App Behavior Research Articles

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 of fields. 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.

Execution Recording and Reconstruction for Detecting Information Flows in Android Apps

Security researchers utilize taint analyses to uncover suspicious behaviors in Android apps. Current static taint analyzers cannot handle ICC, reflection, and lifecycles dependably, increasing the result verification cost. On the other hand, current dynamic taint trackers accurately detect execution paths. However, they depend on specific Android versions and modified devices, reducing their usability and applicability. In addition, they require app exercise every time running the taint analysis. This paper presents a new dynamic taint tracker called T-Recs, tracking information flows by recording and reconstructing the app execution. First, before the taint analysis, the app’s runtime data are obtained by instrumenting logging code into the app’s bytecode and running the app to be independent of specific Android versions and devices. Then, T-Recs performs the taint analysis accurately with the logged data and separately from the app exercise. This paper is an extended version of our work published. Previously, T-Recs’ accuracy was mainly evaluated in privacy leak detection. The results show that T-Recs outperforms compared analyzers, which are FlowDroid (w/ and w/o IC3), Amandroid, DroidSafe, and TaintDroid (w/ and w/o IntelliDroid). This paper also involves DroidRA and IccTA. This paper shows that T-Recs detects ICC- and reflection-related leaks missed by FlowDroid in popular Google Play apps. The other static analyzers fail to analyze most of the apps. These experiments also indicate an advantage of T-Recs: its users can re-execute T-Recs’ taint analysis without re-exercising the app. T-Recs’ app-runtime overhead and parallel execution performance were also evaluated, and the results are acceptable.

A Study on Consumer’s Behavioral Intention and Use Behavior Based on UTAUT Theory: Centered around Mobile Delivery Apps

Purpose – The growth of mobile delivery apps is expanding further due to the increase in nonface- to-face consumption due to social distancing in the COVID-19 situation. It is essential to understand what factors are causing this mobile delivery app market to grow significantly. Design/Methodology/Approach – To achieve the purpose of the study, independent variables were selected by searching for prior literature on UTAUT, and a questionnaire was designed according to the situation in Korea. The survey was conducted through Google Online, and statistical analysis of the research model was conducted using SPSS 25.0. Findings – According to the results of this study, the factors that significantly influence the intention to use mobile delivery apps were Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC). In addition, the result indicated that Behavioral Intention (BI) had a statistically significant effect on Use Behavior (UB). Research Implications – This study will predict the user behavior of mobile delivery apps by dealing with the technical attributes of mobile delivery apps and the influencing factors that consumers recognize. In addition, as a mobile delivery app company, it is expected to play a role in establishing the marketing direction of mobile delivery apps.

Examining Use Behavior of a Goal-Supporting mHealth App in Primary Care Among Patients With Multiple Chronic Conditions: Qualitative Descriptive Study.

Although mobile health (mHealth) apps are increasingly being used to support patients with multiple chronic conditions (multimorbidity), most mHealth apps experience low interaction and eventual abandonment. To tackle this engagement issue, when developing an mHealth program, it is important to understand the social-behavioral factors that affect patients' use behavior. The aim of this study was to explore the social and behavioral factors contributing to patients' use behavior of an mHealth app called the electronic Patient-Reported Outcome (ePRO). The ePRO app supports goal-oriented care delivery in interdisciplinary primary care models. A descriptive qualitative study was used to analyze interview data collected for a larger mixed methods pragmatic trial. The original 15-month trial was conducted in 6 primary care teams across Ontario, Canada, between 2018 and 2019. The eligibility criteria for patients were being aged ≥60 years with ≥10 visits within the previous 12 months of study enrollment. For this analysis, patients were classified as long-term or short-term users based on their length of use of the ePRO app during the trial. The Social Cognitive Theory by Bandura was used to categorize social-behavioral factors that contributed to patients' decision to continue or discontinue using the app. The patient-provider relationship emerged as a key factor that shaped patients' experiences with the app and subsequent decision to continue using the app. Other factors that contributed to patients' decision to continue using the app were personal and social circumstances, perceived usefulness, patients' previous experience with goal-related behaviors, and confidence in one's capability. There was an overlap of experience between long- and short-term app users but, in general, long-term users perceived the app to be more useful and their goals to be more meaningful than short-term app users. This observation was complicated by the fact that patient health-related goals were dynamic and changed over time. Complex patients' use behavior of a goal-supporting mHealth app is shaped by an array of sociobehavioral factors that can evolve. To tackle this dynamism, there should be an emphasis on creating adaptable health technologies that are easily customizable by patients and able to respond to their changing contexts and needs. ClinicalTrials.gov NCT02917954; https://clinicaltrials.gov/ct2/show/NCT02917954.

