Who is more susceptible to phishing emails? : a Saudi Arabian study

Phishing emails cause enormous losses to both users and organisations. The goal of this study is to determine which individuals are more vulnerable to phishing emails. To gain this information an experiment has been developed which involves sending phishing email to users and collecting information about users. The detection deception model has been applied to identify users’ detection behaviour. We find that users who have less email experience and high levels of submissiveness have increased susceptibility. Among those, users who have high susceptibility levels and high openness and extraversion are more likely to carry on the harmful action embedded in phishing emails.

Faster Monte Carlo simulation of radiotherapy geometries

Using Monte Carlo simulation for radiotherapy dose calculation can provide more accurate results when compared to the analytical methods usually found in modern treatment planning systems, especially in regions with a high degree of inhomogeneity. These more accurate results acquired using Monte Carlo simulation however, often require orders of magnitude more calculation time so as to attain high precision, thereby reducing its utility within the clinical environment. This work aims to improve the utility of Monte Carlo simulation within the clinical environment by developing techniques which enable faster Monte Carlo simulation of radiotherapy geometries. This is achieved principally through the use new high performance computing environments and simpler alternative, yet equivalent representations of complex geometries. Firstly the use of cloud computing technology and it application to radiotherapy dose calculation is demonstrated. As with other super-computer like environments, the time to complete a simulation decreases as 1=n with increasing n cloud based computers performing the calculation in parallel. Unlike traditional super computer infrastructure however, there is no initial outlay of cost, only modest ongoing usage fees; the simulations described in the following are performed using this cloud computing technology. The definition of geometry within the chosen Monte Carlo simulation environment - Geometry & Tracking 4 (GEANT4) in this case - is also addressed in this work. At the simulation implementation level, a new computer aided design interface is presented for use with GEANT4 enabling direct coupling between manufactured parts and their equivalent in the simulation environment, which is of particular importance when defining linear accelerator treatment head geometry. Further, a new technique for navigating tessellated or meshed geometries is described, allowing for up to 3 orders of magnitude performance improvement with the use of tetrahedral meshes in place of complex triangular surface meshes. The technique has application in the definition of both mechanical parts in a geometry as well as patient geometry. Static patient CT datasets like those found in typical radiotherapy treatment plans are often very large and present a significant performance penalty on a Monte Carlo simulation. By extracting the regions of interest in a radiotherapy treatment plan, and representing them in a mesh based form similar to those used in computer aided design, the above mentioned optimisation techniques can be used so as to reduce the time required to navigation the patient geometry in the simulation environment. Results presented in this work show that these equivalent yet much simplified patient geometry representations enable significant performance improvements over simulations that consider raw CT datasets alone. Furthermore, this mesh based representation allows for direct manipulation of the geometry enabling motion augmentation for time dependant dose calculation for example. Finally, an experimental dosimetry technique is described which allows the validation of time dependant Monte Carlo simulation, like the ones made possible by the afore mentioned patient geometry definition. A bespoke organic plastic scintillator dose rate meter is embedded in a gel dosimeter thereby enabling simultaneous 3D dose distribution and dose rate measurement. This work demonstrates the effectiveness of applying alternative and equivalent geometry definitions to complex geometries for the purposes of Monte Carlo simulation performance improvement. Additionally, these alternative geometry definitions allow for manipulations to be performed on otherwise static and rigid geometry.

A context-fusion framework for enhancing context-awareness on mobile application

Mobile devices and smartphones have become a significant communication channel for everyday life. The sensing capabilities of mobile devices are expanding rapidly, and sensors embedded in these devices are cheaper and more powerful than before. It is evident that mobile devices have become the most suitable candidates to sense contextual information without needing extra tools. However, current research shows only a limited number of sensors are being explored and investigated. As a result, it still needs to be clarified what forms of contextual information extracted from mo- bile sensors are useful. Therefore, this research investigates the context sensing using current mobile sensors, the study follows experimental methods and sensor data is evaluated and synthesised, in order to deduce the value of various sensors and combinations of sensor for the use in context-aware mobile applications. This study aims to develop a context fusion framework that will enhance the context-awareness on mobile applications, as well as exploring innovative techniques for context sensing on smartphone devices.

