A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Can Tabular Generative Models Generate Realistic Synthetic Near Infrared Spectroscopic Data?

In this thesis, we evaluated the performance of two generative models, Conditional Tabular Gen- erative Adversarial Network (CTGAN) and Tabular Variational Autoencoder (TVAE), from the open-source library Synthetic Data Vault (SDV), for generating synthetic Near Infrared (NIR) spectral data. The aim was to assess the viability of these models in synthetic data generation for predicting Dry Matter Content (DMC) in the field of NIR spectroscopy. The fidelity and utility of the synthetic data were examined through a series of benchmarks, including statistical comparisons, dimensionality reduction, and machine learning tasks. The results showed that while both CTGAN and TVAE could generate synthetic data with statistical properties similar to real data, TVAE outperformed CTGAN in terms of preserving the correlation structure of the data and the relationship between the features and the target variable, DMC. However, the synthetic data fell short in fooling machine learning classifiers, indicating a persisting challenge in synthetic data generation. With respect to utility, neither synthetic dataset produced by CTGAN or TVAE could serve as a satisfactory substitute for real data in training machine learning models for predicting DMC. Although TVAE-generated synthetic data showed some potential when used with Random For- est (RF) and K-Nearest Neighbors (KNN) classifiers, the performance was still inadequate for practical use. This study offers valuable insights into the use of generative models for synthetic NIR spectral data generation, highlighting their current limitations and potential areas for future research

Robustness, Heterogeneity and Structure Capturing for Graph Representation Learning and its Application

Graph neural networks (GNNs) are potent methods for graph representation learn- ing (GRL), which extract knowledge from complicated (graph) structured data in various real-world scenarios. However, GRL still faces many challenges. Firstly GNN-based node classification may deteriorate substantially by overlooking the pos- sibility of noisy data in graph structures, as models wrongly process the relation among nodes in the input graphs as the ground truth. Secondly, nodes and edges have different types in the real-world and it is essential to capture this heterogeneity in graph representation learning. Next, relations among nodes are not restricted to pairwise relations and it is necessary to capture the complex relations accordingly. Finally, the absence of structural encodings, such as positional information, deterio- rates the performance of GNNs. This thesis proposes novel methods to address the aforementioned problems: 1. Bayesian Graph Attention Network (BGAT): Developed for situations with scarce data, this method addresses the influence of spurious edges. Incor- porating Bayesian principles into the graph attention mechanism enhances robustness, leading to competitive performance against benchmarks (Chapter 3). 2. Neighbour Contrastive Heterogeneous Graph Attention Network (NC-HGAT): By enhancing a cutting-edge self-supervised heterogeneous graph neural net- work model (HGAT) with neighbour contrastive learning, this method ad- dresses heterogeneity and uncertainty simultaneously. Extra attention to edge relations in heterogeneous graphs also aids in subsequent classification tasks (Chapter 4). 3. A novel ensemble learning framework is introduced for predicting stock price movements. It adeptly captures both group-level and pairwise relations, lead- ing to notable advancements over the existing state-of-the-art. The integration of hypergraph and graph models, coupled with the utilisation of auxiliary data via GNNs before recurrent neural network (RNN), provides a deeper under- standing of long-term dependencies between similar entities in multivariate time series analysis (Chapter 5). 4. A novel framework for graph structure learning is introduced, segmenting graphs into distinct patches. By harnessing the capabilities of transformers and integrating other position encoding techniques, this approach robustly capture intricate structural information within a graph. This results in a more comprehensive understanding of its underlying patterns (Chapter 6)

Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Finding suitable forecasting methods for an effective management of energy resources is of paramount importance for improving the efficiency in energy consumption and decreasing its impact on the environment. Natural gas is one of the main sources of electrical energy in Algeria and worldwide. To address this demand, this paper introduces a novel hybrid forecasting approach that resolves the two-stage method's deficiency, by designing a Multi Layered Perceptron (MLP) neural network as a nonlinear forecasting monitor. This model estimates the next day gas consumption profile and selects one of several local models to perform the forecast. The study focuses firstly on an analysis and clustering of natural gas daily consumption profiles, and secondly on building a comprehensive Long Short Term Memory (LSTM) recurrent models according to load behavior. The results are compared with four benchmark approaches: the MLP neural network approach, LSTM, seasonal time series with exogenous variables models and multiple linear regression models. Compared with these alternative approaches and their high dependence on historical loads, the proposed approach presents a new efficient functionality. It estimates the next day consumption profile, which leads to a significant improvement of the forecasting accuracy, especially for days with exceptional customers consumption behavior change

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events

Short-term motion prediction of autonomous vehicles in complex environments: A Deep Learning approach