Evaluating the Privacy Policy of Android Apps: A Privacy Policy Compliance Study for Popular Apps in China and Europe

Recently, with the increase in the market share of the Android system and the sharp increase in the number of Android mobile apps, many countries and regions have successively launched laws and regulations related to data security. The EU’s GDPR and China’s Information Security Technology-Personal Information Security Specification are two of the most important bills, affecting vast areas and large populations. Both regulations impose requirements on privacy policy specifications for Android apps. With these requirements, however, apps’ privacy policies have become larger. Researchers have conducted studies on whether the actual privacy behavior of apps conforms to their privacy policy description but have not focused on compliance with the privacy policy itself. In this paper, we propose evaluation metrics for privacy policy compliance and evaluate popular apps by analyzing privacy policies and apps. We applied our method to 1,000 apps from the Google Play Store in Europe and 1,000 apps from the Tencent Appstore in China. We detected a number of app privacy policy noncompliance issues and discovered a number of privacy issues with third-party services and third-party libraries.

AtAwAR Translate: Attention-Aware Language Translation Application in Augmented Reality for Mobile Phones.

As lightweight, low-cost EEG headsets emerge, the feasibility of consumer-oriented brain–computer interfaces (BCI) increases. The combination of portable smartphones and easy-to-use EEG dry electrode headbands offers intriguing new applications and methods of human–computer interaction. In previous research, augmented reality (AR) scenarios have been identified to profit from additional user state information—such as that provided by a BCI. In this work, we implemented a system that integrates user attentional state awareness into a smartphone application for an AR written language translator. The attentional state of the user is classified in terms of internally and externally directed attention by using the Muse 2 electroencephalography headband with four frontal electrodes. The classification results are used to adapt the behavior of the translation app, which uses the smartphone’s camera to display translated text as augmented reality elements. We present the first mobile BCI system that uses a smartphone and a low-cost EEG device with few electrodes to provide attention awareness to an AR application. Our case study with 12 participants did not fully support the assumption that the BCI improves usability. However, we are able to show that the classification accuracy and ease of setup are promising paths toward mobile consumer-oriented BCI usage. For future studies, other use cases, applications, and adaptations will be tested for this setup to explore the usability.

SkillBot: Identifying Risky Content for Children in Alexa Skills

Many households include children who use voice personal assistants (VPA) such as Amazon Alexa. Children benefit from the rich functionalities of VPAs and third-party apps but are also exposed to new risks in the VPA ecosystem. In this article, we first investigate “risky” child-directed voice apps that contain inappropriate content or ask for personal information through voice interactions. We build SkillBot—a natural language processing-based system to automatically interact with VPA apps and analyze the resulting conversations. We find 28 risky child-directed apps and maintain a growing dataset of 31,966 non-overlapping app behaviors collected from 3,434 Alexa apps. Our findings suggest that although child-directed VPA apps are subject to stricter policy requirements and more intensive vetting, children remain vulnerable to inappropriate content and privacy violations. We then conduct a user study showing that parents are concerned about the identified risky apps. Many parents do not believe that these apps are available and designed for families/kids, although these apps are actually published in Amazon’s “Kids” product category. We also find that parents often neglect basic precautions, such as enabling parental controls on Alexa devices. Finally, we identify a novel risk in the VPA ecosystem: confounding utterances or voice commands shared by multiple apps that may cause a user to interact with a different app than intended. We identify 4,487 confounding utterances, including 581 shared by child-directed and non-child-directed apps. We find that 27% of these confounding utterances prioritize invoking a non-child-directed app over a child-directed app. This indicates that children are at real risk of accidentally invoking non-child-directed apps due to confounding utterances.