Socially aware computing constructs

Every day we are confronted with social interactions with other people. Our social life is characterised by norms that manifest as attitudinal and behavioural uniformities among people. With greater awareness about our social context, we can interact more efficiently. Any theory or model of human interaction that fails to include social concepts could be suggested to lack a critical element. This paper identifies the construct of social concepts that need to be supported by future context-aw are systems from an interdisciplinary perspective. It discusses the limitations of existing context-aware systems to support social psychology theories related to the identification and membership of social groups. We argue that social norms are among the core modelling concepts that future context-aware systems need to capture with the view to support and enhance social interactions. A detailed summary of social psychology theory relevant to social computing is given, followed by a formal definition of the process with each such norm advertised and acquired. The social concepts identified in this paper could be used to simulate agent interactions imbued with social norms or use ICT to facilitate, assist, enhance or understand social interactions. They also could be used in virtual communities modelling where the social awareness of a community as well as the process of joining and exiting a community are important.

Informed use of patients' records on trusted health care services

Health care is an information-intensive business. Sharing information in health care processes is a smart use of data enabling informed decision-making whilst ensuring. the privacy and security of patient information. To achieve this, we propose data encryption techniques embedded Information Accountability Framework (IAF) that establishes transitions of the technological concept, thus enabling understanding of shared responsibility, accessibility, and efficient cost effective informed decisions between health care professionals and patients. The IAF results reveal possibilities of efficient informed medical decision making and minimisation of medical errors. Of achieving this will require significant cultural changes and research synergies to ensure the sustainability, acceptability and durability of the IAF

Fast formulas for computing cryptographic pairings

The most powerful known primitive in public-key cryptography is undoubtedly elliptic curve pairings. Upon their introduction just over ten years ago the computation of pairings was far too slow for them to be considered a practical option. This resulted in a vast amount of research from many mathematicians and computer scientists around the globe aiming to improve this computation speed. From the use of modern results in algebraic and arithmetic geometry to the application of foundational number theory that dates back to the days of Gauss and Euler, cryptographic pairings have since experienced a great deal of improvement. As a result, what was an extremely expensive computation that took several minutes is now a high-speed operation that takes less than a millisecond. This thesis presents a range of optimisations to the state-of-the-art in cryptographic pairing computation. Both through extending prior techniques, and introducing several novel ideas of our own, our work has contributed to recordbreaking pairing implementations.

Modelling sea water intrusion in coastal aquifers using heterogeneous computing

The objective of this PhD research program is to investigate numerical methods for simulating variably-saturated flow and sea water intrusion in coastal aquifers in a high-performance computing environment. The work is divided into three overlapping tasks: to develop an accurate and stable finite volume discretisation and numerical solution strategy for the variably-saturated flow and salt transport equations; to implement the chosen approach in a high performance computing environment that may have multiple GPUs or CPU cores; and to verify and test the implementation. The geological description of aquifers is often complex, with porous materials possessing highly variable properties, that are best described using unstructured meshes. The finite volume method is a popular method for the solution of the conservation laws that describe sea water intrusion, and is well-suited to unstructured meshes. In this work we apply a control volume-finite element (CV-FE) method to an extension of a recently proposed formulation (Kees and Miller, 2002) for variably saturated groundwater flow. The CV-FE method evaluates fluxes at points where material properties and gradients in pressure and concentration are consistently defined, making it both suitable for heterogeneous media and mass conservative. Using the method of lines, the CV-FE discretisation gives a set of differential algebraic equations (DAEs) amenable to solution using higher-order implicit solvers. Heterogeneous computer systems that use a combination of computational hardware such as CPUs and GPUs, are attractive for scientific computing due to the potential advantages offered by GPUs for accelerating data-parallel operations. We present a C++ library that implements data-parallel methods on both CPU and GPUs. The finite volume discretisation is expressed in terms of these data-parallel operations, which gives an efficient implementation of the nonlinear residual function. This makes the implicit solution of the DAE system possible on the GPU, because the inexact Newton-Krylov method used by the implicit time stepping scheme can approximate the action of a matrix on a vector using residual evaluations. We also propose preconditioning strategies that are amenable to GPU implementation, so that all computationally-intensive aspects of the implicit time stepping scheme are implemented on the GPU. Results are presented that demonstrate the efficiency and accuracy of the proposed numeric methods and formulation. The formulation offers excellent conservation of mass, and higher-order temporal integration increases both numeric efficiency and accuracy of the solutions. Flux limiting produces accurate, oscillation-free solutions on coarse meshes, where much finer meshes are required to obtain solutions with equivalent accuracy using upstream weighting. The computational efficiency of the software is investigated using CPUs and GPUs on a high-performance workstation. The GPU version offers considerable speedup over the CPU version, with one GPU giving speedup factor of 3 over the eight-core CPU implementation.

