Interactive email filtering: Learning from misclassified examples

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster

A bayesian artificial neural network method to characterise laminar defects using dynamic measurements

This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.

Further evidence of linkage at 7p22 with familial hyperaldosteronism type II and exclusion of genetic defects in the RBAK coding regions

Once thought rare, primary aldosteronism (PAL) is now reported to be responsible for 5–10% of hypertension. Unlike familial hyperaldosteronism type I (FH-I), FH-II is not glucocorticoidremediable and not associated with the hybrid CYP11B1/CYP11B2 gene mutation. At least five times more common than FH-I, FH-II is clinically indistinguishable from apparently sporadic PAL, suggesting an even higher incidence. Studies performed in collaboration with C Stratakis (NIH, Bethesda) on our largest Australian family (eight affected members) demonstrated linkage at chromosome 7p22. Linkage at this region was also found in a South American family (DNA provided by MI New, Mount Sinai School of Medicine, New York) and in a second Australian family. The combined multipoint LOD score for these 3 families is 4.61 (q = 0) with markers D7S462 and D7S517, providing strong support for this locus harbouring mutations responsible for FH-II. A newly identified recombination event in our largest Australian family has narrowed the region of linkage by 1.8 Mb, permitting exclusion of approximately half the genes residing in the originally reported 5 Mb linked locus. Candidate genes that are involved in cell cycle control are of interest as adrenal hyperplasia and adrenal adenomas are common in FH-II patients. A novel candidate gene in this linked region produces the retinoblastoma-associated Kruppel-associated box protein (RBaK) which interacts with the retinoblastoma gene product to repress the expression of genes activated by members of the E2F family of transcription factors.

Visualising collaboration via email: Finding the key players

Email is an important form of asynchronous communication. Visualizing analyses of email communication patterns during a collaborative activity help us better understand the nature of collaboration, and identify the key players. By analysing the contents of email communication and adding reflective comments on its perceived importance from the participants of a collaboration new information can be gleaned not immediately obvious in its original flat form. This paper outlines a proof-of-concept prototype collaborative email visualisation schema. Data from a collaboration case study is analysed and subsequently employed to construct a display of the relative impact of both key players and the types of email used.

Structured ontology and information retrieval for email search and discovery

This paper discusses an document discovery tool based on formal concept analysis. The program allows users to navigate email using a visual lattice metaphor rather than a tree. It implements a virtual file structure over email where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in email discovery. The system described provides more flexibility in retrieving stored emails than what is normally available in email clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems.