Photo-densitometry: radiograph digitization and algorithmic enhancement of x-ray images

Industrial investigation of material structure and composition is an integral part of the manufacturing design flow. It is possible to evaluate these properties by both destructive and non-destructive means. Non-destructive evaluation of materials is attractive for obvious reasons and x-ray NDE (Non-Destructive Evaluation) is a well established discipline. X-ray images of materials (represented and stored in the form of radiographs) are capable of providing valuable information regarding the presence of material defects such as, voids, cracks and inclusions. A common medium used to store an x-ray image is the film or radiograph. This is an analog representation of the x-ray image, produced by the photographic effect. This grayscale representation of the material under investigation, when analyzed, is able to provide the necessary information regarding the presence of defects. The human brain has the ability to recognize patterns and differentiate minute variations in the grayscales of the radiograph, so long as these variations are within a particular range. In order to overcome this limitation of the human visual mechanism and to facilitate the objectives of storage, processing and transmission, it is necessary to transform this representation of the x-ray image as a radiograph, into a digital form. This also helps to extract quantitative physical parameters from a digitized image, which is not possible with an analog image which is only good at providing a qualitative overview of an image

k-Means

The k-means clustering algorithm (k-means for short) provides a method offinding structure in input examples. It is also called the Lloyd–Forgy algorithm as it was independently introduced by both Stuart Lloyd and Edward Forgy. k-means, like other algorithms you will study in this part of the book, is an unsupervised learning algorithm and, as such, does not require labels associated with input examples. Recall that unsupervised learning algorithms provide a way of discovering some inherent structure in the input examples. This is in contrast with supervised learning algorithms, which require input examples and associated labels so as to fit a hypothesis function that maps input examples to one or more output variables

On the integration of conceptual hierarchies with deep learning for explainable open-domain question answering

Question Answering, with its potential to make human-computer interactions more intuitive, has had a revival in recent years with the influx of deep learning methods into natural language processing and the simultaneous adoption of personal assistants such as Siri, Google Now, and Alexa. Unfortunately, Question Classification, an essential element of question answering, which classifies questions based on the class of the expected answer had been overlooked. Although the task of question classification was explicitly developed for use in question answering systems, the more advanced task of question classification, which classifies questions into between fifty and a hundred question classes, had developed into independent tasks with no application in question answering. The work presented in this thesis bridges this gap by making use of fine-grained question classification for answer selection, arguably the most challenging subtask of question answering, and hence the defacto standard of measure of its performance on question answering. The use of question classification in a downstream task required significant improvement to question classification, which was achieved in this work by integrating linguistic information and deep learning through what we call Types, a novel method of representing Concepts. Our work on a purely rule-based system for fine-grained Question Classification using Types achieved an accuracy of 97.2%, close to a 6 point improvement over the previous state of the art and has remained state of the art in question classification for over two years. The integration of these question classes and a deep learning model for Answer Selection resulted in MRR and MAP scores which outperform the current state of the art by between 3 and 5 points on both versions of a standard test set

Flesch or Fumble? Evaluating Readability Standard Alignment of Instruction-Tuned Language Models

Readability metrics and standards such as Flesch Kincaid Grade Level (FKGL) and the Common European Framework of Reference for Languages (CEFR) exist to guide teachers and educators to properly assess the complexity of educational materials before administering them for classroom use. In this study, we select a diverse set of open and closed-source instruction-tuned language models and investigate their performances in writing story completions and simplifying narratives$-$tasks that teachers perform$-$using standard-guided prompts controlling text readability. Our extensive findings provide empirical proof of how globally recognized models like ChatGPT may be considered less effective and may require more refined prompts for these generative tasks compared to other open-sourced models such as BLOOMZ and FlanT5$-$which have shown promising results

SON: PREDICTING THE NATURE OF SERVICE DISRUPTIONS IN CELLULAR NETWORKS

An important aspect of communication is involved in its cellular network. To meet the demands, communication requires the next generation cellular network, i.e., self organizing networks (SON). In order to implement a self-organizing network, its subsections have to be known and optimized using certain rules. The objective of this document is to deal with one of the subsections called “Self-healing: Fault identification,” in particular by conducting analysis on the Telstra cellular network and predicting its disruptions. First, the prediction of the disruptions can be determined by establishing the machine learning algorithms upon Telstra data. Thus, the classification of faults could be used for finding the nature of the disruptions. Because the appropriate algorithm is chosen by the trial-and-error method, there is no one particular algorithm that fits particular data. Thus, data has to be pre-processed for the algorithms to be applied. Here, the Python Sci-kit module was used as a tool for developing the predictive model. As a note, there are many other tools like R, MATLAB, Rattle, KNIME, etc. that can be used for machine learning. Then, the nature of the faults was identified and investigated to drive customer advocacy