Edinburgh Napier University

About: Edinburgh Napier University is a education organization based out in Edinburgh, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2665 authors who have published 6859 publications receiving 175272 citations.

Oh, What a Tangled Web We Weave: Experiences of Doing 'Multiple Perspectives' Research in Families

Abstract: It is not uncommon to explore the views of parents and children in qualitative research with families. Yet the implications and challenges of a multiple perspective approach often remain at a relatively taken‐for‐granted level. In this article we draw on our experience across a range of qualitative interview‐based projects, focusing in particular on two case studies, to illustrate the practical working out of the challenges posed by multiple perspective research in families. The implications of research with parents and children are discussed in relation to two themes – power and truth. This discussion reflects on the challenges and benefits of multiple perspective research at different points in the research process. It is not the intention of this article to try to provide definitive ‘answers’ to some of the challenges posed. Rather we aim to open up discussion by reflecting on a range of scenarios, offering suggestions based on our experience, and so, allow the reader to reflect on the particularities ...

Posttraumatic Stress Disorder and Complex Posttraumatic Stress Disorder in DSM-5 and ICD-11: Clinical and Behavioral Correlates.

Abstract: The American Psychiatric Association and the World Health Organization provide distinct trauma-based diagnoses in the fifth edition of the Diagnostic and Statistical Manual (DSM-5), and the forthcoming 11th version of the International Classification of Diseases (ICD-11), respectively. The DSM-5 conceptualizes posttraumatic stress disorder (PTSD) as a single, broad diagnosis, whereas the ICD-11 proposes two "sibling" disorders: PTSD and complex PTSD (CPTSD). The objectives of the current study were to: (a) compare prevalence rates of PTSD/CPTSD based on each diagnostic system; (b) identify clinical and behavioral variables that distinguish ICD-11 CPTSD and PTSD diagnoses; and (c) examine the diagnostic associations for ICD-11 CPTSD and DSM-5 PTSD. Participants in a predominately female clinical sample (N = 106) completed self-report scales to measure ICD-11 PTSD and CPTSD, DSM-5 PTSD, and depression, anxiety, borderline personality disorder, dissociation, destructive behaviors, and suicidal ideation and self-harm. Significantly more people were diagnosed with PTSD according to the DSM-5 criteria (90.4%) compared to those diagnosed with PTSD and CPTSD according to the ICD-11 guidelines (79.8%). An ICD-11 CPTSD diagnosis was distinguished from an ICD-11 PTSD diagnosis by higher levels of dissociation (d = 1.01), depression (d = 0.63), and borderline personality disorder (d = 0.55). Diagnostic associations with depression, anxiety, and suicidal ideation and self-harm were higher for ICD-11 CPTSD compared to DSM-5 PTSD (by 10.7%, 4.0%, and 7.0%, respectively). These results have implications for differential diagnosis and for the development of targeted treatments for CPTSD.

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Abstract: While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.