answering, and so on. In this paper, we present ongoing research on LLMs capabilities for semantic relations assessment. We queried several LLMs to rate the relationship of pairs of elements from existing semantic measures evaluation datasets, and measured the correlation between the results from the LLMs and gold standard datasets. Furthermore, we performed additional experiments to evaluate which other factors can influence LLMs performance in this task. We present and discuss the results obtained so far.

Abstract
Emerging from a rich heritage, the shoe manufacturing industry stands as one of the world's most enduring and tradition-bound sectors. While renowned for their high-quality craftsmanship, countries like Portugal and Italy share the spotlight with those who focus on mass production methods. Regardless of their manufacturing model, both must adapt to the evolving competitive landscape by embracing innovative manufacturing techniques. Robotics has emerged as a transformative force within the shoe industry, offering a path towards enhanced working conditions for employees while simultaneously reducing reliance on manual labor and bolstering productivity. The main focus of this paper is the comprehensive literature review, which examines the advancements made by researchers in various stages of shoe production, including roughing, gluing, finishing, and lasting. This article sheds light on the industry's response to modernization and efficiency imperatives, providing a thorough understanding of robotics in shoe manufacturing automation. A case study on the real implementation and simulation of a robotic cell for sole roughing is also presented. The results revealed that the robotic cell maintains the production cadence.

Abstract

Abstract

Abstract
This study estimates alighting stops and transfers from entry-only Automatic Fare Collection (AFC) data. The methodology adopted includes two main steps: an implementation of the Trip Chaining Method (TCM) to estimate the alighting stops from AFC records and the subsequent application of criteria for the identification of transfers. For each pair of consecutive AFC records on the same smart card, a transfer is identified considering a threshold for the walking distance, a threshold for the time required to perform an activity, and the validation of different boarding routes. This methodology was applied to the case study of Porto, Portugal, considering all trips performed by a set of 19999 smart cards over one year. The results of this methodology allied with visualization techniques allowed to study Origin-Destination (OD) patterns by type of day, seasonally, and by user frequency, each analyzed at the stop level and at the geographic area level.

Abstract
In this paper, a solution to monitor the location of humans during their activity in the agriculture sector with the aim to boost productivity and efficiency is provided. Our solution is based on map-matching methods, that are used to track the path spanned by a worker along a specific activity in an agriculture culture. Two different cultures are taken into consideration in this study olives and vines. We leverage the symmetry of the geometry of these cultures into our solution and divide the problem three-fold initially, we estimate a path of a worker along the fields, then we apply the map-matching to such path and finally, a post-processing method is applied to ensure local continuity of the sequence obtained from map-matching. The proposed methods are experimentally evaluated using synthetic and real data in the region of Mirandela, Portugal. Evaluation metrics show that results for synthetic data are robust under several sampling periods, while for real-world data, results for the vine culture are on par with synthetic, and for the olive culture performance is reduced.