Cost-effectiveness of recruitment methods in a population-based epidemiological study: the Melbourne Visual Impairment Project

The cost-effectiveness of five recruitment methods was evaluated to determine the best method of encouraging eligible persons to participate in the Melbourne Visual Impairment Project (a population-based epidemiological study). The evaluation was divided into two phases. Phase 1 included one of two types of initial contact, by direct personal contact or by telephone. Phase 2 involved recruiting residents after an attempt had been made by either the telephone or the doorstep approach, and included a second attempt by a field interviewer, subsequent attempts by senior field staff, and finally, financial incentives. The cost-effectiveness of each method was determined by dividing the approach's cost by the effectiveness ratio. We identified 269 eligible households with 356 eligible residents. An 89 per cent response rate was achieved at the examination centre, comprising 61 per cent from Phase 1 and 28 per cent from Phase 2. Although both recruitment methods in Phase 1 were equally cost-effective, there was a significant difference in the effectiveness of each method in actually recruiting residents. The doorstep method was more costly per attender but was far more effective at 76 per cent recruitment than the telephone method at 47 per cent (P < 0.001). We have demonstrated a practical two-stage approach (the doorstep method in Phase 1 and follow-up strategies in Phase 2) to population-based recruitment involving the middle to elderly age group that should be relevant to many epidemiological studies.

Leveraging visual features and hierarchical dependencies for conference information extraction

Traditional information extraction methods mainly rely on visual feature assisted techniques; but without considering the hierarchical dependencies within the paragraph structure, some important information is missing. This paper proposes an integrated approach for extracting academic information from conference Web pages. Firstly, Web pages are segmented into text blocks by applying a new hybrid page segmentation algorithm which combines visual feature and DOM structure together. Then, these text blocks are labeled by a Tree-structured Random Fields model, and the block functions are differentiated using various features such as visual features, semantic features and hierarchical dependencies. Finally, an additional post-processing is introduced to tune the initial annotation results. Our experimental results on real-world data sets demonstrated that the proposed method is able to effectively and accurately extract the needed academic information from conference Web pages. © 2013 Springer-Verlag.

A visual-numeric approach to clustering and anomaly detection for trajectory data

This paper proposes a novel application of Visual Assessment of Tendency (VAT)-based hierarchical clustering algorithms (VAT, iVAT, and clusiVAT) for trajectory analysis. We introduce a new clustering based anomaly detection framework named iVAT+ and clusiVAT+ and use it for trajectory anomaly detection. This approach is based on partitioning the VAT-generated Minimum Spanning Tree based on an efficient thresholding scheme. The trajectories are classified as normal or anomalous based on the number of paths in the clusters. On synthetic datasets with fixed and variable numbers of clusters and anomalies, we achieve 98 % classification accuracy. Our two-stage clusiVAT method is applied to 26,039 trajectories of vehicles and pedestrians from a parking lot scene from the real life MIT trajectories dataset. The first stage clusters the trajectories ignoring directionality. The second stage divides the clusters obtained from the first stage by considering trajectory direction. We show that our novel two-stage clusiVAT approach can produce natural and informative trajectory clusters on this real life dataset while finding representative anomalies.