Smartphone Environments Research Articles

Automated Spatiotemporal Classification Based on Smartphone App Logs

In this paper, a framework for user app behavior analysis using an automated supervised learning method in smartphone environments is proposed. This framework exploits the collective location data of users and their smartphone app logs. Based on these two datasets, the framework determines the apps with a high probability of usage in a geographic area. The framework extracts the app-usage behavior data of a mobile user from an Android phone and transmits them to a server. The server learns the representative trajectory patterns of the user by combining the collected app usage patterns and trajectory data. The proposed method performs supervised learning with automated labeled trajectory data using the user app data. Furthermore, it uses the behavioral characteristics data of users linked to the app usage data by area without a labeling cost.

A Functional Game Application for Korean Words Learning Based on Smartphone Environments

A Functional Game Application for Korean Words Learning Based on Smartphone Environments

Regions-of-interest discovering and predicting in smartphone environments

Location-Based Services (LBSs) provide users especially in smartphones with information and services based on their location and interests. Predicting people’s next location could help in improving the quality of such services and, in turn, boosting people’s confidence on those services. Any successful LBS algorithm or model should target three major goals: Location prediction accuracy, high throughput or fast response and efficiency in terms of utilizing smartphone resources. This paper proposes a new approach to discover and predict people’s next location based on their mobility patterns, while being computationally efficient. The approach starts by discovering Regions-of-Interest (RoIs) in people’s historical trajectories (which denote the locations where users have been previously, frequently). A new model based on Markov Chain (MC) is proposed to overcome the drawback of classical MC. Our proposed model considers both space and time contexts where the space represents a specific location that has been visited by a user while the time represents the location visiting time. We show how classical MC model can be extended to include movement times and how time will improve prediction accuracy. One unique finding in our research is related to the value of integrating users’ mobility location/space with time context. In particular, time context is formulated in a way to add extra information to the space context. For better abstraction during building the model, a general transformation function is used to transform the n-order MC into first order. Results show that the proposed approach provides significant improvements in predicting people’s next location compared with the state-of-the-art models when applied on two large real-life datasets, GeoLife and Gowalla.

Efficient Eye-Blinking Detection on Smartphones: A Hybrid Approach Based on Deep Learning

We propose an efficient method that can be used for eye-blinking detection or eye tracking on smartphone platforms in this paper. Eye-blinking detection or eye-tracking algorithms have various applications in mobile environments, for example, a countermeasure against spoofing in face recognition systems. In resource limited smartphone environments, one of the key issues of the eye-blinking detection problem is its computational efficiency. To tackle the problem, we take a hybrid approach combining two machine learning techniques: SVM (support vector machine) and CNN (convolutional neural network) such that the eye-blinking detection can be performed efficiently and reliably on resource-limited smartphones. Experimental results on commodity smartphones show that our approach achieves a precision of 94.4% and a processing rate of 22 frames per second.

Energy Efficient QoS-Aware Random Network Coding on Smartphones

Random network coding (RNC) technology is known to benefit various facets of information networks; however, there have been concerns for the computational complexity of RNC since its incipience. For instance, RNC's high complexity can be directly translated into high energy consumption and drain fast smartphone batteries, making it unsuitable for mobile environments. In this paper, we optimize the energy consumption of RNC implementations with a given QoS requirement, especially throughput, for smartphone environments. To this end, we propose a duty cycling approach minimizing the energy consumption of RNC with a given throughput constraint. By manipulating the processor clock frequency controlling mechanism (a.k.a. governor) in Android, our approach can indirectly regulate the processor clock frequency and enhance energy efficiency. Real experiments on Android systems with smartphone application processors such as Samsung's Exynos 5410, show that our method can reduce the energy consumption of RNC by up to 67% compared to a RNC implementation relying on ondemand governor for frequency control. Finally, we argue that our method can be applied to a wide range of applications by implementing it with a fast Fourier transform algorithm.

Analysis of User Preferences for Management and Search Features in E-book Reader Libraries in Smartphone Environments

There has been a significant paradigm shift in the book industry from print to digital, with the increased use of electronic books (ebooks) on e-book readers. The major online booksellers and publishers are devoting their energies to the growth of the e-book market, resulting in an upward spiral in e-book usage, and a resultant increase in the number of downloaded e-books in an e-book reader library. However, there are comparatively few features for e-book management and search in most e-book reader libraries, particularly in smartphone environments. In addition, the user interfaces of e-book management in e-book readers are highly diverse, which has led to major usability issues. In this paper, we analyze user preferences for e-book management and search in the libraries of the five most commonly used e-readers for the Android smartphone platform via a questionnaire survey. Then, we suggest ideal alternatives in addition to user-friendly features based on user preferences for managing e-book libraries, to allow users to more easily browse collections, thereby enhancing the usability of e-book readers.

Towards attention-aware adaptive notification on smart phones

As the amount of information to users increases with the trends of an increasing numbers of devices, applications, and web services, the new bottleneck in computing is human attention. To minimize users attentional overload, we propose a novel middleware “Attelia” that detects breakpoints of user’s mobile interactions to deliver notifications adaptively. Attelia detects such timings in real-time, using only users phones, without any external sensors, and without any modifications to applications. Our extensive evaluation proved Attelias effectiveness. In-the-wild user study with 30 participants for 16 days showed that, specifically for the users with greater sensitivity for interruptive notification timings, notification scheduling in Attelia’s breakpoint timing reduced users frustration by 28% in users’ real smart phone environments.

