Mobile Devices Research Articles

The effect of Mobile-Assisted training and counseling on nurses' ventrogluteal injections application of nursing: A mixed method study.

Nurses often administer intramuscular (IM) injections. However, they do not use the ventrogluteal (VG) site, although it is highly recommended. This study investigated whether a mobile-assisted training and counseling program (intervention) encouraged nurses to use the VG site to administer IM injections. This study adopted a pretest-posttest interventional design with no control group and employed a mixed research design. The sample consisted of 105 nurses from a public hospital in a metropolitan city in Turkey. Quantitative data were collected using a descriptive characteristics form and a Ventrogluteal Site Information Form (VSIF). Qualitative data were collected using a semi-structured interview form. First, participants took a pretest and then watched an animation about VG injections on their mobile devices. Afterward, they were provided with counseling and then administered a posttest. Sixteen participants were interviewed after the posttest. Participants had a significantly higher mean posttest VSIF score (17.50 + 3.23) than the pretest score (12.73 + 5.40) (p < 0.05). They stated that the intervention helped them reinforce what they already knew and encouraged them to use the VG site to administer IM injections. They also noted that it boosted their confidence and helped them experience less anxiety. The results showed that the intervention helped participants learn more about IM injections into the VG site. Therefore, hospitals should provide nurses with mobile-assisted training and counseling programs to encourage them to use the VG site to administer IM injections.

VOTEMAT: A Blockchain Based Voting System

This study aims to show that a secure, trustable and immutable voting system can be established with Blockchain technology. Decentralized structure of the Blockchain excludes the central authority and provides transparency. Moreover, its cryptographic functions enable secure transactions. Therefore, the operation is prevented from potential frauds, such as multiple votes, fake vote attempts, and fraudulent vote counts. The proposed method, VOTEMAT, covers both electronic voting and paper ballot as a complete solution. A mobile application and a Website, connected to Ethereum private Blockchain network, were developed for the voters who prefer to cast their votes remotely. It is also possible to vote in the voting centres via the mobile device or paper ballot placed in the vote boxes; but these votes are also recorded in the same Blockchain and equally secure. For the remote users, a two-step authentication is designed, based on the information on the national identity card and face recognition. An additional encryption based security measure is used to avoid hacking attempts, such as man in the middle attacks. Since the proposed system is more practical than the traditional voting methods, it can increase the participation and be utilized in all kinds of local or national elections.

Exploring Normalization for High Convergence on Federated Learning for Drones

The usage of mobile devices like drones has been increasing in various fields, ranging from package delivery to emergency services and environmental monitoring. Intelligent services increasingly use the processing power of these devices in conjunction with techniques such as Federated Learning (FL), which allows machine learning to be carried out in a decentralized way using data accessed by clients or devices. However, in normal operations, the data accessed by clients is distributed heterogeneously among themselves, negatively impacting learning results. This article discusses the normalization in Federated Learning local training to mitigate results obtained in heterogeneous distributions. In this context, we propose Federated Learning with Weight Standardization on Convolutional Neural Networks (FedWS) and evaluate it with Batch Normalization, Layer Normalization, and Group Normalization in experiments with heterogeneous data distributions. The experiments demonstrated that FedWS achieved higher accuracy results ranging from 3% to 6% and reduced the computational and communication costs between 25% and 40%, being more suitable for use in devices with computational resource limitations.

Advancements in Digital Forensics: A Quantitative Analysis of Cell Tower Triangulation Techniques

This study delves into the accuracy and reliability of Cell Tower Triangulation (CTT) in digital forensics, employing a diverse sample of mobile devices across various real-world scenarios—criminal investigations, missing persons, and corporate security breaches. By simulating these situations, the research aims to provide a comprehensive understanding of CTT’s effectiveness in urban and rural settings. Data was meticulously collected on signal strength, tower locations, and timestamps, and analyzed using statistical methods to assess CTT accuracy and reliability. Findings from descriptive statistics, comparative analyses, precision metrics, and correlation analyses reveal significant insights into CTT’s application in digital forensics. Despite certain limitations in precision, indicated by the variability in virtual triangle sizes, the study underscores the potential of CTT in supporting investigations while also highlighting the importance of ethical considerations and legal compliance. This research contributes to the advancement of digital forensics, offering a nuanced understanding of CTT’s capabilities and limitations.

