Multimedia Content Research Articles

Unlocking Potential: Harnessing Language Learning Apps to Support Young Learners with ADHD

This article explores the potential of language learning applications in supporting young learners with Attention Deficit Hyperactivity Disorder (ADHD). It highlights the challenges faced by this demographic in language acquisition, such as difficulty sustaining attention, impulsivity, and hyperactivity. The article emphasizes the importance of early intervention and tailored support in fostering language development in young learners with ADHD. Language learning applications offer benefits such as customizable pacing, engaging multimedia content, instant feedback, and gamification elements. Real-life case studies demonstrate the transformative impact of these apps on the language acquisition journey. Design considerations for ADHD-friendly apps include a simplified user interface, clear instructions, visual aids, structured routines, multisensory elements, rewards, and positive reinforcement. Practical strategies for parents and educators include collaborative learning, realistic goal-setting, progress monitoring, and consistent usage routines. Success stories and testimonials highlight the real-world impact of language learning apps in supporting young learners with ADHD.

Design considerations for the migration from paper to screen-based media in current health education for older adults: a scoping review

ObjectivesTo map the current use of paper-based and/or screen-based media for health education aimed at older people.DesignA scoping review was reported following the Preferred Reporting Items of Systematic Reviews and...

Movie Review Sentiment Analysis and AI-Story Generation Web Application

Abstract: This paper presents a web application designed to enhance the interaction between users and multimedia content through two innovative platforms: "MOVIE REVIEW SENTIMENT ANALYSIS" and "AI-STORY GENERATION." The first platform leverages the Fastai library and LSTM networks to perform sentiment analysis on user-submitted movie reviews, determining their polarity as positive or negative. This analysis directly influences the movie's aggregate rating, with the system capable of processing extensive textual input with high accuracy. Ratings are dynamically updated and stored in JSON format. The second platform, AI-STORY GENERATION, introduces a creative avenue for generating novel stories based on user prompts. It utilizes Natural Language Processing (NLP) techniques and a Generative Pretrained Transformer (GPT-2 Large) model, trained on tokenized movie scripts, to produce coherent and engaging narratives. The Flask web framework supports the application's backend, providing a robust and scalable foundation for user interaction and content delivery. This dual-platform approach not only demonstrates the practical application of advanced machine learning and NLP techniques in enhancing digital entertainment experiences but also showcases the potential for AI-driven tools to foster creative storytelling. The integration of sentiment analysis and story generation within a single application illustrates a novel use case of AI in the context of web-based entertainment, potentially paving the way for more personalized and interactive user experiences

Face Detection Framework for Accelerated Analysis of High-Quality Multimedia Content

Modern face detection algorithms fail to provide optimal results when they have to deal with larger amounts of data per frame while processing higher quality videos. This paper tackles that problem and offers a solution to deploy commercially used state-of-the-art face detection algorithms to process only the regions of interest in a frame, and discard the rest to decrease the data to be processed. The model maintains the accuracy of the base algorithm while decreasing the processing time per frame, thereby increasing the overall efficiency. The selection of region of interest is dependent on the detection of facial window in the previous frame. Therefore, the choice of base algorithm plays an important role in determining the speed of the framework. The model achieves increased processing speeds of about 69–76% more than the standalone usage of the detection algorithms for analyzed frame rates.

Learning achievement in purposive communication of ESL college students: A blended learning approach

