Network

Network has emerged from a contempory worldwide phenomenon, culturally manifested as a consequence of globalization and the knowledge economy. It is in this context that the internet revolution has prompted a radical re-ordering of social and institutional relations and the associated structures, processes and places which support them. Within the duality of virtual space and the augmentation of traditional notions of physical place, the organizational structures pose new challenges for the design professions. Technological developments increasingly permit communication anytime and anywhere, and provide the opportunity for both synchronous and asynchronous collaboration. The resultant ecology formed through the network enterprise has resulted in an often convolted and complex world wherein designers are forced to consider the relevance and meaning of this new context. The role of technology and that of space are thus interwined in the relation between the network and the individual workplace. This paper explores a way to inform the interior desgn process for contemporary workplace environments. It reports on both theoretical and practical outcomes through an Australia-wide case study of three collaborating, yet independent business entities. It further suggests the link between workplace design and successful business innovation being realized between partnering organizations in Great Britain. Evidence presented indicates that, for architects and interior designers, the scope of the problem has widened, the depth of knowledge required to provide solutions has increased, and the rules of engagement are required to change. The ontological and epistemological positions adopted in the study enabled the spatial dimensions to be examined from both within and beyond the confines of a traditional design only viewpoint. Importantly it highlights the significance of a trans-disiplinary collaboration in dealing with the multiple layers and complexity of the contemporary social and business world, from both a research and practice perspective.

User-representative feature selection for keystroke dynamics

Continuous user authentication with keystroke dynamics uses characters sequences as features. Since users can type characters in any order, it is imperative to find character sequences (n-graphs) that are representative of user typing behavior. The contemporary feature selection approaches do not guarantee selecting frequently-typed features which may cause less accurate statistical user-representation. Furthermore, the selected features do not inherently reflect user typing behavior. We propose four statistical based feature selection techniques that mitigate limitations of existing approaches. The first technique selects the most frequently occurring features. The other three consider different user typing behaviors by selecting: n-graphs that are typed quickly; n-graphs that are typed with consistent time; and n-graphs that have large time variance among users. We use Gunetti’s keystroke dataset and k-means clustering algorithm for our experiments. The results show that among the proposed techniques, the most-frequent feature selection technique can effectively find user representative features. We further substantiate our results by comparing the most-frequent feature selection technique with three existing approaches (popular Italian words, common n-graphs, and least frequent ngraphs). We find that it performs better than the existing approaches after selecting a certain number of most-frequent n-graphs.