Thesis (Ph. D.)--University of Hong Kong, 2005.

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (p. 240-245).

Context: Enterprise Cloud Computing (or Enterprise Clouds) is using the Cloud Computing services by a large-scale organisation to migrate its existing IT services or use new Cloud based services. There are many issues and challenges that are barrier to the adoption of Enterprise Clouds. The adoption challenges have to be addressed for better assimilation of Cloud based services within the organisation. Objective: The aim of this research was to develop an assessment model for adoption of Enterprise Clouds. Method: Key challenges reported as barrier in adoption of Cloud Computing were identified from literature using the Systematic Literature Review methodology. A survey research was carried out to elicit industrial approaches and practices from Cloud Computing experts that help in overcoming the key challenges. Both key challenges and practices were used in formulating the assessment model. Results: The results have highlighted that key challenges in the adoption of Enterprise Clouds are security & reliability concerns, resistance to change, vendor lock-in issues, data privacy and difficulties in application and service migration. The industrial practices to overcome these challenges are: planning and executing pilot project, assessment of IT needs, use of open source APIs, involvement of legal team in vendor selection, identification of the processes to change, involvement of senior executive as change champion, using vendor partners to support application/service migration to Cloud Computing and creating employee awareness about Cloud Computing services. Conclusion: Using the key challenges and practices, the assessment model was developed that assesses an organisation’s readiness to adopt Enterprise Clouds. The model measures the readiness in four dimensions: technical, legal & compliance, IT capabilities and end user readiness for the adoption of Enterprise Clouds. The model’s result can help the organisation in overcoming the adoption challenges for successful assimilation of newly deployed or migrated IT services on Enterprise Clouds.

The demand of indoor positioning solution is on the increase dramatically, and WiFi-based indoor positioning is known as a very promising approach because of the ubiquitous WiFi signals and WiFi-compatible mobile devices. Improving the positioning accuracy is the primary target of most recent works, while the excessive deployment overhead is also a challenging problem behind. In this thesis, the author is investigating the indoor positioning problem from the aspects of indoor map information and the ubiquity of WiFi signals. This thesis proposes a set of novel WiFi positioning schemes to improve the accuracy and efficiency. Firstly, considering the access point (AP) placement is the first step to deploy indoor positioning system using WiFi, an AP placement algorithm is provided to generate the placement of APs in a given indoor environment. The AP placement algorithm utilises the floor plan information from the indoor map, in which the placement of APs is optimised to benefit the fingerprinting- based positioning. Secondly, the patterns of WiFi signals are observed and deeply analysed from sibling and spatial aspects in conjunction with pathway map from indoor map to address the problem of inconsistent WiFi signal observations. The sibling and spatial signal patterns are used to improve both positioning accuracy and efficiency. Thirdly, an AP-centred architecture is proposed by moving the positioning modules from mobile handheld to APs to facilitate the applications where mobile handheld doesn’t directly participate positioning. Meanwhile, the fingerprint technique is adopted into the AP-centred architecture to maintain comparable positioning accuracy. All the proposed works in this thesis are adequately designed, implemented and evaluated in the real-world environment and show improved performance.

When asked to define where a geographic place is, people normally resort to using qualitative expressions of location, such as north of and near to. This is evident in the domain of social geography, where qualitative research methods are used to gauge people’s understanding of their neighbourhood. Using a GIS to represent and map the location of neighbourhood boundaries is needed to understand and compare people’s perceptions of the spatial extent of their neighbourhoods. Extending the GIS to allow for the qualitative modelling of place will allow for the representation and mapping of neighbourhoods. On the other hand, a collaborative definition of place on the web will result in the accumulation of large sets of data resources that can be considered “location-poor”, where place location is defined mostly using single point coordinates and some random combinations of relative spatial relationships. A qualitative model of place location on the Linked Data Web (LDW) will allow for the homogenous representation and reasoning of place resources. This research has analysed the qualitative modelling of place location on the LDW and in GIS. On the LDW, a qualitative model of place is proposed, which provides an effective representation of individual place location profiles that allow place information to be enriched and spatially linked. This has been evaluated using the application of qualitative spatial reasoning (QSR) to automatic reasoning over place profiles, to check the completeness of the representation, as well as to derive implicit links not defined by the model. In GIS, a qualitative model of place is proposed that provides a basis for mapping qualitative definitions of place location in GIS, and this has been evaluated using an implementation-driven approach. The model has been implemented in a GIS and demonstrated through a realistic case study. A user-centric approach to development has been adopted, as users were involved throughout the design, development and evaluation stages.

