Abstract
Emergency department crowding has been steadily increasing, with a significant part due to non-emergent pathologies. We developed a self-service kiosk to be used by patients while waiting from triage to treatment room allocation, which collects clinical history, usual medication, main complaint and, also collects vital signs. This information is processed and presented in a comprehensive way to the medical staff in order to accelerate diagnostics and treatment selection. This work describes and analyzes the results of the usability evaluation of this kiosk, taking into account the average time per screen, the average time of a complete kiosk session, the application design and the user interaction with devices and the system. The kiosk was tested in several environments with different types of users, allowing the identification of causes of problems and difficulties experienced, as well as solutions to improve the solution.

Abstract
Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more about the tourist is known, better recommendations can be made. The inclusion of psychological aspects to generate recommendations, such as personality, is a growing trend in RS and they are being studied to provide more personalized approaches. However, although many studies on the psychology of tourism exist, studies on the prediction of tourist preferences based on their personality are limited. Therefore, we undertook a large-scale study in order to determine how the Big Five personality dimensions influence tourists' preferences for tourist attractions, gathering data from an online questionnaire, sent to Portuguese individuals from the academic sector and their respective relatives/friends (n=508). Using Exploratory and Confirmatory Factor Analysis, we extracted 11 main categories of tourist attractions and analyzed which personality dimensions were predictors (or not) of preferences for those tourist attractions. As a result, we propose the first model that relates the five personality dimensions with preferences for tourist attractions, which intends to offer a base for researchers of RS for tourism to automatically model tourist preferences based on their personality. © 2020 ACM.

Abstract
Abstract Background: Analysis of locomotion is often used as a measure for impairment and recovery following experimental peripheral nerve injury. Compared to rodents, sheep offer several attractive features as an experimental model for studying peripheral nerve regeneration. There are no studies on locomotion outcomes after peripheral nerve injury and repair in the sheep model. In the present study, we performed and compared two-dimensional (2D) and, for the first time, three-dimensional (3D) hindlimb kinematics during obstacle avoidance in the ovine model. This study aimed to obtain kinematic data to serve as a template for an objective assessment of the ankle joint motion in future studies of common peroneal nerve (CP) injury and repair in the ovine model. Results: The strategy used by the sheep to bring the hindlimb over a moderately high obstacle, set to 10% of its hindlimb length, was the pronounced knee, ankle and metatarsophalangeal flexion when approaching and clearing the obstacle. Despite the overall time course kinematic patterns about the hip, knee, ankle, and metatarsophalangeal were identical, we found significant differences between values of the 2D and 3D joint angular motion. Conclusions: Our results show that the most apparent changes that occurred during the gait cycle were for the ankle and metatarsophalangeal joints, whereas the hip and knee joints were much less affected. Data and techniques described here are likely to be useful for an objective assessment of altered gait after CP injury and repair in an ovine model.

Abstract
In a platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of the platoon's driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this article, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multi-hop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests. We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.

Abstract
The importance of early detection of diseases with high-mortality is crucial to save lives. Deep Learning algorithms are recurrently used by many researchers that aim to model the progression and treatment of these conditions. There is growing evidence that the complexity of a Deep Learning model is correlated to its performance: the deeper the network, the more accurate it is. However, as the topology deepens, training gets more demanding: (1) increased need of data, (2) increased computational costs, and (3) increased time for evaluation, fine-tuning, and subsequent feedback-based activities inherent to Data Science, with direct impact on the exploration towards finding the best model, due to an inherent trial-and-error approach. We hypothesize that there exist (domain-specific) architectural patterns that, if applied during the model exploration phase, allow an overall improvement of the training performance. Should it be true, it would significantly reduce the exploration phase length, contributing to both Medicine and Computer Science fields.

Abstract
Determining subgraph frequencies is at the core of several graph mining methodologies such as discovering network motifs or computing graphlet degree distributions. Current state-of-the-art algorithms for this task either take advantage of common patterns emerging on the networks or target a set of specific subgraphs for which analytical calculations are feasible. Here, we propose a novel network generic framework revolving around a new data-structure, a Condensed Graph, that combines both the aforementioned approaches, but generalized to support any subgraph topology and size. Furthermore, our methodology can use as a baseline any enumeration based census algorithm, speeding up its computation. We target simple topologies that allow us to skip several redundant and heavy computational steps using combinatorics. We were are able to achieve substantial improvements, with evidence of exponential speedup for our best cases, where these patterns represent up to 97% of the network, from a broad set of real and synthetic networks.