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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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Quantum-Resistant Multi-Feature Attribute-Based Proxy Re-Encryption Scheme for Cloud Services

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Computer Modeling in Engineering & sciences 2024年第1期138卷 917-938页

作者： Jinqiu Hou Changgen Peng Weijie Tan Hongfa Ding State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Advanced Manufacturing Technology Ministry of EducationGuizhou UniversityGuiyang550025China College of Information Guizhou University of Finance and EconomicsGuiyang550025China

Cloud-based services have powerful storage functions and can provide accurate ***,the question of how to guarantee cloud-based services access control and achieve data sharing security has always been a research *** the attribute-based proxy re-encryption(ABPRE)schemes based on number theory can solve this problem,it is still difficult to resist quantum attacks and have limited expression *** address these issues,we present a novel linear secret sharing schemes(LSSS)matrix-based ABPRE scheme with the fine-grained policy on the lattice in the ***,to detect the activities of illegal proxies,homomorphic signature(HS)technology is introduced to realize the verifiability of ***,the non-interactivity,unidirectionality,proxy transparency,multi-use,and anti-quantum attack characteristics of our system are all ***,it can efficiently prevent the loss of processing power brought on by repetitive authorisation and can enable precise and safe data sharing in the ***,under the standard model,the proposed learning with errors(LWE)-based scheme was proven to be IND-sCPA secure.

关键词： Lattice learning with errors attribute-based proxy re-encryption linear secret sharing schemes

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Two-Stage Guided Constraint Differential Evolution Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Neural Machine Translation Models with Attention-Based Dropout Layer

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Computers, Materials & Continua 2023年第5期75卷 2981-3009页

作者： Huma Israr Safdar Abbas Khan Muhammad Ali Tahir Muhammad Khuram Shahzad Muneer Ahmad Jasni Mohamad Zain School of Electrical Engineering and Computer Science(SEECS) National University of Science and TechnologyIslamabadPakistan Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA40450Shah AlamSelangorMalaysia

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for several language ***,there has been little work exploring useful architectures for Urdu-to-English machine *** conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and *** results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen *** trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,*** a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more *** attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear ***,we considered refining the attention-based models by defining an additional attention-based dropout *** dropout fixes alignment errors and minimizes translation errors at the word *** empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation *** ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as *** empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

关键词： Natural language processing neural machine translation word embedding attention perplexity selective dropout regularization Urdu Persian Arabic BLEU

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Dense Pedestrian Detection Algorithm Based on Multiscale Feature Fusion in YOLOv8 5

Dense Pedestrian Detection Algorithm Based on Multiscale Fea...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Hu, Weichao Hu, Qian Pi, Jianyong Huang, Kun Li, Wenhua Wang, Juanmin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Guizhou Mobile Information Technology Company Guiyang China

ISBN: (纸本)9798350373820

The pedestrian target in large-scale crowded places has the characteristics of complex environment, diverse scales and dense arrangement, which makes the current target detection methods appear the phenomenon of false detection and missed detection when dense pedestrians. In order to solve this problem, based on YOLOv8, a dense pedestrian detection algorithm based on multi-scale feature fusion was proposed. Firstly, in view of the limitation of the performance of standard convolutional convolution due to the defect of convolutional kernel parameter sharing, RFCAConv was introduced into the backbone network. The convolution module can effectively improve the performance of convolution operation, so as to improve the feature extraction ability of the backbone network and fully extract the feature information at all levels. Secondly, a 160 × 160 scale small target detection head was added to improve the model's detection ability of small-scale targets. Finally, based on the ASFF design ASFF-4P module, QAFPN is proposed to fully integrate the feature information of four feature layers with different feature scales, effectively avoid the loss and degradation of information, reduce the distance of non-adjacent layer semantic information, and improve the detection accuracy and efficiency of the model for targets at different scales. Experimental results show that compared with YOLOv8n, the proposed method improves AP50% and AP50:95% by 3.7% and 4. 2% respectively on the CrowdHuman datasets, and is also highly competitive compared with other advanced pedestrian detection models. © 2024 IEEE.

