Personalised Procedures for Thoracic Radiotherapy

This thesis presents the investigation, development, and estimation of two personalised procedures for thoracic cancer therapy in Shenzhen, China and two projects were carried out: (1) respiratory motion management of a lung tumour, and (2) the application of a three-dimensional (3D) printing technique for postmastectomy irradiation. For the first project, all subjects attended sessions of free-breathing (FB) and personalised vocal coaching (VC) for respiratory regulation. Thoracic and abdominal breathing signals were extracted from the subjects’ surface area then estimated as kernel density estimation (KDE) for motion visualisation. The mutual information (MI) and correlation coefficient (CC) calculated from KDEs indicate the variation in the relationship between the two signals. From the 1D signal, through VC, the variation of cycle time and the signal value of end-of-exhale/inhale increased in the patient group but decreased in volunteers. Mixed results were presented on KDE and MI. Compared with FB, VC improves movement consistency between the two signals in eight of eleven subjects by increasing MI. The fixed instruction method showed no improvement for day-to-day variation, while the daily generated instruction enhanced the respiratory regularity in three of five volunteers. VC addresses the variation of the single signal, while the outcome of the two signals, thoracic and abdominal signals, requires further interpretation. The second project aims to address both the enhancement of the skin dose and avoidance of hotspots of critical organs, focusing on improving irradiative treatment for post-mastectomy patients. A 3D-printed bolus was presented as a solution for the air gap between the bolus and skin. The results showed no evidence of significant skin dose enhancement with the printed bolus. Additionally, an air gap larger than 5 mm was evident in all patients. Until a solution for complete bolus adhesion is found, this customised bolus is not suitable for clinical use

SocialSensor: sensing user generated input for improved media discovery and experience

SocialSensor will develop a new framework for enabling real-time multimedia indexing and search in the Social Web. The project moves beyond conventional text-based indexing and retrieval models by mining and aggregating user inputs and content over multiple social networking sites. Social Indexing will incorporate information about the structure and activity of the users‟ social network directly into the multimedia analysis and search process. Furthermore, it will enhance the multimedia consumption experience by developing novel user-centric media visualization and browsing paradigms. For example, SocialSensor will analyse the dynamic and massive user contributions in order to extract unbiased trending topics and events and will use social connections for improved recommendations. To achieve its objectives, SocialSensor introduces the concept of Dynamic Social COntainers (DySCOs), a new layer of online multimedia content organisation with particular emphasis on the real-time, social and contextual nature of content and information consumption. Through the proposed DySCOs-centered media search, SocialSensor will integrate social content mining, search and intelligent presentation in a personalized, context and network-aware way, based on aggregation and indexing of both UGC and multimedia Web content

Re-Inventing Public Education:The New Role of Knowledge in Education Policy-Making

This article focuses on the changing role of knowledge in education policy making within the knowledge society. Through an examination of key policy texts, the Scottish case of Integrated Children Services provision is used to exemplify this new trend. We discuss the ways in which knowledge is being used in order to re-configure education as part of a range of public services designed to meet individuals' needs. This, we argue, has led to a 'scientization' of education governance where it is only knowledge, closely intertwined with action (expressed as 'measures') that can reveal problems and shape solutions. The article concludes by highlighting the key role of knowledge policy and governance in orienting education policy making through a re-invention of the public role of education

Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets

Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach

Landmarks play key roles in human wayfinding and mobile navigation systems. Existing computational landmark selection models mainly focus on outdoor environments, and aim to identify suitable landmarks for guiding users who are unfamiliar with a particular environment, and fail to consider familiar users. This study proposes a familiarity-dependent computational method for selecting suitable landmarks for communicating with familiar and unfamiliar users in indoor environments. A series of salience measures are proposed to quantify the characteristics of each indoor landmark candidate, which are then combined in two LambdaMART-based learning-to-rank models for selecting landmarks for familiar and unfamiliar users, respectively. The evaluation with labelled landmark preference data by human participants shows that people’s familiarity with environments matters in the computational modelling of indoor landmark selection for guiding them. The proposed models outperform state-of-the-art models, and achieve hit rates of 0.737 and 0.786 for familiar and unfamiliar users, respectively. Furthermore, semantic relevance of a landmark candidate is the most important measure for the familiar model, while visual intensity is most informative for the unfamiliar model. This study enables the development of human-centered indoor navigation systems that provide familiarity-adaptive landmark-based navigation guidance

Next generation pedagogy: IDEAS for online and blended higher education. Final report of the FUTURA (Future of university teaching: update and a roadmap for advancement) project

Next generation pedagogy: IDEAS for online and blended higher education. Final report of the FUTURA (Future of university teaching: update and a roadmap for advancement) projec