Communicating the dialog between painting and music with a mobile application

Contemporary visual artists often collaborate with musicians and composers to enrich their artworks and provide multisensory viewing experiences by leveraging the power of digital technology. In this paper, we present our approach to follow in their footsteps by creating a mobile application that provides &amp;ldquo;mixed-media interpretations&amp;rdquo; for a variety of visual artworks and music pieces, spanning over different time eras, with the objective to reflect the creative dialog and multifaceted interaction between their creators through time. Targeting the broader public, we attempt to capture a variety of interconnections between works of visual arts and music that may be of interest and are comprehensible to people with varying levels of knowledge on art or music, including not only inspirational but also personal and social relationships among the artists. We provide a six-layered conceptual model to ease the description of the content produced to that end while reporting several concrete examples. Then, we focus on how to communicate and digitally mediate the intertwined and often intricate interconnections in a concise way, proposing a &amp;ldquo;three-act&amp;rdquo; story scheme to progressively display short narratives on screen, combined with music reproduction on the mobile device. Finally, we explore the potential of the proposed approach through a user study with 15 participants and report a series of promising key findings, whereas also discussing future directions and pointing out open issues.

Benchmarking Mobile Deep Learning Software

Deploying deep learning (DL) on mobile devices has become increasingly prevalent. DL software libraries are crucial for efficient on-device inference, alongside algorithms and hardware. However, there has been limited understanding on the performance of modern DL libraries. We fill this gap by benchmarking 6 popular DL libraries and 15 diverse models across 10 mobile devices, which reveal an unsatisfactory landscape of mobile DL: their performance is highly disparate and fragmented across different models and hardware, and the impacts often surpass algorithm or hardware optimizations, such as model quantization and GPU/NPU-based computing. Finally, we provide practical implications for stakeholders in the DL library ecosystem, and envision a more ambitious picture of future mobile AI landscape in the LLM era.

Analysis and Comparison of Social Media Applications Using Forensic Software on Mobile Devices

With the integration of mobile systems into daily life, social media applications used especially on Android and iOS platforms contain a significant amount of sensitive information. Social media applications on mobile systems have huge personal and sensitive content. Therefore, it is important to design effective techniques for forensic analysis of social media applications and to detect personal data. In this research, three different paid mobile forensic software and 4 different brands and models of smartphones with different operating systems were used and analyzed. The study shows that private messages, e-mails, time information, shared data, location and time information, and other personal data can be obtained by a forensic expert who performs an examination, and it is seen that one software can access the deleted data, but another software cannot access it. In proportion to the technology used in today’s world, mobile forensics incidents are increasing day by day, and a competitive environment is created among the software used to illuminate these incidents. With this competition, software companies dealing with forensic informatics are trying to obtain different data to illuminate forensic events that may occur due to the active use of social media accounts with the developing technology, and software that does not meet the needs in the face of this situation remains in the background. The criminal elements in the investigation areas of mobile forensics differ daily, and the scope of crimes in the virtual environment is expanding with the developing technology. Therefore, mobile forensic analysis applications should be successful in social media applications other than standard data.

