Search

The ability of a robot to adapt in-mission to achieve an assigned goal is highly desirable. This thesis project places an emphasis on employing learning-based intelligent control methodologies to the development and implementation of an autonomous unmanned aerial vehicle (UAV). Flight control is carried out by evolving spiking neural networks (SNNs) with Hebbian plasticity. The proposed implementation is capable of learning and self-adaptation to model variations and uncertainties when the controller learned in simulation is deployed on a physical platform.Controller development for small multicopters often relies on simulations as an intermediate step, providing cheap, parallelisable, observable and reproducible optimisation with no risk of damage to hardware. Although model-based approaches have been widely utilised in the process of development, loss of performance can be observed on the target platform due to simplification of system dynamics in simulation (e.g., aerodynamics, servo dynamics, sensor uncertainties). Ignorance of these effects in simulation can significantly deteriorate performance when the controller is deployed. Previous approaches often require mathematical or simulation models with a high level of accuracy which can be difficult to obtain. This thesis, on the other hand, attempts to cross the reality gap between a low-fidelity simulation and the real platform. This is done using synaptic plasticity to adapt the SNN controller evolved in simulation to the actual UAV dynamics.The primary contribution of this work is the implementation of a procedural methodology for SNN control that integrates bioinspired learning mechanisms with artificial evolution, with an SNN library package (i.e. eSpinn) developed by the author. Distinct from existing SNN simulators that mainly focus on large-scale neuron interactions and learning mechanisms from a neuroscience perspective, the eSpinn library draws particular attention to embedded implementations on hardwar

Imaging polarimetry has shown its advantage over other imaging techniques on various sensing tasks such as astronomical sensing, atmospheric sensing, target detection and biomedical diagnostics because it provides information of the imaged scene that are not obtainable to other imaging modalities such as shading and surface roughness of objects. Polarimeters are the devices designed to measure the polarization of light. In general imaging polarimeters can be divided into two broad classes, the wavefront division polarimeters and the modulated polarimeters. This thesis aims to contribute to the development of modulated polarimeters.The essential weakness of the modulated polarimeter is the resolution loss caused by the bandwidth reduction since this class of polarimeters divide the native bandwidth of the underlying detector to measure multiple pieces of polarization information “simultaneously”. In previous studies, researchers attempted to mitigate this resolution loss by designing more advanced modulation schemes that divide the native bandwidth in more economical fashions or combining multiple domains in modulation to expand the bandwidths of the polarimeters by trading off bandwidth of differentdomains. In recent years, our group and others have started looking at the latter approach, but it is still not particularly popular in the wider community. Many advanced multi-domain modulated polarimeter designs have been proposed but have not been verified with experiments. Therefore, the first study presented in this thesis demonstrates an experimental work where a previously proposed spatio-temporally modulated polarimeter is constructed and its advantages over single domain modulated polarimeters are verified.Secondly, this thesis examines the data reconstruction procedure of modulated polarimeters and proposes a novel machine learning based adaptive data reconstruction framework. This adaptive framework is demonstrated with a simulated classic spatially modulated p

The light field camera provides rich textural and geometric information, but it is still challenging to use it efficiently and accurately to solve computer vision problems. Light field image processing is divided into multiple levels. First, low-level processing technology mainly includes the acquisition of light field images and their preprocessing. Second, the middle-level process consists of the depth estimation, light field encoding, and the extraction of cues from the light field. Third, high-level processing involves 3D reconstruction, target recognition, visual odometry, image reconstruction, and other advanced applications. We propose a series of improved algorithms for each of these levels. The light field signal contains rich angular information. By contrast, traditional computer vision methods, as used for 2D images, often cannot make full use of the high-frequency part of the light field angular information. We propose a fast pre-estimation algorithm to enhance the light field feature to improve its speed and accuracy when keeping full use of the angular information.Light field filtering and refocusing are essential cues in light field signal processing. Modern frequency domain filtering technology and wavelet technology have effectively improved light field filtering accuracy but may fail at object edges. We adapted the sub-window filtering with the light field to improve the reconstruction of object edges. Light field images can analyze the effects of scattering and refraction phenomena, and there are still insufficient metrics to evaluate the results. Therefore, we propose a physical rendering-based light field dataset that simulates the distorted light field image through a transparent medium, such as atmospheric turbulence or water surface. The neural network is an essential method to process complex light field data. We propose an efficient 3D convolutional autoencoder network for the light field structure. This network overcomes the severe distort

Metasurfaces are artificial composite materials with subwavelength inclusions which have been shown to enable very versatile manipulation of electromagnetic waves. Especially at microwave frequencies, the concept is widely explored and the scope of previous methods of wavefront manipulation such as frequency selective surfaces and leaky-wave antennas has been largely extended. Emerging applications like next generation wireless communication and radar sensing could benefit from novel metasurface-based antennas which have been recently proposed. Although most of these emerging applications use frequencies of operation in the millimeter wave (mm-wave) band, research on metasurfaces in this band is still scarce. Many secondary effects known in the microwave community such as fabrication constraints and material losses are more severe using mm-waves and they significantly hamper the development of efficient devices. The aim of this thesis is to explore design and characterization methods for mm-wave metasurfaces. In particular, this thesis concentrates on planar metasurface architectures that are compatible with established printed circuit board fabrication, which is a requirement for many consumer applications. Causes for significant performance degradation in printed circuit metasurfaces for mm-waves are identified and synthesis techniques with which they can be minimized are proposed. The effectiveness of the proposed synthesis techniques is verified by comprehensive experimental works. Building on these synthesis approaches, two kinds of antenna systems are experimentally demonstrated, based on transmissive metasurfaces and on leaky-wave antennas.

The research described in this thesis addresses the relative performance and the physical mechanisms underlying that performance, of three fundamentally different fuel injection methods for generating skin friction drag reduction due to combustion within the boundary layer of semi-direct-connect supersonic combustion airbreathing (scramjet) internal flowpaths.The work builds on a significant body of experimental (and supporting analytical) research into skin friction drag reduction in scramjet flowpaths in the T4 hypersonic shock tunnel at the University of Queensland. That earlier research assumed a turbulent boundary layer delivered to the combustion chamber, which included rectangular and axisymmetric cross-sections during different investigations, and in which hydrogen fuel was injected either as transverse jets or tangential films from injection ports on the combustor walls. For the axisymmetric case and for the first time, the present research extends the injection method to that of porous wall sections, employing high temperature CCSiC ceramics developed by the German Aerospace Centre (DLR). The research applies high fidelity physics-based computational fluid dynamics (CFD) simulations to study and compare the skin friction drag reduction and also combustion performance, and underlying flow processes giving rise to that performance, for transverse jet, tangential film and porous wall injection methods.An experimental campaign was firstly conducted in the T4 shock tunnel to probe the assumption of previous work that the combustor inlet boundary layer was turbulent for the axisymmetric configuration studied. Two flow conditions were used – total enthalpies of 3.2 and 7.8 MJ/kg, at total pressures of 26 and 34 MPa respectively. Measured surface temperature time histories and surface heat transfer rates inferred from them, compared with laminar and turbulent analytical and CFD simulations, provided the significant result that the incoming boundary layer was trans

