Multimedia Content Research Articles

The Role of e-Learning During Martial Law: The Ukrainian Experience

The research aim is to reveal the role of e-learning during Martial law by the example of Ukrainian experience. It focuses on the following objectives: to outline the advantages and challenges of e-learning; to describe the peculiarities of implementation of e-learning at the different levels of education; to analyse teaching methods used to integrate e-learning; and to develop the recommendations to implement e-learning. The systemic literature review was carried out to provide an objective summary of the current topic. All sources were published between March, 2022 and May, 2024. The literature focused on e-learning in Ukraine or similar conflict-affected areas. All articles were available in English or Ukrainian. To analyse the data, the synthesis method was applied. The findings proved that e-learning became an important tool during emergencies, providing continuity in education. It was explained that e-learning is introduced at primary, secondary, and higher levels of education. It includes synchronous and asynchronous models. Certain teaching methods are used to implement e-learning effectively. They relate to interactive simulations, multimedia content, gamification, discussion forums, and personalized learning. These methods, combined, create a dynamic e-learning environment that enhances students’ educational outcomes, particularly during emergencies. The implementation of e-learning in Ukraine during Martial law requires careful consideration of the recommendations: enhancement of digital infrastructure, adoption of flexible learning, formation of digital literacy, support of mental health, using interactive methods, increase of content quality, regular assessment, encouragement of positive teacher-student interaction, implementation of adaptive learning tools, and protection of students and educators' data. The research outcomes on the role of e-learning during Martial law can be used for the development of strategic plan of implementation of e-learning tools within the learning process in the regions affected by war or other emergencies. Also, the study can contribute to future educational practices, encouraging the development of more innovative learning solutions.

Penerapan Teknologi Augmented Reality sebagai Media Pembelajaran Komponen Elektronika: Mata Kuliah Elektronika Dasar

This study aims to apply Augmented Reality (AR) technology as a learning media for electronic components using Android. Augmented Reality technology is growing rapidly and is used in various fields such as education. By using Android devices as a platform, users can integrate Augmented Reality into applications that can be used for the introduction of electronic components, by pointing the camera to the marker in the application, augmented reality and information about the function of the electronic component will appear. In this study, the authors used the Multimedia Development Life Cycle (MDLC) method which is a structured approach to designing, developing, and releasing creative and effective multimedia content. In the research that has been done, the results obtained are augmented reality applications for the introduction of android-based electronic components.

Lazer Boa Vista: Web Application of Leisure Spaces in Boa Vista/Roraima

Objective: The main objective of this study was to explore and identify the main leisure spaces in Boa Vista/Roraima, and to develop a web application that allows users to access interactive multimedia content with the information researched about the municipality (leisure spaces, equipment and activities). Theoretical Framework: The concept of leisure has changed over the years and is currently defined as moments of fun and relaxation in one's free time from work. Providing accurate and up-to-date information on leisure spaces enables people to access and occupy them quickly. Method: This study is characterized by exploratory research and applied research, as its purpose was to generate a product. After the exploratory research, the neighborhoods with the greatest potential for leisure spaces were defined to make up the web application. Leisure research tools were used (bibliographic and documentary research) and application development tools (the programming language suited to the application's purpose). Results and Discussion: 28 neighborhoods with preserved leisure spaces were found, including 25 types of leisure equipment and 45 types of leisure activities practiced in these spaces. This information was used to create the “Lazer Boa Vista” web application. Originality/Value: This study contributes by gathering and listing the leisure spaces in the municipality of Boa Vista/RR. The relevance of this study stands out for highlighting tourist leisure spaces, collaborating with this area socio-economically, popularizing and disseminating them through digital marketing.

