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.

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.

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.

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.

Mobile user forecast and power-law acceleration invariance of scale-free networks

This paper studies and predicts the number growth of China＇s mobile users by using the power-law regression. We find that the number growth of the mobile users follows a power law. Motivated by the data on the evolution of the mobile users, we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model, in which the nodes grow following a power-law acceleration. The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process. This result shows that the model generates appropriate power-law connectivity distributions. Therefore, we find a power-law acceleration invariance of the scale-free networks. The numerical simulations of the models agree with the analytical results well.

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.

User Profile System Based on Sentiment Analysis for Mobile Edge Computing

Emotions of users do not converge in a single application but are scattered across diverse applications.Mobile devices are the closest media for handling user data and these devices have the advantage of integrating private user information and emotions spread over different applications.In this paper,we first analyze user profile on a mobile device by describing the problem of the user sentiment profile system in terms of data granularity,media diversity,and server-side solution.Fine-grained data requires additional data and structural analysis in mobile devices.Media diversity requires standard parameters to integrate user data from various applications.A server-side solution presents a potential risk when handling individual privacy information.Therefore,in order to overcome these problems,we propose a general-purposed user profile system based on sentiment analysis that extracts individual emotional preferences by comparing the difference between public and individual data based on particular features.The proposed system is built based on a sentiment hierarchy,which is created by using unstructured data on mobile devices.It can compensate for the concentration of single media,and analyze individual private data without the invasion of privacy on mobile devices.

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.

Mobility-driven user-centric AP clustering in mobile edge computing-based ultra-dense networks

ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it is comparable with or surpasses the density of active mobile users.In order to mitigate inter-AP interference and improve spectrum efficiency,APs in UDNs are usually clustered into multiple groups to serve different mobile users,respectively.However,as the number of APs increases,the computational capability within an AP group has become the bottleneck of AP clustering.In this paper,we first propose a novel UDN architecture based on Mobile Edge Computing(MEC),in which each MEC server is associated with a user-centric AP cluster to act as a mobile agent.In addition,in the context of MEC-based UDN,we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme,in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific area.Simulation results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation,while it guarantees at the same time low transmission delay.

Context-Based Mobile User Interface

Context awareness is increasingly gaining applicability in interactive ubiquitous mobile computing systems. Each context-aware application has its own set of behaviors to react to context modifications. Hence, every software engineer needs to clearly understand the goal of the development and to categorize the context in the application. We incorporate context-based modifications into the appearance or the behavior of the interface, either at the design time or at the run time. In this paper, we present application behavior adaption to the context modification via a context-based user interface in a mobile application. We are interested in a context-based user interface in a mobile device that is automatically adapted based on the context information. We use the adaption tree, named in our methodology, to represent the adaption of mobile device user interface to various context information. The context includes the user’s domain information and dynamic environment changes. Each path in the adaption tree, from the root to the leaf, presents an adaption rule. An e-commerce application is chosen to illustrate our approach. This mobile application was developed based on the adaption tree in the Android platform. The automatic adaption to the context information has enhanced human-computer interactions.

Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.

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.

A Structural Topic Model for Exploring User Satisfaction with Mobile Payments

This study explored user satisfaction with mobile payments by applying a novel structural topic model.Specifically,we collected 17,927 online reviews of a specific mobile payment(i.e.,PayPal).Then,we employed a structural topic model to investigate the relationship between the attributes extracted from online reviews and user satisfaction with mobile payment.Consequently,we discovered that“lack of reliability”and“poor customer service”tend to appear in negative reviews.Whereas,the terms“convenience,”“user-friendly interface,”“simple process,”and“secure system”tend to appear in positive reviews.On the basis of information system success theory,we categorized the topics“convenience,”“user-friendly interface,”and“simple process,”as system quality.In addition,“poor customer service”was categorized as service quality.Furthermore,based on the previous studies of trust and security,“lack of reliability”and“secure system”were categorized as trust and security,respectively.These outcomes indicate that users are satisfied when they perceive that system quality and security of specific mobile payments are great.On the contrary,users are dissatisfied when they feel that service quality and reliability of specific mobile payments is lacking.Overall,our research implies that a novel structural topic model is an effective method to explore mobile payment user experience.

Dynamic data-sharing based user recruitment in mobile crowdsensing

Mobile crowdsensing(MCS) has become an emerging paradigm to solve urban sensing problems by leveraging the ubiquitous sensing capabilities of the crowd. One critical issue in MCS is how to recruit users to fulfill more sensing tasks with budget restriction, while sharing data among tasks can be a credible way to improve the efficiency. The data-sharing based user recruitment problem under budget constraint in a realistic scenario is studied, where multiple tasks require homogeneous data but have various spatio-temporal execution ranges, meanwhile users suffer from uncertain future positions. The problem is formulated in a manner of probability by predicting user mobility, then a dynamic user recruitment algorithm is proposed to solve it. In the algorithm a greedy-adding-and-substitution(GAS) heuristic is repeatedly implemented by updating user mobility prediction in each time slot to gradually achieve the final solution. Extensive simulations are conducted using a real-world taxi trace dataset, and the results demonstrate that the approach can fulfill more tasks than existing methods.

Understanding Why People Change Their Technology Use in Protests: User Study on Ebonyi State Mobile Telecommunication Subscribers Experiences

The rise in consumers complains against their service/tech providers has empowered government of various nations to set up Consumer Complaint Commissions. Some corporate organizations who wish to handle the complaints of their customers usually set up an in-house/internal customer care centres, a platform through which they attend to their customers’ needs. This research therefore is interested in exploring the reasons for user-protests among the mobile telecommunication subscribers in Ebonyi State, Nigeria against their network service providers. To arrive at this, we designed a research questionnaire to capture the experiences of the subscribers of the 4 major mobile telecommunication network service provider companies operating in the state. The participants were recruited through physical administration of research instrument and social media such as facebook, whatsapp and email platforms with a total of 72 participants feedbacks received. The users responses on the various issues raised on our questionnaire were thematically analyzed and represented using adequate data presentation tools. Our results proved that there are growing numbers of disgruntled customers among subscribers of the various network providers. From our survey, we gathered that 81.90% of the respondents have engaged in protest use at one point or the other against their service providers with MTN network topping the list with 36.50%, followed by GLO network 27%, Airtel network 23.80% and 9 Mobile 12.70%. Finally, we proposed a design prototype named “Komplain” that will serve as rallying system for the protest users to use and prosecute their actions. The system can also recommend actions which the protesters may undertake.