The use of ships as wave buoy analogies (SAWB) provides a novel means to estimate sea states, namely wave heights, periods, and directions. While single vessels have shown reasonable results, a previous study has shown improved estimations when using multiple vessels. However, no investigation into the effects of vessel trajectory on multi-vessel sea state estimation (SSE) performance has previously been undertaken. It was hypothesised that the guidance of multiple vessels acting as wave buoys for SSE can improve upon single vessel estimation. Further, it was hypothesised that variation of vessel trajectories will reduce SSE error as it changes the filtering types of the vessels, when compared with common trajectories.While several SAWB methodologies have been developed, the use of machine learning (ML) for SAWB has shown great promise in terms of improved estimation accuracy. Moreover, ML approaches to SAWB-based SSE are model-free, establishing relationships between causal wave properties and vessel motion responses using the input and output data alone. Therefore, neural networks have been designed to intake spectral vessel response data for heave, pitch, and roll, and output causal wave properties, with a high degree of accuracy when compared with similar approaches.As spectra can represent time-series information statistically, it was hypothesised that the proposed SSE methodology could be used to update SSEs online. However, although the spectra had similar shapes over time, the density of spectral ordinates was lower, leading to poor real-time estimation accuracy.Two uncertainty metrics were adopted to test their effectiveness as assessing SSEs, a time-based metric aimed at evaluating random errors, and a spectral agreement metric to assess bias errors. The results showed a combination of the metrics were able to associate uncertainty with SSE error distribution.To investigate the difference in SSE performance when multiple vessels move along different trajec

Medical image segmentation is a procedure to analyse an image’s content to find an organ, cancer, tumour, or possible abnormalities. Since hospitals and medical centres generate billions of images daily worldwide, manual analysis of the images is frustrating. Therefore there is a need to improve automatic techniques to examine the content of images. Deep Convolutional Neural Networks (DCNNs) are one of the most reliable and successful approaches to analyse images’ content. However, the main problem is a lack of rules to design a network, and trial and error is the usual approach to find a network structure along with its training parameters. Regarding the diversity of medical images, existing with various types of noises and artefacts, the limited number of available labelled medical images, and limited available computational facilities, designing a CNN for medical image analysis is even more complicated.Because of the importance of medical image segmentation, during the last decade, various CNNs are designed manually; however, most of these networks work well for the segmentation of a specific dataset or application. One of the solutions to address this problem is developing networks automatically. Neuroevolution, which is the combination of an evolutionary algorithm and Neural Networks (NNs), can automatically design a network.Evolutionary algorithms are relatively easy to understand and implement; however, they need considerable computation to evolve a network. Since Nerouevolution is computationallydemanding, there is very limited previous work regarding applying Neuroevolution for medical image segmentation. Existing works just set up a part of the parameters to develop a network and have been applied to a limited number of datasets. The most significant drawbacks of existing works are lack of robustness and generalizability; also, most of them are computationally expensive.In this thesis, several Neroevolutionary-based frameworks are developed for 2D and 3D m

This thesis is focused on analysis the behaviours of two forms of protective panels, namely (i) steel built-up panel formed by welding of standard beam sections and plates and (ii) steel-concrete sandwich panel (SCS), subjected to combined blast and fragment loadings (CBFL) experimentally and numerically. Three explosion trials, including a bare charge of 2.7 kg TNT and two cased charges of 15 kg of TNT encased in a mild steel pipes, were performed. After the explosion trials, static bending tests of the damaged panels were carried out to estimate the residual capacity of the damaged panels. In addition, in order to investigate the behaviours of both steel and SCS sandwich panels against fragment loading under laboratory conditions, high velocity impact tests were carried out on both panel which have the similar cross-sectional design and dimensions as blast tests. Numerical models were developed for both steel and SCS sandwich panel to simulate experimental test cases using LS-DYNA finite element software package to further investigate the effects of different loading components (fragment and blast) on structural response. After FE model development, it was validated and compared with the experimental results. It was found that FE model can simulate dynamic responses with reasonable accuracy. The validated FE models were then utilised to conduct comprehensive parametric study to further analyse the panels under different loading and geometric conditions for both steel and SCS sandwich panel.The three types of simplified modelling techniques namely (a) Single degree of freedom (SDOF) model, (b) multiple degree of freedom (MDOF) model, and (c) simplified finite element model (SFEM) were developed to predict the responses of the steel and SCS sandwich panels under CBFL generated from cylindrical cased charge. It was found that the responses matched with reasonable accuracy when compared with experimental and detailed FE model results considering its simplicity of the

Polarimetric imaging is a technique for measuring the spatial correlations of the aspectsof the polarisation of light. Since human vision is essentially unable to detect polarisation,the data obtained from this imaging technique must be converted into the channelsof the human visual system in order to visually process the spatial correlations in the data.The technique for converting non-visual data into a visual representation is known as datavisualisation. While the techniques for visualising other types of data is well studied,techniques specific for polarimetric imaging are understudied. This research aims to surveythe current state of polarimetric imaging visualisation, to analyse the current methodsusing metrics from visualisation research, to improve on the existing techniques, to testthe effectiveness of different methods in terms of user performance, and to develop novelcolourmapping methods.

Disasters are hazards that damage the human environment. Disasters have had significant costs in the past and their financial impact is expected to increase in the future. Community Resilience (CR) is the ability of a community to resist, absorb, accommodate and recover from disasters (UNISDR 2015). The methods developed to evaluate CR are broad and varied, however all seek to enhance a community’s understanding of its abilities. By better understanding a CR, decision makers can make better informed decisions on how and where to apply limited resources.Emergencies and natural hazards have seen the extensive application of information communication technologies (ICT). The application of ICT in disasters includes their use to facilitate the exchange of information between emergency management agencies (EMA) and the public. To date, academic research in this field has focused on how social media (SM) platforms and data mining may be used to enhance EMA situational awareness during disasters and their ability to detect and respond to emerging disasters (Stieglitz, Mirbabaie, Fromm, et al. 2018). However, research into using analysis of social media within CR evaluation has been limited to date. This use of social media analysis for community resilience evaluation can be used to inform decision making in pre-event phases.This thesis seeks to explore whether the analysis of SM data may contribute to the evaluation of CR. To do this, it reviews existing CR evaluation frameworks and identifies possible SM variables for employment in CR evaluation. Subsequently, it reports on a case study that applies SM analysis to CR evaluation of three communities in Australia (the City of Lismore, Western Plains Regional Council, and Queanbeyan-Palerang Regional Council). This case study employs two Indicators from SM analysis (EMA influence and engagement within a community, and active participation of users with Institutions) to evaluate the Institutions Domain of CR. The performance o

Reward-based crowdfunding is rapidly shaping up to be a revolutionary avenue for companies, especially start-ups, to broadcast their businesses and solicit capital through digital platforms. The platformized micro-investment mechanism in crowdfunding creates an unprecedented digital market that ensures a more fluid and granular distribution of financial resources. The Information Systems (IS) community has endeavored to shed light on this growing market by studying the decisive factors that motivate funders’ contributions and lead to crowdfunding success. Yet, despite its risky nature, no study thus far systematically investigates how risk perceptions emerge and impact funders’ investment behavior in reward-based crowdfunding and what companies can do to address funders’ risk concerns. To bridge these gaps, this thesis synthesizes contemporary knowledge on the contextual characteristics of crowdfunding and risks around diverse transactional environments to arrive at a typology of the risks encountered by funders when participating in reward-based crowdfunding. Drawing on the indispensable link between trust and risk, a holistic research model is constructed to: (1) postulate the impact of funders’ risk perceptions on their willingness to invest using a reward-based crowdfunding platform, (2) discern how different types of crowdfunding risks can be mitigated by enhancing the assessability of fundraiser’s trustworthiness, and (3) explore the optimum trust-building mechanisms that can facilitate funders’ assessments of fundraiser’s trustworthiness. Three experimental studies are conducted to validate the advanced research model empirically. The findings from the three studies yield theoretical insights on crowdfunding risks and mechanisms to diminish funders’ risk perceptions via trust-building. The results also yield actionable design principles that platform providers can adopt to help funders assess risk and improve their financing experience in this emerging market

