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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： He, CHao Li, Zhi Su, Shoubao Xu, Liukai Wang, Chishe School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing211169 China Provincial Engineering Research Center of Digital Technology for Industrial Economic Jinling Institute of Technology Nanjing211169 China School of Computer Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9783031067662

Aiming at the real-time problem of vehicle routing planning, an improved particle swarm optimization method, called AVSPSO, is proposed in this paper. In the method, the path similarity of vehicle routing is calculated by adopting based on the One-Hot coding vector and by improving the constraint processing through the combination of coding mode and fixed penalty, in which dynamically adjust the inertia weight through the evolution state is to balance the ability of local mining and global development of particles. Experimental results on two practical cases of vehicle routing optimization problems and comparisons with other methods have shown that the proposed method is feasible and stable with better performances. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicle routing

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An Early Detection Model of Prostate Cancer Based on MRI 9

An Early Detection Model of Prostate Cancer Based on MRI

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

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

ISBN: (纸本)9798350391367

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. © 2024 IEEE.

关键词： Urology

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LDD-YOLO: An Improved Lightweight Detection Method for Steel Surface Defects Based on YOLOv8

LDD-YOLO: An Improved Lightweight Detection Method for Steel...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yuechen Zhang Aimin Li Zhiyao Li Xiaotong Kong Wenqiang Li 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: (数字)9781665410205

ISBN: (纸本)9781665410212

Steel is an indispensable raw material in the industrial field, steel surface defects seriously affect the quality of steel, in recent years a lot of research has been carried out on the detection of steel surface defects. Existing steel defect detection methods are unable to fully mine the underlying feature information of the target image and do not achieve a dynamic balance between accuracy and speed. To address the above problems, this paper proposes an optimised target detection algorithm based on YOLOv8. First, we proposed the DMCA module, which combines the ideas of deformable convolution and multi-channel self-attention mechanism. We developed a strengthen self-attention module to enhance the process of deformable convolutional generation of offsets, so that the model can better adapt to the complex shapes of different defective targets and extract features at a deeper level. Secondly, using the idea of LKA (Large Kernel Attention), we propose the LF-MSPP lightweight module with long-range dependence and adaptive capability to capture the tele-relationships with small computational cost and parameters, improved the problem of missing defective feature information. Finally, we replaced the head of the original YOLOv8 with a Dynamic Head and used the split attention mechanism to improve the head detection capabilities while ensuring lightweight. We conduct extensive experiments on the widely used Northeastern University steel defect dataset NEU-DET. Experimental results show that the improved model improves mAP@50, mAP@50-95, AP and AR indicators by 2.4%, 2.0%, 6.1%, and 3.4% respectively compared with the original YOLOv8 model, and the number of model parameters is reduced by 11.2%. The improved model is also better than mainstream defect target detection models such as SSD, Retinanet, FasterRCNN, YOLOv5, YOLOv6, YOLOv7, YOLOv8, etc, and can better meet the accuracy and speed requirements of actual industrial production for steel surface defect detection mode

关键词： YOLO Accuracy Head Three-dimensional displays Shape Convolution Feature extraction Steel Kernel Defect detection

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Coherence entropy during propagation through complex media

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Advanced Photonics 2024年第4期6卷 25-35页

作者： Xingyuan Lu Zhuoyi Wang Qiwen Zhan Yangjian Cai Chengliang Zhao Soochow University School of Physical Science and TechnologyJiangsu Key Laboratory of Frontier Material Physics and DevicesSuzhouChina University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringShanghaiChina Shandong Normal University School of Physics and ElectronicsShandong Provincial Engineering and Technical Center of Light ManipulationsShandong Provincial Key Laboratory of Optics and Photonic DeviceJinanChina East China Normal University Joint Research Center of Light Manipulation Science and Photonic Integrated Chip of East China Normal University and Shandong Normal UniversityShanghaiChina

The deformation,flicker,and drift of a light field owing to complex media such as a turbulent atmosphere have limited its practical ***,research on invariants in randomly fluctuated light fields has garnered considerable attention in recent *** is a statistical property of light,while its full and quantitative characterization is ***,we successfully realize the orthogonal modal decomposition of partially coherent beams and introduce the application of coherence entropy as a global coherence characteristic of such randomly fluctuated light *** is demonstrated that coherence entropy remains consistent during propagation in a unitary system by unraveling complex *** representative examples,we study the robustness of coherence entropy for partially coherent beams as they propagate through deformed optical systems and turbulent *** entropy is anticipated to serve as a key metric for evaluating the propagation of partially coherent beams in complex *** study paves the way for a broader application scope of a customized low-coherence light field through nonideal optical systems and complex media.

关键词： complex media propagation invariant modal decomposition orbital angular momentum statistical optics coherence

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A Privacy-Preserving Logistics Information System with Traceability 1

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13th International Symposium on Cyberspace Safety and Security, CSS 2021

作者： Chen, Quanru Han, Jinguang Li, Jiguo Chen, Liquan Li, Song College of Information Engineering Nanjing University of Finance and Economics Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China College of Computer and Cyber Security Fujian Normal University Fuzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9783030940294

ISBN: (纸本)9783030940287

Logistics Information System (LIS) is an interactive system that provides information for logistics managers to monitor and track logistics business. In recent years, with the rise of online shopping, LIS is becoming increasingly important. However, since the lack of effective protection of personal information, privacy protection issue has become the most problem concerned by users. Some data breach events in LIS released users’ personal information, including address, phone number, transaction details, etc. In this paper, to protect users’ privacy in LIS, a privacy-preserving LIS with traceability (PPLIST) is proposed by combining multi-signature with pseudonym. In our PPLIST scheme, to protect privacy, each user can generate and use different pseudonyms in different logistics services. The processing of one logistics is recorded and unforgeable. Additionally, if the logistics information is abnormal, a trace party can de-anonymize users, and find their real identities. Therefore, our PPLIST efficiently balances the relationship between privacy and traceability. © 2022, Springer Nature Switzerland AG.

