Multimedia Content Research Articles

Armero

The objective of this article is to analyze the impact generated by multimedia videos published on YouTube, Facebook, and Instagram about Armero, to encourage memory tourism. The categories that are evidenced in this research are based on memory tourism and multimedia. This article is based on an analysis with a qualitative approach which will analyze the impact generated by the videos published on social networks about Armero to encourage memory tourism. The results are obtained through an analysis of 20 videos which contemplates: the title, content, and comments from viewers; multimedia content and video platforms have kept alive the history of Armero in different ways and feed memory tourism.

SMSE: A serverless platform for multimedia cloud systems

SummaryAlong with the rise of domain‐specific computing (ASICs hardware) and domain‐specific programming languages, we envision that the next step is the emergence of domain‐specific cloud platforms. Considering multimedia streaming as one of the most trendy applications in the IT industry, the goal of this study is to develop serverless multimedia streaming engine (SMSE), the first domain‐specific serverless platform for multimedia streaming. SMSE democratizes multimedia service development via enabling content providers (or even end‐users) to rapidly develop their desired functionalities on their multimedia contents. Upon developing SMSE, the next goal of this study is to deal with its efficiency challenges and develop a function container provisioning method that can efficiently utilize cloud resources and improve the users' quality of service. In particular, we develop a dynamic method that provisions durable or ephemeral containers depending on the spatiotemporal and data‐dependency characteristics of the functions. Evaluating the prototype implementation of SMSE under real‐world settings demonstrates its capability to reduce both the containerization overhead, and the makespan time of serving multimedia processing functions (by up to 30%) in compare to the function provision methods that are being used in the general‐purpose serverless cloud systems.

Inconsistent Matters: A Knowledge-Guided Dual-Consistency Network for Multi-Modal Rumor Detection

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions. Our codes are available at https://github.com/MengzSun/KDCN.

The Impact of Social Media on the Evolution of Language and Communication Trends

Social media has an unquestionable impact on every aspect of our lives in the quickly changing digital world of the twenty-first century. These social media sites, such as Facebook, Twitter, Instagram, and TikTok, have changed how we interact with one another and permanently altered the way we speak and interact with one another. “The Impact of Social Media on the Evolution of Language and Communication Trends” is a thorough investigation of the complex interaction between the digital world and how we communicate. In addition to giving people a platform to communicate their ideas, opinions, and experiences with a large audience, social media has also sparked the birth of brand-new language in the current digital era, social media has permeated every aspect of our daily lives and significantly impacted how we communicate and the development of language. This abstract examines the complex relationship between social media and the development of language, illuminating how online platforms have shaped communication styles. Social media, linguistic development, and communication styles. The emergence of social media sites like Facebook, Twitter, Instagram, and TikTok has completely changed how we communicate with one another. This change has had a lasting impact on the structure of language itself in addition to the modalities of communication. Language has evolved more quickly thanks to social media’s quick distribution of information. With character constraints and instant messaging, abbreviations, acronyms, and emojis have grown ubiquitous, streamlining communication. We investigate how these language advancements are altering how we communicate our thoughts and feelings online. Social media allows for connections between users from different language backgrounds despite geographical barriers. Due to this, hybrid languages have emerged, in which users combine components of many tongues to produce a distinctive digital vernacular. We investigate how linguistic variety is being woven into the fabric of globalization through social media. Communication habits have changed as a result of social media’s accessibility and immediate nature. The way we interact with one another now happens in real-time, influenced by threaded discussions, multimedia content, and viral trends. We examine how these altering communication styles impact knowledge sharing, interpersonal interactions, and even societal discourse. This abstract highlights how social media has had a significant impact on linguistic development and communication styles. Language is now a dynamic and developing phenomenon that is being influenced by billions of users throughout the world thanks to the digital age’s facilitation of linguistic innovation as well as its democratization. For linguists, communication academics, and anybody interested in the constantly changing nature of human communication in the twenty-first century, understanding the relationship between social media and language evolution is crucial.

