A Mobile App Search Engine

With the popularity of mobile apps on mobile devices based on iOS, Android, Blackberry and Windows Phone operating systems, the numbers of mobile apps in each of the respective native app stores are increasing in leaps and bounds. Currently there are close to one million mobile apps across these four major native app stores. Due to the enormous number of apps, both the constituents in the app ecosytem, consumers and app developers, face problems in ‘app discovery’. For consumers, it is a daunting task to discover the apps they like and need among the huge number of available apps. Likewise, for developers, enabling their apps to be discovered is a challenge. To address these issues, Mobilewalla (MW) an app search engine provides an independent unbiased search for mobile apps with semantic search capabilities. It has also developed an objective scoring mechanism based on user and developer involvement with an app. The scoring mechanism enables MW to provide a number of other ways to discover apps—such as dynamically maintained ‘hot’ lists and ‘fast rising’ lists. In this paper, we describe the challenges of developing the MW platform and how these challenges have been mitigated. Lastly, we demonstrate some of the key functionalities of MW.     

Cross-Compiling Android Applications to iOS and Windows Phone 7

Android is currently leading the smartphone segment in terms of market share since its introduction in 2007. Android applications are written in Java using an API designed for mobile apps. Other smartphone platforms, such as Apple’s iOS or Microsoft’s Windows Phone 7, differ greatly in their native application programming model. App developers who want to publish their applications for different platforms are required to re-implement the application using the respective native SDK. In this paper we describe a cross-compilation approach, whereby Android applications are cross-compiled to C for iOS and to C# for Windows Phone 7. We describe different aspects of our cross-compiler, from byte code level cross-compilation to API mapping. A prototype of our cross-compiler called XMLVM is available under an Open Source license.     

Download or swipe left: The role of complexity,future-oriented emotions and feature overload

With the intense competition in the mobile applications (apps) market, it is imperative for app providers to understand how visual stimuli from their apps can create a positive first impression and enhance the app download rates. In the present study, the mechanism through which visual complexity influences mobile app download intention is examined. Using a combination of convenience and snowball sampling, 218 participants were recruited to take part in a single-group post-test only quasi-experimental design. The findings of the study showed that visual complexity influences mobile app download intentions through the mediating role of two future-oriented emotions (i.e. hope and anticipated regret). Additionally, the study showed that the indirect effect of visual complexity on download intentions was moderated by feature overload with the importance of visual complexity significantly reducing as perceptions of feature overload increases. The proposed model explained 46% variance in the intentions to download a mobile app. The findings not only provide practical insights for mobile app developers and publishers but also theoretical insights on consumer decision making in the pre-use context of mobile apps.     

Religion-focused dating apps: A Q methodology study on the uses of mutual

Dating apps have become an increasingly viable option for individuals seeking interpersonal romantic relationships. While there is significant research regarding user motivation on dating apps such as Bumble, Tinder, and Match.com, there is no published research that discusses the motivations of Mutual app users. Developed as a dating app to target members of The Church of Jesus Christ of Latter-Day Saints, Mutual allows users to find potential mates who share their religious background and specify their relationship readiness (from “Into Dating I Guess” to “Ready for a Ring”). This research aims to illuminate the various motivations, attitudes, and opinions of Mutual app users through Q methodology, which identifies perceptual groups among homogeneous populations through a factor analysis of participants’ agreement with similar statements regarding Mutual use. Findings indicated four factor groups: the Relationship Readies (i.e., those serious about dating), the Swipeaholics (i.e., those looking for entertainment), the Faithless (i.e., those who felt pressured to use Mutual), and the Eligible Optimists (i.e., those who saw the app as a convenient, entertaining way to date). Different from other research on dating apps, this study indicates that people may use a niche religion-focused dating app to find individuals with similar moral values or due to external pressure from others. Results warrant further investigation into niche dating apps.     

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model of Android does not address this threat as it is rather limited to mitigating risks of individual apps. This paper presents a technique for quantifying the collusion threat, essentially the first step towards assessing the collusion risk. The proposed method is useful in finding the collusion candidate of interest which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29,000 Android apps provided by Intel Security^TM.     

