Bayesian Methods in Tensor Analysis

Tensors, also known as multidimensional arrays, are useful data structures in machine learning and statistics. In recent years, Bayesian methods have emerged as a popular direction for analyzing tensor-valued data since they provide a convenient way to introduce sparsity into the model and conduct uncertainty quantification. In this article, we provide an overview of frequentist and Bayesian methods for solving tensor completion and regression problems, with a focus on Bayesian methods. We review common Bayesian tensor approaches including model formulation, prior assignment, posterior computation, and theoretical properties. We also discuss potential future directions in this field.Comment: 32 pages, 8 figures, 2 table

A QoE adaptive management system for high definition video streaming over wireless networks

[EN] The development of the smart devices had led to demanding high-quality streaming videos over wireless communications. In Multimedia technology, the Ultra-High Definition (UHD) video quality has an important role due to the smart devices that are capable of capturing and processing high-quality video content. Since delivery of the high-quality video stream over the wireless networks adds challenges to the end-users, the network behaviors 'factors such as delay of arriving packets, delay variation between packets, and packet loss, are impacted on the Quality of Experience (QoE). Moreover, the characteristics of the video and the devices are other impacts, which influenced by the QoE. In this research work, the influence of the involved parameters is studied based on characteristics of the video, wireless channel capacity, and receivers' aspects, which collapse the QoE. Then, the impact of the aforementioned parameters on both subjective and objective QoE is studied. A smart algorithm for video stream services is proposed to optimize assessing and managing the QoE of clients (end-users). The proposed algorithm includes two approaches: first, using the machine-learning model to predict QoE. Second, according to the QoE prediction, the algorithm manages the video quality of the end-users by offering better video quality. As a result, the proposed algorithm which based on the least absolute shrinkage and selection operator (LASSO) regression is outperformed previously proposed methods for predicting and managing QoE of streaming video over wireless networks.This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" with in the Project under Grant TIN2017-84802-C2-1-P. This study has been partially done in the computer science departments at the (University of Sulaimani and Halabja).Taha, M.; Canovas, A.; Lloret, J.; Ali, A. (2021). A QoE adaptive management system for high definition video streaming over wireless networks. Telecommunication Systems. 77(1):63-81. https://doi.org/10.1007/s11235-020-00741-2638177

Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

Understanding music-selection behavior via statistical learning: Using the percentile-Lasso to identify the most important factors

Music psychological research has either focused on individual differences of music listening behavior or investigated situational influences. The present study addresses the question of how much of people's listening behavior in daily life is due to individual differences and how much is attributable to situational effects. We aimed to identify the most important factors of both levels (i.e., person-related and situational) driving people's music selection behavior. Five hundred eighty-seven participants reported three self-selected typical music listening situations. For each situation, they answered questions on situational characteristics, functions of music listening, and characteristics of the music selected in the specific situation (e.g., fast - slow, simple - complex). Participants also reported on several person-related variables (e.g., musical taste, Big Five personality dimensions). Due to the large number of variables measured, we implemented a statistical learning method, percentile-Lasso, for variable selection, which prevents overfitting and optimizes models for the prediction of unseen data. Most of the variance in music selection behavior was attributable to differences between situations, while individual differences accounted for much less variance. Situation-specific functions of music listening most consistently explained which kind of music people selected, followed by the degree of attention paid to the music. Individual differences in musical taste most consistently accounted for person-related differences in music selection behavior, whereas the influence of Big Five personality was very weak. These results show a detailed pattern of factors influencing the selection of music with specific characteristics. They clearly emphasize the importance of situational effects on music listening behavior and suggest shifts in widely-used experimental designs in laboratory-based research on music listening behavior

Experimenting with sequential allocation procedures

In experiments that consider the use of subjects, a crucial part is deciding which treatment to allocate to which subject – in other words, constructing the treatment allocation procedure. In a classical experiment, this treatment allocation procedure often simply constitutes randomly assigning subjects to a number of different treatments. Subsequently, when all outcomes have been observed, the resulting data is used to conduct an analysis that is specified a priori. Practically, however, the subjects often arrive at an experiment one-by-one. This allows the data generating process to be viewed differently: instead of considering the subjects in a batch, intermediate data from previous interactions with other subjects can be used to influence the decisions of the treatment allocation in future interactions. A heavily researched formalization that helps developing strategies for sequentially allocating subjects is the multi-armed bandit problem. In this thesis, several methods are developed to expedite the use of sequential allocation procedures by (social) scientists in field experiments. This is done by building upon the extensive literature of the multi-armed bandit problem. The thesis also introduces and shows many (empirical) examples of the usefulness and applicability of sequential allocation procedures in practice