The ready availability of image-editing software makes it important to ensure the authenticity of images. This thesis concerns the detection and localization of cloning, or Copy-Move Forgery (CMF), which is the most common type of image tampering, in which part(s) of the image are copied and pasted back somewhere else in the same image. Post-processing can be used to produce more realistic doctored images and thus can increase the difficulty of detecting forgery. This thesis presents three novel methods for CMF detection, using feature extraction, surface fitting and segmentation. The Dense Scale Invariant Feature Transform (DSIFT) has been improved by using a different method to estimate the canonical orientation of each circular block. The Fitting Function Rotation Invariant Descriptor (FFRID) has been developed by using the least squares method to fit the parameters of a quadratic function on each block curvatures. In the segmentation approach, three different methods were tested: the SLIC superpixels, the Bag of Words Image and the Rolling Guidance filter with the multi-thresholding method. We also developed the Segment Gradient Orientation Histogram (SGOH) to describe the gradient of irregularly shaped blocks (segments). The experimental results illustrate that our proposed algorithms can detect forgery in images containing copy-move objects with different types of transformation (translation, rotation, scaling, distortion and combined transformation). Moreover, the proposed methods are robust to post-processing (i.e. blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise) and can detect multiple duplicated objects. In addition, we developed a new method to estimate the similarity threshold for each image by optimizing a cost function based probability distribution. This method can detect CMF better than using a fixed threshold for all the test images, because our proposed method reduces the false positive and the time required to estimate one threshold for different images in the dataset. Finally, we used the hysteresis to decrease the number of false matches and produce the best possible result.

Research into providing support for long term data in lazy functional programming systems is presented in this thesis. The motivation for this work has been to reap the benefits of integrating lazy functional programming languages and persistence. The benefits are: the programmer need not write code to support long term data since this is provided as part of the programming system; persistent data can be used in a type safe way since the programming language type system applies to data with the whole range of persistence; the benefits of lazy evaluation are extended to the full lifetime of a data value. Whilst data is reachable, any evaluation performed on the data persists. A data value changes monotonically from an unevaluated state towards a completely evaluated state over time. Interactive data intensive applications such as functional databases can be developed. These benefits are realised by the development of models for persistence in lazy functional programming systems. Two models are proposed which make persistence available to the functional programmer. The first, persistent modules, allows values named in modules to be stored in persistent storage for later reuse. The second model, stream persistence allows dynamic, interactive access to persistent storage. These models are supported by a system architecture which incorporates a persistent abstract machine, PCASE, integrated with a persistent object store. The resulting persistent lazy functional programming system, Staple, is used in prototyping and functional database modelling experiments.

The questions to be addressed here are all aimed at beginning to assess the emergence and utility of social behaviour and social learning in artificial evolutionary systems. Like any biological adaptation, the adaptation to process and use social information must lead to an overall increase in the long term reproductive capability of any population utilising such an adaptation - this increase in fecundity also being accompanied by increased survivability and therefore adaptability. In nature, social behaviours such as co-operation, teaching and agent aggregation, all seem to provide improved levels of fitness, resulting in an improved and more robust set of general behaviours - in the human case these social behaviours have led to cumulative culture and the ability to rapidly adapt to, and thrive in, an astonishing number of environments. In this thesis we begin to look at why the evolutionary adaptation to process and use social information, leading to social learning and social behaviour, proves to be such a useful adaptation, and under which circumstances we would expect to see this adaptation, and its resulting mechanisms and strategies, emerge. We begin by asking these questions in two contexts; firstly what does social learning enable that incremental genetic evolution alone does not, and secondly what benefit does social learning provide in temporally variable environments. We go on to assess how differing social learning strategies affect the utility of social learning, and whether social information can be utilised by an evolutionary process without any accompanying within-lifetime learning processes (and whether the accommodation of social information results in any notable behavioural changes). By addressing the questions posed here in this way, we can begin to shed some light on the circumstances under which the adaptations for the accommodation and use of social information begin to emerge, and ultimately lead to the emergence of robust socially intelligent artificial agents.

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