Emerging technologies are radically changing the future of work. From artificial intelligence for customer support to robotics for performing surgery, the change is unprecedented in the history of most organisations. This thesis aims to understand and contribute rich, empirically informed insights into the phenomenon of the future of work in the context of emerging technologies from an organisational history perspective. The thesis comprises three related but standalone studies that discuss organisational trajectories and the implications of emerging technologies in three distinct organisational contexts. To explore this emerging and intrinsic phenomenon in organisations, this work adopts the historical narrativist approach and the qualitative case study method. The first study explores the interplay between tradition and technology at a pre-digital organisation when new technologies are introduced. The second study delves into the strategies and practices of realising historically embodied process knowledge when adopting a robotic process automation technology at a digitally reformed organisation. The third study traces the evolution of a strategic path towards technology and data driven innovation, from the foundation to the implementation of artificial intelligence technologies, at a born-digital organisation. Each of the three studies provides a distinct but complementary understanding on the role of organisational history by contributing to the theories of imprinting, organisational memory and path dependence. Collectively, the thesis studies contribute to our understanding of “how history matters” in the future of work. The thesis discussion contributes to the literature by integrating the implications of surface- and deep-level effects of emerging technologies for the future of work and how they are shaped by the distant, intermediate and immediate histories of organisations.

This paper will discuss the security protocols of wireless computer networks. WEP, WPA / WPA2 encryption and the reasons for the existence of such protocols and how they are applied will be explained. The security of WEP and WPA / WPA2 protocols will be tested with practical examples and penetration testing in a secure environment, using Airgeddon and Pixiewps tools. The operating system to be used is ParrotOS.

Impervious surface (IS) not only serves as a key indicator of urbanization, but also affects the micro-ecosystem. Therefore, it is essential to monitor IS distribution timely and accurately. Remote sensing is an effective approach as it can provide straightforward and consistent information over large area with low cost. This thesis integrates multi-source remote sensing data to interpretate urban patterns and provide more reliable IS mapping results.Registration of optical daytime and nighttime lights (NTL) data is developed in the first contribution. An impervious surface based optical-to-NTL image registration algorithm with iterative blooming effect reduction (IS_iBER) algorithm is proposed. This coarse-to-fine procedure investigates the correlation between optical and NTL features. The iterative registration and blooming effect reduction method obtains precise matching results and reduce the spatial extension of NTL.Considering the spatial transitional nature of urban-rural fringes (URF) areas, the second study proposed approach for URF delineation, namely optical and nighttime lights (NTL) data based multi-scale URF (msON_URF).The landscape heterogeneity and development vitality derived from optical and NTL features are analyzed at a series of scales to illustrate the urban-URF-rural pattern. Results illustrate that msON_URF is effective and practical for not only concentric, but also polycentric urban patterns.The third study proposes a nighttime light adjusted impervious surface index (NAISI) to detect IS area. Parallel to baseline subtraction approaches, NAISI takes advantage of features, rather than spectral band information to map IS. NAISI makes the most of independence between NTL-ISS and pervious surface to address the high spectral similarity between IS and bare soil in optical image.An optical and NTL based spectral mixture analysis (ON_SMA) is proposed to achieve sub-pixel IS mapping result in the fourth study. It integrates characteristics of optic

Information Technology (IT) outsourcing is a common practice, being adopted in many organisations in different industries and sectors of the worldwide market. Much of the research into IT outsourcing has focused on the use of IT outsourcing within private sector organisations, with less attention being paid to the use of IT outsourcing by government and the public sector. Relationships in IT outsourcing are of interest, juxtaposed against the older contracts in IT outsourcing; contracts and relationships are complementary, not substitutes. The purpose of this Study is to understand the differences between government versus private sector IT outsourcing, focusing on relationships. The researcher developed a classification of ‘tendencies’ in government IT outsourcing, characteristics that are more pronounced in government than in private sector IT outsourcing. Relational Exchange Theory was used to structure IT outsourcing into attributes, inherent characteristics that support the performance of the relationship (Goles and Chin, 2005). The researcher conducted an online survey, completed by government IT outsourcing managers, giving responses converted into tendency-attribute ‘combinations’ and analysed using statistical tests. The attributes were calculated using differing sample sizes, as some respondents abandoned the survey part-way through. Out of sixty combinations there were eighteen that were statistically significant at the 5% level. Most combinations came from Commitment, Consensus, Flexibility and Trust attributes. There are five tendencies out of ten that were strongly associated with these combinations. Some combinations appeared as if the scores have a bivariate distribution, but no clear evidence of bimodal distribution of the demographic variables was found. The low response rate to the survey was concerning; there was bias and sampling errors, there was inconsistent interpretation of the constructs, and there were respondent concerns about confidentia

This research aims to contribute rich, empirically informed insights into the emerging phenomenon of ‘ICTs and societal challenges’ in the developing country context. Despite the growing body of research on the use of ICTs in addressing societal challenges, our understanding of this phenomenon is limited regarding the active role played by both individuals and the technology itself. This research examines two case studies which offer different ways to advance understanding of the use of ICTs in resolving crucial yet under-researched societal problems in Myanmar that are explored at micro and macro levels of analysis. It uses multiple methods of data collection: documentation, website/social media analysis, interviews, focus groups and participant observation. It applies a ‘technology affordance’ approach as a theoretical lens that provides a relational concept, facilitating different users’ understanding of the available opportunities to use technology. The first study examines the emerging use of social media during a recent flood disaster in Myanmar at the micro (community) level. It focuses on how individuals (e.g., victims, volunteers and government officials) harness the power of social media for disaster response. We identify different affordances of social media and examine how these affordances enable local communities to respond to disaster situations and achieve relevant social outcomes, using the case of the 2015 Myanmar flood. The second study explores how mobile technology can increase financial access and bring socio-economic benefits to the country. We look into the current issues of financial exclusion and examine the potential of mobile money at the macro (ecosystem) level using the case of Wave Money, the first Fintech company to offer financial services in Myanmar. The findings demonstrate that realisation of a technology affordance differs between different user groups due to the various intentions of technology use in specific contexts. However

It has been argued that the current business environment is so dynamic and techno-centric that Chief Information Officers (CIOs) must be transformational leaders capable of providing the strategic vision expected of other C-Suite members. This requires CIOs to have the ability to drive change, generate business value and leverage innovative technologies for competitive advantage. As a result, many CIOs see themselves as nomads, with their role loosely defined by a technology-determined past but bound by a strategic business-focused future. Despite the changing organisational expectations of CIOs, there has been little research on the role of a CIO over the past 10 years and there is a paucity of research on the competencies required by CIOs.This study explores the changing roles and responsibilities of CIOs and identifies the competencies required to fulfil these changing roles and responsibilities. The study is based on over 30 hours of in-depth interviews with 30 CIOs in leading organisations across six industry sectors in Australia. Data analysis consisted of a four-stage process that first identified required competencies before linking these to the roles that CIOs must undertake in order to meet organisational expectations. Analysis revealed that all CIOs require a core set of Social & Behavioural, Cognitive and Functional competencies in order to fulfil their role as a C-Suite member. In addition, the CIO undertakes one or more of five specialised roles depending on the needs of the organisation. These specialised roles are as Catalyst of Change, Leader, Visionary, Architect, and as ‘Chameleon’, who can seamlessly collaborate with and connect a wide variety of internal and external stakeholders. The analysis reveals the additional competencies, beyond the core competencies, required to fulfil each of these roles. This study is the first piece of research in the CIO space to be true to the concept of competency as utilised in other fields. It provides a signifi

