Mobile Multimedia Applications Research Articles

Mathematical Model of Heterogeneous Traffic in LTE Networks

With the introduction of multimedia mobile applications in cellular networks, effective methods have been used to increase throughput. The article defines methods for dividing cellular network resources through mathematical modeling for heterogeneous traffic. The numerical properties of the indicators of joint servicing of requests, the efficiency of joint transmission of real-time traffic and elastic data traffic have been studied. Explores key uses of traffic model resource bandwidth.

Multimedia Mobile Application to Improve the Academic Achievement of Fifth Grade Elementary School Students in Physical Education Classes

Lack of student interest in learning has an impact on low student learning outcomes. One of the factors that causes low student learning outcomes is the lack of learning media. This research aims to examine the effect of multimedia mobile applications to improve the academic achievement of fifth grade elementary school students in physical education classes. This type of research is quantitative research using an experimental design. The population in this study was 112 grade V elementary school students. This research took samples randomly using the Slovin formula. The methods used to collect data are questionnaires and tests. The instruments used to collect data were questionnaire sheets and test questions. The data analysis technique used to analyze the data is inferential statistics. The results of this research, namely the t-test results, show that there is an influence of the use of mobile Apps in learning activities. It can be concluded that the use of mobile Apps in learning activities can improve students' academic achievement in class V physical education. This research implies that the use of mobile Apps in learning activities can make it easier for students to learn.

Design of Secure Authentication Handover Protocol for Innovative Mobile Multimedia Services in 5G MEC Environments

<p>5G advanced technology has introduced innovative multimedia services, thus propelling the rise of the mobile multimedia market. While the ubiquity of mobile multimedia services has boosted customer convenience, it has also unlocked the way to possible security concerns that potentially damage users’ privacy and service providers’ properties. Several standards and researches have been proposed to counter these threats, notably those concentrating on authentication between users and service applications. Unfortunately, these studies are limited to address a new challenge, secure handover among distributed application functions in 5G Multi-access Edge Cloud (MEC) environments. Motivated by this, we present a handover authentication protocol with push-key and pull-key optimization options in 5G mobile multimedia application services. The proposed protocol provides mutual authentication, secure key exchange, confidentiality, and integrity. Furthermore, it also supports perfect forward secrecy, optimal handover, and anonymity, which were not previously considered in prior studies. While formal verification through BAN Logic and Scyther proved that the protocol is secure against attacks that violate the supported security requirements, comparative analysis of the proposed protocol against existing studies demonstrates that the protocol is simultaneously efficient.</p> <p> </p>

A Projection-Free Adaptive Momentum Optimization Algorithm for Mobile Multimedia Computing

In mobile multimedia applications, deep learning has received significant interest. Due to the limited computation and storage resources of mobile devices, however, general training methods are hardly suited for mobile multimedia computing. For this reason, we propose an adaptive momentum training (FWAdaBound) algorithm to reduce computation and storage cost, where the Frank-Wolfe method is employed. Furthermore, we rigorously prove the regret bound in order that O T 3 / 4 can be achieved, where T is a time horizon. Finally, the convergence, cost-reduction, and generalization ability of FWAdaBound are validated through various experiments on public datasets.

Student Engagement With Teacher Feedback in Pronunciation Training Supported by a Mobile Multimedia Application

This study focused on students’ experience with a mobile multimedia application for a pronunciation course. An app was used for after-class pronunciation practice, through which the instructor gave out multimodal feedback to students. Student engagement with teacher feedback in the app was investigated from multiple data sources. Data collected in the form of the instructor’s response and analytics gathered through the mobile system revealed that students demonstrated a high level of engagement in the app, behaviorally, cognitively, and emotionally. Specifically, the instructor observed more submissions from and interactions with students compared with previous classes. Records of student-teacher interactions suggested that students reacted positively and actively to multimodal feedback from the teacher and expressed willingness to use the app for future learning. Questionnaire results further confirmed that a large number of the students perceived the app to be useful for practicing pronunciation tasks, as it made it easy for them to interact with the teacher, receive course materials, and submit recordings. Interviews among selected students revealed more details about students’ experiences and views. Some students reported accessing their peers’ readings and feedback from the teacher as a way of informing their own learning, which is made possible by the app. This study provides implications to enrich pronunciation instruction practice to maximize student engagement, which ultimately contributes to positive learning experiences and gains.

