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Copyright of RIED: Revista Iberoamericana de Educación a Distancia is the property of Revista Iberoamericana de Educacion a Distancia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we proposed a system to reduce the speaker's suffering from the strong light of a beam projector by applying regional brightness control over the screen. Since the original image and the projected one on the screen are quite different in area, brightness, and color, the proposed system first transforms them so that they have the same area and similar color tone. Then, to accurately determine the difference between those images, we have introduced a SSIM map, which is a perception-based method of measuring image similarity. Accordingly, an image segmentation model is used to determine the speaker's silhouette from the SSIM map. We applied a couple of well-trained segmentation models, such as Selfie and DeepLab-v3, provided with MediaPipe. The experimental results showed the operability of the proposed system and that it determines most of a lecturer's body area on the screen. To closely evaluate the system's effectiveness, we have measured error rates consisting of false-positive and false-negative errors in the confusion matrix. With the measured results, the error rates appeared so insignificant and stable that the proposed system provides a practical effect for the speakers, especially in the case of applying DeepLab-v3. With the results, it is implied that an accurate segmentation model can considerably elevate the effectiveness of the system. [ABSTRACT FROM AUTHOR]

Given the rising psychological challenges encountered by university students, there is an imperative to address the pressing need for enhancing their psychological capital. This study is to design an innovative multimedia system that seeks to offer comprehensive psychological support and promotion mechanisms for university students. This is achieved through the integrated use of various media forms. Multimedia system group counseling was employed to assess and enhance the psychological capital of college students. This study comprises two main components: first, an analysis of the application of multimedia technology in education, and second, an empirical investigation into college students’ psychological capital through a questionnaire survey. The findings reveal that the introduction of group counseling via a multimedia system significantly enhances the psychological capital of college students. This improvement in psychological capital positively impacts the well-being and mental states of students and contributes novel ideas to mental health education for college students. The effectiveness of the group counseling intervention scheme within the multimedia system is evident, suggesting its potential for widespread adoption. The utilization of multimedia systems in educational settings emphasizes the importance of positive psychology for students and contributes to cultivating a positive and healthy psychological state. This study serves as a valuable reference for enhancing the psychological capital of college students, focusing on aspects such as independent thinking, decision-making, and execution.

Copyright of UCV Hacer is the property of UCV Hacer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multimedia and multimodal sensing with the edge computing for the personalized healthcare supply chain system data optimization is studied in this manuscript. In the agile supply chain management information system, the cross-platform and heterogeneity of data exchange and business logic invocation is inevitable. For the efficient analysis, our application scenarios will be focused on the personalized healthcare supply chain system. For the theoretical basis novelty, we consider the two core aspects. (1) For the Multimedia System and Multimodal Sensing, they will undertake the role of interface optimization and the comprehensive data optimization and (2) for the Edge Computing and the Data Optimization. The data center will become an important foundation for the hospital to then achieve digital transformation. For the designed system, we use the Unique Device Identification (UDI) for the product tagging, the blockchain for the safety guarantee, and the Supply-Processing-Distribution (SPD) based model are considered. For the optimization of the performance, the Gale-Shapley model considering the complex network is designed. Through the comparison experiment, the proposed model is tested under different environment. It has been reflected that the proposed model is efficient and accurate. For testing the robustness of the model, the dynamic simulation is added. [ABSTRACT FROM AUTHOR]

Abstract Aim In this study, we aimed to describe a multimedia-supported manikin system, compare the new manikin with the traditional manikin and evaluate its effectiveness in preclinical dentistry training. Methods A total of 150 students participated in this study. Amongst these students, 71 in the 2015-year group used traditional manikins (Group TM) for preclinical training courses (endodontics training courses and prosthodontics training courses), and 79 in the 2016-year group used manikins with a multimedia system (Group MM). The scores of the training courses between the two groups were compared. A questionnaire survey was used to collect opinions of the students in Group MM on their experience of using the multimedia-supported manikin system in the preclinical training. Results In the endodontics training courses, the scores of Group MM were higher than those of Group TM, but there was no significant difference (P = 0.379 > .05). However, the scores of prosthodontics training courses in Group MM were significantly higher than those in Group TM (P = 0.018

In this study, we proposed a system to reduce the speaker’s suffering from the strong light of a beam projector by applying regional brightness control over the screen. Since the original image and the projected one on the screen are quite different in area, brightness, and color, the proposed system first transforms them so that they have the same area and similar color tone. Then, to accurately determine the difference between those images, we have introduced a SSIM map, which is a perception-based method of measuring image similarity. Accordingly, an image segmentation model is used to determine the speaker’s silhouette from the SSIM map. We applied a couple of well-trained segmentation models, such as Selfie and DeepLab-v3, provided with MediaPipe. The experimental results showed the operability of the proposed system and that it determines most of a lecturer’s body area on the screen. To closely evaluate the system’s effectiveness, we have measured error rates consisting of false-positive and false-negative errors in the confusion matrix. With the measured results, the error rates appeared so insignificant and stable that the proposed system provides a practical effect for the speakers, especially in the case of applying DeepLab-v3. With the results, it is implied that an accurate segmentation model can considerably elevate the effectiveness of the system.

