Investigation of improved aerodynamic performance of isolated airfoils using CIRCLE method

CC BY-NC-ND licenseThe PhD research of Moin U Ahmed is partly sponsored by Cummins Turbo Technologies Ltd and partly by Queen Mary University of London

Illustrating some issues raised when designing context-aware personalized services for mobile users

International audienceWhen travelling people might seek for help or any sorts of information. For example, travelers might need suggestions about accommodation, transportation, activities, food, etc. while they are travelling. Moreover, they expect to get suggestions which are personalized according to some specific criteria such as preferences, age, location, etc. This paper sketches a framework named "Context-Aware Recommender for Mobile Users" that is responsible for providing users with personalized recommendations in order to deliver them the right service to the right user at the right time with the respect of their privacy

Illustrating some issues raised when designing context-aware personalized services for mobile users

International audienceWhen travelling people might seek for help or any sorts of information. For example, travelers might need suggestions about accommodation, transportation, activities, food, etc. while they are travelling. Moreover, they expect to get suggestions which are personalized according to some specific criteria such as preferences, age, location, etc. This paper sketches a framework named "Context-Aware Recommender for Mobile Users" that is responsible for providing users with personalized recommendations in order to deliver them the right service to the right user at the right time with the respect of their privacy

Context-Aware Service Discovering System for Nomad Users

International audienceThis paper presents an architecture for a system that provides nomad users, context-aware personalised services. Users might need any sort of services: information about the weather forecast for the next day, or about a museum in the neighbour worth to visit. These services are known as stateless services. More complex situations ocurre when services are stateful. Such services are, for example those which need users to be logged in (e.g. booking a room in a hotel). The question discussed in the text are those related to: i) user's privacy, ii) recommendation and discovery of services, iii) composition of recommended services into a composite service, and iv) execution of the resulting composite service

Quality of Experience Experimentation Prediction Framework through Programmable Network Management

Quality of experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has focused on improving network properties such as the quality of service (QoS). In this paper we examine adaptive streaming over a software-defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and the network. This was undertaken to eventually reach a stage of analysing the network’s features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE

Diet and Microbes in the Pathogenesis of Lupus

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder with no known cure. It is characterized by severe and persistent inflammation that damages multiple organs. To date, treatment and prevention of disease flares have relied on long-term use of anti-inflammatory drugs where side effects are of particular concern. There is a need for better understanding of the disease and for better approaches in SLE treatment and management. In this chapter, we delineate the roles of diet and microbes in the pathogenesis of SLE

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Quality of Experience Experimentation Prediction Framework through Programmable Network Management

Quality of experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has focused on improving network properties such as the quality of service (QoS). In this paper we examine adaptive streaming over a software-defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and the network. This was undertaken to eventually reach a stage of analysing the network’s features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE