Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition

Human body pose estimation and hand detection are two important tasks for systems that perform computer vision-based sign language recognition(SLR). However, both tasks are challenging, especially when the input is color videos, with no depth information. Many algorithms have been proposed in the literature for these tasks, and some of the most successful recent algorithms are based on deep learning. In this paper, we introduce a dataset for human pose estimation for SLR domain. We evaluate the performance of two deep learning based pose estimation methods, by performing user-independent experiments on our dataset. We also perform transfer learning, and we obtain results that demonstrate that transfer learning can improve pose estimation accuracy. The dataset and results from these methods can create a useful baseline for future works

Information retrieval in systematic reviews: a case study of the crime prevention literature

OBJECTIVES: A defining feature of a systematic review is the data collection; the assembling of a meticulous, unbiased, and reproducible set of primary studies. This requires specialist skills to execute. The aim of this paper is to marshal tacit knowledge, gained through a systematic search of the crime prevention literature, to develop a ‘how-to guide’ for future evidence synthesists in allied fields. METHODS: Empirical results from a recent systematic search for evidence in crime prevention are supplied to illustrate key principles of information retrieval. RESULTS: Difficulties in operationalizing a systematic search are expounded and possible solutions discussed. Empirical results from optimizing the balance between sensitivity and precision with the criminological literature are presented. An estimation of database overlap for crime prevention studies is provided to guide other evidence synthesists in streamlining the search process. CONCLUSIONS: A high-quality search will involve a substantial time investment in honing the research question, specifying the precise scope of the work, and trialing and testing of search tactics. Electronic databases are a lucrative source of eligible studies, but they have important limitations. The diversity of expression across the criminological literature needs to be captured by the use of many search terms—both natural language and controlled vocabulary—in database searches. Complementary search tactics should be employed to locate eligible studies without common vocabulary. Grey literature should be ardently pursued, for it has a central role in the crime prevention evidence base

Family-Expressed Emotion, Childhood-Onset Depression, and Childhood-Onset Schizophrenia Spectrum Disorders: Is Expressed Emotion a Nonspecific Correlate of Child Psychopathology or a Specific Risk Factor for Depression?

Expressed emotion (EE) was examined, using the brief Five Minute Speech Sample measure, in families of (1) children with depressive disorders, (2) children with schizophrenia spectrum disorders, and (3) normal controls screened for the absence of psychiatric disorder. Consistent with the hypothesis of some specificity in the association between EE and the form of child disorder, rates of EE were significantly higher among families of depressed children compared to families of normal controls and families of children with schizophrenia spectrum disorders. Within the depressed group, the presence of a comorbid disruptive behavior disorder was associated with high levels of critical EE, underscoring the need to attend to comorbid patterns and subtypes of EE in future research

A victim-centred cost–benefit analysis of a stalking prevention programme

Research suggests that stalking inflicts great psychological and financial costs on victims. Yet costs of victimisation are notoriously difficult to estimate and include as intangible costs in cost–benefit analysis. This study reports an innovative cost–benefit analysis that used focus groups with multi-agency teams to collect detailed data on operational resources used to manage stalking cases. This method is illustrated through the presentation of one case study. Best- and worst-case counterfactual scenarios were generated using the risk assessment scores and practitioner expertise. The findings suggest that intervening in high-risk stalking cases was cost-beneficial to the state in all the case studies we analysed (even if it incurs some institutional costs borne by the criminal justice system or health) and was often cost-beneficial to the victims too. We believe that this method might be useful in other fields where a victim- or client-centred approach is fundamental

Evidencing the impact of Neighbourhood Watch

This report outlines the routes or processes through which Neighbourhood Watch activities might have an impact on crime reduction and other, associated, outcomes. These are broken down into chains of events (the ‘theories of change’ or ‘mechanisms’). Commonly used in evaluation projects, a theory of change is intended to simply but elegantly explain how and why something works . The first step is determining the intended outcomes of the activity, i.e. crime reduction or increased neighbourliness; the second is determining the logical sequence of specific actions and processes that are required to make that outcome likely to happen. The result is a process map that links activities and required conditions to produce intermediate changes and final outcomes. Articulating a theory of change before conducting any evaluation has the advantage of exposing measurement points along the process where data can be collected to evidence whether something is working as assumed. Therefore, measurement points along each theory of change we present are highlighted. Subsequently, the advantages and disadvantages of different data sets that can measure and evidence these points in the theory of change are summarised