关键词： Convolution

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Probe Machine Based Computing Model for Maximum Clique Problem

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Chinese Journal of Electronics 2022年第2期31卷 304-312页

作者： CUI Jianzhong YIN Zhixiang TANG Zhen YANG Jing Department of Computer Huainan Union University School of Electronic and Information Engineering Anhui University of Science & Technology School of Mathematics Physics and Statistics Shanghai University of Engineering Science School of Mathematics and Big Data Anhui University of Science & Technology

Probe machine(PM) is a recently reported mathematic model with massive parallelism. Herein,we presented searching the maximum clique of an undirected graph with six vertices. We constructed data library containing n sublibraries, each sublibrary corresponded to a vertex in the given graph. Then, probe library according to the induced subgraph was designed in order to search and generate all maximal cliques. Subsequently,we performed probe operation, and all maximal cliques were generated in parallel. The advantages of the proposed model lie in two aspects. On one hand, solution to NP-complete problem is generated in just one step of probe operation rather than found in vast solution *** the other hand, the proposed model is highly *** work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP-complete problem.

关键词： maximal cliques maximum clique problem searching capacity data library NP-complete problem computational complexity model lie induced subgraph probe operation undirected graph mathematic model graph theory parallel algorithms optimisation parallelism computing model vertex probe library probe machine

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Jiacheng Liu, Jiaxuan Ji, Shengwei Yang, Hongbin School of Computer Science and Information Engineering Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei230027 China School of Artificial Intelligence and Big Data Hefei University China

ISBN: (纸本)9798350337020

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets. © 2023 IEEE.

关键词： Information extraction Reliable pattern Writing style data

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Flame Detection Algorithm Based on Improved YOLOv8 5

Flame Detection Algorithm Based on Improved YOLOv8

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Huang, Kun Hu, Qian Pi, Jianyong Hu, Weichao Li, Wenhua Wang, Juanmin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Guizhou Mobile Information Technology Company Guiyang China

ISBN: (纸本)9798350373820

Flame detection is a key technology in intelligent firefighting, using object detection methods to locate and count flames in videos, thereby enabling intelligent monitoring of fire incidents. This has significant research value in areas such as public safety and community intelligent governance. In complex scenes, due to the complicated backgrounds of flame targets, non-uniform sizes and shapes, and sensitivity to lighting, there's a tendency for missed detections. This paper proposes an improved flame detection algorithm based on YOLOv8, named Fire-YOLOv8, addressing these issues. Firstly, a Tri-RepVGG module is introduced into the backbone network for better flame feature extraction without increasing computational costs, ensuring the timeliness of detection. Secondly, in the neck part of the YOLOv8 network, a Progressive Feature Pyramid Network (AFPN) structure is introduced to enhance the fusion of information between layers, solving the issues of information loss and degradation present in YOLOv8 feature fusion. Finally, based on the SE channel attention mechanism, an SE-Detect detection head is proposed to effectively mitigate the issues of flame detection sensitivity to lighting. Experimental results show that Fire-YOLOv8 achieves 74.5% mAP50 and 47.9% mAP50-95 on a self-made flame dataset Multi-Fire, representing improvements of 4.4% and 3.3% respectively over the original YOLOv8n, demonstrating strong competitive capabilities. © 2024 IEEE.

关键词： Lighting

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Research and Application of Music Personalized Recommendation System Based on Random Forest Algorithm

Research and Application of Music Personalized Recommendatio...

引用

IEEE Information technology, Networking, Electronic and Automation Control Conference

作者： Ziyan Shu Qing Shen Tianlun Zeng Department of Information Engineering Wuhan Business University Hubei China Major in Data Science and Big Data Technology Major in Internet of Things Engineering

In recent years, with the advent of the era of big data and the rise of data mining technology, people used to waste a lot of time looking for advantageous information, which was very inefficient. Music is an indispensable part of everyone’s daily life, and major music platforms are gradually emerging. Finding music that users like is the key to attracting users. At present, there are relatively few studies on personalized music recommendation using data mining algorithms. In this paper, the random forest algorithm is used to pre-process the collected data, and then the data is modeled, the model parameters and fitting coefficients are adjusted, the appropriate index is selected as the learning parameter, and the learning curve is obtained at the same time. Finally, the prediction is carried out through examples, and it is found that the prediction accuracy is high, and the WeChat applet is used for development and application. The approach in this article is simple and constructive, and it is dedicated to recommending personalized and exclusive music for particular users, so that you can spend every day happily.

关键词： Social networking (online) Fitting big data Prediction algorithms Message services data models data mining

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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