Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing Networks:An Online Algorithm

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

Information behavior in the mobile environment:A study of undergraduate smartphone users in China

Purpose:As smartphones become ubiquitous,it is important to understand emerging information behavior as a result of wide spread use of smartphones.The purpose of this study is to investigate information behavior in the mobile environment by studying undergraduate smartphone users in China.Design/methodology/approach:This study is based on a survey of 205 undergraduate students in China.Findings:Smartphones are used predominantly for accessing news and connecting to social media,rather than for academic purposes such as accessing library resources or researching.While students use smartphones for reading e-books,much of this reading is recreational during their spare time.Research limitations:The inherent limitations of self-reported measures and the small sample size of this study mean that the results cannot be generalized across different age groups and cultures.Practical implications:When targeting users on the move,information professionals should be aware that the needs and behaviors of smartphone readers are significantly different compared to users of fixed devices,and should provide services in a mobile-friendly way.Originality/value:The younger generation is accustomed to instant information access.For libraries to relevant,they must redesign their services.It is important for libraries to leverage the strengths of mobile technology and to balance traditional services with mobile delivery.Even though many mobile users will use desktop or laptop computers to access library resources,they will benefit from the availability of mobile-friendly library services.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

SAC-Based Computation Offloading for Reconfigurable Intelligent Surface-Aided Mobile Edge Networks

In this paper,we concentrate on a reconfigurable intelligent surface(RIS)-aided mobile edge computing(MEC)system to improve the offload efficiency with moving user equipments(UEs).We aim to minimize the energy consumption of all UEs by jointly optimizing the discrete phase shift of RIS,UEs’transmitting power,computing resources allocation,and the UEs’task offloading strategies for local computing and offloading.The formulated problem is a sequential decision making across multiple coherent time slots.Furthermore,the mobility of UEs brings uncertainties into the decision-making process.To cope with this challenging problem,the deep reinforcement learning-based Soft Actor-Critic(SAC)algorithm is first proposed to effectively optimize the discrete phase of RIS and the UEs’task offloading strategies.Then,the transmitting power and computing resource allocation can be determined based on the action.Numerical results demonstrate that the proposed algorithm can be trained more stably and perform approximately 14%lower than the deep deterministic policy gradient benchmark in terms of energy consumption.

A Literature Review: Potential Effects That Health Apps on Mobile Devices May Have on Patient Privacy and Confidentiality

Purpose: This research aims to evaluate the potential threats to patient privacy and confidentiality posed by mHealth applications on mobile devices. Methodology: A comprehensive literature review was conducted, selecting eighty-eight articles published over the past fifteen years. The study assessed data gathering and storage practices, regulatory adherence, legal structures, consent procedures, user education, and strategies to mitigate risks. Results: The findings reveal significant advancements in technologies designed to safeguard privacy and facilitate the widespread use of mHealth apps. However, persistent ethical issues related to privacy remain largely unchanged despite these technological strides.

Optimization and Innovation of the Operation Model of Mobile Social E-commerce under AI Empowerment:Taking the Female Community Platform“Little Red Book”as an Example

This article takes the female community platform“Little Red Book”as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence(AI)empowerment.Firstly,the relevant concepts were defined,and then the unique attributes of mobile community e-commerce were analyzed.As a typical representative of mobile community e-commerce,Little Red Book introduces the background and characteristics of its platform,analyzes its mobile community operation mode,and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology,to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.

Behavioral Intention to Continue Using a Library Mobile App

To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA.A research model was constructed based on the technology acceptance model.A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data.The analysis was based on structural equation modeling.The empirical results show that the perceived ease of use,the perceived usefulness,the social influence,and the facilitating conditions affected the users’behavioral intention,but their impacts were different at different times.As the usage time increases,the technology acceptance model is still universal for understanding the user perception of LMA.In addition,two extended variables(social impact and convenience)also affect the user’s behavior intention.User behavior is dynamic and changed over time.This study is important both theoretically and practically,as the results could be used to improve the service quality of LMAs and reduce the loss rate of users.Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.

