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2nd IEEE International Conference on Electrical, Automation and computer engineering, ICEACE 2024

作者： Wu, Yanxu Liu, Kai Zhou, Xianghao Zhang, Xiumei University of Science and Technology Liaoning School of Electronics and Information Engineering Anshan China South-Central Minzu University School of Computer Science Wuhan China Beihang University School of Automation Science and Electrical Engineering Beijing China University of Science and Technology Liaoning School of Computer and Software Engineering Anshan China

ISBN: (纸本)9798350368208

To address the issue of challenging detection tasks for tiny and medium-sized objects because of backdrop confusion and inadequate feature representation of steel surface flaws, this paper proposes an efficient feature-enhanced, feature-fused SAM-YOLO detector based on the YOLOv8 model. It adds a new feature fusion module MSFF based on the original YOLOv8 model and replaces the bottleneck module in the C2f module with a feature enhancement module SACM. These two modules improve the enhancement and detection of defect edge features, local area sensing, and the network capability of multi-scale feature fusion, and the effect of SAM-YOLO is verified through several experiments on the NEU-DET steel surface defect dataset. The mAP50 of SAM-YOLO reaches 77.9%. mAP50:95 reaches 49.1%, exceeding the original YOLOv8 baseline model by 6.9% and surpassing several other benchmark models. To confirm the legitimacy of each SAM-YOLO model module, ablation tests were conducted on the model in the interim. © 2024 IEEE.

关键词： Target tracking

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A Hybrid Algorithm based on Autoencoder and Text Convolutional Network for Integrating scATAC-seq and scRNA-seq Data

A Hybrid Algorithm based on Autoencoder and Text Convolution...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Xiaoshu Liu, Jiahui Lan, Wei Teng, Fei Guilin University of Electronic Technology School of Computer Science and Information Security School of Software Engineering Guilin China Guangxi University School of Computer Electronics and Information Nanning China

ISBN: (纸本)9798350386226

Integrating single-cell RNA-seq (scRNA-seq) data and single-cell ATAC-seq (scATAC-seq) data provides a more comprehensive view of cellular heterogeneity. However, the high sparsity in scATAC-seq data presents significant challenges for cell type identification, while scRNA-seq offers richer gene expression information for more accurate annotation. We propose scEDI, a method that integrates scRNA-seq and scATAC- seq data using autoencoders and text convolutional networks. In scEDI, both data types are processed through an autoencoder to create a shared low-dimensional space, followed by a text convolutional network for label transfer. We evaluated scEDI against three state-of-the-art methods on datasets from the adult mouse cerebral cortex and human peripheral blood monocytes. Experimental results demonstrate that scEDI improves label transfer accuracy, particularly in small datasets. © 2024 IEEE.

关键词： Dimensionality reduction

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Efficiently Detecting Anomalies in IoT: A Novel Multi-Task Federated Learning Method 19th

Efficiently Detecting Anomalies in IoT: A Novel Multi-Task ...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Hao, Junfeng Chen, Juan Chen, Peng Wang, Yang Niu, Xianhua Xu, Lei Xia, Yunni School of Computer and Software Engineering Xihua University Chengdu China School of Emergency Management Xihua University Chengdu China School of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9783031545306

With the development of IoT technology, a significant amount of time series data is continuously generated, and anomaly detection of this data is crucial. However, time series data in IoT is dynamic and heterogeneous, and most centralized learning also suffers from security and privacy issues. To address these issues, we propose a multi-task anomaly detection approach based on federated learning (MTAD-FL) to address these problems. First, we propose a distributed framework based on Multi-Task Federated Learning (MT-FL), which aims to solve multiple tasks simultaneously while exploiting similarities and differences between tasks;second, to identify complex anomaly patterns and features in the IoT environment, we construct a Squeeze Excitation (SE) based and External Attention (EA) based Enhance Dual Network (SE-EA-EDN) feature extractor to monitor real-time data features from IoT systems efficiently;finally, we design a Local-Global Feature-based Parallel Knowledge Transfer (LGF-PKT) to parallelize the updating of weights of local and global features. To validate the effectiveness of our approach, we conducted comparative experiments on three publicly available datasets, SMD, SWaT, and SKAB, and MTAD-FL improved F1 by 11%, 67.8%, and 27.5%, respectively, over the other methods. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Anomaly detection

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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