Beyond Hypoglossal Hype: Social Media Perspectives on the Inspire Upper Airway Stimulation System

In the landscape of sleep surgery, the Inspire® Upper Airway Stimulation (UAS) device has gained prominence as an increasingly popular treatment option for obstructive sleep apnea, prompting significant discourse across social media platforms. This study explores the social media narrative of the UAS device, particularly the nature of multimedia content, author demographics, and audience engagement on Instagram, Facebook, and TikTok. Our analysis encompassed 423 public posts, revealing images (67.4%) and videos (28.1%) as the dominant content types, with over a third of posts authored by physicians. A notable 40% of posts were advertisements, whereas patient experiences comprised 34.5%. TikTok, although presenting a smaller sample size, showed a substantially higher engagement rate, with posts averaging 152.9 likes, compared with Instagram and Facebook at 32.7 and 41.2 likes, respectively. The findings underscore the need for otolaryngologists and healthcare professionals to provide clear, evidence-based information on digital platforms. Given social media’s expanding role in healthcare, medical professionals must foster digital literacy and safeguard the accuracy of health information online. In this study, we concluded that maintaining an evidence-based, transparent digital dialogue for medical innovations such as the UAS device necessitates collaborative efforts among physicians, health institutions, and technology companies.

Optimizing Multimedia Data Delivery in Wireless Multimedia Sensor Network: A QoS-Driven Routing Protocol Design

In recent years, there has been a shift towards utilizing wireless sensor networks (WSN) for multimedia applications, even though these networks were originally designed for transmitting small data volumes. In order to satisfy the increasing need for improved throughput and real-time performance when handling multimedia content, it becomes essential to streamline and enhance the routing protocols. In this document, we introduce a routing algorithm called Honey Badger optimization Algorithm (HBOA). This algorithm is designed to minimize end-to-end packet delay and reduce packet loss in wireless sensor networks (WSN). Instead of selecting shortest path to the destination HBOA selects optimised high throughput path to transport multimedia packets ovet the nertwork. Furthermore, it incorporates a network void-bypass mechanism to enhance network reliability when it encounters gaps in the network. Energy management is integrated to enhance the longevity of sensor nodes by adjusting the radio transmission coverage to a distance that sensor nodes can efficiently reach. The simulation results when compared with existing routing protocols demonstrate that HBOA achieves a 8% reduction in end-to-end delay and a 18% decrease in packet loss ratio while effectively managing energy consumption when compared to alternative algorithms. Load balancing occurs as a result of energy management, which makes the remaining energy of sensor nodes uniformly dispersed throughout the entire network. Through Simulation, the performance of HBOA is also compared to that of AGEM, TPGF, GPSR, and AODV.the esults suggests the HBOA performance better under conditions that are similat to these, such as low packet end-to-end- delay, low packet loss, and load balancing of routing channels.

Preparing undergraduates for the post-pandemic workplace: Teams of education and engineering students teach engineering virtually

When schools and universities across the world transitioned online due to the COVID-19 pandemic, Ed+gineering, a National Science Foundation (NSF) project that partners engineering and education undergraduates to design and deliver engineering lessons to elementary students, also had to shift its hands-on lessons to a virtual format. Through the lens of social cognitive theory (SCT), this study investigates engineering and education students’ experiences during the shift to online instruction to understand how they perceived its influence on their learning. As a result of modifying their lessons for online delivery, students reported learning professional skills, including skills for teaching online and educational technology skills, as well as Science, Technology, Engineering, and Mathematics (STEM) content. Some also lamented missed learning opportunities, like practice presenting face-to-face. Students’ affective responses were often associated with preparing and delivering their lessons. SCT sheds light on how the mid-semester change in their environment, caused by the shift in designing and teaching from face-to-face to online, affected the undergraduate engineering and education students’ personal experiences and affect. Overall, the transition to fully online was effective for students’ perceived learning and teaching of engineering. Though students experienced many challenges developing multimedia content for delivering hands-on lessons online, they reported learning new skills and knowledge and expressed positive affective responses. From the gains reported by undergraduates, we believe that this cross-disciplinary virtual team assignment was a successful strategy for helping undergraduates build competencies in virtual skills. We posit that similar assignment structures and opportunities post-pandemic will also continue to prepare future students for the post-pandemic workplace.

