Army, police and society in Ireland: Civil, military and police relations in King's County and County Donegal c1870-1902

This thesis makes a detailed contribution to the study of social relations between tenantry, landowners, police and the military, and reveals continuities and complexities often missing from more generalised accounts. It begins by arguing that the prevailing framework of traditionalism versus revisionism in Irish history is too restricting, and agrees with those who want it opened up to wider approaches for a better understanding. By comparing two selected counties, this work uses local studies to examine Irish history in general - which is a well-established method for the period 1916-1923, but less so for the late nineteenth century. Evidence of continuous disturbances throughout the period, albeit of varying intensity, supports the theory of a ‘Long Land War’. It is important, however, to notice that there were other causes of friction apart from the pressures of nationalism and agrarian reform. In this conflict, the evidence suggests that widespread intimidation was both commonplace and effective on an often reluctant population, and questions how far nationalism was a really popular ideal. It is argued here that nationalism did slowly become stronger, and was fostered during the South African War. Agrarian reform made more tangible progress, partly through the actions of the various leagues, but also through a collapse of government and landlord resolve. The position of the Catholic clergy as leaders of agitation is well established and supported here – but less discussed in the secondary literature is the role played by the women of tenant families. This study argues that ordinary women played a vital part in all agitation and resistance, and that this role deserves much wider recognition.. It is well documented that sport and culture were used by the nationalists to nurture support, but this study will argue that the authorities did the same thing. Army reforms also helped to identify military units with specific areas, and economic considerations about the buying power of the army played a moderating role in limiting opposition. The militia, whilst certainly ill-disciplined at times, have too often been dismissed as ineffectual, but it is argued here that they served a useful social function, and that their importance was as imperial reinforcements, not as a gendarmerie for dealing with discontent at home. It is also argued that excessive violence was sometimes used by the authorities, but it will be shown that Catholics in both the RIC and the army performed their duties with very few exceptions, and so recruiting Catholics was not a cause of weakness for the authorities. The RIC in particular, however, were vilified for the work they carried out and the way that they sometimes performed it

Glyph visualization: A fail-safe design scheme based on quasi-hamming distances

© 1981-2012 IEEE. In many spatial and temporal visualization applications, glyphs provide an effective means for encoding multivariate data. However, because glyphs are typically small, they are vulnerable to various perceptual errors. This article introduces the concept of a quasi-Hamming distance in the context of glyph design and examines the feasibility of estimating the quasi-Hamming distance between a pair of glyphs and the minimal Hamming distance for a glyph set. The authors demonstrate the design concept by developing a file-system event visualization that can depict the activities of multiple users

Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation

Mutual information (MI) is a popular similarity measure for performing image registration between different modalities. MI makes a statistical comparison between two images by computing the entropy from the probability distribution of the data. Therefore, to obtain an accurate registration it is important to have an accurate estimation of the true underlying probability distribution. Within the statistics literature, many methods have been proposed for finding the 'optimal' probability density, with the aim of improving the estimation by means of optimal histogram bin size selection. This provokes the common question of how many bins should actually be used when constructing a histogram. There is no definitive answer to this. This question itself has received little attention in the MI literature, and yet this issue is critical to the effectiveness of the algorithm. The purpose of this paper is to highlight this fundamental element of the MI algorithm. We present a comprehensive study that introduces methods from statistics literature and incorporates these for image registration. We demonstrate this work for registration of multi-modal retinal images: colour fundus photographs and scanning laser ophthalmoscope images. The registration of these modalities offers significant enhancement to early glaucoma detection, however traditional registration techniques fail to perform sufficiently well. We find that adaptive probability density estimation heavily impacts on registration accuracy and runtime, improving over traditional binning techniques. © 2013 Elsevier Ltd

Caught in the act of an insider attack: detection and assessment of insider threat

