This paper presents an estimation of Quality of Experience (QoE) metrics based on Quality of Service (QoS) metrics in WiMAX networks. Applications used to generate such estimations were EvalVid and Network Simulator 2 (NS-2). The QoE was estimated by employing a Multilayer Artificial Neural Network by means of the WEKA tool. The results show a very efficient estimation of metrics of QoE parameters with respect to QoS parameters.

The safety-critical systems we are building today employ software, use physical and cybernetic components, and have more interactions (including hierarchical controllers). Systems-Theoretic Process Analysis (STPA) is a hazard analysis method that is used in the concept stage of the safety-critical systems life-cycle. It is claimed that STPA identifies more loss scenarios and safety recommendations when compared to traditional safety analysis methods. However, the STPA analyses are lengthy and complex, and it is common to miss some loss scenarios and safety recommendations. Few STPA-based tools allow systematic and automated analyses. We propose an ontology that represents the STPA analysis domain knowledge and we built an STPA ontology-based tool, called AppSTPA, that aids the analyst providing a more systematic, automated and guided analyses. We evaluated the STPA analyses made with AppSTPA and without the assistance of tools.We also assessed the benefits of AppSTPA when compared to an existing STPA tool. The results show that our ontology-based tool provides pertinent guidance and allows a more complete analysis. We conclude that our STPA ontology-based tool is able to support analysts and provide valuable suggestions, resulting in better safety analyses.

Situation-awareness-based decision-making (SABDM) models constructed using cognitive maps and goal-direct task analysis techniques have been successfully used in decision support systems in safety-critical and mission-critical environments such as air traffic control and electrical energy distribution. Reinforcement learning (RL) and other machine learning techniques are used to automate situational awareness mental model parameter adjustments, reducing the expert work on the initial configuration and long-term maintenance without affecting the mental model’s structure and maintaining the situation-awareness-based decision-making model’s cognitive and explainability characteristics. Real-world models should evolve to cope with changes in the environmental conditions. This study evaluates the application of reinforcement learning as an online adaptive technique to adjust the situational-awareness mental model’s parameters under evolving conditions. We conducted evaluation experiments using real-world public datasets to compare the performance of the SABDM model with that of the reinforcement learning adaptation technique (SABDM/RL) and other adaptive machine learning methods under distinct concept drift evolving conditions. We measured the techniques’ overall and dynamic performance to understand how well they adapt to evolving environmental conditions. The experiments show that SABDM/RL performs similarly to modern online adaptive machine learning classification methods with the support of concept drift detection techniques while maintaining the mental model strength of the situation awareness-based systems.