Regulating Fake News and Hoaxes: A Comparative Analysis of Indonesia and Malaysia

Indonesia and Malaysia already have regulations prohibiting the spreading of fake news and hoaxes. However, the critical question is whether these regulations can tackle the spread of fake news and hoaxes, considering their detrimental impact on the economy and reputation. This is aimed at comprehensively understanding the legal framework in both countries. This research was designed using the normative juridical method. The approaches used are statutory approach, conceptual approach, and comparative approach. The result show Indonesia's penal policy was recently created by passing a new Criminal Code. It seeks to protect public order, public welfare, and democratic values, emphasizing a balance between freedom of speech and combating the adverse effects of hoax and fake news. Meanwhile, Malaysia employs regulatory measures through the Communication and Multimedia Content Forum, relying on voluntary compliance and cooperation from various stakeholders. Looking ahead, emerging technologies and methodologies in digital forensics offer promise for more effective means of identifying the origins of fake news

A Lightweight Algorithm for Detecting Fake Multimedia Contents on Social Media

The significant growth of the fourth industrial revolution (Industry 4.0) coupled with the widespread adoption of social media across the world has initiated new challenges that deserve the attention of researchers and industry leaders especially in detecting and preventing fake multimedia contents on social media. The forging of multimedia contents like videos and images for malicious activities is gradually becoming very rampant and this has serious psychological, health, political and economic consequences on the targeted individuals or close associates of the victims. The application of deepfake algorithms to make manipulated videos and images has contributed in making it very difficult to identify fake videos and images from the real multimedia contents. The availability of the internet and social media has made the spread of deepfake videos and images very fast and at an alarming rate. This research work understanding the dire need to detect deepfake videos and images (multimedia contents) proposes a lightweight algorithm to detect deepfake videos and images on social media platforms. The need for a lightweight algorithm is essential to enable low computational devices to be able to apply the algorithm without computational challenges and overheads. The proposed model has demonstrated a significant reduction in the computational and time complexities. The research work also presented a comparative analysis of some selected deep learning models with emphasis on the datasets used, their features and challenges identified.

Text-to-movie authoring of anatomy lessons

There is a need for a simple yet comprehensive tool to produce and edit pedagogical anatomy video courses, given the widespread usage of multimedia and 3D content in anatomy instruction. Anatomy teachers have minimal control over the present anatomical content generation pipeline. In this research, we provide an authoring tool for instructors that takes text written in the Anatomy Storyboard Language (ASL), a novel domain-specific language (DSL) and produces an animated video. ASL is a formal language that allows users to describe video shots as individual sentences while referencing anatomic structures from a large-scale ontology linked to 3D models. We describe an authoring tool that translates anatomy lessons written in ASL to finite state machines, which are then used to automatically generate 3D animation with the Unity 3D game engine. The proposed text-to-movie authoring tool was evaluated by four anatomy professors to create short lessons on the knee. Preliminary results demonstrate the ease of use and effectiveness of the tool for quickly drafting narrated video lessons in realistic medical anatomy teaching scenarios.

