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Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

Improving the Performance of K-Nearest Neighbor Algorithm by Reducing the Attributes of Dataset Using Gain Ratio 4

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Improving the Performance of K-Nearest Neighbor Algorithm by...

4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Hasdyna, N. Sianipar, B. Zamzami, E.M. Graduate Program of Computer Science Universitas Sumatera Utara Medan20155 Indonesia Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia

Data that has many attributes or higher dimensions will affect the performance of the K-NN classification algorithm. In this study, the Gain Ratio implemented for selecting and reducing the dataset attributes to form a new dataset for the classification process is carried out with the K-NN. The dataset used in this study are the Breast Cancer Coimbra dataset and Hepatitis C Virus dataset obtained from the UCI Machine Learning Repository. The results showed that the Breast Cancer Coimbra dataset, Gain Ratio can improve the performance of K-NN with average value 0.535596 TPR, TNR = 1, NPV = 0.608279, FNR = 1, FOR = 0.391721, Accuracy = 72.85%. In Hepatitis C Virus dataset also managed to improve the performance of K-NN with average value TPR = 0.665596, TNR = 0,876667, NPV=0,738279, FNR=0,88, FOR=0,521721, and Accuracy=86,25%. © Published under licence by IOP Publishing Ltd.

关键词： Nearest neighbor search

Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

Proposing a visualized comparative review analysis model on tourism domain using Naïve Bayes classifier

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Procedia computer science 2023年 227卷 482-489页

作者： Albert Verasius Dian Sano Adriel Anderson Stefanus Evaristus Didik Madyatmadja Hendro Nindito Agung Purnomo Corinthias P.M. Sianipar Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Department of Global Ecology Graduate School of Global Environmental Studies (GSGES) Division of Environmental Science and Technology Graduate School of Agriculture (GSA) Kyoto University Kyoto 606-8502 Japan

This study, using the Naïve Bayes classifier, proposes a new descriptive model for conducting a comparative review analysis on the tourism domain. The proposed model seeks to improve the understanding of tourists' experiences by visually representing their opinions between various tourist destinations. The model predicts the sentiment of tourist reviews using the Naive Bayes classifier, which is then used to generate visualizations that facilitate comparative analysis. The comparison takes two tourism sites as experimental examples. The results, in terms of accuracy, show 80% and 64% for the first and the second site respectively. The study discusses the model's implementation, effectiveness, and potential applications in the tourism industry. The findings of the study suggest that the proposed model can provide valuable insights into tourist experiences and help tourism stakeholders, such as the managements of tourism sites, the future or soon coming tourists to make informed decisions based on comparative analysis.

关键词： Comparative review analysis Naïve Bayes classifier tourism

Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

Pillow for Detecting Snoring with Embedded Techniques for Elderly People with Snoring Problems

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Pillow for Detecting Snoring with Embedded Techniques for El...

Cybernetics and Innovations (ICCI), International Conference on

作者： Worawut Yimyam Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Narumol Chumuang Department. of Business Information Management Phetchaburi Rajabhat University Phetchaburi Thailand Department of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Department of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand

This research aimed to design, produce, and assess a snoring detection neck pillow for elderly individuals with snoring symptoms. The research process began with finding information on snoring. The data obtained were used to design a snore detection neck pillow in the form of a U-shaped neck pillow. The functional principle was that when the microphone module detects the snoring sound, it activates a vibrating motor to alert the snorer to awake or change sleeping positions and stop snoring. There was a program to control the operation. The results of the snoring detection neck pillow test with a sample size of 20 people showed an average efficiency of 80%.

关键词： Performance evaluation Technological innovation Sleep apnea Neck Delays Older adults Cybernetics

Identification of Beef Freshness Using Linear Discriminant Analysis (LDA) Algorithm and Extraction of HSV Values

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Identification of Beef Freshness Using Linear Discriminant A...

Electronic and Electrical Engineering and Intelligent System (ICE3IS), International Conference on

作者： Riduwan Napianto Agung Tri Prastowo Agung Deni Wahyudi Wajiran Yohanes Egi Pratama Yudoutomo Arico Ayani Suparto Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Information Technology Education Study Program STKIP PGRI Situbondo Situbondo Indonesia

ISBN: (纸本)9781665465427

To determine the quality of beef, it is usually seen using a flashlight and matching it with a predetermined color standard. However, this method is time consuming and the results are inconsistent due to human visual limitations. This study aims to identify the freshness of beef based on its color characteristics using feature extraction method with HSV and LDA method for identification. The identification process begins with the transformation process from RGB images to L*a*b, then image segmentation serves to separate one object from another and image identification uses the LDA method with feature extraction using HSV. The use of color feature extraction with the HSV algorithm to assist in obtaining information from the colors contained in the image for easy identification. Furthermore, the LDA algorithm has a function to obtain the maximum projection to obtain a smaller dimensional space so that the desired patterns can be separated through the boundary line of a linear equation so as to form a certain class or group. Based on the test results using the confusion matrix, it produces a precision value of 80%, recall of 84%, and accuracy of 82%.

关键词： Visualization Image segmentation Image color analysis Shape Feature extraction Mathematical models Linear discriminant analysis

Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin Poor, H. Vincent The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices. Our code is available at https://***/HongyangDu/DistributedDiffusion. © 2023, CC BY.

关键词： Wireless networks

Globally Harmonized System Label detection using Color Segmentation

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Globally Harmonized System Label detection using Color Segme...

Cybernetics and Innovations (ICCI), International Conference on

作者： Thidarat Pinthong Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Narumol Chumuang Department. of Business Information Management Phetchaburi Rajabhat University Phetchaburi Thailand Department of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Department of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand

This paper presents an approach for color detection and segmentation based on Image Processing Process to retrieve candidate regions of GHS Sign. For color classification, where the dimension of each vector can be extended by a group of neighboring pixels. The experimental results are highly accurate and robust for our testing database, where samples are recorded on various states of environment. But in the meantime, it was found that the error processing. It is an image that looks asymmetrical image. It is impossible to detect the symbol GHS.

关键词： Image segmentation Technological innovation Image resolution Image color analysis Shape Symbols Transportation