Android-based Mobile Multimedia Application Development Training

<p align="center">Covid-19 demands the use of technology as an online learning media tool. The technology that becomes a low-cost developer is the Android Technology operating system. The main target of this community service activity is the 3rd & 5th semester Computer System Study Program students at the Faculty of Science and Technology at the Panca Budi Development University in using Android technology, where learning about Android has been carried out but not maximized in building a Mobile Application. The method used is to provide training and demonstrations in making Multimedia Applications using Android Studio with online media support in the form of Zoom, YouTube, and Google Drive links. The results of the feedback response to material questions submitted by the resource person were 67% very good, participant responses to the material presented were 41% good, the relationship between material and participant needs was 59% very good, the relationship between material and applications that could be absorbed by participants was 54% very good, the linkage of the material with needs is 42% good, the presentation technique of the presenter is 56% very good, the time spent by the presenter is 44% giving a good response, the clarity of the material is 59% clear, the interest of the participants is 47% good, and overall satisfaction level of 51% gave a satisfied response to the training activities.</p>

Interactive Mobile Multimedia Application Usage to Improve Fractional Algebra Thinking among Students with Learning Disabilities

Penggunaan aplikasi mobile multimedia dalam pendidikan kini mendapat perhatian semua pihak dan menghasilkan interaksi hubungan murid dan guru. Penggunaan aplikasi mudah alih dalam pengajaran dan pembelajaran dipercayai dapat menyelesaikan banyak masalah yang melibatkan matematik dan mengubah persepsi murid terhadap pemahaman algebra pecahan yang dianggap rumit dan membosankan. Kesukaran ini lebih terkesan kepada murid yang mempunyai masalah pembelajaran berbanding dengan murid biasa. Rasional kajian untuk meneroka penggunaan aplikasi mobile multimedia interaktif berasaskan permainan (P-PKSSMPK) sebagai alat bantu pengajaran untuk meningkatkan kemahiran pemikiran algebra pecahan dan pencapaian murid masalah pembelajaran dalam topik pecahan. Aplikasi mobile P-KSSMPK digunakan sebagai bahan bantu pengajaran bagi memudahkan murid memahami kemahiran pembelajaran pecahan. Penyelidikan ini melibatkan penguasaan kemahiran pemikiran algebra pecahan dan pemahaman dalam pengajaran dan pembelajaran topik pecahan bagi murid masalah pembelajaran. Objektif kajian yang pertama ialah merekabentuk dan membangunkan aplikasi mobile multimedia interaktif dengan menggunakan model Prototaip Pantas (menilai, menentukan objektif, mereka bentuk dan membangunkan prototaip, pelaksanaan prototaip, penilaian dan pemurnian). Objektif kajian kedua pula, mengkaji kesan aplikasi mobile P-KSSMPK melalui sembilan adengan pengajaran dalam membantu meningkatkan pemahaman dan penguasaan kemahiran pemikiran algebra pecahan bagi murid masalah pembelajaran. Reka bentuk kuasi-eksperimen digunakan ke atas kumpulan kawalan dan kumpulan rawatan di mana setiap responden diberi ujian pra, rawatan, dan ujian pasca. Kajian ini melibatkan 152 orang murid masalah pembelajaran dari sepuluh sekolah berbeza di daerah kinta utara di Perak. Dapatan kajian akhir mendapati bahawa pembelajaran menerusi aplikasi mobile P-KSSMPK berjaya mempertingkatkan tahap penguasaan kemahiran dan pemahaman pecahan. Min keputusan pencapaian ujian pra adalah 44.59 (sisihan piawai: 4.233) bagi kumpulan rawatan manakala min purata bagi ujian pasca meningkat sebanyak 65.88 (sisihan piawai: 21.520) bagi kumpulan rawatan. Justeru, kajian terhadap pembelajaran topik pecahan perlu diteruskan dengan melibatkan sampel kajian yang lebih besar supaya penglibatan murid dalam aktiviti pembelajaran pecahan dapat dipertingkatkan.

On the Performance of Self-Concatenated Coding for Wireless Mobile Video Transmission Using DSTS-SP-Assisted Smart Antenna System

