This action research-based dissertation aims to explain how belonging is understood of a group of adults who spent a significant portion of their childhood in globally nomadic families. A hermeneutic phenomenological lens was used throughout the inquiry The research process revealed that belonging and identity are deeply intertwined and that for these adults, belonging is defined by relationship rather than physical proximity; their sense of belonging was varied and defined by multiple dimensions which is consistent with the multi-layered cultural identities of the participants; and that belonging can be experienced imperfectly due to issues of permanence and socio-cultural perceptions of not fitting in. The second aim of this dissertation was to examine how a temporary, online community built participants’ understanding of their lived experiences, particularly among the axes of belonging and identity. The analysis indicated that a meaningful depth of understanding can be created among relative strangers, given the design of the online community and willingness among the participants to meet each other with intention and generosity within that design. This study adds to an under-researched area within existing literature by offering an authentic description of the lifeworld of adults beyond their globally nomadic childhood and makes actionable suggestions for current ex-patriate families and the sponsoring organizations who send them.

The technological advances in the past few decades have made possible creation and consumption of digital visual content at an explosive rate. Consequently, there is a need for efficient quality monitoring systems to ensure minimal degradation of images and videos during various processing operations like compression, transmission, storage etc. Objective Image Quality Assessment (IQA) algorithms have been developed that predict quality scores which match well with human subjective quality assessment. However, a lot of research still remains to be done before IQA algorithms can be deployed in real world systems. Long runtimes for one frame of image is a major hurdle. Graphics Processing Units (GPUs), equipped with massive number of computational cores, provide an opportunity to accelerate IQA algorithms by performing computations in parallel. Indeed, General Purpose Graphics Processing Units (GPGPU) techniques have been applied to a few Full Reference IQA algorithms which fall under the. We present a GPGPU implementation of Blind Image Integrity Notator using DCT Statistics (BLIINDS-II), which falls under the No Reference IQA algorithm paradigm. We have been able to achieve a speedup of over 30x over the previous CPU version of this algorithm. We test our implementation using various distorted images from the CSIQ database and present the performance trends observed. We achieve a very consistent performance of around 9 milliseconds per distorted image, which made possible the execution of over 100 images per second (100 fps).