Logical Image Acquisition and Analysis of Android Smartphones

Android smartphones largely dominate the smartphone market. For this reason, it is very important to examine these smartphones in terms of digital forensics since they are often used as evidence in trials. It is possible to acquire a physical or logical image of these devices. Acquiring physical and logical images has advantages and disadvantages compared to each other. Creating the logical image is done at the file system level. Analysis can be made on this logical image. Both logical image acquisition and analysis of the image can be done by software tools. In this study, the differences between logical image and physical image acquisition in Android smartphones, their advantages and disadvantages compared to each other, the difficulties that may be encountered in obtaining physical images, which type of image contributes to obtaining more useful and effective data, which one should be preferred for different conditions, and the benefits of having root authority are discussed. The practice of getting the logical image of the Android smartphones and making an analysis on the image is also included. Although root privileges are not required for logical image acquisition, it has been observed that very limited data will be obtained with the logical image created without root privileges. Nevertheless, logical image acquisition has advantages too against physical image acquisition.

Relationship between depression,smartphone addiction,and sleep among Chinese engineering students during the COVID-19 pandemic

BACKGROUND Existing research has demonstrated that depression is positively related to smartphone addiction, but the role of sleep has not been discussed thoroughly, especially among engineering undergraduates affected by the coronavirus disease 2019 pandemic.AIM To evaluate sleep as a mediator of the association between smartphone addiction and depression among engineering undergraduates.METHODS Using a multistage stratified random sampling method, a cross-sectional survey was conducted among 692 engineering undergraduates from a top engineering university in China, and data were collected by self-reported electronic questionnaires. The data included demographic characteristics, such as age, gender, the Smartphone Addiction Scale-Short Version(SAS-SV), the 9-item Patient Health Questionnaire, and the Pittsburgh Sleep Quality Index. Pearson correlation and multiple linear regression analyses were used to examine the association between smartphone addiction and depression, while structural equation models were established to evaluate the possible mediating role of sleep.RESULTS Based on the cutoffs of the SAS-SV, the rate of smartphone addiction was 63.58 percent, with 56.21 percent for women and 65.68 percent for men, among 692 engineering students. The prevalence of depression among students was 14.16 percent, with 17.65 percent for women, and 13.18 percent for men. Smartphone addiction was positively correlated with depression, and sleep played a significant mediating effect between the two, accounting for 42.22 percent of the total effect. In addition, sleep latency, sleep disturbances, and daytime dysfunction significantly mediated the relationship between depression and smartphone addiction. The mediating effect of sleep latency was 0.014 [P < 0.01;95% confidence interval(CI): 0.006-0.027], the mediating effect of sleep disturbances was 0.022(P < 0.01;95%CI: 0.011-0.040), and the mediating effect of daytime dysfunction was 0.040(P < 0.01;95%CI: 0.024-0.059). The influence of sleep latency, sleep disturbances, and daytime dysfunction accounted for 18.42%, 28.95%, and 52.63% of the total mediating effect, respectively.CONCLUSION The results of the study suggest that reducing excessive smartphone use and improving sleep quality can help alleviate depression.

A non-contact measurement method for rock mass discontinuity orientations by smartphone

Smartphones are usually packed with a large number of features.An increasing number of researchers are paying attention to the technological capabilities of smartphones,which is a new topic and research interest.This paper proposes a method using smartphones and digital photogrammetry to measure the discontinuity orientation of a rock mass.Smartphone photos satisfying a certain overlap rate provide an efficient method for generating point cloud models of rock outcrops based on image matching.Using the target and the generated point cloud model allows for determining actual geographic coordinates and the measurement of discontinuity orientations.The method proposed has been applied to two different study areas.The discontinuity orientations measured by the proposed method are compared with those measured by the manual method in two cases.The results show a good agreement,verifying the reliability and accuracy of the proposed method.The main contribution of this paper is to use knowledge of coordinate rotation to determine the actual geographic location of the model through a square target.The equipment used in this study is simple,and photogrammetric field surveys are easy to carry out.

Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Smartphones have now become an integral part of our everyday lives.User authentication on smartphones is often accomplished by mechanisms(like face unlock,pattern,or pin password)that authenticate the user’s identity.These technologies are simple,inexpensive,and fast for repeated logins.However,these technologies are still subject to assaults like smudge assaults and shoulder surfing.Users’touch behavior while using their cell phones might be used to authenticate them,which would solve the problem.The performance of the authentication process may be influenced by the attributes chosen(from these behaviors).The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of smartphones.We began by concentrating on feature selection methods utilizing the grey wolf optimization strategy.The random forest classifier is used to evaluate these tactics.The testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public dataset.It achieved a 97.89%accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly;processing power of the device and memory to validate individuals.Simultaneously,the findings revealed that our approach has a lower equal error rate(EER)of 0.5104,a false acceptance rate(FAR)of 1.00,and a false rejection rate(FRR)of 0.0209 compared to the methods discussed in the literature.These promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.

Smartphone-based bridge frequency identification using vehicle contact-point response

Bridge frequency(BF)identification using the vehicle scanning method has attracted considerable attention during the last two decades.However,most previous studies have adopted unrealistic vehicle models,thus finding limited practical applications.This study proposes a smartphone-based BF identification method that uses the contact-point acceleration response of a four degree-of-freedom vehicle model.The said response can be inferred from the vehicle body response measured by a smartphone.For realizing practical applications,this method is incorporated into a self-developed smartphone app to obtain data smoothly and identify BFs in a timely manner.Numerical and experimental investigations are performed to verify the effectiveness of the proposed method.In particular,the robustness of this method is investigated numerically against various factors,including the vehicle speed,bridge span,road roughness,and bridge type.Furthermore,laboratory calibration tests are performed to investigate the accuracy of the smartphone gyroscope in measuring the angular velocity,where anomalous data are detected and eliminated.Laboratory experiment results for a simply supported bridge indicate that the proposed method can be used to identify the first two BFs with acceptable accuracy.

Prevalence and Factors Associated with Smartphone Addiction among Adolescents–A Nationwide Study in Malaysia

Smartphone ownership among adolescents is getting common in this decade especially in Malaysia;Adolescent are strongly devoted to their smartphone and this may lead to smartphone addiction.Studies have reported that smartphone addiction has become an emerging social and health problem especially among the youth in many countries however there is lack of study among adolescents in Malaysia.This study aimed to examine the prevalence and factors associated with smartphone addiction among adolescents in Malaysia.This was a cross-sectional study involving adolescents from 15 primary care clinics throughout the country.Respondents were assessed on their smartphone activities using the Malaysian short version of the Smartphone addiction scale(SAS-M-SV).Multiple logistic regression was used to determine the predictors of smartphone addiction among adolescents.The study was conducted among 921 adolescents with 49.6%male(n=457).The mean age of adolescents was 16.4±2.4 years.The ethnicity distribution were 74.6%Malay,7.3%Chinese,4.7%Indian and 13.4%other ethnicities.The prevalence of smartphone addiction was 37.1%(342/921);37.4%in male and 36.9%in female.Based on multiple logistic regression analysis,longer duration of smartphone use per week was associated with higher odds of smartphone addiction among adolescent(odd ratio=1.005%,95%confidence interval=1.000–1.009,p-value=0.039).Smartphone addiction is present in nearly four in ten adolescents in Malaysia.Adolescents who spend longer duration in smartphone usage per week were associated with higher odds of having smartphone addiction.Parents should be more alert and vigilant about this finding.Hence,parents should limit their children from spending too much of time with smartphone in order to prevent their children from getting smartphone addiction.

Optimal Unification of Static and Dynamic Features for Smartphone Security Analysis

Android Smartphones are proliferating extensively in the digital world due to their widespread applications in a myriad offields.The increased popularity of the android platform entices malware developers to design malicious apps to achieve their malevolent intents.Also,static analysis approaches fail to detect run-time behaviors of malicious apps.To address these issues,an optimal unification of static and dynamic features for smartphone security analysis is proposed.The proposed solution exploits both static and dynamic features for generating a highly distinct unified feature vector using graph based cross-diffusion strategy.Further,a unified feature is subjected to the fuzzy-based classification model to distinguish benign and malicious applications.The suggested framework is extensively experimentally validated through both qualitative and quantitative analysis and results are compared with the existing solutions.Performance evaluation over benchmarked datasets from Google Play Store,Drebin,Androzoo,AMD,and CICMalDroid2020 revealed that the suggested solution outperforms state-of-the-art methods.We achieve average detection accuracy of 98.62%and F1 Score of 0.9916.