In the current age of advanced technologies, there is an escalating demand for reliable wireless systems, catering to the high data rates of mobile multimedia applications. This article presents a novel approach to the concept of Self-Concatenated Convolutional Coding (SECCC) with Sphere Packing (SP) modulation via Differential Space-Time Spreading- (DSTS-) based smart antennas. The two transmitters provide transmit diversity which is capable of recuperating the signal from the effects of fading, even with a single receiving antenna. The proposed DSTS-SP SECCC scheme is probed for the Rayleigh fading channel. The SECCC structure is developed using the Recursive Systematic Convolutional (RSC) code with the aid of an interleaver. Interleaving generates randomness in exchange for extrinsic information between the constituent decoders. Iterative decoding is invoked at the receiving side to enhance the output performance by attaining fruitful convergence. The convergence behaviour of the proposed system is investigated using EXtrinsic Information Transfer (EXIT) curves. The performance of the proposed system is ascertained with the H.264 standard video codec. The perceived video quality of DSTS-SP SECCC is found to be significantly better than that of the DSTS-SP RSC. To be more precise, the proposed DSTS-SP SECCC system exhibits an E b / N 0 gain of 8 dB at the PSNR degradation point of 1 dB, relative to the equivalent rate DSTS-SP RSC. Similarly, an E b / N 0 gain of 10 dB exists for the DSTS-SP SECCC system at 1 dB degradation point when compared with the SECCC scheme dispensing with the DSTS-SP approach.

System of automatic analysis of methods for recognizing the emotional state of a person

The introduction of methods and means of digital transformation of industry and the social sphere poses new challenges. One of these tasks is the management of active systems, in which simple registration and identification of the initiators of actions is not enough, but a deeper assessment of their state, including psychophysical and emotional, is required. The article is devoted to the analysis of methods and means of recognizing the emotional state of a person. Approaches to automated recognition of a person's emotional state based on primary, secondary and more complex features are analyzed. The main focus is on a comprehensive approach to recognition of a person's emotional state based on the analysis of visual and audio channels using neural networks and computer psyche algorithms. The target of the article is a formalization of methods and development of means to recognize a person's emotional state according to complex audiovisual criteria. Analyzed software for recognizing the emotional state of a person: FaceReaderNoldus, EmoDetect, FaceSecurity, Microsoft Oxford Project Emotion Recognition, eMotion Software, MMER_FEASy. Methods used to recognize the emotional state of a person by his face have been investigated, such as: the method of basic components, the Viola-Jones method, template comparison, the Hopfield neural network, the method based on the localization of key points on the face and the method based on texture information. Separately analyzed methods of recognizing the emotional state of a person from his speech. invention proposes a solution for multilevel recognition of emotional state of a person based on using algorithms of neural networks and computer psyche. Software for recognizing the emotional state of a person was analyzed: the developed approach can be used for various digital applications, ranging from the analysis of the psychophysiological and emotional state of a person - an operator (pilot, driver, etc.), to multimedia mobile applications for analyzing the emotional state of an interlocutor. The trend for remote work and self-employment opens up such areas of application of these applications as consulting psychologists, interviewing online, optimizing the human resources of companies and organizations. Prospects for using the latest versions of emotional analyzers are shown.

Editorial: Recent Advances on the Mobile Multimedia Services and Applications

Editorial: Recent Advances on the Mobile Multimedia Services and Applications

Mobility Management With Transferable Reinforcement Learning Trajectory Prediction

Future mobile networks will enable the massive deployment of mobile multimedia applications anytime and anywhere. In this context, mobility management schemes, such as handover and proactive multimedia service migration, will be essential to improve network performance. In this article, we propose a proactive mobility management approach based on group user trajectory prediction. Specifically, we introduce a mobile user trajectory prediction algorithm by combining the Long-Short Term Memory networks (LSTM) with Reinforcement Learning (RL) to automate the model training procedure. We further develop a group user trajectory predictor to reduce prediction calculation overheads of users with similar movement patterns. To validate the impact of the proposed mobility management approach, we present a virtual reality (VR) service migration scheme built on the top of the proactive handover mechanism that benefits from trajectory predictions. Experiment results validate our predictor's outstanding accuracy and its impacts on enhancing handover and service migration performance to provide quality of service assurance.

Optimized Lossless Embedded Compression for Mobile Multimedia Applications

Power consumption is a critical design factor in modern mobile chip design, in which the memory system with dynamic random-access memory (DRAM) consumes more than half of the entire system’s power. Without DRAM bandwidth compression, extreme multimedia operations such as 8K high dynamic range (HDR) recording and 8K video conference calling are not possible without sacrificing image quality or trimming because of thermal limitations or battery time sustainability constraints. Since heterogeneous processors are substantially involved in managing various types of fallbacks or software solutions, complicated compression algorithms for high-compression ratios are not actually adaptable owing to timing closure problems or high throughput requirements. In this paper, we propose evaluation metrics to assess lossless embedded compression (LEC) algorithms to reflect realistic design considerations for mobile multimedia scenarios. Furthermore, we introduce an optimized LEC implementation for contemporary multimedia applications in mobile devices based on the proposed metrics. The proposed LEC implementation enhances the compression ratio of LEC algorithms in other commercial application processors for contemporary premium smartphones by up to 9.2% on average, while maintaining the same timing closure condition.