With the development of recent economic globalization, international exchanges and cooperation are increasingly frequent and in-depth. In this process, there is a serious obstacle, that is, language differences. Therefore, the development of high-quality and practical machine translation system is of great significance. Japan has a close relationship with China, so it is necessary to acquire and process Japanese information. Japanese translation is the basis of Japanese information processing, which plays an important role in cross language information retrieval, machine translation, information extraction and other practical applications. In recent years, machine translation has made remarkable progress, but there is still much room for improvement in the quality of translation. Multimedia assisted instruction is an important application of computer technology. Therefore, this paper realizes the feature recognition and hardware structure of Japanese machine translation; for the efficient implementation of the model, the edge-driven model is combined. The proposed model is implemented through the edge devices and the performance of the model is validated through the testing. The recognition accuracy is much higher than the traditional models and the robustness is higher. [ABSTRACT FROM AUTHOR]

Due to its potential influence, Wireless Multimedia Sensor Networks (WMSNs) receive a lot of attention from numerous appealing applications and the scientific studies that surround them. Over the past few years, the most active area of the research field has been routing in WMSNs. Additionally, the routing protocol is used to transfer the multimedia data that identifies the most effective resources and stable paths, as well as to guarantee various degrees of Quality-of-Experience (QoE) and Quality Of Service (QoS). It remains a difficult aspect, nevertheless, due to the complex responsibilities of multimedia, dynamic variations in network state, and a shortage of network resources. In order to choose the most appropriate cooperative nodes, this research seeks to implement a Deep Belief Network (DBN) based decision-making system. In addition, restrictions such as Tri-level energy consumption of nodes (Level 1: Low energy level for textual data, Level 2: Mid energy level for text, image, and video information, Level 3: Highly energetic level for high definition pictures), (ii) Reliability, and (iii) Delay are investigated while transferring the multimedia data in the network. Self-Improved Butterfly Optimization Algorithm (SIBOA) is proposed to tune the optimal weight of the DBN in order to make the correct choice. Finally, a comparison of the selected scheme's performance to various conventional schemes is made in terms of alive nodes, delay, residual energy, network lifetime, and reliability analysis.

With the exponential increase in energy demands (commercial as well as residential), the traditional grid infrastructure significantly shifted to intelligent ICT-based Smart Grid (SG) infrastructure. In the SG environment, only efficient energy management may not be sufficient as the SG dynamics have significant impacts on multimedia communications such as video surveillance of the technical/non-technical losses of energy and many more. The inevitable energy losses can be identified by process and analyze the massive amount of heterogeneous data, i.e., Big Data (BD) generated through smart devices such as sensors, Smart Meters (SMs), and others. The key challenges in analyzing multimedia BD are computational complexity, operational integration complexity, data security, and privacy. To overcome the aforementioned issues, this paper proposes a blockchain-based data analytics scheme called ChoIce, which offers secure data collection, analysis, and decision support for the SG systems. It works in two phases; (i) secure data collection over Ethereum and (ii) BD analytics and decision-making using deep learning (DL). The robust and secure data analytics, efficient network management, and high-performance computing for BD are crucial towards the optimization of SG operation. The performance of ChoIce is evaluated considering parameters such as the data storage cost, multimedia communication latency, and prediction accuracy. Thus, the results of ChoIce shows that it outperforms in contrast to other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Aim: In this study, we aimed to describe a multimedia-supported manikin system, compare the new manikin with the traditional manikin and evaluate its effectiveness in preclinical dentistry training. Methods: A total of 150 students participated in this study. Amongst these students, 71 in the 2015-year group used traditional manikins (Group TM) for preclinical training courses (endodontics training courses and prosthodontics training courses), and 79 in the 2016-year group used manikins with a multimedia system (Group MM). The scores of the training courses between the two groups were compared. A questionnaire survey was used to collect opinions of the students in Group MM on their experience of using the multimedia-supported manikin system in the preclinical training. Results: In the endodontics training courses, the scores of Group MM were higher than those of Group TM, but there was no significant difference (P = 0.379 >.05). However, the scores of prosthodontics training courses in Group MM were significantly higher than those in Group TM (P = 0.018 <.05). The questionnaire results indicated that the students in Group MM were satisfied with the device in usability, clarity, effectiveness and improvement in operation proficiency. Conclusions: In the groups studied, for preclinical dental training, the multimedia-supported manikin system was a good alternative to traditional manikin in preclinical dentistry training. [ABSTRACT FROM AUTHOR]