Computation Rate Maximization in Multi-User Cooperation-Assisted Wireless-Powered Mobile Edge Computing with OFDMA

In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply.A wireless-powered mobile edge computing(WPMEC)system consisting of a hybrid access point(HAP)combined with MEC servers and many users is considered in this paper.In particular,a novel multiuser cooperation scheme based on orthogonal frequency division multiple access(OFDMA)is provided to improve the computation performance,where users can split the computation tasks into various parts for local computing,offloading to corresponding helper,and HAP for remote execution respectively with the aid of helper.Specifically,we aim at maximizing the weighted sum computation rate(WSCR)by optimizing time assignment,computation-task allocation,and transmission power at the same time while keeping energy neutrality in mind.We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques.Simulation results demonstrate that the proposed multi-user cooperationassisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes.In addition,OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.

Factors influencing academic library users' intention to use mobile systems: A comparison of current users and potential adopters

Purpose: The study intends to examine the factors influencing the behavioral intention to use academic libraries’ mobile systems from the perspective of current users and potential adopters, respectively. Design/methodology/approach: Our study investigates the mobile library system’s acceptance by using a context-specific extension of the theory of reasoned action(TRA) and the technology acceptance model(TAM), which includes such factors as mobile self-efficacy, personal innovativeness and perceived playfulness. Structural equation modeling was used to test the validity of the proposed model based on the empirical data which was collected from 210 questionnaire survey participants.Findings: The result shows that 1) for both current users and potential adopters, attitude toward use and subjective norm both have a significant and positive impact on behavioral intention to use; 2) perceived usefulness and perceived ease of use are significantly correlated to potential adopters’ attitude toward use whereas perceived usefulness and perceived playfulness are significantly related to current users’ attitude toward use; 3) as for the comparison between the two groups of users, personal innovativeness not only affects perceived usefulness of both current users and potential adopters, but also affects potential adopters’ perceived playfulness positively. Mobile self-efficacy has a significant effect on perceived ease of use for both types of users.Research limitations: Although the sample size met the basic statistics requirements for the social research, the participants were mainly college students, and other mobile system users like faculty members and researchers were not investigated. In addition, some influencing factors, such as information quality, system quality and service quality were not considered in the research model.Practical implications: This study reveals main factors which influence both current users and potential adopters’ intention to use the mobile system, providing academic libraries withinsights into management strategies to offer customized mobile services to different types of users. Originality/value: Previous studies did not distinguish current users from potential adopters, which is not conducive for academic libraries to provide customized services and attract potential users. We presented an exploratory study to address this issue.

Prevalence of Ringing Delusion among Mobile Phone Users

The exponential application of mobile technology has led to a concern about implications of electromagnetic radiation on human health. As we are aware that mobile phone radiates EMR when users communicate to others and that time subscribers of the device are regularly exposed nearby 40% - 50% of total mobile irradiation. We analyzed the risk of "Ringing Delusion" among normal users, moderate users and heavy users when compared to low users. Although the "Ringing Delusion" has not been added in medical terminology but we found frequently such kind of symptoms among mobile phone users. "Ringing Delusion" may be considered as an imagination of ringing voice from cellular phone. The risk was also compared between urban and rural, male and female and adult and children population. The information was gathered through well designed questionnaires for cellular user’s demographic and social characteristics, adopted safety measures and calling duration. Prevalence of “Ringing Delusion” among rural users was higher than the urban users. A trend for the risk was also observed in male users in comparison to female. Study may support innovators to re-examine health effects of mobile phones.

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Different types of pandemics that have appeared from time to time have changed many aspects of daily life.Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown.The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store.A huge number of reviews are written daily by users to express their opinions,which include significant information to improve these applications.The manual processing and extracting of information from users’reviews is an extremely difficult and time-consuming task.Therefore,the use of intelligent methods is necessary to analyse users’reviews and extract issues that can help in improving these apps.This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods.To the best of our knowledge,this is the first study to explore users’opinions about governmental apps in Saudi Arabia.The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases.A new dataset used for this research includes 8000 user reviews gathered from social media,Google Play and the app store.Different methods are applied to the dataset,and the results show that the k nearest neighbourhood(KNN)method generates the highest accuracy compared to other implemented methods.