Intra- and Inter-frame Features for Automatic Speech Recognition

In this paper, alternative dynamic features for speech recognition are proposed. The goal of this work is to improve speech recognition accuracy by deriving the representation of distinctive dynamic characteristics from a speech spectrum. This work was inspired by two temporal dynamics of a speech signal. One is the highly non-stationary nature of speech, and the other is the inter-frame change of a speech spectrum. We adopt the use of a sub-frame spectrum analyzer to capture very rapid spectral changes within a speech analysis frame. In addition, we attempt to measure spectral fluctuations of a more complex manner as opposed to traditional dynamic features such as delta or double-delta. To evaluate the proposed features, speech recognition tests over smartphone environments were conducted. The experimental results show that the feature streams simply combined with the proposed features are effective for an improvement in the recognition accuracy of a hidden Markov model-based speech recognizer.

The Design and Implementation of Real Time Encoder System

This paper designed and implemented a next-generation solution for the Internet Broadcasting system applying FlashVideo. Compressing HD Class images in real time currently being broadcast in the field, this is a system that can service VOD as well as Internet live broadcasting, which is a solution that supports the operation of Internet Live broadcasting, VOD service and UCC service easily online. It is a system that supports real time streaming of MPEC4 and WMV images on the Internet or Smartphone by compressing camera and VOD images using H264 Codec in real time, which is first, the first real time encoder system in Korea with the real time recording feature of camera images and a software product that supports the up-to-date Codec technology suitable for the Web and Smartphone environments. Second, it is a two-way Internet broadcasting system that can play videos with an MP4 player and support user chatting and customizing. Third, the Contents Management System (CMS) feature can service streaming of video contents and lecture management contents in real time through Android phone or iPhone.

An intelligent mobile based decision support system for retinal disease diagnosis

Diabetes and Cataract are the key causes of retinal blindness for millions of people. Current detection of diabetes and Cataract from retinal images using Fundus camera is expensive and inconvenient since such detection is not portable and requires specialists to perform an operation. This paper presents an innovative development of a low cost Smartphone based intelligent system integrated with microscopic lens that allows patients in remote and isolated areas for regular eye examinations and disease diagnosis. This mobile diagnosis system uses an artificial Neural Network algorithm to analyze the retinal images captured by the microscopic lens to identify retinal disease conditions. The algorithm is first of all trained with infected and normal retinal images using a personal computer and then further developed into a mobile-based diagnosis application for Android environments. The application is optimized by using the rooted method in order to increase battery lifetime and processing capacity. A duty cycle method is also proposed to greatly improve the energy efficiency of this retinal scan and diagnosis system in Smartphone environments. The proposed mobile-based system is tested and verified using two well-known medical ophthalmology databases to demonstrate its merits and capabilities. The evaluation results indicate that the system shows competitive retinal disease detection accuracy rates (>87%). It also offers early detection of retinal diseases and shows great potential to be further developed to identify skin cancer.

A Cultural Dimensions Model based on Smart Phone Applications

One of the major factors influencing the phenomenal growth of the smart phone market is the active development applications based on open environments. Despite difficulties in finding and downloading applications due to the small screens and inconvenient interfaces of smart phones, users download applications nearly every day. Such user behavior patterns indicate the significance of smart phone applications. So far, studies on applications have focused mainly on technical approaches, including recommendation systems. Meanwhile, the issue of culture, as an aspect of user characteristics regarding smart phone use, remains largely unexamined throughout the world. Hence, the present study attempts to analyze the highest ranked smart phone applications downloaded and paid for that are ranked the highest in 10 countries (Korea, Japan, China, India, the UK, USA, Indonesia, Canada, France, and Mexico) and we then derive the CDSC (Cultural Dimensions Score of Content) for these applications. The results derived are, then, mapped to the cultural dimensions model to determine the CISC (Cultural Index Score for Country). Further, culturally significant differences in smart phone environments are identified using MDS analysis.

A QoS Scheme for a Congestion Core Network Based on Dissimilar QoS Structures in Smart-Phone Environments

This study suggests an approach to effective transmission of multimedia content in a rapidly changing Internet environment including smart-phones. Guaranteeing QoS in networks is currently an important research topic. When transmitting Assured Forwarding (AF) packets in a Multi-DiffServ network environment, network A may assign priority in an order AF1, AF2, AF3 and AF4; on the other hand, network B may reverse the order to a priority AF4, AF3, AF2 and AF1. In this case, the AF1 packets that received the best quality of service in network A will receive the lowest in network B, which may result in dropping of packets in network B and vice versa. This study suggests a way to guarantee QoS between hosts by minimizing the loss of AF packet class when one network transmits AF class packets to another network with differing principles. It is expected that QoS guarantees and their experimental value may be utilized as principles which can be applied to various mobile-web environments based on smart-phones.