In the world of technology today, the hardware, applications, and online computing service have transformed instructional approaches and classroom changes. In various ways, artificial intelligence has shown its function as triggering transformations in the multimedia educational platform in an unimaginable manner. In this paper, the multimedia systems can be programmed to communicate with the students using Intellectual Computer-Aided Artificial Intelligence (ICA-AI) framework through visual vision, voice recognition, and intelligent behavior that the researchers consider inherently human. The ICA-AI scheme facilitates a variety of curricula for teaching and studying diverse topics in information technology. In general, it offers multimedia-based lectures, drills, research, reviews, and debate moderation based on students' success and development in higher education platforms. The framework, instructional design, students' modeling, and the knowledge base's organization are defined effectively using ICA-AI. The Genetic Algorithm has been designed for integration into the instruction mechanism of all records, information, and multimedia objects using ICA-AI. The case study has been compared based on the students' performance ratio and feedback following conventional methods to validate the proposed method's reliability. [ABSTRACT FROM AUTHOR]

Copyright of RIED: Revista Iberoamericana de Educación a Distancia is the property of Revista Iberoamericana de Educacion a Distancia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing stand-alone multimedia machines and online multimedia machines in the market have certain deficiencies, so they cannot meet the actual needs. Based on this, this research combines the actual needs to design and implement a multi-media system based on the Internet of Things and cloud service platform. Moreover, through in-depth research on the MQTT protocol, this study proposes a message encryption verification scheme for the MQTT protocol, which can solve the problem of low message security in the Internet of Things communication to a certain extent. In addition, through research on the fusion technology of the Internet of Things and artificial intelligence, this research designs scheme to provide a LightGBM intelligent prediction module interface, MQTT message middleware, device management system, intelligent prediction and push interface for the cloud platform. Finally, this research completes the design and implementation of the cloud platform and tests the function and performance of the built multimedia system database. The research results show that the multimedia database constructed in this paper has good performance. [ABSTRACT FROM AUTHOR]

Quality of service (QoS) is the overall performance of a computer network, which not support or guaranteed well with the current Internet technology. It is hard to track, locate, and recognize the reason of a QoS violation in volatile situations. Even though this issue is solved, other issues may rise, such as the Internet service providers not willing to open their management interfaces. Besides, not all Internet hardware can support QoS controlling. End-to-end QoS controlling is an effective way to overcome these issues. The existing end-to-end QoS solutions have a problem that they analyze and exploit the relationships between the QoS metrics and contexts in an isolated way. This paper proposes a novel comprehensively context-aware approach to exploit these relationships based on the Bayesian networks and fuzzy set. This approach divides into three processes: context discretization, context and QoS metric mapping, and QoS quantitative guarantee. We validate the effectiveness of the proposed approach in our real multimedia software system and compare the performance of our approach with other existing bandwidth-aware and playback-buffer-aware schemes. The experimental results demonstrate that the proposed approach performs better than existing solutions in improving the video Peak Signal-to-Noise Ratio. [ABSTRACT FROM AUTHOR]

Given the rising psychological challenges encountered by university students, there is an imperative to address the pressing need for enhancing their psychological capital. This study is to design an innovative multimedia system that seeks to offer comprehensive psychological support and promotion mechanisms for university students. This is achieved through the integrated use of various media forms. Multimedia system group counseling was employed to assess and enhance the psychological capital of college students. This study comprises two main components: first, an analysis of the application of multimedia technology in education, and second, an empirical investigation into college students' psychological capital through a questionnaire survey. The findings reveal that the introduction of group counseling via a multimedia system significantly enhances the psychological capital of college students. This improvement in psychological capital positively impacts the well-being and mental states of students and contributes novel ideas to mental health education for college students. The effectiveness of the group counseling intervention scheme within the multimedia system is evident, suggesting its potential for widespread adoption. The utilization of multimedia systems in educational settings emphasizes the importance of positive psychology for students and contributes to cultivating a positive and healthy psychological state. This study serves as a valuable reference for enhancing the psychological capital of college students, focusing on aspects such as independent thinking, decision-making, and execution., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In this paper, we present a memory subsystem for a burst that is unaligned to a block of synchronous dynamic random access memory (SDRAM) columns. In the case that a processor demands such an unaligned burst, a memory subsystem is required to rearrange SDRAM transfers in the order demanded by the processor. Our memory subsystem modifies a memory address given by a processor, then accesses SDRAMs with the modified memory address, and thus makes SDRAMs output transfers in an intermediate order. The transfers can be rearranged in the order demanded by the processor with the minimal performance degradation. Next, urgent transfers within the burst are delivered via priority service, and nonurgent transfers within the burst can be independently delivered via best-effort service. Experimental results show that the proposed memory subsystem achieves, on average, 16.6% shorter program execution time and 13.5% shorter memory latency than a conventional memory subsystem for wrapping bursts in the case that a quadcore processor concurrently executes four benchmarks. In addition, the proposed memory subsystem achieves, on average, 5.4% shorter application execution time and 3.4% shorter memory latency than a conventional memory subsystem for incrementing bursts in case of double data rate (DDR) 3 SDRAMs. [ABSTRACT FROM AUTHOR]

