Recognition as a distinguishing criterion of IS journals

The number of journals publishing information systems (IS) research has grown dramatically over the past few decades. This has resulted in an environment where authors have a wider choice of journals in which to place articles. Electronic journals are now as readily recognised by authorities as print journals. This paper provides firm evidence in support of the assertion that the number of journals publishing IS research has increased. The paper also examines the Australian context where the selection of a journal in which to place an article is influenced by recognition from the Department of Education Science and Training (DEST). In Australia, obtaining DEST recognition as a recognised research journal is not an onerous task, and yet a significant number of IS journals have not done this. Publishing in a DEST recognised journal is essential for Australian researchers to contribute to their organisation&rsquo;s research quantum and hence research funding. Attention is drawn to an increasing number of IS journals not recognised by DEST, and consequent action is recommended.<br /

“Excellence R Us”: university research and the fetishisation of excellence

The rhetoric of “excellence” is pervasive across the academy. It is used to refer to research outputs as well as researchers, theory and education, individuals and organisations, from art history to zoology. But does “excellence” actually mean anything? Does this pervasive narrative of “excellence” do any good? Drawing on a range of sources we interrogate “excellence” as a concept and find that it has no intrinsic meaning in academia. Rather it functions as a linguistic interchange mechanism. To investigate whether this linguistic function is useful we examine how the rhetoric of excellence combines with narratives of scarcity and competition to show that the hypercompetition that arises from the performance of “excellence” is completely at odds with the qualities of good research. We trace the roots of issues in reproducibility, fraud, and homophily to this rhetoric. But we also show that this rhetoric is an internal, and not primarily an external, imposition. We conclude by proposing an alternative rhetoric based on soundness and capacity-building. In the final analysis, it turns out that that “excellence” is not excellent. Used in its current unqualified form it is a pernicious and dangerous rhetoric that undermines the very foundations of good research and scholarship

Shape descriptors for mode-shape recognition and model updating

The most widely used method for comparing mode shapes from finite elements and experimental measurements is the Modal Assurance Criterion (MAC), which returns a single numerical value and carries no explicit information on shape features. New techniques, based on image processing (IP) and pattern recognition (PR) are described in this paper. The Zernike moment descriptor (ZMD), Fourier descriptor (FD), and wavelet descriptor (WD), presented in this article, are the most popular shape descriptors having properties that include efficiency of expression, robustness to noise, invariance to geometric transformation and rotation, separation of local and global shape features and computational efficiency. The comparison of mode shapes is readily achieved by assembling the shape features of each mode shape into multi-dimensional shape feature vectors (SFVs) and determining the distances separating them. © 2009 IOP Publishing Ltd

Fuzzy logic based intention recognition in STS processes

This paper represents a fuzzy logic based classifier that is able to recognise human users' intention of standing up from their behaviours in terms of the force they apply to the ground. The research reported focused on the selection of meaningful input data to the classifier and on the determination of fuzzy sets that best represent the intention information hidden in the force data. The classifier is a component of a robot chair which provides the users with assistance to stand up based on the recognised intention by the classifier