Default prediction of online credit loans based on mobile application usage behaviors

Credit scoring is widely used by financial institutions for default prediction, however, a significant portion of online credit loan customers have inadequate or unverifiable credit histories, making it difficult for financial institutions to make effective credit decisions. Since the widespread use of smartphones and the popularity of mobile applications, it is worth investigating whether mobile application usage behaviors (App behaviors) of customers can effectively predict online loan defaults. This paper proposes a combined algorithm of CNN and LightGBM, and establishes credit scoring models with App behaviors to evaluate the default risk of online credit loans based on logistic regression, LightGBM, CNN and the combined algorithm, respectively. The experimental results suggest that App behaviors have an obvious effect on the default prediction of customers applying for online credit loans, and the combined model outperforms the other models in terms of the area under the curve (AUC). Furthermore, integrated credit scoring models are developed by combining App behaviors with traditional scoring features. A comparison of the integrated models and the traditional scoring model indicates that the integrated models have achieved a significant improvement in classification performance and App behaviors can be a powerful complement to the traditional credit scoring model.

Watch Over Your TV: A Security and Privacy Analysis of the Android TV Ecosystem

The rapid adoption of Smart TVs has resulted in them becoming another app-based ecosystem. In this context, Android TV is one of the major players as it is widely available across multiple TV manufacturers and has a high integration with other Google products. Yet, the Android TV ecosystem has remained unexplored. This paper presents a deep analysis of the Android TV ecosystem using a large dataset of TV apps. We give an insight into the stakeholder ecosystem, including developers, streaming services, and thirdparty libraries. We analyze the behavior of TV apps in terms of sensitive data collection and communication with other devices using a pipeline of static analysis tools, network traffic collection, and verification via manual analysis. We compare the mobile and TV version of popular streaming apps and found a significant degradation of TV apps in terms of quality and different data collection practices. Our study shows that most TV apps present potentially harmful behaviors, and in most cases, these can be attributed to tracking and advertisement services. We found a prevalence of static identifiers for tracking purposes despite this not being the recommendation. This finding suggests that Google’s new policies limiting advertising identifiers will not have a tangible effect on the TV ecosystem.

Cross-device behavioral consistency: Benchmarking and implications for effective android malware detection

Most of the proposed solutions using dynamic features for Android malware detection collect and test their systems using a single and particular data collection device, either a real device or an emulator. The results obtained using these particular devices are then generalized to any Android platform. This extensive generalization is based on the assumption of consistent behavior of apps across devices. This study performs an extensive benchmarking of this assumption for system calls, executing Android malware and benign samples under the same conditions in 9 different collection devices, including real and virtual devices. The results indicate the existence of significant differences between real devices and emulators in system calls usage and, consequently, in the collected behavioral profiles obtained from running the same set of applications on different devices. Furthermore, the impact of these differences on machine learning-based malware detection models is evaluated. In this regard, a significant degenerative effect on the detection performance of the model is produced when data collected on different devices are used in the training and testing sets. Therefore, the empirical findings do not support the assumption of cross-device consistent behavior of Android apps when system calls are used as descriptive features.

Real-world Benefits of Diabetes Management App Use and Self-monitoring of Blood Glucose on Glycemic Control: Retrospective Analyses.

