13,677 research outputs found

    mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds

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    Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity data. Despite connectomes inhabiting a matrix manifold, most analytical frameworks ignore the underlying data geometry. This is largely because simple operations, such as mean estimation, do not have easily computable closed-form solutions. We propose a geometrically aware neural framework for connectomes, i.e., the mSPD-NN, designed to estimate the geodesic mean of a collections of symmetric positive definite (SPD) matrices. The mSPD-NN is comprised of bilinear fully connected layers with tied weights and utilizes a novel loss function to optimize the matrix-normal equation arising from Fr\'echet mean estimation. Via experiments on synthetic data, we demonstrate the efficacy of our mSPD-NN against common alternatives for SPD mean estimation, providing competitive performance in terms of scalability and robustness to noise. We illustrate the real-world flexibility of the mSPD-NN in multiple experiments on rs-fMRI data and demonstrate that it uncovers stable biomarkers associated with subtle network differences among patients with ADHD-ASD comorbidities and healthy controls.Comment: Accepted into IPMI 202

    Marketing Through Microcultures on Social Media: An Examination of BookTok and Independent Bookstores

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    This thesis explores the role of microcultures in social media marketing, by focusing on the BookTok microculture and its significant role in the marketing strategies of independent bookstores. The research includes an examination of microcultures and their impact on businesses, the effectiveness of the key social media marketing tactics used on TikTok, what BookTok is, how independent bookstores engage with BookTok, and an interview with an independent bookstore on their uses of BookTok

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

    Feature Extraction Methods for CT-Scan Images Using Image Processing

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    Medical image processing covers various types of images such as tomography, mammography, radiography (X-Ray images), cardiogram, CT scan images etc. Once the CT scan image is captured, Doctors diagnose it to detect abnormal or normal condition of the captured of the patient’s body. In the computerized image processing diagnosis, CT-scan image goes through sophisticated phases viz., acquisition, image enhancement, extraction of important features, Region of Interest (ROI) identification, result interpretation etc. Out of these phases, a feature extraction phase plays a vital role during automated/computerized image processing to detect ROI from CT-scan image. This phase performs scientific, mathematical and statistical operations/algorithms to identify features/characteristics from the CT-scan image to shrink image portion for diagnosis. In this chapter, I have presented an extensive review on “Feature Extraction” step of digital image processing based on CT-scan image of human being

    Organizations decentered: data objects, technology and knowledge

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    Data are no longer simply a component of administrative and managerial work but a pervasive resource and medium through which organizations come to know and act upon the contingencies they confront. We theorize how the ongoing technological developments reinforce the traditional functions of data as instruments of management and control but also reframe and extend their role. By rendering data as technical entities, digital technologies transform the process of knowing and the knowledge functions data fulfil in socioeconomic life. These functions are most of the times mediated by putting together disperse and steadily updatable data in more stable entities we refer to as data objects. Users, customers, products, and physical machines rendered as data objects become the technical and cognitive means through which organizational knowledge, patterns, and practices develop. Such conditions loosen the dependence of data from domain knowledge, reorder the relative significance of internal versus external references in organizations, and contribute to a paradigmatic contemporary development that we identify with the decentering of organizations of which digital platforms are an important specimen

    Developing a new approach for machine learning explainability combining local and global model-agnostic approaches

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    The last couple of past decades have seen a new flourishing season for the Artificial Intelligence, in particular for Machine Learning (ML). This is reflected in the great number of fields that are employing ML solutions to overcome a broad spectrum of problems. However, most of the last employed ML models have a black-box behavior. This means that given a certain input, we are not able to understand why one of these models produced a certain output or made a certain decision. Most of the time, we are not interested in knowing what and how the model is thinking, but if we think of a model which makes extremely critical decisions or takes decisions that have a heavy result on people’s lives, in these cases explainability is a duty. A great variety of techniques to perform global or local explanations are available. One of the most widespread is Local Interpretable Model-Agnostic Explanations (LIME), which creates a local linear model in the proximity of an input to understand in which way each feature contributes to the final output. However, LIME is not immune from instability problems and sometimes to incoherent predictions. Furthermore, as a local explainability technique, LIME needs to be performed for each different input that we want to explain. In this work, we have been inspired by the LIME approach for linear models to craft a novel technique. In combination with the Model-based Recursive Partitioning (MOB), a brand-new score function to assess the quality of a partition and the usage of Sobol quasi-Montecarlo sampling, we developed a new global model-agnostic explainability technique we called Global-Lime. Global-Lime is capable of giving a global understanding of the original ML model, through an ensemble of spatially not overlapped hyperplanes, plus a local explanation for a certain output considering only the corresponding linear approximation. The idea is to train the black-box model and then supply along with it its explainable version

    Investigating and mitigating the role of neutralisation techniques on information security policies violation in healthcare organisations

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    Healthcare organisations today rely heavily on Electronic Medical Records systems (EMRs), which have become highly crucial IT assets that require significant security efforts to safeguard patients’ information. Individuals who have legitimate access to an organisation’s assets to perform their day-to-day duties but intentionally or unintentionally violate information security policies can jeopardise their organisation’s information security efforts and cause significant legal and financial losses. In the information security (InfoSec) literature, several studies emphasised the necessity to understand why employees behave in ways that contradict information security requirements but have offered widely different solutions. In an effort to respond to this situation, this thesis addressed the gap in the information security academic research by providing a deep understanding of the problem of medical practitioners’ behavioural justifications to violate information security policies and then determining proper solutions to reduce this undesirable behaviour. Neutralisation theory was used as the theoretical basis for the research. This thesis adopted a mixed-method research approach that comprises four consecutive phases, and each phase represents a research study that was conducted in light of the results from the preceding phase. The first phase of the thesis started by investigating the relationship between medical practitioners’ neutralisation techniques and their intention to violate information security policies that protect a patient’s privacy. A quantitative study was conducted to extend the work of Siponen and Vance [1] through a study of the Saudi Arabia healthcare industry. The data was collected via an online questionnaire from 66 Medical Interns (MIs) working in four academic hospitals. The study found that six neutralisation techniques—(1) appeal to higher loyalties, (2) defence of necessity, (3) the metaphor of ledger, (4) denial of responsibility, (5) denial of injury, and (6) condemnation of condemners—significantly contribute to the justifications of the MIs in hypothetically violating information security policies. The second phase of this research used a series of semi-structured interviews with IT security professionals in one of the largest academic hospitals in Saudi Arabia to explore the environmental factors that motivated the medical practitioners to evoke various neutralisation techniques. The results revealed that social, organisational, and emotional factors all stimulated the behavioural justifications to breach information security policies. During these interviews, it became clear that the IT department needed to ensure that security policies fit the daily tasks of the medical practitioners by providing alternative solutions to ensure the effectiveness of those policies. Based on these interviews, the objective of the following two phases was to improve the effectiveness of InfoSec policies against the use of behavioural justification by engaging the end users in the modification of existing policies via a collaborative writing process. Those two phases were conducted in the UK and Saudi Arabia to determine whether the collaborative writing process could produce a more effective security policy that balanced the security requirements with daily business needs, thus leading to a reduction in the use of neutralisation techniques to violate security policies. The overall result confirmed that the involvement of the end users via a collaborative writing process positively improved the effectiveness of the security policy to mitigate the individual behavioural justifications, showing that the process is a promising one to enhance security compliance
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