Unpacking user relations in an emerging ubiquitous computing environment : introducing the bystander

The move towards technological ubiquity is allowing a more idiosyncratic and dynamic working environment to emerge that may result in the restructuring of information communication technologies, and changes in their use through different user groups' actions. Taking a ‘practice’ lens to human agency, we explore the evolving roles of, and relationships between these user groups and their appropriation of emergent technologies by drawing upon Lamb and Kling's social actor framework. To illustrate our argument, we draw upon a study of a UK Fire Brigade that has introduced a variety of technologies in an attempt to move towards embracing mobile and ubiquitous computing. Our analysis of the enactment of such technologies reveals that Bystanders, a group yet to be taken as the central unit of analysis in information systems research, or considered in practice, are emerging as important actors. The research implications of our work relate to the need to further consider Bystanders in deployments other than those that are mobile and ubiquitous. For practice, we suggest that Bystanders require consideration in the systems development life cycle, particularly in terms of design and education in processes of use.

Outsourcing data storage without outsourcing trust in cloud computing

The main theme of this thesis is to allow the users of cloud services to outsource their data without the need to trust the cloud provider. The method is based on combining existing proof-of-storage schemes with distance-bounding protocols. Specifically, cloud customers will be able to verify the confidentiality, integrity, availability, fairness (or mutual non-repudiation), data freshness, geographic assurance and replication of their stored data directly, without having to rely on the word of the cloud provider.

Dynamic robot path planning using an enhanced simulated annealing approach

Evolutionary computation is an effective tool for solving optimization problems. However, its significant computational demand has limited its real-time and on-line applications, especially in embedded systems with limited computing resources, e.g., mobile robots. Heuristic methods such as the genetic algorithm (GA) based approaches have been investigated for robot path planning in dynamic environments. However, research on the simulated annealing (SA) algorithm, another popular evolutionary computation algorithm, for dynamic path planning is still limited mainly due to its high computational demand. An enhanced SA approach, which integrates two additional mathematical operators and initial path selection heuristics into the standard SA, is developed in this work for robot path planning in dynamic environments with both static and dynamic obstacles. It improves the computing performance of the standard SA significantly while giving an optimal or near-optimal robot path solution, making its real-time and on-line applications possible. Using the classic and deterministic Dijkstra algorithm as a benchmark, comprehensive case studies are carried out to demonstrate the performance of the enhanced SA and other SA algorithms in various dynamic path planning scenarios.

In vitro and in vivo MMP gene expression localisation by In Situ-RT-PCR in cell culture and paraffin embedded human breast cancer cell line xenografts

Background Members of the matrix metalloproteinase (MMP) family of proteases are required for the degradation of the basement membrane and extracellular matrix in both normal and pathological conditions. In vitro, MT1-MMP (MMP-14, membrane type-1-MMP) expression is higher in more invasive human breast cancer (HBC) cell lines, whilst in vivo its expression has been associated with the stroma surrounding breast tumours. MMP-1 (interstitial collagenase) has been associated with MDA-MB-231 invasion in vitro, while MMP-3 (stromelysin-1) has been localised around invasive cells of breast tumours in vivo. As MMPs are not stored intracellularly, the ability to localise their expression to their cells of origin is difficult. Methods We utilised the unique in situ-reverse transcription-polymerase chain reaction (IS-RT-PCR) methodology to localise the in vitro and in vivo gene expression of MT1-MMP, MMP-1 and MMP-3 in human breast cancer. In vitro, MMP induction was examined in the MDA-MB-231 and MCF-7 HBC cell lines following exposure to Concanavalin A (Con A). In vivo, we examined their expression in archival paraffin embedded xenografts derived from a range of HBC cell lines of varied invasive and metastatic potential. Mouse xenografts are heterogenous, containing neoplastic human parenchyma with mouse stroma and vasculature and provide a reproducible in vivo model system correlated to the human disease state. Results In vitro, exposure to Con A increased MT1-MMP gene expression in MDA-MB-231 cells and decreased MT1-MMP gene expression in MCF-7 cells. MMP-1 and MMP-3 gene expression remained unchanged in both cell lines. In vivo, stromal cells recruited into each xenograft demonstrated differences in localised levels of MMP gene expression. Specifically, MDA-MB-231, MDA-MB-435 and Hs578T HBC cell lines are able to influence MMP gene expression in the surrounding stroma. Conclusion We have demonstrated the applicability and sensitivity of IS-RT-PCR for the examination of MMP gene expression both in vitro and in vivo. Induction of MMP gene expression in both the epithelial tumour cells and surrounding stromal cells is associated with increased metastatic potential. Our data demonstrate the contribution of the stroma to epithelial MMP gene expression, and highlight the complexity of the role of MMPs in the stromal-epithelial interactions within breast carcinoma.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Distributed low-power image processing in wireless sensor networks for intelligent video surveillance applications

