Hanchett-Stamford V (1) HM Attorney-General and (2) Dr William Johnston Jordan [2008] Ewhc 330 (CH), 2008 WL 1968855

Casenote considers meaning and impact of ruling of High Court in Hanchett Stamford v HM A.G. The decision of Mr Justice Lewison in Hanchett-Stamford v HM Attorney General and Dr William Johnston Jordan1 provides us with a useful analysis of the legal principles relating to the thorny issues of: (i) how unincorporated associations hold property; (ii) the applicability of the law of charities to unincorporated associations and (iii) the property rights of a declining membership upon the dissolution of such associations.

"Well did you feel jealous?" Control and ideology in police interviews with rape complainants

The methods used by the UK Police to investigate complaints of rape have unsurprisingly come under much scrutiny in recent times, with a 2007 joint report on behalf of HM Crown Prosecution Service Inspectorate and HM Inspectorate of Constabulary concluding that there were many areas where improvements should be made. The research reported here forms part of a larger project which draws on various discourse analytical tools to identify the processes at work during police interviews with women reporting rape. Drawing on a corpus of video recorded police interviews with women reporting rape, this study applies a two pronged analysis to reveal the presence of these ideologies. Firstly, an analysis of the discourse markers ‘well’ and ‘so’ demonstrates the control exerted on the interaction by interviewing officers, as they attach importance to certain facts while omitting much of the information provided by the victim. Secondly, the interpretative repertoires relied upon by officers to ‘make sense’ of victim’s accounts are subject to scrutiny. As well as providing micro-level analyses which demonstrate processes of interactional control at the local level, the findings of these analyses can be shown to relate to a wider context – specifically prevailing ideologies about sexual violence in society as a whole.

Is gender a factor in perceived prison officer competence?

Background The introduction of women officers into HM Prison Service raised questions regarding women's ability to perform what had traditionally been a male role. Existing research is inconclusive as to whether female prison officers are as competent as male prison officers, and whether there are gender differences in job performance. This study examined prisoners' perceptions of male and female prison officers' performance. Hypotheses The hypotheses were that overall competence and professionalism ratings would not differ for men and women officers, but that there would be differences in how men and women were perceived to perform their roles. Women were expected to be rated as more communicative, more empathic and less disciplining. Method The Prison Officer Competency Rating Scale (PORS) was designed for this study. Ratings on the PORS for male and female officers were given by 57 adult male prisoners. Results There was no significant difference in prisoners' ratings of overall competence of men and women officers. Of the PORS subscales, there were no gender differences in Discipline and Control, Communication or Empathy, but there was a significant difference in Professionalism, where prisoners rated women as more professional. Conclusion The failure to find any differences between men and women in overall job competence, or on communication, empathy and discipline, as perceived by prisoners, suggests that men and women may be performing their jobs similarly in many respects. Women were rated as more professional, and items contributing to this scale related to respecting privacy and keeping calm in difficult situations, where there may be inherent gender biases. Copyright © 2005 Whurr Publishers Ltd.

The significance of microaneurysms and haemorrhages within one optic disc diameter of the fovea

Free paper session INTRODUCTION. Microaneurysms and haemorrhages within the macula area are a poor predictor of macular oedema as shown by optical coherence tomography (OCT). Our research suggests that it is safe and cost effective to screen patients who present with these surrogate markers annually. PURPOSE. To determine whether microaneurysms (ma) and haemorrhages (hm) within one optic disc diameter of the fovea (ma/hm<1DD) are significant predictors of macular oedema. METHODS. Data were collected over a one-year period from patients attending digital diabetic retinopathy screening. Patients who presented with ma/hm<1DD also had an OCT scan. The fast macula scan on the Stratus OCT was used and an ophthalmologist reviewed the scans to determine whether macular oedema was present. Macular oedema was identified by thickening on the OCT cross-sections. Patients were split into two groups. Group one (325 eyes) included those with best VA?6/9 and group two (30 eyes) with best VA =6/12. Only patients who had no other referable features of diabetic retinopathy were selected. RESULTS. In group one, 6 (1.8%) out of 325 eyes showed thickening on the OCT and were referred to hospital eye service (HES) for further investigation. In group two, 6 (20%) out of 30 eyes showed thickening and were referred to HES. CONCLUSIONS. Ma/hm<1DD become more significant predictors of macular oedema when VA is reduced. Results confirm the grading criteria concerning microaneurysms predicting macular oedema for referable maculopathy in the English national screening programme. OCT is a useful method to accurately identify patients requiring referral to HES.

A hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus

We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM-SVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract annotations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the HM-SVMs. The proposed framework has been tested on the DARPA Communicator Data. Experimental results show that an improvement over the baseline HVS parser has been observed using the hybrid framework. When compared with the HM-SVMs trained from the fully-annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences. © 2008. Licensed under the Creative Commons.

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Ensuring efficiency and robustness in MANET

This paper introduces a joint load balancing and hotspot mitigation protocol for mobile ad-hoc network (MANET) termed by us as 'load_energy balance + hotspot mitigation protocol (LEB+HM)'. We argue that although ad-hoc wireless networks have limited network resources - bandwidth and power, prone to frequent link/node failures and have high security risk; existing ad hoc routing protocols do not put emphasis on maintaining robust link/node, efficient use of network resources and on maintaining the security of the network. Typical route selection metrics used by existing ad hoc routing protocols are shortest hop, shortest delay, and loop avoidance. These routing philosophy have the tendency to cause traffic concentration on certain regions or nodes, leading to heavy contention, congestion and resource exhaustion which in turn may result in increased end-to-end delay, packet loss and faster battery power depletion, degrading the overall performance of the network. Also in most existing on-demand ad hoc routing protocols intermediate nodes are allowed to send route reply RREP to source in response to a route request RREQ. In such situation a malicious node can send a false optimal route to the source so that data packets sent will be directed to or through it, and tamper with them as wish. It is therefore desirable to adopt routing schemes which can dynamically disperse traffic load, able to detect and remove any possible bottlenecks and provide some form of security to the network. In this paper we propose a combine adaptive load_energy balancing and hotspot mitigation scheme that aims at evenly distributing network traffic load and energy, mitigate against any possible occurrence of hotspot and provide some form of security to the network. This combine approach is expected to yield high reliability, availability and robustness, that best suits any dynamic and scalable ad hoc network environment. Dynamic source routing (DSR) was use as our underlying protocol for the implementation of our algorithm. Simulation comparison of our protocol to that of original DSR shows that our protocol has reduced node/link failure, even distribution of battery energy, and better network service efficiency.