关键词： Privacy-preserving techniques

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FPPNet: Fast Privacy-Preserving Neural network via Three-Party Arithmetic Secret Sharing 15th

FPPNet: Fast Privacy-Preserving Neural Network via Three-Pa...

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15th EAI International Conference on Mobile Multimedia Communications, MobiMedia 2022

作者： Bi, Renwan Xiong, Jinbo Li, Qi Liu, Ximeng Tian, Youliang Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal University Hangzhou311121 China Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9783031239014

Jointing multi-source data for model training can improve the accuracy of neural network. To solve the raising privacy concerns caused by data sharing, data are generally encrypted and outsourced to a group of cloud servers for computing and processing. In this client-cloud architecture, we propose FPPNet, a fast and privacy-preserving neural network for secure inference on sensitive data. FPPNet is deployed in three cloud servers, who collaboratively execute privacy computing via three-party arithmetic secret sharing. We develop the secure conversion method between additive shares and multiplicative shares, and propose three secure protocols to calculate non-linear functions, such as comparison, exponent and division that are superior to prior three-party works. Some secure modules for running convolutional, ReLU, max-pooling and Sigmoid layers are designed to implement FPPNet. We theoretically analyze the security and complexity of the proposed protocols. With MNIST dataset and two types of neural networks, experimental results validate that our FPPNet is faster than the related works, and the accuracy is the same as that of plaintext neural network. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Sensitive data

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Enhancing Privacy-Preserving Federated Learning: A Robust and Resilient Approach

Enhancing Privacy-Preserving Federated Learning: A Robust an...

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International Conference on networking and network Applications (NaNA)

作者： Zheng Yuan Youliang Tian Jinbo Xiong Linjie Wang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China School of Data Science Tongren University Tongren China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

Federated learning has garnered considerable attention for its capacity to aggregate efficient and accurate models while preserving user’s data privacy. Nonetheless, conventional federated learning frameworks remain susceptible to malicious threats like collusion and inference attacks. We design a robust and resilient enhancing privacy-preserving federated learning framework based on blockchain (EPFL) to address these threats. Initially, we meticulously design a two-round encryption scheme that allows users to upload encryption gradients while the server can still perform aggregation. Subsequently, a secure communication protocol termed the “server-blockchain-user” is devised based on blockchain architecture. This protocol guarantees the openness, transparency, and traceability of the entire training process, while also offering resilience in users’ drop-out and joining that may arise during the aggregation process. Finally, experimental assessments are conducted to appraise the robustness and accuracy of EPFL. The empirical findings demonstrate that EPFL not only demonstrates robustness against collusion and inference attacks, but also adeptly handles user exits and entries without compromising the confidentiality of user keys. Moreover, EPFL attains model accuracy commensurate with the FedAvg.

关键词： Training Data privacy Protocols Accuracy Federated learning Robustness Blockchains

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OpenViewer: Openness-Aware Multi-View Learning

arXiv

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arXiv 2024年

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Invulnerability Optimization of Communication network Based on Analog Attack Strategy 11th

Invulnerability Optimization of Communication Network Based ...

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11th International Conference on computer Engineering and networks, CENet2021

作者： Xia, Lingling Ni, Xueli Jing, Zhengjun Liu, Jiayin Zhang, Yan Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China College of Computer Engineering Jiangsu University of Technology Changzhou213001 China Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center Nanjing210031 China Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department Nanjing210031 China

ISBN: (纸本)9789811665530

Invulnerability of communication networks directly related to security and reliability of networks has received increasing attention in recent years. In order to enhance the destruction resisting capacity of communication network, we propose a new invulnerability optimization method based on analog attack strategy and present an improved invulnerability measure with consideration of network connectivity and construction cost. The experimental simulations are conducted to construct four different optimized communication networks using analog attack strategy with different indicators of node centrality on the initial network respectively. Results demonstrate that the improved metric used to evaluate the economic benefits of network invulnerability is reasonable, and the proposed topology optimization method can improve the invulnerability of communication network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Structural optimization

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DCCT-Net: A network Combined Dynamic CNN and Transformer for Image Compressive Sensing

DCCT-Net: A Network Combined Dynamic CNN and Transformer for...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lijuan Xu Haixiao Mei Fenghua Tong Dawei Zhao Fuqiang Yu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent end-to-end image compressive sensing networks primarily use Convolutional Neural networks (CNNs) and Transformers, each with distinct limitations: CNNs struggle with global feature capture, while Transformers lack local feature extraction. We propose a novel network, DCCT-Net, which combines Dynamic CNN (DCNN) and Transformer. This integration leverages DCNN’s local feature strengths and the Transformer’s global representation capabilities, resulting in superior image reconstruction quality. To further enhance the network’s performance, we propose a Feature Dynamic Augment Module (FDAM), which dynamically extracts features based on the saliency of segmented image regions, thereby amplifying the CNN’s local feature expression. Additionally, we design a Weighted Fusion Module (WFM), which optimizes the combination of local and global features extracted by the DCNN and Transformer, respectively. Extensive experiments demonstrate that our proposed DCCT-Net significantly outperforms most existing state-of-the-art methods in the field.

关键词： Image segmentation Image coding Signal processing Transformers Feature extraction Sensors Convolutional neural networks Speech processing Compressed sensing Image reconstruction

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