With the ongoing studies of blended learning in different areas, the study contrasts blended learning with online classes by analyzing pre/post-test scores, grades, and activity scores, indicating blended learning might offer superior outcomes. Additionally, it explores college students’ experiences in a blended learning class, providing insights into its effectiveness and areas for improvement. Participants are engineering students from Camarines Sur Polytechnic Colleges, Camarines Sur, Philippines, which includes 45 participants for each group (controlled and experimental groups). The study was based on a triangulation approach of integrating Independent Samples T-test and Multiple Regression Analysis for quantitative data and semi-structured interviews for qualitative insights. Accordingly, certain activities were crucial in improving non-verbal communication, persuasive writing, and research skills that led to confidence build-up and improved students’ performances. Narrative descriptions give more enlightening information about boosted confidence and better presentation competence due to better communication skills. Writing skills go a long way in improving students’ academic achievement, and Research and writing practices are essential skills that help students achieve higher grades. Integrating the qualitative narratives and quantitative results adds credibility to the study since it gives a different perspective on the blended learning environment. The recommendations to instructors and instructional designers call for blending unique instruction methods, multimedia content, and individualized feedback options. It is also advised to make pedagogical revisions for less impactful activities and provide customized exercises to build student confidence. Finally, this study illustrates the transformative capabilities of blended learning to enhance ESL teaching practice. The insights deepen pedagogue practices and broadly contribute to ongoing discussions around effective ESL education strategies toward better learning.

The effect of augmented reality book on the proficiency of local anesthesia administration of the inferior alveolar nerve.

Advancements in augmented reality (AR) technology enable developers to create augmented reality books (AR Books). AR Books can show interactive 3D objects and multimedia content to the user. The purpose of this study was to investigate the effect of a developed AR Book on the theoretical and practical abilities in the administration of local anesthesia of the inferior alveolar nerve block. An exclusive AR Book was developed based on Chapter 14, Handbook of Local Anesthesia, seventh edition, Stanley F. Malamed, which enabled students to use their book (printed or electronic) as an AR Book. Forty dental students were randomly divided into control and study groups. In the control group, students were asked to use regular books, while in the study group, students were instructed to use interactive AR Books. All students took the written exam, scoring 0 to 10. Also, the administration of mandibular local anesthesia was evaluated in students. The level of concentration during the study, the written exam score, the practical score, the success rate of local anesthesia, and the needed time for injection were compared between the two groups. There was a significant difference in the level of concentration during the study (p<0.001), practical skill score (p=0.037), and needed time for injection (p=0.040) between the two groups. Conversely, the success rate of local anesthesia was almost similar (p=0.705). Although the written exam score was higher in the study group than in the control group, the difference was not significant (p=0.414). Using AR Books can engage students and help them to remain focused during the study. AR Books may be beneficial in enhancing the practical abilities of dental students.

Access to Resources and Preservation of the Cultural Heritage of Old Believers in North America: a Case Study of an Online Bibliography

This article discusses the history of the development and significance of the “Old Believers Bibliography in North America”, a digital resource dedicated to the documentation and preservation of the cultural legacy of Old Believers in North America. It addresses how this comprehensive digital bibliography provides access to a broad range of resources, including scholarly articles, historical documents, and multimedia content, pertinent to the study of Old Believer communities. The article highlights the role of this online platform in enhancing research accessibility, facilitating academic scholarship, and promoting cultural preservation as well as underlines the bibliography’s impact on both scholarly research and community awareness of Old Believers. It reflects on the broader implications of digital bibliographies in preserving intangible cultural heritage and fostering diverse historical narratives.

The Transformative Role of Digital Resources in Teaching and Learning

This study examines the evolving terrain of Digital Resources (DR) by investigating its influence on teaching methodologies and student achievement and exploring the diverse aspects of integrating these resources to improve teaching methods and establish more efficient learning settings. The study focuses on diverse DR, such as Learning Management Systems (LMS); interactive software, online platforms, and Collaboration and Communication Tools; including multimedia content used to produce Educational Software and Applications, and evaluates their influence on engagement, accessibility, and overall student success. These examples of digital resources represent the diverse array of digital tools that contribute to the transformation of teaching and learning in various educational settings. This list is not exhaustive, as the field of digital resources is continually evolving with the advancement of technology. The impact of these tools extends to areas such as accessibility, personalized learning, and the enhancement of both in-person and online education. Examining the integration of adaptive learning platforms seeks to understand how technology adapts to individual learning needs, providing tailored support to students and promoting a deeper understanding of educational content. Furthermore, the study explores the global connectivity and collaboration facilitated by DR, investigating the impact of virtual classrooms, video conferencing, and collaborative platforms on students' ability to connect with their peers and educators worldwide. It seeks to identify how this interconnectedness contributes to cultural awareness, diverse perspectives, and the development of global competencies. It also addresses challenges related to digital literacy, ethical use, and equitable access as it unfolds. The research aims to provide insights that inform best practices for educators, policymakers, and stakeholders in the ongoing endeavour to optimise integrating DR into teaching and learning. Ultimately, this research aspires to contribute valuable knowledge supporting education's evolution in the digital age. The researcher explores the transformative role of DR in education, examining how these tools contribute to a more dynamic and inclusive learning environment.

