Field Of Multimedia Research Articles

RJAN: Region‐based joint attention network for 3D shape recognition

AbstractAs an essential field of multimedia and computer vision, 3D shape recognition has attracted much research attention in recent years. Multiview‐based approaches have demonstrated their superiority in generating effective 3D shape representations. Typical methods usually extract the multiview global features and aggregate them together to generate 3D shape descriptors. However, there exist two disadvantages: First, the mainstream methods ignore the comprehensive exploration of local information in each view. Second, many approaches roughly aggregate multiview features by adding or concatenating them together. The information loss for some discriminative characteristics limits the representation effectiveness. To address these problems, a novel architecture named region‐based joint attention network (RJAN) was proposed. Specifically, the authors first design a hierarchical local information exploration module for view descriptor extraction. The region‐to‐region and channel‐to‐channel relationships from different granularities can be comprehensively explored and utilised to provide more discriminative characteristics for view feature learning. Subsequently, a novel relation‐aware view aggregation module is designed to aggregate the multiview features for shape descriptor generation, considering the view‐to‐view relationships. Extensive experiments were conducted on three public databases: ModelNet40, ModelNet10, and ShapeNetCore55. RJAN achieves state‐of‐the‐art performance in the tasks of 3D shape classification and 3D shape retrieval, which demonstrates the effectiveness of RJAN. The code has been released on https://github.com/slurrpp/RJAN.

Development Augmented Reality of CNC Lathe G-Code Programming

In the realm of education, the most recent novel methods, such as augmented reality (AR), are becoming increasingly popular since students can apply the learning circumstances seen in virtual form to the real-world environment. The study's goal is to create an AR application for G code programming on CNC Lathe machines for the Machining course at Batu Pahat Vocational College. The ADDIE model was employed as the process model in this development. The phases of this paradigm are as follows: analysis, design, development, implementation, and assessment. SolidWorks, Blender, and Blippar are used to create AR apps. Product functionality was evaluated by five experts in the fields of machining and multimedia. The data is collected using an expert confirmation form and analysed using the frequency of acceptance and expert feedback. The study's findings suggest that the built application achieved its purpose. Overall, this AR application for G code programming was created with vocational college students in mind and is suited for use as a supplementary reference that can be used in theoretical or practical lessons.

ANALYSIS OF BLOCKCHAIN TYPES AND THEIR SUITABILITY FOR IMAGE STORAGE

There different types of blockchains and their possible use for creating an image repository are investigated. The purpose of the study was to evaluate the advantages and limitations of different types of blockchains in terms of image storage. Data processing methods were applied to analyze the technical characteristics of various types of blockchains and comparative analysis of efficiency and reliability parameters. The results were obtained, which made it possible to formulate the principles of choosing the type of blockchain for creating image storage and to identify the advantages and limitations of each type from the point of view of image storage, depending on the priorities of the software product. The conclusion is that the use of blockchain provides a high level of security and integrity of images, some types of blockchains exhibit high speed and scalability. However, it is important to understand that the preservation process may remain centralized, so more research is needed to optimally use and develop these technologies. Future research may include an analysis of the possibilities of ensuring the privacy of participants and the development of standards for the sharing of multimedia content via blockchain. It is important to consider that the use of blockchain can contribute to increasing transparency and trust in the process of storing and sharing multimedia content, which is important for the development of the digital economy. However, in order to achieve the full potential of blockchain in the field of multimedia, it is necessary to develop effective strategies to solve the problems of privacy, scalability and centralization that arise when implementing these technologies. Such a comprehensive approach will provide a stable and effective infrastructure for managing multimedia content in the digital environment.

Lightweight Food Recognition via Aggregation Block and Feature Encoding

Food image recognition has recently been given considerable attention in the multimedia field in light of its possible implications on health. The characteristics of the dispersed distribution of ingredients in food images put forward higher requirements on the long-range information extraction ability of neural networks, leading to more complex and deeper models. Nevertheless, the lightweight version of food image recognition is essential for improved implementation on end devices and sustained server-side expansion. To address this issue, we present Aggregation Feature Net(AFNet), a lightweight network that is capable of effectively capturing both global and local features from food images. In AFNet, we develop a novel convolution based on a residual model by encoding global features through row-wise and column-wise information integration. Merging aggregation block with classic local convolution yields a framework that works as the backbone of the network. Based on the efficient use of parameters by the aggregation block, we constructed a lightweight food image recognition network with fewer layers and a smaller scale, assisted by a new type of activation function. Experimental results on four popular food recognition datasets demonstrate that our approach achieves state-of-the-art performance with higher accuracy and fewer FLOPs and parameters. For example, in comparison to the current state-of-the-art model of MobileViTv2, AFNet achieved 88.4% accuracy of the top-1 level on the ETHZ Food-101 dataset, with similar parameters and FLOPs but 1.4% more accuracy. The source code will be provided in supplementary materials.

