Measuring and predicting the user's Quality of Experience (QoE) of a multimedia stream is the first step towards improving and optimizing the provision of mobile streaming services. This enables us to better understand how Quality of Service (QoS) parameters affect service quality, as it is actually perceived by the end user. Over the last years this goal has been pursued by means of subjective tests and through the analysis of the user's feedback. Existing statistical techniques have lead to poor accuracy (order of 70%) and inability to evolve prediction models with the system's dynamics. In this paper, we propose a novel approach for building accurate and adaptive QoE prediction models using Machine Learning classification algorithms, trained on subjective test data. These models can be used for real-time prediction of QoE and can be efficiently integrated into online learning systems that can adapt the models according to changes in the environment. Providing high accuracy of above 90%, the classification algorithms become an indispensible component of a mobile multimedia QoE management system

The Quality of Experience (QoE) is an irreplaceable metric for evaluating the perceived quality of consumers of multimedia content. Due to the subjectiveness of QoE the most suitable way to measure it is by executing subjective studies. However, executing subjective studies is a complex and expensive process. Careful recreation of the viewing conditions is necessary, and a strict selection of the test subjects is required based on many criteria. This is why solutions are often found in various objective methodologies for measuring the QoE of multimedia. These solutions even though more practical are less accurate and cannot reflect the user expectations. In this work we present a method for building QoE prediction models using machine learning techniques from continuous real-time customer feedback, i.e., during the service execution. This online learning approach builds and adapts prediction models that estimate the QoE based on given Quality of Service metrics from real-time user feedback and does not require apriori execution of subjective studies.

Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applications or exhaustive parameter scans only simple approximations are currently fast enough. We address this lack of fast simulators using neural partial differential equation (PDE) surrogates, data-driven neural network-based surrogate models trained using solutions generated with a classical numerical method. The surrogate approximates a time-stepping operator that evolves the full spatial solution of a reference physics-based model over time. We use DIV1D, a 1D dynamic model of the divertor plasma, as reference model to generate data. DIV1D’s domain covers a 1D heat flux tube from the X-point (upstream) to the target. We simulate a realistic TCV divertor plasma with dynamics induced by upstream density ramps and provide an exploratory outlook towards fast transients. State-of-the-art neural PDE surrogates are evaluated in a common framework and extended for properties of the DIV1D data. We evaluate (1) the speed-accuracy trade-off; (2) recreating non-linear behavior; (3) data efficiency; and (4) parameter inter- and extrapolation. Once trained, neural PDE surrogates can faithfully approximate DIV1D’s divertor plasma dynamics at sub real-time computation speeds: In the proposed configuration, 2 ms of plasma dynamics can be computed in ≈ 0.63 ms of wall-clock time, several orders of magnitude faster than DIV1D.

Deep learning velocity signals allow quantifying turbulence intensity from very short signals and with extremely high accuracy.

Efficient management of multimedia services necessitates the understanding of how the quality of these services is perceived by the users. Estimation of the perceived quality or Quality of Experience (QoE) of the service is a challenging process due to the subjective nature of QoE. This process usually incorporates complex subjective studies that need to recreate the viewing conditions of the service in a controlled environment. In this paper we present Machine Learning techniques for modeling the dependencies of different network and application layer quality of service parameters to the QoE of network services using subjective quality feedback. These accurate QoE prediction models allow us to further develop a geometrical method for calculating the possible remedies per network stream for reaching the desired level of QoE. Finally we present a set of possible network techniques that can deliver the desired improvement to the multimedia streams.