With the increasing volume of text documents, it is crucial to identify the themes and topics contained within. Labelling documents with the identified topics is called multi-label classification. Interdependencies exist between not just words, but sentences and paragraphs. These longer sequences and more complex relationships increase the label identification challenge. Five novel deep neural networks are proposed and evaluated for their performance classifying longer documents. The RCLNN applies the RCL to NLP, combining that model with a CNN which has demonstrated success on short text. The QRCNN similarly extends a CNN in addition to implementing it with a QRNN. The remaining three models build on these base models, integrating them in a novel pseudo-Siamese approach. Experiments find QRCNN highest performing overall, with the PSRCNNA model a close second, indicating that the pseudo-Siamese approach can be performant when married with attention., 2020-11

This thesis argues that the Digitally-Enabled Social Innovation (DESI) has contributed to generate a large number of important social benefits for the communities across the globe over the last decade. Yet, the essence of DESI has not been discussed to a certain degree through empirical studies. In particular, the process of attaining DESI from internal intervention perspective remains ambiguous. However, conceptualised process models based on the rich empirical data have the great potential to make the contribution to DESI and Information Systems (IS) research. Therefore, this thesis is intended to highlight the significance of studying process theory in developing a better understating of DESI, and eventually raise the awareness of IS researchers in exploring the social implications of Information Communication Technologies (ICTs) in the contemporary society.To attain the research goals, this thesis will introduce the emerging phenomena which provide the in-depth and comprehensive insight of DESI to answer the research question: How do communities achieve DESI? Mainly, three examples of DESI are selected and discussed, they are Digital Inclusion, Digital Empowerment and Digital Entrepreneurship. By identifying their traits and research gaps of each phenomenon, this proposal argues that Self-determination, Self-organization and Belief-Action-Outcome are the appropriate theoretical foundations to explore the underlying mechanisms of achieving DESI.Accordingly, in this thesis, interpretive case study method will be applied to address the research questions in order to achieve the proposed research goals. Using three case studies of Shaji Taobao Village, Daiji Taobao Village and Peixie Taobao Village, the agriculture communities in remote area of rural China that have grown into a vibrant e-commerce hub, the process model of each phenomenon will be depicted and validated through the empirical data.Last but not the least, a comparative study is also conducted, which ap

There has been enduring interest in the Information Systems (IS) literature in discovering and analysing the elements that characterise leadership and value creation. This thesis reviews the body of literature concerning shared and distributed leadership and examines how the perspectives found therein might add to the knowledge of IS leadership. Specifically, it examines IS leadership and value creation activities, by considering an expanded scope of IS leadership beyond the notion of a single heroic individual leader, often the Chief Information Officer (CIO) into a view which encapsulates leadership across multiple hierarchical levels within an organisation. This thesis presents its research in the form of three separate studies. Study 1 contains a review of the role of IS leadership specifically concerning its historical development, and how the expectations and pressures on IS leaders have changed over time. This study reviews how the IS leader role has evolved alongside various waves of technology changes, and identifies key themes in the literature including a summary of the number and types of roles that have been identified for the IS leader. Study 2 is a study of a particular company that builds on the information gleaned from the literature study. It examines the context in which IS leaders create value, and surveys over 100 IS leaders from within the IS department of a single organisation. The IS leaders were from multiple hierarchical levels, across middle management, and senior leadership jobs. Data was collected from triangulated sources that included interviews, a focus group, survey and archival documents. This company study research identified the key factors contributing to performance for IS leaders.Finally, Study 3 builds on the work completed in Studies 1 and 2. It uses an in-depth interview-type data collection method called the Repertory Grid Technique (RepGrid) to further the findings and insights. The results of 16 in-depth interviews were o

Cybersecurity emerges as an exemplary case of human-technology entanglement. Cybersecurity technologies that monitor security breaches, protect Information Technology (IT) infrastructure, and respond to and counter-attack cybercrimes, are getting deployed at an increasing rate across organisations worldwide. These technologies are highly complex, and their deployment involves profound technological, organisational and social transformation in an environment of ever-increasing cyber threats and the dramatic rise of interconnected digital devices. These present an urgent challenge for all stakeholders who need to understand the cybersecurity phenomenon in order to deal with its complex social and technological implications. This thesis explores the cybersecurity phenomenon in the context of government organisations responsible for the deployment, implementation and use and effects of cybersecurity technologies. A broad literature review on cybersecurity demonstrates that the dominant research focus has been on the technical aspects of cybersecurity technologies, including their design, functionality and performance, as well as recommending best practices. Limited research in information systems (IS) and social sciences has also addressed social and organisational aspects, primarily concerned with issues around privacy, surveillance and human rights, data sharing and protection, legislation and law enforcement, and psychological profiling of attackers. On the other hand, IS literature has addressed IT implementation, organisational change and adaptation in organisational context, focusing on specific IT systems (for example on ERP and CRM) but not cybersecurity technologies. Overall, the literature review shows that there is a lack of understanding of the cybersecurity phenomenon and the complex processes of the deployment, implementation and use of cybersecurity technologies.This thesis therefore aims to contribute to better understanding of cybersecurity technologies

Plasmonic is an emerging technology used to increase the performance of thin-film devices via light-trapping mechanisms. Recently, plasmonic devices have been used due to their nanoscale fabrication abilities. Metamaterials are promising candidates for improving the light absorption of plasmonic devices. The extraordinary properties of metamaterial-plasmonic devices (such as the perfect absorption of light) open up applications such as solar energy harvesting, photo-detecting and making batteries. This thesis focuses on the theoretical design and experimental studies of metamaterial-based perfect absorbers (MMPA) to improve their optical properties and applications (e.g., energy harvesting). I also design a high-power MMPA for high-power laser applications.First, I present the design and analysis of a high-power broadband perfect absorber. An array of rectangular patterns was placed to broaden the bandwidth so that the array can function between 950 nm and 1400 nm. Here, I propose a tungsten boride (a refractory ceramic) broadband metamaterial absorber and characterize its optical properties numerically and experimentally. I have also analyzed the damage characteristics of this absorber using a femtosecond laser and compared these with an ordinary gold metamaterial absorber.Second, a multilayer-based perfect absorber for omnidirectional capabilities is theoretically and experimentally analyzed. The device provides a reasonable amount of light absorption and thermal stability has been checked. Applying this device to high-temperature solar cell applications is discussed. I demonstrate a thermally stable broadband absorber based on an ultrathin layer of refractory ceramic,tungsten boride. I experimentally analyze and compare the performance of the absorber with an aluminum-based absorber. The multilayer perfect absorber has absorption higher than 85% in the wavelength range between 500 and 1600 nm over a large range of incident angles (up to 60 degrees). I show that a

