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Recognising the Role of the Emotion of Fear in Offences and Defences
Get PDFAbstract Anger, its part in human conduct and in crime commission has been much discussed and accorded a privileged status within the law, whilst the role of fear has been less considered. Notwithstanding, fear and related emotional states have received some recognition as intrinsic elements of the perpetrator’s object integral to the actus reus of certain offences and relevant to the defendant’s mens rea of some defences. The harm caused by deliberately or negligently instilling fear in another is inconsistently considered in law as is its impact on criminal responsibility and mens rea. Fear has been recently acknowledged as a permissible cause of loss of self-control in a partial defence to murder. It remains a contested emotion and as with anger the male experience of what circumstances trigger fear predominates
An evaluation of partial differential equations based digital inpainting algorithms
Get PDFPartial Differential equations (PDEs) have been used to model various phenomena/tasks in different scientific and engineering endeavours. This thesis is devoted to modelling image inpainting by numerical implementations of certain PDEs. The main objectives of image inpainting include reconstructing damaged parts and filling-in regions in
which data/colour information are missing. Different automatic and semi-automatic approaches to image inpainting have been developed including PDE-based, texture synthesis-based, exemplar-based, and hybrid approaches. Various challenges remain unresolved in reconstructing large size missing regions and/or missing areas with highly textured surroundings. Our main aim is to address such challenges by developing new advanced schemes with particular focus on using PDEs of different orders to preserve continuity of textural and geometric information in the surrounding of missing regions. We first investigated the problem of partial colour restoration in an image region whose greyscale channel is intact. A PDE-based solution is known that is modelled as minimising total variation of gradients in the different colour channels. We extend the applicability of this model to partial inpainting in other 3-channels colour spaces (such as RGB where information is missing in any of the two colours), simply by exploiting the known linear/affine relationships between different colouring models in the derivation of a modified PDE solution obtained by using the Euler-Lagrange minimisation of the corresponding gradient Total Variation (TV). We also developed two TV models on the relations between greyscale and colour channels using the Laplacian operator and the directional derivatives of gradients. The corresponding
Euler-Lagrange minimisation yields two new PDEs of different orders for partial colourisation. We implemented these solutions in both spatial and frequency domains.
We measure the success of these models by evaluating known image quality measures in inpainted regions for sufficiently large datasets and scenarios. The results reveal that our schemes compare well with existing algorithms, but inpainting large regions remains a challenge. Secondly, we investigate the Total Inpainting (TI) problem where all colour channels are missing in an image region. Reviewing and implementing existing PDE-based total inpainting methods reveal that high order PDEs, applied to each colour channel separately, perform well but are influenced by the size of the region and the quantity of
texture surrounding it. Here we developed a TI scheme that benefits from our partial inpainting approach and apply two PDE methods to recover the missing regions in the image. First, we extract the (Y, Cb, Cr) of the image outside the missing region, apply the above PDE methods for reconstructing the missing regions in the luminance channel (Y), and then use the colourisation method to recover the missing (Cb, Cr) colours in the region. We shall demonstrate that compared to existing TI algorithms, our proposed method (using 2 PDE methods) performs well when tested on large datasets of natural and face images. Furthermore, this helps understanding of the impact of the texture in the surrounding areas on inpainting and opens new research directions. Thirdly, we investigate existing Exemplar-Based Inpainting (EBI) methods that do not use PDEs but simultaneously propagate the texture and structure into the missing region by finding similar patches within the rest of image and copying them into the boundary of the missing region. The order of patch propagation is determined by a priority function, and the similarity is determined by matching criteria. We shall exploit recently emerging Topological Data Analysis (TDA) tools to create innovative EBI schemes, referred to as TEBI. TDA studies shapes of data/objects to quantify image texture in terms of connectivity and closeness properties of certain data landmarks. Such quantifications help determine the appropriate size of patch propagation and will be used to modify the patch propagation priority function using the geometrical properties of curvature of isophotes, and to improve the matching criteria of patches by calculating the correlation coefficients from the spatial, gradient and Laplacian domains. The performance of this TEBI method will be tested by applying it to natural dataset