Exploring the feasibility and acceptability of using internet technology to promote physical activity within a defined community

ISSUE ADDRESSED: To explore the feasibility of using the Internet and e-mail to promote physical activity in a defined community. METHODS: An online survey was conducted through a community-based Internet Service Provider (ISP). ISP clients were recruited via electronic newsletter and direct e-mail. Data were collected on preferred sources of assistance for physical activity advice and stage of motivational readiness for physical activity. RESULTS: Valid surveys were completed by 797 (9% response rate). Participants were: 55% men; 56% aged >45 years; 57% worked full time; mean BMI was 28+/-8. Thirty-six per cent were in the early stages of motivational readiness for physical activity. More than 70% were somewhat to extremely interested in having access to a physical activity website. CONCLUSION: Promoting physical activity via the Internet and e-mail is feasible and appealing to some people. Expanding the reach, appeal and use of this technology to deliver physical activity programs will be a challenge.

Incremental learning for interactive e-mail filtering

In this article, we propose a framework, namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling misclassified documents. Whenever a user points out misclassified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications. If the classifier fails to accept relevant documents or reject irrelevant documents on certain categories, then PLD will assign those documents as new positive/negative training instances. The classifier can then strengthen its weakness by learning from these new training instances. Our experiments’ results have demonstrated that the proposed algorithm can learn from user-identified misclassified documents, and then distil the rest successfully.

Recursive filtering of images with symmetric extension

Recursive filters are widely used in image analysis due to their efficiency and simple implementation. However these filters have an initialisation problem which either produces unusable results near the image boundaries or requires costly approximate solutions such as extending the boundary manually. In this paper, we describe a method for the recursive filtering of symmetrically extended images for filters with symmetric denominator. We begin with an analysis of symmetric extensions and their effect on non-recursive filtering operators. Based on the non-recursive case, we derive a formulation of recursive filtering on symmetric domains as a linear but spatially varying implicit operator. We then give an efficient method for decomposing and solving the linear implicit system, along with a proof that this decomposition always exists. This decomposition needs to be performed only once for each dimension of the image. This yields a filtering which is both stable and consistent with the ideal infinite extension. The filter is efficient, requiring less computation than the standard recursive filtering. We give experimental evidence to verify these claims. (c) 2005 Elsevier B.V. All rights reserved.