The main aim of this research is to develop an on-line tool wear condition monitoring intelligent system for single-point turning operations. This is to provide accurate and reliable information on the different states of tool wear. Calibrated acoustic emission and vibration techniques were implemented to monitor the progress of wear on carbide tool tips. Previous research has shown that acoustic emission (AE) is sensitive to tool wear. However, AE, as a monitoring technique, is still not widely adopted by industry. This is because it is as yet impossible to achieve repeatable measurements of AE. The variability is due to inconsistent coupling of the sensor with structures and the fact that the tool structure may have different geometry and material property. Calibration is therefore required so that the extent of variability becomes quantifiable, and hence accounted for or removed altogether. Proper calibration needs a well-defined and repeatable AE source. In this research, various artificial sources were reviewed in order to assess their suitability as an AE calibration source for the single-point machining process. Two artificial sources were selected for studying in detail. These are an air jet and a pulsed laser; the former produces continuous-type AE and the latter burst type AE. Since the air jet source has a power spectrum resembling closely the AE produced from single-point machining and since it is readily available in a machine shop, not to mention its relative safety compared to laser, an air-jet source is a more appealing choice. The calibration procedure involves setting up an air jet at a fixed stand-off distance from the top rake of the tool tip, applying in sequence a set of increasing pressures and measuring the corresponding AE. It was found that the root-mean-square value of the AE obtained is linearly proportional to the pressure applied. Thus, irrespective of the layout of the sensor and AE source in a tool structure, AE can be expressed in terms of the common currency of 'pressure' using the calibration curve produced for that particular layout. Tool wear stages can then be defined in terms of the 'pressure' levels. In order to improve the robustness of the monitoring system, in addition to AE, vibration information is also used. In this case, the acceleration at the tool tip in the tangential and feed directions is measured. The coherence function between these two signals is then computed. The coherence is a function of the vibration frequency and has a value ranging from 0 to 1, corresponding to no correlation and full correlation respectively between the two acceleration signals. The coherence function method is an attempt to provide a solution, which is relatively insensitive to the dynamics and the process variables except tool wear. Three features were identified to be sensitive to tool wear and they are; AErms, and the coherence function of the acceleration at natural frequency (2.5-5.5 kHz) of the tool holder and at high frequency end (18-25kHz) respectively. A belief network, based on Bayes' rule, was created providing fusion of data from AE and vibration for tool wear classification. The conditional probabilities required for the belief network to operate were established from examples. These examples were presented to the belief network as a file of cases. The file contains the three features mentioned earlier, together with cutting conditions and the tool wear states. Half of the data in this file was used for training while the other half was used for testing the network. The performance of the network gave an overall classification error rate of 1.6 % with the WD acoustic emission sensor and an error rate of 4.9 % with the R30 acoustic emission sensor.

Static authentication, where user identity is checked once at login time, can be circumvented no matter how strong the authentication mechanism is. Through attacks such as man-in-the-middle and its variants, an authenticated session can be hijacked later after the initial login process has been completed. In the last decade, continuous authentication (CA) using biometrics has emerged as a possible remedy against session hijacking. CA consists of testing the authenticity of the user repeatedly throughout the authenticated session as data becomes available. CA is expected to be carried out unobtrusively, due to its repetitive nature, which means that the authentication information must be collectible without any active involvement of the user and without using any special purpose hardware devices (e.g. biometric readers). Stylometry analysis, which consists of checking whether a target document was written or not by a specific individual, could potentially be used for CA. Although stylometric techniques can achieve high accuracy rates for long documents, it is still challenging to identify an author for short documents, in particular when dealing with large author populations. In this dissertation, we propose a new framework for continuous authentication using authorship verification based on the writing style. Authorship verification can be checked using stylometric techniques through the analysis of linguistic styles and writing characteristics of the authors. Different from traditional authorship verification that focuses on long texts, we tackle the use of short messages. Shorter authentication delay (i.e. smaller data sample) is essential to reduce the window size of the re-authentication period in CA. We validate our method using different block sizes, including 140, 280, and 500 characters, and investigate shallow and deep learning architectures for machine learning classification. Experimental evaluation of the proposed authorship verification approach based on the Enron emails dataset with 76 authors yields an Equal Error Rate (EER) of 8.21% and Twitter dataset with 100 authors yields an EER of 10.08%. The evaluation of the approach using relatively smaller forgery samples with 10 authors yields an EER of 5.48%. / Graduate

