4,435 research outputs found

    Semi-Federated Learning of an Embedding Space Across Multiple Machine Clusters

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    Provided are systems and methods for privacy-preserving learning of a shared embedding space for data split across multiple separate clusters of computing machines. In one example, the multiple separate clusters of computing machines can correspond to multiple separate data silos

    Hybrid feature selection based ScC and forward selection methods

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    Operational data is always huge. A preprocessing step is needed to prepare such data for the analytical process so the process will be fast. One way is by choosing the most effective features and removing the others. Feature selection algorithms (FSAs) can do that with a variety of accuracy depending on both the nature of the data and the algorithm itself. This inspires researchers to keep on developing new FSAs to give higher accuracies than the existing ones. Moreover, FSAs are essential for reducing the cost and effort of developing information system applications. Merging multiple methodologies may improve the dimensionality reduction rate retaining sensible accuracy. This research proposed a hybrid feature selection algorithm based on ScC and forward selection methods (ScCFS). ScC is based on stability and correlation while forward selection is based on Random Forest (RF) and Information Gain (IG). A lowered subset generated by ScC is fed to the forward selection method which uses the IG as a decision criterion for selecting the attribute to split the node of the RF to generate the optimal reduct. ScCFS was compared to other known FSAs in terms of accuracy, AUC, and F-score using several classification algorithms and several datasets. Results showed that the ScCFS excels other FSAs employed for all classifiers in terms of accuracy except FLM where it comes in second place. This proves that ScCFS is the pioneer in generating the reduced dataset with remaining high accuracies for the classifiers used

    ENHANCING CLOUD SYSTEM RUNTIME TO ADDRESS COMPLEX FAILURES

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    As the reliance on cloud systems intensifies in our progressively digital world, understanding and reinforcing their reliability becomes more crucial than ever. Despite impressive advancements in augmenting the resilience of cloud systems, the growing incidence of complex failures now poses a substantial challenge to the availability of these systems. With cloud systems continuing to scale and increase in complexity, failures not only become more elusive to detect but can also lead to more catastrophic consequences. Such failures question the foundational premises of conventional fault-tolerance designs, necessitating the creation of novel system designs to counteract them. This dissertation aims to enhance distributed systems’ capabilities to detect, localize, and react to complex failures at runtime. To this end, this dissertation makes contributions to address three emerging categories of failures in cloud systems. The first part delves into the investigation of partial failures, introducing OmegaGen, a tool adept at generating tailored checkers for detecting and localizing such failures. The second part grapples with silent semantic failures prevalent in cloud systems, showcasing our study findings, and introducing Oathkeeper, a tool that leverages past failures to infer rules and expose these silent issues. The third part explores solutions to slow failures via RESIN, a framework specifically designed to detect, diagnose, and mitigate memory leaks in cloud-scale infrastructures, developed in collaboration with Microsoft Azure. The dissertation concludes by offering insights into future directions for the construction of reliable cloud systems

    On the real world practice of Behaviour Driven Development

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    Surveys of industry practice over the last decade suggest that Behaviour Driven Development is a popular Agile practice. For example, 19% of respondents to the 14th State of Agile annual survey reported using BDD, placing it in the top 13 practices reported. As well as potential benefits, the adoption of BDD necessarily involves an additional cost of writing and maintaining Gherkin features and scenarios, and (if used for acceptance testing,) the associated step functions. Yet there is a lack of published literature exploring how BDD is used in practice and the challenges experienced by real world software development efforts. This gap is significant because without understanding current real world practice, it is hard to identify opportunities to address and mitigate challenges. In order to address this research gap concerning the challenges of using BDD, this thesis reports on a research project which explored: (a) the challenges of applying agile and undertaking requirements engineering in a real world context; (b) the challenges of applying BDD specifically and (c) the application of BDD in open-source projects to understand challenges in this different context. For this purpose, we progressively conducted two case studies, two series of interviews, four iterations of action research, and an empirical study. The first case study was conducted in an avionics company to discover the challenges of using an agile process in a large scale safety critical project environment. Since requirements management was found to be one of the biggest challenges during the case study, we decided to investigate BDD because of its reputation for requirements management. The second case study was conducted in the company with an aim to discover the challenges of using BDD in real life. The case study was complemented with an empirical study of the practice of BDD in open source projects, taking a study sample from the GitHub open source collaboration site. As a result of this Ph.D research, we were able to discover: (i) challenges of using an agile process in a large scale safety-critical organisation, (ii) current state of BDD in practice, (iii) technical limitations of Gherkin (i.e., the language for writing requirements in BDD), (iv) challenges of using BDD in a real project, (v) bad smells in the Gherkin specifications of open source projects on GitHub. We also presented a brief comparison between the theoretical description of BDD and BDD in practice. This research, therefore, presents the results of lessons learned from BDD in practice, and serves as a guide for software practitioners planning on using BDD in their projects