Beyond Grounding: Extracting Fine-Grained Event Hierarchies across Modalities

Events describe happenings in our world that are of importance. Naturally, understanding events mentioned in multimedia content and how they are related forms an important way of comprehending our world. Existing literature can infer if events across textual and visual (video) domains are identical (via grounding) and thus, on the same semantic level. However, grounding fails to capture the intricate cross-event relations that exist due to the same events being referred to on many semantic levels. For example, the abstract event of "war'' manifests at a lower semantic level through subevents "tanks firing'' (in video) and airplane "shot'' (in text), leading to a hierarchical, multimodal relationship between the events. In this paper, we propose the task of extracting event hierarchies from multimodal (video and text) data to capture how the same event manifests itself in different modalities at different semantic levels. This reveals the structure of events and is critical to understanding them. To support research on this task, we introduce the Multimodal Hierarchical Events (MultiHiEve) dataset. Unlike prior video-language datasets, MultiHiEve is composed of news video-article pairs, which makes it rich in event hierarchies. We densely annotate a part of the dataset to construct the test benchmark. We show the limitations of state-of-the-art unimodal and multimodal baselines on this task. Further, we address these limitations via a new weakly supervised model, leveraging only unannotated video-article pairs from MultiHiEve. We perform a thorough evaluation of our proposed method which demonstrates improved performance on this task and highlight opportunities for future research. Data: https://github.com/hayyubi/multihieve

CAMEL: Capturing Metaphorical Alignment with Context Disentangling for Multimodal Emotion Recognition

Understanding the emotional polarity of multimodal content with metaphorical characteristics, such as memes, poses a significant challenge in Multimodal Emotion Recognition (MER). Previous MER researches have overlooked the phenomenon of metaphorical alignment in multimedia content, which involves non-literal associations between concepts to convey implicit emotional tones. Metaphor-agnostic MER methods may be misinformed by the isolated unimodal emotions, which are distinct from the real emotions blended in multimodal metaphors. Moreover, contextual semantics can further affect the emotions associated with similar metaphors, leading to the challenge of maintaining contextual compatibility. To address the issue of metaphorical alignment in MER, we propose to leverage a conditional generative approach for capturing metaphorical analogies. Our approach formulates schematic prompts and corresponding references based on theoretical foundations, which allows the model to better grasp metaphorical nuances. In order to maintain contextual sensitivity, we incorporate a disentangled contrastive matching mechanism, which undergoes curricular adjustment to regulate its intensity during the learning process. The automatic and human evaluation experiments on two benchmarks prove that, our model provides considerable and stable improvements in recognizing multimodal emotion with metaphor attributes.

UMIE: Unified Multimodal Information Extraction with Instruction Tuning

Multimodal information extraction (MIE) gains significant attention as the popularity of multimedia content increases. However, current MIE methods often resort to using task-specific model structures, which results in limited generalizability across tasks and underutilizes shared knowledge across MIE tasks. To address these issues, we propose UMIE, a unified multimodal information extractor to unify three MIE tasks as a generation problem using instruction tuning, being able to effectively extract both textual and visual mentions. Extensive experiments show that our single UMIE outperforms various state-of-the-art (SoTA) methods across six MIE datasets on three tasks. Furthermore, in-depth analysis demonstrates UMIE's strong generalization in the zero-shot setting, robustness to instruction variants, and interpretability. Our research serves as an initial step towards a unified MIE model and initiates the exploration into both instruction tuning and large language models within the MIE domain. Our code, data, and model are available at https://github.com/ZUCC-AI/UMIE.

