Video streaming is an increasingly popular way to consume media content. Adaptive video streaming is an emerging delivery technology which aims to increase user QoE and maximise connection utilisation. Many implementations naively estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behaviour results in unfairness and could potentially lower QoE for all clients. We propose an OpenFlow-assisted QoE Fairness Framework that aims to fairly maximise the QoE of multiple competing clients in a shared network environment. By leveraging a Software Defined Networking technology, such as OpenFlow, we provide a control plane that orchestrates this functionality. The evaluation of our approach in a home networking scenario introduces user-level fairness and network stability, and illustrates the optimisation of QoE across multiple devices in a network

Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. Our survey provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction,tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies,including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data.We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.

The growing demand for online distribution of high quality and high throughput content is dominating today's Internet infrastructure. This includes both production and user-generated media. Among the myriad of media distribution mechanisms, HTTP adaptive streaming (HAS) is becoming a popular choice for multi-screen and multi-bitrate media services over heterogeneous networks. HAS applications often compete for network resources without any coordination between each other. This leads to quality of experience (QoE) fluctuations on delivered content, and unfairness between end users, while new network protocols, technologies, and architectures, such as software defined networking (SDN), are being developed for the future Internet. The programmability, flexibility, and openness of these emerging developments can greatly assist the distribution of video over the Internet. This is driven by the increasing consumer demands and QoE requirements. This paper introduces a novel user-level fairness model UFair and its hierarchical variant UFair ^HA , which orchestrate HAS media streams using emerging network architectures and incorporate three fairness metrics (video quality, switching impact, and cost efficiency) to achieve user-level fairness in video distribution. UFair ^HA has also been implemented in a purpose-built SDN testbed using open technologies, including OpenFlow. Experimental results demonstrate the performance and feasibility of our design for video distribution over future networks.

Living Labs provide a ‘human-centric’ research approach for the design of new ICT artefacts. In Living Labs users participate over several design stages, providing insights into unexpected ICT use, co-creation and evaluation of new IT solutions. Although this approach is becoming more popular, there is little comparative and reflective work on its practical dynamics, problems and possibilities. In this study, we analyse two 4-year Living Lab projects in Lancaster, UK and Siegen, Germany within the domain of Social TV, and compare experiences. We focus on documenting the purposes, methods and user dynamics that affect the trajectory of such long-term research initiatives, focusing inter alia on the dynamics of researcher/user interaction and the developing issues of trust and managing expectations; emphasizing some often neglected ethical issues and the impact of users' individual characteristics and their role in the community dynamics of Living Labs.

Existing network QoS models do not sufficiently reflect the challenges faced by high-throughput, always-on, inelastic multimedia applications. In this paper, a utility-based QoS model is proposed as a user layer extension to existing communication QoS models to better assess the requirements of multimedia applications and manage the QoS provisioning of multimedia flows. Network impairment utility functions are derived from user experiments and combined to application utility functions to evaluate the application quality. Simulation is used to demonstrate the validity of the proposed QoS model.