Acceptance and profitability modelling for consumer loans

This thesis explores and models the relationships between offers of credit products, credit scores, consumers' acceptance decisions and expected profits generated using data that records actual choices made by customers and their monthly account status after being accepted. Based on Keeney and Oliver's theoretical work, this thesis esti¬ mates the expected profits for the lender at the time of application, draws the iso-profit curves and iso-preference curves, derives optimal policy decisions subject to various constraints and compares the economic benefits after the segmentation analysis.This thesis also addresses other research issues that have emerged during the explo¬ ration into profitability and acceptance. We use a Bivariate Sample Selection model to test the existence of sample selection bias and found that acceptance inference may not be necessary for our data. We compared the predictive performance of Support Vector Machines (SVMs) vs. Logistic Regression (LR) on default data as well as on accep¬ tance data, without finding that SVMs outperform LR. We applied different Survival Analysis models on two events of interest, default and paying back early. Our results favoured semi-parametric PH-Cox models separately estimated for each hazard

Deep audio-visual speech recognition

Decades of research in acoustic speech recognition have led to systems that we use in our everyday life. However, even the most advanced speech recognition systems fail in the presence of noise. The degraded performance can be compensated by introducing visual speech information. However, Visual Speech Recognition (VSR) in naturalistic conditions is very challenging, in part due to the lack of architectures and annotations. This thesis contributes towards the problem of Audio-Visual Speech Recognition (AVSR) from different aspects. Firstly, we develop AVSR models for isolated words. In contrast to previous state-of-the-art methods that consists of a two-step approach, feature extraction and recognition, we present an End-to-End (E2E) approach inside a deep neural network, and this has led to a significant improvement in audio-only, visual-only and audio-visual experiments. We further replace Bi-directional Gated Recurrent Unit (BGRU) with Temporal Convolutional Networks (TCN) to greatly simplify the training procedure. Secondly, we extend our AVSR model for continuous speech by presenting a hybrid Connectionist Temporal Classification (CTC)/Attention model, that can be trained in an end-to-end manner. We then propose the addition of prediction-based auxiliary tasks to a VSR model and highlight the importance of hyper-parameter optimisation and appropriate data augmentations. Next, we present a self-supervised framework, Learning visual speech Representations from Audio via self-supervision (LiRA). Specifically, we train a ResNet+Conformer model to predict acoustic features from unlabelled visual speech, and find that this pre-trained model can be leveraged towards word-level and sentence-level lip-reading. We also investigate the Lombard effect influence in an end-to-end AVSR system, which is the first work using end-to-end deep architectures and presents results on unseen speakers. We show that even if a relatively small amount of Lombard speech is added to the training set then the performance in a real scenario, where noisy Lombard speech is present, can be significantly improved. Lastly, we propose a detection method against adversarial examples in an AVSR system, where the strong correlation between audio and visual streams is leveraged. The synchronisation confidence score is leveraged as a proxy for audio-visual correlation and based on it, we can detect adversarial attacks. We apply recent adversarial attacks on two AVSR models and the experimental results demonstrate that the proposed approach is an effective way for detecting such attacks.Open Acces

PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis

Debugging performance anomalies in real-world databases is challenging. Causal inference techniques enable qualitative and quantitative root cause analysis of performance downgrade. Nevertheless, causality analysis is practically challenging, particularly due to limited observability. Recently, chaos engineering has been applied to test complex real-world software systems. Chaos frameworks like Chaos Mesh mutate a set of chaos variables to inject catastrophic events (e.g., network slowdowns) to "stress" software systems. The systems under chaos stress are then tested using methods like differential testing to check if they retain their normal functionality (e.g., SQL query output is always correct under stress). Despite its ubiquity in the industry, chaos engineering is now employed mostly to aid software testing rather for performance debugging. This paper identifies novel usage of chaos engineering on helping developers diagnose performance anomalies in databases. Our presented framework, PERFCE, comprises an offline phase and an online phase. The offline phase learns the statistical models of the target database system, whilst the online phase diagnoses the root cause of monitored performance anomalies on the fly. During the offline phase, PERFCE leverages both passive observations and proactive chaos experiments to constitute accurate causal graphs and structural equation models (SEMs). When observing performance anomalies during the online phase, causal graphs enable qualitative root cause identification (e.g., high CPU usage) and SEMs enable quantitative counterfactual analysis (e.g., determining "when CPU usage is reduced to 45\%, performance returns to normal"). PERFCE notably outperforms prior works on common synthetic datasets, and our evaluation on real-world databases, MySQL and TiDB, shows that PERFCE is highly accurate and moderately expensive

Investigating the Lombard Effect Influence on End-to-End Audio-Visual Speech Recognition

Several audio-visual speech recognition models have been recently proposed which aim to improve the robustness over audio-only models in the presence of noise. However, almost all of them ignore the impact of the Lombard effect, i.e., the change in speaking style in noisy environments which aims to make speech more intelligible and affects both the acoustic characteristics of speech and the lip movements. In this paper, we investigate the impact of the Lombard effect in audio-visual speech recognition. To the best of our knowledge, this is the first work which does so using end-to-end deep architectures and presents results on unseen speakers. Our results show that properly modelling Lombard speech is always beneficial. Even if a relatively small amount of Lombard speech is added to the training set then the performance in a real scenario, where noisy Lombard speech is present, can be significantly improved. We also show that the standard approach followed in the literature, where a model is trained and tested on noisy plain speech, provides a correct estimate of the video-only performance and slightly underestimates the audio-visual performance. In case of audio-only approaches, performance is overestimated for SNRs higher than -3dB and underestimated for lower SNRs.Comment: Accepted for publication at Interspeech 201

Towards Practical Federated Causal Structure Learning

Understanding causal relations is vital in scientific discovery. The process of causal structure learning involves identifying causal graphs from observational data to understand such relations. Usually, a central server performs this task, but sharing data with the server poses privacy risks. Federated learning can solve this problem, but existing solutions for federated causal structure learning make unrealistic assumptions about data and lack convergence guarantees. FedC2SL is a federated constraint-based causal structure learning scheme that learns causal graphs using a federated conditional independence test, which examines conditional independence between two variables under a condition set without collecting raw data from clients. FedC2SL requires weaker and more realistic assumptions about data and offers stronger resistance to data variability among clients. FedPC and FedFCI are the two variants of FedC2SL for causal structure learning in causal sufficiency and causal insufficiency, respectively. The study evaluates FedC2SL using both synthetic datasets and real-world data against existing solutions and finds it demonstrates encouraging performance and strong resilience to data heterogeneity among clients