Leveraging app usage contexts for app recommendation: a neural approach

The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods.

     

Network-based detection of Android malicious apps

Users leverage mobile devices for their daily Internet needs by running various mobile applications (apps) such as social networking, e-mailing, news-reading, and video/audio streaming. Mobile device have become major targets for malicious apps due to their heavy network activity and is a research challenge in the current era. The majority of the research reported in the literature is focused on host-based systems rather than the network-based; unable to detect malicious activities occurring on mobile device through the Internet. This paper presents a detection app model for classification of apps. We investigate the accuracy of various machine learning models, in the context of known and unknown apps, benign and normal apps, with or without encrypted message-based app, and operating system version independence of classification. The best resulted machine learning(ML)-based model is embedded into the detection app for efficient and effective detection. We collect a dataset of network activities of 18 different malware families-based apps and 14 genuine apps and use it to develop ML-based detectors. We show that, it is possible to detect malicious app using network traces with the traditional ML techniques, and results revealed the accuracy (95–99.9 %) in detection of apps in different scenarios. The model proposed is proved efficient and suitable for mobile devices. Due to the widespread penetration of Android OS into the market, it has become the main target for the attackers. Hence, the proposed system is deployed on Android environment.     

Understanding the evaluation of mHealth app features based on a cross-country Kano analysis

While mobile health (mHealth) apps play an increasingly important role in digitalized health care, little is known regarding the effects of specific mHealth app features on user satisfaction across different healthcare system contexts. Using personal health record (PHR) apps as an example, this study identifies how potential users in Germany and Denmark evaluate a set of 26 app features, and whether evaluation differences can be explained by the differences in four pertinent user characteristics, namely privacy concerns, mHealth literacy, mHealth self-efficacy, and adult playfulness. Based on survey data from both countries, we employed the Kano method to evaluate PHR features and applied a quartile-based sample-split approach to understand the underlying relationships between user characteristics and their perceptions of features. Our results not only reveal significant differences in 14 of the features between Germans and Danes, they also demonstrate which of the user characteristics best explain each of these differences. Our two key contributions are, first, to explain the evaluation of specific PHR app features on user satisfaction in two different healthcare contexts and, second, to demonstrate how to extend the Kano method in terms of explaining subgroup differences through user characteristic antecedents. The implications for app providers and policymakers are discussed.

     

Discovering and understanding android sensor usage behaviors with data flow analysis

Today’s Android-powered smartphones have various embedded sensors that measure the acceleration, orientation, light and other environmental conditions. Many functions in the third-party applications (apps) need to use these sensors. However, embedded sensors may lead to security issues, as the third-party apps can read data from these sensors without claiming any permissions. It has been proven that embedded sensors can be exploited by well designed malicious apps, resulting in leaking users’ privacy. In this work, we are motivated to provide an overview of sensor usage patterns in current apps by investigating what, why and how embedded sensors are used in the apps collected from both a Chinese app. market called “AppChina” and the official market called “Google Play”. To fulfill this goal, We develop a tool called “SDFDroid” to identify the used sensors’ types and to generate the sensor data propagation graphs in each app. We then cluster the apps to find out their sensor usage patterns based on their sensor data propagation graphs. We apply our method on 22,010 apps collected from AppChina and 7,601 apps from Google Play. Extensive experiments are conducted and the experimental results show that most apps implement their sensor related functions by using the third-party libraries. We further study the sensor usage behaviors in the third-party libraries. Our results show that the accelerometer is the most frequently used sensor. Though many third-party libraries use no more than four types of sensors, there are still some third-party libraries registering all the types of sensors recklessly. These results call for more attentions on better regulating the sensor usage in Android apps.     

An analysis of the relationship between quality and user acceptance in smartphone apps

This study investigates consumer intentions within the smartphone app environment. More specifically, it studies the factors influencing the intention to use banking apps based on the smartphone by employing the information system success model and a revised technology acceptance model. The study examines how quality factors and attitudes toward mobile apps-based banking influence the intention to use banking apps, and whether trust influences the relationship between quality factors and intention to use. In it, we collect data from 520 users and estimate the structural model. The results indicate that attitudes to mobile apps-based banking, as well as information and service quality, affect consumers’ intention to use banking apps. We further confirm that three particular quality factors, moderated by trust, affect the intention to use these apps. This study helps to explain consumers’ mobile apps-based banking behaviours, by combining the information system success model with a technology acceptance model.     

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently, various mobile apps have included more features to improve user convenience. Mobile operating systems load as many apps into memory for faster app launching and execution. The least recently used (LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low. However, the LRU-based cached app termination does not distinguish between frequently or infrequently used apps. The app launch performance degrades if LRU terminates frequently used apps. Recent studies have suggested the potential of using users’ app usage patterns to predict the next app launch and address the limitations of the current least recently used (LRU) approach. However, existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again. In this paper, we present a new approach for predicting future app launches by utilizing the relaunch distance. We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction (M2ARP). M2ARP utilizes past app usage patterns to predict the relaunch distance. It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.     

An automatically vetting mechanism for SSL error-handling vulnerability in android hybrid Web apps

A large set of diverse hybrid mobile apps, which use both native Android app UIs and Web UIs, are widely available in today’s smartphones. These hybrid apps usually use SSL or TLS to secure HTTP based communication. However, researchers show that incorrect implementation of SSL or TLS may lead to serious security problems, such as Man-In-The-Middle (MITM) attacks and phishing attacks. This paper investigates a particular SSL vulnerability that results from error-handling code in the hybrid mobile Web apps. Usually such error-handling code is used to terminate an ongoing communication, but the vulnerability of interest is able to make the communication proceed regardless of SSL certificate verification failures, eventually lead to MITM attacks. To identify those vulnerable apps, we develop a hybrid approach, which combines both static analysis and dynamic analysis to (1) automatically distinguish the native Android UIs and Web UIs, and execute the Web UIs to trigger the error-handling code; (2) accurately select the correct paths from the app entry-point to the targeted code, meanwhile avoiding the crash of apps, and populate messaging objects for the communication between components. Specifically, we construct inter-component call graphs to model the connections, and design algorithms to select the paths from the established graph and determine the parameters by backtracing. To evaluate our approach, we have implemented and tested it with 13,820 real world mobile Web apps from Google Play. The experimental results demonstrate that 1,360 apps are detected as potentially vulnerable ones solely using the static analysis. The dynamic analysis process further confirms that 711 apps are truly vulnerable among the potentially vulnerable set.     

Effects of the design of mobile security notifications and mobile app usability on users’ security perceptions and continued use intention

The explosive global adoption of mobile applications (i.e., apps) has been fraught with security and privacy issues. App users typically have a poor understanding of information security; worse, they routinely ignore security notifications designed to increase security on apps. By considering both mobile app interface usability and mobile security notification (MSN) design, we investigate how security perceptions of apps are formed and how these perceptions influence users’ intentions to continue using apps. Accordingly, we designed and conducted a set of controlled survey experiments with 317 participants in different MSN interface scenarios by manipulating the types of MSN interfaces (i.e., high vs. low disruption), the context (hedonic vs. utilitarian scenarios), and the degree of MSN intrusiveness (high vs. low intrusiveness). We found that both app interface usability and the design of MSNs significantly impacted users’ perceived security, which, in turn, has a positive influence on users’ intention to continue using the app. In addition, we identified an important conundrum: disruptive MSNs—a common approach to delivering MSNs—irritate users and negatively influence their perceptions of app security. Thus, our results directly challenge current practice. If these results hold, current practice should shift away from MSNs that interrupt task performance.     

A study of the relation of mobile device attributes with the user-perceived quality of Android apps

The number of mobile applications (apps) and mobile devices has increased considerably over the past few years. Online app markets, such as the Google Play Store, use a star-rating mechanism to quantify the user-perceived quality of mobile apps. Users may rate apps on a five point (star) scale where a five star-rating is the highest rating. Having considered the importance of a high star-rating to the success of an app, recent studies continue to explore the relationship between the app attributes, such as User Interface (UI) complexity, and the user-perceived quality. However, the user-perceived quality reflects the users’ experience using an app on a particular mobile device. Hence, the user-perceived quality of an app is not solely determined by app attributes. In this paper, we study the relation of both device attributes and app attributes with the user-perceived quality of Android apps from the Google Play Store. We study 20 device attributes, such as the CPU and the display size, and 13 app attributes, such as code size and UI complexity. Our study is based on data from 30 types of Android mobile devices and 280 Android apps. We use linear mixed effect models to identify the device attributes and app attributes with the strongest relationship with the user-perceived quality. We find that the code size has the strongest relationship with the user-perceived quality. However, some device attributes, such as the CPU, have stronger relationships with the user-perceived quality than some app attributes, such as the number of UI inputs and outputs of an app. Our work helps both device manufacturers and app developers. Manufacturers can focus on the attributes that have significant relationships with the user-perceived quality. Moreover, app developers should be careful about the devices for which they make their apps available because the device attributes have a strong relationship with the ratings that users give to apps.     

App store mining is not enough for app improvement

The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. However, app users’ feedback does not only exist on the app store. In fact, despite the large quantity of posts that are made daily on social media, the importance and value that these discussions provide remain mostly unused in the context of mobile app development. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analyzing a total of 30,793 apps over a period of six weeks, we found strong correlations between the number of reviews and tweets for most apps. Moreover, through applying machine learning classifiers, topic modeling and subsequent crowd-sourcing, we successfully mined 22.4% additional feature requests and 12.89% additional bug reports from Twitter. We also found that 52.1% of all feature requests and bug reports were discussed on both tweets and reviews. In addition to finding common and unique information from Twitter and the app store, sentiment and content analysis were also performed for 70 randomly selected apps. From this, we found that tweets provided more critical and objective views on apps than reviews from the app store. These results show that app store review mining is indeed not enough; other information sources ultimately provide added value and information for app developers.     

Automated generation of colluding apps for experimental research

Colluding apps bypass the security measures enforced by sandboxed operating systems such as Android. App collusion can be a real threat in cloud environments as well. Research in detecting and protecting against app collusion requires a variety of colluding apps for experimentation. Presently the number of (real or manually crafted) apps available to researchers is very limited. In this paper we propose a system called Application Collusion Engine (ACE) to automatically generate combinations of colluding and non-colluding Android apps to help researchers fairly evaluate different collusion detection and protection methods. Our initial implementation includes a variety of components that enable the system to create more than 5,000 different colluding and non-colluding app sets. ACE can be extended with more functional components to create even more colluding apps. To show the usefulness of our system, we have applied different risk evaluation and collusion detection methods to the created set of colluding apps.     

Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the overall impression of their app. In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobile app industry, it is essential for developers of cross-platform apps to maintain a consistent level of star ratings and user reviews for their apps across the various platforms on which they are available. In this paper, we investigate whether cross-platform apps achieve a consistent level of star ratings and user reviews. We manually identify 19 cross-platform apps and conduct an empirical study on their star ratings and user reviews. By manually tagging 9,902 1 & 2-star reviews of the studied cross-platform apps, we discover that the distribution of the frequency of complaint types varies across platforms. Finally, we study the negative impact ratio of complaint types and find that for some apps, users have higher expectations on one platform. All our proposed techniques and our methodologies are generic and can be used for any app. Our findings show that at least 79% of the studied cross-platform apps do not have consistent star ratings, which suggests that different quality assurance efforts need to be considered by developers for the different platforms that they wish to support.     

A parental perspective on apps for young children

Touchscreen applications (apps) for young children have seen increasingly high rates of growth with more than a hundred thousand now available apps. As with other media, parents play a key role in young children’s app selection and use. However, to date, we know very little about how parents select apps for their children. Guided by uses and gratification theory, a survey was conducted with 600 Dutch parents who had at least one child between three and seven years old. Across two studies, we identified parents’ most important needs that drive their selection of children’s apps as well as the extent to which these needs differ by parenting style. Results indicate five overarching parental needs when it comes to children’s apps, and confirm that these needs vary by parenting style. Findings offer important insight into how parents select apps for their children.     

Impact of the density of the elements belonging to the quick access area of a smartphone app on the visual search efficiency and user experience of elderly people

In the digital age, the dependence of the elderly on smartphones is growing; and the frequency of use and time spent on various apps continue to grow. The display elements belonging to the quick access area on the homepage of an app act as shortcuts for navigation, directly affecting the search efficiency and user experience of the elderly when they use the app. In this study, we aim to investigate the impact of the density of the elements (icons and their text labels) located on the quick access area of smartphone apps on the visual search efficiency and user experience of elderly people. First, three typical density designs were extracted by collating and analyzing the density rules for the elements belonging to the quick access areas on the homepages of existing elderly-oriented versions of mainstream apps. Then, the densities of three elements in the quick access area on the homepage of a takeout app were used for a case study involving 96 elderly subjects, who were invited to participate in the task search test and user interviews. The results showed that the density of the elements in the app’s quick access area had no significant effect on the visual search efficiency of the subjects but had a certain impact on the user experience. Additionally, the older elderly subjects preferred designs with lower density, while the younger elderly ones, who had more online shopping experience, preferred designs with higher density. The subjects were more concerned about the ease of use and the overall user experience of the quick access area than its visual aesthetics. The research results not only provide a theoretical reference and design basis for designing icons and text labels belonging to the quick access area of apps, considering the density of these elements in the context of elderly-oriented apps, but also offers inspiration for improving the user experience of elderly people that use apps.     

The Dating and Domestic Violence App Rubric: Synthesizing Clinical Best Practices and Digital Health App Standards for Relationship Violence Prevention Smartphone Apps

ABSTRACT

Smartphones are used to both perpetrate and intervene in dating and domestic violence (DV). However, existing DV literature primarily evaluates technology as a tool for perpetration and emerging frameworks that measure eHealth app interventions have not yet considered DV.