BackgroundAmong self-care measures, the self-monitoring of blood glucose (SMBG) is a critical component for checking blood glucose levels. In addition, there is growing evidence suggesting that digital technologies are being adopted as an additional method for health care systems to increase patient contact. However, for patients with non–insulin-treated diabetes mellitus type 2 (DMT2), the value of SMBG was inconsistent among studies, and the evidence for digital technologies from real-world clinical practice is still limited.ObjectiveOur study aimed to assess patients with non–insulin-treated DMT2 who were receiving care from a single clinic and analyze whether the use of a diabetes management app and SMBG behavior would affect glycemic control in a real-world clinical setting.MethodsWe collaborated with a large clinic focused on diabetes care in Taiwan that had been using the Health2Sync mobile app and web-based Patient Management Platform to collect the data. The patients were divided into 2 groups (app-engaged-user group and only-data-uploader group) according to different activities in the app, and blood glucose was recorded every month from 1 to 6 months after registration in the app. A sample of 420 patients was included in the analysis, and a linear mixed model was built to investigate which factors affected the patients’ blood glucose percentage change.ResultsUsing the mixed model coefficient estimates, we found that the percentage change was significantly negative when the only-data-uploader group was set as the baseline (t=–3.873, df=1.81 × 104; P<.001 for the patients of the app-engaged-user group). We found that for patients with shorter diabetes duration, their blood glucose decreased more than patients with longer diabetes duration (t=2.823, df=1.71 × 104; P=.005 for the number of years of diabetes duration). In addition, we found that for younger patients, their blood glucose decreased more than older patients (t=2.652, df=1.71 × 104; P=.008 for the age of the patients). Furthermore, the patients with an education level of junior high school or lower saw a significantly greater decrease in blood glucose percentage change than the patients with an education level of senior high school or higher (t=4.996, df=1.72 × 104; P<.001 for the patients with an education level of senior high school or higher). We also found that the count of blood glucose measured enlarged the decrease along the interaction months (t=–8.266, df=1.97 × 104; P<.001 for the nth month × the count of blood glucose in the nth month). Lastly, the gender of the patients did not significantly affect the percentage change (t=0.534, df=1.74 × 104; P=.59 for female patients).ConclusionsOur analysis showed the following: the blood glucose percentage change of the patients in the app-engaged-user group dropped more than that in the only-data-uploader group; shorter diabetes duration is associated with a steeper decrease in the patients’ blood glucose percentage change; the percentage decrease in blood glucose change in younger patients is greater than older patients; the blood glucose percentage change of the patients with an education level of junior high school or lower dropped more than those with an education level of senior high school or higher; and the more frequently the patients test SMBG each month, the greater the decrease in the patients’ blood glucose percentage. Further studies can be performed to consider the differences in daily behaviors such as exercise and diet across the patients and whether these factors could have vital effects on glycemic control.

Eating for 2: A Systematic Review of Dutch App Stores for Apps Promoting a Healthy Diet during Pregnancy

ABSTRACTA healthy diet during pregnancy has been associated with beneficial child and maternal health outcomes but is challenging to achieve. Recent technological advances offer new opportunities to support pregnant women in their food choices—for instance, via apps. This is already reflected by a wide availability of pregnancy-related apps, but health care professionals feel unsure about their potential. Therefore, the Dutch Google Play Store and Apple App Store were reviewed to identify existing apps on diet and pregnancy. App quality was assessed using the 1) Mobile App Rating Scale (MARS; i.e., assessing functionality, aesthetics, engagement, information quality), 2) Dutch dietary guidelines for pregnant women, and 3) App Behavior Change Scale (ABACUS). Fifty-seven unique apps were identified with an average star rating of 4.2 ± 0.6 and MARS quality score of 3.2 ± 0.3, indicating a moderate quality. Most apps scored best in terms of functionality and aesthetics (4.0 ± 0.4 and 3.3 ± 0.6), but lowest in terms of engagement (2.5 ± 0.6). Regarding nutrition information provision, most apps were incomplete or deviated from the Dutch guidelines. Folic acid supplementation (91%), hygiene (81%), caffeine (79%), and alcohol (77%) were the most commonly addressed nutrition aspects, whereas licorice (11%), iodine (19%), and soy (18%) were only addressed in a few apps. Moreover, a median of 2 out of 21 ABACUS behavior change items were identified per app, which were predominantly related to the category “knowledge and information.” Thus, despite the abundance of apps supporting a healthy diet during pregnancy in the Dutch app stores, there is an urgent need for apps with complete and scientifically sound dietary information that is supported by effective behavior change techniques.

