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Resource Allocation for MEC in Ultra-dense networks

Journal of Computers (Taiwan) 2025年第1期36卷 143-162页

作者： Ma, Huan Li, Shuyun Wang, Zhihui Dou, Xubing Zhang, Xinchang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China

In the complex context of 5G ultra-dense networks, the unprecedented growth in traffic and computing demand has made the interaction between edge devices and edge servers extremely intricate due to the time-varying nature of the network environment and resource heterogeneity. The combination of UDN and Mobile Edge computing not only significantly improves network performance but also expands application scenarios and enhances user experience, marking it as one of the crucial trends in future network development. In this paper, we review recent results and research progress on MEC-based resource allocation in 5G ultra-dense networks. We analyze resource allocation strategies from multiple dimensions, including user association, interference management, energy efficiency optimization, delay reduction, access selection, and user mobility. Finally, we discuss the challenges and open issues that need to be addressed for future development in this area. © 2025 Codon Publications. All rights reserved.

关键词： 5G mobile communication systems

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A research framework for constructing the knowledge database of public security information

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International Journal of Wireless and Mobile computing 2021年第3期21卷 191-197页

作者： Zhong, Han Zhang, Shiqiang Liu, Jianli College of Information Technology and Network Security People’s Public Security University of China Xicheng Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China

At present, the public security organs in China have accumulated a great deal of public security data. These data have broad sources, complex structures, large and increasing scales. How to effectively integrate, manage and mine these data has become a new problem faced by all public security organs. This paper proposes a research framework for constructing the knowledge database of public security information. Based on this multi-dimension framework, data features can be effectively extracted and modelled for improving the management and utilisation of public safety data. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Information management

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Combine Superpixel-Wise GCN and Pixel-Wise CNN for Polsar Image Classification

Combine Superpixel-Wise GCN and Pixel-Wise CNN for Polsar Im...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Haiyan Jin Tiansheng He Junfei Shi Shanshan Ji Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

Superpixel-based graph convolution network (SGCN) can extract global features well and reduce computing time greatly, which has been widely used in image classification. However, SGCN ignores individual feature for each pixel within a superpixel. Pixel-wise convolutional neural network (CNN) can learn local features with fixed-square convolution kernel. Combine with both the advantages of SGCN and CNN, we proposed a novel SGCN-CNN method, which can combine the global and local features together. Superpixel-wise SGC-N and pixel-wise CNN cannot be combined into a network directly since they are with different scales. To alleviate this issue, encoder and decoder are designed by defining an association matrix, which can covert features between superpixel and pixel. In addition, complex matrix-based Wishart metric is used to construct the edge weights for SGCN. The proposed method can obtain both global and local features to improve classification performance. Experimental results demonstrate the effectiveness of the proposed method.

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Visual malware classification and detection method based on convolutional neural network 9

Visual malware classification and detection method based on ...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Huang, Hua Sheng, Hua Han, Xingwang Zhang, Wenbin Zhao, Zilin State Grid Shandong Electric Power Company Information and Telecommunications Company Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

With the rapid development of Internet technology, leading to the evolution of malware variants, and malicious threats are also increasing. Therefore, rapid and accurate classification of malware becomes crucial. In response to this challenge, this paper proposes a malware classification method based on deep learning and visualization techniques. The method first extracts sample features, then converts malware binary samples into image format, thus transforming abstract binary data into images with visual information. Subsequently, color mapping technique is applied to convert the processed images into color images. Furthermore, to address imbalanced datasets, this paper introduces data augmentation methods to ensure more balanced and comprehensive training of the model across different sample categories. Finally, an improved convolutional neural network architecture is employed for the classification detection of malware families, achieving accurate classification and identification of different malware families through learning and analysis of sample features. To evaluate the effectiveness of the proposed method, experiments are conducted on the Google Code Jam (GCJ) benchmark dataset. The experimental results demonstrate an accuracy rate of 99.29% on the GCJ dataset. Additionally, we also compared the performance of the improved method before and after, and found that the proposed method can provide more comprehensive feature representation and significantly improve classification accuracy and detection efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Fusion of Dynamic Hypergraph and Clinical Event for Sequential Diagnosis Prediction 29

Fusion of Dynamic Hypergraph and Clinical Event for Sequenti...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Xin Peng, Xueping Guan, Hongjiao Zhao, Long Qiao, Xinxiao Lu, Wenpeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