    Applications of Deep Learning Models in Financial Forecasting

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    In financial markets, deep learning techniques sparked a revolution, reshaping conventional approaches and amplifying predictive capabilities. This thesis explored the applications of deep learning models to unravel insights and methodologies aimed at advancing financial forecasting. The crux of the research problem lies in the applications of predictive models within financial domains, characterised by high volatility and uncertainty. This thesis investigated the application of advanced deep-learning methodologies in the context of financial forecasting, addressing the challenges posed by the dynamic nature of financial markets. These challenges were tackled by exploring a range of techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders (AEs), and variational autoencoders (VAEs), along with approaches such as encoding financial time series into images. Through analysis, methodologies such as transfer learning, convolutional neural networks, long short-term memory networks, generative modelling, and image encoding of time series data were examined. These methodologies collectively offered a comprehensive toolkit for extracting meaningful insights from financial data. The present work investigated the practicality of a deep learning CNN-LSTM model within the Directional Change framework to predict significant DC events—a task crucial for timely decisionmaking in financial markets. Furthermore, the potential of autoencoders and variational autoencoders to enhance financial forecasting accuracy and remove noise from financial time series data was explored. Leveraging their capacity within financial time series, these models offered promising avenues for improved data representation and subsequent forecasting. To further contribute to financial prediction capabilities, a deep multi-model was developed that harnessed the power of pre-trained computer vision models. This innovative approach aimed to predict the VVIX, utilising the cross-disciplinary synergy between computer vision and financial forecasting. By integrating knowledge from these domains, novel insights into the prediction of market volatility were provided

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    Advances in machine learning algorithms for financial risk management

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    In this thesis, three novel machine learning techniques are introduced to address distinct yet interrelated challenges involved in financial risk management tasks. These approaches collectively offer a comprehensive strategy, beginning with the precise classification of credit risks, advancing through the nuanced forecasting of financial asset volatility, and ending with the strategic optimisation of financial asset portfolios. Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression model is then applied to predict the probability of default using the heuristically balanced datasets. The results underscore the effectiveness of our proposed technique, with superior performance observed in comparison to other imbalanced preprocessing approaches. This advancement in credit risk classification lays a solid foundation for understanding individual financial behaviours, a crucial first step in the broader context of financial risk management. Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a Triple Discriminator Generative Adversarial Network with a continuous wavelet transform is proposed. The proposed model has the ability to decompose volatility time series into signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a Generative Adversarial Network consisting of triple Discriminator and Generator networks. The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised loss and reconstruction loss as part of its framework. Data from nine financial assets are employed to demonstrate the effectiveness of the proposed model. This approach not only enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis. Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio optimisation using historical Low, High, and Close prices of assets as input with weights of assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return on investment based on deep reinforcement learning. To provide more learning stability in an online training process, a Markov Differential Sharpe Ratio reward function has been proposed as the reinforcement learning objective function. Additionally, a Multi-Memory Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout a specified trading period. The use of the insights gained from volatility forecasting into this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving superior results based on risk-adjusted reward performance measures. In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the accuracy of credit risk classification, through the improvement and understanding of market volatility, to optimisation of investment strategies. These methodologies collectively show the potential of the use of machine learning to improve financial risk management

    A novel approach to intrusion detection using zero-shot learning hybrid partial labels

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    Computer networks have become the backbone of our interconnected world in today's technologically driven landscape. Unauthorized access or malicious activity carried out by threat actors to acquire control of network resources, exploit vulnerabilities, or undermine system integrity are examples of network intrusion. ZSL(Zero-Shot Learning) is a machine learning paradigm that addresses the problem of detecting and categorizing objects or concepts that were not present in the training data. . Traditional supervised learning algorithms for intrusion detection frequently struggle with insufficient labeled data and may struggle to adapt to unexpected assault patterns. In this article We have proposed a unique zero-shot learning hybrid partial label model suited to a large image-based network intrusion dataset to overcome these difficulties. The core contribution of this study is the creation and successful implementation of a novel zero-shot learning hybrid partial label model for network intrusion detection, which has a remarkable accuracy of 99.12%. The suggested system lays the groundwork for future study into other feature selection techniques and the performance of other machine learning classifiers on larger datasets. Such research can advance the state-of-the-art in intrusion detection and improve our ability to detect and prevent the network attacks. We hope that our research will spur additional research and innovation in this critical area of cybersecurity

    Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

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    Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice

    Towards predicting and tailoring properties of energetic materials

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    The field of energetic materials (EMs) involves the study of materials (explosives, propellants, and pyrotechnics) that can release a significant amount of energy when initiated. This property renders EMs particularly useful to a wide array of industries including space travel (rocket propellants), mining (demolition charges), and defence applications. The propensity to release a significant amount of energy upon initiation means these materials are inherently dangerous, as such they are subjected to stringent safety requirements, and must be rigorously characterised prior to use. The safety of an EM is often quantified through the evaluation of the sensitivity (propensity to initiate) with respect to different stimuli such as impact, shock, friction, and electric spark. The focus of this work is the impact sensitivity, a solid-state property which can be influenced through changes in the orientation of molecules in 3D space (polymorphism or co-crystallisation) as well as through changing the structure or bonding environment of the molecules comprising the material. Prediction of this metric has been shown in previous work within the group to be computationally achievable for molecular EMs if the crystal structure of the material is known. This is completed through use of the vibrational up-pumping methodology. Vibrational up-pumping refers to the process by which mechanical impact energy excites delocalised low energy motions in a material and is subsequently channelled upwards into localised molecular vibrations. The vibrational states excited through up-pumping are termed the two-phonon density of states, which represents a measure of how efficiently the initial energy can become trapped on the molecular vibrations. Projection of the twophonon density of states onto the underlying vibrational character yields the up-pumped density which shows a correlation with experimental impact sensitivity. To this date, this method has been applied exclusively to molecular EMs, successfully reproducing experimental sensitivities. While important, focusing on solely molecular materials overlooks those of growing importance such as co-crystals, salts and coordination polymers. Application of the vibrational up-pumping methodology to materials from these areas of growing interest forms the backbone for the work presented in this thesis. Chapter 2 addresses a number of areas within the vibrational up-pumping methodology that could be improved upon, namely, the generation of consistent phonon density of states (g(w)) spectra as well as partial g(w) spectra, the determination of the location of uppermost phonon frequency (Wmax) and the interrogation of vibrational modes within the solid-state vibrations to track the local modes of vibration (bond stretches and angle bends). Three Python scripts have been developed to address these problems and improve the efficiency and applicability of the process by which the impact sensitivity of an EM is predicted via the vibrational up-pumping methodology. Chapter 3 focuses on two unexpected findings that had recently come to light in the EMs group at Edinburgh: a co-crystal of FOX-7 with the non-energetic p-phenylenediamine (PPD) that appeared to be more hazardous to mechanical impact than the pure EM, and a new high-pressure polymorph of 3,4,5-trinitro-1H-pyrazole (TNP) that was markedly more sensitive to initiation than the ambient pressure polymorph. For the former study, strong hydrogen bonding interactions significantly altered the molecular conformation of FOX- 7. For the latter, the molecular conformation remained unchanged in the ambient and high-pressure polymorphs, meaning that crystal packing or pressure-induced vibrational mode hardening must account for the increase in mechanical sensitivity. Taken together both studies present challenges for the up-pumping model, which if successful would allow important structure/property connections to be made. Chapter 4 focuses on salt coordination polymers, all of which present as exceptionally sensitive EMs. The study began with lead azide (LA), which is often used in small quantities as a detonator for a much larger mass of a less sensitive EM. It is well documented that lead has drastic adverse effects to both people and the environment and as such REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) has issued a ban on the use of LA. This has necessitated the development of a number of ‘green’ copper-containing replacements (DBX-1, DBX-2, DBX-3 and Cu(ADNP)) with comparable impact sensitivity and detonation characteristics such that they could potentially be used as drop-in replacements. This type of EM has not been studied before using the vibrational up-pumping procedure; they present a number of unique challenges, exemplified primarily by the need to separate the lattice modes from the molecular modes, which is a key requirement of the vibrational up-pumping model. In this chapter a full discussion on a range of mechanochemical models are investigated, from simple phonon heating, through to up-pumping and consideration of target (i.e. trigger mode) activation. Culminating in the development of a workflow for the treatment of such materials in the future within the vibrational up-pumping methodology. In Chapter 5 the emphasis switches towards applying the up-pumping model in a wider capacity to explore the effects of molecular structure on the impact sensitivity of molecular energetics. Here, the investigation centred on a series of chemically related EMs from three common families, namely pyrazoles, tetrazoles and nitrate esters. A number of these materials only differ by the location or substitution of a single functional group, and yet taken together cover a wide range of impact sensitivity response. Successful predictions of their respective impact sensitivities by the up-pumping model would therefore present a unique opportunity to fully explore structure/property relationships, with molecular flexibility, functional group identity and proximity being key structural features to explore. The data set also allowed further exploration of the trigger mode activation introduced in Chapter 4, where only the weakest bonds in the molecules are vibrationally excited by up-pumping. This approach improves the physical basis for impact sensitivity prediction. Collectively, this thesis explores the application of the vibrational up-pumping methodology to various EMs that present with greater structural complexity than the single-component molecular materials that it was initially designed to model. This work has been aided by the development of supplementary Python scripts which attempt to improve both the efficiency and applicability of the vibrational up-pumping methodology. If successful this work will act to considerably validate vibrational up-pumping, as well as to provide the opportunity to explore in-depth structure/property relationships, to understand the physical basis of impact sensitivity. Such understanding may lead to the development of tailored EMs with desired physical properties in the future
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