Investigating Transfer Learning based CNN for Multi-Crop Disease Recognition in Horticulture Crops using Wireless Multimedia Sensor Network

Introduction: Achieving sustainable growth in agricultural productivity is crucial for addressing various challenges confronting food systems globally. One of the key aspects is effectively identifying and addressing diseases that can adversely affect crop quality and yield. Method: This research study proposes the use of Wireless Multimedia Sensor Network (WMSN) in conjunction with deep transfer learning model for disease detection in horticulture crops. The proposed system utilizes the image data of horticulture crops obtained using WMSN for performing a comparative assessment of several pre-trained deep learning models, encompassing VGG, ResNet, MobileNet, Inception, Xception, DenseNet, and EfficientNet, to ascertain their effectiveness in identifying diseases across a range of horticultural plants. The models undergo fine-tuning using transfer learning technique and are assessed on different measures, such as accuracy, precision, recall, F1 score, and the number of epochs needed for convergence using the image data obtained from WMSN. Results: Among the compared models, EfficientNetB3 appears as the best performing model, surpassing others in all evaluation metrics while also exhibiting low parameters and a compact size. Notably, EfficientNetB3 achieves a testing accuracy of 93.69% and a training accuracy of 98.85% after only 21 epochs of training. In contrast, DenseNet201, DenseNet169, and EfficientNetB0 demonstrate performance close to the best-performing models, but they have higher parameters or sizes. On the contrary, ResNet50, InceptionV3, and Xception exhibit poorer performance, presumably due to their substantial size in relation to the dataset. Conclusion: This analysis provides valuable insights for optimizing the top-performing models for disease classification across various plant species. Moreover, the fine-tuned EfficientNetB3 model shows potential for integration with mobile devices, facilitating real-time disease classification.

Applying Mobile Technology to Address Gender-Based Violence in Rural Nigeria: Experiences and Perceptions of Users and Stakeholders

ABSTRACT This paper documents the results of an intervention conducted in Nigeria to test the effectiveness of a mobile phone technology, text4life, in enabling women to self-report gender-based violence (GBV). Women experiencing GBV and other challenges related to sexual and reproductive health and rights were requested to use their mobile phones to text a code to a central server. In turn, the server relayed the messages to trained nearby health providers and civil society organization (CSO) officials who reached out to provide health care and social management services to the callers. Interviews were conducted with some callers, health care providers, and CSO staff to explore their experiences with the device. The interviews and data from the server were analyzed qualitatively and quantitatively. The results indicate that over a 27-month period, 3,403 reports were received by the server, 34.9% of which were reporting GBV. While interviewees perceived that a large proportion of the women were satisfied with the use of text4life, and many received medical treatment and psychological care, the consensus opinion was that many women reporting GBV did not wish to pursue police or legal action. This was due to women’s perceptions that there would be negative cultural and social backlash should they pursue civil punishments for their partners. We conclude that a mobile phone device can be used effectively to report GBV in low-resource settings. However, the device would be more useful if it contributes to equitable primary prevention of GBV, rather than secondary prevention measures.

Flexible Composites with Rare-Earth Element Doped Polycrystalline Particles for Piezoelectric Nanogenerators

Energy harvesting plays an important role in advancing personalized wearables by enabling continuous monitoring, enhancing wearable functionality and facilitating sustainable solutions. We aimed to develop a flexible piezoelectric energy harvesting system based on inorganic piezoelectric materials that convert mechanical energy into electricity to power a wide range of mobile and portable electronic devices. There is significant interest in flexible piezoelectric energy harvesting systems that use inorganic piezoelectric materials due to their exceptional physical features and prospective applications. Herein, we successfully demonstrated a flexible piezoelectric nanogenerator (PENG) designed by the co-doped rare-earth element ceramics (RE-PMN-PT) embedded in PVDF and PDMS composite film and attained a significant output performance while avoiding electrical poling process. The impact of dielectric characteristics on the electrical output of nanogenerators was investigated, together with the structure of the composites. The Sm/La-PMN-PT particles effectively amplify both the voltage and current output, showcasing their potential to power portable and wearable devices, as demonstrated by their capacity to illuminate LEDs. The maximal output power of 2 mW was correlated with the high voltage (220 V) and current (90 µA) of Sm/La-PMN-PT/PVDF, which demonstrated that the device has the potential for energy harvesting in biomedical applications.