Professionals' digital competences and user profiles in social agencies and their impact on professional practice, family autonomy and wellbeing.

The health crisis of the last 3 years has revealed the weaknesses of the child and family support system based on the professional use of digital resources in social agencies. This study addresses three aims: to examine the level of professional digital competence; to analyze the user profiles in a variety of digital resources; and to test the impact of level of competences and user profiles on four aspects: professional practice, family satisfaction with the services, child and family wellbeing, and family autonomy in the exercise of the parenting role. Participants were 148 practitioners from social agencies who voluntarily responded to an online survey with 47 questions. Results showed that professionals perceive themselves as more competent in areas of information / data management and communication / collaboration than in the creation of digital content, security measures, and technical problem solving. Websites, email, and instant messaging were the sources more frequently used and with higher satisfaction, than structured programs, social networks and multimedia content. Variability in the user profiles showed three clusters: Cluster 1 Social network user (n = 13), Cluster 2 Diversified user (n = 75) and Cluster 3 Communicative instant user (n = 60). Participants in Cluster 2 compared to those in the other clusters were the most proficient on their digital competences and acknowledge the positive impact of digital resources on their professional practice and the psychological and social wellbeing of families. This study points the need for improvement in professionals' digital competences in some of the measured areas and the user profile of digital resources since both provide benefits on professional practice and family autonomy and wellbeing.

Multi-task Hierarchical Heterogeneous Fusion Framework for multimodal summarization

With the rise of multimedia content on the internet, Multimodal Summarization has become a challenging task to help individuals grasp vital information fast. However, previous methods mainly learn the different modalities indistinguishably, which is ineffective in capturing the fine-grained content and hierarchical correlation in multimodal articles. To resolve the present problem, this paper proposes a Multi-task Hierarchical Heterogeneous Fusion Framework (MHHF) to learn the hierarchical structure and heterogeneous correlation existing in the multimodal data. Specifically, we propose a Hierarchical Cross-modality Feature Fusion module to progressively explore the different levels of interaction from object-word features to sentence-scene features. Besides, a Multi-task Cross-modality Decoder is constructed to coalesce different levels of features with three sub-tasks, i.e., Abstractive Summary Generation, Relevant Image Selection, and Extractive Summary Generation. We conduct extensive experiments on three datasets, i.e., MHHF-dataset, CNN, and Daily Mail, which consist of 62880, 1970, and 203 multimodal articles, respectively. Our method achieves state-of-the-art performance on most metrics. Moreover, MHHF consistently outperforms the baseline model on MHHF-dataset by 5.88%, 4.41%, and 0.4% of Rouge-1, Rouge-2, and Rouge-L for the abstractive summarization task. Ablation studies show that both Hierarchical Cross-modality Feature Fusion and Multi-task Cross-modality Decoder can improve the quality of multimodal summarization output. Further diversity analysis and human evaluation also demonstrate that MHHF can generate more informative and fluent summaries.

Movie genre prediction based on the bidirectional encoder representations from transformer

The rapid expansion of digital media has underscored the growing significance of predicting genres to target audiences effectively and to enhance filmmakers' understanding of viewer preferences. In this study, we introduced a novel method for forecasting movie genres, leveraging the power of the Bidirectional Encoder Representations from Transformer (BERT) deep learning model. The research team employed a dataset sourced from Douban's website, which featured 5,000 movies, complete with cover images, titles, and genre information. This undertaking tackled several key challenges, including the extraction of features from textual data, the categorization of movie genres, and the incorporation of cultural nuances. BERT's bidirectional representation, especially its variant tailored for Chinese language tasks, 'bert-base-chinese', was employed to extract textual features. Meanwhile, the visual features from movie covers were processed using the Wide ResNet-50-2 architecture. The combined features underwent classification, and the resulting model achieved an accuracy of 34.67%, a recall rate of 74.5%, and an F1 score of 0.4755. The study validates the potential of the BERT model in predicting movie genres and offers significant insights for future research in multimedia content classification.

