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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

IEEE Transactions on Mobile Computing 2025年第07期24卷 6446-6459页

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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DNAS: Depth-First Neural Architecture Search

DNAS: Depth-First Neural Architecture Search

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhou, Jianjun Chen, Junying Cai, Yi School of Software Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9781665410205

The key challenge of neural architecture search (NAS) methods lies in efficiently exploring search spaces. To solve this problem, Breadth-First Search (BFS) method uses two trees to represent a search space, and performs bi-level BFS on these trees to find the optimal network architecture. However, the BFS method did not discover the best network in NAS-Bench-201, and its search efficiency is still not high enough. In this work, we propose a one-shot method called Depth-first Neural Architecture Search (DNAS) to efficiently explore the best architecture. Given a search space with N-candidate operations, we represent it as a single N-ary tree and employ depth-first search on this tree to explore high-performance network architectures while significantly reducing the resource consumption during the exploration. While the BFS method explores multiple networks simultaneously in each exploration, DNAS efficiently explores along only one direction at a time, eliminating the need to synchronize the search processes of multiple networks. The proposed DNAS, performed on a single RTX 2080Ti GPU, finds the optimal architectures for CIFAR-10 and ImageNet16-120 in 0.3 hour on NAS-Bench-201, and in 1.0 hour on CIFAR-100. While finding the best network, the search time of the DNAS method has been reduced by around 2 to 8 GPU hours as compared to other well-performed methods, especially reducing the search time by 91.67 % when compared to the BFS method. The best searched network was further transferred to medical image classification tasks and achieved high classification accuracy across multiple datasets. In addition, the results of ablation experiments substantiate the effectiveness and efficiency of the proposed method. Our source code is available at: https://***/Bob5090/DNAS. © 2024 IEEE.

关键词： Trees (mathematics)

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Heterogeneous UAV Resource Scheduling for Dynamic Time Sensitive Target Detection and Interference

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IEEE Internet of Things Journal 2025年第12期12卷 20030-20041页

作者： Wan, Liangtian Wang, Jiashuai Sun, Lu Li, Kuixian Xiong, Xuanrui Lin, Yun Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian116620 China Dalian Maritime University Institute of Information Science Technology Department of Communication Engineering Dalian116026 China Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China

In complex electromagnetic environments, targets that need to be interfered with often possess high levels of concealment and anti-interference capabilities. Additionally, due to the dynamic characteristics of these targets, interference tasks must be conducted within strict time constraints to ensure interference effect. In this paper, we adopt a reconnaissance-first approach for concealed targets. After detecting the accurate location of the target, we deploy Unmanned Aerial Vehicles (UAVs) to interfere with the targets. First, we established a UAV swarm task scheduling optimization model after considering constraints such as target threat range, priority of reconnaissance and interference tasks, interference task time, and UAV energy consumption. Meanwhile, we model the anti-interference capability of the target as a threat range. Second, we propose a non-dominated sorting genetic algorithm based on distance in the solution space and a dynamic parent selection strategy (DPSNSGA-II) to solve UAV Resource Scheduling optimization problem. The diversity of the population is increased and the situation of falling into local optima is reduced by improving the parent individual selection strategy, mutation strategy, and elite solution retention mechanism. Finally, we construct two data sets of varying sizes to evaluate the quality of solution sets and the convergence performance of the proposed algorithm. The simulation results indicate that the proposed DPSNSGA-II algorithm has better result for population diversity and convergence compared to state-of-the-art algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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Enhancing Emotion Detection in Chatbots for Improved Mental Health Support: Addressing Limitations in Mental Disorders and Emotion Detection 17

Enhancing Emotion Detection in Chatbots for Improved Mental ...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Krabashini, Nagendran Pillai Guo, Jiandong Chen, Junzuo Han, Bolin Dai, Zihan School of Information & Software Engineering University of Electronic Science & Technology of China China