images, resulting in improved inpainting when compared with other EBI methods. Fourthly, the recent hybrid-based inpainting techniques are reviewed and a number of highly performing innovative hybrid techniques that combine the use of high order PDE methods with the TEBI method for the simultaneous rebuilding of the missing texture and structure regions in an image are proposed. Such a hybrid scheme first decomposes the image into texture and structure components, and then the missing regions in these components are recovered by TEBI and PDE based methods respectively. The performance of our hybrid schemes will be compared with two existing hybrid algorithms. Fifthly, we turn our attention to inpainting large missing regions, and develop an innovative inpainting scheme that uses the concept of seam carving to reduce this problem to that of inpainting a smaller size missing region that can be dealt with efficiently using the inpainting schemes developed above. Seam carving resizes images based on content-awareness of the image for both reduction and expansion without affecting those image regions that have rich information. The missing region of the seam-carved version will be recovered by the TEBI method, original image size is restored by adding the removed seams and the missing parts of the added seams are then repaired using a high order PDE inpainting scheme. The benefits of this approach in dealing with large missing regions are demonstrated. The extensive performance testing of the developed inpainting methods shows that these methods significantly outperform existing inpainting methods for such a challenging task. However, the performance is still not acceptable in recovering large missing regions in high texture and structure images, and hence we shall identify remaining challenges to be investigated in the future. We shall also extend our work by investigating recently developed deep learning based image/video colourisation, with the aim of overcoming its limitations and shortcoming. Finally, we should also describe our on-going research into using TDA to detect recently growing serious “malicious” use of inpainting to create Fake images/videos
Texture Analysis based Machine Learning Algorithms For Ultrasound Ovarian Tumour Image Classification within Clinical Practices
Get PDFResearch investigations reported in this thesis, aim to contribute to the efforts of developing reliable ovarian tumour classification tools using texture features extracted from B-mode ultrasound ovarian tumour scan images. This kind of research is necessitated by the shortage of highly trained sonographers and gynaecologists in order to reduce the heavy pressure on healthcare systems throughout the world. Our ultimate aim is to automate the error-prone process of the laborious manual examination of the ultrasound scan images, and we, therefore, exploit advances in Machine learning and computer vision to develop informative software to be integrated within clinical setup.
Our research was guided by an extensive literature review of existing research in this and related fields, building on existing collaborations with medical expertise, and evidence from systems biology research that carcinogenesis results in changing the texture of cysts cellular network. These considerations led to adapting image texture analysis approaches as an adequate source for Machine learning algorithms and software tools. Most existing research works in general biomedical image-based diagnostics are directed towards identifying one or few best performing texture features. Instead, our analysis aimed at extracting a suit of texture-based image features that together contribute to effective ultrasound ovarian tumour image classification. This open-minded strategy unearthed a plethora of texture-based features and in different image domains beyond the spatial domain, which depicts a visual image of the scanned tissue. There is a significant variation in the dimensionality of the texture features, included in our investigations, and although we use different well-known classifiers in evaluating performances, the focus of the comparisons made are not on the choice of classifiers.
This thesis includes many contributions; the most significant ones can be summarised as
follows:
1. Established that even without pre-processing the scanned images spatial domain is a rich source of 7 microscale texture primitives that can distinguish malignant tumour scans from benign ones with accuracy well above being a case of random chance prediction (70% -83%). The simple majority rule fusion of an odd number of features yield accuracy in the range 83% - 90%.
2. Developed a smart adaptive speckle-noise reduction scheme that applies noise reduction in blocks of the cropped tumour images (not the entire image) only if ii(Skewness, Kurtosis) pair in the block satisfies a criterion determined by training. This adaptive pre-processing is shown to significantly improve the performance of all investigated texture schemes, not only the spatial domain ones.
3. Modified the existing frequency domain texture feature (FFGF), by adaptively pre-processing the cropped tumour image prior to computing its Fourier Spectrum, and using a different binarization scheme to extract the bright elliptical shape at the centre of the FFT spectrum. These modifications improved the accuracy of the original FFGF scheme 85.9% to more than 92%.
