Beyond the pixels: learning and utilising video compression features for localisation of digital tampering.

Video compression is pervasive in digital society. With rising usage of deep convolutional neural networks (CNNs) in the fields of computer vision, video analysis and video tampering detection, it is important to investigate how patterns invisible to human eyes may be influencing modern computer vision techniques and how they can be used advantageously. This work thoroughly explores how video compression influences accuracy of CNNs and shows how optimal performance is achieved when compression levels in the training set closely match those of the test set. A novel method is then developed, using CNNs, to derive compression features directly from the pixels of video frames. It is then shown that these features can be readily used to detect inauthentic video content with good accuracy across multiple different video tampering techniques. Moreover, the ability to explain these features allows predictions to be made about their effectiveness against future tampering methods. The problem is motivated with a novel investigation into recent video manipulation methods, which shows that there is a consistent drive to produce convincing, photorealistic, manipulated or synthetic video. Humans, blind to the presence of video tampering, are also blind to the type of tampering. New detection techniques are required and, in order to compensate for human limitations, they should be broadly applicable to multiple tampering types. This thesis details the steps necessary to develop and evaluate such techniques

A review of digital video tampering: from simple editing to full synthesis.

Video tampering methods have witnessed considerable progress in recent years. This is partly due to the rapid development of advanced deep learning methods, and also due to the large volume of video footage that is now in the public domain. Historically, convincing video tampering has been too labour intensive to achieve on a large scale. However, recent developments in deep learning-based methods have made it possible not only to produce convincing forged video but also to fully synthesize video content. Such advancements provide new means to improve visual content itself, but at the same time, they raise new challenges for state-of-the-art tampering detection methods. Video tampering detection has been an active field of research for some time, with periodic reviews of the subject. However, little attention has been paid to video tampering techniques themselves. This paper provides an objective and in-depth examination of current techniques related to digital video manipulation. We thoroughly examine their development, and show how current evaluation techniques provide opportunities for the advancement of video tampering detection. A critical and extensive review of photo-realistic video synthesis is provided with emphasis on deep learning-based methods. Existing tampered video datasets are also qualitatively reviewed and critically discussed. Finally, conclusions are drawn upon an exhaustive and thorough review of tampering methods with discussions of future research directions aimed at improving detection methods

Working Together to Get It For Them: ILL and Document Delivery at the UNT Libraries

This presentation provides an overview of the document delivery process for UNT Libraries. Slides include reorganization of process to include use of ILLiad. Presented at the 2016 OCLC ILLiad International Conference

Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers.

The generalisability of deep neural network classifiers is emerging as one of the most important challenges of our time. The recent COVID-19 pandemic led to a surge of deep learning publications that proposed novel models for the detection of COVID-19 from chest x-rays (CXRs). However, despite the many outstanding metrics reported, such models have failed to achieve widespread adoption into clinical settings. The significant risk of real-world generalisation failure has repeatedly been cited as one of the most critical concerns, and is a concern that extends into general medical image modelling. In this study, we propose a new dataset protocol and, using this, perform a thorough cross-dataset evaluation of deep neural networks when trained on a small COVID-19 dataset, comparable to those used extensively in recent literature. This allows us to quantify the degree to which these models can generalise when trained on challenging, limited medical datasets. We also introduce a novel occlusion evaluation to quantify model reliance on shortcut features. Our results indicate that models initialised with ImageNet weights then fine-tuned on small COVID-19 datasets, a standard approach in the literature, facilitate the learning of shortcut features, resulting in unreliable, poorly generalising models. In contrast, pre-training on related CXR imagery can stabilise cross-dataset performance. The CXR pre-trained models demonstrated a significantly smaller generalisation drop and reduced feature dependence outwith the lung region, as indicated by our occlusion test. This paper demonstrates the challenging problem of model generalisation, and the need for further research on developing techniques that will produce reliable, generalisable models when learning with limited datasets

