Multimedia Content Research Articles

News Alert App

The News Alert Android App is a revolutionary mobile application designed to keep users informed with real- time access to breaking news from across the globe. With its user-friendly interface, the app ensures that users can easily navigate through various news categories and receive instant notifications for critical updates and emergencies. Offering personalized news preferences, users can customize their experience by selecting specific topics, regions, or industries of interest. Beyond text-based news, the app provides multimedia content, including images and videos, to enhance the storytelling experience. Social media integration allows users to share news updates with their networks, encouraging meaningful discussions and raising awareness about important issues. With AI-driven algorithms, the app delivers smart notifications to avoid overwhelming users with redundant or irrelevant information. Whether online or offline, users can access up-to-date news, bookmark articles, and stay connected to the world's events. The News Alert Android App is a powerful tool for those seeking timely and reliable information, fostering an informed and engaged community in today's fast-paced information age. The news alert app includes modules like home, science technology, sports, health, entertainment for users. Keyword: Real-Time News Delivery, User Experience, User- Centric Design, Push Notification.

Watermarking and Encryption Technique to Avoid Data Leakage in Cloud Storage with Block Chain Technology

Multimedia data sharing is increasingly becoming an essential part of the daily life for end users to access different systems, services, and applications. Data disclosure frequently occurs in real-world cloud storage services. Authentication and copyright protection of multimedia contents has always been a concern in secure data transfer media. The problem has become more critical with the increasing use of the Internet and digital technologies. However, making the protection of copyright is more complex and difficult. In proposed approach both Watermarking and Proxy Re-encryption (PRE) approach utilized for efficient multimedia content sharing. Watermarking is used to hiding the information such as hide secret information in digital media like images. Encryption techniques used to provide security to data. In proposed work, secret key can be encrypted using encryption algorithm with the help of key. Then user’s private key combined with encrypted key information and can be embedded within the image or audio using LSB (Least Significant Bit). After embedding of secret information, image or audio can be encrypting using ECC Encryption algorithm. Finally, authorized user can extract decryption key with the help of embedded data verification process. Unauthorized or illegal access can identify, when user information does not match with embedded information. And also secure these transaction details using Block chain technology.

On the Consumption of Multimedia Content Using Mobile Devices: a Year to Year User Case Study

In the early days, consumption of multimedia content related with audio signals was only possible in a stationary manner. The music player was located at home, with a necessary physical drive. An alternative way for an individual was to attend a live performance at a concert hall or host a private concert at home. To sum up, audio-visual effects were only reserved for a narrow group of recipients. Today, thanks to portable players, vision and sound is at last available for everyone. Finally, thanks to multimedia streaming platforms, every music piece or video, e.g. from one’s favourite artist or band, can be viewed anytime and everywhere. The background or status of an individual is no longer an issue. Each person who is connected to the global network can have access to the same resources. This paper is focused on the consumption of multimedia content using mobile devices. It describes a year to year user case study carried out between 2015 and 2019, and describes the development of current trends related with the expectations of modern users. The goal of this study is to aid policymakers, as well as providers, when it comes to designing and evaluating systems and services.

Padlet Mobile Training Model for Novice Omani EFL In-service Teachers

In Oman, novice in-service primary English teachers struggle with the theoretical content of professional training programs, which impedes knowledge acquisition and transfer into teaching practices. This research stems from the benefits of continuous professional development, communities of practice, and practical and reflective learning on teachers’ exposure to teaching methods resulting in improved teaching skills and techniques. Considering the affordances of mobile learning, Padlet training was designed as an alternative to face-to-face programs during the Covid-19 pandemic to support in-service primary teachers with online synchronous teaching. The objectives were to improve participation, content acquisition, and knowledge transfer into teaching practices. Six novice Omani in-service English primary teachers participated in training for seven months. The data from semi-structured interviews, observations, interactions, peer feedback, and teaching e-portfolios were analysed with a systematic grounded theory three-step coding, which were performed until model emergence and saturation. The findings confirmed the contributions of the Padlet training to the enhancement of content acquisition and transfer due to ubiquitous access, simplified multimedia content, hands-on practice, instructor-peer feedback, and digital self-reflective e-portfolios. The teachers were enthusiastic about the program, which led to a virtual circle. Participants viewed Padlet as an engaging platform to host a community, access, create, and share content. Ubiquitous access and support within a peer community resulted in regular attendance, engaged participation, and increased confidence, which led to improved content acquisition and content transfer. The resulting Padlet training model presented in this article could be replicated to design mobile professional development training programs in similar contexts.

