3,733 research outputs found
Investigating UI displacements in an Adaptive Mobile Homescreen
The authors present a system that adapts application shortcuts (apps) on the homescreen of an Android smartphone, and investigate the effect of UI displacements that are caused by the choice of adaptive model and the order of apps in the homescreen layout. They define UI displacements to be the distance that items move between adaptations, and they use this as a measure of stability. An experiment with 12 participants is performed to evaluate the impact of UI displacements on the homescreen. To make the distribution of apps in the experiment task less contrived, naturally generated data from a pilot study is used. The authors’ results show that selection time is correlated to the magnitude of the previous UI displacement. Additionally, selection time and subjective rating improve significantly when the model is easy to understand and an alphabetical order is used, conditions that increase stability. However, rank order is preferred when the model updates frequently and is less easy to understand. The authors present their approach to adapting apps on the homescreen, and initial insights into UI displacements
On the Feature Discovery for App Usage Prediction in Smartphones
With the increasing number of mobile Apps developed, they are now closely
integrated into daily life. In this paper, we develop a framework to predict
mobile Apps that are most likely to be used regarding the current device status
of a smartphone. Such an Apps usage prediction framework is a crucial
prerequisite for fast App launching, intelligent user experience, and power
management of smartphones. By analyzing real App usage log data, we discover
two kinds of features: The Explicit Feature (EF) from sensing readings of
built-in sensors, and the Implicit Feature (IF) from App usage relations. The
IF feature is derived by constructing the proposed App Usage Graph (abbreviated
as AUG) that models App usage transitions. In light of AUG, we are able to
discover usage relations among Apps. Since users may have different usage
behaviors on their smartphones, we further propose one personalized feature
selection algorithm. We explore minimum description length (MDL) from the
training data and select those features which need less length to describe the
training data. The personalized feature selection can successfully reduce the
log size and the prediction time. Finally, we adopt the kNN classification
model to predict Apps usage. Note that through the features selected by the
proposed personalized feature selection algorithm, we only need to keep these
features, which in turn reduces the prediction time and avoids the curse of
dimensionality when using the kNN classifier. We conduct a comprehensive
experimental study based on a real mobile App usage dataset. The results
demonstrate the effectiveness of the proposed framework and show the predictive
capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape
Leveraging Program Analysis to Reduce User-Perceived Latency in Mobile Applications
Reducing network latency in mobile applications is an effective way of
improving the mobile user experience and has tangible economic benefits. This
paper presents PALOMA, a novel client-centric technique for reducing the
network latency by prefetching HTTP requests in Android apps. Our work
leverages string analysis and callback control-flow analysis to automatically
instrument apps using PALOMA's rigorous formulation of scenarios that address
"what" and "when" to prefetch. PALOMA has been shown to incur significant
runtime savings (several hundred milliseconds per prefetchable HTTP request),
both when applied on a reusable evaluation benchmark we have developed and on
real applicationsComment: ICSE 201
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