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2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Wang, Kun Gao, Shaofeng Chen, Xinze Fu, Yong 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 Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510677685

During the process of embedded firmware upgrade, to protect the security of the firmware during transmission, this paper proposes an improved firmware transmission scheme based on the combination of the SM2 threshold key agreement algorithm. Between the server, client, and downloader, the SM2 key agreement algorithm is used to generate session key pairs to encrypt the firmware information, and the method of reinforcing and verifying re-transmission is adopted to ensure data security. In order to improve the security of the algorithm, a threshold algorithm is used in the key agreement process. The server acts as a trusted center to execute the threshold algorithm and divide the main secret private keys dA and dB. During the phase of key exchange, at least t+1 sub-secrets are required to recover the complete main secret. This paper successfully implements and verifies the feasibility of this scheme. © 2024 SPIE.

关键词： Firmware

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Beyond 5G networks: Integration of Communication, Computing, Caching, and Control

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China Communications 2023年第7期20卷 137-174页

作者： Musbahu Mohammed Adam Liqiang Zhao Kezhi Wang Zhu Han State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China Department of Computer and Information Sciences Northumbria UniversityNewcastle upon TyneUK Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004 USAand also with the Department of Computer Science and EngineeringKyung Hee UniversitySeoul 02447 South Korea

In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular *** challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)*** this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use ***,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration *** review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration *** also highlight the need for intelligence in resources ***,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various ***,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.

关键词： 4C 6G integration of communication,computing,caching,and control i4C multi-access edge computing(MEC)

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A firmware upgrade solution based on hardware unique ID and SM4 cryptographic algorithm

A firmware upgrade solution based on hardware unique ID and ...

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2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Gao, Shaofeng Wang, Kun Chen, Xinze Fu, Yong 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 Jinan250000 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

ISBN: (纸本)9781510677685

This article presents and implements a firmware upgrade solution for embedded systems, which aims to address the vulnerability of using the same file that can be easily copied or stolen. When upgrading firmware, a txt file is sent by the upper-level software, which is encrypted using the SM4 algorithm. This encryption is based on a hardware-unique ID and a pre-stored upper-level software-unique ID, which act as encryption keys. During the upgrade, a secure downloader utilizes its hardware-unique ID and a pre-stored upper-level software-unique ID as decryption keys to decrypt the file. If the decryption key does not match the encryption key, the decrypted txt file will not be readable. Once decrypted, the firmware is downloaded to the target device using BSL (Bootstrap Loader) technology. Practical tests have shown that this method can achieve "one file, one key,"effectively preventing unauthorized use of the upgraded firmware. © 2024 SPIE.

关键词： Firmware

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MFFLEN: Multi-Label Text Classification Based on Multi-Feature Fusion and Label Embedding

MFFLEN: Multi-Label Text Classification Based on Multi-Featu...

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

作者： Gu, Qiliang Zhao, Shuo Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Jinan China Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

To address the challenges associated with insufficiently extracting and utilizing features at different levels, overlooking the connection between label meanings and text, and facing problems of over-compression or information loss when extracting global information using recurrent neural networks in the field of multi-label text categorization, this paper introduces an innovative model known as MFFLEN (MultiFeature Fusion and Label Embedding Neural Network). First, a back-translated enhanced label set is constructed by back-translated splicing enhancement of the original label set. This set, together with the text, is then input into the embedding layer, which consists of the pre-trained model of bert-baseChinese, thus establishing the initial connection between the text and the labels within the same vector space. Then, to comprehensively extract multi-level semantic features, the model uses a convolutional layer to extract local features and an embedding layer to extract sentence-level features. A bidirectional attention embedded GRU (BAE-GRU) layer is used to extract hybrid finegrained features, which are then fed into the attention layer to further extract hybrid labeled features based on labeling information. Finally, these three different types of features are fused and multi-label text classification results are obtained using a classifier. The experiments proved that the MFFLEN model achieved 73.82% and 88.44% macro-F1 and 88.00% and 88.86% micro-F1 on the two datasets CAIL 2018 Small and CAIL 2018 Split, respectively, which is better than other baseline models. © 2024 IEEE.

关键词： Vector spaces

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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

作者： Lu, Lin Zou, Qingzhi Key Laboratory of Computing Power Network and Information Se-curity 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Due to the exceptional performance of Transform-ers in 2D medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%. © 2024 IEEE.

关键词： Image segmentation

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Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

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BEW-YOLO: An Improved Method for PCB Defect Detection Based on YOLOv7 29

BEW-YOLO: An Improved Method for PCB Defect Detection Based ...

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

作者： Li, Zhiyao Li, Aimin Zhang, Yuechen Kong, Xiaotong Li, Wenqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350330717

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models. © 2023 IEEE.

关键词： attention mechanism defect detection PCB YOLOv7

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Indoor Human Fall Detection Algorithm Based on Wireless Sensing

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Tsinghua Science and Technology 2022年第6期27卷 1002-1015页

作者： Chao Wang Lin Tang Meng Zhou Yinfan Ding Xueyong Zhuang Jie Wu Key laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai UniversityShanghai 200444China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6096USA

As the main health threat to the elderly living alone and performing indoor activities,falls have attracted great attention from institutions and ***,fall detection systems are mainly based on wear sensors,environmental sensors,and computer vision,which need to be worn or require complex equipment ***,they have limitations and will interfere with the daily life of the *** the basis of the indoor propagation theory of wireless signals,this paper proposes a conceptual verification module using Wi-Fi signals to identify human fall *** module can detect falls without invading privacy and affecting human comfort and has the advantages of noninvasive,robustness,universality,and low *** module combines digital signal processing technology and machine learning *** paper analyzes and processes the channel state information(CSI)data of wireless signals,and the local outlier factor algorithm is used to find the abnormal CSI *** support vector machine and extreme gradient boosting algorithms are used for classification,recognition,and comparative *** results show that the average accuracy of fall detection based on wireless sensing is more than 90%.This work has important social significance in ensuring the safety of the elderly.

关键词： wireless signal channel status information fall detection wireless sensing

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Multidimensional data fusion detection method based on time series data model 24

Multidimensional data fusion detection method based on time ...

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8th International Conference on High Performance Compilation, Computing and Communications, HP3C 2024

作者： Zhao, Lina Li, Ming Ni, Jinchao Liu, Xue Xu, Lijuan Yao, Zhiang Information & Telecommunications Company State Grid Shandong Electric Power Company Shandong Jinan China Shandong Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan China

ISBN: (纸本)9798400716904

Network security is currently a topic of significant concern in today's society, as it directly impacts national security, social stability, and public welfare. Consequently, the issue of network security cannot be overlooked. In current research, log anomaly detection faces various challenges, including the neglect of the correlation and complementarity between traffic data and log data, as well as inadequate response capabilities to novel and complex attack behaviors. To address these challenges, this paper presents an intrusion detection method based on traffic-log data fusion. By merging the decision results of traffic and log data, this method maximizes the utilization of their correlation and complementarity, thereby enhancing the intrusion detection system's capability to identify complex attack behaviors. Experimental results demonstrate that the proposed intrusion detection method based on traffic-log data fusion exhibits significant advantages in key performance metrics such as accuracy, recall, and F-measure. Compared to traditional methods such as PCA, N-gram, and IM, this method achieves more accurate identification of normal and abnormal log data, as well as a balanced trade-off between recall and precision, indicating higher practical utility. © 2024 ACM.

关键词： Network intrusion

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