Multimedia Content of the Blogosphere as a Tool for Informational Influence

The purpose of the study is to determine the resources of the information influence of the content of the blogosphere of the Internet publication "Censor.NET". To realise this goal, it is necessary to solve the following tasks: perform a thematic analysis of the multimedia content of the blogosphere; analyse the performance of content topics; establish a typology of visual materials; determine the productivity and artistic features of using different types of content visualisation. The research methodology lies in the use of general scientific methods of analysis, synthesis, logical generalisations, the application of which made it possible to establish the thematic focus, typology of visualisation of textual information, their role in the implementation of informational influence on users of the blogosphere of the Internet publication "Censor.NET". The use of synchronous slice and statistical methods made it possible to establish quantitative indicators of the leading topics of posts in the blogosphere and types of visualisation of the analysed text. The synchronous slice is formed on the basis of a sample population. Visualisation was used to present the results of the study, in particular, regarding the topic of posts in the blogosphere and the types of visualisation of the analysed text. The scientific novelty of the work consists in the synchronous monitoring of the thematic direction and forms of visualisation of the content of the online publication, which makes it possible to evaluate its possibilities of exerting an informational influence on readers. Conclusions. The content of the blogosphere on the "Censor.NET" platform is a combination of textual and visual information. The thematic structure of the blogs indicates the top problems that are related to the political, military, socio-economic conditions of life in the country caused by the Russian-Ukrainian war. The content of the blogs indicates a high level of social instability, the expectation of aggression, as well as the activities of the Ukrainian authorities in relation to today's complex challenges. The most common visualisation methods include photos, screenshots, and videos. The specificity of the multimedia content of the blog is determined by the features of the blog platform and the nature of the publications. A blog with an optimal ratio of text and visual information helps to attract a wide target audience of readers, to exert an informational and emotional influence on them. Keywords: blog, blogosphere, thematic text structure, content visualisation, photography, video content.

Teaching Chest Tube Insertion by Blended Learning: A Multi-Dimensional Analysis.

Emerging technologies are being incorporated in surgical education. The use of such technology should be supported by evidence that the technology neither distracts nor overloads the learner and is easy to use. To teach chest tube insertion, we developed an e-learning module, as part of a blended learning program delivered prior to in-person hands-on simulation. This pilot study was aimed to assess learning effectiveness of this blended learning, and cognitive load and the usability of e-learning. The interactive e-learning module with multimedia content was created following learning design principles. In advance of the standard simulation, 13 first-year surgical residents were randomized into two groups: 7 received the e-learning module and online reading materials (e-learning group); 6 received only the online reading materials (controls). Knowledge was evaluated by pre-and post-tests; technical performance was assessed using a Global Rating Scale by blinded assessors. Cognitive load and usability were evaluated using rating scales. The e-learning group showed significant improvement from baseline in knowledge (P = .047), while controls did not (P = .500). For technical skill, 100% of residents in the e-learning group reached a predetermined proficiency level vs 60% of controls (P = .06). The addition of e-learning was associated with lower extrinsic and greater germane cognitive load (P = .04, .03, respectively). Usability was evaluated highly by all participants in e-learning group. Interactive e-learning added to hands-on simulation led to improved learning and desired cognitive load and usability. This approach should be evaluated in teaching of other procedural skills.

“What Do I Say? How Do I Say it?” Twitter as a Knowledge Dissemination Tool for Mental Health Research

This study aims to generate evidence-based guidelines for researchers regarding how to effectively disseminate mental health research via Twitter. Three hundred mental health research Tweets posted from September 2018 to September 2019 were sampled from two large Australian organizations. Twenty-seven predictor variables were coded for each Tweet across five thematic categories: messaging; research area; mental health area; external networks; and media features. Regression analyses were conducted to determine associations with engagement outcomes of Favourites, Retweets, and Comments. Less than half (n = 10) of predictor variables passed validity tests. Notably, conclusions could not reliably be drawn on whether a Tweet featured evidence-based information. Tweets were significantly more likely to be Retweeted if they contained a hyperlink or multimedia. Tweets were significantly more likely to receive comments if they focused on a specific population group. These associations remain significant when controlling for organization. These findings indicate that researchers may be able to maximize engagement on Twitter by highlighting the population groups that the research applies to and enriching Tweets with multimedia content. In addition, care should be taken to ensure users can infer which messages are evidence-based. Guidelines and an accompanying resource are proposed.

