149,545 research outputs found
Implicit Measures of Lostness and Success in Web Navigation
In two studies, we investigated the ability of a variety of structural and temporal measures computed from a web navigation path to predict lostness and task success. The user’s task was to find requested target information on specified websites. The web navigation measures were based on counts of visits to web pages and other statistical properties of the web usage graph (such as compactness, stratum, and similarity to the optimal path). Subjective lostness was best predicted by similarity to the optimal path and time on task. The best overall predictor of success on individual tasks was similarity to the optimal path, but other predictors were sometimes superior depending on the particular web navigation task. These measures can be used to diagnose user navigational problems and to help identify problems in website design
Ranking Spaces for Predicting Human Movement in an Urban Environment
A city can be topologically represented as a connectivity graph, consisting
of nodes representing individual spaces and links if the corresponding spaces
are intersected. It turns out in the space syntax literature that some defined
topological metrics can capture human movement rates in individual spaces. In
other words, the topological metrics are significantly correlated to human
movement rates, and individual spaces can be ranked by the metrics for
predicting human movement. However, this correlation has never been well
justified. In this paper, we study the same issue by applying the weighted
PageRank algorithm to the connectivity graph or space-space topology for
ranking the individual spaces, and find surprisingly that (1) the PageRank
scores are better correlated to human movement rates than the space syntax
metrics, and (2) the underlying space-space topology demonstrates small world
and scale free properties. The findings provide a novel justification as to why
space syntax, or topological analysis in general, can be used to predict human
movement. We further conjecture that this kind of analysis is no more than
predicting a drunkard's walking on a small world and scale free network.
Keywords: Space syntax, topological analysis of networks, small world, scale
free, human movement, and PageRankComment: 11 pages, 5 figures, and 2 tables, English corrections from version 1
to version 2, major changes in the section of introduction from version 2 to
Predicting Phishing Websites using Neural Network trained with Back-Propagation
Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates
Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions
Technology has recently been recruited in the war against the ongoing obesity
crisis; however, the adoption of Health & Fitness applications for regular
exercise is a struggle. In this study, we present a unique demographically
representative dataset of 15k US residents that combines technology use logs
with surveys on moral views, human values, and emotional contagion. Combining
these data, we provide a holistic view of individuals to model their physical
exercise behavior. First, we show which values determine the adoption of Health
& Fitness mobile applications, finding that users who prioritize the value of
purity and de-emphasize values of conformity, hedonism, and security are more
likely to use such apps. Further, we achieve a weighted AUROC of .673 in
predicting whether individual exercises, and we also show that the application
usage data allows for substantially better classification performance (.608)
compared to using basic demographics (.513) or internet browsing data (.546).
We also find a strong link of exercise to respondent socioeconomic status, as
well as the value of happiness. Using these insights, we propose actionable
design guidelines for persuasive technologies targeting health behavior
modification
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