Large systems often have a long life-span and their system and software architecture design comprise many intricately related elements. The verification and maintenance of these architecture designs require an understanding of how and why the system are constructed. Design rationale is the reasoning behind a design and it provides an explanation of the design. However, the reasoning is often undocumented or unstructured in practice. This causes difficulties in the understanding of the original design, and makes it hard to detect inconsistencies, omissions and conflicts without any explanations to the intricacies of the design. Research into design rationale in the past has focused on argumentation-based design deliberations. Argumentation-based design rationale models provide an explicit representation of design rationale. However, these methods are ineffective in communicating design reasoning in practice because they do not support tracing to design elements and requirements in an effective manner. In this thesis, we firstly report a survey of practising architects to understand their perception of the value of design rationale and how they use and document this knowledge. From the survey, we have discovered that practitioners recognize the importance of documenting design rationale and frequently use them to reason about their design choices. However, they have indicated certain barriers to the use and documentation of design rationale. The results have indicated that there is no systematic approach to using and capturing design rationale in current architecture design practice. Using these findings, we address the issues of representing and applying architecture design rationale. We have constructed a rationale-based architecture model to represent design rationale, design objects and their relationships, which we call Architecture Rationale and Element Linkage (AREL). AREL captures both qualitative and quantitative rationale for architecture design. Quantitative rationale uses costs, benefits and risks to justify architecture decisions. Qualitative rationale documents the issues, arguments, alternatives and tradeoffs of a design decision. With the quantitative and qualitative rationale, the AREL model provides reasoning support to explain why architecture elements exist and what assumptions and constraints they depend on. Using a causal relationship in the AREL model, architecture decisions and architecture elements are linked together to explain the reasoning of the architecture design. Architecture Rationalisation Method (ARM) is a methodology that makes use of AREL to facilitate architecture design. ARM uses cost, benefit and risk as fundamental elements to rank and compare alternative solutions in the decision making process. Using the AREL model, we have proposed traceability and probabilistic techniques based on Bayesian Belief Networks (BBN) to support architecture understanding and maintenance. These techniques can help to carry out change impact analysis and rootcause analysis. The traceability techniques comprise of forward, backward and evolution tracings. Architects can trace the architecture design to discover the change impacts by analysing the qualitative reasons and the relationships in the architecture design. We have integrated BBN to AREL to provide an additional method where probability is used to evaluate and reason about the change impacts in the architecture design. This integration provides quantifiable support to AREL to perform predictive, diagnostic and combined reasoning. In order to align closely with industry practices, we have chosen to represent the rationale-based architecture model in UML. In a case study, the AREL model is applied retrospectively to a real-life bank payment systems to demonstrate its features and applications. Practising architects who are experts in the electronic payment system domain have been invited to evaluate the case study. They have found that AREL is useful in helping them understand the system architecture when they compared AREL with traditional design specifications. They have commented that AREL can be useful to support the verification and maintenance of the architecture because architects do not need to reconstruct or second-guess the design reasoning. We have implemented an AREL tool-set that is comprised of commercially available and custom-developed programs. It enables the capture of architecture design and its design rationale using a commercially available UML tool. It checks the well-formedness of an AREL model. It integrates a commercially available BBN tool to reason about the architecture design and to estimate its change impacts.