Computer Feed based Text Processing Using NLP

The goal of this research project is to create a comprehensive multimedia and text processing framework that can be used in a variety of applications. The objective is to combine a collection of libraries and instruments for the purpose of extracting, analyzing, and modifying textual information obtained from multimedia files, including images, videos, and audio files. The strategy involves applying cutting-edge techniques such as computer vision, audio processing, natural language processing (NLP), optical character recognition (OCR), and computer vision. Important findings include accurate text extraction from multimedia inputs, accurate textual data analysis using natural language processing techniques, and the production of insightful information via sentiment analysis, summarization, and translation. Its main contributions are related to the development of a flexible system that can process a wide range of multimedia inputs and provide useful text processing features. This project provides a foundation for future research and useful applications in the fields of text processing and multimedia.

Virtual Reality (VR) and Digital Storytelling (DS) Technology to Improve English Speaking Skills of Vocational Students

In an era characterized by globalization and digitalization, proficiency in English is a vital skill, particularly for vocational students specializing in multimedia fields. This research provides a concise overview of a study that investigates the use of virtual reality (VR) and digital storytelling (DS) technology as innovative tools to enhance the English-speaking skills of vocational multimedia students. This study aims to assess the efficacy of VR and DS technology in improving the English-speaking abilities of vocational multimedia students, exploring their potential to provide immersive language learning experiences and foster creativity and engagement. The integration of VR and DS technology into English language instruction can play a pivotal role in improving the speaking skills of vocational multimedia students. These technologies offer immersive experiences and creative opportunities that not only enhance language skills but also prepare students for success in a digitally driven, globally connected workforce.
 Keywords: virtual reality, business model, entrepreneurial mindset

Open Resource Binary & Interactive Technology (ORBIT): Platform Asset Digital untuk Diseminasi Karya Mahasiswa

Students in the Multimedia Engineering Technology Program (MET) at Telkom University possess the ability to create a diverse range of creative multimedia products, including 2D visual works, 3D modeling, and game applications. However, paradoxically, the documentation of their works often falls short, with their creations sporadically stored and inadequately cataloged. To address this challenge, we introduce Open Resource Binary & Interactive Technology (ORBIT), a digital asset platform designed to streamline the organization and management of multimedia projects generated by MET students. Through ORBIT, MET students can effortlessly upload, share, and document various types of multimedia projects, fostering collaboration among peers and ensuring that innovations in the multimedia field remain accessible and can be further developed by future generations

Cross-modality integration framework with prediction, perception and discrimination for video anomaly detection

Video anomaly detection is an important task for public security in the multimedia field. It aims to distinguish events that deviate from normal patterns. As important semantic representation, the textual information can effectively perceive different contents for anomaly detection. However, most existing methods primarily rely on visual modality, with limited incorporation of textual modality in anomaly detection. In this paper, a cross-modality integration framework (CIForAD) is proposed for anomaly detection, which combines both textual and visual modalities for prediction, perception and discrimination. Firstly, a feature fusion prediction (FUP) module is designed to predict the target regions by fusing the visual features and textual features for prompting, which can amplify the discriminative distance. Then an image-text semantic perception (ISP) module is developed to judge semantic consistency by associating the fine-grained visual features with textual features, where a strategy of local training and global inference is introduced to perceive local details and global semantic correlation. Finally, a self-supervised time attention discrimination (TAD) module is built to explore the inter-frame relation and further distinguish abnormal sequences from normal sequences. Extensive experiments on the three challenging benchmarks indicate that our CIForAD obtains state-of-the-art anomaly detection performance.