Conceptual framework for the security of mobile health applications on Android platform

Mobile Health (mHealth) applications are readily accessible to the average user of mobile devices, and despite the potential of mHealth applications to improve the availability, affordability and effectiveness of delivering healthcare services, they handle sensitive medical data, and as such, have also the potential to carry substantial risks to the security and privacy of their users. Developers of applications are usually unknown, and users are unaware of how their data are being managed and used. This is combined with the emergence of new threats due to the deficiency in mobile applications development or the design ambiguities of the current mobile operating systems. A number of mobile operating systems are available in the market, but the Android platform has gained the topmost popularity. However, Android security model is short of completely ensuring the privacy and security of users’ data, including the data of mHealth applications. Despite the security mechanisms provided by Android such as permissions and sandboxing, mHealth applications are still plagued by serious privacy and security issues. These security issues need to be addressed in order to improve the acceptance of mHealth applications among users and the efficacy of mHealth applications in the healthcare systems. Thus, this paper presents a conceptual framework to improve the security of medical data associated with Android mHealth applications, as well as to protect the privacy of their users. Based on the literature review that suggested the need for the intended security framework, three-distinct and successive phases are presented, each of which is described in a separate section. First, discussed the design process of the first phase to develop a security framework for mHealth apps to ensure the security and privacy of sensitive medical data. The second phase is discussed who to achieve the implementation of a prototypic proof-of-concept version of the framework. Finally, the third phase ending discussed the evaluation process in terms of effectiveness and efficiency for the proposed framework.     

基于电子围栏技术的校园监护App的应用研究

随着移动互联网的快速发展，基于地理位置的信息服务App也得到了广泛应用。针对高校校园学生安全需求日益强烈的现状，为进一步加强学生安全，确保正常的教学和生活秩序，文中基于安卓平台开发了校园监护App，利用第三方开源地图来实现电子围栏的功能，以此实现学生行程的精细化管理。电子地图围栏结合GPS、北斗、基站等3种定位方式实现了对学生位置信息的实时定位，使整个系统运行得更为可靠和高效。电子围栏App可以实现位置监控、健康打卡、电子围栏报警、外出审批与监控等具体功能。     

Systematic evaluation of mobile fitness apps: Apps as the Tutor,Recorder, Game Companion,and Cheerleader

Mobile fitness applications are innovating the ways in which smartphone users self-manage their health. Prior research found that app functions may impact app efficacy. However, research to date has not sought to systematically investigate how different combinations of app functions impact user response to apps, especially adoption intent. This article describes two studies on mobile fitness app characteristics and user attitudes. Study One used content analysis and hierarchical cluster analysis on 98 iPhone fitness apps and identified four app clusters: “Tutor”, “Recorder”, “Game Companion”, and “Cheerleader.” Tracking was the predominant function in current market, but tracking-focused Recorder apps received lowest user ratings among all app clusters. Users favored Tutor apps that combine exercise education and tracking, and Game Companion apps that combine gamification, tracking, and social functions. Function combinations, rather than standalone functions, impact app success. Following a Reasoned Action Approach, Study Two found various effects of individual differences (age, gender, BMI, eHealth literacy, smartphone experience, function preference) on user attitude toward different fitness app types. A comparison between two studies demonstrated a mismatch between market offerings and user needs regarding app functions. Implications of results for mobile fitness app design to improve consumer health and for theories are discussed.     

Crowdsourced hotspot validation and data visualisation for location-based haze mitigation

ABSTRACT

Haze over Sumatera and Kalimantan has been a prolonged trans-boundary issue in South East Asia mainly due to setting fire to drained peatland. At present, fire sources (i.e. hotspots) are located based on satellite data. Sensors such as MODIS and AVHRR detect extremes in average temperatures of an area. The hotspots have low spatial resolution and large spatial footprints, thus making it harder to detect fires. This research proposed a ground-based spatial validation of satellite data based on crowdsourcing in order to obtain more accurate estimates of the location and severity of the fire. We developed an Android application for reporting and validating fires in peatlands. Crowd data collected were integrated with satellite hotspot data by the dashboard system as a monitoring platform for government agencies. The 110,888 hectares of Padang Island, in Riau Province, were chosen as the study area given its vulnerability to peatland fire and imminent danger of subsidence as the collateral effect of draining peatlands. Residents of Padang Island tested the use-case scenario of the app to assess its applicability. The study showed the potential use of mobile apps for local communities to help the government validate hotspots for haze mitigation.     

Facilitation of consumer loyalty toward branded applications: The dual-route perspective

Although many brands develop mobile applications (apps) to build relationships with consumers, most branded apps fail to retain consumers’ loyalty. This study examines the facilitation of consumer loyalty toward branded apps (continuance intention, in-app purchase intention, and word-of-mouth intention) from the dual-route perspective. One route is the affective (relationship) route, where brand benefits (functional benefits, experiential benefits, symbolic benefits, and monetary benefits) drive parasocial interactions between consumers and the brand, which, in turn, influences branded app loyalty. The other route is the utility route, where system characteristics (system quality and information quality) affect perceived usefulness, which, in turn, facilitates branded app loyalty. An online survey was conducted, and the research model was empirically tested using partial least squares structural equation modeling. The findings support the dual-route perspective according to which both affective and utilitarian paths facilitate branded app loyalty. The key theoretical contribution of this study is that it moves beyond the utilitarian path and finds the affective (relationship) path to give a more complete picture of the facilitation of consumer loyalty in the branded app context. A strategy is provided to suggest to practitioners how to design branded apps to facilitate consumer loyalty.     

