This work, published over a five-year-period, focuses upon the availability of mental condition defences to vulnerable offenders. The question addressed is ‘to what extent do mental condition defences adequately accommodate the circumstances of vulnerable offenders within the criminal justice system?’ The publications are timely in charting a pathway for the interpretation and application of the (then) recently introduced partial defences to murder, and recent reviews/reforms to mental condition defences across England and Wales (‘E&W’), Scotland, New South Wales (‘NSW’), Victoria, New Zealand (‘NZ’), and the United States (‘US’), before advancing optimal reform solutions. The publications fall under four themes, each addressing essential aims and objectives of the study. The overarching aim is to provide optimal reform solutions to problems faced by vulnerable offenders in claiming mental condition defences. The objective is to provide a critical exposition of these problems before advancing reform proposals based upon the experiences of the jurisdictions identified. The research method is largely doctrinal 'black-letter', comparative, and reform- focused. The nature of the research means that socio-legal factors also play a significant role. This collection provides a leading point of reference in the field of mental condition defences, which represents one of the most important and sensitive criminal law areas; this work reveals significant problems in the operation and application of the law. The central conclusion reached is that in the context of mental condition defences, a rebalancing exercise must take place, which ensures vulnerable offenders are at the centre of discourse, policy, and reform initiatives. In this regard, these publications provide insights into the interpretation of proposed, new and existing law as it applies to vulnerable offenders. This focus upon making mental condition defences more accessible to the vulnerable offender, and the optimal reform framework advanced demonstrates the extent to which this can be achieved, without risking the integrity of mental condition defences; the NZ Law Commission is considering several of the proposals.

Since the time when legal education became the preserve of universities and other higher education providers there has been an uneasy relationship between the ‘aims’ of a liberal course of study and the demands of a practising profession. In addition, educators have dominated formal legal learning with a rules-driven curriculum delivered principally in the format of lectures – where students are seen and treated primarily as passive recipients of knowledge. On joining academia from legal practice in the early 1990s my work has been principally focused on addressing the tensions implicit in the above – looking at what we as educators, practitioners, students and the wider public need and want from legal education and the most effective ways of achieving that. Taking the importance of knowledge, skills and values as a framework I have published widely on both the content and means of delivery of legal education. Whilst the former (content) must be seen in context – undergraduates for example may have very different outcomes set for them as compared with those an overtly vocational course – the latter (delivery) has a common denominator. This is to do with how students learn and how they can be encouraged to take responsibility for, and make the most of, that. Learning through experience supported by the means to conduct critical reflection is at the centre of my work. I have looked, in particular, at developing models of 'clinic' where students, under requisite supervision, engage in real or realistic casework in order to comprehend relevant content, be that legal doctrine, lawyering skills or professional and ethical considerations. This work has been recognised as ‘pioneering’ in the development of clinical legal education both in the UK and further afield, as evidenced by the volume of publications, numerous citations in other works and requests made by others for assistance, particularly in developing clinics. Most recently I have been looking at whether these pedagogic and professional practice models have transferable value in civil and common law jurisdictions and to what extent they aide access to justice. The publications – a mix of books, book chapters, articles in refereed journals and other material in the public domain – that I rely on are listed in Appendix 1 to this commentary and show, I submit, how I have made a significant contribution to knowledge through the development of clinic as pedagogy and the utility of this, for student, client, profession and the wider public in a domestic and international context. This submission looks initially at the context of my published work and recounts a journey encompassing different models of clinical legal education. This is followed by a section setting out a framework for understanding the nature, scope and extent of ‘clinic’. In order the encapsulate what clinic is and what potential is offered a taxonomy is suggested based on the variants that determine the educational, public service and professional reach of experiential learning in general and clinical legal education in particular. I then focus on three aspects of my work represented by the publications on which I rely for this award – simulation, model modification and international transferability and provide examples of these manifestations of clinic alongside analysis from other scholars. The final substantive section of the submission attempts to locate what I have published within the wider context of current changes and proposed reforms in legal education. The conclusions reached include the recognition of the importance of clinic (actual and potential) in serving the various agendas held by relevant stakeholders.

This research examines the treatment of terror detainees during pre-charge detention in Pakistan and the UK. Pakistan is the principal focus and the UK acts as a comparator thereto. Suspected terrorists are more vulnerable to maltreatment during pre-charge detention. Their vulnerability increases more in a country like Pakistan where more than 60,000 people have died in various terrorist attacks. Arguably, there is no case-study on the topic in Pakistan and the UK has not been used as a comparator. This scholarship, therefore, attempts to fill the gap by evaluating the treatment of terror detainees during pre-charge detention in Pakistan by using relevant human rights law and principles as a yardstick and the UK as a comparator to the main case. This scholarship uses liberal critique research methodology assessing pre-charge terror detention in the following six themes: the period of pre-charge terror detention; police interrogation and questioning; internal police review mechanisms; police records; the rights of a terror suspect to contact the outside world; and the detention conditions. The relevant anti-terror legislation of the two countries will be used to find the law on the topic. The related provisions in the UDHR, ICCPR and UNCAT will also be used to find out how we ought to treat terror detainees in a criminal justice system. The results show that the UK fulfils most of its human rights obligations, while Pakistan does not. The UK provides a maximum period of 14 days for pre-charge detention, while Pakistan has 90 days. A terror suspect can be interrogated for up to two hours at a time in the UK, while police interrogation sessions in Pakistan are unlimited. The UK includes internal police review mechanism as a check on the special powers of the police, while there is no such arrangement in Pakistan. The countries also differ in their police records, the rights of a terror suspect to contact the outside world and the detention conditions. Consequently, Pakistan can arguably learn from the UK's experience on the topic.