SemanticFormer: Hyperspectral image classification via semantic transformer

Hyperspectral image (HSI) classification is an active research problem in computer vision and multimedia field. Contrary to traditional image data, HSIs contain rich spectral, spatial and semantic information. Thus, how to extract the discriminative features for HSIs by integrating spectral, spatial and semantic cues together is the core issue to address HSI classification task. Existing works mainly focus on exploring spectral and spatial information which usually fail to fully explore the rich semantic information in HSIs. To address this issue, in this paper, we first propose a novel semantic Transformer scheme, named SemanticFormer, which aims to learn discriminative visual representations for semantics by exploiting the interaction among different semantic tokens. Using the proposed SemanticFormer, we then propose a novel heterogeneous network that contains both spectral–spatial convolution network branch and SemanticFormer branch to extract spectral–spatial and semantic features simultaneously for HSIs. Experiments on two widely used datasets demonstrate the effectiveness of our SemanticFormer and HSI classification network method. Our codes will be available in https://github.com/SissiW/SemanticFormer.

DA-Net: Density-Aware 3D Object Detection Network for Point Clouds

3D object detection is an important but demanding task, which has become an active research topic in the field of multimedia. Much recent research has been devoted to exploiting end-to-end trainable object detection networks with point clouds. However, most state-of-the-art methods have bottlenecks in detecting occluded objects and small objects, because the sparseness of point clouds is exacerbated on these objects. In this paper, a Density-Aware 3D object detection network (DA-Net) is proposed to improve the perception performance for detecting occluded and small objects, which contains four components: a backbone module with an inverse density scoring module (IDM) and a point-wise attention module (PAM), a 3D intersection over union Estimation Module (3DEM), a Consistent Label Assignment (CLA) method and an Adaptive-Soft-NMS method. The proposed backbone module makes the network concentrate on low-density points of occluded objects, and suppresses outliers and background points. Then, the 3DEM is introduced to evaluate the localization quality of the prediction boxes. Furthermore, the proposed CLA method can more accurately select positive and negative samples for small objects. Finally, Adaptive-Soft-NMS is proposed in our method to reduce the number of false detections during inference and thereby improve detection performance substantially. Extensive experiments demonstrated that the proposed method achieves state-of-the-art performance on two large-scale datasets, SUN RGB-D (62.1% in terms of mAP@0.25) and ScanNetV2 (67.1% in terms of mAP@0.25), and in particular, the detection accuracy of small objects and occluded objects are extremely improved.

Multi-Media Field Test: Cognitive-Behavioral Therapy at Our Fingertips: Sanvello Provides On-Demand Support for Mental Health

Sanvello is a cognitive-behavioral therapy (CBT) app available as a web platform and a native app for both Android and iOS. Sanvello can be used for free, with additional premium content available in a paid version. It is targeted for adults with mild to moderate symptoms of depression, anxiety, and stress and has a solid evidence base to support its efficacy. Sanvello is user-friendly and intuitive, with good user experience and appropriate privacy and data security measures. Sanvello’s free version consists mainly of self-care and peer support content, allowing users to engage with tools such as in-app activities personalized through initial user assessments, and community pages. A paid version provides additional content through a subscription model and coaching available for a monthly fee. Professional support is available either through insurance coverage of a pay-per-appointment model. Despite a solid evidence base, various features, and a library of content, identified areas of improvement include expanding the psychoeducation materials and articles to include more timely resources and the diversity of its potential users. For these reasons, we have rated Sanvello with a 5 in utility, and ease of use/overall appeal along with a 4 in appropriateness of content and research evidence. Overall, Sanvello provides a variety of evidence-based cognitive and behavioral skills and, for those with the paid version, the opportunity to connect with professionals for additional support.

Project Management and File Management in Multimedia Applications Version of ActionScript 2.0

Multimedia is a key focus within the Information Systems Study Program at Universitas Komputer Indonesia. With increasing student interest and certification from the National Professional Certification Agency, more students are producing quality multimedia theses. However, many lack understanding of theoretical principles such as project and file management, which this research aims to address for both academic and real-world applications. This research was conducted at PS-SI UNIKOM. The research data was taken from the collection of thesis students of the UNIKOM PS-SI even semester academic year 2019/2020. The focus of this research is using the Adobe Flash CS 6 version ActionScript 2.0 approach. This study expects students to understand multimedia scientific principles and implement them in a real thesis and multimedia project. Implementing project management and file management in the multimedia field will help developers develop multimedia application products effectively and efficiently.