4. When attempted to reduce the dependencies between the 3 ellipse parameters of FFGF has shown that even better accuracy (> 95%) can be achieved using a single parameter (the minor axes). These results led to establishing that the FFT-spectrum image is a very rich source of texture information only obscured by its somewhat visually “meaningless” display. We found that all of the features extracted from the FFT-spectrum outperform their spatial domain counterpart, and the fusion of the 7 FFT-spectrum based schemes achieved accuracy of > 97.5%.
5. We further extended the list of texture-based image features beyond the spatial domain and beyond the FFGF schemes by extracting some of the previously defined texture features not only from the FFT-spectrum but also in any image transform domain such as the LBP domain. Again, the texture features in the LBP domain outperformed the spatial domain counterparts, with FFGF from the LBP domain achieving accuracy of 94% which even outperforms the modified FFGF.
6. Finally, the extensive experiments simply opened a Pandora Box of image textures. Instead, of continue other image transform domain, we created two versions of an ML-based software that incorporate 9 spatial domain texture-based features (the original 7 + the Skewness + the Kurtosis) to be used for a prospective test of 100 fresh cases, collected and examined histologically by an IOTA expert gynaecologist at Queen Charlotte and Hammersmith Hospital in London during the period (Oct 2018 – Jan 2019). Version 2 incorporates the smart adaptive speckle noise removal resulted in an accuracy of 94%
Intelligence gathering, issues of accountability, and Snowden
Get PDFWe could ask the question as to whether Snowden’s actions, which place
him in exile in Russia facing multiple years in jail should he return to the US,
caused a retrenchment of industrial-scale Western surveillance and interception activities. The answer, perhaps ironically, is that the opposite seems to
have happened. Taking the UK’s IPA as an example, many Western states
continue to have considerable interception capabilities and powers and have
arguably deepened and strengthened these powers in many cases. Indeed, it
could be argued that the state’s professed need to continue to be able to tackle
the security threats of the twenty-first century despite significant changes in
technology have won out over any public concerns that may exist about erosions of privacy. As a side issue, continuing questions about whether Western
oversight and accountability regimes have sufficient teeth to be able to take on
the security agencies seem only to have been exacerbated.
This chapter will consider the chronology of events in the UK case study,
starting in the late 2000s and moving on to the passing of the IPA in 2016.
It interprets this story in terms of whether and how the state interacted with
its critics in developing a refreshed surveillance regime; how the oversight
bodies fared throughout the period; and where this leaves questions of
privacy-versus-security in the final analysis
Agents based Context-Aware Framework for Facial Identification System
No full textResearch in face recognition systems has been focused on improving algorithm performance under specific conditions
or to increase the average performance under heterogeneous conditions. Typical systems do not adjust well to perform at optimal level for a given instant of identification because the algorithms, face feature representations are fixed to achieve the best average result. Therefore, there is a real need to design a context-aware adaptive face identification system that can select the best pre-processing, features, and classifier for any given instance of identification. This paper focuses on the practical implementation and evaluation of our proposed framework [1] [2] that is aware of its operational context and adapt itself to select a suitable approach to identify a given face image. This is by using the agent technology to give the system an intelligent and adaptive mechanism to make decisions at the key stages of the facial identification process. The agents will use context information such as environment conditions and application requirements to select the most appropriate pre-processing, features and match scores to optimise the best identification accuracy for a given test image. Within our framework, we propose the use of agents in two strategies: 1) an agent-based adaptive score selection technique as an alternative to the traditional fusion approaches, and 2) an agent-based integrated technique as an improvement to the existing adaptive and non-adaptive techniques. The experimental results displayed here demonstrate that our techniques of using agents outperform the traditional fusion strategy that is commonly used in face recognition systems as well as the performance of other existing techniques
G protein alpha-q gene expression plays a role in alcohol tolerance in Drosophila melanogaster
Get PDFEthanol is a psychoactive substance causing both short and long-term behavioural changes, in humans and animal models. We have used the fruit fly Drosophila melanogaster to investigate the effect of ethanol exposure on the expression of the Gɑq protein subunit. Repetitive exposure to ethanol causes a reduction in sensitivity (tolerance) to ethanol which we have measured as the time for 50% of a set of flies to become sedated after exposure to ethanol (ST50). We demonstrate that the same treatment that induces an increase in ST50 over consecutive days (tolerance) also causes a decrease of Gɑq protein subunit expression both at the mRNA and protein level. To identify whether there may be a causal relationship between these two outcomes, we have developed strains of flies in which Gαq mRNA expression is suppressed in a time and tissue specific manner. In these flies, the sensitivity to ethanol and the development of tolerance is altered. This work further supports the value of Drosophila as a model to dissect the molecular mechanisms of the behavioural response to alcohol and identifies G proteins as potentially important regulatory targets for alcohol use disorders
What Role for Media in Security Crises?