Modern mobile platforms have formed their distinct hybrid concurrency system containing both traditional multi-threading and asynchronous event dispatching to support a diverse range of applications. However, this system introduces prevalent concurrency bugs, causing misbehaviour of mobile apps. It is challenging to detect concurrency bugs in mobile apps. Static analysis is prone to a high false positive rate due to the complexity of the hybrid concurrency system, while dynamic analysis suffers from the code coverage problem and the existence of conditional statements in programs creates obstacles to dynamically reaching the target statements. This thesis presents two new techniques for detecting event-based races in Android apps, where Android is the dominant mobile platform and has been plagued by event-based races.First, a novel static analysis tool, called Sard is introduced for detecting event-based races causing use-after-free violations in Android apps. This tool systematically models Android’s concurrency mechanism and applies a novel flow- and context-sensitive static happens-before analysis to reason the interleavings relations between different events and threads. Compared to the prior state-of-the-art tool, Sard achieves better precision (by reporting less false positives and false negatives) and better efficiency.Second, we propose Sieve, which combines both static and dynamic analysis to detect event-based races in Android apps. Sieve first applies static analysis to find all suspicious races, and then attempts to expose these races dynamically by leveraging a new selective branch instrumentation. For the conditionals potentially affecting a suspicious race, Sieve fixes the true/false outcomes of some of these conditionals based on a systematic branch analysis. By instrumenting certain branches selectively this way, Sieve can not only expose event-based races more effectively than the prior state-of-the-art techniques, but also substantially reduce the

Today’s business climate requires the IT infrastructure of organizations to be agile and dynamic so that it can respond quickly to new business models and requirements. Every organization is faced with the need to predict change in a global business environment, to rapidly respond to competitors, and to best exploit organizational assets for growth. Large organizations are adopting a service-oriented approach to adapt to changing circumstances caused by the complexity of implementing modern technologies from different vendors and uncertainty of business needs. Research shows that the lack of clarity surrounding the role of the governance of service-oriented systems by organizations is among the major reasons of service orientation failures. The purpose of this research is to increase our understanding of the governance of service orientation in practice. Based on a literature review, this study first proposes a list of aspects to be considered when implementing Service-Oriented Governance (SOG). By adopting an interpretive research methodology using interview-based study of practices with professionals as a first phase and case studies in the second phase, this research makes three contributions: i) it studies the positioning of SOG in organizations and its relationship to other types of governance ii) it discusses how organizations practice SOG and presents which frameworks are being implemented by organizations, and iii) it discourses the importance of the governance aspects as perceived by practitioners and their actual usage in practice. This study reveals the discrepancy between governance aspects’ importance and their usage and offers possible explanation of that discrepancy. It finds that IT Governance, EA Governance and Data Governance can be regarded as prerequisites to implement effective SOG. The results provide a theoretical contribution to conceptualizing SOG (in particular its key aspects) that can be used to assess SOG practices and provide guidance t

The growth of technology and systems personalisation is leading to increasing personal choice and power. Hence, it becomes increasingly difficult to force people to appropriate, adopt, use, etc., any technology. Relationship marketing sees relations as key to appropriation contexts and that the nature of relations is closely tied to individual identity concerns. Identity literature in IS posits that appropriation or use of technology is based not what the person necessarily is but what they hope to become, which is highly idiosyncratic. Therefore, it becomes increasingly important to understand the ‘why’ behind IS phenomena rather than stopping at lists of characteristics. Furthermore, personal construct theory and existentialism posit that quite often individuals’ identity concerns are highly contradictory to their actual everyday actions in moving towards a preferred self. Hence, to progress the relational literature in IS, this thesis employs the following research objective:To elicit and document the relational dimensions of people’s daily work existence at the intersection of people and technology.The findings challenge many fundamental assumptions underlying various theories employed in IS and management, particularly regarding change. They suggest that 1) people are not averse to change, rather, they demand it, 2) inertia depends largely on where the change is coming from as well as where in the individual’s personal construct system that relations with the object tied to the change are strongest or weakest, and 3) identity and existential concerns are at the heart of the explanation. However, this research offers a theoretical lens (existential phenomenology) and methodology (repertory grid plus laddering to core constructs) that provides a way forward. The methodology employs the repertory grid technique followed by laddering to core constructs. Both of these techniques consist of in-depth semi-structured interviews, and are considered qualitative in nature

Virtual worlds, as the computer-based simulated environments incorporating various representations of real-world elements, have great potential to not only transform the structures and operation modes of various industries but also change the way people work, do business, learn, play, and communicate. However, the existing sharp distinctions between virtual worlds and the real world also bring critical challenges. To address these challenges, the concept of telepresence—the user’s feeling of ‘being there’ in the virtual environments—is adopted as it is considered a direct and essential consequence of a virtual world’s reality. To cultivate this feeling, it is essential to understand what factors can lead to telepresence. However, some literature gaps on telepresence antecedents impede the understanding of telepresence antecedents and affect the adoption of the telepresence construct in the design of virtual worlds. To address these issues, this study explores the concept of telepresence in the context of virtual worlds. Specifically, by adopting means-end chain (MEC) theory, the study aims to investigate the antecedents of telepresence; to reveal the inter-relationships among these antecedents by building a hierarchical structure; and to develop an innovative approach for user segmentation to understand in-depth individual differences in perceiving telepresence. To achieve these objectives, the laddering interview technique was adopted to collect qualitative data from 25 participants. Analysis of the data identified 21 antecedents of telepresence. Based on their inter-relationships, a hierarchical structure was developed to capture the process of cultivating telepresence. Moreover, three types of users with distinctive paths leading to telepresence were classified. This study contributes to both the virtual world research and the telepresence literature and develops a new and meaningful user segmentation method. Finally, this study provides sufficient information an

VoIP user accounts are a prime target for hackers to compromise for profit. VoIP accounts are targets of the same types of attacks as any other Internet account that is authorized with a username and password. Unlike many other Internet accounts VoIP has a direct monetary cost to the user being compromised. Toll-fraud perpetrated using a compro- mised VoIP account can accrue expensive toll-charges that either the user or the service provider are liable to pay for. This paper discusses the prior research in detecting unau- thorized usage on VoIP accounts. The researched methods are based on machine learning techniques. A new technique of using a Recurrent Neural Network for detecting unau- thorized usage periods on a VoIP account is developed and demonstrated. The technique uses a Long-Short Term Memory style of Recurrent Neural Network to achieve over a 99% accuracy when testing against calls tagged as occurring during a toll-fraud event., 2018-10-22

Information and communication technology (ICT or IT) provides benefits to an organisation. However, a large number of IT projects fail. The research literature shows extant governance frameworks are not adequately protecting against project failures and are not as effective on many of these IT projects as they should be.This thesis developed a new IT governance framework using an agile benefit management approach, aimed to achieve benefits realisation for any scale of IT projects, particularly for projects in the public sector such as Defence. The framework objectively targets digital transformation and technological agility, however, is shown in the thesis to assist in other enterprise challenges with IT acquisition and through-life management, such as cyber-resilience.The framework is based on many theories, principles, and practices, such as: Transaction Cost Economics Theory, Prospect Theory (Decision-Making Under Uncertainty), Reference Class Forecasting, Stratification and the Incremental Enlargement Principle and Fuzzy Logic. The framework is shown to be effective primarily through better informed decision-making.Benefits realisation is critical for information technology project success. The framework provides a systematic methodology on how to define economic benefit, technical benefit, and strategic benefit. It provides benefit realisation measures through fuzzy inference system, and it provides decision support based on the benefit performance measures and dis-benefit risk management. When compared to industry-based frameworks for IT acquisition and sustainment this new framework is unique because it includes all internal and external stakeholders such as users, providers, industry, and academia to continuously collaborate for innovating, iterating, and evaluating technology for realisation of benefits to achieve the organisational goals and objectives. The proof of concept has been conducted through a detailed case study in the Defence public sector, and

