Device-aware desktop web page transformation for rendering on handhelds

Artail, Abdalla; Raydan, Mackram

doi:10.1007/s00779-005-0348-5

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…This approach may also resize images proportionally to the display area. Different favours of this approach can be found in the literature (Artail and Raydan, 2005;Blekas, Garofalakis and Stefanis, 2006;Mohomed, Cai andZhang, 2007). Some mobile browsers such as Pocket Internet Explorer and NetFront had adopted this approach to render the display of the web content.…”

Section: Content Re-authoringmentioning

confidence: 99%

Personalised mobile learning content delivery: a learner centric approach

Eap

Gašević

Kinshuk³

et al. 2009

IJMLO

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This research proposes a context-aware framework that automatically adapts learning content designed for personal computers for mobile learners. The framework is a web application plug-in that captures access patterns and uses the patterns with trained configurations, learner preferences and device profiles to tailor learning content to the learners' needs. Learners can switch devices without losing learning context, and the framework would use Semantic technology to search and make content learners may eventually need available to them. The framework is inline with the surveys recently conducted on mobile learning and uses only proven technologies to guarantee a successful implementation.

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Section: Content Re-authoringmentioning

confidence: 99%

Personalised mobile learning content delivery: a learner centric approach

Eap

Gašević

Kinshuk³

et al. 2009

IJMLO

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show abstract

“…As a result, there has been an increasing amount of research in the past few years on mobile Web navigation, aiming to improve the effectiveness and user experience of mobile Web (e.g., [3,4,12,35,36,38]). Based on our comprehensive review of more than one hundred academic papers published in the past decade on mobile Web, one surprising finding is that among prior studies that involved empirical evaluation of the proposed methods for mobile Web navigation, almost 40 percent of them solely used emulators, a software that emulates the functions of a handheld device, on desktop computers in their experiments, instead of using physical mobile handheld devices.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Investigation of Emulators in Mobile Web Research

Zhang

Lai

Dash

2014

Journal of Computer Information Systems

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In spite of affordability, portability, and convenience of mobile handheld devices, there are a number of usability problems associated with Web access through those devices, largely attributable to their inherent physical constraints. In the past decade, there have been increasing empirical studies on interface issues and usability of mobile handheld devices and mobile Web. However, researchers have been frequently using emulators of handheld devices running on desktop computers instead of real, physical handheld devices in those studies. Such a phenomenon raises validity and generalizability concerns given the differences between emulators and physical devices. This research empirically investigates whether the user performance and perception of mobile Web on an emulator is equivalent to or at least consistent with that on a physical device. The findings suggest that an emulator may not be able to emulate user performance and perception with physical handheld devices, and thus should be used with caution.

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“…Mobile devices have evolved and experienced a great success over the last few years . Such devices are capable of performing sophisticated tasks and communicate through various wireless interfaces .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of anomaly‐based IDS for mobile devices using machine learning classifiers

Damopoulos

Menesidou

Kambourakis

et al. 2011

Security Comm Networks

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Mobile devices have evolved and experienced an immense popularity over the last few years. This growth however has exposed mobile devices to an increasing number of security threats. Despite the variety of peripheral protection mechanisms described in the literature, authentication and access control cannot provide integral protection against intrusions. Thus, a need for more intelligent and sophisticated security controls such as intrusion detection systems (IDSs) is necessary. Whilst much work has been devoted to mobile device IDSs, research on anomaly-based or behaviour-based IDS for such devices has been limited leaving several problems unsolved. Motivated by this fact, in this paper, we focus on anomaly-based IDS for modern mobile devices. A dataset consisting of iPhone users data logs has been created, and various classification and validation methods have been evaluated to assess their effectiveness in detecting misuses. Specifically, the experimental procedure includes and cross-evaluates four machine learning algorithms (i.e. Bayesian networks, radial basis function, K-nearest neighbours and random Forest), which classify the behaviour of the end-user in terms of telephone calls, SMS and Web browsing history. In order to detect illegitimate use of service by a potential malware or a thief, the experimental procedure examines the aforementioned services independently as well as in combination in a multimodal fashion. The results are very promising showing the ability of at least one classifier to detect intrusions with a high true positive rate of 99.8%.

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Device-aware desktop web page transformation for rendering on handhelds

Cited by 11 publications

References 13 publications

Personalised mobile learning content delivery: a learner centric approach

Personalised mobile learning content delivery: a learner centric approach

An Empirical Investigation of Emulators in Mobile Web Research

Evaluation of anomaly‐based IDS for mobile devices using machine learning classifiers

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