Content sharing strategy for blind popularity distribution in D2D communications

AbstractThe development of multimedia content continuously encourages the appearances of new multimedia applications. Meanwhile, Device to Device (D2D) is regarded as an important 5G technology that creates a direct connection between two mobile devices. We combine the content sharing traits in D2D network situations in response to the problem of massive amounts of multimedia content being distributed. As a result, in this paper, we provide a popularity‐based information mining and content placement strategy for blind popularity distribution in D2D scenarios. To analyze the cache hit performance in D2D networks under the presumption of deterministic content popularity, we first construct a D2D content caching framework. Then, we design a multiarm bandits model and suggest a single and multicache placement policy based on online learning for blind popularity in D2D networks. Finally, the experimental results demonstrate that the proposed method achieves better convergence and a better cache hit ratio than the other strategies.

A Novel Automatic Content Generation and Optimization Framework

With the rapid growth of IoT multimedia devices, more and more content is being delivered in multimedia forms which generally requires more effort and resources from users to create and could be challenging to streamline. In this paper, we propose Text2Animation (T2A), a framework that helps to generate complex multimedia content, and animation, from the simple textual script input. By leveraging recent advances in computational cinematography and video understanding, the purposed workflow further reduces associated knowledge requirements dramatically for cinematography. By jointly incorporating the fidelity and aesthetic models, T2A jointly considers the comprehensiveness of the visual presentation of the input script and the compliance of generated video with given cinematography specifications. The virtual camera placement in a 3D environment is mapped into an optimization problem that can be resolved by using dynamic programming to achieve the lowest computational complexity. Experimental results show that T2A can reduce the manual animation production process by around 74%, and the new optimization framework can improve the perceptual quality of the output video by up to 35%. More video footage can be found at https://www.youtube.com/watch?v=MMTJbmWL3gs.

Ghost Mines for Geoheritage Enhancement in the Umbria Region (Central Italy)

The paper proposes a method to valorize abandoned mines whose traces were lost in the territory and in the collective memory. We selected two case studies in the Umbria region (central Italy) that were used as examples. The evidence of the presence of lignite mines on the Upper Tiber River Valley (northern Umbria) has been completely erased, and since they were located in rural areas, they represent an interesting challenge regarding recovering the memory of the places and proposing a no-longer-existent site as a geosite. The recovery and valorization of historical documents of the two lignite mines (Caiperino–Terranera and Carsuga) and their conversion into a digital format was carried out before constructing a geolocalized database in a GIS environment. This framework is the starting point for a promising dissemination process via a digital media app, using multimedia contents as video, 3D models and the principles of augmented reality (AR) to enhance the touristic or didactic experience and promote the cultural heritage of the territory by keeping the memory of ’ghost places’.

CPS: Cross-interface network Partitioning and Scheduling towards QoS-aware data flow delivery in multimedia IoT

In the Internet of Things (IoT), multimedia traffic for audio, image, and video accounts for the largest proportion (over 78.7%) of the total traffic, bringing forward the vision of multimedia IoT (M-IoT). As part of the realization of loT, M-IoT is a general network paradigm that constitutes many smart objects equipped with the capability to collect multimedia data from the physical environment and deliver the data to other things. To satisfy a certain level of user experience, Quality of Service (QoS) is required to be regulated to ensure acceptable delivery of the multimedia content. As the most widely-used wireless technology, WiFi has been recommended for IoT communications for its high data rate, native IP compatibility, and good reusability of the existing infrastructures. However, WiFi suffers from channel contention, especially during multi-hop communications, which degrades the QoS performance and hinders its use for many M-IoT services. Although numerous protocols have been proposed to mitigate WiFi contention, they often consume much WiFi bandwidth for network control, lowering the level of achievable QoS performance. To address this issue, we propose a distributed Cross-interface network Partitioning and Scheduling (CPS) protocol, which leverages the co-existing ZigBee communications to divide the network into partitions and allows only one node in each partition to use its WiFi interface to transmit data at any time, for bandwidth-efficient and delay-constrained data flow delivery in M-IoT. A prototype node is implemented by integrating COTS ZigBee and WiFi interfaces into a BeagleBone Green wireless platform for IoT. Extensive field experiments are conducted in a multi-hop network of 24 prototype nodes that deliver real multimedia data (images and videos). The experiment results show that CPS outperforms the standard WiFi and a state-of-the-art contention control scheme (by 62.6% and 26.4% under high data traffic, respectively) in terms of a QoS metric capturing two basic performance metrics (i.e., bandwidth efficiency and end-to-end delay) of multi-hop communications, while retaining fair QoS performance and high energy efficiency.

