Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos

We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a VGG16 network. We establish the contribution of different layers and loss coefficients to the learning of style, minimization of artefacts and maintenance of readability of corporate logos. We find layers and loss coefficients that produce a good tradeoff between heavy metal style and corporate logo readability. This is the first step both towards sparse font style transfer and corporate logo decoration using generative networks. Heavy metal and corporate logos are very different artistically, in the way they emphasize emotions and readability, therefore training a model to fuse the two is an interesting problem

Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos

We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a VGG16 network. We establish the contribution of different layers and loss coefficients to the learning of style, minimization of artefacts and maintenance of readability of corporate logos. We find layers and loss coefficients that produce a good tradeoff between heavy metal style and corporate logo readability. This is the first step both towards sparse font style transfer and corporate logo decoration using generative networks. Heavy metal and corporate logos are very different artistically, in the way they emphasize emotions and readability, therefore training a model to fuse the two is an interesting problem

Computational complexity of elitist population-based evolutionary algorithms: a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

Evolutionary Algorithms (EAs) are a modern heuristic algorithm that have proven efficiency on a large number of real-life problems. Despite the rich history of applications understanding of both how and why EAs work is lagging far behind. This is especially true for one of the main components of EAs, that is hypothesized by many to underlie their efficiency: population. The first problem considered in this thesis is the introduction of a recombination operator, K Bit-Swap (KBS) and its comparison to mainstream operators, such as mutation and different types of crossover. A vast amount of statistical evidence is presented that shows that EAs using KBS outperform other algorithms on a whole range of problems. Two problems are selected for a deep theoretical analysis: OneMax and Royal Roads. The main problem of modeling EAs that use both population and a pool of parents is the complexity of the structures that arise from the process of evolution. In most cases either one type of species is considered or certain simple assumptions are made about fitness of the species. The main contribution of this thesis is the development of a new approach to modeling of EAs that is based on approximating the structure of the population and the evolution of subsets thereof. This approach lies at the core of the new tool presented here, the Elitism Levels Traverse Mechanism that was used to derive upper bounds on the runtime of EAs. In addition, lower bounds were found using simpler assumptions of the underlying distribution of species in the population.The second important result of the approach is the derivation of limiting distributions of a subset of the population, a problem well-known in areas such as epidemiology. To the best of the author's knowledge, no such findings have been published in the EA community so far

K-Bit-Swap: a new operator for real-coded evolutionary algorithms

There have been a variety of crossover operators proposed for real-coded genetic algorithms (RCGAs). Such operators recombine values from pairs of strings to generate new solutions. In this article, we present a recombination operator for RCGAs that selects the string locations for change separately randomly in the parent and offspring, enabling solution parts to move within a string, and compare it to mainstream crossover operators in a set of experiments on a range of standard multidimensional optimization problems and a real-world clustering problem. We present two variants of the operator, either selecting bits uniformly at random in both strings or sampling the second bit from a normal distribution centered at the selected location in the first string. While the operator is biased toward exploitation of fitness space, the random selection of the second bit for swapping reduces this bias slightly. Statistical analysis of the experimental results using a nonparametric test shows the advantage of the new recombination operators on our test optimization functions

One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention Mechanism

We present a model that fuses lesion segmentation with Attention Mechanism to predict COVID-19 from chest CT scans. The model segments lesions, extracts Regions of Interest from scans and applies Attention to them to determine the most relevant ones for image classification. Additionally, we augment the model with Long-Short Term Memory Network layers that learn features from a sequence of Regions of Interest before computing attention. The model is trained in one shot for both problems, using two different sets of data. We achieve 0.4683 mean average precision for lesion segmentation, 95.74% COVID-19 sensitivity and 98.15% class-adjusted F1 score for image classification on a large CNCB-NCOV dataset. Source code is available on https: //github.com/AlexTS1980/COVID-LSTM-Attention

Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network

In this paper we present a novel instance segmentation algorithm that extends a fully convolutional network to learn to label objects separately without prediction of regions of interest. We trained the new algorithm on a challenging CCTV recording of beef cattle, as well as benchmark MS COCO and Pascal VOC datasets. Extensive experimentation showed that our approach outperforms the state-of-the-art solutions by up to 8% on our data

Single-Shot Lightweight Model For The Detection of Lesions And The Prediction of COVID-19 From Chest CT Scans

Abstract—We introduce a lightweight model derived from Mask R-CNN that segments lesions and predicts COVID-19 from chest CT scans in a single shot. The model requires a small dataset to train, and is evaluated on a large set of images to achieve a 42.45% average precision on the segmentation test split, and 93.00% COVID-19 sensitivity and F1-score of 96.76% on the classification test split across 3 classes: COVID-19, Common Pneumonia and Negative. We introduce an augmented Region of Interest layer that disentangles lesion detection functionality for segmentation and classification problems. Efficiency of the solution is confirmed by comparing it to a suite of the state-of-the-art models across both problems. Full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-Single-Shot-Model

Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis

This paper introduces a new approach to the long-term tracking of an object in a challenging environment. The object is a cow and the environment is an enclosure in a cowshed. Some of the key challenges in this domain are a cluttered background, low contrast and high similarity between moving objects - which greatly reduces the efficiency of most existing approaches, including those based on background subtraction. Our approach is split into object localization, instance segmentation, learning and tracking stages. Our solution is benchmarked against a range of semi-supervised object tracking algorithms and we show that the performance is strong and well suited to subsequent analysis. We present our solution as a first step towards broader tracking and behavior monitoring for cows in precision agriculture with the ultimate objective of early detection of lameness

Incremental Adaptation Strategies for Neural Network Language Models

It is today acknowledged that neural network language models outperform backoff language models in applications like speech recognition or statistical machine translation. However, training these models on large amounts of data can take several days. We present efficient techniques to adapt a neural network language model to new data. Instead of training a completely new model or relying on mixture approaches, we propose two new methods: continued training on resampled data or insertion of adaptation layers. We present experimental results in an CAT environment where the post-edits of professional translators are used to improve an SMT system. Both methods are very fast and achieve significant improvements without overfitting the small adaptation data

Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance

Predictive maintenance, which has traditionally used anomaly detection methods on sensory data, is now being replaced by event-based techniques. These methods utilise events with multiple temporal features, produced by diagnostic modules. This raises the need for predicting the next fault event in industrial machines, specially vehicles, that use Diagnostic Trouble Codes (DTCs). We propose a predictive maintenance approach, named Sequential Multivariate Fault Prediction (SMFP), for predicting the next multivariate DTC fault in an event sequence, using Long Short-Term Memory Networks (LSTMs) and jointly learned event embeddings. By performing an in-depth comparison of different architectural choices and contextual preprocessing techniques, we provide an initial baseline for SMFP that achieves top-3 accuracy of 63% on predicting multivariate fault with 3 collective output layers, using vehicle maintenance data as a case study