Casting a Wider (Neural) Net: Introducing Data Science and Machine Learning to a Larger Audience
A presentation introducing foundational concepts related to Data Science and Machine learning was given five times over a one year period to audiences with different backgrounds. This report summarizes the successes and failures in delivering this material. Insights about the topic and useful teaching resources are presented.
What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets
Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety (the 3Vs), but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s taxonomy of seven Big Data traits to 26 datasets drawn from seven domains, each of which is considered in the literature to constitute Big Data. The results demonstrate that only a handful of datasets possess all seven traits, and some do not possess either volume and/or variety. Instead, there are multiple forms of Big Data. Our analysis reveals that the key definitional boundary markers are the traits of velocity and exhaustivity. We contend that Big Data as an analytical category needs to be unpacked, with the genus of Big Data further delineated and its various species identified. It is only through such ontological work that we will gain conceptual clarity about what constitutes Big Data, formulate how best to make sense of it, and identify how it might be best used to make sense of the world.
ImageCLEF 2017: ImageCLEF Tuberculosis Task - the SGEast Submission
In this paper, we describe our methodologies in an attempt to improve the diagnosis accuracy of drug resistant tuberculosis and also of identifying the type of tuberculosis present in the patient, as per the requirements of the ImageCLEF tuberculosis task of ImageCLEF 2017. Firstly, we employed the concept of Convolutional Neural Networks (CNN), which can be used to identify useful features in the Computerized Tomography (CT) scans that were provided in the competition and perform the classification based on them. Secondly, Recurrent Neural Networks (RNN) were used on top of CNNs by utilizing CNNs as a feature extractor and the RNNs as a classifier. In order for our model to produce acceptable results, proper preprocessing was performed prior to providing the input data to the models for training, such as image slicing and data augmentation. Our methods were able to reach rank 4 and 5 for the subtask involving drug-resistant tuberculosis and rank 1 and 2 for the other subtask of identifying the tuberculosis type, according to the evaluation performed by ImageCLEF.