Study on Accessible Long-Term Care Mobile Application for Visually Impaired Users

A result revealed by the Department of Statistics, 29,000 elderly people have registered for Visually Impaired Card in 2014, which was 51.2% of all visually impaired people. With annual growth rate of 1.56%, this number increased yearly by 1,100 people. According to WHO, 285 million people worldwide were estimated to be visually impaired, which is 4.24% of overall population in 2012. From age distribution point of view, most visually impaired people, accounted for 2.76% of overall population, were above age 50. In average, there are 7 visually impaired people in every 100 people above 50 years old. In Taiwan, 549,000 people over age 50 are estimated to be visually impaired. Therefore we expect there is a large amount of visually impaired people. The main purpose of this research is to collect adaptability information on the cognitive model of senior visually impaired people on their work status, social participation status, and leisure activities via questionnaire survey. Furthermore, descriptive video service is used to as an accessible long-term care application for visually impaired senior users. To solve disability circumstances and improve home care quality through visually impaired APP POe （proof-of-concept）. The result of this research will serve as an important basis for other researches in related study field, a reference for practice application. As a result in encouraging profit-seeking enterprises design a user oriented products. And even be an opening for mobile accessibility services benchmark on technology social care for disability and senior users.

Empirical Research on Mobile Phone Reading Based on User Behavior Analysis

Based on the technology acceptance model （TAM） as the foundation, according to the function of mobile phone reading, the hardware design characteristics, software design characteristics, perceived entertainment, perceived risk and perceived price, adding them to the user behavior model, and modify the TAM model. By self-designed questionnaire, taking college students for social mobile phone reading using behavior survey, using SPSS19.0, AMOS21.0 to validate modified TAM model. The empirical results show that hardware design characteristics, software design characteristics, perceived ease of use have significant positive influence on user behavior, while perceived entertainment, perceived risk, have no significant influence on user behavior, behavior Intention has positive influence on user actual behavior.

The Research on E-mail Users' Behavior of Participating in Subjects Based on Social Network Analysis

The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.

Multifactorial study of mobile phone dependence in medical students: Relationship to health-related lifestyle, Type A behavior, and depressive state

We investigated factors contributing to mobile phone dependence. To 139 medical students, we administered a self-reporting questionnaire designed to evaluate mobile phone dependence, health-related lifestyle, patterns of behavior, and depressive state. Multivariate logistic regression analysis revealed that scores for poor health-related lifestyle, Type A behavior pattern, and presence of depression are independently associated with degree of mobile phone dependency. These findings suggest that persons with an unhealthy lifestyle, Type A behavior traits, or depression might benefit from mobile phone use guidance.

Usability-Driven Mobile Application Development

Recently,a specific interest is being taken in the development of mobile application(app)via Model-Based User Interface Development(MBUID)approach.MBUID allows the generation of mobile apps in the target platform(s)from conceptual models.As such it simplified the development process of mobile app.However,the interest is only focused on the functional aspects of the mobile app while neglecting the non-functional aspects,such as usability.The latter is largely considered as the main factor leading to the success or failure of any software system.This paper aims at addressing non-functional aspects of mobile apps generated using MBUID approach.As such,we propose a usability-driven approach for the development of mobile apps.The main stages of the proposed approach are defined in a generic way so that they can be integrated with any MBUID method.A case study is presented,in the paper,with the aim of illustrating the feasibility of this approach.

Two-Timescale Online Learning of Joint User Association and Resource Scheduling in Dynamic Mobile Edge Computing

For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge server.With the limitations imposed on transmission capacity,computing resource,and connection capacity,the per-slot online learning algorithm is first proposed to minimize the time-averaged network cost.In particular,by leveraging the theories of stochastic gradient descent and minimum cost maximum flow,the user association is jointly optimized with resource scheduling in each time slot.The theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network environment.Moreover,to alleviate the high network overhead incurred during user handover and task migration,a two-timescale optimization approach is proposed to avoid frequent changes in user association.With user association executed on a large timescale and the resource scheduling decided on the single time slot,the asymptotic optimality is preserved.Simulation results verify the effectiveness of the proposed online learning algorithms.

Evaluation of Microblog Users’ Influence Based on PageRank and Users Behavior Analysis

This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and followed phenomenon of microblog users. Informed by the microblog user behavior analysis, the paper also addresses a model for calculating weights of users’ influence. It proposes a U-R model, using which we can evaluate users’ influence based on PageRank algorithms and analyzes user behaviors. In the U-R model, the effect of user behaviors is explored and PageRank is applied to evaluate the importance and the influence of every user in a microblog network by repeatedly iterating their own U-R value. The users’ influences in a microblog network can be ranked by the U-R value. Finally, the validity of U-R model is proved with a real-life numerical example.