Automation is fundamentally changing many industries and retail is no exception. Moonshopis a South African venture trying to solve the problem of autonomous grocery storesusing cameras and computer vision. This project is the continuation of a hackathon heldto explore different methods for Human Action Recognition in Moonshop’s stores.Throughout the project a pipeline for data processing has been developed and two typesof Graph-Convolutional Networks, CTR-GCN and ST-GCN, have been implementedand evaluated on the data produced by this pipeline. The resulting scores aren’t goodenough to call it a success. However, this is not necessarily a fault of the models. Rather,there wasn’t enough data to train on and the existing data was of varying to low quality.This makes it complicated to justly judge the models’ performances. In the future, moreresources should be spent on generating more and better data in order to really evaluatethe feasibility of using Human Action Recognition and Graph-Convolutional Networksat Moonshop.

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This thesis is divided into two distinct chapters. In the first chapter, we apply network representation learning to the field of materials science in order to predict aluminum grain boundaries' properties and locate the most influential atoms and subgraphs within each grain boundary. We create fixed-length representations of the aluminum grain boundaries that successfully capture grain boundary structure and allow us to accurately predict grain boundary energy. We do this through two distinct methods. The first method we use is a graph convolutional neural network, a semi-supervised deep learning algorithm, and the second method is graph2vec, an unsupervised representation learning algorithm. The second chapter presents our dynamic global value chain network, the combination of the dynamic global supply chain network and the dynamic global strategic alliance network. Our global value chain network provides a level of scope and accessibility not found in any other global value chain network, commercial or academic. Through applications of network theory, we discover business applications that would increase the robustness and resilience of the global value chain. We accomplish this through an analysis of the static, dynamic, and community structure of our global value chain network.

Proteins are considered the primary actors in living organisms. Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. PPI identification has been addressed by various experimental methods such as the yeast two-hybrid, mass spectrometry, and protein microarrays, to mention a few. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. Therefore a sequence-based framework called ProtInteract is developed to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequential pattern by extracting uncorrelated attributes and more expressive descriptors. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction. Three different scenarios formulate the prediction task. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The present study makes two significant contributions to the field of protein-protein interaction (PPI) prediction. Firstly, it addresses the computational challenges posed by the high dimensionality of protein datasets through the use of dimensionality reduction techniques, which extract highly informative sequence attributes. Secondly, the proposed framework, ProtInteract, utilises this information to identify the interaction characteristics of a protein based on its amino acid configuration. ProtInteract encodes the protein's primary structure into a lower-dimensional vector space, thereby reducing the computational complexity of PPI prediction. Our results provide evidence of the proposed framework's accuracy and efficiency in predicting protein-protein interactions.