Strategy for Developing Android-Based Teaching Materials for Text-based Indonesian Learning as an Implementation of the Independent Curriculum in Junior High School

This study aims to determine strategies for developing android-based teaching materials for text-based Indonesian language learning as an implementation of the independent curriculum in junior high schools. Based on the achievements of learning Indonesian in the independent curriculum, the various texts studied constitute a line of thought that can optimize the use of language for work and lifelong learning. This research method is literature study, namely the method of collecting data by understanding and studying theories from various literature related to research. Therefore, the research data is in the form of data that is relevant to the topic. The data comes from books and research articles that have been published in national and international journals. The strategy in developing android-based teaching materials for text-based Indonesian learning can be carried out by applying the ADDIE development model to produce teaching material prototypes in the form of applications that contain multimedia content by combining several learning media such as text, audio, graphics, images, and video to improve the quality of learning Indonesian and increase student learning independence.

The influence of sociodemographic factors on students' attitudes toward AI-generated video content creation

Artificial Intelligence (AI) and Machine Learning (ML) technologies offer the potential to support digital content creation and media production, providing opportunities for individuals from diverse sociodemographic backgrounds to engage in creative activities and enhance their multimedia video content. However, less attention has been paid to recent research exploring any possible relationships between AI-generated video creation and the sociodemographic variables of undergraduate students. This study aims to investigate the multifaceted relationship between AI-generated video content and sociodemographics by examining its implications for inclusivity, equity, and representation in the digital media landscape. An empirical study about the use of AI in video content creation was conducted with a diverse cohort of three hundred ninety-eighth undergraduate (n = 398) students. Participants voluntarily took part and were tasked with conceiving and crafting their AI-generated video content. All instruments used were combined into a single web-based self-report questionnaire that was delivered to all participants via email. Key research findings demonstrate that students have a favorable disposition when it comes to incorporating AI-supported learning tasks. The factors fostering this favorable attitude among students include their age, the number of devices they use, the time they dedicate to utilizing technological resources, and their level of experience. Nevertheless, it is the student’s participation in AI training courses that exerts a direct impact on students’ ML attitudes, along with their level of contentment with the reliability of these technologies. This study contributes to a more comprehensive understanding of the transformative power of AI in video content creation and underscores the importance of considering instructional contexts and policies to ensure a fair and equitable digital media platform for students from diverse sociodemographic backgrounds.

Robust dense-field based copy-move forgery localization using generic radial harmonic Fourier moment invariants.

As the importance of reliable multimedia content increases in today's society, image forensics is a growing field of research. The act of copying and pasting specific parts of an image, known as copy-move forgery, may be utilized for illegal or unethical purposes. Just as with other vision-related technologies, the accuracy of forensic analysis depends on having an appropriate image representation. Most existing feature extraction techniques do not accurately reflect the underlying image content leading to reduced performance. In this article, to detect the copy-move forgery attack, the Generic Radial Harmonic Fourier Moment (GRHFM) is proposed for reliable and distinctive image representation. The algorithm has the ability to effectively manipulate the distribution of zeros to emphasize certain image regions. Additionally, the relationships between complex exponentials and trigonometric functions are exploited to efficiently compute and easily implement the transform kernels. The efficacy of the algorithm is illustrated through experiments on dense-domain-based matching patterns. Experimental results on five benchmarking databases prove the effectiveness of the proposed approach compared with the state-of-the-art methods. According to the average scores, the proposed method demonstrates superior accuracy in overall localization performance. The F1 score, precision, and recall percentage values obtained are 92.5, 95.44, and 91.96, respectively. Robustness experiments on more challenging attacks are also conducted on FAU dataset. Results show that the proposed framework satisfies invariance to the various image variations, and thus an enhanced robustness compared to the previous methods. Moreover, the advantage of reasonable computational cost implies its potential use in real-world forensic applications.

