City University London

About: City University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Health care. The organization has 5735 authors who have published 17285 publications receiving 453290 citations.

Development of a Measure of Postpartum PTSD: The City Birth Trauma Scale

Abstract: Post-traumatic stress disorder (PTSD) affects 4% of women after birth yet there are very few questionnaire measures of postpartum PTSD that have been validated in this population In addition, none of the available questionnaires assess postpartum PTSD in accordance with criteria specified in the latest edition of the Diagnostic and Statistical Manual [DSM-5, (1)] The City Birth Trauma Scale is a 29-item questionnaire developed to measure birth-related PTSD according to DSM-5 criteria of: stressor criteria (A), symptoms of re-experiencing (B), avoidance (C), negative cognitions and mood (D), and hyperarousal (E), as well as duration of symptoms (F), significant distress or impairment (E), and exclusion criteria or other causes (H) Two additional items from DSM-IV were also included on the basis of evidence suggesting they might be important in this population The first was criterion A2 that women responded to events during birth with intense fear, helplessness or horror The second was symptoms of emotional numbing Items were first reviewed by researchers (n = 9) and postpartum women (n = 8) and revised accordingly The questionnaire was then completed by 950 women recruited online Results showed the City Birth Trauma Scale had excellent reliability (Cronbach's α = 092) and is easy to understand (Flesch reading score 6417) Exploratory factor analysis found two factors which together accounted for 56% of the variance: (i) Birth-related symptoms (408% variance) and (ii) General symptoms (155% variance) PTSD symptoms were highly associated with distress, impaired functioning, and women reporting they wanted treatment (r = 050–061) Removing DSM-IV A2 criteria only increased births classified as traumatic by 2% Adding the item on emotional numbing did not change the psychometric properties of the scale These items were therefore removed The City Birth Trauma Scale has good psychometric properties and the two symptom clusters identified are consistent with previous research on symptoms of postpartum PTSD This scale therefore provides a promising measure of PTSD following childbirth that can be used in research and clinical practice Future research should examine the scale's predictive validity using clinical interviews

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks

Abstract: Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language—a set of logical rules that we call confidence rules —and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural–symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

A Distributed Consensus Algorithm for Decision Making in Service-Oriented Internet of Things

Abstract: In a service-oriented Internet of things (IoT) deployment, it is difficult to make consensus decisions for services at different IoT edge nodes where available information might be insufficient or overloaded. Existing statistical methods attempt to resolve the inconsistency, which requires adequate information to make decisions. Distributed consensus decision making (CDM) methods can provide an efficient and reliable means of synthesizing information by using a wider range of information than existing statistical methods. In this paper, we first discuss service composition for the IoT by minimizing the multi-parameter dependent matching value. Subsequently, a cluster-based distributed algorithm is proposed, whereby consensuses are first calculated locally and subsequently combined in an iterative fashion to reach global consensus. The distributed consensus method improves the robustness and trustiness of the decision process.