Factors Affecting the Intention to Use Virtual Stores: Perspectives of Consumers in Saudi Arabia

Companies have explored various forms of virtual reality (VR) shopping, yet what is known about the user adoption behavior of VR apps is minimal and research into VR shopping from a user acceptance perspective is limited. This study investigates the factors that affect the intention to use VR for online shopping through undergoing a virtual shopping experience in the VR app. This is an exploratory study using a quantitative methodology. The findings indicate that telepresence, attitude, perceived control (PC), satisfaction, hedonic motivation (HM), and perceived usefulness (PU) are direct determinants of intention to use, whereas perceived security risk has no effect. Moreover, PEOU, PU, PC, and telepresence are indirect determinants of intention to use. This research is valuable for its identification of the determinants that can affect the adoption of VR in online shopping. This study helps store owners to understand what features will enable a successful implementation of virtual stores (VSs). This study has several limitations. First, the study was conducted using a convenience sample, making it difficult to generalize the results to the whole population. In terms of method, the study was entirely quantitative, using a questionnaire entirely consisting of close-ended questions, so participants were limited in their response options. The final limitation is in the scope of the research, which covered the perceptions of consumers only, while employees’ and ICT experts’ perspectives were not considered. In light of these limitations, this study allows some suggestions for future research directions to be made. (a) The perspectives of employees and ICT experts could be included so that more factors will be generated. (b) Future research could use different methodologies such as mixed methods to gain a deeper understanding of the responses. (c) A longitudinal study could be designed to address any developments occurring in VS features, as e-commerce is continually being developed.

The Quality of Health Apps and Their Potential to Promote Behavior Change in Patients With a Chronic Condition or Multimorbidity: Systematic Search in App Store and Google Play.

BackgroundMobile apps offer an opportunity to improve the lifestyle of patients with chronic conditions or multimorbidity. However, for apps to be recommended in clinical practice, their quality and potential for promoting behavior change must be considered.ObjectiveWe aimed to investigate the quality of health apps for patients with a chronic condition or multimorbidity (defined as 2 or more chronic conditions) and their potential for promoting behavior change.MethodsWe followed the Cochrane Handbook guidelines to conduct and report this study. A systematic search of apps available in English or Danish on App Store (Apple Inc) and Google Play (Google LLC) for patients with 1 or more of the following common and disabling conditions was conducted: osteoarthritis, heart conditions (heart failure and ischemic heart disease), hypertension, type 2 diabetes mellitus, depression, and chronic obstructive pulmonary disease. For the search strategy, keywords related to these conditions were combined. One author screened the titles and content of the identified apps. Subsequently, 3 authors independently downloaded the apps onto a smartphone and assessed the quality of the apps and their potential for promoting behavior change by using the Mobile App Rating Scale (MARS; number of items: 23; score: range 0-5 [higher is better]) and the App Behavior Change Scale (ABACUS; number of items: 21; score: range 0-21 [higher is better]), respectively. We included the five highest-rated apps and the five most downloaded apps but only assessed free content for their quality and potential for promoting behavior change.ResultsWe screened 453 apps and ultimately included 60. Of the 60 apps, 35 (58%) were available in both App Store and Google Play. The overall average quality score of the apps was 3.48 (SD 0.28) on the MARS, and their overall average score for their potential to promote behavior change was 8.07 (SD 2.30) on the ABACUS. Apps for depression and apps for patients with multimorbidity tended to have higher overall MARS and ABACUS scores, respectively. The most common app features for supporting behavior change were the self-monitoring of physiological parameters (eg, blood pressure monitoring; apps: 38/60, 63%), weight and diet (apps: 25/60, 42%), or physical activity (apps: 22/60, 37%) and stress management (apps: 22/60, 37%). Only 8 out of the 60 apps (13%) were completely free.ConclusionsApps for patients with a chronic condition or multimorbidity appear to be of acceptable quality but have low to moderate potential for promoting behavior change. Our results provide a useful overview for patients and clinicians who would like to use apps for managing chronic conditions and indicate the need to improve health apps in terms of their quality and potential for promoting behavior change.

