Abstract
Incidental visualizations are meant to be perceived at-a-glance, on-the-go, and during short exposure times, but are not seen on demand. Instead, they appear in people's fields of view during an ongoing primary task. They differ from glanceable visualizations because the information is not received on demand, and they differ from ambient visualizations because the information is not continuously embedded in the environment. However, current graphical perception guidelines do not consider situations where information is presented at specific moments during brief exposure times without being the user's primary focus. Therefore, we conducted a crowdsourced user study with 99 participants to understand how accurate people's incidental graphical perception is. Each participant was tested on one of the three conditions: position of dots, length of lines, and angle of lines. We varied the number of elements for each combination and the display time. During the study, participants were asked to perform reproduction tasks, where they had to recreate a previously shown stimulus in each. Our results indicate that incidental graphical perception can be accurate when using position, length, and angles. Furthermore, we argue that incidental visualizations should be designed for low exposure times (between 300 and 1000 ms).

Abstract
Palaeoclimate time series, reflecting the state of Earth's climate in the distant past, occasionally display very large and rapid shifts showing abrupt climate variability. The identification and characterisation of these abrupt transitions in palaeoclimate records is of particular interest as this allows for understanding of millennial climate variability and the identification of potential tipping points in the context of current climate change. Methods that are able to characterise these events in an objective and automatic way, in a single time series, or across two proxy records are therefore of particular interest. In our study the matrix profile approach is used to describe Dansgaard-Oeschger (DO) events, abrupt warmings detected in the Greenland ice core, and Northern Hemisphere marine and continental records. The results indicate that canonical events DO-19 and DO-20, occurring at around 72 and 76 ka, are the most similar events over the past 110 000 years. These transitions are characterised by matching transitions corresponding to events DO-1, DO-8, and DO-12. They are abrupt, resulting in a rapid shift to warmer conditions, followed by a gradual return to cold conditions. The joint analysis of the delta 18O and Ca2+ time series indicates that the transition corresponding to the DO-19 event is the most similar event across the two time series.

Abstract
The notable expansion of technologies related to automated processes has been observed in recent years, largely driven by the significant advantages they provide across diverse industries. Concurrently, there has been a rise in simulation technologies aimed at replicating these complex systems. Nevertheless, in order to fully leverage the potential of these technologies, it is crucial to ensure the highest possible resemblance of simulations to real-world scenarios. In brief, this work consists of the development of a data acquisition and processing pipeline allowing a posterior search for the optimal physical parameters in MuJoCo simulator to obtain a more accurate simulation of a dexterous robotic hand. In the end, a Random Search optimization algorithm was used to validate this same pipeline.

Abstract

Abstract
Effective navigation in mobile robotics relies on precise environmental mapping, including the detection of complex objects as geometric primitives. This work introduces a deep learning model that determines the pose, type, and dimensions of 2D primitives using a mobile robot equipped with a noisy LiDAR sensor. Simulated experiments conducted in Webots involved randomly placed primitives, with the robot capturing point clouds which were used to progressively build a map of the environment. Two mapping techniques were considered, a deterministic and probabilistic (Bayesian) mapping, and different levels of noise for the LiDAR were compared. The maps were used as input to a YOLOv5 network that detected the position and type of the primitives. A cropped image of each primitive was then fed to a Convolutional Neural Network (CNN) that determined the dimensions and orientation of a given primitive. Results show that the primitive classification achieved an accuracy of 95% in low noise, dropping to 85% under higher noise conditions, while the prediction of the shapes' dimensions had error rates from 5% to 12%, as the noise increased. The probabilistic mapping approach improved accuracy by 10-15% compared to deterministic methods, showcasing robustness to noise levels up to 0.1. Therefore, these findings highlight the effectiveness of probabilistic mapping in enhancing detection accuracy for mobile robot perception in noisy environments.

Abstract
The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost-benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.