Challenges and Opportunities in QoS Enhancement for Internet-Based Multimedia Communication

Advancements in communication technologies have fueled the rapid expansion of the multimedia content delivery market, encompassing various media types such as images, audio, and video. Personal communication, computing, broadcasting, and entertainment heavily rely on multimedia content, which is transmitted through diverse communication and network technologies to reach a wide range of devices. QoS requirements for multimedia services are determined by parameters including bandwidth flexibility, low end-to-end delay, minimal delay variation, and acceptable error rates. These parameters are interconnected and vary depending on the type of multimedia application. However, the QoS needs of multimedia communications cannot be fully met by the internet's basic infrastructure. Mechanisms for ensuring QoS guarantees over the Internet have been developed through extensive research and development. This study examines developments, difficulties, and strategies for improving the transmission of multimedia content over the Internet, providing an overview of the methodologies and strategies used to satisfy QoS criteria in multimedia communications.

Text and Context in Multimedia Translation

Multimedia translation refers to the process of translating multimedia content, such as videos, audio, and images, from one language to another. This type of translation is becoming increasingly important as more and more content is being produced and distributed globally. Multimedia translation is a complex field that requires in-depth technical and linguistic expertise and a thorough knowledge of the culture and communication norms of the target language. Plurisemiotic and micro-textual analyses of translations can help to develop an effective translation grid. The aim of this article would be, on one side, to restore certain implicit and explicit parameters of a silent source text so that a multimedia translation faithfully communicates the original and multidimensional meaning of a source text. On the other side, this article will try to demonstrate the usefulness of applying some concepts from a branch of socio-semiotics concerned with the production of multimodal texts that can be easily incorporated into existing methods in translation studies. As technology continues to evolve, we can expect multimedia translation to become even more sophisticated and effective. We will try to show in this article that the real context occupies a large place in which the work of translating audiovisual programs is carried out while presenting the various challenges of audiovisual translation, which is part of an extremely dynamic field that combines theory and practical. The aim of this article is to attempt to provide answers to these questions due to the increasing complexity of textual concepts and text production today. Translation and text production sometimes become almost indistinguishable, which will increase the emphasis on text analysis and different forms of intertextuality rather than on translation in the strictest sense.

Protecting Cloud Data: A Machine Learning Approach for Data Classification in Cloud Computing

Cloud computing (CC) is a modern framework that enables users to store data on remote servers accessible via the internet. This model facilitates easy access and transfer of personal and critical data, leading to increased demand. Users can store various types of data, including financial transactions, documents, and multimedia content. Additionally, CC reduces reliance on local storage and lowers operational and maintenance costs. However, existing systems typically encrypt all data with the same key size, irrespective of its confidentiality level, resulting in higher processing costs and time. Moreover, these methods often classify data with low accuracy and fail to provide adequate confidentiality. This research introduces a cloud computing approach that employs automated data classification to assess data sensitivity. The proposed model categorizes data into three sensitivity levels: basic, confidential, and highly confidential. It utilizes Random Forest (RF), Naïve Bayes (NB), k-nearest neighbor (KNN), and Support Vector Machine (SVM) classifiers, incorporating automated feature extraction. The model achieved an accuracy of 92%, as demonstrated in simulation results. The findings indicate that RF, NB, and KNN outperform SVM. The research also offers valuable guidelines for cloud service providers (e.g., Dropbox and Google Drive) and researchers.