MeST-Former: Motion-enhanced Spatiotemporal Transformer for generalizable Deepfake detection

The rise of Deepfake technology has sparked significant concerns due to its potential for misuse and malicious manipulation of multimedia content. Various detection approaches aimed at detecting Deepfake videos have been proposed, mostly relying on the identification of spatial and temporal artifacts. However, due to the different contexts of source images and the variety of generation techniques, current Deepfake detection methods usually perform well on training datasets, and yet generalize poorly to those unseen identities in new datasets. This issue is widely known as the generalization challenge of Deepfake detection. To address this challenge, this paper proposes an advanced spatiotemporal Deepfake video detector, named Motion-enhanced Spatiotemporal Transformer (MeST-Former). MeST-Former is based on the spatiotemporal modeling capacity of the video Swin Transformer. The spatial and temporal features are obtained from the RGB and motion images, respectively. To enhance the generalization ability of MeST-Former to unseen identities in unseen datasets, the ID-related components in the spatial and temporal features are detached. Specifically, MeST-Former adopts the newly proposed Identity-Decoupling Attention (IDC-Att) module to disentangle the ID-related and ID-unrelated components. Only the ID-unrelated components are used to construct more generalizable spatiotemporal representations. This process makes the constructed spatiotemporal features identity-agnostic and more generalizable to unseen identities. We conducted extensive experiments to evaluate the performance of the MeST-Former. Our results indicate that MeST-Former achieves accurate and generalizable Deepfake detection performance. Notably, MeST-Former also demonstrates high efficacy in detecting AI-animated talking-head videos.

Content-Based Image Retrieval and Feature Extraction: Analysing the Literature

A significant amount of multimedia data consists of digital images, and multimedia content analysis is used in many real-world computer vision applications. Multimedia information, especially photos, has become much more complicated in the last several years. Every day, millions of photos are posted to various websites, such as Instagram, Facebook, and Twitter. Finding a suitable image in an archive is a difficult research subject for the field of computer vision. Most search engines use standard text-based techniques that depend on metadata and captions in order to fetch photos. Over the past 20 years, a great deal of research has been conducted on content-based image retrieval (CBIR), picture categorization, and analysis. In image classification models and CBIR, high-level picture representations are represented as feature vectors made up of numerical values. Empirical evidence indicates a considerable disparity between picture feature representation and human visual understanding. Reducing the semantic gap between human visual understanding and picture feature representation is the aim of this study. This study aims to do a thorough analysis of the latest advancements in the domains of Content-Based picture Retrieval and picture representation. We performed a comprehensive analysis of many models for image retrieval and picture representation, encompassing the most recent advancements in semantic deep-learning methods and feature extraction. This paper provides an in-depth analysis of the key ideas and important studies related to image representation and content-based picture retrieval. In an effort to stimulate more research in this field, it also offers a preview of potential future study topics.

Effects of Kahoot! on K-12 Students’ Mathematics Achievement and Multi-Screen Addiction

Digital platforms are increasingly prevalent among young students in K-12 education, offering significant opportunities but also raising concerns about their effects on self-assessment and academic performance. This study investigates the effectiveness of Kahoot! compared to traditional instructional methods in enhancing mathematics achievement and its impact on multiple screen addiction (MSA) among Greek students aged 9 to 12 during a STEM summer camp. A quasi-experimental design was employed with a purposefully selected sample of one hundred and ten (n = 110) students, who were non-randomly divided into two groups: (a) an experimental group of fifty-five students (n = 55) who engaged with Kahoot! (using dynamic visual aids and interactive content) and (b) a control group of fifty-five students (n = 55) who received traditional instruction (using digital textbooks and PowerPoint slides with multimedia content) on laptops and tablets. The findings revealed a statistically significant difference in MSA scores, with the experimental group exhibiting lower MSA scores compared to their counterparts, indicating a positive impact on reducing screen addiction levels. While Kahoot! led to lower MSA levels, it significantly improved overall mathematical achievement, with a substantial effect size, suggesting a strong positive impact on learning outcomes. The current study highlights the importance of aligning educational tools with the intended outcomes and recommends further research to explore the broader impact of gamified learning on student engagement, screen addiction, and learning outcomes.