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps.

Established Internet of Things (IoT) platforms suffer from their inability to determine whether an IoT app is secure or not. A security analysis system (SAS) is a protective shield against any attack that breaks down data privacy and security. Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. Most of them build on utilizing static or dynamic analysis separately. However, the hybrid analysis is the best for obtaining accurate results. The SAS provides an effective outcome according to many criteria related to the analysis process, such as analysis type, characteristics, sensitivity, and analysis techniques. This paper proposes a new hybrid (static and dynamic) SAS based on the model-checking technique and deep learning, called an HSAS-MD analyzer, which focuses on the holistic analysis perspective of IoT apps. It aims to analyze the data of IoT apps by (1) converting the source code of the target applications to the format of a model checker that can deal with it; (2) detecting any abnormal behavior in the IoT application; (3) extracting the main static features from it to be tested and classified using a deep-learning CNN algorithm; (4) verifying app behavior by using the model-checking technique. HSAS-MD gives the best results in detecting malware from malicious smart Things applications compared to other SASs. The experimental results of HSAS-MD show that it provides 95%, 94%, 91%, and 93% for accuracy, precision, recall, and F-measure, respectively. It also gives the best results compared with other analyzers from various criteria.

Russian-Language Mobile Apps for Reducing Alcohol Use: Systematic Search and Evaluation.

BackgroundPersonalized prevention tools such as mobile apps designed to reduce alcohol consumption are widespread in mobile app stores accessible in Russia. However, the quality and content of these mobile apps have not been systematically evaluated.ObjectiveThis study aimed to identify Russian-language mobile apps for reducing alcohol use and to evaluate their quality and potential to change alcohol-related health behavior. It further aimed to identify apps that could facilitate screening and brief interventions in primary health care in Russia.MethodsA systematic search for mobile apps available in Russia was carried out between April 1 and 15, 2020, December 1 and 15, 2020, and in March 2021 in the iPhone App Store, Google Play Store, and the 4PDA forum. App quality was assessed using the Mobile App Rating Scale (MARS), and structured searches in electronic libraries and bibliographic databases were used to evaluate the apps’ evidence base. The number of features facilitating changes in lifestyle behavior was assessed using the App Behavior Change Scale (ABACUS).ResultsWe identified 63 mobile apps for reducing alcohol use. The mean MARS quality ratings were high for the subscales of functionality (3.92 out of 5, SD 0.58) and aesthetics (2.96, SD 0.76) and low for engagement (2.42, SD 0.76) and information (1.65, SD 0.60). Additional searches in electronic libraries and bibliographic databases (eLibrary, CyberLeninka, Google Scholar) yielded no studies involving the identified apps. ABACUS scores ranged from 1 to 15 out of 25, with a mean of 5 (SD 3.24). Two of the identified apps might be useful for screening and brief interventions in Russian primary health care after improvements in content and scientific testing.ConclusionsRussian-language mobile apps for reducing alcohol use are accessible in the app stores. Many of them are aesthetically pleasing, functional, and easy to use. However, information about their scientific trialing or testing is lacking. Most apps contain a low number of features that facilitate changes in lifestyle behavior. Further research should examine the context of Russian-language mobile apps for reducing alcohol use. Our findings underline the need to develop evidence-based apps to mitigate alcohol consumption in Russia and elsewhere.Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42020167458; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=167458