ISBN: (纸本)9798331507398

The increase in the adoption of AI-driven chatbots in mental health support, accurately detecting and responding to users' emotions is crucial for effective communication. This paper proposes a novel framework that enhances emotion detection in chatbots by leveraging BERT (Bidirectional Encoder Representations from Transformers) and ensemble learning techniques. Our approach addresses the inherent limitations in existing emotion detection systems, particularly when dealing with complex emotional states and mental health disorders. Using an array of tests, we show that our suggested model works better than conventional approaches, providing enhanced precision, resilience, and the ability to more effectively assist mental health therapies. This research contributes to the ongoing development of AI in mental health, providing a more nuanced and effective approach to emotion detection. © 2024 IEEE.

关键词： Electronic health record

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Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

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Computer Modeling in engineering & Sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang Yao Zhou Guangchun Luo Peng Chen Dezhong Peng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer Science Sichuan UniversityChengdu610065China National Innovation Center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

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Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

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Computer Modeling in engineering & Sciences 2024年第4期139卷 187-224页

作者： Weisi Chen Walayat Hussain Francesco Cauteruccio Xu Zhang School of Software Engineering Xiamen University of TechnologyXiamen361024China Peter Faber Business School Australian Catholic UniversityNorth Sydney2060Australia Department of Information Engineering Polytechnic University of MarcheAncona60121Italy

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last *** traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction ***,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and *** review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other *** illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related *** the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only *** remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency *** principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and t

关键词： Financial time series prediction convolutional neural network long short-term memory deep learning attention mechanism finance

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Network Traffic Anomaly Detection Based on DGBi-SA Model

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engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional Networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, International Association of Engineers. All rights reserved.

关键词： Long short-term memory

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Integrating Federated Learning for Privacy Protection in Intelligent Home Care Robots 4

Integrating Federated Learning for Privacy Protection in Int...

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Liu, Bin Blancaflor, Eric B. Mapua University School of Information Technology Manila Philippines Xiamen University of Technology School of Software Engineering Xiamen China

ISBN: (纸本)9798350364798

In the field of intelligent home care robots, privacy protection has become a focus of attention, especially in improving service quality while ensuring user privacy and security. The study explores the application of federated learning privacy protection technology in this field and analyzes the performance of different privacy technologies. The results indicate that as the privacy budget increases to 3, the overall accuracy performance of all technologies has improved. After 93 iterations, the accuracy of federated learning privacy protection technology is 96.08%;The accuracy of homomorphic encryption is 83.59%. The accuracy of secure multi-party computation and differential privacy is 73.31% and 66.74%, respectively. The increase in privacy budget has a positive correlation with the accuracy of privacy protection technology. This study provides data support and direction guidance for the optimization of privacy protection technology applications in similar fields in the future. © 2024 IEEE.

关键词： Federated learning

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HSM:Hand Surface Menu Solution for Interaction in VR 10

HSM:Hand Surface Menu Solution for Interaction in VR

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10th International Conference on Virtual Reality, ICVR 2024

作者： Zhang, Hailong Li, Lin Xue, Chang Wei, XiaoCai Zhou, Yin Hefei University of Technology School of Computer Science and Information Engineering Hefei China Hefei University of Technology School of The Software Engineering Hefei China

ISBN: (纸本)9798350364231

The menu interaction methods in VR, such as floating menus, are still considered unnatural. A solution is proposed in this paper where menus are tightly attached to the user's palm. Firstly, use UV mapping technology to attach the menu to the palm. Secondly, optimal layout parameters for the hand surface menu were determined through user experiments. Finally, a dataset was built to train the interaction error detection model, aiming to improve interaction success rates under various hand poses in VR. Experimental results demonstrate that compared to other VR menu methods, HSM reduces the average interaction error rate by 30.1% and shortens the average completion time by 12.4% under the optimal hand pose. © 2024 IEEE.

关键词： Virtual environments

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