Faster, richer, better: rapid appraisal techniques for the study of IS implementation in virtual communities

A major issue in Information Systems (IS) research is how to combine relevance and rigor (Benbasat and Zmud, 1999) and reduce the widening gap between research results and adoption (Dunn, 1994). Qualitative researchers make use of interpretivist methods to add richness and depth to their understanding of user problems. Interpretivist methods applied to IS implementations can thus result in research which communicates those findings more effectively. However standard interpretivist data-collection and analysis methods can be time-consuming and expensive. Findings based on these methods may be irrelevant to practitioners by the time they reach publication stage. A potential solution to this problem lies in Rapid Appraisal or RA, a qualitative appraisal methodology derived from rural development-related research. It offers IS researchers an additional technique for learning and acquiring relevant information in a limited period of time that  supplements current data collection and analysis techniques. RA adds value to the traditional approach for studying diffusion of innovation, supporting and extending the IS researcher’s qualitative ‘tool-kit’. In this paper we review an electronic gateway designed to facilitate the diffusion of an Australian government to business [G2B] export documentation system, EXDOC, which was first implemented with meat producers. RA techniques were used to collect and analyse data regarding the implementation of the first regional Electronic Trade Facilitation Center [ETFC] successfully established for Australian exporters in the horticulture sector. The findings from the original EXDOC implementation in the meat sector were confirmed and extended through this study. These include the importance of developing a governance structure that ensures all community members share the benefits of an implementation and the fact that virtual trading communities are attractive to users only if they add value to their business and extend standard ways of operating. Interactive interviews, part of the RA approach; also enabled us to expand our understanding of the way in which procedures developed in the course of implementing an electronic market represent value-adding opportunities for virtual trading communities. The paper has special relevance for researchers investigating adoption and diffusion issues experienced by small-scale producers with low exposure to technology in remote and rural settings.

An extended negative selection algorithm for anomaly detection

This paper proposes an extended negative selection algorithm for anomaly detection. Unlike previously proposed negative selection algorithms which do not make use of non-self data, the extended negative selection algorithm first acquires prior knowledge about the characteristics of the Problem space from the historial sample data by using machine learning techniques. Such data consists of both self data and non-self data. The acquired prior knowledge is represented in the form of production rules and thus viewed as common schemata which characterise the two subspaces: self-subspace and non-self-subspace, and provide important information to the generation of detection rules. One advantage of our approach is that it does not rely on the structured representation of the data and can be applied to general anomaly detection. To test the effectiveness, we test our approach through experiments with the public data set iris and KDDrsquo99 published data set.

Bounded lower subdifferentiability optimization techniques : applications

In this article we develop a global optimization algorithm for quasiconvex programming where the objective function is a Lipschitz function which may have "flat parts". We adapt the Extended Cutting Angle method to quasiconvex functions, which reduces significantly the number of iterations and objective function evaluations, and consequently the total computing time. Applications of such an algorithm to mathematical programming problems inwhich the objective function is derived from economic systems and location problems are described. Computational results are presented.

Enhancing medical named entity recognition with an extended segment representation technique

Objective : The objective of this paper is to formulate an extended segment representation (SR) technique to enhance named entity recognition (NER) in medical applications.

Methods : An extension to the IOBES (Inside/Outside/Begin/End/Single) SR technique is formulated. In the proposed extension, a new class is assigned to words that do not belong to a named entity (NE) in one context but appear as an NE in other contexts. Ambiguity in such cases can negatively affect the results of classification-based NER techniques. Assigning a separate class to words that can potentially cause ambiguity in NER allows a classifier to detect NEs more accurately; therefore increasing classification accuracy.

Results : The proposed SR technique is evaluated using the i2b2 2010 medical challenge data set with eight different classifiers. Each classifier is trained separately to extract three different medical NEs, namely treatment, problem, and test. From the three experimental results, the extended SR technique is able to improve the average F1-measure results pertaining to seven out of eight classifiers. The kNN classifier shows an average reduction of 0.18% across three experiments, while the C4.5 classifier records an average improvement of 9.33%.

Precipitation and solute clustering in aluminium: advanced characterisation techniques

Recent development of characterisation techniques and computer simulation has extended our ability to access atomic scale information regarding materials microstructure evolution. New results from such techniques have significantly progressed our knowledge about solute behaviour during the earliest stages of decomposition of the solid solution. This chapter updates current understanding about solute clustering and discusses the effect of solute clustering and micro-alloying on precipitate microstructure evolution in aluminium alloys. In addition, a brief review is given on the effect of severe plastic deformation on precipitate evolution in Al alloys.