(Dis)connecting families: Parents’ versus children's perspectives on the role of mobile devices within family interactions

The characteristics of mobile technology devices have enabled new ways of integrating technology in familial interactions. This study aimed to investigate (1) differences in perspectives of technoference and family-oriented mobile technology use between parents and children, (2) the relation between parent–child interactions that take place via mobile technology and the quality of the parent–child relationship, and (3) its association with the well-being of parents and preadolescents separately. Structural equation modelling was performed on cross-sectional data from 390 parent–child dyads. The findings revealed discrepancies in child technoference perceptions and varying associations of the different types of mobile technology use with the parent–child relationship and parents' and children's well-being. From children's perspective, both parent and child technoference was negative for the parent–child relationship, whereas family-oriented mobile technology use when physically together was beneficial for the relationship and the child's well-being. From parents’ perspective, only child technoference was negatively related to the parent–child relationship and parent's well-being. Overall, the findings underline that mobile technology use can be both detrimental and beneficial, however, the effects can differ depending on the family member and the type of technology use.

Multi-Camera Lighting Estimation for Mobile Augmented Reality

Lighting estimation is an important and long-standing task in computer vision and graphics communities[1]. Over the past few years, we have observed a rising demand for creating immersive user experiences in mobile augmented reality (AR) applications. In particular, environment lighting estimation is essential to render visually coherent virtual objects that need to be overlaid on physical scenes in mobile AR. In this work, we explore the key research questions in lighting understanding for an emerging application domain, mobile AR on multi-camera handheld devices, particularly try-on applications where end users leverage handheld mobile devices, such as smartphones, to overlay products of interest on their faces. Mobile AR try-on apps promote online shopping and often require photorealism to provide user experiences on par with physical try-on.

Enhancing Mobile Central Bank Digital Currency Payment Participation Through Creative Communication on Social Media

With the development of mobile technology, mobile devices have dramatically changed the way people entertain and pay. Social media platforms are booming, and social media is gradually becoming the main form of entertainment for people. Enterprises and organisations with promotional needs are paying more attention to the high efficiency and high input–output ratio of social media communication. Social media advertising has shown more vitality than traditional advertising. Meanwhile, mobile devices have also changed the way people pay, and mobile payment is gradually becoming popular. China has taken the lead in launching mobile Central Bank Digital Currency (CBDC) payment, a new payment technology, and social media communication also plays an important role in promoting CBDC payment. The purpose of this article is to explore the impact of social media creative communication on mobile CBDC payment participation. A research model suitable for this study was created by integrating privacy concern and elaboration likelihood model theory. A valid response from 300 respondents was obtained through an online self-administered questionnaire. Hypotheses were tested through a two-stage research method of partial least squares structural equation modelling with the method of artificial neural network. The results of the study showed that social media advertisements’ creativity and social media information quality significantly and positively influence users’ social media engagement. Users’ social media engagement significantly and positively influences users’ mobile CBDC payment participation. The study provides valuable practical guidance for managers of mobile CBDC payments to promote mobile CBDC payments through social media.

Development of an Algorithm for Determining the Class of an Obstacle in the Process of its Interaction with the Manipulator

The article discusses the problem of determining the nature of an obstacle using a force sensor for terrain mapping. Three classes of objects have been introduced: the first class is "static" obstacles, the second class is "moving" obstacles, the third class is "unstable" obstacles. To conduct experiments, a simulation system was built in the Gazebo modeling environment, including a manipulator with the ability to horizontally move a probe with a force sensor and a set of obstacles corresponding to three classes of obstacles. All experiments were carried out using the ROS framework, the Gazebo simulator and the Movelt manipulation software. Processing of signals from the force sensor showed that the sig­nals for the three classes of obstacles are different, which makes it possible to identify the class based on sensor readings. Based on the analysis of data received from the sensor, rules were formed that make it possible to determine the class of an obstacle during its interaction with the manipulator in real time. To determine the obstacle class, the values of the force sensor signals are considered, smoothed by the moving average method. According to the rules discussed in the article, an algorithm was developed to determine the class of obstacle, which was implemented in the Gazebo modeling environment. Testing of the algorithm showed the stability of its operation under the given experimental conditions. Successful determination of the obstacle class will allow one not only to construct a map of the area, but also to prevent damage to the device when exposed to a "static" object with excessive force. The presented approach can be expanded to narrower classes of obstacles that characterize objects that can be encountered by a mobile device when mapping a forest area.

