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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

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Detecting Artificial Inflation of SMS Traffic Using Machine Learning

Detecting Artificial Inflation of SMS Traffic Using Machine ...

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Mobile, Intelligent, and Ubiquitous computing Conference (MIUCC), International

作者： Samy Elsayed Teleb Hassan Ammar Mohammed Dept. of Data Science FGSSR Cairo University Cairo Egypt Dept. of CS FGSSR Cairo University Cairo Egypt School of Computing and Digital Tech ESLSCA University Cairo Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

The rapid expansion of SMS traffic has led to an increase in artificial inflation, where entities artificially manipulate message volumes for various purposes. Detecting such inflation is essential for maintaining the integrity of communication systems, yet effective solutions to this problem remain limited. This paper introduces a machine learning approach to identify artificial inflation in SMS traffic. Several models were developed and evaluated-including Random Forest, Gradient Boosting, Support Vector Machine (SVM), and Gaussian Naive Bayes (GaussianNB)-using a dataset provided by a telecommunications company. The models were trained on features related to recipient numbers and other contextual factors, and their performance was compared on the basis of accuracy metrics. The results indicate that the Gradient Boosting model significantly outperforms the others, achieving an accuracy score of 91%, and shows considerable potential to effectively detect artificial

关键词： Support vector machines Accuracy Computational modeling Message services Boosting Ubiquitous computing data models Telecommunications Context modeling Testing

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From General to Specific: Tailoring Large Language Models for Personalized Healthcare

arXiv

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arXiv 2024年

作者： Shi, Ruize Huang, Hong Zhou, Wei Yin, Kehan Zhao, Kai Zhao, Yun Huazhong University of Science and Technology Wuhan China Tongji Medical College China Hubei Maternity and Child Health Care Hospital China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

The rapid development of large language models (LLMs) has transformed many industries, including healthcare. However, previous medical LLMs have largely focused on leveraging general medical knowledge to provide responses, without accounting for patient variability and lacking true personalization at the individual level. To address this, we propose a novel method called personalized medical language model (PMLM), which explores and optimizes personalized LLMs through recommendation systems and reinforcement learning (RL). Specifically, by utilizing self-informed and peer-informed personalization, PMLM captures changes in behaviors and preferences to design initial personalized prompts tailored to individual needs. We further refine these initial personalized prompts through RL, ultimately enhancing the precision of LLM guidance. Notably, the personalized prompt are hard prompt, which grants PMLM high adaptability and reusability, allowing it to directly leverage high-quality proprietary LLMs. We evaluate PMLM using real-world obstetrics and gynecology data, and the experimental results demonstrate that PMLM achieves personalized responses, and it provides more refined and individualized services, offering a potential way for personalized medical LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Reusability

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Triangle Graph Interest Network for Click-through Rate Prediction

arXiv

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arXiv 2022年

作者： Jiang, Wensen Jiao, Yizhu Wang, Qingqin Liang, Chuanming Guo, Lijie Zhang, Yao Sun, Zhijun Xiong, Yun Zhu, Yangyong Alibaba Group China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Shanghai Institute for Advanced Communication and Data Science Fudan University China

Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users’ preference for different interest units. By selecting diverse and relative triangles, TGIN brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both of public and industrial datasets clearly verify the effectiveness of our framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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Artificial intelligence assisted recognition and diagnosis of Magnetically controlled capsule gastroscopy

Artificial intelligence assisted recognition and diagnosis o...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Susu Wei, Chuyu Li, Dongqi Borchert, Glen M. Yu, Zhong Lin, Jiawen Huang, Jingshan College of Computer and Data Science Fuzhou University Fuzhou China Sun Yat-sen Memorial Hospital Sun Yat-sen University Department of Gastroenterology Guangzhou China School of Information and Computer Sciences School of Social and Behavioral Sciences University of California Irvine IrvineCA United States College of Medicine University of South Alabama Department of Pharmacology MobileAL United States School of Computing College of Medicine University of South Alabama Mobile United States

ISBN: (纸本)9798350386226

To enhance the diagnostic efficiency and accuracy of gastric mucosal lesions in magnetically controlled capsule endoscopy (MCCE) images, a Jigsaw Guided Deep Feature Fusion (JG-DFF) artificial intelligence (AI)-assisted recognition model based on the ResNet-50 convolutional neural network (CNN) is presented in this paper. Based on a dataset consisting of 4,053 MCCE images retrospectively collected from Sun Yat-sen Memorial Hospital, we constructed a ResNet-50 AI recognition model, as well as a JG-DFF AI recognition model under the jigsaw guided deep feature fusion. Additionally, the diagnostic ability and run time of the JG-DFF model were compared against a digestive endoscopist using the same data. The results indicate that the JG-DFF model has the potential for clinical application in improving diagnostic efficiency and reducing false positive rates. The overall accuracy of the JG-DFF artificial intelligence assisted recognition model established in this study is 92.1%. Compared to the ResNet-50 model, recognition by the JG-DFF model was more consistent;Compared with the digestive endoscopist, observed specificity of the JG-DFF model in identifying positive lesions was similar (P>0.05). That said, the JG-DFF model was more sensitive in identifying certain classes (P1 © 2024 IEEE.

