Software Requirements Classification Using Word Embeddings and Convolutional Neural Networks

Software requirements classification, the practice of categorizing requirements by their type or purpose, can improve organization and transparency in the requirements engineering process and thus promote requirement fulfillment and software project completion. Requirements classification automation is a prominent area of research as automation can alleviate the tediousness of manual labeling and loosen its necessity for domain-expertise. This thesis explores the application of deep learning techniques on software requirements classification, specifically the use of word embeddings for document representation when training a convolutional neural network (CNN). As past research endeavors mainly utilize information retrieval and traditional machine learning techniques, we entertain the potential of deep learning on this particular task. With the support of learning libraries such as TensorFlow and Scikit-Learn and word embedding models such as word2vec and fastText, we build a Python system that trains and validates configurations of Naïve Bayes and CNN requirements classifiers. Applying our system to a suite of experiments on two well-studied requirements datasets, we recreate or establish the Naïve Bayes baselines and evaluate the impact of CNNs equipped with word embeddings trained from scratch versus word embeddings pre-trained on Big Data

Australian public health policy in 2003 – 2004

In Australia, compared with other developed countries the many and varied programs which comprise public health have continued to be funded poorly and unsystematically, particularly given the amount of publicly voiced political support. In 2003, the major public health policy developments in communicable disease control were in the fields of SARS, and vaccine funding, whilst the TGA was focused on the Pan Pharmaceutical crisis. Programs directed to health maintenance and healthy ageing were approved. The tertiary education sector was involved in the development of programs for training the public health workforce and new professional qualifications and competencies. The Abelson Report received support from overseas experts, providing a potential platform for calls to improve national funding for future Australian preventive programs; however, inconsistencies continued across all jurisdictions in their approaches to tackling national health priorities. Despite 2004 being an election year, public health policy was not visible, with the bulk of the public health funding available in the 2004/05 federal budget allocated to managing such emerging risks as avian flu. We conclude by suggesting several implications for the future

Investigating inhibition of return with converging interdisciplinary methods

This dissertation investigates inhibition of return (IOR) as two forms of inhibitory cueing effects operating on spatially uninformative visual stimuli (i.e., cues): an output form of IOR that is generated when saccades are permitted, and an input form of IOR that arises when saccades are not allowed. Using paradigms adapted from Posner’s (1980) spatial cueing task, our first set of experiments in Chapter 2 attempts to dissociate the two forms of IOR by incorporating an incompatible response paradigm that requires either saccadic or manual keypress responses to targets. This design allowed us to examine separately the input form of IOR at the stimulus level versus the output form that is response related. The event-related potential (ERP) study in Chapter 3 builds upon the previous paradigm but uses saccades to cues to activate the oculomotor system. The activation of the oculomotor system allowed us to probe the neural mechanisms underlying the inhibitory cueing effects that are usually exhibited and studied in terms of behavioural response times. By manipulating stimulus-response compatibility in combination with activation or suppression of the oculomotor system in Chapters 2 and 3, we showed that the input form of IOR can be observed behaviourally when the oculomotor system is supressed. However, since we are ultimately looking for evidence of output-based IOR, which we have not been able to show with the anti-localisation paradigm, we decided that a change in direction was necessary. Chapters 4 and 5 present a shift in focus towards investigating modulations of behavioural cueing effects associated with the inclusion of non-targets (i.e., distractors) in a discrimination-localisation task. Our time-course study laid out the development of IOR in a distractor paradigm, and the results indicate that when distractors are present, oculomotor IOR starts early and slowly decays, whereas sensory-based IOR emerges later but decays relatively faster. The visually balanced ERP experiment in Chapter 6 allowed us to study the N2pc component as a neurophysiological marker of the output form of IOR while the oculomotor system is activated. We provide convincing evidence for behavioural IOR despite the presence of distractors, although ERP results are less clear cut. This dissertation provides converging evidence in support of an input based sensory/attentional IOR that is distinct from output based oculomotor IOR

The future of public health: the importance of workforce

Health workforce has become a major concern and a significant health policy issue around the world in recent years. With recent international and national initiatives and models being developed and implemented in Australia and other countries, it is timely to understand the need and the rationale for a better trained and educated public health workforce for the future. Much more attention should also be given to evaluation and research in this field

Timing Matters: How Delaying College Enrollment Affects Earnings Trajectories

High school graduates often delay college enrollment, but few studies have looked at the effects of this choice on their educational attainment and success in the labor market. Using data from the National Longitudinal Survey of Youth 1997, this paper compares the academic and labor market outcomes of high school graduates who delay college enrollment (“delayers”) and those who enroll in college immediately (“on-time enrollees”) up to 13 years after high school completion. The results show that delaying college enrollment decreases individuals’ likelihood of enrolling in college and increases their tendency to enroll in two-year colleges if they do return to school. Delayers experience early earnings benefits, but these fade out after their mid-20s and turn to significant losses over time. Differences in student characteristics only explain one third of the pay gap between delayers and on-time enrollees; 60 percent of the pay gap is explained by delayers’ reduced likelihood of attending and obtaining a degree at a four-year college

Fibulin-4 is essential for maintaining arterial wall integrity in conduit but not muscular arteries

Homozygous or compound heterozygous mutations in fibulin-4 (FBLN4) lead to autosomal recessive cutis laxa type 1B (ARCL1B), a multisystem disorder characterized by significant cardiovascular abnormalities, including abnormal elastin assembly, arterial tortuosity, and aortic aneurysms. We sought to determine the consequences of a human disease-causing mutation in FBLN4 (E57K) on the cardiovascular system and vascular elastic fibers in a mouse model of ARCL1B. Fbln4E57K/E57K mice were hypertensive and developed arterial elongation, tortuosity, and ascending aortic aneurysms. Smooth muscle cell organization within the arterial wall of large conducting vessels was abnormal, and elastic fibers were fragmented and had a moth-eaten appearance. In contrast, vessel wall structure and elastic fiber integrity were normal in resistance/muscular arteries (renal, mesenteric, and saphenous). Elastin cross-linking and total elastin content were unchanged in large or small arteries, whereas elastic fiber architecture was abnormal in large vessels. While the E57K mutation did not affect Fbln4 mRNA levels, FBLN4 protein was lower in the ascending aorta of mutant animals compared to wild-type arteries but equivalent in mesenteric arteries. We found a differential role of FBLN4 in elastic fiber assembly, where it functions mainly in large conduit arteries. These results suggest that elastin assembly has different requirements depending on vessel type. Normal levels of elastin cross-links in mutant tissue call into question FBLN4\u27s suggested role in mediating lysyl oxidase-elastin interactions. Future studies investigating tissuespecific elastic fiber assembly may lead to novel therapeutic interventions for ARCL1B and other disorders of elastic fiber assembly. 2017 © The Authors, some rights reserved

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Media Framing Of Waste Issues In Selected Malaysian English Newspapers: A Study Of The New Straits Times And The Sun

This study deals with quantitative and qualitative aspects of content analysis to examine how environmental and waste issues were covered and how wastes issues were framed in selected Malaysian English newspapers. Articles regarding environmental issues published from January 2003 to December 2007 in the New Straits Times and The Sun were analyzed based on a coding protocol. Waste-related articles were analyzed to determine the presence of Entman’s (1993) four framing functions: to define problems, to diagnose causes, to make moral judgments and to suggest solutions, as well as other applicable function. A total of 616 articles on waste issues were collected from January 2003 to December 2007