Could Military Commanders’ Good LeadershipInfluence Subordinates’ Smartphone Overdependence? A Serial Mediation Analysis

Owing to the ubiquitous use of smartphones by soldiers, military researchers have an increasing interest in potentially problematicside effects such as smartphone overdependence. This raises a question regarding the psychological mechanisms underlying thepotentially self-damaging use of smartphones. Here, we address this question by analyzing how heterogeneity in commander’sgood leadership explains subordinate soldiers’ differences in self-control and smartphone use. Specifically, we found thatsubordinate soldiers who thought their commander's leadership was good were self-regulated, less dependent on smartphones,less stressed, and finally had good mental health. This result indicates that commander’s good leadership can be used toestimate whether subordinate soldiers exert control over their impulses and use their smartphones properly. Thus, the currentfindings help to identify external factors that lead to a better understanding of problematic smartphone use and can potentiallyhelp to design appropriate preventive mechanisms or interventions that target commander’s good leadership.

Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors

Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits.

Prevalence and predictors of smartphone addiction and insomnia among nurses working in the outpatient department after the second wave of the coronavirus disease 2019 pandemic

Objectives:The present study aims to assess the prevalence and predictors of smartphone addiction and insomnia among nurses working in the outpatient department(OPD)after the second wave of the coronavirus disease 2019(COVID-19)pandemic.Materials and Methods:A descriptive,cross-sectional study was carried out among 117 OPD nurses between October and December 2021 using a purposive sampling technique.Two self-reported standardized scales,the Smartphone Addiction Scale-ShortVersion and Insomnia Severity Index were used.Kolmogorov–Smirnov test,Mann–Whitney U,and Kruskal–Wallis Htest were used.Pearson’s correlation and Scatter plot were used to determine the relationship between the study variables.A stepwise multiple linear regression analysis was also performed.Results:The majority of participants had slight smartphone addiction(78.6%)and suffered from sub-threshold to severe forms of insomnia(73.5%).A significant mild positive correlation was found between smartphone addiction and insomnia(r=0.195,P<0.05).Stepwise multivariate logistic regression analysis predicted factors such as female gender and exposure to smartphones for more than 5 years influencing smartphone addiction.A strong influence of exposure to the smartphone for more than 5 years was found on insomnia severity.Conclusion:Smartphone addiction and insomnia were identified problems among nurses working in the OPD after the second wave of the COVID-19 pandemic,requiring an urgent need to identify and manage various factors responsible for smartphone addiction and insomnia such as female gender and years of exposure to smartphones.

Systematic Review of Literature for Smartphones Technology Acceptance Using Unified Theory of Acceptance and Use of Technology Model (UTAUT)

Systematic Review and Meta-analysis are techniques which attempt to associate the findings from similar studies and deliver quantitative summaries of the research literature [1]. The Systematic review of research literature identifies the common research methods, research design, sample size, parameters used, survey instruments, etc. used by the group of researchers. This study intends to fulfill this purpose in order to identify common research mythologies, dependent variables, sample sizes, moderators and mediators used in the field of analysing technology adoption based studies that utilizes the UTAUT2 model. This research collected over 59 published articles and conducted descriptive analytics. The results have revealed performance expectancy/perceived usefulness, trust and habit as the best predictors of consumer behavioural intentions towards the adoption of mobile application. Behavioural intention was the best predictor of use behaviour among the 57 articles selected. 274 was the mean sample size of research with 25 mean questionnaire items. SPSS and AMOS were the most common softwares used in all 57 studies, and 32 of those studies used UTAUT1 model while 14 researches incorporated the UTAUT2 model. There were also two promising predictors such as perceived risk on behavioural intention and habit on use behaviour.

Measurement of cervical range of motion using smartphone compass in the healthy people:a cross-sectional pilot study

Objective:This study evaluates the reliability of smartphone compass software in measuring the cervical range of motion in healthy people.Methods:We selected 40 healthy intern college students from Tianjin Hospital from June to August 2022 to participate in this study.Two physiotherapists used a smartphone(iPhone 11256 Gb(model A2223))compass software to measure six directions of motion of the cervical spine in 40 subjects in a total of 3 rounds each.The intraclass correlation coefficient was used to compare the reliability intra-group,and the Pearson correlation coefficient was also used to compare the correlation between groups,with P<0.05 being statistically significant.Results:The intraclass correlation coefficient showed good reliability(>0.5)in cervical range of motion(CROM),especially in cervical flexion and right rotation(>0.9).In the correlation comparison between the two groups,the Spearman comparison was used,and the six directions of the cervical spine were significantly correlated(P<0.05).Conclusion:The built-in compass software in smartphones has good reliability in measuring CROM in healthy people.