Factors affecting physicians using mobile health applications: an empirical study

BackgroundMobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians’ participation in mHealth apps.ObjectiveThis study aims to empirically explore factors affecting physicians’ usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings.MethodsWe collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians’ usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias.ResultsOur results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians’ cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention.ConclusionsThis study contributes to the existing literature on UTAUT2 extension of physicians’ acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians’ usage behaviors on mHealth apps in China and provide such apps’ managers with an insight into the promotion of physicians’ active acceptance and usage behaviors.

GDL90fuzz: Fuzzing - GDL-90 Data Interface Specification Within Aviation Software and Avionics Devices–A Cybersecurity Pentesting Perspective

As the core technology of next-generation air transportation systems, the Automatic Dependent Surveillance-Broadcast (ADS-B) is becoming very popular. However, many (if not most) ADS-B devices and implementations support and rely on Garmin’s Datalink 90 (GDL-90) protocol for data exchange and encapsulation. This makes it essential to investigate the integrity of the GDL-90 protocol especially against attacks on the core subsystem availability, such as denial-of-service (DoS), which pose high risks to safety-critical and mission-critical systems such as in avionics and aerospace. In this paper, we consider GDL-90 protocol fuzzing options and demonstrate practical DoS attacks on popular electronic flight bag (EFB) software operating on mobile devices. Then we present our own specially configured avionics pentesting platform and the GDL-90 protocol. We captured legitimate traffic from ADS-B avionics devices. We ran our samples through the state-of-the-art fuzzing platform American Fuzzy Lop (AFL) and fed the AFL’s output to EFB apps and the GDL-90 decoding software via the network in the same manner as legitimate GDL-90 traffic would be sent from ADS-B and other avionics devices. The results showed worrying and critical lack of security in many EFB applications where the security is directly related to the aircraft’s safe navigation. Out of the 16 tested configurations, our avionics pentesting platform managed to crash or otherwise impact 9 (56%). The observed problems manifested as crashes, hangs, and abnormal behaviors of the EFB apps and GDL-90 decoders during the fuzzing test. Our developed and proposed systematic pentesting methodology for avionics devices, protocols, and software can be used to discover and report vulnerabilities as early as possible.

Host–guest complexation of APP+ with cucurbit[7]uril. Theoretical and experimental studies on the supramolecular inhibition of its cytotoxicity on SERT

The supramolecular binding behavior of APP+ and CB[7] in aqueous solution was studied by different techniques. APP+ showed cytotoxicity towards HEK293 cells expressing hSERT. This cytotoxicity was inhibited by the treatment of the cells with CB[7].

Analysis of Pre-existing Investment Behavior and Influence of Trading Apps

The last few years have seen a rise in trading apps, and Robinhood is one trading app that has attracted millennials. This paper explores trading apps such as Robinhood and their role in providing financial inclusion and safe trading opportunities. This paper discusses investment behavior in the status quo, explaining overconfidence, sociability, and the disposition effect. Investment behavior can include the behavioral biases and common notions investors utilize for trading. Furthermore, this paper assesses the design and business model of Robinhood. Five expert investors were interviewed (such as a professor and other MBA graduates from Wharton School of Business, financial experts from private equity firms in the US and Mexico, and a JP Morgan investment banking professional), and five casual investors were interviewed to understand their opinions on investment behavior, certain trading apps, common criticisms of stock trading, and solutions to these concerns. The findings led to the conclusion that investment behavior is harmful in the status quo. Results did indicate that Robinhood does promote at least some dangerous behavior through excessive active trading and is one example of a problematic trading app through the 4th Industrial Revolution, but trading apps can only amplify behavioral biases most retail investors already display.