Tropical coral reefs are among the most diverse ecosystems of the world but facing increasing threats to their health. Over the last thirty years, many Caribbean coral reefs have undergone dramatic changes and experienced large losses in coral cover, due to direct and indirect anthropogenic disturbances. The results of which are reefs with low rugosity, changed trophic dynamics and low fish diversity. In recent times reefs have failed to recover from disturbances due to an increase in frequency and severity of disturbances and stresses. In the Caribbean on many coral reefs this has resulted in a shift towards macroalgal dominance by species of the phylum Phaeophyta. The processes and factors affecting the standing crop of macroalgae are many and complex. Two main hypotheses are identified in the literature as being the driving forces of algal dynamics: nutrient dynamics (availability, supply and uptake) and herbivory. However, many studies have been found to be inconclusive because of the complexity of the coral reef ecosystem, which makes it difficult if not impossible to control for all factors and processes influencing the standing crop of macroalgae such as light, water flow and sedimentation. The inherent characteristics of macroalgae, like morphology and life history, make them behave differently. Whilst herbivore characteristics, like size of mouth parts, feeding modes and preferences, will influence the amount of algal biomass removed. The spatial context (i.e. coral fore reef vs. back reef) will influence the effects of both bottom-up and top-down controls. Besides these inter-habitat differences, macroalgae within similar habitats but differing geographical locations may respond differently, for example, a forereef exposed to the open ocean or a forereef located in a sheltered bay. This thesis attempts to provide insight into the dynamics of two dominant brown macroalgae on Caribbean coral reefs, Dictyota spp. and Lobophora variegata. This aim was addressed by developing a model for the macroalga species Dictyota to model the various processes and factors on a coral forereef affecting percentage cover. Further, the patch dynamics of both Lobophora variegata and Dictyota were investigated to gain an insight into their dynamics under varying environmental conditions: the windward and leeward sides of an atoll. Finally, herbivory is identified as one of the key process affecting macroalgal cover. I investigated this process by deploying cages on both the windward and leeward side of the atoll to investigate the effects of grazing pressure under varying environmental conditions. A Bayesian Belief Network model was developed for Dictyota spp. to model the bottom-up and top-down processes on a coral forereef determining the percentage cover. The model was quantified using relationships identified in the scientific literature and from field data collected over a nine moth period in Belize. This is the first BBN model developed for brown macroalgae. The fully parameterized model identified areas of limited knowledge and because of its probabilistic nature it can explicitly communicate the uncertainties associated with the processes and interactions on standing crop. As such the model may be used as a framework for scientific research or monitoring programmes and it is expected that the model performance to predict macroalgal percentage cover will improve once new information becomes available. Size-based transition matrices were developed for both Dictyota spp. and Lobophora variegata to investigate the patch dynamics under varying environmental conditions: the windward and leeward sides of an atoll. The matrices reveal that standard measures of algal percent cover might provide a misleading insight into the underlying dynamics of the species. Modelling the patch dynamics with matrices provided insight into the temporal behaviour of macroalgae. This is an important process to understand because patch dynamics are determining competitive interactions with other coral reef benthic organisms. The outcome of competitive interactions will differ with macroalgal species. This study indicate that Dictyota spp. responded strongly to differing environmental conditions in that it has reduced growth rates and lower percent cover on the leeward side of the atoll, whilst Lobophora variegata showed far less sensitivity to environmental conditions. The patch dynamics of Dictyota spp. also showed a higher temporal variation than Lobophora variegata but only on the exposed forereef. A caging experiment was set up to investigate the response of both macroalgal species to different grazing pressure scenarios, under varying environmental conditions. Dictyota spp. had a significant response to environmental conditions in that a higher percentage cover was found on the exposed side of the atoll, whilst for Lobophora variegata the response was far less obvious. The less clear response of Lobophora variegata was very likely caused by competition of Dictyota with Lobophora due to the very high cover Dictyota obtained in the cages where all herbivores were excluded. The low grazing pressure treatments also showed an increase in cover of Dictyota, whilst for Lobophora, only a reduction in the rate of increase could be observed. The results indicate that on the leeward side of the atoll, fish grazing alone seems sufficient to control the standing crop of Dictyota and Lobophora variegata. Retrospective analysis of the experimental design showed that the limited size of the experimental set up could have confounded the results for Lobophora as well. In future experiments it is recommended to increase number replicates. Management of coral reef habitats is frequently constrained by a lack of funds and resources. The BBN Model once fully parameterized can provide a useful tool for coral reef management, because the model allows exploration of different reef scenario’s, which in turn can aid in prioritizing management strategies. Furthermore, the thesis provided an insight into the complexities of macroalgal dynamics. The responses of macroalgae to physiological factors and ecological processes are species specific and dependent on the location, and caution against generalizing on what controls the standing crop of macroalgae. Therefore it is argued that future investigations into algal ecology should clearly define the species, habitat and location. This can help to make informed management decisions.