Thesis (Ph. D.)--Case Western Reserve University, 2009. / Frances Payne Bolton School of Nursing. Includes bibliographical references.

This research project aims to provide a clear and concise guide to latent dirichlet allocation which is a form of topic modelling. The aim is to help researchers who do not have a strong background in mathematics or statistics to feel comfortable with using topic modelling in their work. In order to achieve this, the thesis provides a step-by-step explanation of how topic modelling works. A range of tools that can be used to perform a topic model analysis are also described. The first chapter gives an explanation of how topic modelling, and (more specifically), latent dirichlet allocation works; it offers a very basic explanation and then provides an easy to follow mathematical explanation. The second chapter explains how to perform a topic model analysis; this is done through an explanation of each step used to run a topic model analysis, starting from the type of dataset through to the software packages available to use. The third section provides an example topic model analysis, based on the Philpapers dataset. The final section provides a discussion on the highlights of each chapter and areas for further research.

Latent topic models are statistical structures in which a "latent topic" describes some relationship between parts of the data. Co-maintenance is defined as an observable property of software systems under source control in which source code fragments are modified together in some time frame. When topic models are applied to software systems, latent topics emerge from code fragments. However, it is not yet known what these latent topics mean. In this research, we analyse software maintenance history, and show that latent topics often correspond to code fragments that are maintained together. Moreover, we show that latent topic models can identify such co-maintenance relationships even with no supervision. We can use this correlation both to categorize and understand maintenance history, and to predict future co-maintenance in practice. The relationship between co-maintenance and topics is directly analysed within changelists, with respect to both local pairwise code fragment similarity and global system-wide fragment similarity. This analysis is used to evaluate topic models used with a domain-specific programming language for web service similarity detection, and to estimate appropriate topic counts for modelling source code. / Thesis (Ph.D, Computing) -- Queen's University, 2012-04-30 18:16:04.05

The subject of this research is institutional racism and how it can be addressed as an organisational change process post the Race Relations (Amendment) Act 2000. The research was carried out using a range of methods within an over-arching constructivist approach to grounded theory in a single site case study. The case study was the Royal College of Psychiatrists which provided the researcher with full access to its organisational change programme for race equality. During the five year period of this programme the researcher was a participant observer and was able to use this role to make an in depth study of the organisational change processes. The constructivist grounded theory approach has been used with both qualitative and quantitative methods of data collection and analysis to construct new theory about organisational change and institutional racism. This is an under-developed area of research and there are no examples in the literature of a similar study of this depth and duration. Soft Systems Methodology was used as a sense making tool by which the College's change programme was evaluated. The methodology reveals significant gaps between desirable and culturally feasible change. These gaps arise because standard approaches to organisational change based on systems thinking fail to address the discursive effects of institutional racism. The new contribution to knowledge provided by this research includes the identification of four dimensions of change by which institutional racism can be operationalised: organisational structure, professional boundary, group difference and personal agency. These dimensions of change are used to construct new theory about the discursive effects of institutional racism. The research concludes with the development of a new approach to organisational change based on these insights. This approach takes the professional field as the prime unit of change and uses communicative action as a means of overcoming the discursive effects of institutional racism.

In response to conflicting claims within the field (Fox & Thomas, 1990; Thomas, Chaigne & Fox, 1989; Jolley, 1995), a series of experiments was conducted to explore the experimental conditions under which children might alter the formal and content properties of their drawings to depict topics which have received differential topic characterisation. In Experiment 1, children produced three copies of shaded models of men, dogs and trees. All children drew a baseline drawing, and two further drawings following nice and nasty topic characterisation. It was found that nice drawings were scaled up from baseline drawings and that nasty drawings were less consistently scaled down from baseline drawing size. Topic type did not interact with-this main effect. Experiment 2 examined children's choice of colour to complete pre-drawn models of men, dogs and trees following affective topic characterisation. Children altered their colour choice for the affectively characterised topics, and colour choice was related to colour preference. Experiment 3 investigated children's use of both size and colour in spontaneous drawings, and examined which additional strategies children might use to differentiate emotional character. Children's drawings of nice figures were again found to be increased from baseline size, whilst only drawings of nasty trees were reduced in surface area from baseline figure size. Children used a wide range of strategies in response to differential topic characterisation, and it was also found that children were able to report the techniques which they had used to represent emotional character. Experiments 4-6 assessed potential effects of drawing materials, emotional terms and educational group on children's drawings of positively and negatively characterised men. More consistent evidence was found to suggest that children increase the size of positively salient figures than reduce the size of negatively salient figures. Colour choice in relation to preference was found in Experiments 4 and 5, and the same range of additional strategies was observed throughout Experiments 4-6. There were only slight variations in the use of the strategies in situations where children used different drawing materials, received different sets of emotional characterisations, and in drawings by children from different educational groups. The experiments showed that effects of topic characterisation on a range of properties of children's drawings can be measured when task demands are systematically varied.

