Abstract
Blob features are particularly common in acoustic imagery, as isolated objects (e.g., moorings, mines, rocks) appear as blobs in the acquired images. This work focuses the application of the SIFT, SURF, KAZE and U-SURF feature extraction algorithms for blob feature tracking towards Simultaneous Localization and Mapping applications. We introduce a modified feature extraction and matching pipeline intended to improve feature detection and matching precision, tackling performance deterioration caused by the differences between optical and acoustic imagery. Experimental evaluation was undertaken resorting to datasets collected from a water tank structure.

Abstract

Abstract
Autonomous UAVs offer advantages in industrial, agriculture, environment inspection, and logistics applications. Sometimes the use of cooperative UAVs is important to solve specific demands or achieve productivity gain in these applications. An important technical challenge is the precise positioning between two or more UAVs in a cooperative task flight. Some techniques provide solutions, like the GNSS positioning, visual and LIDAR slam, and computer vision intelligent algorithms, but all these techniques present limitations that must be solved to work properly in specific environments. The proposal of new cooperative position methods is important to face these challenges. The present work proposes an evaluation of a visual relative positioning architecture between two small UAV multi-rotor aircraft working in a master-slave operation, based on an Augmented Reality tag tool. The simulation results obtained absolute error measurements lower than 0.2 cm mean and 0.01 standard deviation for X, Y and Z directions. Yaw measurements presented an absolute error lower than 0.5 degrees C with a 0.02-5 degrees C standard deviation. The real-world experiments executing autonomous flight with the slave UAV commanded by the master UAV achieved success in 8 of 10 experiment rounds, proving that the proposed architecture is a good approach to building cooperative master-slave UAV applications.

Abstract

Abstract
Seven years after the seminal paper on FAIR was published, that introduced the concept of making research outputs Findable, Accessible, Interoperable, and Reusable, researchers still struggle to understand how to implement the principles. For many researchers, FAIR promises long-term benefits for near-term effort, requires skills not yet acquired, and is one more thing in a long list of unfunded mandates and onerous requirements for scientists. Even for those required to, or who are convinced that they must make time for FAIR research practices, their preference is for just-in-time advice properly sized to the scientific artifacts and process. Because of the generality of most FAIR implementation guidance, it is difficult for a researcher to adjust to the advice according to their situation. Technological advances, especially in the area of artificial intelligence (AI) and machine learning (ML), complicate FAIR adoption, as researchers and data stewards ponder how to make software, workflows, and models FAIR and reproducible. The FAIR+ Implementation Survey Tool (FAIRIST) mitigates the problem by integrating research requirements with research proposals in a systematic way. FAIRIST factors in new scholarly outputs, such as nanopublications and notebooks, and the various research artifacts related to AI research (data, models, workflows, and benchmarks). Researchers step through a self-serve survey process and receive a table ready for use in their data management plan (DMP) and/or work plan. while gaining awareness of the FAIR Principles and Open Science concepts. FAIRIST is a model that uses part of the proposal process as a way to do outreach, raise awareness of FAIR dimensions and considerations, while providing timely assistance for competitive proposals.

Abstract
In order to address one of the most challenging problems in hospital management - patients' absenteeism without prior notice - this study analyses the risk factors associated with this event. To this end, through real data from a hospital located in the North of Portugal, a prediction model previously validated in the literature is used to infer absenteeism risk factors, and an explainable model is proposed, based on a modified CART algorithm. The latter intends to generate a human-interpretable explanation for patient absenteeism, and its implementation is described in detail. Furthermore, given the significant impact, the COVID-19 pandemic had on hospital management, a comparison between patients' profiles upon absenteeism before and during the COVID-19 pandemic situation is performed. Results obtained differ between hospital specialities and time periods meaning that patient profiles on absenteeism change during pandemic periods and within specialities.