Wildlife re-identification aims to match individuals of the same species across different observations. Current state-of-the-art (SOTA) models rely on class labels to train supervised models for individual classification. This dependence on annotated data has driven the curation of numerous large-scale wildlife datasets. This study investigates self-supervised learning Self-Supervised Learning (SSL) for wildlife re-identification. We automatically extract two distinct views of an individual using temporal image pairs from camera trap data without supervision. The image pairs train a self-supervised model from a potentially endless stream of video data. We evaluate the learnt representations against supervised features on open-world scenarios and transfer learning in various wildlife downstream tasks. The analysis of the experimental results shows that self-supervised models are more robust even with limited data. Moreover, self-supervised features outperform supervision across all downstream tasks. The code is available here https://github.com/pxpana/.

Meta-learning, decision fusion, hybrid models, and representation learning are topics of investigation with significant traction in time-series forecasting research. Of these two specific areas have shown state-of-the-art results in forecasting: hybrid meta-learning models such as Exponential Smoothing- Recurrent Neural Network (ES-RNN) and Neural Basis Expansion Analysis (N-BEATS) and feature-based stacking ensembles such as Feature-based FORecast Model Averaging (FFORMA). However, a unified taxonomy for model fusion and an empirical comparison of these hybrid and feature-based stacking ensemble approaches is still missing. This study presents a unified taxonomy encompassing these topic areas. Furthermore, the study empirically evaluates several model fusion approaches and a novel combination of hybrid and feature stacking algorithms called Deep-learning FORecast Model Averaging (DeFORMA). The taxonomy contextualises the considered methods. Furthermore, the empirical analysis of the results shows that the proposed model, DeFORMA, can achieve state-of-the-art results in the M4 data set. DeFORMA, increases the mean Overall Weighted Average (OWA) in the daily, weekly and yearly subsets with competitive results in the hourly, monthly and quarterly subsets. The taxonomy and empirical results lead us to argue that significant progress is still to be made by continuing to explore the intersection of these research areas.

Maintenance data from power transformers are typically in the form of dissolved gas analysis time series data. This research attempts consolidating industry knowledge on the maintenance of power transformers and time series forecasting techniques into a coherent system for the purpose of predictive maintenance of power transformers. The generalisability of forecasting models is investigated by measuring performance of single models across multiple transformers, and hence, multiple data sets. A novel method of data preprocessing is utilized; an exponential smoothing technique specifically designed for the type of raw data received (aperiodically sampled, noisy data). In addition, industry specified features for fault classification from the literature were added to the data set. These other features were examined to see if they improved forecasting. The forecasting techniques evaluated included Least-Squares Support Vector Machine (LS-SVM) with hyper-parameters optimized using a Particle Swarm Optimisation; Support Vector Regressors; Naive forecasts; Mean forecasts; Auto-Regressive Integrated Moving average or ARIMA; and Exponential Smoothing. Two sets of experiments were run, which differed in how the Training, Validation, and Testing sets were chosen. These experiments were run for different input vectors; the original input vector and an input vector augmented with the industry specified features. Models trained in the second experiment outperformed all other models, when the distributions of the Testing errors were considered. When generalisability was considered, it was found that the models trained across all transformers outperformed the per-transformer models that treated the data as univariate time series.

This paper tackles face recognition in videos employing metric learning methods and similarity ranking models. The paper compares the use of the Siamese network with contrastive loss and Triplet Network with triplet loss implementing the following architectures: Google/Inception architecture, 3D Convolutional Network (C3D), and a 2-D Long short-term memory (LSTM) Recurrent Neural Network. We make use of still images and sequences from videos for training the networks and compare the performances implementing the above architectures. The dataset used was the YouTube Face Database designed for investigating the problem of face recognition in videos. The contribution of this paper is two-fold: to begin, the experiments have established 3-D Convolutional networks and 2-D LSTMs with the contrastive loss on image sequences do not outperform Google/Inception architecture with contrastive loss in top n rank face retrievals with still images. However, the 3-D Convolution networks and 2-D LSTM with triplet Loss outperform the Google/Inception with triplet loss in top n rank face retrievals on the dataset; second, a Support Vector Machine (SVM) was used in conjunction with the CNNs' learned feature representations for facial identification. The results show that feature representation learned with triplet loss is significantly better for n-shot facial identification compared to contrastive loss. The most useful feature representations for facial identification are from the 2-D LSTM with triplet loss. The experiments show that learning spatio-temporal features from video sequences is beneficial for facial recognition in videos.