The greatest asset that any organisation has are its people, but they may also be the greatest threat. Those who are within the organisation may have authorised access to vast amounts of sensitive company records that are essential for maintaining competitiveness and market position, and knowledge of information services and procedures that are crucial for daily operations. In many cases, those who have such access do indeed require it in order to conduct their expected workload. However, should an individual choose to act against the organisation, then with their privileged access and their extensive knowledge, they are well positioned to cause serious damage. Insider threat is becoming a serious and increasing concern for many organisations, with those who have fallen victim to such attacks suffering significant damages including financial and reputational. It is clear then, that there is a desperate need for more effective tools for detecting the presence of insider threats and analyzing the potential of threats before they escalate. We propose Corporate Insider Threat Detection (CITD), an anomaly detection system that is the result of a multi-disciplinary research project that incorporates technical and behavioural activities to assess the threat posed by individuals. The system identifies user and role-based profiles, and measures how users deviate from their observed behaviours to assess the potential threat that a series of activities may pose. In this paper, we present an overview of the system and describe the concept of operations and practicalities of deploying the system. We show how the system can be utilised for unsupervised detection, and also how the human analyst can engage to provide an active learning feedback loop. By adopting an accept or reject scheme, the analyst is capable of refining the underlying detection model to better support their decisionmaking process and significant reduce the false positive rate

Automated insider threat detection system using user and role-based profile assessment

© 2007-2012 IEEE. Organizations are experiencing an ever-growing concern of how to identify and defend against insider threats. Those who have authorized access to sensitive organizational data are placed in a position of power that could well be abused and could cause significant damage to an organization. This could range from financial theft and intellectual property theft to the destruction of property and business reputation. Traditional intrusion detection systems are neither designed nor capable of identifying those who act maliciously within an organization. In this paper, we describe an automated system that is capable of detecting insider threats within an organization. We define a tree-structure profiling approach that incorporates the details of activities conducted by each user and each job role and then use this to obtain a consistent representation of features that provide a rich description of the user's behavior. Deviation can be assessed based on the amount of variance that each user exhibits across multiple attributes, compared against their peers. We have performed experimentation using ten synthetic data-driven scenarios and found that the system can identify anomalous behavior that may be indicative of a potential threat. We also show how our detection system can be combined with visual analytics tools to support further investigation by an analyst

Multimodal retinal imaging: Improving accuracy and efficiency of image registration using Mutual Information

This thesis addresses the challenging task of multi-modal image registration. Registration is often required in a number of applications, whereby two images are aligned to give matching correspondence between the features in each image. Such techniques have become popular in many different fields, especially in medical imaging. Multi-modal registration would allow for anatomical structure to be studied concurrently in both modalities, providing the clinician with a greater insight of the patient's condition. Glaucoma is a serious condition that damages the optic nerve progressively, leading to irreversible blindness. The disease can be treated so to prevent any further infection, however it can not be reversed. Therefore it is paramount that the disease is detected in the early stages so to minimise the affect of the condition. The work in this thesis focuses on two particular imaging modalities: colour fundus photographs and scanning laser ophthalmoscope images. Both images are captured from the human eye and show the appearance and reflectivity of the retina respectively. Registration of these two modalities would significantly improve demarcation and monitoring of the optic nerve head, a crucial stage for glaucoma diagnosis. In recent years, Mutual Information has become a popular technique used to perform multi-modal registration. This thesis provides a comprehensive overview of the algorithm. Firstly, an investigation is performed that shows how probability estimation can improve the algorithm performance. Secondly, the weaknesses of the current technique are revealed and so a novel solution is proposed that overcomes these problems. Finally, the proposed solution is incorporated in a non-rigid registration scheme that provides excellent registration accuracy for our intended application

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. © 1995-2012 IEEE

Hierarchical event selection for video storyboards with a case study on snooker video visualization

Video storyboard, which is a form of video visualization, summarizes the major events in a video using illustrative visualization. There are three main technical challenges in creating a video storyboard, (a) event classification, (b) event selection and (c) event illustration. Among these challenges, (a) is highly application-dependent and requires a significant amount of application specific semantics to be encoded in a system or manually specified by users. This paper focuses on challenges (b) and (c). In particular, we present a framework for hierarchical event representation, and an importance-based selection algorithm for supporting the creation of a video storyboard from a video. We consider the storyboard to be an event summarization for the whole video, whilst each individual illustration on the board is also an event summarization but for a smaller time window. We utilized a 3D visualization template for depicting and annotating events in illustrations. To demonstrate the concepts and algorithms developed, we use Snooker video visualization as a case study, because it has a concrete and agreeable set of semantic definitions for events and can make use of existing techniques of event detection and 3D reconstruction in a reliable manner. Nevertheless, most of our concepts and algorithms developed for challenges (b) and (c) can be applied to other application areas. © 2010 IEEE