Complex environments manifest a high level of complexity and it is of critical importance that the safety systems embedded within autonomous vehicles (AVs) are able to accurately anticipate short-term future motion of agents in close proximity. This problem can be further understood as generating a sequence of coordinates describing the plausible future motion of the tracked agent. Number of recently proposed techniques that present satisfactory performance exploit the learning capabilities of novel deep learning (DL) architectures to tackle the discussed task. Nonetheless, there still exists a vast number of challenging issues that must be resolved to further advance capabilities of motion prediction models.This thesis explores novel deep learning techniques within the area of short-term motion prediction of on-road participants, specifically other vehicles from a points of autonomous vehicles. First and foremost, various approaches in the literature demonstrate significant benefits of using a rasterised top-down image of the road to encode the context of tracked vehicle’s surroundings which generally encapsulates a large, global portion of the environment. This work on the other hand explores a use of local regions of the rasterised map to more explicitly focus on the encoding of the tracked vehicle’s state. The proposed technique demonstrates plausible results against several baseline models and in addition outperforms the same model that instead uses global maps. Next, the typical method for extracting features from rasterised maps involves employing one of the popular vision models (e.g. ResNet-50) that has been previously pre-trained on a distinct task such as image classification. Recently however, it has been demonstrated that this approach can be sub-optimal for tasks that strongly rely on precise localisation of features and it can be more advantageous to train the model from scratch directly on the task at hand. In contrast, the subsequent part of this thesis investigates an alternative method for processing and encoding of spatial data based on the capsule networks in order to eradicate several issues that standard vision models exhibit. Through several experiments it is established that the novel capsule based motion predictor that is trained from scratch is able to achieve competitive results against numerous popular vision models. Finally, the proposed model is further extended with the use of generative framework to account for the fact that the space of possible movements of the tracked vehicle is not strictly limited to single trajectory. More specifically, to account for the multi-modality of the problem a conditional variational auto-encoder (CVAE) is employed which enables to sample an arbitrary amount of diverse trajectories. The final model is examined against methods from literature on a publicly available dataset and as presented it significantly outperforms other models whilst drastically reducing the number of trainable parameters

Knowledge Elicitation in Deep Learning Models

Embora o aprendizado profundo (mais conhecido como deep learning) tenha se tornado uma ferramenta popular na solução de problemas modernos em vários domínios, ele apresenta um desafio significativo - a interpretabilidade. Esta tese percorre um cenário de elicitação de conhecimento em modelos de deep learning, lançando luz sobre a visualização de características, mapas de saliência e técnicas de destilação. Estas técnicas foram aplicadas a duas arquiteturas: redes neurais convolucionais (CNNs) e um modelo de pacote (Google Vision). A nossa investigação forneceu informações valiosas sobre a sua eficácia na elicitação e interpretação do conhecimento codificado. Embora tenham demonstrado potencial, também foram observadas limitações, sugerindo espaço para mais desenvolvimento neste campo. Este trabalho não só realça a necessidade de modelos de deep learning mais transparentes e explicáveis, como também impulsiona o desenvolvimento de técnicas para extrair conhecimento. Trata-se de garantir uma implementação responsável e enfatizar a importância da transparência e compreensão no aprendizado de máquina. Além de avaliar os métodos existentes, esta tese explora também o potencial de combinar múltiplas técnicas para melhorar a interpretabilidade dos modelos de deep learning. Uma mistura de visualização de características, mapas de saliência e técnicas de destilação de modelos foi usada de uma maneira complementar para extrair e interpretar o conhecimento das arquiteturas escolhidas. Os resultados experimentais destacam a utilidade desta abordagem combinada, revelando uma compreensão mais abrangente dos processos de tomada de decisão dos modelos. Além disso, propomos um novo modelo para a elicitação sistemática de conhecimento em deep learning, que integra de forma coesa estes métodos. Este quadro demonstra o valor de uma abordagem holística para a interpretabilidade do modelo, em vez de se basear num único método. Por fim, discutimos as implicações éticas do nosso trabalho. À medida que os modelos de deep learning continuam a permear vários setores, desde a saúde até às finanças, garantir que as suas decisões são explicáveis e justificadas torna-se cada vez mais crucial. A nossa investigação sublinha esta importância, preparando o terreno para a criação de sistemas de inteligência artificial mais transparentes e responsáveis no futuro.Though a buzzword in modern problem-solving across various domains, deep learning presents a significant challenge - interpretability. This thesis journeys through a landscape of knowledge elicitation in deep learning models, shedding light on feature visualization, saliency maps, and model distillation techniques. These techniques were applied to two deep learning architectures: convolutional neural networks (CNNs) and a black box package model (Google Vision). Our investigation provided valuable insights into their effectiveness in eliciting and interpreting the encoded knowledge. While they demonstrated potential, limitations were also observed, suggesting room for further development in this field. This work does not just highlight the need for more transparent, more explainable deep learning models, it gives a gentle nudge to developing innovative techniques to extract knowledge. It is all about ensuring responsible deployment and emphasizing the importance of transparency and comprehension in machine learning. In addition to evaluating existing methods, this thesis also explores the potential for combining multiple techniques to enhance the interpretability of deep learning models. A blend of feature visualization, saliency maps, and model distillation techniques was used in a complementary manner to extract and interpret the knowledge from our chosen architectures. Experimental results highlight the utility of this combined approach, revealing a more comprehensive understanding of the models' decision-making processes. Furthermore, we propose a novel framework for systematic knowledge elicitation in deep learning, which cohesively integrates these methods. This framework showcases the value of a holistic approach toward model interpretability rather than relying on a single method. Lastly, we discuss the ethical implications of our work. As deep learning models continue to permeate various sectors, from healthcare to finance, ensuring their decisions are explainable and justified becomes increasingly crucial. Our research underscores this importance, laying the groundwork for creating more transparent, accountable AI systems in the future

An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

Artificial Intelligence for Complex Network: Potential, Methodology and Application

Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world networks. Despite these advancements, there remain considerable challenges in exploring more realistic systems and enhancing practical applications. The emergence of artificial intelligence (AI) technologies, coupled with the abundance of diverse real-world network data, has heralded a new era in complex network science research. This survey aims to systematically address the potential advantages of AI in overcoming the lingering challenges of complex network research. It endeavors to summarize the pivotal research problems and provide an exhaustive review of the corresponding methodologies and applications. Through this comprehensive survey-the first of its kind on AI for complex networks-we expect to provide valuable insights that will drive further research and advancement in this interdisciplinary field.Comment: 51 pages, 4 figures, 10 table

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation