Curtin University

About: Curtin University is a education organization based out in Perth, Western Australia, Australia. It is known for research contribution in the topics: Population & Zircon. The organization has 14257 authors who have published 48997 publications receiving 1336531 citations. The organization is also known as: WAIT & Western Australian Institute of Technology.

The International Pulsar Timing Array project: using pulsars as a gravitational wave detector

Abstract: The International Pulsar Timing Array project combines observations of pulsars from both northern and southern hemisphere observatories with the main aim of detecting ultra-low frequency (similar to 10(-9)-10(-8) Hz) gravitational waves. Here we introduce the project, review the methods used to search for gravitational waves emitted from coalescing supermassive binary black-hole systems in the centres of merging galaxies and discuss the status of the project.

Managing brands and customer engagement in online brand communities

Abstract: Purpose – Given the dramatic technology‐led changes that continue to take place in the marketplace, researchers and practitioners alike are keen to understand the emergence and implications of online brand communities (OBCs). The purpose of this paper is to explore OBCs from both consumer and company perspectives.Design/methodology/approach – The study provides a synthesis of the extant OBC literature to further our understanding of OBCs, and also puts forth future priorities for OBC research.Findings – A conceptual framework is provided that extends our understanding of OBCs and consumer engagement. Four key OBC dimensions (brand orientation, internet‐use, funding and governance) are identified and three antecedents (brand‐related, social and functional) are proposed of consumer‐OBC engagement.Originality/value – This study is the first to explore key dimensions of OBCs, and the differing but related perspectives of the consumers and organizations involved.

Human Rights and Social Work: Towards Rights-Based Practice

Abstract: Now in its third edition, Human Rights and Social Work explores how the principles of human rights inform contemporary social work practice. Jim Ife considers the implications of social work's traditional Enlightenment heritage and the possibilities of 'post-Enlightenment' practice in a way that is accessible, direct and engaging. The world has changed significantly since the publication of the first edition in 2000 and this book is situated firmly within the context of present-day debates, concerns and crises. Ife covers the importance of relating human rights to the non-human world, as well as the consequences of political and ecological uncertainty. Featuring examples, further readings and a glossary, readers are able to identify and investigate the important issues and questions arising from human rights and social work. Now more than ever, Human Rights and Social Work is an indispensable resource for students, scholars and practitioners alike.

Activity recognition and abnormality detection with the switching hidden semi-Markov model

Abstract: This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the switching hidden semi-markov model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.