The notion of social capital (SC) is increasingly used as a framework for describing social issues in terrestrial communities. For more than a decade, researchers use the term to mean the set of trust, institutions, social norms, social networks, and organizations that shape the interactions of actors within a society and that are considered to be useful and assets for communities to prosper both economically and socially. Despite growing popularity of social capital especially, among researchers in the social sciences and the humanities, the concept remains ill-defined and its operation and benefits limited to terrestrial communities. In addition, proponents of social capital often use different approaches to analyze it and each approach has its own limitations. <p>This thesis examines social capital within the context of technology-mediated communities (also known as virtual communities) communities. It presents a computational model of social capital, which serves as a first step in the direction of understanding, formalizing, computing and discussing social capital in virtual communities. The thesis employs an eclectic set of approaches and procedures to explore, analyze, understand and model social capital in two types of virtual communities: virtual learning communities (VLCs) and distributed communities of practice (DCoP). <p>There is an intentional flow to the analysis and the combination of methods described in the thesis. The analysis includes understanding what constitutes social capital in the literature, identifying and isolating variables that are relevant to the context of virtual communities, conducting a series of studies to further empirically examine various components of social capital identified in three kinds of virtual communities and building a computational model. <p>A sensitivity analysis aimed at examining the statistical variability of the individual variables in the model and their effects on the overall level of social capital are conducted and a series of evidence-based scenarios are developed to test and update the model. The result of the model predictions are then used as input to construct a final empirical study aimed at verifying the model.<p>Key findings from the various studies in the thesis indicated that SC is a multi-layered, multivariate, multidimensional, imprecise and ill-defined construct that has emerged from a rather murky swamp of terminology but it is still useful for exploring and understanding social networking issues that can possibly influence our understanding of collaboration and learning in virtual communities. Further, the model predictions and sensitivity analysis suggested that variables such as trust, different forms of awareness, social protocols and the type of the virtual community are all important in discussion of SC in virtual communities but each variable has different level of sensitivity to social capital. <p>The major contributions of the thesis are the detailed exploration of social capital in virtual communities and the use of an integrated set of approaches in studying and modelling it. Further, the Bayesian Belief Network approach applied in the thesis can be extended to model other similar complex online social systems.

