120 research outputs found
QoE Modelling, Measurement and Prediction: A Review
In mobile computing systems, users can access network services anywhere and
anytime using mobile devices such as tablets and smart phones. These devices
connect to the Internet via network or telecommunications operators. Users
usually have some expectations about the services provided to them by different
operators. Users' expectations along with additional factors such as cognitive
and behavioural states, cost, and network quality of service (QoS) may
determine their quality of experience (QoE). If users are not satisfied with
their QoE, they may switch to different providers or may stop using a
particular application or service. Thus, QoE measurement and prediction
techniques may benefit users in availing personalized services from service
providers. On the other hand, it can help service providers to achieve lower
user-operator switchover. This paper presents a review of the state-the-art
research in the area of QoE modelling, measurement and prediction. In
particular, we investigate and discuss the strengths and shortcomings of
existing techniques. Finally, we present future research directions for
developing novel QoE measurement and prediction technique
corrected soft photon theorem from a CFT Ward identity
Classical soft theorems applied to probe scattering processes on AdS
spacetimes predict the existence of corrections to the soft photon and
soft graviton factors of asymptotically flat spacetimes. In this paper, we
establish that the corrected soft photon theorem can be derived from a
large CFT Ward identity. We derive a perturbed soft photon mode
operator on a flat spacetime patch in global AdS in terms of an integrated
expression of the boundary CFT current. Using the same in the CFT Ward
identity, we recover the corrected soft photon theorem derived from
classical soft theorems.Comment: 32 pages, 1 figur
Toward distributed, global, deep learning using IoT devices
Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed training platform/system. We present a novel approach where to train one DL model on the hardware of thousands of mid-sized IoT devices across the world, rather than the use of GPU cluster available within a data center. We analyze the scalability and model convergence of the subsequently generated model, identify three bottlenecks that are: high computational operations, time consuming dataset loading I/O, and the slow exchange of model gradients. To highlight research challenges for globally distributed DL training and classification, we consider a case study from the video data processing domain. A need for a two-step deep compression method, which increases the training speed and scalability of DL training processing, is also outlined. Our initial experimental validation shows that the proposed method is able to improve the tolerance of the distributed training process to varying internet bandwidth, latency, and Quality of Service metrics
Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries
Abstract
Background
Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres.
Methods
This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries.
Results
In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia.
Conclusion
This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries
Quality of experience measurement, prediction and provisioning in heterogeneous access networks
In mobile computing systems, users can access network services anywhere and anytime using mobile devices such as tablets and smart phones. Users usually have some expectations about the services provided to them by different service providers, for example, telecommunication operators and network providers. Users' expectations along with additional factors such as cognitive and behavioural states, cost, and network quality of service (QoS) may determine their quality of experience (QoE). If users are not satisfied with their QoE, they may switch to different providers or may stop using a particular application or service. QoE measurement and prediction can benefit users to avail personalized services from service providers. On the other hand, it can help service providers to achieve lower user-operator switchover. Users with mobile devices can roam in heterogeneous access networks (HANs). A mobile device may go through handoffs while roaming in HANs i.e., it may switch from one access point (AP) to another. These APs within a network can belong to different network technologies, for example, WLAN or 4G. Handoffs may cause severe QoE degradation due to increased delay and packet losses. Thus, there is a need to facilitate QoE-aware handoffs for users roaming in HAN. The mobile devices can learn from the prior network conditions and users' QoE to make timely and proactive QoE-aware handoffs. In this thesis, we propose, develop and validate a novel method, CaQoEM-Contextaware Quality of Experience, Modelling, Measurement and Prediction. CaQoEM is based on Bayesian networks and utility theory. It provides a straightforward and efficient way of dealing with a plethora of parameters to model, measure and predict users' QoE under uncertainty on a single scale. We validate CaQoEM using a number of case studies, user tests and simulations performed in OPNET. Our results validate that CaQoEM can efficiently model, measure and predict users' QoE. It achieves an average QoE prediction accuracy of 98.93% in stochastic wireless network conditions such as wireless signal fading, handoffs and wireless network congestion. We further extend our CaQoEM to develop SCaQoEM-Sequential Context-aware Quality of Experience Measurement and Prediction where we use dynamic Bayesian networks and utility theory to model, measure and predict users' QoE over time. We performed a case study and our results validate the efficiency of SCaQoEM. In this thesis, we also propose, develop and validate a novel approach called PRO NET-Proactive Context-aware Mobility Support in HANs. PRONET incorporates a novel method for QoE estimation and prediction using hidden Markov models and Multihomed Mobile IP. It also incorporates a method for QoE-aware handoffs using Q-learning function. Using extensive simulations and experimental analysis, we show that PRONET achieves an average QoE prediction accuracy of 97%. Further, PRONET can maximize users' QoE by reducing the average number of handoffs by 60.65%, compared to the state-of-the-art methods. The outcomes of this thesis have resulted in eleven peer-reviewed conference, workshop and journal papers along with three technical reports.Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Dual award at Monash University and Luleå University of Technology)
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