5,036 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    The relationship between environmental statistics and predictive gaze behaviour during a manual interception task: Eye movements as active inference

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    This is the final version. Available from Springer via the DOI in this record. Data Availability: All relevant data and code is available online from: https://osf.io/tgx6r/.Human observers are known to frequently act like Bayes-optimal decision-makers. Growing evidence indicates that the deployment of the visual system may similarly be driven by probabilistic mental models of the environment. We tested whether eye movements during a dynamic interception task were indeed optimised according to Bayesian inference principles. Forty-one participants intercepted oncoming balls in a virtual reality racquetball task across five counterbalanced conditions in which the relative probability of the ball’s onset location was manipulated. Analysis of pre-onset gaze positions indicated that eye position tracked the true distribution of onset location, suggesting that the gaze system spontaneously adhered to environmental statistics. Eye movements did not, however, seek to minimise the distance between the target and foveal vision according to an optimal probabilistic model of the world and instead often reflected a ‘best guess’ about onset location. Trial-to-trial changes in gaze position were, however, found to be better explained by Bayesian learning models (hierarchical Gaussian filter) than associative learning models. Additionally, parameters relating to the precision of beliefs and prediction errors extracted from the participant-wise models were related to both task-evoked pupil dilations and variability in gaze positions, providing further evidence that probabilistic context was reflected in spontaneous gaze dynamics.Leverhulme Trus

    Opportunities and risks of stochastic deep learning

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    This thesis studies opportunities and risks associated with stochasticity in deep learning that specifically manifest in the context of adversarial robustness and neural architecture search (NAS). On the one hand, opportunities arise because stochastic methods have a strong impact on robustness and generalisation, both from a theoretical and an empirical standpoint. In addition, they provide a framework for navigating non-differentiable search spaces, and for expressing data and model uncertainty. On the other hand, trade-offs (i.e., risks) that are coupled with these benefits need to be carefully considered. The three novel contributions that comprise the main body of this thesis are, by these standards, instances of opportunities and risks. In the context of adversarial robustness, our first contribution proves that the impact of an adversarial input perturbation on the output of a stochastic neural network (SNN) is theoretically bounded. Specifically, we demonstrate that SNNs are maximally robust when they achieve weight-covariance alignment, i.e., when the vectors of their classifier layer are aligned with the eigenvectors of that layer's covariance matrix. Based on our theoretical insights, we develop a novel SNN architecture with excellent empirical adversarial robustness and show that our theoretical guarantees also hold experimentally. Furthermore, we discover that SNNs partially owe their robustness to having a noisy loss landscape. Gradient-based adversaries find this landscape difficult to ascend during adversarial perturbation search, and therefore fail to create strong adversarial examples. We show that inducing a noisy loss landscape is not an effective defence mechanism, as it is easy to circumvent. To demonstrate that point, we develop a stochastic loss-smoothing extension to state-of-the-art gradient-based adversaries that allows them to attack successfully. Interestingly, our loss-smoothing extension can also (i) be successful against non-stochastic neural networks that defend by altering their loss landscape in different ways, and (ii) strengthen gradient-free adversaries. Our third and final contribution lies in the field of few-shot learning, where we develop a stochastic NAS method for adapting pre-trained neural networks to previously unseen classes, by observing only a few training examples of each new class. We determine that the adaptation of a pre-trained backbone is not as simple as adapting all of its parameters. In fact, adapting or fine-tuning the entire architecture is sub-optimal, as a lot of layers already encode knowledge optimally. Our NAS algorithm searches for the optimal subset of pre-trained parameters to be adapted or fine-tuned, which yields a significant improvement over the existing paradigm for few-shot adaptation

    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Nationwide tracing of two top freshwater fish invaders in Greece using environmental DNA sampling

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    Alien fish invasions are causing devastating impacts on native freshwater fauna; thus, rigorous, non-invasive and cost-effective biomonitoring of lotic and lentic freshwaters to design and implement appropriate prevention and control measures is now a priority. In this study, we used a species-specific qPCR eDNA assay to monitor two of the most invasive fish species (Gambusia holbrooki and Pseudorasbora parva) in 15 river basins of Greece and validated these results with conventional fish sampling as well as existing field sampling data. Our monitoring provided new records of invasive species indicating basins for rigorous future monitoring and possible eradication attempts. The eDNA proved more sensitive as a detection tool (56% detection rate) compared to conventional electrofishing (50% detection rate) for G. holbrooki. In contrast, it proved less sensitive for detecting P. parva (38% accuracy) compared to electrofishing (44% accuracy), as evident by the two locations where the eDNA failed to detect the target species. Our study illustrates the potential of the eDNA method for regular, standardised monitoring of riverine habitats for invasive fish species by local managers for early detection. Finally, we discuss the application of eDNA in management interventions for identifying invasive species’ hotspots for management prioritisation, for early detection of invaders and for the monitoring of eradication/control actions

    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 Trust Management Framework for Vehicular Ad Hoc Networks

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    The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers
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