Detect with Style: A Contrastive Learning Framework for Detecting Computer-Generated Images

The detection of computer-generated (CG) multimedia content has become of utmost importance due to the advances in digital image processing and computer graphics. Realistic CG images could be used for fraudulent purposes due to the deceiving recognition capabilities of human eyes. So, there is a need to deploy algorithmic tools for distinguishing CG images from natural ones within multimedia forensics. Here, an end-to-end framework is proposed to tackle the problem of distinguishing CG images from natural ones by utilizing supervised contrastive learning and arbitrary style transfer by means of a two-stage deep neural network architecture. This architecture enables discrimination by leveraging per-class embeddings and generating multiple training samples to increase model capacity without the need for a vast amount of initial data. Stochastic weight averaging (SWA) is also employed to improve the generalization and stability of the proposed framework. Extensive experiments are conducted to investigate the impact of various noise conditions on the classification accuracy and the proposed framework’s generalization ability. The conducted experiments demonstrate superior performance over the existing state-of-the-art methodologies on the public DSTok, Rahmouni, and LSCGB benchmark datasets. Hypothesis testing asserts that the improvements in detection accuracy are statistically significant.

Minat Generasi Z Terhadap Belanja Online Tiktok Shop di Kabupaten Bener Meriah

Advances in technology and access to information have significantly changed the dynamics of society, as evidenced by the rise of online shopping platforms, commonly referred to as e-commerce. One of the benefits of the digital era is the ease of shopping online. Through online shopping platforms and product images, we can sell and buy goods practically. The purpose of this study is to test and analyse the effect of generation Z's interest in online shopping at TikTok Shop and also to test and analyse generation Z's digital behaviour towards online shopping at TikTok Shop. The method used in this research is quantitative using a sample of 100 respondents selected through purposive sampling method in Redelong City, Bener Meriah Regency. Data were collected through questionnaires distributed via social media and analysed using multiple linear regression. The results showed that the variables of interest (X1) and digital behaviour (X2) had a positive and significant effect on generation Z online shopping in Bener Meriah Regency. Then the independent variables contribute simultaneously 53.1% to generation Z shopping at TikTok Shop (Y). While other factors contributed 46.9%, the rest was influenced by other variables. It can be concluded that the preference of generation Z who has an interest and also a behavioural attitude towards the TikTok shop platform to do this shopping is due to interactive, attractive multimedia content, consumer confidence, and the platform's ability to provide a shopping experience with good service.

Cloud media video encoding: review and challenges

AbstractIn recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user.

Datafication of audiovisual archives: from practice mapping to a thinking model

PurposeRecent archiving and curatorial practices took advantage of the advancement in digital technologies, creating immersive and interactive experiences to emphasize the plurality of memory materials, encourage personalized sense-making and extract, manage and share the ever-growing surrounding knowledge. Audiovisual (AV) content, with its growing importance and popularity, is less explored on that end than texts and images. This paper examines the trend of datafication in AV archives and answers the critical question, “What to extract from AV materials and why?”.Design/methodology/approachThis study roots in a comprehensive state-of-the-art review of digital methods and curatorial practices in AV archives. The thinking model for mapping AV archive data to purposes is based on pre-existing models for understanding multimedia content and metadata standards.FindingsThe thinking model connects AV content descriptors (data perspective) and purposes (curatorial perspective) and provides a theoretical map of how information extracted from AV archives should be fused and embedded for memory institutions. The model is constructed by looking into the three broad dimensions of audiovisual content – archival, affective and aesthetic, social and historical.Originality/valueThis paper contributes uniquely to the intersection of computational archives, audiovisual content and public sense-making experiences. It provides updates and insights to work towards datafied AV archives and cope with the increasing needs in the sense-making end using AV archives.