SARAWAK MULTIMEDIA AUTHORITY: CURRENT FUNCTIONS AND POWERS AND THE WAY FORWARD

This article seeks to outline the establishment of the Sarawak Multimedia Authority (hereinafter referred to as “SMA”) and critically examine the extent of its limited effectiveness in serving as a supposed regulatory body over communications and multimedia matters in the region of Sarawak as compared to its counterpart, Malaysian Communications and Multimedia Commission (hereinafter referred to as “MCMC”) which has wider powers to regulate communications and multimedia matters over the entire Malaysia in general, including that of Sarawak. The purpose of this article is to compare the powers of SMA and MCMC with the aim to propose for the empowerment of the SMA with more legal enforcement powers, as it rightfully should be empowered, so that SMA can serve its purpose not only as an executive body in charge of implementing policies of the Sarawak Government, but also as an effective enforcement agency in the scope of communications and multimedia fields in Sarawak. The first section of this article provides an overview of SMA and MCMC. The second section covers the issues of the limited powers and functions of the SMA to regulate relevant communications and multimedia matters in Sarawak. The third section covers the problems of gap and conflict of the powers of SMA. The fourth section discusses comparative study that supports the proposition for law reform to empower SMA with more legal enforcement powers. The fifth section covers critical study of the way to maintain separation of powers between SMA and MCMC if SMA is accorded with regulatory and enforcement powers in future. At the end of this paper, the author offers a recommendation for law reform of empowerment of the SMA with more legal enforcement powers over communications and multimedia matters in Sarawak.

Workshop Pemanfaatan Aplikasi Filmora sebagai Alternatif Pembuatan Media Pembelajaran pada Guru Pondok Pesantren Ar-Rayyan Kabupaten Pacitan

The development of information technology is very important in the world of education. Education taught in Islamic boarding schools is no exception. A teacher at an Islamic boarding school also needs to master technology in the field of multimedia in providing learning videos. This is done in order to help teachers deliver material in a more interesting way to their students. Therefore, a workshop on using Filmora as an alternative for making learning videos is needed. The methods used in this service activity are survey activities, preparation stage, implementation stage, and evaluation stage. The results obtained from this workshop activity were an increase in the abilities of the teachers at the Ar-Rayyan Islamic boarding school. Previously only around 30% of teachers were able to make learning videos, after the workshop was held there are now 85% of teachers who can use technology to make learning videos. Apart from that, the results of the questionnaire analysis distributed to the participants also obtained an average score of 4.1 which is in the good category. The participants (teachers) at the Ar-Rayyan Islamic Boarding School were very enthusiastic in taking part in the workshop activities that had been carried out.

Computer Laboratory Management In Improving Students' Hard Skills In The Field Of Multimedia

The computer laboratory is a support for learning, skills and school infrastructure. Then required planning, organizing, implementation and evaluation. To make the computer laboratory directed and managerial so that the goal of procuring a computer laboratory is achieved effectively and efficiently. As for improving the hard skills themselves, it requires teaching that is repeated consistently and always evaluates so that the hard skills are even better. This research is included in qualitative research with a descriptive approach, the data collection methods used are interviews, observation and documentation. The key informant in this research is the head of the computer laboratory. While the supporting informants were the Head of School, Deputy Head of School for Facilities and Infrastructure, Deputy Head of School for Curriculum, and students. This research was conducted at MAN 1 Jombang. Based on the results of research on computer laboratory management including planning activities or goals for managing computer laboratories as well as organizing which includes the selection of good human resources and the preparation of organizational structures, the implementation of evaluation which is a managerial activity here also describes how to increase hard skills in the multimedia field. It also describes the supporting and inhibiting factors in managing computer laboratories in improving students' hard skills in the field of multimedia at MAN 1 Jombang.

Deep Learning for Logo Detection: A Survey

Logo detection has gradually become a research hotspot in the field of computer vision and multimedia for its various applications, such as social media monitoring, intelligent transportation, and video advertising recommendation. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, and loss functions have been employed. This article reviews the advance in applying deep learning techniques to logo detection. First, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and their strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection. This study aims better to inform readers about the current state of logo detection and encourage more researchers to get involved in logo detection.

ADOBE AFTER EFFECTS SOFTWARE VIDEO EDITING DAN MOTION GRAPHICS YANG POPULER

Adobe After Effects is a popular software for video editing and motion graphics that was developed by Adobe Inc and was first released in 1993 by CoSA. After Adobe Systems acquired CoSA in 1994, Adobe After Effects became a multi-platform software and has continued to evolve over the years with more advanced features. This software is widely used in Hollywood films and by many companies in the production of promotional videos, advertisements, and other videos. Adobe After Effects is used to create special effects in videos such as animation, compositing, and color grading. It offers up to 50 different standard effects to modify and animate objects, and can even use Expressions to produce more dynamic movement. The software has several functions in the field of multimedia, including creating animations and motion graphics, adding special effects, compositing, color grading, and integration with other software. The main purpose of Adobe After Effects is to help users create high-quality videos with complex and detailed special effects. It allows users to explore their creativity by using various tools and features available in the software. Adobe After Effects is very important in video production because it can produce very complex and detailed visual effects such as explosion effects, fire effects, and special effects in Hollywood films.

