Empirical Evaluation of Rhythm-Based Authentication Method for Mobile Devices

2017 ◽  
pp. 529-538
Author(s):  
Takahiro Hori ◽  
Yoshihiro Kita ◽  
Kentaroh Toyoda ◽  
Naonobu Okazaki ◽  
Mirang Park
Keyword(s):  
Mobile Devices ◽  
Empirical Evaluation

Interfaces for accessing location-based information on mobile devices: An empirical evaluation

2015 ◽  
Vol 67 (12) ◽  
pp. 2882-2896 ◽  
Author(s):  
Dion Hoe-Lian Goh ◽  
Chei Sian Lee ◽  
Khasfariyati Razikin
Keyword(s):  
Mobile Devices ◽  
Empirical Evaluation

scholarly journals Location-Based Test Case Prioritization for Software Embedded in Mobile Devices Using the Law of Gravitation

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaolin Wang ◽  
Hongwei Zeng ◽  
Honghao Gao ◽  
Huaikou Miao ◽  
Weiwei Lin
Keyword(s):  
Mobile Devices ◽  
Optimization Technique ◽  
Challenge Test ◽  
Empirical Evaluation ◽  
Location Information ◽  
Test Case ◽  
Test Case Prioritization ◽  
Specific Calculation ◽  
Intelligent Software ◽  
The Law

Considering that some intelligent software in mobile devices is related to location of sensors and devices, regression testing for it faces a major challenge. Test case prioritization (TCP), as a kind of regression test optimization technique, is beneficial to improve test efficiency. However, traditional TCP techniques may have limitations on testing intelligent software embedded in mobile devices because they do not take into account characteristics of mobile devices. This paper uses a smart mall as a scenario to design a novel location-based TCP technique for software embedded in mobile devices using the law of gravitation. First, test gravitation is proposed by applying the idea of universal gravitation. Second, a specific calculation model of test gravitation is designed for a smart mall scenario. Third, how to create a faulted test case set is designed by the pseudocode. Fourth, a location-based TCP using the law of gravitation algorithm is proposed, which utilizes test case information, fault information, and location information to prioritize test cases. Finally, an empirical evaluation is presented by using one industrial project. The observation, underlying the experimental results, is that our proposed TCP approach performs better than traditional TCP techniques. In addition, besides location information, the level of devices is also an important factor which affects the prioritization efficiency.

An Empirical Evaluation of User Movement Data on Smartphones

2019 ◽  
Vol 18 (04) ◽  
pp. 1950025
Author(s):  
Christopher Kelley ◽  
Janelle Mason ◽  
Albert Esterline ◽  
Kaushik Roy
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Empirical Evaluation ◽  
Machine Learning Algorithms ◽  
Accelerometer Data ◽  
K Nearest Neighbor ◽  
Movement Data ◽  
User Movement

Movement data can be collected and used to add new security features and functionality to users’ mobile devices. Measuring a user’s movement using mobile devices allows for the use of behavioral biometrics. This assessment could introduce a shift in our current methods for securing mobile devices: instead of physical attributes like fingerprints or our face, the use of behavioral attributes like the way we walk or perform some personal activity. In this paper, an empirical evaluation of different classification techniques is conducted on user movement data. The datasets used in this empirical evaluation contain accelerometer data that were collected during various experiments from several mobile devices, including smartphones, smart watches, and other accelerometer sensors. We aggregated the user movement data and provided them as input into five traditional machine learning algorithms. The classification performances of the data were compared with a deep learning technique, the Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN). The LSTM-RNN achieved its highest accuracy at 89% compared to 97% from a traditional machine learning algorithm, specifically the k-Nearest Neighbor (k-NN) algorithm on wrist-worn accelerometer data, thus showing the LSTM to be a less viable option.

scholarly journals Simple Energy Aware Scheduler: An Empirical Evaluation

2018 ◽  
Vol 21 (2) ◽  
Author(s):  
Alexander Perez Campos ◽  
Juan Manuel Rodriguez ◽  
Alejandro Zunino
Keyword(s):  
Distributed Computing ◽  
Mobile Devices ◽  
Job Scheduling ◽  
Research Work ◽  
Empirical Evaluation ◽  
Real Life ◽  
General Purpose ◽  
Energy Aware ◽  
Computing Platform ◽  
Accessible Information

Mobile devices have evolved from single purpose devices, such as mobile phone, into general purpose multi-core computers with considerable unused capabilities. Therefore, several researchers have considered harnessing the power of these battery-powered devices for distributed computing. Despite their ever-growing capabilities, using battery as power source for mobile devices represents a major challenge for applying traditional distributed computing techniques. Particularly, researchers aimed at using mobile devices as resources for executing computationally intensive task. Different job scheduling algorithms were proposed with this aim, but many of them require information that is unavailable or difficult to obtain in real-life environments, such as how much energy would require a job to be finished. In this context, Simple Energy Aware Scheduler (SEAS) is a scheduling technique for computational intensive Mobile Grids that only require easily accessible information. It was proposed in 2010 and it has been the base for a range of research work. Despite being described as easily implementable in real-life scenarios, SEAS and other SEAS-improvements works have always been evaluated using simulations. In this work, we present a distributed computing platform for mobile devices that support SEAS and empirical evaluation of the SEAS scheduler. This evaluation followed the methodology of the original SEAS evaluation, in which Random and Round Robin schedulers were used as baselines. Although the original evaluation was performed by simulation using notebooks profile instead of smartphones and tablets, results confirms that SEAS outperforms the baseline schedulers.

scholarly journals Designing a Natural User Interface to Support Information Sharing among Co-Located Mobile Devices

2018 ◽  
Vol 30 (2) ◽  
Author(s):  
Timothy Lee Son ◽  
Janet Wesson ◽  
Dieter Vogts
Keyword(s):  
User Interface ◽  
Information Sharing ◽  
Mobile Devices ◽  
Gesture Recognition ◽  
User Satisfaction ◽  
Empirical Evaluation ◽  
Analytical Evaluation ◽  
Natural User Interface ◽  
Touch Gestures ◽  
Spatial Environment

Users of mobile devices share their information through various methods, which are supported by mobile devices. However, the information sharing process of these methods are typically redundant and sometimes tedious. This is because it may require the user to repeatedly perform a series of steps to share one or more selected files with another individual. The proliferation of mobile devices support new, more intuitive, and less complicated solutions to information sharing in the field of mobile computing. The aim of this paper is to present MotionShare, which is a NUI application that supports information sharing among co-located mobile devices. Unlike other existing systems, MotionShare’s distinguishing attribute is its inability of relying on additional and assisting technologies in determining the positions of devices. A primary example is using an external camera to determine device positioning in a spatial environment. An analytical evaluation investigated the accuracy of device positioning and gesture recognition, where the results were positive. The empirical evaluation investigated any usability issues. The results of the empirical evaluation showed high levels of user satisfaction and that participants preferred touch gestures to point gestures.

Sign in / Sign up

Export Citation Format

Share Document