REDISCOVERING CULTURAL HERITAGE SITES BY INTERACTIVE 3D EXPLORATION: A PRACTICAL REVIEW OF OPEN-SOURCE WEBGL TOOLS

Abstract. Georeferenced reconstructions can help understand the dynamic evolution of the urban context surrounding a historical site, supporting decision-making processes in the field of urban planning. The development of web applications that allow the interaction between 2D and 3D products, as well as their exploration, can facilitate virtual inspections and foster collaboration in digitization of interventions and site evolutions over time. The article discusses how virtual scene reconstructions and visits can provide alternatives to traditional in-situ tourism promotion, through digital storytelling solutions for the exploration of sites. The article also describes how 3D storytelling technologies are currently being used for dissemination cultural heritage sites. The case study of the Castello Farnese, an old XVI century heritage site in Piacenza (Italy), test the adoption of two distinct free and open-source JavaScript WebGL, Potree and Cesium, for the rendering of photogrammetric and laser scanning georeferenced scaled products and for the integration of narrative features such as annotations, camera animations, texts, and other multimedia contents. Potentials and limitations of both tools are discussed in detail, highlighting how they can be implemented for enhancing user experience in virtual tour and exploration of 3D products. In order to guarantee replicability for other case studies, source code of the implemented application is shared on GitHub along with its documentation for contributions.

Scope and Challenges of Multimedia in Education Sector

The multimedia-based education system is a dynamic and innovative approach that integrates various forms of multimedia, including text, images, audio, video, and interactive content, to enhance the teaching and learning process. This system aims to create engaging and immersive learning experiences, catering to diverse learning styles and promoting active participation.

Delineating the concept of (digital) slow journalism and its future through an international Delphi study

ABSTRACT This study seeks to evaluate the current status of journalism and digital slow journalism and identify its future challenges. A Delphi method has been used, with international experts responding to a qualitative questionnaire. This methodological technique is used to analyze experts’ perceptions regarding one subject of study and detect future tendencies. In this particular case, an iterative process was conducted in two waves, with 28 professionals and academics from different continents (North America, South America, Europe and Australia), to stimulate a critical debate about digital slow journalism. The most prominent result of the study is that slow journalism is understood as more critical, contrasted and contextualized than fast journalism. The participants of the study believe there is a need for the truthful and quality information offered by slow journalism and narrative journalistic genres. Additionally, the findings indicate that the future of slow journalism involves the incorporation of new multimedia content and formats into its practice. This is particularly important if slow journalism is to be adapted to digital consumption demands. In this direction, future audience investigations will be key to offer readers better experiences and narrative styles.

Improved Arabic image captioning model using feature concatenation with pre-trained word embedding

Automatic captioning of images contributes to identifying features of multimedia content and helps in the detection of interesting patterns, trends, and occurrences. English image captioning has recently made incredible progress; however, Arabic image captioning is still lagging. In the field of machine learning, Arabic image-caption generation is generally a very difficult problem. This paper presents a more accurate model for Arabic image captioning by using transformer models in both the encoder and decoder phases as feature extractors from images in the encoder phase and a pre-trained word embedding model in the decoder phase. The models are demonstrated, and all of them are implemented, trained, and tested on Arabic Flickr8k datasets. For the image feature extraction subsystem, we compared using three different individual vision models (SWIN, XCIT, and ConvNexT) with concatenation to get among them the most expressive extracted feature vector of the image, and for the caption generation lingual subsystem, which is tested by four different pre-trained language embedding models: (ARABERT, ARAELECTRA, MARBERTv2, and CamelBERT), to select from them the most accurate pre-trained language embedding model. Our experiments showed that building an Arabic image captioning system that uses a concatenation of the three transformer-based models ConvNexT combined with SWIN and XCIT as an image feature extractor, combined with the CamelBERT language embedding model produces the best results among the other combinations, having scores of 0.5980 with BLEU-1 and with ConvNexT combined with SWIN the araelectra language embedding model having a score of 0.1664 with BLEU-4 which are higher than the previously reported values of 0.443 and 0.157.

Scaffolding the Open: Transforming an International Studies Course using Open Pedagogy

This case study describes how two librarians transformed an international information literacy course by creating a scaffolded open pedagogy experience for students to build transferable skills while exploring how information is produced, disseminated, and interpreted across the world. The authors discuss how we collaborated on the project to incorporate open pedagogy, tools, and strategies to enhance learning. The result was a scaffolded course using open pedagogy to help students engage with global information issues. Using Pressbooks, students published multimedia content exploring topics including global news media, censorship, misinformation, and digital divides. Then students chose to either openly license their work, share it publicly while retaining their copyright, or hide it from public view. Additionally, students used Hypothesis to socially annotate reading assignments outside of class. We reflect on our experience revising this course, what worked and what did not, and how we will adapt the course in the future.