Distributed Wireless Smart Camera (DWSC) network is a special type of Wireless Sensor Network (WSN) that processes captured images in a distributed manner. While image processing on DWSCs sees a great potential for growth, with its applications possessing a vast practical application domain such as security surveillance and health care, it suffers from tremendous constraints. In addition to the limitations of conventional WSNs, image processing on DWSCs requires more computational power, bandwidth and energy that presents significant challenges for large scale deployments. This dissertation has developed a number of algorithms that are highly scalable, portable, energy efficient and performance efficient, with considerations of practical constraints imposed by the hardware and the nature of WSN. More specifically, these algorithms tackle the problems of multi-object tracking and localisation in distributed wireless smart camera net- works and optimal camera configuration determination. Addressing the first problem of multi-object tracking and localisation requires solving a large array of sub-problems. The sub-problems that are discussed in this dissertation are calibration of internal parameters, multi-camera calibration for localisation and object handover for tracking. These topics have been covered extensively in computer vision literatures, however new algorithms must be invented to accommodate the various constraints introduced and required by the DWSC platform. A technique has been developed for the automatic calibration of low-cost cameras which are assumed to be restricted in their freedom of movement to either pan or tilt movements. Camera internal parameters, including focal length, principal point, lens distortion parameter and the angle and axis of rotation, can be recovered from a minimum set of two images of the camera, provided that the axis of rotation between the two images goes through the camera's optical centre and is parallel to either the vertical (panning) or horizontal (tilting) axis of the image. For object localisation, a novel approach has been developed for the calibration of a network of non-overlapping DWSCs in terms of their ground plane homographies, which can then be used for localising objects. In the proposed approach, a robot travels through the camera network while updating its position in a global coordinate frame, which it broadcasts to the cameras. The cameras use this, along with the image plane location of the robot, to compute a mapping from their image planes to the global coordinate frame. This is combined with an occupancy map generated by the robot during the mapping process to localised objects moving within the network. In addition, to deal with the problem of object handover between DWSCs of non-overlapping fields of view, a highly-scalable, distributed protocol has been designed. Cameras that follow the proposed protocol transmit object descriptions to a selected set of neighbours that are determined using a predictive forwarding strategy. The received descriptions are then matched at the subsequent camera on the object's path using a probability maximisation process with locally generated descriptions. The second problem of camera placement emerges naturally when these pervasive devices are put into real use. The locations, orientations, lens types etc. of the cameras must be chosen in a way that the utility of the network is maximised (e.g. maximum coverage) while user requirements are met. To deal with this, a statistical formulation of the problem of determining optimal camera configurations has been introduced and a Trans-Dimensional Simulated Annealing (TDSA) algorithm has been proposed to effectively solve the problem.

Making sense of purpose, direction and innovation : an embedded design led innovation case study in the Australian mining industry

The mining equipment technology services sector is driven by a reactive and user-centered design approach, with a technological focus on incremental new product development. As Australia moves out of its sustained mining boom, companies need to rethink their strategic position, to become agile to stay relevant in an enigmatic market. This paper reports on the first five months on an embedded case study within an Australian, family-owned mining manufacturer. The first author is currently engaged in a longitudinal design led innovation project, as a catalyst to guide the company’s journey to design integration. The results find that design led innovation could act as a channel for highlighting and exploring company disconnections with the marketplace and offer a customer-centric catalyst for internal change. Data collected for this study is from 12 analysed semistructured interviews, a focus group and a reflective journal, over a five-month period. This paper explores limitations to design integration, and highlights opportunities to explore and leverage entrepreneurial characteristics to stay agile, broaden innovation and future-proof through the next commodity cycle in the mining industry.

Cloud computing : how does it differ from IT outsourcing and what are the implications for practice and research?

The purpose of this paper is to provide an evolutionary perspective of cloud computing (CC) by integrating two previously disparate literatures: CC and information technology outsourcing (ITO). We review the literature and develop a framework that highlights the demand for the CC service, benefits, risks, as well as risk mitigation strategies that are likely to influence the success of the service. CC success in organisations and as a technology overall is a function of (i) the outsourcing decision and supplier selection, (ii) contractual and relational governance, and (iii) industry standards and legal framework. Whereas CC clients have little control over standards and/or the legal framework, they are able to influence other factors to maximize the benefits while limiting the risks. This paper provides guidelines for (potential) cloud computing users with respect to the outsourcing decision, vendor selection, service-level-agreements, and other issues that need to be addressed when opting for CC services. We contribute to the literature by providing an evolutionary and holistic view of CC that draws on the extensive literature and theory of ITO. We conclude the paper with a number of research paths that future researchers can follow to advance the knowledge in this field.