AdaptiveMutate: a technique for privacy preservation

Mobile apps are known to be rich sources for gathering privacy-sensitive information about smartphone users. Despite the presence of encryption, passive network adversaries who have access to the network infrastructure can eavesdrop on the traffic and therefore fingerprint a user’s app by means of packet-level traffic analysis. Since it is difficult to prevent the adversaries from accessing the network, providing secrecy in hostile environments becomes a serious concern.In this study, we propose AdaptiveMutate, a privacy-leak thwarting technique to defend against the statistical traffic analysis of apps. First, we present a method for the identification of mobile apps using traffic analysis. Further, we propose a confusion system in which we obfuscate packet lengths, and/or inter-arrival time information leaked by the mobile traffic to make it hard for intruders to differentiate between the altered app traffic and the actual one using statistical analysis. Our aim is to shape one class of app traffic to obscure its features with the minimum overhead. Our system strives to dynamically maximize its efficiency by matching each app with the corresponding most dissimilar app. Also, AdaptiveMutate has an adaptive capability that allows it to choose the most suitable feature to mutate, depending on the type of apps analyzed and the classifier used, if known.We evaluate the efficiency of our model by conducting a comprehensive simulation analysis that mutates different apps to each other using AdaptiveMutate. We conclude that our algorithm is most efficient when we mutate a feature of one app to its most dissimilar one in another app. When applying the identification technique, we achieve a classification accuracy of 91.1%. Then, using our obfuscation technique, we are able to reduce this accuracy to 7%. Also, we test our algorithm against a recently published approach for mobile apps classification and we are able to reduce its accuracy from 94.8% to 17.9%. Additionally, we analyze the tradeoff between the shaping cost and traffic privacy protection, specifically, the associated overhead and the feasibility for real-time implementation.     

基于数字证书的自有APP签名保护方案

为应对移动应用(APP)盗版、仿冒、篡改风险,保证自有APP来源可信、内容完 整,本文提出了一套基于数字证书的APP签名保护与安全控制技术方案,实现了对 自有应用进行签名认证和全流程安全管控。该方案规范了自有APP的签名证书,明 确了正版标识;采用可信副署签名技术,加强代码保护,实现被篡改应用可识别可 追溯;通过中央监控平台,及时发现并限制异常应用,实现了APP的全流程实时安全管控。     

面向Android的音视频传输策略的研究

谷歌发布的Android操作系统为应用层开发者提供了MediaRecoder对象和MediaPlayer对象用于音视频应用的开发,但它们主要针对音视频的摄录和播放需求,无法满足开发者基于摄像头和麦克等硬件设备的实时音视频流化传输需求.因此,在对Android操作系统进行深入研究的基础上,提出了一种高效灵活的音视频传输策略,然后依据此策略设计了一个第三方音视频流化传输组件libavstream.最后基于libavstream设计了一个音视频直播应用BLife,验证了本文所提出策略的可用性和有效性.     

Mobile apps usage and dynamic capabilities: A structural equation model of SMEs in Lagos,Nigeria

Significant knowledge exists regarding the application of dynamic capability (DC) frameworks in large firms, but their impact on smaller organisations is yet to be fully researched. This study surveyed 1162 small and medium sized enterprises (SMEs) in Lagos in an effort to understand how SMEs in developing country contexts use mobile apps to enhance their businesses through DCs. Through the use of the covariance-based structural equation modelling (SEM) technique, the study explored the fitness of a conceptual formative model for SMEs. The model assembled 7 latent variables namely: mobile app usage, adaptive capability, absorptive capability, innovative capability, opportunity sensing ability, opportunity shaping ability and opportunity seizing ability. Subsequently, 15 hypotheses aimed at testing the relationships between the latent variables were developed and tested. The findings revealed that mobile app usage increases the adaptive, absorptive and innovative capabilities of SMEs. Absorptive capabilities help SMEs to maximise opportunities, while innovative capabilities negatively influence SMEs’ tendency to maximise opportunities. The results failed to establish a direct relationship between mobile app usage and SMEs’ ability to maximise opportunities. The research outcomes indicate that SMEs in Lagos respond to opportunities innovatively but they seldom exhibit innovation in order to create opportunities. The heterogeneous nature of SMEs complicates any clear-cut narrative as to how SMEs in Lagos should employ mobile apps to create and maximise opportunities. However, mobile apps could induce innovation and, as such, impact significantly when developed and applied to the contextual requirements of SMEs. The research revealed the untapped potential of SMEs’ mobile app usage in Lagos.     