Adversarial android malware detection for mobile multimedia applications in IoT environments

In this paper, we propose two defense methods against adversarial attack to a malware detection system for mobile multimedia applications in IoT environments. They are Robust-NN and a combination of convolutional neural network and 1- nearest neighbors(C4N) which modify training data that has been poisoned by an adversarial attack. As a result, the trained machine learning model will be accurate and if the malicious program is entered by any IoT device, the model generates necessary alerts. We provide an explanation of the used attack method and the algorithms proposed to defend against this attack. In order to evaluate the suitability of the proposed defense methods, sufficient analysis is presented, i.e. Drebin, Contagio and Genome datasets which include benign and malware Android apps are applied to perform experiments. To confirm the effectiveness of the suggested defense algorithms, this paper compared their performance with two state-of-the-art defense algorithms used to detect adversarial samples, namely e2SAD and EAT. The experiments are performed on two types of API and Permission features from the mentioned datasets. The results confirm that accuracy rates of classification algorithms decrease to 40% after attack in some cases (related to Drebin dataset by reviewing API feature sets). Additionally, the accuracy rates increase to 94.94% and 96.03% by applying Robust-NN and C4N algorithms, respectively. Therefore, they are comparable with existing cutting-edge defense algorithms. Also, the adversarial attack increased the FPR to 45.81% which will be reduced to 4.84% and 4.15% using Robust-NN and C4N, respectively. Consequently, the proposed methods will be robust against adversarial attacks.

Rapid Re-Identification Risk Assessment for Anonymous Data Set in Mobile Multimedia Scene

Ubiquitous mobile multimedia applications bring great convenience to users. However, when enjoying mobile multimedia services, users provide personal data to service platforms. Although the service platforms always claim that the collected personal data are de-identified, the risk of re-identifying users through linkage attacks still exists and is incalculable. This paper proposes a rapid prediction model for the overall re-identification risk based on the statistics of data sets (i.e., the number of individuals, number of attributes, distribution of attribute values, and attribute dependency). Our proposed model reveals the impact of statistics on the overall re-identification risk and adopts random sampling and semi-random sampling methods to predict the overall re-identification risk of data sets with and without strong dependency ordered attribute pairs. Experimental results show that for the data sets without strong dependency ordered attribute pairs, the random sampling method has a high prediction accuracy (the prediction error is less than 0.05). For the data sets with strong dependency ordered attribute pairs, the semi-random sampling method has a high prediction accuracy (the prediction error is less than 0.09). Exploiting our model, governments and individuals can quickly assess the privacy leakage risk of their data sets, given only the statistic of the data sets. Besides, this model can also evaluate the privacy risk of data collection schemes in advance according to historical statistics, and identify suspected services.

The millimeter frequency range as a perspective for 5G networks

The currently widely used 4G cellular networks face many challenges. The growing demand for high-resolution mobile multimedia applications is bringing these networks to their practical limits. 5G networks, the next generation of wireless communications, are standardized to ease the burden on current infrastructure by offering significantly higher data rates through increased channel bandwidth. Given the lack of accessible frequencies traditionally used for mobile communications, millimeter bandwidths (mmWave) are quite a challenging alternative. This technology is a cornerstone of upcoming 5G networks, allowing faster data speeds and much higher bandwidth than ever before, but not every 5G network will necessarily use this frequency band, at least not all the time. As with any new technology, there are unavoidable problems that need to be overcome. The purpose of the research and analysis in this article is to systematize issues and solutions related to the potential problems of spectrum sharing in mmWave frequency range, its suitability for long-distance communications, indoors, in bad weather conditions, as well as biological aspects, and relevant conclusions have been done to meet these challenges.

Novel Fast User-Placement Ushering Algorithms and Performance Analysis for LTE Femtocell Networks

Nowadays, the sufficient quality-of-service (QoS) provision for multimedia mobile applications remains a major challenge to any wireless network. Conventional sufficient QoS provision techniques using resource allocation, data scheduling, and cross-layer optimization have been proposed to tackle this problem. Nevertheless, due to the unpredictable nature of wireless channel conditions, the QoS improvements resulting from the aforementioned approaches are often unsatisfactory. In this paper, we would like to further exploit our previous concept, namely user-placement ushering (UPU). A mobile user can be relocated to an optimal, or at least a better place to boost the QoS for a given application. The QoS is measured by the packet-success rate (PSR), which can be further computed from the code-block error-rate (CBER). Novel fast algorithms are devised here to usher (guide) a mobile user to an appropriate spot with sufficient QoS, which is nearby. We address the UPU problem to accommodate the realistic complex indoor environments, where obstacles are present. Our simulation results have demonstrated that our new fast UPU algorithms are capable of finding new appropriate locations satisfying the required QoSs for different wireless network applications.The preliminary results are presented in [1] .