ISBN: (纸本)9798350330717

Sequential diagnosis prediction (SDP) is a challenging task, aiming to predict patients' future diagnoses based on their historical medical records. While methods based on graph neural networks (GNNs) have proven successful for this task, they typically focus on modeling pairwise diseases using a global disease combination graph. However, these approaches neglect the fine-grained higher-order relations among persistent and emerging diseases within a single visit, which may contain crucial clues to predict the next diagnosis. Additionally, they fail to fully leverage patient-related clinical information present in electronic health records (EHRs). To address these challenges, this paper proposes a novel approach called the fusion of Dynamic Hypergraph and Clinical Event (DHCE) for sequential diagnosis prediction. The proposed method aims to exploit the fine-grained higher-order relations among diagnoses within a visit and leverage clinical event information from EHRs to improve the accuracy of predicting the next diagnosis. Specifically, DHCE categorizes diagnoses within a single visit in a fine-grained granularity into persistent and emerging categories based on a patient's historical diagnoses. It then constructs dynamic hypergraphs to capture higher-order disease relations within each visit. Next, we design a transition function to extract the transitional context from previous visits in order to generate the visit representation. Furthermore, to fully leverage patient-related clinical events in a visit, we utilize Bio-Clinical BERT to encode them and generate the clinical event representation for each visit. Finally, we combine the visit representation and event representation to generate a comprehensive patient representation, which is then used to predict the patient's next diagnosis. Experimental results on two benchmark datasets consistently demonstrate that DHCE outperforms state-of-the-art methods. © 2023 IEEE.

关键词： Bio-Clinical BERT clinical event dynamic hypergraph sequential diagnosis prediction

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Research on Classroom Behavior Target Detection Algorithm based on YOLOv7 6

Research on Classroom Behavior Target Detection Algorithm ba...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Qiu, Wenke Wu, Xiaoming Liu, Xiangzhi Computer Science and Qilu University of Technology Shandong Academy of Sciences ShanDong Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350316520

The integration of video information for the purpose of detecting students' behavior in the classroom, with the aim of enhancing teaching efficiency and maintaining a stable classroom environment, offers significant assistance. However, existing models usually struggle to achieve good accuracy due to the lack of datasets, dense targets, interclass occlusion, and other issues. To address these difficulties, the CD-YOLO classroom behavior detection algorithm is proposed in this paper. And the classroom behavior dataset (DSL-Dataset) is created. We propose a loss function named VAIoU, which can be combined with vector ratio to filter the prediction box. To further improve the detection accuracy of occluded targets, we propose an attention module CM module with command feature fusion. Moreover, in this paper, we improve the convolutional kernel to design a lightweight convolutional kernel FFconv. Experimental results on DSL-Dataset show that CD-YOLO outperforms existing one-stage detection models in terms of detection accuracy. © 2024 IEEE.

关键词： Convolution

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GA-Net: Gated Attention Mechanism Based Global Refinement network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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Scheduling Model of Scenic Tour Task Based on Heuristic Intelligent Search Algorithm

Scheduling Model of Scenic Tour Task Based on Heuristic Inte...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Hongbo Wang Rong Fei Zilong Wang Aimin Li Yajun Liu Fuyu Zhu School of Computer Science and Engineering Xi'an University of Technology Xi’an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi’an China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

This paper constructs a scenic tour task scheduling model (hereinafter referred to as the model) based on heuristic intelligent search algorithm, puts forward the heuristic intelligent search solution scheme under single-task and multi-task scenarios, aims to unify the planning of tour routes according to the scenic spot flow situation, prevents the formation of attraction passenger flow peaks, and can effectively solve the problem of the traditional scheduling model's dynamic scheduling ability lack. In particular, the performance of different scheduling algorithms is compared and simulation tests are designed to verify that the proposed model and solution method are effective for solving the optimal path problem of scenic tour, which is of practical significance for realizing the intelligent management of scenic spots and promoting the intelligent development of tourism.

关键词： Scheduling algorithms Heuristic algorithms Dynamic scheduling Search problems Multitasking Planning Artificial intelligence

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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An Ecg Signal Synthesis Approach Based on the Synergy of Wgan and Attention Mechanisms

SSRN

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SSRN 2024年

作者： Zhou, Hongfang Li, Yijie Guo, Yifan Xue, Haolin School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an710048 China

Electrocardiogram (ECG) synthesis plays a key role in the diagnosis of heart diseases. However, ECG data involves patient privacy and has obvious imbalance issue. This leads to a disconnect between theoretical research methods and practical applications. Based on this, the paper will explore the application of generative adversarial networks (GANs) in ECG signal synthesis. First, the paper proposes a WGAN-SD-GP model that uses the conditional WGAN networks incorporating an attention mechanism to enhance the ability to capture contextual information and effectively focus contextual information by scaling dot products. Secondly, the paper explores whether the design concepts of circular convolution, feature extraction and layer-hopping connection in R2U-Net can be applied to the synthesis of ECGs, and proposes the WGAN-SKD-GP model. The model combines the hopping layer connection with the attention mechanism, which enables it to process the input data more flexibly and enhances the feature expression ability. Finally, the experiments were designed to automatically classify arrhythmia using 1DResNet50 on a mixed dataset of synthetic and real signals, which was evaluated using several metrics, including Precision, Recall, F1 Score, Macro Avg, Weighted Avg, and Overall Accuracy. The experimental results not only verify the effectiveness of WGAN-SKD-GP in ECG signal synthesis, but also provide a new idea for solving the problem of insufficient data of ECG signals, which has significant potential application value. © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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