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Frontiers of computer science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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Profit Maximization of Delay-Sensitive, Differential Accuracy Inference Services in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6209-6224页

作者： Zhang, Yuncan Liang, Weifa Xu, Zichuan Jia, Xiaohua Yang, Yuanyuan City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Dalian University of Technology School of Software Dalian China Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY United States

The integration of Artificial Itelligence (AI) and edge computing has sparked significant interest in edge inference services. In this paper, we consider delay-sensitive, differential accuracy inference services in a Mobile Edge Computing (MEC) network while meeting user stringent delay and accuracy requirements. We formulate two novel profit maximization problems under static and dynamic settings of service request arrivals, with the aim of maximizing the accumulative profit of admitted requests. We assign differential accuracy service requests to the corresponding resolution instances of their requested service models, assuming that each resolution instance can serve up to L≥ 1 the same type of service requests. Since the profit maximization problem is NP-hard, we first formulate an Integer Linear Program (ILP) solution if the problem size is small or medium;otherwise, we devise a constant randomized algorithm with high probability. Then, we consider dynamic service request admissions without the knowledge of future request arrivals for a given finite time horizon, for which we develop a simple yet effective prediction mechanism to accurately predict the number of different resolution instances of each model needed, and pre-deploy the predicted number of resolution instances into cloudlets to reduce instantiating delays. We then devise an online algorithm with a provable competitive ratio for the dynamic profit maximization problem by leveraging the primal-dual dynamic updating technique. Finally, we evaluate the performance of the proposed algorithms by simulations. The simulation results demonstrate that the proposed algorithms are promising. © 2002-2012 IEEE.

关键词： Mobile edge computing

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True Rays Model Integration in OMNeT++ for IIoT Network Planning in Underground Mines 1

True Rays Model Integration in OMNeT++ for IIoT Network Plan...

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1st IEEE Latin American Conference on Antennas and Propagation, LACAP 2024

作者： Suancha, Cristian Montanez, Oscar Medina, Fabian Fernandez, Eduardo Avendano Cespedes, Sandra School UPTC Systems and Computing Engineering Sogamoso Colombia School UPTC Electronic Engineering Sogamoso Colombia Concordia University Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9798331527587

This work develops a new module in the OMNeT++ simulator incorporating and validating the True Rays propagation model for underground environments. The integration enables the planning and evaluation of Industrial Internet of Things deployments of wireless sensor networks (WSNs) in mining scenarios. The tool can read the mine blueprints and deploy nodes at specified locations. It also defined the environment and network communication parameters for network coverage and performance analysis, allowing an understanding of how the WSN behaves in the underground mine before its deployment. The simulation predicts variables such as received power and path losses, optimizing sensing coverage and connectivity while reducing the need for site surveys. A large number of simulations using blueprints from real mines show that the signal propagation measurements are consistent with the theoretical models proposed in the state of the art. Experimental results were compared with a simulation WSN using LoRa, the maximum range obtained in simulation was 47 m, the measurements under real conditions in the underground only achieved 25 m of reach, but were limited at a threshold of -100 dBm to ensure connectivity and reliability of transmission among the nodes. As a result, the simulations and measurements performed better at the 433 MHz frequency, and the main contribution is a simulation tool simplifying planning IIoT networks in mining tunnels. © 2024 IEEE.

关键词： Sensor nodes

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GSD-YOLO: a Lightweight Framework for Traffic Sign Detection

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第6期52卷 1815-1823页

作者： Jianbang, Ding Tianwei, Shi Zhehao, Zhang Ruiqi, Wang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

To address the challenges of detecting small traffic signs, low detection accuracy, complex detection network models, and large parameter counts, this paper proposes a lightweight traffic sign detection framework called GSD-YOLO. This framework integrates the GCSconv module, SENetV2 module, and DySample module. Specifically, the Slim-net architecture, formed by combining the GCSconv and VOVGSCP modules, is employed to replace the Conv and C2f modules in the original YOLOv8 neck network. This substitution not only reduces the number of parameters and computational load during training but also achieves a lightweight design. The lightweight SENetV2 module is incorporated into the backbone of the detection model, further lightening the backbone. In the neck network, the DySample module replaces the original upsampling module, enhancing the model’s upsampling capability while reducing the parameter count. Compared to the original YOLOv8 detection network, Grad-CAM visualization shows that traffic signs in the detected regions appear darker and more concentrated. On the CCTSDB dataset, the proposed framework achieves an mAP@0.5 of 95.5%, which is 2.3% higher than the original YOLOv8 (93.3%). The model size is reduced to 4.2MB, which is 32.2% smaller than the original YOLOv8. The GFLOPs are reduced by 35%, and the overall parameter count is decreased by 40%. The lightweight detection framework proposed in this paper effectively reduces model size, simplifies model complexity, decreases parameter count, and improves detection accuracy, achieving significant improvements in the field of traffic sign detection. © (2025), (International Association of Engineers). All rights reserved.

