A latent profile analysis of the interplay between PC and smartphone in problematic internet use

As modern-day adolescents use the Internet on both personal computer (PC) and smartphone, this study examined the phenomenon of problematic internet use by taking account of Internet usage on both PC and smartphone together, based on the theoretical framework of substitution/complementarity of media use. For this, latent profile analysis, nonlinear canonical correlation analysis, and logistic/probit regression analyses were performed on 653 Korean adolescents. Latent profile analysis identified six classes of distinct problematic internet use patterns. In brief, two latent classes showed substituting patterns, two other classes showed complementing patterns, and the last two showed neither. According to nonlinear canonical correlation analysis, classification by latent profile analysis was mainly associated with individual variables such as ‘PC game,’ ‘instant messaging,’ ‘gender,’ and ‘decreased PC usage time.’ Further, logistic/probit regression analyses revealed that male adolescents were more likely to be included in the complementation class, because they played PC games more than female adolescents. Implications and limitations of the study are discussed.     

Early diffusion of smartphones in OECD and BRICS countries: An examination of the effects of platform competition and indirect network effects

Employing the Gompertz model, this study examines macro-level adoption factors of early smartphone diffusion in OECD and BRICS countries. The results of the data analysis suggest that mobile OS competition, mobile network standard competition, open source OS share (platform openness), and price influence the early diffusion of smartphones in OECD countries. However, in BRICS countries, open source OS share (platform openness) and price are the main factors of smartphone diffusion. As the smartphone market continues to grow, it seems that the effects of mobile platform competition and indirect network effects on smartphone diffusion may co-exist in global smartphone markets. In addition, the results of the data analysis suggest that a platform (standard)-neutral policy is important in the growth stage of smartphone markets.     

Information disclosure on mobile devices: Re-examining privacy calculus with actual user behavior

The use of mobile applications continues to experience exponential growth. Using mobile apps typically requires the disclosure of location data, which often accompanies requests for various other forms of private information. Existing research on information privacy has implied that consumers are willing to accept privacy risks for relatively negligible benefits, and the offerings of mobile apps based on location-based services (LBS) appear to be no different. However, until now, researchers have struggled to replicate realistic privacy risks within experimental methodologies designed to manipulate independent variables. Moreover, minimal research has successfully captured actual information disclosure over mobile devices based on realistic risk perceptions. The purpose of this study is to propose and test a more realistic experimental methodology designed to replicate real perceptions of privacy risk and capture the effects of actual information disclosure decisions. As with prior research, this study employs a theoretical lens based on privacy calculus. However, we draw more detailed and valid conclusions due to our use of improved methodological rigor. We report the results of a controlled experiment involving consumers (n=1025) in a range of ages, levels of education, and employment experience. Based on our methodology, we find that only a weak, albeit significant, relationship exists between information disclosure intentions and actual disclosure. In addition, this relationship is heavily moderated by the consumer practice of disclosing false data. We conclude by discussing the contributions of our methodology and the possibilities for extending it for additional mobile privacy research.     

Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing

Road surface monitoring is an important problem in providing smooth road infrastructure to the commuters. The key to road condition monitoring is to detect road potholes and bumps, which affect the driving comfort and transport safety. This paper presents a smartphone based sensing and crowdsourcing technique to detect the road surface conditions. The in-built sensors of the smartphone like accelerometer and GPS¹ have been used to observe the road conditions. It has been observed that several techniques in the past have been proposed using these sensors. Such techniques either use fixed threshold values which are road or vehicle condition dependent or use machine learning based classified training which requires intensive and continuous training. The motivation of our work is to improve classification accuracy of detecting road surface conditions using DTW² technique which has not been researched on data based on motion sensors. The main features of DTW is its ability to automatically cope with time deformations and different speeds associated with time data, its simplicity is to be used in resource constrained devices such as smartphones and also the simplicity in its training procedure which is must as fast as compared to techniques such as SVM,³ HMM⁴ and ANN.⁵ Our technique shows better accuracy and efficiency with detection rate of 88.66% and 88.89% for potholes and bumps respectively, when compared with the existing techniques with the use of the proposed technique, prioritization of the road repair and maintenance can be decided based on real-time data and facts.     