Towards Food Image Retrieval via Generalization-Oriented Sampling and Loss Function Design

Food computing has increasingly received widespread attention in the multimedia field. As a basic task of food computing, food image retrieval has wide applications, that is, food image retrieval can help users to find the desired food from a large number of food images. Besides, the retrieved information can be applied to establish a richer database for the subsequent food content-related recommendation. Food image retrieval aims to achieve better performance on novel categories. Thus, it is worth studying to transfer the embedding ability from the training set to the unseen test set, that is, the generalization of the model. Food is influenced by various factors, such as culture and geography, leading to great differences between domains, such as Asian food and western food. Therefore, it is challenging to study the generalization of the model in food image retrieval. In this article, we improve the classical metric learning framework and propose a generalization-oriented sampling strategy, which boosts the generalization of the model by maximizing the intra-class distance from a proportion of positive pairs to avoid the excessive distance compression in the embedding space. Considering that the existing optimization process is in an opposite direction to our proposed sampling strategy, we further propose an adaptive gradient assignment policy named gradient-adaptive optimization , which can alleviate the intra-class distance compression during optimization by assigning different gradients to different samples. Extensive evaluation on three popular food image datasets demonstrates the effectiveness of the proposed method. We also experiment on three popular general datasets to prove that solving the problem from the generalization can also improve the performance of general image retrieval. Code is available at https://github.com/Jiajun-ISIA/Generalization-oriented-Sampling-and-Loss .

Peningkatan kompetensi siswa SMK melalui pelatihan desain grafis dan animasi

Multimedia is a crucial field of study in the present era, as all professional domains require its application to aid individuals in effectively completing their tasks. However, the Multimedia field is contributing significantly to unemployment rates today. This issue partly arises from the fact that graduates in Multimedia lack the required proficiency, skills, and knowledge sought by various companies and institutions. SMK NW Kembang Kerang is a private vocational school located in East Lombok, which offers a multimedia program attracting a substantial number of students each year. Despite graduating students in this field annually, many face the challenge of inadequate competencies upon completion of their education. The lack of practical exposure during their school years hinders the alumni's ability to compete in the job market. Although numerous photo and video editing services are available in the Kembang Kerang region, it is observed that few SMK graduates are employed in this area. The school's teachers and principal comprehend this issue, attributing it to the limited opportunities for practical training experienced by the students. Consequently, to address this concern, the author conducted a competence training activity for students at SMK NW Kembang Kerang, with a specific focus on multimedia and graphic design. The objective of this training program is to enhance the students' multimedia and graphic design skills, empowering them with competent capabilities that will prove beneficial both for their personal growth and the broader community. Through this initiative, it is anticipated that upon completing their vocational education, students will possess the necessary competencies to thrive in the professional realm and contribute positively to society at large.

Application of the multimedia fugacity model in predicting the environmental behaviors of PCBs: Based on field measurements and level III fugacity model simulation

The comprehensive understanding of PCBs' fate has been impeded by the lack of simultaneous monitoring of PCBs in multiple environmental media in the background areas, which were considered long-term sinks for highly chlorinated PCBs. To address this gap, this study analyzed soils, willow tree barks, water, suspended particulate matter (SPM), and sediment samples collected from the middle reach of the Huaihe River in China for 27 PCBs. The results showed that the levels of ∑27PCBs in the soils were comparable to or lower than the background values worldwide. There were no significant correlations between organic matter and ∑27PCB concentrations in the soils and sediments. Additionally, the contamination of dioxin-like PCBs in the aquatic environment of the study area deserves more attention than in the soils. Applying the level III fugacity model to PCB 52, 77, 101, and 114 revealed that the soil was the primary reservoir, and air-soil exchange was the dominant intermedia transfer process, followed by air-water exchange. Furthermore, simulated results of air-soil and air-water diffusion were compared with those calculated from the field concentrations to predict the potential environmental behaviors of PCBs. Results indicated that the studied river would be a "secondary source" for PCB 52, 77, and 101. However, PCB 52, 77, 101, and 114 would continue to transfer from the air to the soil. This study combines multimedia field measurements and the fugacity model, providing a novel approach to predicting the potential environmental behaviors of PCBs.