A threefold chaotic image encryption technique for efficacious image data protection

With the profound use of digital contents in education and social media, multimedia content have become a prevalent means of communication and with such rapid increase, information security is still a major concern. This paper provides a summary of the techniques of securing image data focusing primarily on encryption. This paper additionally also explores the contribution of chaos theory in the domain of image encryption and applies a threefold chaotic map sequence to design an image encryption technique. This chaos-based encryption technique renders characteristics like sensitivity, determinacy, and ergodicity, and hence provides a better trade-off between security and computational speed. The efficacy of proposed algorithm is also evaluated based on well-known statistical metrics like Number of Changing Pixel Rate (NPCR), Unified Average Changing Intensity (UACI), Information Entropy, local Entropy, Correlation analysis, key sensitivity analysis, and NIST Randomness test. Furthermore, the key sensitivity analysis is also discussed which is a major part of any encryption algorithm analysis.

EFFECTIVE PEDAGOGICAL STRATEGIES FOR DIGITAL LEARNERS

This study investigated the effectiveness of various pedagogical strategies in digital learning environments, focusing on blended learning, gamification, adaptive learning technologies, interactive multimedia content, and frequent feedback. Employing a mixed-methods approach, the research combined quantitative surveys and qualitative interviews with students and educators to assess the impact of these strategies on engagement, motivation, and academic performance. The results revealed that blended learning enhances flexibility and personalization, gamification increases motivation through interactive elements, and adaptive technologies tailor content to individual needs. Interactive multimedia content improves retention and understanding of complex concepts, while frequent feedback supports continuous learner development. The findings emphasized integrating diverse pedagogical strategies to optimize digital education and address varying learner needs. Future research should explore the long-term impacts of these strategies, emerging technologies, and their effectiveness across different educational contexts to ensure equitable and compelling learning experiences. KEYWORDS: blended learning, gamification, adaptive learning technologies, multimedia content, frequent feedback, digital learning, educational technology

Modeling Android Security Vulnerabilities: Insights from Statistical Distributions

Android operating system is a mobile operating system that supports multimedia features. Android offers a wide range of applications and integrated features for playing, recording, editing and sharing audio, video, images and other multimedia content. Most Android devices include cameras, speakers, microphones, and other multimedia components. In software security, vulnerabilities are critical concerns that often emerge during software development. Predicting these vulnerabilities post-release is essential for risk assessment and mitigation. While various models have been explored, the Android operating system remains relatively uncharted. This study delves into modeling Android security vulnerabilities using different statistical distributions, comparing their suitability to the widely-used Alhazmi-Malaiya Logistic (AML) model. Data from the National Vulnerability Database (NVD) spanning 2016 to 2018, along with Common Vulnerability Scoring System (CVSS) scores, was analyzed. The study evaluates several distribution models, including Logistic, Weibull, Nakagami, Gamma, and Log-logistic, for monthly vulnerability counts and average monthly impact values. Goodness-of-fit tests and information criteria were applied for model robustness assessment. The findings offer valuable insights for researchers and Android software developers, aiding prediction, risk assessment, resource allocation, and research direction. Logistic and Nakagami distributions emerged as the best-fit models for average monthly impact values and monthly vulnerability counts, respectively. Finally, statistical methods perform better against known artificial intelligence methods for small data sets or more clearly defined data due to their flexible features such as comprehensibility, amount of data, need for calculation, and data independence.

A novel approach for long-term secure storage of domain independent videos

Long-term protection of multimedia contents is a complex task, especially when the video has critical elements. It demands sophisticated technology to ensure confidentiality. In this paper, we propose a blended approach which uses proactive visual cryptography scheme along with video summarization techniques to circumvent the aforementioned issues. Proactive visual cryptography is used to protect digital data by updating periodically or renewing the shares, which are stored in different servers. And, video summarization schemes are useful in various scenarios where memory is a major concern. We use a domain independent scheme for summarizing videos and is applicable to both edited and unedited videos. In our scheme, the visual continuity of the raw video is preserved even after summarization. The original video can be reconstructed through the shares using auxiliary data, which was generated during video summarization phase. The mathematical studies and experimental results demonstrate the applicability of our proposed method.

Pengembangan Media Pembelajaran Multimedia Interaktif pada Materi Ekologi dan Keanekaragaman Hayati Indonesia untuk Peserta Didik Kelas VII di SMP Negeri 2 Darma

The purpose of this research is to develop and determine the feasibility of interactive multimedia content in science class VII of Indonesia's ecology and biodiversity material. This research uses a Research and Development (R&D) method with a development model called ADDIE. This development model has five phases, namely analysis, design, development, implementation, and evaluation. The sample in this study was taken from 1 (one) class at SMP Negeri 2 Darma which amounted to 25 students. The data collection techniques in this study are observation, interviews, and questionnaires. The end result of this research is the development of products in the form of interactive multimedia content. The development of interactive multimedia was tested through a questionnaire for validation by media experts and obtained a feasibility level of 85% which is included in the "Very Feasible" feasibility level, and material experts realized that they got a feasibility percentage of 92% including the "Very Feasible" category. A small group experiment involving five students of SMP Negeri 2 Darma produced a result of 93% including the "Very Feasible" category. The results of the large group test with a total of 20 students showed a feasibility of 93% including the "Very Feasible" category. The average percentage of results from media experts, material experts, small-scale and large-scale trials got 91% with the category of "Very Feasible"

Architectural Design for Data Security in Cloud-based Big Data Systems

التصميم المعماري للنظام هو عملية إبداعية بعد كل شيء. المتطلبات غير الوظيفية وبنية البرامج لها علاقة قوية. يعد أمن النظام أحد الشواغل الكبيرة وهو حاجة ومتطلب غير وظيفي. يجب أن يعتمد الأسلوب والهيكل المعماري المختاران للنظام على متطلبات النظام غير الوظيفية. تُستخدم تقنيات البيانات الضخمة لتخزين كميات هائلة من البيانات التشغيلية في المجال العام. تم اقتراح مخططات هيكل وسلوك النظام التي تستخدم تصميمًا معماريًا آمنًا لنظام يعمل عبر السحابة. تم التحقيق في مكونات مختلفة من البيانات الضخمة في هذا العمل البحثي نظرًا لخطورة القضايا الأمنية التي تركز على الخصوصية والسرية. يعد تأمين البيانات عبر السحابة التي يتم الاستعانة بمصادر خارجية لها مصدر قلق متزايد. هناك مسؤولو قواعد بيانات ضارة من الخارج ومن الداخل يمكنهم الوصول إلى البيانات الخاصة والحساسة وتعديلها ، وبالتالي يمكن أن يكون مزود خدمة قاعدة البيانات مخادعًا. آليات الأمان مطلوبة بالضرورة لحماية البيانات الحساسة الموجودة على السحابات العامة. على مستوى التطبيق ، يضمن الحل المقترح خصوصية وسرية البيانات الموجودة على السحابة. تم إجراء اختبارات مكثفة ، وكانت نتائج هذا البحث كما يلي: كشفت النتائج المسجلة عن اختلافات ذات دلالة إحصائية في النص العادي ومحتويات الوسائط المتعددة (أي الصوت والفيديو) مع قياس الوقت بالمللي ثانية لمجموعات البيانات الصغيرة والمتوسطة والكبيرة.

Harmonizing Fine-tuned Llama 2 for Content Generation with Stable Diffusion for Image Synthesis in Article Creation

The research explores the integration of generative AI in multimedia content production using a fine-tuned Llama 2 model for text generation and the Stable Diffusion algorithm for image synthesis. The research analyses the fine-tuned Llama 2-7b-chat model's adaptability to specific content generation contexts, enhanced by a unique dataset and QLoRa, a Quantized Low-Rank Adaptation for parameter-efficient fine-tuning, achieving significant reductions in training loss and nuanced quality in the generated content. Notably, the model's evaluation yielded an impressive perplexity score of 1.49, indicating advanced predictive performance. Additionally, stable diffusion's ability to transform textual descriptions into intricate images, highlighting its potential in AI-mediated content creation is demonstrated. The experiments and qualitative analyses reveal improvements in efficiency and creativity, emphasizing the collaborative potential of these models to revolutionize multidisciplinary content generation. The research underscores the transformative impact of fine-tuned generative models on content creation and offers insights into the broader implications for future AI research, while acknowledging the critical need for ethical considerations in the deployment of such technologies.

Integrating Cloud Services with Mobile Applications for Seamless User Experience

In the modern digital era, the integration of cloud services with mobile applications has emerged as a pivotal strategy for delivering a seamless and dynamic user experience. This approach not only enhances the functionality of mobile applications but also ensures that users can access services and data in real-time, regardless of their location. As mobile devices continue to dominate the technology landscape, the demand for responsive, scalable, and user-centric applications has skyrocketed. Cloud services, with their inherent capabilities such as storage, computing power, and advanced analytics, provide the backbone for developing applications that meet these demands. The integration of cloud services with mobile applications offers numerous advantages. It allows for the offloading of heavy computational tasks to cloud servers, thereby preserving the limited processing power and battery life of mobile devices. This capability enables mobile applications to perform complex tasks such as real-time data processing, machine learning inference, and multimedia content rendering without compromising user experience. Additionally, cloud services facilitate the synchronization of data across multiple devices, ensuring that users have consistent access to their data and settings regardless of the device they are using. This is particularly important in scenarios where users switch between devices, such as moving from a smartphone to a tablet or laptop. Moreover, the use of cloud services in mobile applications enhances security and reliability. Cloud providers typically offer robust security features, including data encryption, identity and access management, and continuous monitoring, which are essential for protecting sensitive user data. By leveraging these features, mobile applications can offer a higher level of security than would be feasible with on-device storage and processing. Furthermore, the reliability of cloud services ensures that applications remain available and functional even during peak usage times or when the user is in a location with poor network connectivity. Through techniques such as caching and offline access, cloud-integrated mobile applications can continue to operate smoothly even when real-time access to cloud resources is temporarily unavailable.

Assessing Usefulness, Ease of Use and Recognition Performance of Semi-Automatic Mulsemedia Authoring

Mulsemedia (Multiple Sensorial Media) authoring poses a considerable challenge as authors navigate the intricate task of identifying moments to activate sensory effects within multimedia content. A novel proposal is to integrate content recognition algorithms that use machine learning (ML) into authoring tools to alleviate the authoring effort. As author subjectivity is very important, it is imperative to allow users to define which sensory effects should be automatically extracted. This paper conducts a twofold evaluation of the proposed semi-automatic authoring. The first is from a user perspective within the STEVE 2.0 mulsemedia authoring tool, employing the Goal-Question-Metric (GQM) methodology and a user feedback questionnaire. Our user evaluation indicates that users perceive the semi-automatic authoring approach as a positive enhancement to the authoring process. The second evaluation targets sensory effect recognition using two different content recognition modules, quantifying their automatic recognition capabilities against manual authoring. Metrics such as precision, recall, and F1 scores provide insights into the strengths and nuances of each module. Differences in label assignments underscore the need for ML module result combination methodologies. These evaluations contribute to a comprehensive understanding of the effectiveness of sensory effect recognition modules in enhancing mulsemedia content authoring.

Multimedia content recommendation algorithm based on behavior and knowledge feature embedding

Multimedia content recommendation algorithm based on behavior and knowledge feature embedding

What Can Physiology Tell Us about State of Interest?