White Box Watermarking for Convolution Layers in Fine-Tuning Model Using the Constant Weight Code.

Deep neural network (DNN) watermarking is a potential approach for protecting the intellectual property rights of DNN models. Similar to classical watermarking techniques for multimedia content, the requirements for DNN watermarking include capacity, robustness, transparency, and other factors. Studies have focused on robustness against retraining and fine-tuning. However, less important neurons in the DNN model may be pruned. Moreover, although the encoding approach renders DNN watermarking robust against pruning attacks, the watermark is assumed to be embedded only into the fully connected layer in the fine-tuning model. In this study, we extended the method such that the model can be applied to any convolution layer of the DNN model and designed a watermark detector based on a statistical analysis of the extracted weight parameters to evaluate whether the model is watermarked. Using a nonfungible token mitigates the overwriting of the watermark and enables checking when the DNN model with the watermark was created.

Identifying multimodal misinformation leveraging novelty detection and emotion recognition.

With the growing presence of multimodal content on the web, a specific category of fake news is rampant on popular social media outlets. In this category of fake online information, real multimedia contents (images, videos) are used in different but related contexts with manipulated texts to mislead the readers. The presence of seemingly non-manipulated multimedia content reinforces the belief in the associated fabricated textual content. Detecting this category of misleading multimedia fake news is almost impossible without relevance to any prior knowledge. In addition to this, the presence of highly novel and emotion-invoking contents can fuel the rapid dissemination of such fake news. To counter this problem, in this paper, we first introduce a novel multimodal fake news dataset that includes background knowledge (from authenticate sources) of the misleading articles. Second, we design a multimodal framework using Supervised Contrastive Learning (SCL) based novelty detection and Emotion Prediction tasks for fake news detection. We perform extensive experiments to reveal that our proposed model outperforms the state-of-the-art (SOTA) models.

Future dynamic multimedia content access via aerial computing system

Multimedia content access sovereignty arises due to the intention of producers to enable subscribers from pre–defined regions access multimedia content. This limits the number of locations (with subscribers) that can access producer content. Therefore, the ability to access multimedia content across previously unconsidered locations is limited. The presented research addresses this challenge and focuses on multimedia content sharing among subscribers in arid and hyper–arid regions. The use of stratosphere based data centres (SBDCs) is proposed. The paper also presents multi–tier network architecture for network traffic management. This ensures that network traffic congestion does not limit access to multimedia content by subscribers across multiple regions. The use of SBDCs increases the number of locations that engage in the sharing of multimedia content. Evaluation shows that the proposed solution increases the number of data sharing locations by (75.8 – 88.2) % on average.

CHALLENGES OF CREATIVE DEVELOPMENT BY ADVERTISING INDUSTRY PLAYERS IN MALAYSIA

This study focused on understanding challenges within creative development of advertising industry in Malaysia. This will be analysed through the adaptation that needed to be made by key industry players from two advertising self-regulatory guidelines in Malaysia which are Malaysia Advertising Codes of Practice and Malaysian (The Code) and Multimedia Content Forum of Malaysia (the Content Forum). Both code of conduct is a crucial part of the advertising regulatory framework as it justifies the mechanism behind industry implementation in any kind of advertising messages intended. Using Sternberg & Lubart (1995) Investment Theory of Creativity, this article analyses the idea behind regulatory bodies' rules, focusing on the role of the contemporary advertising creative quality and whether it generates the ability in moulding freedom or restrictions towards industry players. Through purposive sampling, informants were selected within advertising industry professionals such as advertising agencies and brand experts. The historical context and current state of Malaysia's advertising environment were better understood due to the insights from and in-depth interview conducted between regulatory bodies, brand experts, and advertising agency representatives. Outcome of the findings have developed six key concepts to reflect the challenges such as Relationship between client and advertising agencies, Societal acceptance of the creative work, Invasion of social media in advertising, Limited resources for prestige award submission, Layers of approval authority, and Decreasing creative talent pool within Malaysia.