The rapid development of current wisdom education has become the mainstream that leads the development of education informatization. The development of wisdom education relies on big data network resources to adapt to online teaching resources and online learning environment. Wisdom education can meet the needs of teachers, students, researchers, and the public areas under the support of the Internet of Things, cloud computing, big data, and ubiquitous networks. As a very important part of wisdom education, the wisdom education environment plays a fundamental role in wisdom education. In an intelligent learning environment, personalized learners need to build an efficient community intelligence education model, which acquires and stores the data produced by the learners touching the related physical elements in the intelligent situation through the sensing device. An intelligent learning environment is able to analyze and infer the data and adapt the present educational research according to the individual needs of learners' program providing "intelligent" learning services. In addition, guided by the system model and design principles of the wisdom education environment, this paper put forward the initial design ideas of the wisdom education inquiry base in order to provide some guidance to the construction of the wisdom education environment. This paper introduces XML language to standardize the representation of learning situation model and illustrates it with a concrete example. [ABSTRACT FROM AUTHOR]

In this research, we propose the thermal power generation fault diagnosis and prediction model based on deep learning and multimedia systems. The application of multimedia technology in the power dispatching communication system not only greatly enhances the stability and reliability of the power system, but also enriches the application of science and technology in the power system. It is one of the main directions for the development of power communication and information processing systems. The paper's novelty and contribution are major reflected from the three aspects. First, we optimize the traditional neural network model to fix it more suitable for multimedia applications. We improve the forecasting accuracy; and then for each type of sample of B-neural network model, the up-front of meteorological data. Second, the deep neural network is optimized for better evaluation efficiency. The number of convolution kernels in each convolutional layer in the network is different. The more the number of the post-convolution kernels, the more efficient the model will be. Therefore, the multi-kernel structure is proposed. Third, we integrate the multimedia into the prediction scenario to visualize the data and results. The experiment result is conducted to validate the performance of the proposed method. Results compared with the other state-of-the-art models demonstrate the robustness of our method. [ABSTRACT FROM AUTHOR]

In the large-scale Distributed Virtual Environment (DVE) multimedia systems, one of key challenges is to distributedly preserve causal order delivery of messages in real time. Most of the existing causal order control approaches with real-time constraints use vector time as causal control information which is closely coupled with system scales. As the scale expands, each message is attached a large amount of control information that introduces too much network transmission overhead to maintain the real-time causal order delivery. In this article, a novel Lightweight Real-Time Causal Order (LRTCO) algorithm is proposed for large-scale DVE multimedia systems. LRTCO predicts and compares the network transmission times of messages so as to select the proper causal control information of which the amount is dynamically adapted to the network latency variations and unconcerned with system scales. The control information in LRTCO is effective to preserve causal order delivery of messages and lightweight to maintain the real-time property of DVE systems. Experimental results demonstrate that LRTCO costs low transmission overhead and communication bandwidth, reduces causal order violations efficiently, and improves the scalability of DVE systems. [ABSTRACT FROM AUTHOR]

Self-training is essential in sports exercise. However, without the instruction of a coach, a practitioner may progress to a limited extent. Improper postures may even cause serious harm to muscles and ligaments of the body. Hence, the development of computer-assisted self-training systems for sports exercise is a recently emerging research topic. In this paper, we propose a yoga self-training system, which aims at instructing the practitioner to perform yoga poses correctly, assisting in rectifying poor postures, and preventing injury. Integrating computer vision techniques, the proposed system analyzes the practitioner’s posture from both front and side views by extracting the body contour, skeleton, dominant axes, and feature points. Then, based on the domain knowledge of yoga training, visualized instructions for posture rectification are presented so that the practitioner can easily understand how to adjust his/her posture. Experiments on twelve yoga poses performed by different practitioners validate the feasibility of the proposed system in yoga training. [ABSTRACT FROM AUTHOR]