Fast Neural Network Predictions from Constrained Aerodynamics Datasets

Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simulate these systems faster but with sufficient accuracy. While many approaches involve simplified models of the underlying physics, others are model-free and make predictions based only on existing simulation data. We present a novel model-free approach in which we reformulate the simulation problem to effectively increase the size of constrained pre-computed datasets and introduce a novel neural network architecture (called a cluster network) with an inductive bias well-suited to highly nonlinear computational fluid dynamics solutions. Compared to the state-of-the-art in model-based approximations, we show that our approach is nearly as accurate, an order of magnitude faster, and easier to apply. Furthermore, we show that our method outperforms other model-free approaches

Dynamics of a self--gravitating magnetized source

We consider a magnetized degenerate gas of fermions as the matter source of a homogeneous but anisotropic Bianchi I spacetime with a Kasner--like metric. We examine the dynamics of this system by means of a qualitative and numerical study of Einstein-Maxwell field equations which reduce to a non--linear autonomous system. For all initial conditions and combinations of free parameters the gas evolves from an initial anisotropic singularity into an asymptotic state that is completely determined by a stable attractor. Depending on the initial conditions the anisotropic singularity is of the ``cigar'' or ``plate'' types.Comment: 7 pages, 1 figur

A systematic review of tagging as a method to reduce theft in retail environments

Background: Retailers routinely use security tags to reduce theft. Presently, however, there has been no attempt to systematically review the literature on security tags. Guided by the acronym EMMIE, this paper set out to (1) examine the evidence that tags are effective at reducing theft, (2) identify the key mechanisms through which tags are expected to reduce theft and the conditions that moderate tag effectiveness, and (3) summarise information relevant to the implementation and economic costs of tagging. Methods: In this mixed-methods review, we performed systematic keyword searches of the published and unpublished literature, hand searched relevant journals, conducted forward and backward citation searches and consulted with four retailers. Studies were included if they reported an explicit goal of reducing the theft or shrinkage of items through the use of security tags in retail environments. Results: We identified 50 eligible studies, eight of which reported quantitative data on the effectiveness of tags in retail environments. Across these eight studies, five showed positive results associated with the introduction of tags, but heterogeneity in the type of tag and reported outcome measures precluded a meta-analysis. We identified three mechanisms through which tags might plausibly reduce theft-increase the risks, reduce the rewards, increase the effort-which were found to vary by tag type, and their activation dependent on five broad categories of moderator: retail store and staff, customers (including shoplifters), tag type, product type, and the involvement of the police and criminal justice system. Implementation challenges documented in the literature related mainly to staffing issues and tagging strategy. Finally, although estimates are available on the costs of tagging, our searches identified no highquality published economic evaluations of tagging. Conclusions: Through applying the EMMIE framework this review highlighted the complexity involved in security tagging in retail environments, whereby different kinds of tags are expected to reduce theft through different casual mechanisms which are dependent on a distinctive configuration of conditions. Based on the available evidence it is difficult to determine the effectiveness of tags as a theft reduction measure, albeit there is suggestive evidence that more visible tags are associated with greater reductions in theft than less visible tags

How Strong is the Evidence-Base for Crime Reduction End Users?

To support the development and implementation of evidence-based crime reduction, we systematically identified and appraised 70 systematic reviews of single crime reduction measures published between 1975 and 2015. Using the EMMIE framework, we find that the quality of reporting on the Effectiveness of crime reduction measures is reasonably strong, particularly in systematic reviews published by the Cochrane and Campbell Collaborations. In contrast, evidence concerning the Mechanisms underpinning a crime reduction intervention, the conditions that Moderate effectiveness, Implementation challenges and the Economic costs and benefits of crime reduction was largely absent from the assessed systematic reviews. We conclude that there is a distinct lack of systematic review evidence in crime reduction that currently speaks to the knowledge needs of practitioners (i.e., how to make an intervention “work” for them)