Object-sensitivity is widely used as a context abstraction for computing the points-to information context-sensitively for object-oriented languages like Java. Due to the combinatorial explosion of contexts in large programs, k-object-sensitive pointer analysis (under k-limiting), k-obj, is scalable only for small values of k, where k≤2 typically. A few recent solutions attempt to improve its efficiency by instructing k-obj to analyse only some methods in the program context-sensitively, determined heuristically by a pre-analysis. While already effective, these heuristics-based pre-analyses do not provide precision guarantees, and consequently, are limited in the efficiency gains achieved. This thesis introduces a radically different approach, EAGLE, that makes k-obj run significantly faster than the prior art while maintaining its precision. The novelty of EAGLE is to enable k-obj to analyse a method with partial context-sensitivity, i.e., context-sensitively for only some of its selected variables/allocation sites. EAGLE makes these selections during a lightweight pre-analysis by reasoning about context-free-language (CFL) reachability at the level of variables/objects in the program, based on a new CFL-reachability formulation of k-obj. This thesis demonstrates the advances made by EAGLE by comparing it with the prior art in terms of a set of popular Java benchmarks and applications.

type:Journal Article, In this paper, we consider the uniform deployment problem of mobile agents in asynchronous unidirectional rings, which requires the agents to uniformly spread in the ring. The uniform deployment problem is in striking contrast to the rendezvous problem which requires the agents to meet at the same node. While rendezvous aims to break the symmetry, uniform deployment aims to attain the symmetry. It is well known that the symmetry breaking is difficult in distributed systems and the rendezvous problem cannot be solved from some initial configurations. Hence, we are interested in clarifying what difference the uniform deployment problem has on the solvability and the number of agent moves compared to the rendezvous problem. We consider two problem settings, with knowledge of k (or n) and without knowledge of k or n where k is the number of agents and n is the number of nodes. First, we consider agents with knowledge of k (or n since k and n can be easily obtained if one of them is given). In this case, we propose two algorithms. The first algorithm solves the uniform deployment problem with termination detection. This algorithm requires O(k log n) memory space per agent, O(n) time, and O(kn) total moves. The second algorithm also solves the uniform deployment problem with termination detection. This algorithm reduces the memory space per agent to O(log n), but uses O(n log k) time, and requires O(kn) total moves. Both algorithms are asymptotically optimal in terms of total moves since there are some initial configurations such that agents re- quire Ω(kn) total moves to solve the problem. Next, we consider agents with no knowledge of k or n. In this case, we show that, when termination detection is required, there exists no algorithm to solve the uniform deployment problem. For this reason, we consider the relaxed uniform deployment problem that does not require termination detection, and we propose an algorithm to solve the relaxed uniform deployment problem. This algo

type:Journal Article, Objective: We developed and demonstrated laser speckle flowgraphy (LSFG) for two-dimensional (2D) skin blood flow (SBF) measurements to facilitate the noninvasive comparisons of SBF between individuals. Approach: By using morphing technology with a face mesh to compare SBF spatial distributions among individuals, we examined the practicability of SBF measurement with LSFG. Main results: (1) The uniformity of SBF measurement was demonstrated by examinations at different distances and angles for healthy subjects. (2) Mean blur rate (MBR)—a blood flow index of LSFG—exhibited significant correlation with the thermal diffusion method (TDM)—an established blood flow measurement method—suggesting that MBR is an effective index of SBF. (3) Blowout time, the half-width duration/duration of one cardiac cycle, exhibited significant negative correlation with age and positive correlation with stratum corneum hydration. Significance: These results suggest that LSFG is useful for evaluating SBF-related skin properties, and it has significant potential in medicine and cosmetology.

Context. The growing prevalence and potential impact of cloud computing technology have sparked significant attention amongst IT industry and academia. It offers potential clients a wide range of services which are universally accessible, acquirable, releasable, and payable on the fly based on the amount of usage. Many IT-based organisations are moving their legacy software applications to cloud environments. An effective method for conducting such transition is crucial to a successful migration. The literature review reveals that the methodological aspect of the cloud migration has not been well addressed, despite calls expressed by several researchers to resolve this issue. The existing knowledge about cloud migration process is fragmented over the literature, narrow in tactical focus with heterogeneous viewpoints, and lacking support for creating customised methods, that fit given cloud migration scenarios.Objective. By adopting the design science paradigm, this research proposes a framework, which provides a generic process model of cloud migration with associated guidelines. The model can be reused and tailored for creating, configuring, and maintaining situational methods for various cloud migration scenarios.Method. The proposed framework is based a model-driven software development approach and offers two functionalities: (i) an overarching and platform-independent process model which captures domain constructs relevant to the cloud migration which a typical cloud migration may entail; and (ii) a tailoring procedure which aids creating, fine-tuning, standardising, and sharing situational methods for given cloud migration scenarios through reusing the process model. The framework is iteratively evaluated and refined by feedback received from domain experts and case studies.Contributions. The framework facilitates consistent communication and efficient knowledge sharing and exchange in the cloud migration domain. It also underpins a substrate for creating, sta

The current study responds to calls for research to increase the knowledge on how development firms could generate and enhance value from their distributed IT projects. Recent literature indicates the need for studies to address significant challenges of distributed information systems development teams, such as cultural incompatibility, lack of trust, customer collaboration, communication, lack of control and coordination (Mattsson et al. 2010). Based on a qualitative exploratory single-case study, this dissertation is concerned with the institution of effective governance frameworks to address distributed development project challenges, an area of research that is currently lacking empirical studies. In particular, the current study is seeking to understand how social governance mechanisms affect the governance of distributed software development projects. The current study shows the role and intervening processes of social governance mechanisms (Jones et al. 1997), including restricted access, macroculture, collective sanctions and reputation, to coordinate activities and safeguard exchanges. Furthermore, the current study suggests that to apply these social governance mechanisms effectively, it is critical that organisations maintain congruency among them. The study also found that all the four social governance mechanisms of the Jones et al. (1997) model interact with each other, thus showing the critical importance of macroculture among the social governance mechanisms and the impact that macroculture has on other mechanisms. Moreover, the current study found that the mentioned four social governance mechanisms are context dependent and have different impacts on safeguarding and coordinating exchanges in various contexts including Open Source Software. The current study also found a new construct, ‘ideological similarity’, which is about a preference for more frequent interactions among project teams with similar interests to facilitate smooth interactions and

Recent advancement in technology is breaking the barriers to societal change. From crowdfunding for the underserved community to mobilization for political injustice, the rise of social media has opened up unprecedented opportunities to address a wide range of complex societal challenges. This dissertation aims to illuminate and contribute rich, empirically informed insights into the phenomenon of social media enablement for societal challenges. Specifically, this dissertation comprises three related but standalone essays that discuss the use and implications of social media in addressing three different societal challenges. The essays are preceded by a background review on the phenomenon of interest, a discussion on the research significance and an overview of the dissertation structure, followed by an overview on the research methodology. The first essay explores the use of social media for disaster response; the second essay explores the use of social media for environmental sustainability; and the third essay explores the use of social media for civic engagement. Collectively, this dissertation makes two contributions: (1) to shed light on the enabling power of social media in addressing complex societal challenges and (2) to advance future studies on the under-researched phenomenon of social media enablement for societal challenges

Authors report the reoccurrence of Pareuchaetes pseudoinsulata Rego Barros larvae at Technopark campus, Thiruvananthapuram. The larvae of P. pseudoinsulata were found feeding voraciously on terminal and axillary buds and leaves of Chromolena odorata. The recent report helps to identify the elite and resistant population for further field release to control C. odorata.