The state of interest as a positive emotion is associated with the ability to comprehend new information and/or to better consolidate already perceived information, to increase the attention level to the object, to increase informational processing, and also to influence such processes as learning and motivation. The aim of this study was to reveal oculomotor correlates that can predict the locus of interest in cases of people perceiving educational information from different areas of knowledge presented as text or multimedia content. Sixty (60) volunteers participated in the study (50% males, mean age 22.20 ± 0.51). The stimuli consisted of 16 texts covering a wide range of topics, each accompanied by a comprehension question and an interest assessment questionnaire. It was found that the multimedia content type triggered more visual attention and gave an advantage in the early stages of information processing. The first fixation duration metric for the multimedia stimuli allowed u to characterize the subjective interest assessment. Overall, the results suggest the potential role of eye-tracking in evaluating educational content and it emphasizes the importance of developing solutions based on this method to enhance the effectiveness of the educational process.

Comparative randomized trial addressing childhood emotional problems: Computerized vs. traditional transdiagnostic Super Skills program

BackgroundSuper Skills for Life is a structured intervention rooted in cognitive-behavioral therapy and a transdiagnostic approach, aimed at preventing childhood emotional problems. Over the past decade, it has demonstrated positive outcomes in various formats and countries. This study evaluated the comparative effectiveness of two individual modalities: the computerized and the traditional version enriched with multimedia content, through a comparative randomized effectiveness trial. Method109 children (55.24 % female) aged 8–12 years participated, randomly assigned to either the traditional (n = 54) or computerized (n = 51) groups. Pre- and post-intervention assessments involved emotional problem measures completed by children and parents. ResultsBoth groups exhibited significant reductions in all outcomes according to parent reports, including depression, anxiety, and anxiety-related life interference. Children's self-reports indicated improvements across all general measures. Generalized Estimating Equations indicated marginally better improvements in the traditional group. LimitationsLimitations included the absence of follow-up assessment, variability in participant regions across Spain, and input from other informants like teachers. ConclusionsThis study pioneers the examination of short-term effects of the program in both a fully computerized format supervised by a specialized therapist and an enriched traditional individual format, and the promising results suggest their potential for indicated prevention of childhood emotional problems.

Chaos based image encryption scheme to secure sensitive multimedia content in cloud storage

Cloud computing offers a variety of on-demand services to users and has gained significant prominence in the contemporary era. The security of information stored in cloud data centers has become a central concern, especially for sensitive data like medical images, videos, and multimedia content that require heightened protection when stored in data centers. Cloud users have a responsibility to ensure the security of their data through strategic measures. Focusing on maintaining the privacy of images stored in the cloud, this research introduces an innovative image encryption technique that is based on a modified Skew Tent chaotic map. The suggested modification to the chaotic map includes combining the Skew Tent map with both the sine trigonometric function and perturbation technology. This results in enhanced randomness, more intricate dynamical behavior, a broader chaotic range, and increased sensitivity to initial values in contrast to alternative chaotic maps. The incorporation of this adapted map into a stepwise procedure, involving two rounds of permutation followed by diffusion, effectively accomplishes image data encryption for cloud storage systems. These consecutive operations collectively enhance the encryption method’s randomness and robustness. Through simulations conducted using Cloudsim, the cipher-image exhibits a uniform distribution and achieves a commendable information entropy score of 7.996749. The encryption algorithm significantly reduces correlation coefficients from 1 in the original image to 0 in the encrypted image, while maintaining NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changing Intensity) values within the critical range. Additionally, both theoretical analysis and practical evaluations confirm the algorithm’s resilience against exhaustive, occlusion, and classical attacks. Moreover, extending this encryption framework to video data, a novel video encryption approach is proposed.

Retraction Note: A comprehensive survey on generative adversarial networks used for synthesizing multimedia content

Retraction Note: A comprehensive survey on generative adversarial networks used for synthesizing multimedia content