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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Xiaoteng Yang Lei Liu Xifei Song Jie Feng Qingqi Pei Xiaoming Yuan Jianqiao Li School of Telecommunication Engineering Xidian University Xi'an China Guangzhou Institute of Technology Xidian University Guangzhou China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an Shaanxi China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an China Northeastern University Qinhuangdao Branch Campus Qinhuangdao China China Academy of Space Technology-Xi'an Xi'an China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

In view of the huge satellite images, which will consume a large amount of resources if they are completely transmitted to the ground for processing, an improved lightweight on-orbit image classification model is proposed in this study. By pushing the image processing task to the satellite, real-time processing is achieved and the ground communication burden is significantly reduced. The model is based on MobileNetV3,which utilises depth-separable convolution and inverted residual linear structure to maintain the accuracy of the model while keeping the computational efficiency. The integrated channel attention mechanism and spatial pyramid pooling structure further enhance the model's classification accuracy and multi-scale sensing capability. Experiments demonstrate that the accuracy of the model is improved by 1.09% and 1.84% on the two datasets while keeping the model parameters consistent, which validates the effectiveness and accuracy of the method in this paper. This innovative lightweight model provides an efficient and feasible solution for satellite in-orbit image classification tasks.

关键词： Accuracy Satellites Computational modeling Image processing Real-time systems Satellite images Sensors

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Research on information security Protection System of Industrial Control System

Research on Information Security Protection System of Indust...

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IEEE Joint International information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zhenwan Zou Yingsa Hou Huiting Yang Bin Wang Ruxia Yang Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Maintenance company of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Global Energy Interconnection Research Institute Co. Ltd. Nanjing Jiangsu China State Grid Key Laboratory of Information & Network Security Nanjing Jiangsu China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Aiming at the current status and risks of industrial control system information security, a security protection system was proposed from the technical and management levels. At the technical level, it strengthen security protection by setting up firewall protection, isolating engineer stations, conducting system security tests, and deploying network monitoring. At the management level, it improves safety management through measures such as real-time maintenance and updating of existing management systems and safety technology standards. Finally, the feasibility of this protection system was proved by experiments, which has certain reference significance for the research on safety protection of industrial control systems.

关键词： Industrial control information security Safety management Maintenance engineering Real-time systems Standards Monitoring

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Remote Healthcare Monitoring System for Aging Population Based on IoT and Big Data Analysis

Remote Healthcare Monitoring System for Aging Population Bas...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Qiang Liu Songlin Sun Liang Gong Zhongle Wu Mohamed Cheriet Michel Kadoch National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Academy of Broadcasting Planning NRTA China ETS University of Quebec Montreal Canada

ISBN: (数字)9781728157849

ISBN: (纸本)9781728157856

As the population ages, this will put tremendous pressure on pensions and the medical system. Health insurance for the elderly poses a major challenge to the allocation of limited medical resources. The Internet of Things (IoT) is widely regarded as a potential solution to relieve pressure on healthcare systems. With the rapid development of wireless communication technologies such as backscatter communication, Wi-Fi and 5G, in vivo and in vitro medical devices have been greatly developed. In this article, we propose a real-time big data healthcare analysis system based on the combination of shallow neural networks and deep neural networks. The system consists of two parts: the detection of fog layer abnormality based on shallow neural network and the diagnosis of cloud disease based on deep neural network. The system can not only make full use of the real-time nature of the fog layer but also give full play to its powerful computing power based on reducing the computing pressure of the cloud layer.

关键词： Cloud computing Neural networks Sociology Real-time systems Internet of Things Statistics Medical diagnostic imaging

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On the ADEP and DOR Analysis of RIS-aided URLLC Systems with Partial CSI in Smart Factory

On the ADEP and DOR Analysis of RIS-aided URLLC Systems with...

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IEEE Globecom Workshops

作者： Tingnan Bao Haiming Wang Hong-Chuan Yang Wen-Jing Wang Mazen O. Hasna Lenovo Research + Institute Of Computing Technology Chinese Academy of Sciences Beijing China Lenovo Reseach Lenovo Group Limited Bejiing China Department of Electrical and Computer Engineering University of Victoria Victoria Canada Shaanxi Key Laboratory of Information Communication Network and Security XUPT Xi'an China Department of Electrical Engineering Qatar University Doha Qatar

ISBN: (数字)9781665423908

ISBN: (纸本)9781665423915

Factory automation applications impose stringent quality of service (QoS) over ultra-reliable and low-latency communication (URLLC) system. However, the existing wireless systems cannot meet the requirement for these mission-critical industrial internet-of-things (IIoT) applications. In this paper, we consider the reconfigurable intelligent surface (RIS)-aided URLLC systems in the smart factory. To facilitate the performance analysis of the practical situation, the presence of phase error at RIS is discussed. We analyze and derive approximate closed-form expression of average decoding error probability (ADEP) and the closed-form expression of performance limits de-lay outage rate (DOR) under our proposed scenario, respectively. Through analytical results and associated numerical examples, we demonstrate the effect of the number of reflecting elements at the RIS, channel blocklength and packet size in achieving high reliability and low latency. Therefore, the deployment of RIS can offer an attractive solution to enhance the reliability over URLLC systems in the smart factory.