Get PDFThe chapter examines the impact of media on public opinion and leadership decision-making during security crises. Leaders pay particular attention to media outlets during crises in an effort to collect as much information as possible from open sources. While intelligence from state services and allies plays a crucial role in reaching decisions, the impact of electronic and social media in shaping leadership perceptions is increasingly hard to ignore. The fact that governments have access to “accurate” intelligence should mitigate, in principle, the danger of misperception arising from erroneous media reports. Nevertheless, we have no way of limiting the potential “contamination” of leadership perceptions by inaccurate media information. Intelligence, after all, may be inconclusive, and intelligence assessments could themselves be affected by factors such as hostile images of the “other” engineered by the media. But the media’s independence is being gradually compromised, with the post-Cold War trend being particularly revealing. From the “CNN effect” of the 1990s to the “War on Terror” campaign in the 2000s and the Hybrid Warfare doctrines of the 2010, it becomes increasingly evident that governments aspire to “weaponise” information so as to achieve their military objectives. Maintaining, therefore, accurate perceptions in an environment where disinformation, fake news, and propaganda are pervasive is a demanding task. As a result, governments will have to exercise effective oversight across media outlets in the future in order to ensure that public opinion and leadership perceptions are unaffected by disinformation and propaganda campaigns. With more governments engaging into the “weaponisation” of media, however, it is up to media professionals and journalists to defend their field and ensure that global audiences have access to impartial coverage of security crises
The Biopsychosocial Factors Associated With Pain In People with Spinal Cord Injury
Get PDFBackground: It is estimated that over 62% of people with a spinal cord injury (SCI) experience chronic pain, (Ullrich, Jensen, Loesser & Cardenas, 2007). Much research has demonstrated that a variety of biopsychosocial factors can impact on pain outcomes (Tran, Dorstyn & Burke, 2016) and, consequently on adjustment to injury. It is also well established that cognitive appraisal of SCI impacts on psychological adjustment during rehabilitation (Eaton, Jones & Duff, 2018). SCI pain is unusually resistant to standard pain management programmes (Perry, Nicholas & Middleton, 2010). However, the development of a tailored programme requires a profile of the biological, psychological, and social characteristics of chronic pain sufferers with SCI, but the existing knowledge base is fragmented. This study aimed to investigate how biopsychosocial factors interact to impact on pain-related outcomes for people with SCI.
Method: A longitudinal, multiple assessment-point design was used with 60 spinal cord injured in-patients at the NSIC, Stoke Mandeville. Participants were asked to complete a set of two pain and six psychological assessments at three different time points over a nine-month period, and to provide salivary samples on each occasion to assess concentration levels of cortisol. Additionally, a cross-sectional study using the same questionnaires and cortisol sampling was undertaken with 47 out-patients, who had been discharged a minimum of two years previously from the NSIC. Cohen’s (2009) power primer was used to calculate sample size. Independent t-tests measured differences between in-patient and out-patient groups on each questionnaire. Multiple regression was used to determine which biopsychosocial factors have greater predictive power in accounting for a range of functional, affective and sensory pain outcomes, highlighting how variables may individually and in combination influence the pain experience. Repeated measures ANOVAs were used to assess how the data changed over time and each measure was additionally correlated with time since injury. Additional exploratory analyses were undertaken to see whether pain catastrophising, appraisal of injury and pain acceptance mediated the effects of the other biopsychosocial variables on the pain outcomes. Lastly, multiple regressions explored whether the psychosocial variables predicted the way in which the injury was appraised.