Cement production is a multi-billion dollar industry, of which one of the main sub-processes, cement milling, is complex and non-linear. There is a need to model the fineness of particles exiting the milling circuit to better control the cement plant. This paper explores the relationship between the particle size of cement produced and the operation of the cement mill circuit. This paper aims to provide a model for predicting the fineness of particles exiting the milling circuit using data on the current and past states of the plant. A comprehensive literature review of the problem, as well as a discussion of potential modelling solutions, is provided. Blaine (particle fineness)is modelled using many different linear and non-linear models on 5 months of data from a Chinese cement plant. On a holdout test set a multi-layered perceptron achieved an MAE of 8.799 and a linear regression achieved a R2of 0.481. discussion of the significance of various features for predicting Blaine is also presented. The results show some limited success from non-linear data-driven models and highlight some of the unique difficulties in modelling the cement mill and present recommendations for future research.

Soccer match prediction has been a difficult domain for machine learning, currently outperformed by bookmakers' odds and human predictions due to the stochastic nature of soccer. In response, we focus on a Similarity learning approach. Our research involves using Siamese networks and RankNet pair-wise models alongside Transfer learning to predict soccer match rankings. We implemented these models in conjunction with traditional sports tally ranking as well as graph-based PageRank to attain list-wise seasonal rankings for English Premier League spanning seasons 2006 to 2018. Our models used two datasets- EPL seasonal team statistics and an augmentation of these team statistics with external (monetary and transfer) data. Our models have a lower F1 score than a standard neural network, however, perform better in identifying draws and seasonal teams rankings. Transfer Learning models performed better on match-wise rankings while for seasonal rankings, augmented data provides the best predictor (0.72 average). Our proposed Tally Rank provides more accurate seasonal rankings than a graph-based PageRank. Since there is no consistently best-performing model, other Similarity Learning/Ranking models can be considered in the future.

A significant challenge faced within the field of chemical plant design and optimisation is the uncertainty in the reaction parameter, indeterminate component failures and their impacts on the design and operation. Additionally, real-world data accumulated from these plants would contain stochastic elements that are difficult to model. To this end, stochastic and deterministic methods have been proposed to simulate the uncertainty and enable an understanding of the plant and how it may be optimised. Within the existing literature investigated, the optimisation is done under the assumption that the simulation (target function) is non-stochastic. We have found that the use of an Evolutionary Algorithm in the form of a Genetic Algorithm can find an optimal solution even when we allow the simulation to behave stochastically as it would in practical applications. Further, we note that the use of a surrogate Machine Learning model as a substitute for the stochastic simulation model leads to substantively improved solutions in significantly less time (1.82 times speedup). We argue that the use of Genetic Algorithms in the optimisation of chemical plant design, taking into account the stochastic nature of the plant and including indeterminate failures, is a worthwhile solution and that surrogate assisted evolutionary algorithms will improve this solution further.

The Galaxy Zoo project is a crowd-sourced astronomy galaxy classification endeavour whose results can have significant benefits to astronomers. The project has evolved into using crowd-sourced labelling together with machine learning to automate the classification of galaxies. If this process is to be automated using crowd-sourcing and machine learning, then understanding how these results will hold up against expert classifications on an academically accepted classification such as the Hubble tuning fork is timely. We propose a rules-based approach for deriving the Hubble type using the responses in the Galaxy Zoo as well as a Transfer Learning approach for solving this problem. The dataset we used to get the Hubble type for galaxies is the Revised Shapley-Ames catalogue of bright galaxies. Previous work in this field has mainly revolved around the Galaxy Zoo project with little to no attempt to map the Galaxy Zoo responses to a more robust method of classifying galaxies such as the Hubble tuning fork classification system. Previous research has tried to map the Galaxy Zoo responses to a set of classes like elliptical, spiral and irregular. Their work has shown promising results. Our experiments showed that by using the Galaxy Zoo response vectors, our rules-based approach was able to separate the elliptical and spiral shapes, however, it did not perform particularly well at separating the spiral shapes from one another. Our Transfer Learning model showed better potential for separating not only elliptical and spiral shapes but also for separating spiral shapes into exact Hubble types (e.g Sa, Sb and Sc).

The unique automated identification of animals of various species is a pressing challenge ecologically, environmentally and economically. A broader question relates to how one might exploit the somewhat more mature technologies and techniques used within human visual biometrics to automate this same task for other species. One specific technique is the use of region proposal networks and deep transfer learning in siamese networks for individual animal identification. We report that although it is relatively easy to achieve state of the art performance in uniquely identifying individuals for the easy target of zebras, trying to use the same pipeline to obtain useable, top-10> 85%, results for a more challenging species such as nyala is still an open research problem. We argue that uniquely identifying individuals such as nyala who actively try to disguise themselves in their environments require improved few-shot learning techniques and perhaps more data than the current open dataset we have provided to stimulate this area of research.