Interactive advertising as a new factor of influence on consumer behaviour of youth

The article is devoted to the analysis of interactive advertising as a relatively new factor influencing the consumer behaviour of Moscow youth. The empirical basis of the work are statistical and sociological research data, including the authors’ own research conducted using the methods of mass survey and indepth interview. As the study has showed, the Moscow youth are quite familiar with the interactive advertising, most often encountering it on mobile devices and in gaming applications. The article analyses several types of the interactive advertising, differing in the degree of their impact on the consumer behaviour of young people. The authors come to the conclusion that the most effective in this regard are playable and video advertising placed in mobile device applications. As advantages of the interactive advertising, in comparison with the traditional format, the authors highlight its innovation and playful nature. At the same time, many young Muscovites express a negative attitude towards the interactive advertising, mainly because of its intrusiveness. Moreover, young men demonstrate greater loyalty to this format of advertising influence in comparison with young ladies. The authors come to the conclusion that, despite the noted shortcomings, the interactive advertising has great potential for development in modern society.

A Compressed Domain Robust Video Watermarking Resists Geometric Attack

The widespread utilization of mobile devices and the proliferation of video content, facilitated by communication networks, has unfortunately led to issues such as data piracy, illegal usage, and unauthorized sharing. To address these challenges, digital watermarking emerges as a crucial tool, serving purposes such as authentication, maintaining integrity, and identifying rightful owners. In the realm of video watermarking, two primary categories exist: watermarking in the compressed domain and watermarking in the uncompressed domain. Compressed domain watermarking involves expedited data processing through partial video decoding, whereas uncompressed domain watermarking requires more time. This article proposes a novel video watermarking methodology designed specifically to withstand geometric attacks. The proposed method aims to dynamically embed watermarks within video content, thereby enhancing resilience against various geometric distortions. Geometric attacks, including scaling, rotation, cropping, and translation, pose significant challenges in digital watermarking and are thus a focal point of this research. The objective is to integrate this approach with complex video coding standards, leveraging compression domain techniques to fortify resistance against geometric attacks. Tests show that the suggested video watermarking system works well. Not only does it excel in safeguarding against geometric attacks, but it also mitigates perceptual quality degradation, thus ensuring a balance between robustness and visual fidelity. As the prevalence of video content continues to grow, robust watermarking techniques like the one presented here play a vital role in preserving intellectual property rights and ensuring the integrity of multimedia content in the digital age.

A MODEL FOR PHYSICS PRACTICAL WORK USING MOBILE LEARNING AND LEARNING MANAGEMENT SYSTEM (LMS) IN HIGHER EDUCATION: DESIGN, VALIDATION, AND EVALUATION

Despite the importance of physics practical work in higher education, its implementation is often hampered by various constraints and problems. Technology, such as learning management systems (LMS) and mobile learning, can offer solutions to some of these problems and enrich students' learning experiences. Therefore, this research proposes a model called Practical Works in Physics via Mobile Learning and LMS (PWP-MLMS) that exploits features of LMSs and mobile devices to overcome specific challenges encountered in physics practical works and improve students' performance in these works. The model was designed, validated, and evaluated within the teaching context of a Moroccan university. To assess the model's effectiveness,128 students in the Bachelor of Education, Physics-Chemistry specialization were randomly divided into two groups of 64 students each: an experimental group using the model for practical work on the topic of rectification and filtering in the electronics module, and a control group following the conventional method for the same practical work. The results of the evaluation showed that the proposed model can significantly reduce the time required to complete the practical work, have a positive influence on the students' technical skills, and improve the quality of their laboratory reports. Keywords: mobile learning, LMS, practical work, physics education, higher education

