Abstract
Trajectory clustering is one of the most important issues in mobility patterns data mining. It is applied in several cases such as hot-spots detection, urban transportation control, animal migration movements, and tourist visiting routes among others. In this paper, we describe how to identify the most frequent trajectories from raw GPS data. By making use of the Ramer-Douglas-Peucker (RDP) mechanism we simplify the trajectories in order to obtain fewer points to check without losing information. We construct a similarity matrix by using the Fréchet distance metric and then employ density-based clustering to find the most similar trajectories. We perform experiments over three real-world datasets collected in the city of Porto, Portugal, and in Beijing China, and check the results of the most frequent trajectories for the top-k origins x destinations for the moves. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Remote collaborative work has become pervasive in many settings, ranging from engineering to medical professions. Users are immersed in virtual environments and communicate through life-sized avatars that enable face-to-face collaboration. Within this context, users often collaboratively view and interact with virtual 3D models, for example to assist in the design of new devices such as customized prosthetics, vehicles or buildings. Discussing such shared 3D content face-to-face, however, has a variety of challenges such as ambiguities, occlusions, and different viewpoints that all decrease mutual awareness, which in turn leads to decreased task performance and increased errors. To address this challenge, we introduce MAGIC, a novel approach for understanding pointing gestures in a face-to-face shared 3D space, improving mutual understanding and awareness. Our approach distorts the remote user's gestures to correctly reflect them in the local user's reference space when face-to-face. To measure what two users perceive in common when using pointing gestures in a shared 3D space, we introduce a novel metric called pointing agreement. Results from a user study suggest that MAGIC significantly improves pointing agreement in face-toface collaboration settings, improving co-presence and awareness of interactions performed in the shared space. We believe that MAGIC improves remote collaboration by enabling simpler communication mechanisms and better mutual awareness.

Abstract
Traditional historical data analytics is at risk in a world where volatility, uncertainty, complexity, and ambiguity are the new normal. While Streaming Machine Learning (SML) and Time-series Analytics (TSA) attack some aspects of the problem, we still need a comprehensive solution. SML trains models using fewer data and in a continuous/adaptive way relaxing the assumption that data points are identically distributed. TSA considers temporal dependence among data points, but it assumes identical distribution. Every Data Scientist fights this battle with ad-hoc solutions. In this paper, we claim that, due to the temporal dependence on the data, the existing solutions do not represent robust solutions to efficiently and automatically keep models relevant even when changes occur, and real-time processing is a must. We propose a novel and solid scientific foundation for Time-Evolving Analytics from this perspective. Such a framework aims to develop the logical, methodological, and algorithmic foundations for fast, scalable, and resilient analytics.

Abstract

Abstract
In the last decades most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral datasets, and local surveys. The first approach is only updated every 10 years and does not provide building locations, the second type of data is only available for restricted urban centers, and the third approach requires surveyors with an engineering background, which is cost-prohibitive for large-scale risk studies. It is thus clear that methods to characterize the built environment for large-scale risk analysis at the asset level are currently missing, which hampers the assessment of the impact of natural hazards for the purposes of risk management. Some recent efforts have demonstrated how deep learning algorithms can be trained to recognize specific architectural and structural features of buildings, which is needed for earthquake risk analysis. In this paper we describe how convolutional neural networks can be combined with data from OpenStreetMap and Google Street View to help develop exposure models for multi-hazard risk analysis. This project produced an original comprehensively annotated (15 characteristics) dataset of approximately 5000 images of buildings from the parish of Alvalade (Lisbon, Portugal). The dataset was used to train and test different deep learning networks for building exposure models. The best results were obtained with ResNet50V2, InceptionV3 and DenseNet201, all with accuracies above 82%. These results will support future developments for assessing exposure models for seismic risk analysis. The novelty of our work consists in the number of characteristics of the images in the dataset, the number of deep learning models trained and the number of classes that can be used for building exposure models.

Abstract
Low-code tools are a trend in software development for business solutions due to their agility and ease of use. There are a certain number of vendors with such solutions. Still, in most Western countries, there is a clear need for the existence of greater quantities of certified and experienced professionals to work with those tools. This means that companies with more resources can attract and maintain those professionals, whilst other smaller organizations must rely on an endless search for this scarce resource. We will present and validate a model designed to transform ChatGPT into a low-code developer, addressing the demand for a more skilled human resource solution. This innovative tool underwent rigorous validation via a focus group study, engaging a panel of highly experienced experts. Their invaluable insights and feedback on the proposed model were systematically gathered and meticulously analysed.