Generation and detection of manipulated multimodal audiovisual content: Advances, trends and open challenges

Generative deep learning techniques have invaded the public discourse recently. Despite the advantages, the applications to disinformation are concerning as the counter-measures advance slowly. As the manipulation of multimedia content becomes easier, faster, and more credible, developing effective forensics becomes invaluable. Other works have identified this need but neglect that disinformation is inherently multimodal. Overall in this survey, we exhaustively describe modern manipulation and forensic techniques from the lens of video, audio and their multimodal fusion. For manipulation techniques, we give a classification of the most commonly applied manipulations. Generative techniques can be exploited to generate datasets; we provide a list of current datasets useful for forensics. We have reviewed forensic techniques from 2018 to 2023, examined the usage of datasets, and given a comparative analysis of each modality. Finally, we give another comparison of end-to-end forensics tools for end-users. From our analysis clear trends are found with diffusion models, dataset granularity, explainability techniques, synchronisation improvements, and learning task diversity. We find a roadmap of deep challenges ahead, including multilinguality, multimodality, improving data quality (and variety), all in an adversarial ever-changing environment.

Pipeline Design for Data Preparation for Social Media Analysis

In a data-driven culture, in which analytics applications are the main resources for supporting decision-making, the use of high-quality datasets is mandatory to minimize errors and risks. For this reason, data analysis tasks need to be preceded by a data preparation pipeline. The design of such a pipeline is not trivial: the data analyst must carefully choose the appropriate operations considering several aspects. This is often performed by adopting a trial-and-error approach that does not always lead to the most effective solution. In addition, extracting information from social media poses specific problems due to the need to consider only posts relevant for the analysis, for its dependence from the context being considered, for its multimedia contents, and for the risk of filtering out informative posts with automatic filters. In this article, we propose a systematic approach to support the design of pipelines that are able to effectively extract a relevant dataset for the goal of the analysis of data from social media. We provide a conceptual model for designing and annotating the data preparation pipeline with quality and performance information, thus providing the data analyst preliminary information on the expected quality of the resulting dataset in a context-aware manner. The generation of metadata related to the processing tasks has been recognized as essential for enabling data sharing and reusability. To this aim, the dataset resulting from the pipeline application is automatically annotated with provenance metadata to get a detailed description of all the activities performed by the pipeline on them. As a case study, we consider the design of a pipeline for creating datasets of images extracted from social media in order to analyze behavioural aspects during COVID-19.

Automatic Intelligence Caption Generator

An Image Caption Generator is a sophisticated AI system that combines computer vision and natural language processing to automatically create descriptive textual captions for images. This technology utilizes deep learning, particularly Convolutional Neural Networks (CNNs), to analyze and extract meaningful visual features from the input image. These features capture details about the objects, scenes, and elements within the image. Subsequently, a natural language processing model, often built on Recurrent Neural Networks (RNNs) or Transformers, processes these visual features and generates coherent, contextually relevant captions. Post-processing steps may be applied to enhance the quality of the generated text. The primary aim of Image Caption Generators is to facilitate image understanding, improve accessibility, and enhance content search ability by providing human-readable descriptions for visual content. This technology is instrumental in various fields, including content tagging, accessibility tools for the visually impaired, and enhancing user experiences in multimedia content management systems, ultimately bridging the gap between visual and textual information for a more comprehensive and human-like interpretation of image. Key Words:Image Recognition, Internet, Image-To-Caption, Contextual Understanding,Image Captioning.

Role of Social Media In Business Management Students’ Communication Skills

This study explores the role of social media in business management students' communication skills. Specifically, the study aims to answer ten research questions related to social media use, benefits and challenges, perceptions, improvements, engagement, workplace attitudes, communication patterns and behaviours, training strategies, and academic performance. Quantitative and qualitative methods, such as surveys, interviews, and social media analysis, were utilized to gather data from business management students. The findings suggest that social media is widely used by business management students for communication purposes, with a preference for certain platforms. Business management students recognize the benefits of social media but also acknowledge challenges such as privacy and security concerns. The use of social media in communication positively influences engagement among students, but improvements in multimedia content creation and knowledge of privacy and security settings are needed. The survey findings suggest that business management students can benefit from several recommendations to effectively use social media for professional purposes. They should prioritize engaging in conversations, invest in multimedia content creation and promotions, increase their knowledge of privacy settings, improve their proficiency in social media analytics, participate in online courses, and seek opportunities to collaborate. By following these recommendations, students can leverage social media in their professional lives, enhance their communication skills, and adapt to changes in the business environment.