Utilizing AspectJ for Defense Against Evasive Malware Attacks in Android System

Introduction: Mobile devices have become an integral part of our digital lives, facilitating various tasks and storing a treasure trove of sensitive information. However, as more people utilize mobile devices, sophisticated cyber threats are emerging to elude traditional security measures. Method: The use of evasion techniques by malicious actors presents a significant challenge to mobile security, necessitating creative solutions. In this work, we investigate the potential critical role that the aspect-oriented programming paradigm AspectJ can play in strengthening mobile security against evasion attempts. Evasion techniques cover a wide range of tactics, including runtime manipulation, code obfuscation, and unauthorized data access. Results: These tactics usually aim to bypass detection and avoid security measures. In order to address the aforementioned issues, this paper uses AspectJ to give developers a flexible and dynamic way to add aspects to their coding structures so they can monitor, intercept, and respond to evasive actions. It illustrates how AspectJ can improve mobile security and counteract the long-lasting risks that evasion techniques present in a dynamic threat landscape. Conclusion: Consequently, this work proposes a novel defense mechanism incorporating AspectJ into a significant paradigm of security against evasion with 91.33% accuracy and demonstrates the successful detection of evasive attacks.

Minimum data sampling requirements for accurate detection of terrain-induced gait alterations change with mobile sensor position

Human gait is a key biomarker for health, independence and quality of life. Advances in wearable inertial sensor technologies have paved the way for out-of-the-lab human gait analysis, which is important for the assessment of mobility and balance in natural environments and has applications in multiple fields from healthcare to urban planning. Automatic recognition of the environment where walking takes place is a prerequisite for successful characterisation of terrain-induced gait alterations. A key question which remains unexplored in the field is how minimum data requirements for high terrain classification accuracy change depending on the sensor placement on the body. To address this question, we evaluate the changes in performance of five canonical machine learning classifiers by varying several data sampling parameters including sampling rate, segment length, and sensor configuration. Our analysis on two independent datasets clearly demonstrate that a single inertial measurement unit is sufficient to recognise terrain-induced gait alterations, accuracy and minimum data requirements vary with the device position on the body, and choosing correct data sampling parameters for each position can improve classification accuracy up to 40% or reduce data size by 16 times. Our findings highlight the need for adaptive data collection and processing algorithms for resource-efficient computing on mobile devices.

Private compression for intermediate feature in IoT-supported mobile cloud inference

In the emerging Internet of Things (IoT) paradigm, mobile cloud inference serves as an efficient application framework that relieves the computation and storage burden on resource-constrained mobile devices by offloading the workload to cloud servers. However, mobile cloud inference encounters computation, communication, and privacy challenges to ensure efficient system inference and protect the privacy of mobile users’ collected information. To address the deployment of deep neural networks (DNN) with large capacity, we propose splitting computing (SC) where the entire model is divided into two parts, to be executed on mobile and cloud ends respectively. However, the transmission of intermediate data poses a bottleneck to system performance. This paper initially demonstrates the privacy issue arising from the machine analysis-oriented intermediate feature. We conduct a preliminary experiment to intuitively reveal the latent potential for enhancing the privacy-preserving ability of the initial feature. Motivated by this, we propose a framework for privacy-preserving intermediate feature compression, which addresses the limitations in both compression and privacy that arise in the original extracted feature data. Specifically, we propose a method that jointly enhances privacy and encoding efficiency, achieved through the collaboration of the encoding feature privacy enhancement module and the privacy feature ordering enhancement module. Additionally, we develop a gradient-reversal optimization strategy based on information theory to ensure the utmost concealment of core privacy information throughout the entire codec process. We evaluate the proposed method on two DNN models using two datasets, demonstrating its ability to achieve superior analysis accuracy and higher privacy preservation than HEVC. Furthermore, we provide an application case of a wireless sensor network to validate the effectiveness of the proposed method in a real-world scenario.