运用XD与HBuilderX在Android平台的智能手机App设计及应用

随着智能手机时代的到来,大多数人的生活方式已经改变,如今,智能手机已成为人们工作的一部分,并在他们的工作中起着重要作用。作为移动互联网行业的关键领域,智能手机在互联网行业中变得越来越重要。人们一直在密切关注智能手机App设计的用户体验。以智能手机App为核心的用户体验构建设计是智能手机应用程序设计的核心和关键点,也是使得用户需求得以满足的重要方式。智能手机App的存在时间相对较短。其开发过程缺少完整的设计过程和理论支持。如何给用户提供良好体验的App设计就显得非常重要。针对此问题,一方面,需要从逻辑设计入手考虑用户体验。另一方面,通过调查来分析用户体验,进行有效的图形设计。在手机App层出不穷的新时期,要合理设计智能手机的应用程序,以增强用户体验。     

Android系统架构及应用程序开发研究

Android系统是一个开放源码的平台,所有的开发人员都可以使用该软件开发工具包(SDK)在自己平台上开发应用程序。该系统包含一系列功能,如语音控制、智能应用程序、虚拟机等。文中主要研究了Android系统的架构及应用程序开发。     

Intelligent Vehicle Parking System (IVPS) Using Wireless Sensor Networks

Android smartphones are employed widely due to its flexible programming system with several user-oriented features in daily lives. With the substantial growth rate of smartphone technologies, cyber-attack against such devices has surged at an exponential rate. Majority of the smartphone users grant permission blindly to various arbitrary applications and hence it weakens the efficiency of the authorization mechanism. Numerous approaches were established in effective malware detection, but due to certain limitations like low identification rate, low malware detection rate as well as category detection, the results obtained are ineffective. Therefore, this paper proposes a convolutional neural network based adaptive red fox optimization (CNN-ARFO) approach to detect the malware applications as benign or malware. The proposed approach comprising of three different phases namely the pre-processing phase, feature extraction phase and the detection phase for the effective detection of android malware applications. In the pre-processing phase, the selected dataset utilizes Minmax technique to normalize the features. Then the malicious APK and the collected benign apps are investigated to identify and extract the essential features for the proper functioning of malware in the extraction phase. Finally, the android mobile applications are detected using CNN based ARFO approach. Then the results based on detecting the benign and malicious applications from the android mobiles are demonstrated by evaluating certain parameters like model accuracy rate, model loss rate, accuracy, precision, recall and f-measure. The resulting outcome revealed that the detection accuracy achieved by the proposed approach is 97.29%.

     

Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables

Artificial intelligence (AI) is a future-defining technology, and AI applications are becoming mainstream in the developed world. Many consumers are adopting and using AI-based apps, devices, and services in their everyday lives. However, research examining consumer behavior in using AI apps is scant. We examine critical factors in AI app adoption by extending and validating a well-established unified theory of adoption and use of technology, UTAUT2. We also explore the possibility of unobserved heterogeneity in consumers’ behavior, including potentially relevant segments of AI app adopters. To augment the knowledge of end users’ engagement and relevant segments, we have added two new antecedent variables into UTAUT2: technology fear and consumer trust. Prediction-orientated segmentation was used on 740 valid responses collected using a pre-tested survey instrument. The results show five segments with different behaviors that were influenced by the variables of the proposed model. Once known, the profiles were used to propose apps to AI developers to improve consumer engagement. The moderating effects of the added variables—technology fear and consumer trust—are also shown. Finally, we discuss the theoretical and managerial implications of our findings and propose priorities for future research.     

A data reduction scheme for active authentication of legitimate smartphone owner using informative apps ranking

Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms. Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices. Machine-learning-based frameworks are effective for active authentication. However, the success of any machine-learning-based techniques depends highly on the relevancy of the data in hand for training. In addition, the training time should be very efficient. Keeping in view both issues, we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate users. We hypothesized that every user has a unique pattern hidden in his/her usage of apps. Motivated by this observation, we’ve designed a way to obtain training data, which can be used by any machine learning model for effective authentication. To achieve better accuracy with reduced training time, we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority list. An information theoretic app ranking scheme was used to prepare a user-targeted apps priority list. Predictability of each instance related to a candidate app was calculated by using a knockout approach. Finally, a weighted rank was calculated for each app specific to every user. Instances with low ranked apps were removed to derive the reduced training set. Two datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.     

Predicting numeric ratings for Google apps using text features and ensemble learning

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.