President’s Column: Maximizing Your Membership by Leveraging SPE’s Multimedia Content

_ Editor’s Note: This month we are introducing a summary of the full transcript. We encourage you to listen to the podcast episode to hear the full conversation on your favorite podcast platform under “The SPE Podcast.” _ In this podcast episode, I am joined by Dana Otillio, vice president of marketing and communications at SPE, to discuss how members can maximize their membership by leveraging multimedia content. The episode covers various aspects, including SPE Live, webinars, online training, and the Distinguished Lecturer (DL) Program. The conversation delves into the history and evolution of SPE’s multimedia content and its impact on connecting members globally. I introduce the episode by highlighting the goal of helping members understand how to get the most out of their SPE membership. As I like to do, I liken an SPE membership to a gym membership, emphasizing the importance of exploring all available offerings to create your energy future. Our discussion delves into the birth of SPE Live during the pandemic, emphasizing its role in overcoming the challenges posed by the inability to hold in-person events. Its success in delivering timely, relevant technical knowledge led to it becoming a series on LinkedIn providing conversational content on global topics. I also highlight its popularity and mention the addition of Tech Talks, sponsored content sessions that have garnered significant views. To date these have been very popular, streaming four to five SPE Lives a month and garnering 1,600 to 1,700 views live and on-demand per presentation. It’s one of our many growing outlets for multimedia. Evolution of Multimedia Content Our discussion then turns to the accessibility of multimedia content through the SPE Energy Stream website, launched in 2022. I describe how it is a one-stop shop for all multimedia content, easily accessible from the spe.org homepage. The website offers webinars, online training, SPE Live, and podcast episodes showcasing the organization’s commitment to providing a comprehensive multimedia experience. I like to think of SPE Energy Stream as the “OnePetro” for multimedia content. The conversation explores the integration of the DL program with SPE Live, allowing DLs to reach a broader audience through live broadcasts. We take a moment to review the DL program’s significance in connecting with sections worldwide, and the addition of SPE Live as a feature expands the program’s reach. We distinguish between SPE Live, which focuses on conversational content, and webinars, which are more technical, lasting 60 to 90 minutes. The availability of webinars on-demand and the opportunity to earn continuing education credits are emphasized. Online training courses, both SPE-developed and in partnership with IHRDC, are discussed as more extensive, allowing members to learn at their own pace. In 2019, SPE partnered with IHRDC to link their training content to SPE members. It’s offered through the SPE website and provides a reduced rate for our members.

Diversified realistic face image generation GAN for human subjects in multimedia content creation

AbstractFace image generation plays an important role in generating innovative and unique multimedia content using the GAN model. With these qualities of the GAN model, they have numerous challenges in the human face image generation. The problems encountered in the generation of facial images are like blurriness in images, incomplete details in the generated facial images, high computational power requirements, and so forth. In this manuscript, we proposed a GAN model that utilizes the composite strength of VGG‐16 and ResNet‐50's models to overcome those difficulties. It uses VGG‐16 to build a discriminator model to discriminate between real and fake images. The generator model utilizes a combination of components from the ResNet‐50 and VGG‐16 models to enhance the image generation process at each iteration, resulting in the creation of realistic face images. The proposed DRFI GAN (Diversified and Realistic Face Image Generation GAN) model's generator achieves an impressive low FID score of 20.50, which is less than existing state‐of‐the‐art approaches. Furthermore, our findings indicate that the images generated by the DRFI GAN model exhibit 10%–15% greater efficiency and realism with reduced training time compared to existing state‐of‐the‐art methods with lower FID scores.