Intelligent computational techniques for multimodal data

This guest editorial introduces the special issue on “Intelligent Computational Techniques for Multimodal Data”. The goal of this special issue was to solve a variety of real-life problems which have uncertainty, imprecision, vagueness, resulting in high performance applications or prototypes for real time system. The special issue touched different hot topics related to Computer Vision, Computational Biology, Multimedia data mining, High-dimensional multimedia data, Deep convolution network, Deep semantic preserving hashing, High-dimensional multimedia classification, Deep CNN and extended residual units, particle swarm optimization, Cyberbullying detection on social multimedia, Multimedia detection algorithm of malicious nodes, Multimedia based fast face recognition, Diversifying personalized mobile multimedia application, Multimedia image edge extraction algorithm, Rumour veracity detection.

Mobile Multimedia Evaluation: Development of Stop Drugs Tutorial

This research is intended to evaluate the development of mobile multimedia based stop drug tutorial. This tutorial as a campaign to stop drugs, that should encourage people’s ability to integrate all information on mobile application. The tutorial focuses on how people understand about drugs and what to do if someone is drugs addicted. Stopping drug tutorial is created using ADDIE instructional development method, and during the developing stage, it is done using Multimedia Development Life Cycle according to Luther. Subjects of the research are experts in education, visual communication and information technology in the preliminary testing. Evaluation was conducted using the analytic descriptive method on mobile multimedia evaluation method, and interpreted in a narrative way based on the research findings. The evaluation of mobile multimedia application uses combination of multimedia evaluation that is used by educationalist with ISO 9126 that was used by software developer. The research findings show that mobile multimedia stop drugs application can be used with minor revision.

Performance Evaluation of Wordlength Reduction Based Area and Power Efficient Approximate Multiplier for Mobile Multimedia Applications

Hardware multiplier circuits decide the speed and power consumption in the execution of digital signal processing algorithms. The desirable feature of reduced area and power consumption for battery-driven multimedia gadgets can be realized by replacing the power hungry multiplier circuits with approximate multiplier circuits. The approximation techniques reduce the complexity of the design and improve the energy efficiency of the circuit. This paper proposes an area and power efficient approximate unsigned integer multiplier architecture based on wordlength reduction. It is designed to meet a pre-specified error performance with improved area and power reduction compared with similar designs. It is extended further for the signed multiplier architecture. The circuit characteristics are analyzed to establish the suitability of the proposed design for low-power applications. Synthesis results show that the proposed unsigned multiplier consumes 65% less power than the exact Wallace multiplier. The area requirement of the proposed multiplier reduces by 50% compared to an exact multiplier. The multiplier is tested for image filtering to establish the efficacy of the design in multimedia applications.

Diversifying personalized mobile multimedia application recommendations through the Latent Dirichlet Allocation and clustering optimization

The rapid development of mobile multimedia applications explosively increased the availability of the number of applications in the apps market. Among the crowd of mobile multimedia applications with diverse functions, identifying the appropriate application is a significant challenge. Hence, it is essential for the app stores to recommend the desired applications to the users in the surge of applications. Several research works lack to consider the interactions among the contextual information of applications such as application category and features in different aspects instead of user preferences. Thus, it is of significance to developing a practical approach that provides high-quality application recommendations for users according to personal preferences. This paper presents the DIversifying Personalized Mobile Multimedia Application Recommendation (DIPMMAR) by fusing the user ratings, review texts, application description, and application popularity. Initially, the DIPMMAR approach analyzes the user reviews and application descriptions by applying the Latent Dirichlet Allocation (LDA) based topic model. It employs the Principle Component Analysis (PCA) principal components are the continuous solutions to the discrete cluster membership indicators for K-means clustering among all the extracted features of the applications and retains the optimal latent features alone. Further, the DIPMMAR approach computes the user-specific local popularity score on applications and exploits the application-specific global popularity score to generate the top-N personalized recommendation. Moreover, by exploring the mobile application categories and sub-categories, the DIPMMAR approach ensures the relevance and diversified applications in the recommendation list. The experiments on the real-world mobile app store dataset demonstrate the accuracy of the personalized recommendation.