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The ability to present effectively is essential for professionals; therefore, oral communication courses have become part of the curricula for higher education studies. However, speaking in public is still a challenge for many graduates. To tackle this problem, driven by the recent advances in computer vision techniques and prosody analysis, multimodal tools have been designed to support the development of public speaking skills. One of these tools is the "Presentation Trainer," a research prototype able to provide learners with real-time feedback on a set of nonverbal communication aspects. Despite initial positive evaluations, the application still lacks grounding in a valid assessment model for nonverbal communication aspects in the context of presentations. To come up with such a model, we conducted semi-structured interviews with experts in the public speaking domain. Furthermore, the objective of these interviews was also to have a formative evaluation of the "Presentation Trainer," analysing how it suits with common practices for teaching and learning public speaking skills. The results of this study identify 131 nonverbal communication practices that affect the quality of a presentation and summarize experts' points of view regarding multimodal public speaker instructors.

Ensuring medical support of patients of advanced age in rural areas is a major challenge. Moreover, the number of registered doctors—medical specialists in particular—will decrease in such areas over the next years. These unmet medical needs in combination with communication deficiencies among different types of health-care professionals pose threats to the quality of patient treatment. This work presents a novel solution combining telemedicine, telecooperation, and IHE profiles to tackle these challenges. We present a telecooperation platform that supports longitudinal electronic patient records and allows for intersectoral cooperation based on shared electronic medication charts and other documents. Furthermore, the conceived platform allows for an integration into the planned German telematics infrastructure.

There are many manufacturers on the market offering various types of Unmanned Aerial Vehicles (UAV). The multitude of drones available on the market means that the choice of a UAV for a specific application appears to be a decision problem to be solved. The aim of this article is a comparative analysis of drones used in photogrammetric surveys. The criteria for evaluating the UAVs were: availability and product support, payload (min. 5 kg), price (PLN 100,000), as well as space available for measurement modules. These are the requirements that must be met for the implementation of the INNOBAT project, the aim of which is to develop an integrated system using autonomous unmanned aerial and surface vehicles, intended for bathymetric monitoring in the coastal zone. The comparative analysis of drones was based on 27 companies producing UAV. Based on the analysis, 6 drones that met the project requirements were selected. They were: Aurelia X6 Pro, Aurelia X8 Standard LE, DroneHexa AG, FOX-C8 XT, Hercules 10 and Zoe X4. Selected UAVs differ from each other, among others, in the number of rotors, flight duration and resistance to weather conditions. Individual characteristics of drones may have a different rank depending on their application, therefore the selection of UAVs should be made after prioritisation criteria of a given project. [ABSTRACT FROM AUTHOR]

This article explores the use of Light Detection And Ranging (LiDAR) derived point clouds to extract the shoreline of the Lake Kłodno (Poland), based on their geometry properties. The data collection was performed using the Velodyne VLP-16 laser scanner, which was mounted on the HydroDron Unmanned Surface Vehicle (USV). A modified version of the shoreline extraction method proposed by Xu et al. was employed, comprising of the following steps: (1) classifying the point cloud using the Euclidean cluster extraction with a tolerance parameter of 1 m and min. cluster size of 10,000 points, (2) further filtration of boundary points by removing those with height above 1 m from the measured elevation of water surface, (3) manual determination of a curve consisting of 5 points located along the entire shoreline extraction region at a relatively constant distant from the coast, (4) removal of points that are further from the curve than the average distance, repeated twice. The method was tested on the scanned section of the lake shoreline for which Ground Control Points (GCP) were measured using a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) receiver. Then, the results were compared to the ground truth data, obtaining an average position error of 2.12 m with a standard deviation of 1.11 m. The max error was 5.54 m, while the min. error was 0.41 m, all calculated on 281 extracted shoreline points. Despite the limitations of this parametric, semi-supervised approach, those preliminary results demonstrate the potential for accurate shoreline extraction based on LiDAR data obtained using an USV. Further testing and optimisation of this method for larger scale and better generalisation for different waterbodies are necessary to fully assess its effectiveness and feasibility. In this context, it is essential to develop computationally efficient methods for approximating shorelines that can accurately determine their course based on a set of points. [ABSTRACT FROM AUTHOR]