SmartPhone类手机应用开发框架研究

通过对SmartPhone类型手机的研究与消息机制的分析,最终创建起较为通用高效的开发框架,对于后继软件开发建立了良好的开发平台.

使用VB2005设计Smartphone智能手机应用程序入门

Smartphone是一个兴起中的智能手机系统,Microsoft将智能手机编程赋予给高级语言编程系统VB2005,实现了大众化的程序开发。通过实例分析,从Smartphone程序的界面设计、程序代码编写,到程序的调试、应用,适合程序员的入门使用。

一种基于Smartphone类的注册表实现方案

目前Smartphone类型的手机注册表都是用数据库实现的,现在要改用COMS来实现,具体做法是采用大小为64KB的2块FLASH来模拟CMOS,在此基础上实现注册表的管理,直接从FLASH驱动层做起。注册表的记录改变了传统的Windows做法,采用一种简化的方法,不分目录级,由一个主键加一个键值构成,其主键是一个null结束的字符串,可以包含目录信息,这对后继手机软件的应用开发建立了良好的开发基础。

基于Windows CE 的Smartphone操作系统界面响应研究

本文在.NET Compact Framework环境下结合Visual Studio 2008开发了一个基于Windows CE平台的Smartphone操作系统主题自制软件,对其中的界面响应问题做了简要阐述,并给出了解决办法。

基于Windows CE的Smartphone操作系统图片研究

本文在.NET Compact Framework环境下结合Visual Studio 2008开发了一个基于Windows CE平台的Smartphone操作系统主题自制软件,对其中的图片选取、图片路径获得及图片加载时遇到的问题做了简要阐述,并给出了这些问题的解决办法。

Predictive Factors for Smartphone Dependence: Relationship to Demographic Characteristics, Chronotype, and Depressive State of University Students

We investigated factors contributing to smartphone dependence. To 196 medical university students, we administered a set of self-reporting questionnaires designed to evaluate demographic characteristics, smartphone dependence, chronotype, and depressive state. Smartphone dependence was evaluated using the Wakayama Smartphone-Dependence Scale (WSDS) with 3 subscales: Subscale 1, immersion in Internet communication;Subscale 2, using a smartphone for extended periods of time and neglecting social obligations and other tasks;Subscale 3, using a smartphone while doing something else and neglect of etiquette. Multiple regression analyses revealed that living in a family, eveningness, and presence of depression were associated with Subscale 1, that living in a family and eveningness were also associated with Subscale 2, and that being a man was associated with Subscale 3. These findings suggest that smartphone dependence can be predicted by factors such as gender, mode of residence, chronotype, or depressive state.

Development of Japanese Version of Smartphone Dependence Scale

We designed a Japanese version of the Smartphone Dependence Scale (J-SDS) for university students, and assessed its validity and reliability. For development of this scale, we initially selected 43 preliminary items based on previous studies of Internet and mobile phone dependence. Each response was scored using a Likert scale (0, 1, 2, 3), with higher scores indicating greater dependence. After administering the preliminary scale to 149 university students, the final 29 items were selected according to exploratory factor analysis. The final scale consisted of 5 factors: 1) craving and withdrawal, 2) overuse and tolerance, 3) virtual life orientation, 4) disturbance of concentration in class, and 5) physical symptoms. Reliability coefficient (Cronbach’s alpha) values for the 5 factors and total J-SDS were 0.87, 0.87, 0.76, 0.77, 0.69, and 0.92, respectively. We then summed the Likert scores for each item to provide a quantitative overall smartphone dependence score, with higher scores indicating greater dependence. The J-SDS score became significantly higher with longer hours of smartphone use (p < 0.001). Our results suggest that the J-SDS is a reliable and valid scale for screening university students who may be at risk of developing smartphone dependence.

Relationship of Smartphone Dependence to General Health Status and Personality Traits among University Students

We investigated associations between smartphone dependence and general health status or personality traits. To 197 medical university students, we administered a set of self-reporting questionnaires designed to evaluate these parameters. For males, smartphone dependence positively correlated with somatic symptoms, anxiety and insomnia, and emotional instability, and negatively correlated with agreeableness. For females, smartphone dependence positively correlated with somatic symptoms, severe depression, and extraversion, and negatively correlated with social dysfunction. These findings suggest that smartphone dependence may be associated with general health status or personality traits and that there may be a gender difference in these associations.