关键词： Endoscopy

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Multi-Agent System based Medical Diagnosis Using Particle Swarm Optimization in Healthcare

Multi-Agent System based Medical Diagnosis Using Particle Sw...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Jagjit Singh Dhatterwal Mahaveer Singh Naruka Kuldeep Singh Kaswan Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India G L Bajaj Institute of Technology and Management Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India

Healthcare is an area that is challenging, dynamic, and diverse. Healthcare in the twenty-first century brings a variety of difficulties (population growth and rising healthcare costs). Compared to the conventional medical system, agent technology can offer superior healthcare for that. Agents in the hospital might deal with a variety of medical issues. We list collaboration across hospital wards, the development of diagnostics, the gathering of patient data, etc. as examples of issues that arise in hospitals. These issues can be resolved with a cooperative This paper proposes a generic architecture that integrates swarm intelligence with multi-agent healthcare systems to provide therapy as efficiently as possible.

关键词： Hospitals Sociology Semantics Medical services Medical diagnosis Particle swarm optimization Statistics

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Confidential Spatio-Temporal Graph Convolutional Networks with Differential Privacy for Traffic Forecasting

Confidential Spatio-Temporal Graph Convolutional Networks wi...

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Xiangyang Chen Aiwen Xiao Weiling Pang Xinyi Jiang Zehua Ren Min Tang Key Laboratory of Data Analysis and Computation School of Mathematics and Computing Science Guangxi Colleges and Universities Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

Traffic forecasting is essential for urban traffic management in intelligent transportation systems (ITS). However, it involves personal sensitive information, such as user location data. In this paper, we propose an innovative secure protocol for addressing the privacy challenges of traffic prediction in ITS. Our model integrates Spatio-Temporal Graph Convolutional Network (STGCN) and differential privacy (DP) to effectively protect user privacy on the premise of predictive performance. Our experimental results demonstrate that the model maintains a high level of prediction accuracy while offering privacy protection for the user data. On the PeMSD7 dataset, compared to the STGCN benchmark model, our model shows a slight increase of 0.5% in performance metrics (e.g., mean absolute error) while safeguarding the user data. Additionally, the model's average training time increases by roughly 8.4% in the ciphertext domain.

关键词： Training Differential privacy Privacy Protocols Graph convolutional networks Predictive models data models

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A TV-Module for DC-Baseline Compensation in Analog Front-End Design

A TV-Module for DC-Baseline Compensation in Analog Front-End...

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2022 International Conference on computing, Networking, Telecommunications and Engineering sciences Applications, CoNTESA 2022

作者： Hertz, Pancha Yannick Kamdem Jerome, Folla Evariste, Wembe Tafo Zobo Bernard, Essimbi Sobhan Bhuiyan, Mohammad Arif Miraz, Mahdi H. University of Yaounde I Department of Physics Yaounde Cameroon Department of Physics University of Douala Douala Cameroon Xiamen University Malaysia Electrical and Electronics Engineering Sepang Malaysia Xiamen University Malaysia School of Computing and Data Science Sepang Malaysia

ISBN: (纸本)9798350398014

In the design of analog front-end electronics (FEE) for particle beam energies, the circuit is prone to several shortcomings to ensure accurate extraction of particle data in the semiconductor device. Among these problems, the frequency variability that leads to baseline shift affecting the amplitude /energy detection of particles is recurrent. It impinges on the effective energy extracted from the semiconductor material system. In this work, we propose a correction circuit to solve the above-mentioned problem while preserving the bandwidth characteristics of the analog filter. The proposed circuit is a time-variant high-pass filter based on complementary switches S1 and S2, which stabilise the output baseline of the front-end circuit. Post-layout simulation analysis has been performed and the results are quite promising. That allows the output swing to be switched within the detected baseline and a given voltage, controlled by the discriminator threshold level. The design has been validated by post-layout simulation results, performed in a 65 nm CMOS process from TSMC, under 1.2 V supply voltage. © 2022 IEEE.

关键词： High pass filters

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Multi-Scale Semantic Communication for Object Detection: Single and Cross-Domain Scenarios

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Jie Yin, Hang Song, Bin Chi, Yuhao Zhang, Zhaoyang Yuen, Chau Niyato, Dusit Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore Nanyang Technological University College of Computing and Data Science 639798 Singapore

With the rapid popularity of vision-driven communication applications, object detection has become one of the fundamental techniques for performing practical vision tasks. In traditional communication systems, images are compressed for transmission, reconstructed at the receiver, and then processed by existing object detection algorithms. However, transmitting large amounts of images consumes significant storage and communication resources. To address this challenge, a semantic communication-based image reconstruction scheme has been proposed for object detection, which transmits only the semantic information relevant to image reconstruction. However, this method is prone to losing key information, such as object position and texture details, leading to degraded object detection performance. Additionally, it is sensitive to environmental factors such as weather and lighting, resulting in poor adaptability across multiple scenarios. To address these issues, we propose a multiscale semantic communication framework for object detection that transmits only multi-scale semantic features relevant to the task and employs decoupling at the receiver to separate positional and classification information of target objects without requiring image reconstruction. To improve adaptability across multiple scenarios, we introduce a cross-domain object detection technique that ensures reliable object detection in new scenarios by optimizing the framework’s multi-scale semantic encoder through domain adversarial learning. Numerical results demonstrate that the proposed framework achieves mean average precision improvements of 15.4% ~ 38.5% over the traditional communication framework within low to medium signal-to-noise ratio regions in additive white Gaussian noise and Rayleigh fading channels. © 2002-2012 IEEE.

关键词： Fading channels

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism

Image Denoising Method Based on Channel Compression and Spli...

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Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Hao Sun Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Shandong

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks.

关键词： Image coding PSNR Convolution Computational modeling Refining Image restoration Computational efficiency Computational complexity Image reconstruction Image denoising

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