This dissertation investigated teaching presence on Athabasca University’s e-learning commons: The Landing. As a qualitative, exploratory research study, it investigated teaching presence as it is defined in the community of inquiry framework using the transcript assessment tool developed by Anderson, Rourke, Garrison, and Archer (2001). Booth and Hultén’s (2003) taxonomy of discussion indicators was also used to investigate the nature and depth of discussion on the Landing and shed light on how teaching presence prompted learning. Finally, the study also investigated the Landing’s value to stakeholders using Wenger, Trayner, and de Laat’s (2011) conceptual framework for assessing value creation in communities and networks. The investigation of teaching presence on the Landing, the depth of discussion, and the creation of learning value as measured in value creation stories established a preliminary indication of the value the Landing offers to students, staff, and faculty at Athabasca University. While this study was limited to the examination of two self-paced courses on one e-learning commons, it added to the very limited literature measuring teaching presence in self-paced university courses supported by such an environment and shed light on the teaching affordances and attendant value e-learning commons can offer teaching and learning organizations. The investigation revealed that the instructional design and organization of the courses created a cooperative, dialogue, not discussion-based learning environment. It also attempted to code educational groups, nets, and sets within the environment and showed some evidence of a correlation between instructional design and organization and students feeling a sense of being a member of a supportive community. As a result, a new model for dialogue-based, cooperative e-learning is proposed., 2019-06

Over the past decade, crowdsourcing marketplaces — online exchange platforms which facilitate commercial outsourcing of services — have witnessed a dramatic growth in the number of participants (service providers and customers) and the value of outsourced services. On crowdsourcing marketplaces, provider profiles and especially previous customers’ feedback on these profiles are the main sources of information for the choice decisions of prospective customers. The information on these profiles signals the reputation and past performance of their owners. However, deciding about the most appropriate provider based on profiles is a key challenge for customers because the information on profiles may be incomplete and sometimes irrelevant for customer decisions. By adversely affecting customers’ choices, such ineffective profiles impede many service providers to develop long-term relationships with customers, transact on a regular basis and survive on crowdsourcing marketplaces. Given the important role of provider profile information in the customer–provider relationship development, previous studies have investigated the impact of simple components, such as “average ratings,” while more complex, compound information components have often been overlooked. Accordingly, the main objectives of this research are to reveal 1) the effects of main feedback information components on provider profiles on customers’ choice decisions, and 2) the impact of all common, simple and compound, profile information components on the long-term business outcomes of providers on crowdsourcing marketplaces. Two discrete choice experiments were conducted, one in a controlled laboratory (120 participants) and one on a crowdsourcing marketplace (695 participants), and the results were analysed through nested logit modelling to address the first objective. A longitudinal partial least squares (PLS) analysis of the impact of 4,545 provider profiles on a large crowdsourcing marketplace was also cond

Previous research has shown students that have Attention Deficit Hyperactivity Disorder (ADHD), a mental health disorder that displays excessive inattentive, impulsive, and hyperactive behavioural symptoms, have an increased risk of having Written Language Disorder (WLD), a learning disorder that displays written composition issues (multiple spelling, grammar and punctuation mistakes, sentences that lack cohesion and topic flow, and written assignment completion). The prevalence of WLD without ADHD or another mental health disorder is quite rare. To measure the prevalence of ADHD and WLD in a student, this research created a computational 'artificial neural network (ANN)' model that combined the outcomes of common screening tools for ADHD with written performance tasks as a measure for WLD. A study based on this ANN shows that ADHD students have a high prevalence of WLD in comparison to typical (non-ADHD or control) students, demonstrating the link between ADHD and WLD., 2019-11

Published Thesis, Environmental pollution caused by mines within the district of Lejweleputswa in Free State is a major contributor to health issues and the inability to grow crops within the mining communities. Mining industries continue to develop environmental management systems/plans to mitigate the impact their operations has on the society. Even with these plans, there are still issues of environmental pollution affecting the society. Though there are Information Communication and Technology (ICT) based pollution monitoring solutions, their use is dismal due to lack of appreciation or understanding of how they disseminate information. Furthermore, non-adopting community members are being regarded as inherently conservative or irrational, but these community members argue that the recommendations and technologies brought to them are not always appropriate to their circumstances. There was concern that local people’s knowledge of their environment, farming systems, and their social as well as economic situation had been ignored and underestimated when ICTs solutions are being implemented (Warburton & Martin, 1999). Another challenge is that there is no station to monitor pollution for small communities such as Nyakallong in the district. This result in mining communities depending on their own local knowledge to observe and monitor mining related environmental pollution. However, this local knowledge has never been tested scientifically or analysed to recognize its usability or effectiveness. Mining companies tend to ignore this knowledge from the communities as it is treated like common information with no much scientific value. As a step towards verifying or validating this local knowledge, fuzzy cognitive maps were used to model, analyse and represent this linguistic local knowledge. Although this local knowledge assists in mitigating environmental pollution, incorporating it with scientific knowledge will improve its relevance, trustworthiness and acceptability by majority of

Publishe Thesis, Information and Communication Technology (ICT) has given rise to new technologies and solutions that were not possible a few years ago. One of these new technologies is electronic voting, also known as e-voting, which is the use of computerised equipment to cast a vote. One of the subsets of e-voting is mobile voting (m-voting). M-voting is the use of mobile phones to cast a vote outside the restricted electoral boundaries. Mobile phones are pervasive; they offer connection anywhere, at any time. However, utilising a fast-growing medium such as the mobile phone to cast a vote, poses various new security threats and challenges. Mobile phones utilise equivalent software design used by personal computers which makes them vulnerable or exposed to parallel security challenges like viruses, Trojans and worms. In the past, security solutions for mobile phones encountered several restrictions in practice. Several methods were used; however, these methods were developed to allow lightweight intrusion detection software to operate directly on the mobile phone. Nevertheless, such security solutions are bound to fail securing a device from intrusions as they are constrained by the restricted memory, storage, computational resources, and battery power of mobile phones. This study compared and evaluated two intrusion detection systems (IDSs), namely Snort and Suricata, in order to propose a cloud-based intrusion detection and prevention system (CIDPS) for m-voting in South Africa. It employed simulation as the primary research strategy to evaluate the IDSs. A quantitative research method was used to collect and analyse data. The researcher established that as much as Snort has been the preferred intrusion detection and prevention system (IDPS) in the past, Suricata presented more effective and accurate results close to what the researcher anticipated. The results also revealed that, though Suricata was proven effective enough to protect m-voting while saving the computational res