The notion of social capital (SC) is increasingly used as a framework for describing social issues in terrestrial communities. For more than a decade, researchers use the term to mean the set of trust, institutions, social norms, social networks, and organizations that shape the interactions of actors within a society and that are considered to be useful and assets for communities to prosper both economically and socially. Despite growing popularity of social capital especially, among researchers in the social sciences and the humanities, the concept remains ill-defined and its operation and benefits limited to terrestrial communities. In addition, proponents of social capital often use different approaches to analyze it and each approach has its own limitations. <p>This thesis examines social capital within the context of technology-mediated communities (also known as virtual communities) communities. It presents a computational model of social capital, which serves as a first step in the direction of understanding, formalizing, computing and discussing social capital in virtual communities. The thesis employs an eclectic set of approaches and procedures to explore, analyze, understand and model social capital in two types of virtual communities: virtual learning communities (VLCs) and distributed communities of practice (DCoP). <p>There is an intentional flow to the analysis and the combination of methods described in the thesis. The analysis includes understanding what constitutes social capital in the literature, identifying and isolating variables that are relevant to the context of virtual communities, conducting a series of studies to further empirically examine various components of social capital identified in three kinds of virtual communities and building a computational model. <p>A sensitivity analysis aimed at examining the statistical variability of the individual variables in the model and their effects on the overall level of social capital are conducted and a series of evidence-based scenarios are developed to test and update the model. The result of the model predictions are then used as input to construct a final empirical study aimed at verifying the model.<p>Key findings from the various studies in the thesis indicated that SC is a multi-layered, multivariate, multidimensional, imprecise and ill-defined construct that has emerged from a rather murky swamp of terminology but it is still useful for exploring and understanding social networking issues that can possibly influence our understanding of collaboration and learning in virtual communities. Further, the model predictions and sensitivity analysis suggested that variables such as trust, different forms of awareness, social protocols and the type of the virtual community are all important in discussion of SC in virtual communities but each variable has different level of sensitivity to social capital. <p>The major contributions of the thesis are the detailed exploration of social capital in virtual communities and the use of an integrated set of approaches in studying and modelling it. Further, the Bayesian Belief Network approach applied in the thesis can be extended to model other similar complex online social systems.

Though adaptational effects are found throughout the visual system, the underlying mechanisms and benefits of this phenomenon are not yet known. In this work, the visual system is modeled as a Deep Belief Network, with a novel “post-training” paradigm (i.e. training the network further on certain stimuli) used to simulate adaptation in vivo. An optional sparse variant of the DBN is used to help bring about meaningful and biologically relevant receptive fields, and to examine the effects of sparsification on adaptation in their own right. While results are inconclusive, there is some evidence of an attractive bias effect in the adapting network, whereby the network’s representations are drawn closer to the adapting stimulus. As a similar attractive bias is documented in human perception as a result of adaptation, there is thus evidence that the statistical properties underlying the adapting DBN also have a role in the adapting visual system, including efficient coding and optimal information transfer given limited resources. These results are irrespective of sparsification. As adaptation has never been tested directly in a neural network, to the author’s knowledge, this work sets a precedent for future experiments. / text

Modeling comprehensive human decision behaviors in a unified and extensible framework is quite challenging. In this research, an integrated Belief-Desire-Intention (BDI) modeling framework is proposed to represent the human decision behavior, whose submodules (Belief, Desire, Decision-Making, and Emotion modules) are based on a Bayesian belief network (BBN), Decision-Field-Theory (DFT), a probabilistic depth first search (PDFS) technique, and a BBN-reinforcement (Q-Learning) hybrid learning algorithm. A key novelty of the proposed model is its ability to represent various human decision behaviors such as decision-making, decision-planning, and learning in a unified framework.To this end, first, we extend DFT (a widely known psychological model for preference evolution) to cope with dynamic environments. The extended DFT (EDFT) updates the subjective evaluation for the alternatives and the attention weights on the attributes via BBN under the dynamic environment. To illustrate and validate the proposed EDFT, a human-in-the-loop experiment is conducted for a virtual stock market. Second, a new approach to represent learning (a dynamic evolution process of underlying modules) in the human decision behavior is proposed under the context of the BDI framework. Our research focuses on how a human adjusts his perception process (involving BBN) dynamically against his performance (depicted via a confidence index) in predicting the environment as part of his decision-planning. To this end, Q-learning is employed and further developed.To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE). The proposed modeling framework is demonstrated for a human's evacuation behaviors in response to a terrorist bomb attack. The constructed simulation has been used to test the impact of several factors (e.g., demographics, number of police officers, information sharing via speakers) on evacuation performance (e.g., average evacuation time, percentage of casualties).In addition, the proposed human decision behavior model is extended for decisions of many stakeholders that form a complex social network in the community-based development of software systems.To the best of our knowledge, the proposed human decision behavior modeling framework is one of the first efforts to represent various human decision behaviors (e.g., decision-making, decision-planning, dynamic learning) in a unified BDI framework.