To address this gap, the Dating and Domestic Violence App Rubric assesses smartphone-based DV intervention apps along common eHealth app measures such as user responsiveness and security as well as DV-appropriateness – categories derived from eHealth intervention theory and evidence-based DV interventions. As proof of concept, 38 DV intervention apps for iPhone were measured using this rubric.

K-means cluster analysis identified three clusters (high, medium, low). Apps targeting specific users or a specific intervention strategy tended to score higher overall. Findings suggest high-quality DV intervention apps may depend on active collaboration between stakeholders including app developers, DV advocates, and other professionals. Future research should expand this research to include additional DV apps and explore how individuals use smartphone apps to prevent or intervene in DV.     

Component-based permission management of Android applications

Most Android applications include third-party libraries (3PLs) to make revenues, to facilitate their development, and to track user behaviors. 3PLs generally require specific permissions to realize their functionalities. Current Android systems manage permissions in app (process) granularity. As a result, the permission sets of apps with 3PLs (3PL-apps) may be augmented, introducing overprivilege risks. In this paper, we firstly study how severe the problem is by analyzing the permission sets of 27 718 real-world Android apps with and without 3PLs downloaded in both 2016 and 2017. We find that the usage of 3PLs and the permissions required by 3PL-apps have increased over time. As a result, the possibility of overprivilege risks increases. We then propose Perman, a fine-grained permission management mechanism for Android. Perman isolates the permissions of the host app and those of the 3PLs through dynamic code instrumentation. It allows users to manage permission requests of different modules of 3PL-apps during app runtime. Unlike existing tools, Perman does not need to redesign Android apps and systems. Therefore, it can be applied to millions of existing apps and various Android devices. We conduct experiments to evaluate the effectiveness and efficiency of Perman. The experimental results verify that Perman is capable of managing permission requests of the host app and those of the 3PLs. We also confirm that the overhead introduced by Perman is comparable to that by existing commercial permission management tools.     

“Masculine Guys Only”: The effects of femmephobic mobile dating application profiles on partner selection for men who have sex with men

Mobile dating applications (apps) have changed the way gay men find others in their geographic area for sexual activity and romantic relationships. Many of these apps are branded in relation to traditional masculinity and have become a breeding ground for femmephobic, or anti-effeminate, language. Past research has not examined the effects of femmephobic language in social networking apps designed for men who have sex with men (MSM) on app users' perceptions. This research employed an online experiment of 143 MSM app users to test how users respond to femmephobic and non-femmephobic language use in MSM dating profiles. Participants rated the profile users, as well as reported their desire to meet the user in an offline context. Results indicated that the use of femmephobic language in dating profiles affects a potential partner's perceived intelligence, sexual confidence, and dateability, as well as one's desire to meet potential partners offline for friendship or romantic purposes. Anti-effeminacy was an important moderator of the main effect.     

Digital narrative conventions in heritage trail mobile apps

ABSTRACT

Galleries, libraries, archives and museums (GLAMs) are increasingly using digital technologies for storytelling and creating mobile applications (apps) for cultural heritage content, but how apps are used in practice to communicate information to users has not been widely studied. A team of people from a heritage organisation, a university, and mobile app development group plan to create a bespoke heritage trail app for Ireland, but to date design conventions/recommendations for this genre are lacking. This article applies a systematic approach to digital narrative content analysis to better understand how apps are being used specifically for heritage trails with the aim of identifying what the common features are, which modalities and narrative techniques are used. The selected corpus included 55 apps downloaded from the Google Play Store. The results of this content analysis—based on the App Walkthrough Method (Light, B., Burgess, J., & Duguay, S. (2018). The walkthrough method: An approach to the study of apps. New Media & Society, 20(3), 881–900)—show that there is a gap between academic research themes/trends and how digital narrative is actually being communicated in the current market, and it aims to inform the future development of heritage trail apps by including a list of design and content features common to this genre.     

What's going on behind the screens?

Since their introduction in 2010, much has been said and written about Apple's iPad (Apple Inc., Cupertino, CA, USA) and its potential to transform when and how students learn. Much of this hype has focused on attributes of the device such as its touch screen interface, light and portable form factor, easy‐to‐use operating system, and large array of apps. Emerging studies mainly report positive outcomes for students from using iPads in specific learning situations. These studies have cited enhanced motivation and learner engagement, often linking this with improved learning outcomes. However, there is a dearth of research exploring how students interact with these devices, and the factors that affect the quality and learning value from that interaction. This article reports on the use of an innovative recording solution to capture video and audio data of young students using iPads to develop literacy, numeracy and problem‐solving/decision‐making skills. The study offers insights into how students go about solving problems within apps, and highlights the importance of knowledge, affective and dispositional elements, and good app design to profitable interaction. It also flags considerations for teachers embarking on initiatives involving iPads, and suggests app developers need to work more closely with teachers to improve the learning value of their products.