Temporal data is ubiquitous nowadays and can be easily found in many applications. Consider the extensively studied social media website Twitter. All the information can be associated with time stamps, and thus form different types of data sequences: a sequence of feature values of users who retweet a message, a sequence of tweets from a certain user, or a sequence of the evolving friendship networks. Mining these data sequences is an important task, which reveals patterns in the sequences, and it is a very challenging task as it usually requires different techniques for different sequences. The problem becomes even more complicated when the sequences are correlated. In this dissertation, we study the following two types of data sequences, and we show how to carefully exploit within-sequence and across-sequence correlations to develop more effective and scalable algorithms. 1. Multi-dimensional value sequences: We study sequences of multi-dimensional values, where each value is associated with a time stamp. Such value sequences arise in many domains such as epidemiology (medical records), social media (keyword trends), etc. Our goals are: for individual sequences, to find a segmentation of the sequence to capture where the pattern changes; for multiple correlated sequences, to use the correlations between sequences to further improve our segmentation; and to automatically find explanations of the segmentation results. 2. Social media post sequences: Driven by applications from popular social media websites such as Twitter and Weibo, we study the modeling of social media post sequences. Our goal is to understand how the posts (like tweets) are generated and how we can gain understanding of the users behind these posts. For individual social media post sequences, we study a prediction problem to find the users' latent state changes over the sequence. For dependent post sequences, we analyze the social influence among users, and how it affects users in generating posts and links. Our models and algorithms lead to useful discoveries, and they solve real problems in Epidemiology, Social Media and Critical Infrastructure Systems. Further, most of the algorithms and frameworks we propose can be extended to solve sequence mining problems in other domains as well. / Ph. D.

Context. Software testing is the process, where a particular software product, or a system is executed, in order to find out the bugs, or issues which may otherwise degrade its performance. Software testing is usually done based on pre-defined test cases. A test case can be defined as a set of terms, or conditions that are used by the software testers to determine, if a particular system that is under test operates as it is supposed to or not. However, in numerous situations, test cases can be so many that executing each and every test case is practically impossible, as there may be many constraints. This causes the testers to prioritize the functions that are to be tested. This is where the ability of topic models can be exploited. Topic models are unsupervised machine learning algorithms that can explore large corpora of data, and classify them by identifying the hidden thematic structure in those corpora. Using topic models for test case prioritization can save a lot of time and resources. Objectives. In our study, we provide an overview of the amount of research that has been done in relation to topic models. We want to uncover various quality criteria, evaluation methods, and metrics that can be used to evaluate the topic models. Furthermore, we would also like to compare the performance of two topic models that are optimized for different quality criteria, on a particular interpretability task, and thereby determine the topic model that produces the best results for that task. Methods. A systematic mapping study was performed to gain an overview of the previous research that has been done on the evaluation of topic models. The mapping study focused on identifying quality criteria, evaluation methods, and metrics that have been used to evaluate topic models. The results of mapping study were then used to identify the most used quality criteria. The evaluation methods related to those criteria were then used to generate two optimized topic models. An experiment was conducted, where the topics generated from those two topic models were provided to a group of 20 subjects. The task was designed, so as to evaluate the interpretability of the generated topics. The performance of the two topic models was then compared by using the Precision, Recall, and F-measure. Results. Based on the results obtained from the mapping study, Latent Dirichlet Allocation (LDA) was found to be the most widely used topic model. Two LDA topic models were created, optimizing one for the quality criterion Generalizability (TG), and one for Interpretability (TI); using the Perplexity, and Point-wise Mutual Information (PMI) measures respectively. For the selected metrics, TI showed better performance, in Precision and F-measure, than TG. However, the performance of both TI and TG was comparable in case of Recall. The total run time of TI was also found to be significantly high than TG. The run time of TI was 46 hours, and 35 minutes, whereas for TG it was 3 hours, and 30 minutes.Conclusions. Looking at the F-measure, it can be concluded that the interpretability topic model (TI) performs better than the generalizability topic model (TG). However, while TI performed better in precision, Conclusions. Looking at the F-measure, it can be concluded that the interpretability topic model (TI) performs better than the generalizability topic model (TG). However, while TI performed better in precision, recall was comparable. Furthermore, the computational cost to create TI is significantly higher than for TG. Hence, we conclude that, the selection of the topic model optimization should be based on the aim of the task the model is used for. If the task requires high interpretability of the model, and precision is important, such as for the prioritization of test cases based on content, then TI would be the right choice, provided time is not a limiting factor. However, if the task aims at generating topics that provide a basic understanding of the concepts (i.e., interpretability is not a high priority), then TG is the most suitable choice; thus making it more suitable for time critical tasks.