Abstract
The optimal Coalition Structure Generation (CSG) problem for a given set of agents finds a partition of the agent set that maximises social welfare. The CSG problem is an NP-hard optimisation problem, where the search space grows exponentially. The exact and approximation algorithms focus on finding an optimal solution or a solution within a known bound from the optimum. However, as the number of agents increases linearly, the search space increases exponentially and a practical option here is to use heuristic algorithms. Heuristic algorithms are suitable for solving the optimisation problems because of their less computational complexity. TACOS is a heuristic method for the CSG problem that finds high-quality solutions quickly using a neighbourhood search performed with a memory. However, some of the neighbourhood searches by TACOS can be performed simultaneously. Therefore, this paper proposes a parallel version of the TACOS algorithm (P-TACOS) for the CSG problem, intending to find a better solution than TACOS. We evaluated P-TACOS using eight (8) benchmark data distributions. Results show that P-TACOS achieves better results for all eight (8) data distributions. P-TACOS achieves the highest gain, 74.23%, for the Chisquare distribution and the lowest gain, 0.01%, for the Normal distribution. We also examine how often P-TACOS generates better results than TACOS. In the best case, it generates better results for 92.30% of the time (for the Rayleigh and Agent-based Normal distributions), and in the worst case, 38.46% of the time (for the Weibull distribution).

Abstract
Identifying stress in people is not a trivial or straightforward task, as several factors are involved in detecting the presence or absence of stress. The problem of detect stress has attracted much attention in the last decade and is mainly addressed with physiological signals and in a controlled ambience with specific tasks. However, the widespread use of video cameras permitted the creation of a new non-invasive data collection techniques. The goal of this work is to provide an alternative way to detect stress in the workplace without the need of specific laboratory conditions. For that, a stress detection model based on images analysed with deep learning neural networks was developed. The trained model achieved a F1 = 79.9% on a binary dataset, of stress/non-stress, with an imbalanced ratio of 0.49. This model can be used in a non-invasive application to detect stress and provide recommendations to the collaborators in the workplace in order to help them to control their stress condition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Stress is a common feeling in people's day-to-day life, especially at work, being the cause of several health problems and absenteeism. Despite the difficulty in identifying it properly, several studies have established a correlation between stress and perceivable human features. The problem of detecting stress has attracted significant attention in the last decade. It has been mainly addressed through the analysis of physiological signals in the execution of specific tasks in controlled environments. Taking advantage of technological advances that allow to collect stress-related data in a non-invasive way, the goal of this work is to provide an alternative approach to detect stress in the workplace without requiring specific controlled conditions. To this end, a video-based plethysmography application that analyses the person's face and retrieves several physiological signals in a non-invasive way was used. Moreover, in an initial phase, additional information that complements and labels the physiological data was obtained through a brief questionnaire answered by the participants. The data collection pilot took place over a period of two months, having involved 28 volunteers. Several stress detection models were developed; the best trained model achieved an accuracy of 86.8% and a F1 score of 87% on a binary stress/non-stress prediction.

Abstract

Abstract

Abstract
Content generation is a heavy task in virtual worlds design. Procedural content generation techniques aim to agile this process by automating the 3D modelling with some degree of parametrisation. The novelty of this work is the procedural generation of the marine alga (Asparagopsis armata), taking into consideration the underwater environmental factors. The depth and the occlusion were the two parameters in this study to simulate how the alga growth is influenced by the environment where the alga grows. Starting by building a prototype to explore different L-systems categories to model the alga, the stochastic L-systems with parametric features were selected to generate different alga plasticities. Qualitative methods were used to evaluate the designed grammar and alga's animation results by comparing videos and images of the Asparagopsis armata with the computer-generated versions. © 2022 University of West Bohemia. All rights reserved.