关键词： Road and street markings

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Collaborative Estimating Multiple Gaussian Graphical Models on Resource Constrained Devices in IoT Networks

Collaborative Estimating Multiple Gaussian Graphical Models ...

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

作者： Zhan, Ying Wang, Beilun School of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350337020

In recent years, the rapid development of the Internet of Things (IoT) has attracted significant interest in smart healthcare. However, such collaborative IoT applications still face three major challenges: multi-task data heterogeneity, high communication cost, decentralized computation and privacy preservation. As a result, efficiently integrating and processing such complex data has become a crucial problem to solve. In this paper, we propose COFFE, a collaborative federated learning framework that allows edge devices to participate in the model training. Our framework leverages the local computing power of edge devices and utilizes the communication resources of the IoT network to enable distributed learning. We apply a compression algorithm which optimizes the communication cost by minimizing the amount of exchanged data between the edge devices and the cloud server. We demonstrate the effectiveness of our approach through a set of experiments on a simulated IoT network. Our results show that our proposed COFFE approach achieves significant performance gains compared to traditional centralized learning approaches. Overall, our work provides a promising direction in smart healthcare for applying federated learning to IoT setting and enabling edge device collaboration. © 2023 IEEE.

关键词： Environmental Sensing Medical Informatics Systems Safety and Security

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Federated Social Recommendation with Rényi Differential Privacy 3

Federated Social Recommendation with Rényi Differential Pri...

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3rd International Symposium on computer Technology and Information science, ISCTIS 2023

作者： Wang, Tian Jiao Wu, Jie School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China

ISBN: (纸本)9798350325386

A social recommendation system based on graph neural networks is a system that uses social relationships between users to generate personalized recommendations. To improve recommendation accuracy, it is usually necessary to collect a large amount of user behavior data, which may lead to user privacy breaches and data misuse. Existing privacy protection methods often sacrifice recommendation effectiveness or increase computational costs. Therefore, this article proposes a federated social recommendation system (FRSRec) based on Rényi differential privacy. By combining hash encryption technology with Rényi differential privacy, user data privacy can be protected while ensuring recommendation effectiveness and computational efficiency. This article conducted experimental evaluations on three real datasets. The experimental results show that the FRSRec model achieves optimal results in both MAE and RMSE, demonstrating that the FRSRec model can effectively achieve privacy protection while improving recommendation quality. © 2023 IEEE.

关键词： Recommender systems

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MedNetV3: A Deep Learning Model for MRI Brain Tumor Detection with MobileNetV3 and Advanced Data Augmentation 21

MedNetV3: A Deep Learning Model for MRI Brain Tumor Detectio...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Tariq, Hussain Ahmed Fengli, Zhang Salim Shakib, Muhamud Md Javed, Iram School of Information and Software Engineering University of Electronic Science and Technology of China China University of Agriculture Department of Computer Science Faisalabad Pakistan

ISBN: (纸本)9798331519254

In this paper, we present a novel deep learning model medical network (MedNetV3) developed for brain tumor detection. It incorporates advanced data augmentation techniques based on the MobileNetV3 architecture. MedNetV3 is developed for higher classification accuracy at the cost of slightly lower computational efficiency and is therefore suitable for clinical use. The proposed model overcomes the class imbalance problem and can make the model robust to the variations of MRI scans included in the extensive data augmentation methods, such as random rotations, flips. The proposed model trained and evaluated using a publicly available MRI dataset. It achieved a high Area Under the Curve (AUC) value of 0.99 for both the Precision-Recall and Receiver Operating Characteristic (ROC) curves. The results show that MedNetV3 is effective in correctly distinguishing brain tumors and therefore has the potential to be used in medical image classification. © 2024 IEEE.

关键词： Deep learning

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