R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

The performance in context model: A 21st century tertiary dance teaching pedagogy

Despite tertiary institutions acknowledging that reflective practice is an essential component of undergraduate dance teacher training, there is often a disparity between the tertiary students’ reflective skills and the more sophisticated reflective ability needed to navigate the 21st century workforce (Silva 2008). This paper charts the evolution of a dance teaching reflective pedagogy within a suite of three units across a three-year undergraduate dance teacher-training course for school, community and studio dance teachers. This reflective pedagogy based on exploration, collaboration, critical questioning and connections with community forms the basis of a model of tertiary dance teacher- training; the Performance in Context Model (PCM). Over the past four years, through four cycles of action research, the PCM pedagogy, context and engagement with community has developed into a successful model integrating practical dance teaching skills, artistry and community engagement. The PCM represents a holistic collaborative approach to dance teacher education: the marrying of ‘teacher-as-artist’, ‘teacher-as-performer’ and ‘teacher-as-researcher’. More specifically, it emphasises the need for mature, reflective, receptive and flexible approaches in response to dance teaching and learning. These are enacted in a variety of contexts, with tertiary dance teaching students identifying as teaching artists, as well as researchers of their own practice.

Teaching about deliberative politics: Case studies of classroom–community learning projects in four nations

Theories of deliberative politics position grass-roots community members as more than spectators of politics, and instead recognize their capacity for political engagement by discussing and evaluating options in order to make decisions about issues affecting community life. The processes and products of journalism can assist deliberative politics by providing community members with information resources that are vital for understanding the root causes of problems, weighing up competing claims, forming networks around shared concerns, reaching decisions and undertaking action. This article presents the findings of case studies of four community–classroom projects--one each from Australia, New Zealand, the United States and South Africa--that develop the capacity of journalism students to be effective contributors to deliberative politics. The research points to the importance of learning activities that prepare students to work in diverse communities, map significant community places and structures, identify leaders and stakeholders, engage in respectful dialogue about problems and perspectives, and appreciate community frames and values.