基于智能手机与信任评价的商品防伪系统架构

提出一种基于智能手机与信任评价体系相结合的商品防伪系统架构．满足新型应用领域的需要。与旧的系统比较,能大幅提升了终端消费者的消费体验,并且降低系统复杂性．减少了硬件成本的费用,符合防伪溯源技术的未来发展趋势。     

Investigating decision factors in mobile application purchase: A mixed-methods approach

The growth of the smart devices market and the development of mobile applications (Apps) for them have given rise to an App economy. Sales of mobile applications are a key revenue source in this economy, with the expected worldwide market growth of US $75 billion by 2017. Despite the trend, many mobile Apps fail to attract customers, yet there has been a lack of research and understanding of the factors that affect the decisions to buy them. This study is thus motivated to examine the factors that people consider in their buying decisions of mobile Apps for their smartphones. This mixed-methods investigation first adopts an exploratory and qualitative approach to identify the purchase decision factors based on interviews with consumers. It then undertakes a quantitative, confirmatory study using a survey to test the model derived using mental accounting theory and the findings of the exploratory study. The results show the direct and indirect effects of five factors – word of mouth about App, App usefulness, monetary value of App, App trialability, and App enjoyment – on the intention to purchase an App. In this manner, this study advances our understanding of the decision-making factors leading to the purchase of mobile Apps. It also facilitates developers and marketers to promote the sales of their Apps for revenue generation.     

智能手机音频通信的实现

随着智能手机的兴起,手机与手机外设间的通信日益为人们关注,从而对两者间的通信提出了更高的要求。本文通过对码元特征数组提取与使用,完成了在资源有限环境下对数字信号处理,达到了实现音频通信的目的。验证表明该方案是有效的。     

The prevalence of neck pain and associated risk factors among undergraduate students: A large-scale cross-sectional study

BackgroundNeck pain is a pervasive ailment causing work absenteeism, disabilities, and sleep disturbance among working adults. While the onset of neck pain in many individuals may date back to college-age, little is known regarding the prevalence of neck pain and associated risk factors among undergraduates. The current study aimed to compare the prevalence of neck pain among students in different undergraduate programs and to investigate their risk factors.MethodsUndergraduates from two universities were invited to participate in a self-administered online survey. The survey collected data regarding demographics, previous and the current neck pain symptoms, and potential risk factors (e.g., gender, age, body mass index, study programs, electronic devices usage, study hours, sports participation, and anxiety and depression levels, etc.). Multiple logistic regressions were conducted to identify risk factors for neck pain.ResultsA total of 5,195 invitation emails were sent. Of 1,002 respondents, 22.3% reported having current neck pain. Physiotherapy (26.5%) and nursing students (26.1%) had significantly higher prevalence of neck pain as compared to business students (13.2%). Anxiety (odds ratio (OR):1.11, 95%CI:1.07–1.16), concurrent low back pain (OR:3.28, 95%CI:2.15–5.00) and senior years of studies (OR:1.19,95%CI:1.01–1.41) were significantly associated with the presence of neck pain. Taller students (OR:1.02,95%CI:0.99–1.05) and prolonged smartphone usage (OR:1.05,95%CI:0.99–1.12) appeared to be associated with the presence of neck pain.ConclusionThis study not only revealed the high prevalence of neck pain among undergraduates but also identified several modifiable and non-modifiable risk factors for neck pain in this population. Specific prevention strategies should be developed and implemented to reduce the risk of neck pain in vulnerable students.     

A smartphone based technique to monitor driving behavior using DTW and crowdsensing

Safety issues while driving in smart cities are considered to be top-notch priority in contrast to traveling. Today’s fast paced society, often leads to accidents. In order to reduce the road accidents, one key area of research is monitoring the driving behavior of drivers. Understanding the driver behavior is an essential component in Intelligent Driver Assistance Systems. One of potential cause of traffic fatalities is aggressive driving behavior. However, drivers are not fully aware of their aggressive actions. So, in order to increase awareness and to promote driver safety, a novel system has been proposed. In this work, we focus on DTW based event detection technique, which have not been researched in motion sensors based time series data to a great extent. Our motivation is to improve the classification accuracy to detect sudden braking and aggressive driving behaviors using sensory data collected from smartphone. A very significant feature of DTW is to be able to automatically cope with time deformations and different speeds associated with time-dependent data which makes it suitable for our chosen application where data might get affected due to factors such as: high variability in road and vehicle conditions, heterogeneous smartphone sensors, etc. Our technique is novel as it uses fusion of sensors to enhance detection accuracy. The experimental results show that proposed algorithm outperforms the existing machine learning and threshold-based techniques with 100% detection rate of braking events and 97% & 86.67% detection rate of normal left & right turns and aggressive left & right turns respectively.     

新型智能终端取证技术研究

随着移动互联网的广泛应用,智能手机、平板等新型智能终端设备在各种各样的违法犯罪活动中开始扮演越来越重要的角色,从涉案手机中提取的数据常常包含与违法犯罪行为相关的重要线索和证据。然而,移动智能终端设备不断提升的安全设计可能使得取证人员无法从设备中提取数据,给电子数据取证鉴定工作提出了新的挑战。本文详细分析当前主流的iOS、Android和Windows Phone等平台下的移动设备的安全机制,研究了主要的安全机制破解和取证技术及其在目前电子数据取证工作中的应用。最后,对未来面向新型移动智能终端电子数据取证技术研究发展方向进行了探讨。