Much of the discourse on cultural change has been descriptive and explanatory, with few attempts to be predictive. Where indicators of and buffers to change are identified, they tend only to be post-event assessable. The need for a tool with strong predictive power is fundamental to cultural (and environmental) impact assessment and the rationale behind this developmental work. Focusing on traditional cultures and their environmental context, and based on a case study of the Juatinga Ecological Reserve, Brasil, this research advances knowledge on modelling cultural and environmental changes, and how to manage these changes for accepted goals. A heuristic tool is presented for assessing the impacts of pressures on a culture and its related environment as well as the efficacy of management responses. This tool is associated with methods to assist in developing predictive models representing the change processes. The change model building process involves consulting stakeholders as a way of integrating different perceptions, to identify pressures, responses and links associated with cultural and environmental change. This assists in creating a co-learning environment, which facilitates communication between stakeholders. The change modelling approach permits incorporation of the complexity and uncertainty of the system represented, and enables scenario analyses. These allow expected local and flow-on impacts of management interventions to be tested. This approach is more efficient than stand-alone performance indicators that do not allow for the synergic impacts of management interventions to be observed and assessed. Using the models representing the cultural and environmental change processes of the Caiçaras of the Juatinga Ecological Reserve, this research identifies that tourism is a major pressure for change (at that locality). This study also identified that tourist numbers at new and small tourism destinations, as well as on a continental scale, can be forecast using exponential and polynomial functions. Yet, tourism flow may be perturbed at any given time by, for example, acts of violence and when the type of marketing changes. In addition, tourist numbers cannot be greater than the total population, therefore it cannot grow indefinitely as exponential and polynomial functions suggest. Hence, the use of exponential and polynomial functions to forecast tourist numbers is more reliable for short periods, such as four or five years, and when based on six or more sets of data points. The greatest contribution of this research to the cultural change discourse is its innovative approach to study, forecast and manage cultural and environmental changes. The continuation of this research may lead to identifying general theories relating pressures and responses to indicators of cultural and environmental changes.

Much of the discourse on cultural change has been descriptive and explanatory, with few attempts to be predictive. Where indicators of and buffers to change are identified, they tend only to be post-event assessable. The need for a tool with strong predictive power is fundamental to cultural (and environmental) impact assessment and the rationale behind this developmental work. Focusing on traditional cultures and their environmental context, and based on a case study of the Juatinga Ecological Reserve, Brasil, this research advances knowledge on modelling cultural and environmental changes, and how to manage these changes for accepted goals. A heuristic tool is presented for assessing the impacts of pressures on a culture and its related environment as well as the efficacy of management responses. This tool is associated with methods to assist in developing predictive models representing the change processes. The change model building process involves consulting stakeholders as a way of integrating different perceptions, to identify pressures, responses and links associated with cultural and environmental change. This assists in creating a co-learning environment, which facilitates communication between stakeholders. The change modelling approach permits incorporation of the complexity and uncertainty of the system represented, and enables scenario analyses. These allow expected local and flow-on impacts of management interventions to be tested. This approach is more efficient than stand-alone performance indicators that do not allow for the synergic impacts of management interventions to be observed and assessed. Using the models representing the cultural and environmental change processes of the Caiçaras of the Juatinga Ecological Reserve, this research identifies that tourism is a major pressure for change (at that locality). This study also identified that tourist numbers at new and small tourism destinations, as well as on a continental scale, can be forecast using exponential and polynomial functions. Yet, tourism flow may be perturbed at any given time by, for example, acts of violence and when the type of marketing changes. In addition, tourist numbers cannot be greater than the total population, therefore it cannot grow indefinitely as exponential and polynomial functions suggest. Hence, the use of exponential and polynomial functions to forecast tourist numbers is more reliable for short periods, such as four or five years, and when based on six or more sets of data points. The greatest contribution of this research to the cultural change discourse is its innovative approach to study, forecast and manage cultural and environmental changes. The continuation of this research may lead to identifying general theories relating pressures and responses to indicators of cultural and environmental changes.