Thesis (Master of Engineering (Electrical)) -- Central University of Technology, Free State, 2018, Much effort is spent in regulating the power quality in alternating current power supplies for electronic devices. Many electronic devices, however, do not use alternating current, but rather direct current. The output of most small scale renewable energy systems are also direct current, so it can be connected to the loads more efficiently by eliminating the inverter stage. In a circuit with a number of rectification stages the conversion losses can add up to a significant amount. By reducing the number of conversion stages or possibly eliminating some of the stages the overall system could be more efficient. The purpose of this dissertation is to present the simulation design and results of a direct current distribution system, containing common household appliances connected to a direct current grid supply and a renewable energy source. A bottom-up design approach is used where a list of household appliances with their voltage needs is identified and the distribution voltage is then selected based on the voltage needs. The distribution system is modelled using Matlab and Simulink software. Results show that common household loads can be supplied directly with direct current, from either a main direct current grid supply, or a renewable energy system with direct current output. This direct current distribution system is compared to two other systems: (1) Existing alternating current system and (2) Hybrid system (converting alternating current to direct current for distribution in the house). The three systems are compared to each other in terms of power efficiency and material cost. The existing alternating current system is shown to be the most efficient, with an average power efficiency of 87.85 %. The second most efficient system is the hybrid system with average power efficiency of 86.95 %, and the least efficient of the three is the direct current distribution system with 86.45 %. The main reason why the direct current system is less efficient is because of th

Every year, millions of patients in the world suffer from health care-associated infections (HCAIs) caused by antibiotic-resistant bacteria (ARB). ARB are transmitted to patients in a ward by temporarily contaminated hands of health care workers (HCWs) in patient-care activities. Therefore, hand hygiene of HCWs is an important and effective way to prevent ARB transmission among patients and ensure patient safety. However, HCWs wash their hands with high compliance in some situations, but with low compliance in other situations. Namely, they wash their hands strategically. HCWs’ strategic hand hygiene behaviour has been observed to lead to a generally low compliance. This research studies HCWs’ strategic hand hygiene behaviour and provides guidance to hospitals about intervention decision-making to improve compliance. In particular, a strategic behaviour model is developed by using an evolutionary game to understand how HCWs adjust their hand washing behaviour because of peer influence in intensive interactions. This model suggests three contingent behaviours that depend heavily on peers’ choices: bandwagoning, free-riding, and prosocial hand hygiene behaviour. The conditions for how contingent behaviours appear are also identified. In addition, this study investigates the drivers for these contingent behaviours by conducting a discrete choice experiment (DCE) and best-worst scaling experiment (BWS). The DCE and BWS are also used to provide further insights on how hospitals can design effective interventions, from the perspective of a HCW. This study uses inspection policy as an example of an intervention in order to illustrate how to use a decision model that integrates an evolutionary game model and a transmission dynamics model for the allocation of resources for hand hygiene interventions.

To explain how websites are used to encourage volunteering and philanthropy. Chapter 1 explains (i) why encouraging prosocial behaviour (i.e., volunteering, philanthropy and activism) through Information and Communications Technology (ICT) is socially important and (ii) how a lack of relevant research is limiting the effectiveness of organisations at using websites (a form of ICT) to encourage prosocial behaviour: a research gap. It then discusses how this study helps to address this research gap by explaining how websites are used to encourage volunteering and philanthropy (two forms of prosocial behaviour). As using websites to encourage prosocial behaviour involves persuasion (i.e., changing individuals’ attitudes and behaviours), Chapter 2 uses two extensive literature searches to provide a foundation for understanding persuasion, which informs the later studies. Chapter 3 uses the repertory grid interview technique, and six live volunteering websites, to explain how and why websites lead to prosocial intention (to engage in volunteering). Chapter 4 uses the perceptions identified in Chapter 3 within a model and evaluates if this model can predict users' prosocial behaviour (i.e. volunteering and philanthropy) after using a prosocial website. (Chapter 2) A common frame of reference for IS on the concept of persuasion, explaining its definition, conceptualisation, and differentiation from related terms. (Chapter 3) A comprehensive explanation of how, through the roles of specific features, websites create specific perceptions, which, through specific motivational triggers, lead to prosocial intention (to engage in volunteering). (Chapter 4) A theoretical model showing how specific perceptions of website influence subsequent prosocial behaviour (i.e. volunteering and philanthropy). The research contributes (1) a comprehensive explanation of the concept of persuasion for future IS research to draw on where

Objective – To better understand participation and non-participation in online communities.Research questions – Understanding of participation focuses on a digital divide denying access to online communities. Policy assumes individuals will participate given access to technology. Social capital theory claims online participation benefits society through increased collective action. Critique of prevalent ontological assumptions underlying existing understanding of participation challenges the separation of the social and the technological. The objective is to understand participation from a relational perspective, explaining (1) why and how people participate and (2) why people do not participate and the impacts.Methodology – An ethnographic field study in a parenting community using interviewing and participant observation techniques yielded rich data on participatory behaviour and relationships with social media. Data were interpreted through iterative thematic and narrative analysis and theorized using sociomaterial concepts.Major findings – Participating online is motivated by needs for well-being, information sharing, autonomy, social contact, and entertainment. Online participation materializes as changes to what is communicated, when and where. Reasons for not participating online emerge as perceived lack of value, discouraged by some feature of online communication, and fear of repercussions. Not participating online affects relationships, limits participation in the lives of community members and reduces ability to capitalise on opportunities. This challenges assumptions of participation with access, creating an opportunity to better inform policies.Contributions – The study extends understanding of what it means to participate in community in the digital age. Participation, performed as a sociomaterial practice, is enacted online and offline contemporaneously through entanglements of social actors, social media, community values, beliefs, norms and rules fo

Online group buying, as a new form of e-commerce, is one of the most innovative e-commerce business models. This model offers great opportunities for e-marketers, but also brings new challenges. In the last four years, more than 5000 online group buying vendors in China have gone out of business. Existing literature suggests that online group buying is distinctive from online shopping, and consumers are motivated to use online group buying to enjoy different benefits. As little research has been conducted on understanding consumers in this context, group buying websites have few guidelines to follow to improve competitiveness.This thesis addresses this challenge by exploring the hierarchical motivations underlying consumers’ online group buying behaviour and the development of consumer typologies from a new perspective. Specifically, utilising a Uses and Gratifications (U&G) approach and Means-End Chain (MEC) theory perspective, this study aims not only to explore the content of online group buyers’ motivations, but also uncover the inter-relationships among these motivations. Moreover, it aims to introduce a new method to segment consumer, based on benefit-level motivations, which can provide more accurate consumer typologies.To reach these objectives the laddering interview technique was used to collect data from 58 online group buyers in China. A context-specific hierarchical motive model was developed, based on the 35 motivations identified, which not only indicated consumer value/goal fulfilment paths, but also illustrated the relative importance of different paths. Moreover, three typologies of group buyers with distinct value/goal fulfilment paths were identified, fundamentally different from existing shopper typologies in the e-commerce context.As a timely topic using a novel approach to explore consumer online group buying motivations, this study adds to the online group buying literature, introduces a new segmentation approach that overcomes the limitation

В статье рассмотрена динамика экономически активного населения Архангельской области, проведен статистический анализ, составлен интервальный прогноз численности экономически активного населения региона на 2017, 2018 гг.

В статье рассматривается роль визуализации при изучении высшей математики, а также возможности применения интерактивной геометрической среды GeoGebra для создания изображений кривых второго порядка. Применение интерактивной геометрической среды GeoGebra при изучении кривых второго порядка позволяет обеспечить визуализацию изучаемых математических объектов.

В статье рассматриваются возможности применения интерактивной геометрической среды GeoGebra для визуализации такого абстрактного раздела высшей математики, как комплексные числа, при изучении студентами инженерных специальностей и направлений подготовки.