Results: Out-patients appraised their injury more negatively (p = .04) and had lower determined resilience (p = .05) than in-patients at time one. They also demonstrated less pain acceptance (p = .04) and received fewer solicitous responses from a significant other person (p = .01). In-patients at time three had higher depression scores than out-patients (p = .006). In the multiple regression analyses, negative psychological variables predicted pain intensity (p = .002-.005), interference from pain (p = .001), and pain-related distress (p = .001). Positive psychological variables did not predict pain intensity but did predict pain interference (p = .029 - .071) and distress (p <.001). The way a significant other responded to the individual in pain did not predict pain intensity but did predict life interference (p = .001) and distress (p = .018 - .049). Cortisol did not predict any of the pain outcomes directly. Of all the variables, cortisol concentration was only significantly related to pain catastrophising (p = .008). The in-patient longitudinal analysis showed that over the three time points determined resilience decreased (p <.001), and depression scores increased (p = .025). The magnification sub scale of pain catastrophizing also increased between the first and second time point (p = .021). Time since injury was positively correlated with mental defeat (p = .047) and the helplessness sub scale of pain catastrophizing (p = .010), and negatively correlated with cortisol concentration levels (p = .001). In the mediation analyses, pain catastrophizing and appraisal of injury mediated the effects of most of the biopsychosocial variables on a wide range of pain outcomes. Pain catastrophizing was most influential on sensory and functional pain outcomes, and injury appraisal had greater effects on affective and functional outcomes. Pain acceptance was not influential as a mediating variable. In the final multiple regression analyses, the psychosocial variables were entered into regression models to see if they would predict the way the spinal injury was appraised. The psychological variables model (catastrophizing, acceptance, perceived stress, anxiety and mental defeat) predicted catastrophic negativity (p <.001) and determined resilience (p <.001). The way a significant person responded to the individual in pain did not predict catastrophic negativity with regard to injury appraisal but did predict determined resilience (p = .001).
Conclusion: The results of this study clearly indicate that biopsychosocial variables combine and interact to affect the consequences of pain for people with spinal cord injury. Pain treatment programmes that fail to take account of each of the components of the biopsychosocial model will not be addressing all of the factors associated with the pain experience, and this will have a negative impact for those in pain. This is especially concerning as the study found that psychosocial variables worsen on transition to the community. Appraisal of injury and pain catastrophizing are particularly influential, both as predictors and mediators, so focusing on these factors in pain management could improve pain outcomes and injury adjustment for people with spinal cord injury
SLDPC: Towards Second Order Learning for Detecting Persistent Clusters in Data Streams
Get PDFThe main attention of research on data stream clustering algorithms so far has been focused on the adaptation of the algorithms for static datasets to the data streams and improvements of the existing adapted algorithms. Such algorithms fulfil the purpose of the first-order learning from data to clusters. This paper prompts a new question on second-order learning of cluster models from data streams and presents a learning algorithm that detects persistent clusters from consecutive clustering snapshots in data streams. In this work, we first collect a sequence of cluster snapshots as the output clusters at selected query points and then identify the persistent clusters within a given timeframe. The algorithm is evaluated on collections of synthetic datasets. The experimental results have demonstrated the effectiveness of the algorithm in detecting such persistent clusters
Exploring the role of Egocentrism and Fear of Missing Out on Online Risk Behaviours among Adolescents in South Africa
Get PDFThe study explored the potential for developmental and social factors to predict adolescent online risk behaviour. Employing a sample of 1184 adolescents aged 12-18 in South Africa, the study examined gender, age, egocentrism (Personal Fable and Imaginary Audience) and Fear of Missing Out (FoMO) on online risk taking. Results showed that all variables were significant predictors of online risk behaviour. Higher Imaginary Audience, higher FoMO and older age emerged as strongest predictors, and males engaged in more online risks. FoMO also correlated significantly with egocentrism constructs. The findings indicate that egocentrism is a relevant developmental construct for understanding adolescent online risk taking along with social factors like FoMO, which can inform more targeted online safety efforts at particular developmental stages