Tackling the Password Problem: Image- and Gesture-Based Alternatives to Alphanumeric Passwords

Abstract

Alphanumeric passwords remain a ubiquitous means of user authentication, yet they are plagued by a fundamental problem: Secure passwords are difficult to create and remember. This paper suggests that image- or gesture-based passwords might strike a better balance between security and usability. It examines two such systems that are currently in widespread commercial use and examines alternative approaches that may offer insights for future improvements. Finally, it considers the possibility that touch-screen gesture passwords may become a viable biometric measure, which may allow them to provide multi-factor gesture-based authentication.     

基于Android和GPS轨迹记录和能耗测算研究

现在城市里处于亚健康的人数比例越来越大,锻炼是改善亚健康状态的有效手段,跑步和走路是较方便快捷的锻炼方式。为了帮助人们科学合理地进行日常锻炼如户外跑步、走路等运动,文中基于Android平台和GPS技术,利用百度地图开源SDK,设计并研发了一个较准确记录并测算终端持有者运动轨迹和人体运动消耗量的系统。该系统提出了怎样根据运动速度计算能耗的相关算法,而且系统设置固定的经纬度步长排除了GPS不稳定性带来的异常点干扰。该系统通过采集到的用户相关参数,按照设计出的算法,测算出运动者的运动时间、速度、路程、运动的能耗等相关数据,而且能较准确记录下运动轨迹。     

浅析Android手机病毒的防范策略

介绍Android系统及病毒特点,分析Android病毒的危害,针对流行的Android手机病毒案例浅析了相关的防范策略。     

基于角色的Android手机平台木马检测系统

有效保障手机各类通信的安全,是当前手机安全软件研究的热点问题。基于Android手机操作系统平台,引入角色思想,提出一种基于支持向量机(SVM)二分类算法、Android OS中的权限机制及用户自定义策略的角色安全检测模型。在该模型的基础上详细描述设计方案,并实现一个针对典型木马的检测系统,实验结果证实该系统的有效性。     

基于增强现实技术的移动LBS系统的设计与实现

基于位置服务发展迅速,利用增强现实技术开发LBS系统,对人机界面与GIS服务整合具有重要的理论和现实意义。在此基础上,设计一个空间信息服务的构建与发布机制,利用智能终端配备的传感装置,在Android平台手持设备下扩展增强现实功能,融合真实场景和虚拟信息,搭建远程Web服务器完成数据交互,得到整体基于Web服务的LBS原型系统。     

安卓Android与苹果ios优劣与发展前景

文章主要介绍了谷歌的安卓系统与苹果公司的IOS系统,介绍了2个系统的应用范围,详细评价了系统之间存在的差异及优劣,并展望在未来的发展趋势和前景。     

Android智能手机天气预报系统设计及实现

在分析讨论Android应用系统设计原理的基础上,提出了Android智能手机天气实况预报系统用户界面以及获取并解析城市列表数据的设计方法,给出了在用户界面上呈现列表数据的设计过程,实现了一个简单的Android智能手机城市天气实况预报系统.系统在模拟器上通过调试并正常运行.     

基于Android技术的界面设计与研究

详细介绍了google发布的新一代智能手机平台Android的基本架构,通过对其应用程序构成框架的分析,分析了构成Android应用程序的四种架构块的功能。最后通过对视图和布局的介绍,给出一种使用Android技术的界面设计实现方案,并通过数据绑定实现了用户事件映射。     

Android IoT Lifelog System and Its Application to Motion Inference

In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users’ daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users’ daily lives. The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards. In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system. Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly. As an application of the proposed system, we also propose motion inference based on three multi-class ML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling). When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall. Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values. The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively.     

Studying users’ perception of IoT mobile companion apps

Internet of Things (IoT) products provide over-the-net capabilities such as remote activation, monitoring, and notifications. An associated mobile app is often provided for more convenient usage of these capabilities. The perceived quality of these companion apps can impact the success of the IoT product. We investigate the perceived quality and prominent issues of smart-home IoT mobile companion apps with the aim of deriving insights to: (i) provide guidance to end users interested in adopting IoT products; (ii) inform companion app developers and IoT producers about characteristics frequently criticized by users; (iii) highlight open research directions. We employ a mixed-methods approach, analyzing both quantitative and qualitative data. We assess the perceived quality of companion apps by quantitatively analyzing the star rating and the sentiment of 1,347,799 Android and 48,498 iOS user reviews. We identify the prominent issues that afflict companion apps by performing a qualitative manual analysis of 1,000 sampled reviews. Our analysis shows that users’ judgment has not improved over the years. A variety of functional and non-functional issues persist, such as difficulties in pairing with the device, software flakiness, poor user interfaces, and presence of issues of a socio-technical impact. Our study highlights several aspects of companion apps that require improvement in order to meet user expectations and identifies future directions.