In With The New: Adapting To Change Using ILLiad

This presentation discusses adapting to change using ILLiad

The Economic Burden of Prematurity in Canada

Background Preterm birth is a major risk factor for morbidity and mortality among infants worldwide, and imposes considerable burden on health, education and social services, as well as on families and caregivers. Morbidity and mortality resulting from preterm birth is highest among early (< 28 weeks gestational age) and moderate (28–32 weeks) preterm infants, relative to late preterm infants (33–36 weeks). However, substantial societal burden is associated with late prematurity due to the larger number of late preterm infants relative to early and moderate preterm infants. Methods The aim in this study was to characterize the burden of premature birth in Canada for early, moderate, and late premature infants, including resource utilization, direct medical costs, parental out-of-pocket costs, education costs, and mortality, using a validated and published decision model from the UK, and adapting it to a Canadian setting based on analysis of administrative, population-based data from Québec. Results Two-year survival was estimated at 56.0% for early preterm infants, 92.8% for moderate preterm infants, and 98.4% for late preterm infants. Per infant resource utilization consistently decreased with age. For moderately preterm infants, hospital days ranged from 1.6 at age two to 0.09 at age ten. Cost per infant over the first ten years of life was estimated to be $67,467 for early preterm infants,$52,796 for moderate preterm infants, and id="mce_marker"0,010 for late preterm infants. Based on population sizes this corresponds to total national costs of id="mce_marker"23.3 million for early preterm infants, $255.6 million for moderate preterm infants,$208.2 million for late preterm infants, and $587.1 million for all infants. Conclusion Premature birth results in significant infant morbidity, mortality, healthcare utilization and costs in Canada. A comprehensive decision-model based on analysis of a Canadian population-based administrative data source suggested that the greatest national-level burden is associated with moderate preterm infants due to both a large cost per infant and population size while the highest individual-level burden is in early preterm infants and the largest total population size is in late preterm infants. Although the highest medical costs are incurred during the neonatal period, greater resource utilization and costs extend into childhood

Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19.

This paper presents an experience report of online attendance and associated behavioural patterns during a module in the first complete semester undertaken fully online in the autumn of 2020, and the corresponding module deliveries in 2021 and 2022. The COVID-19 pandemic of 2020 resulted in a sudden move of most university teaching online, at a global and large-scale level. This, combined with the need to maintain "business as usual" resulted in new levels of student engagement data for largely unchanged pedagogical processes. Engagement data continued to be gathered throughout the subsequent, phased return to face-to-face and hybrid learning, although at a lesser level of granularity. The wealth of student engagement data gathered during this time allows quantitative insights into how student behaviour continued to adapt during and after the enforced online learning during the COVID-19 pandemic. The anonymous subjects of this case study are computing science students in their final year of undergraduate study. We examine their engagement with the virtual learning environment, including engagement with recorded lecture material, attendance in online sessions and engagement during in-person labs. We relate this to both the students' final grades and the content of the module itself. A number of conclusions are drawn based on this empirical data, relating to observations made by staff and pedagogical theory. There was a moderate, but significant, correlation between engagement in synchronous online lecture sessions and grades during thelockdown phase, but the strength of this correlation has reduced in subsequent years as normality has returned. From monitoring behaviour in online sessions down to minute-by-minute accuracy, it can also be seen that some students strategised their engagement based on sessions they perceived to be most directly contributory to their assessment, placing little value on live guest lecturer sessions. During enforced online learning, the most successful students, on average, engaged with less repeat content than less successful students, instead apparently utilising lecture recordings to "catch up" with missed live lectures

Optimisation and validation of a PCR for Antigen Receptor Rearrangement (PARR) assay to detect clonality in canine lymphoid malignancies

PCR for antigen receptor gene rearrangements (PARR) analysis is being increasingly used to assist diagnosis of canine lymphoma. In this study, PARR was carried out on consecutive samples received as part of routine diagnostic practice from 271 patients: 195 with lymphoid malignancies, 53 with reactive conditions and 23 with other neoplasms. Initially, published primer sets were used but later minor primer modifications were introduced and primers were rationalised to give a PARR panel that provides a good compromise between sensitivity and cost. Results were compared to diagnoses made by histology or cytology, coupled with immunophenotyping by flow cytometry or immunohistochemistry where possible. After exclusion of 11 poor quality samples, 230/260 (88%) gave a clear result with 162/163 (99%) of samples classified as clonal and 56/67 (84%) classified as polyclonal giving results concordant with the cytological/histological diagnosis. Among 30 samples with equivocal results, 21 had clonal peaks in a polyclonal background and nine showed little amplification. These were from patients with a range of neoplastic and non-neoplastic conditions emphasising the need to interpret such results carefully in concert with other diagnostic tests. The combination of primer sets used in this study resulted in a robust, highly specific and sensitive assay for detecting clonality