关键词： Wireless communication Closed-form solutions Error probability Rician channels Quality of service Ultra reliable low latency communication Reconfigurable intelligent surfaces

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IBAQ: Frequency-Domain Backdoor Attack Threatening Autonomous Driving via Quadratic Phase

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Jinghan Qiu Honglong Chen Junjian Li Yudong Gao Junwei Li Xingang Wang College of Control Science and Engineering China University of Petroleum China the Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology China

The rapid evolution of backdoor attacks has emerged as a significant threat to the security of autonomous driving models. An attacker injects a backdoor into the model by adding triggers to the samples, which can be activated to manipulate the model’s inference. Backdoor attacks can lead to severe consequences, such as misidentifying traffic signs during autonomous driving, posing a risk of causing traffic accidents. Recently, there has been a gradual evolution of frequency-domain backdoor attacks. However, since the change of both amplitude and its corresponding phase will significantly affect image appearance, most of the existing frequency-domain backdoor attacks change only the amplitude, which results in a suboptimal efficacy of the attack. In this work, we propose an attack called IBAQ, to solve this problem by blurring semantic information of the trigger image through the quadratic phase. Initially, we convert the trigger and benign sample to YCrCb space. Then, we perform the fast Fourier transform on the Y channel, blending the trigger image’s amplitude and quadratic phase linearly with the benign sample’s amplitude and phase. IBAQ achieves covert injection of trigger information within amplitude and phase, enhancing the attack effect. We validate the effectiveness and stealthiness of IBAQ through comprehensive experiments.

关键词： backdoor attack quadrature phase artificial intelligence security

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Research on network security Risk and security Countermeasures of 5G Technology in power System Application

Research on Network Security Risk and Security Countermeasur...

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IEEE Advanced information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Zhenwan Zou Tao Chen Jia Chen Yingsa Hou Ruxia Yang Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Maintenance company of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Global Energy Interconnection Research Institute Co. Ltd. Nanjing China State Grid Key Laboratory of Information & Network Security Nanjing China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

As a key area of new infrastructure, 5G development strategy is constantly clear, and various industries have developed around 5G. However, 5G new services, new architecture, new technologies, and openness of the network make 5G security issues become the focus. This article focuses on the relevant application scenarios of 5G in the power field, analyzes and discusses the security risks that 5G technology brings to the power grid, and on this basis, proposes technical solutions to enhance security for power industry applications, and proposes follow-up development suggestions.

关键词： 5G mobile communication Transportation Solids power grids security Communication networks Sustainable development

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An Early Detection Model of Prostate Cancer Based on MRI

An Early Detection Model of Prostate Cancer Based on MRI

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yuankun Liu Lumin Xing Di Wang Xin Nie Xueyan Zhang Di Wu Wenjian Liu Faculty of Data Science City University of Macau China Shandong Provincial Qianfoshan Hospital The First Affiliated Hospital of Shandong First Medical University Jinan China 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 Chongqing Jianzhu College Chongqing China School of Information Work Office Shandong Youth University of Political Science Jinan China School of Mathematics Physics and Computing University of Southern Queensland Queensland Australia

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively.

关键词： Computer vision Visualization Accuracy Magnetic resonance imaging Mortality Transformers Feature extraction Reliability Prostate cancer Medical diagnostic imaging

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuo Zhao Qiliang Gu Jianqiang Zhang Gongpeng Song Qin Lu 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets.

关键词： Deep learning Computational modeling Semantics Neural networks Feature extraction Natural language processing Multilingual Convolutional neural networks Cybernetics Overfitting

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Semantic Relation-Based Cross Attention network for Image-Text Retrieval

Semantic Relation-Based Cross Attention Network for Image-Te...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image-text retrieval refers to querying the text or image of another modality given an image or text, and its key lies in the ability to accurately measure the similarity between text and image. Most of the existing retrieval methods only utilize the intramodal relations of each modality or the intermodal relations of the two modalities to perform the retrieval task. To address the above problems, we integrate the intra-modal and inter-modal relations and propose a Semantic Relation-based Cross Attention network(SRCAN). In our proposed method, we first mine the possible intra-modal associations between regions in an image and between words in a text to obtain features with semantic relationships, and then capture the fine-grained associations between segments through the cross-attention mechanism. Finally, we balance the intra-modal and inter-modal relationships to improve the performance of the model. Our proposed method is experimentally validated on two datasets, Flickr30K and MS-COCO, and the experimental results show that our method achieves superior results.

关键词： Image segmentation Semantics Bidirectional control Feature extraction Cognition Feeds Cybernetics

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Semantic information Reasoning and Multi-Step Cross-Modal Interaction network for Image-Text Retrieval

Semantic Information Reasoning and Multi-Step Cross-Modal In...

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International Joint Conference on Neural networks (IJCNN)

作者： Xishan Ma Yushui Geng Jing Zhao Huanxiao Zhou 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Image-text retrieval is a basic cross-modal task, and the basic idea is to learn image-text matching. The key to this task is to accurately measure the similarity between these two modalities. However, most existing methods tend to measure image-text similarity based on highly correlated region-word pairs, which lacks the ability to capture complex cross-modal semantic associations, thereby resulting in failure to distinguish hard negative examples that are similar in semantic content. Furthermore, existing methods focus more on alignment of different modal features, thus neglecting deep reasoning about the semantic concepts within the modality. In this paper, we propose a new semantic information reasoning and multi-step cross-modal interaction (SRMCI) network to simultaneously explore intra-modal and inter-modal relationships. Specifically, we first propose a semantic information reasoning module to fully explore potential semantic relationships within modalities and focus on fragments with important semantic information. Then, we design a multi-step cross-modal interaction module that utilizes two different inter-modal interactive attentions and an effective gated fusion strategy to capture fragment-level and fragment-instance-level cross-modal semantic associations. In particular, this gated fusion strategy is used to learn better fine-grained feature representations to aid in understanding and establishing semantic associations between different modalities. Experiments on the benchmark datasets MS-COCO and Flickr30K demonstrate the effectiveness of our method.

关键词： Correlation Semantics Neural networks Logic gates Benchmark testing Cognition

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