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

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong 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 Qilu University of Technology Shandong Academy of Sciences 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

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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ILP-Based Heuristics for the Multi-Modal Stable Matching Problem

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Tsinghua Science and Technology 2025年第2期30卷 479-487页

作者： Yang Yang Rolf H.Möhring Junteng Song Yicheng Xu Yong Zhang Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 101408China School of Applied Mathematics and Computer Science Technische Universität BerlinBerlin 10623Germany School of Mathematics and Information Science Hebei UniversityBaoding 071002China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China

In this paper,we investigate the stable matching problem with multiple preferences in bipartite graphs,where each agent has various preference lists for all available partners with respect to different *** problem requires that each matched agent must have exactly one partner and the obtained matching should be stable for all *** our main contribution,we present an integer linear programming(ILP)model for determining whether there exists a globally stable matching in bipartite graphs,which has been proved to be *** the time consumed for solving ILPs might dramatically increase as the size of instances grows,we develop a preprocessing technique that helps to eliminate pairs that will never be a member of any globally stable matching and thus accelerates the computing *** perform experiments on randomly generated preference lists and observe a significant speedup when we preprocess the instance before solving the *** there does not need to exist a perfect matching that is stable for all given criteria,we extend our ILP to the optimized version of the aforementioned problem,which asks to find a matching with maximum cardinality that is stable among all matched agents.

关键词： stable matching multi-modal integer linear programming(ILP)

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A Multi-Channel Composite Recommendation Method in Federated Learning

A Multi-Channel Composite Recommendation Method in Federated...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Chen, Shi Li, Qianmu Wang, Qi Zhao, Xinjian Ge, Hao Li, Nianzhe State Grid Jiangsu Information & Telecommunication Company Nanjing China School of Computer Science Ana Engineering Nanjing University of Science and Technology Nanjing China State Grid Smart Grid Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Nanjing China

ISBN: (纸本)9798350304602

There are various data security risks in power grid systems, and recommendation systems are a good solution to the selection of security strategies. Federated learning is a machine learning framework that can protect the data privacy of each participant and can effectively solve the data security external collaborative training modeling challenges. This paper proposed a flexible implantable recommendation algorithm based on knowledge graph and multi-channel composite to enhance federated learning on demand. The algorithm designs three different feature extraction modules to learn to construct multi-dimensional features on multi-attribute collaborative knowledge graphs. Among them, the distance translation module utilizes the Trans R recommendation algorithm to learn the internal semantic structure information in knowledge graph triples and terminal-security policy selection history;the random walk module uses the Node2Vec algorithm to learn the homogeneous and Structured information in the multi-attribute collaborative knowledge graph;the graph convolutional network module learns the high-order neighborhood multi-attribute collaborative information of terminal/security policy nodes through multi-layer feature propagation and aggregation. The composite network module adopts a convolutional neural network-like operation to first locally optimize the three modules, and then extracts the highly informative elements to obtain the final feature representation of the terminal/security policy. Finally, the algorithm merges the vectors of terminal-security policy pairs into the multi-layer perceptron to learn and calculate the prediction score for recommendation. © 2023 IEEE.

关键词： Recommender systems

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An Approach to New Bearing Fault Diagnosis Utilizing Deep Transfer Learning

An Approach to New Bearing Fault Diagnosis Utilizing Deep Tr...

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Smart World Congress (SWC), IEEE

作者： HuiJuan Hao Yue Feng HongGe Zhao SiJian Zhu 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 Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Bearings, serving as crucial components in mechanical equipment, exert a direct influence on the overall performance of the system. Deep transfer learning has garnered significant attention in the realm of intelligent fault diagnosis for machines. Nevertheless, extant deep transfer learning algorithms encounter challenges when detecting newly emerging faults within the target domain. To address this limitation, this study introduces a novel bearing fault diagnosis approach for newly emerging faults, leveraging deep transfer learning principles. Initially, a multi-grained hierarchical feature extraction network is constructed to effectively capture intricate data characteristics. Subsequently, these extracted features are employed to train a fault classifier, thereby establishing a fault diagnosis network grounded in deep transfer learning. This network is capable of discriminating unlabeled new faults from labeled known faults with remarkable precision. Furthermore, an adaptive hierarchical clustering algorithm, informed by the elbow method, is incorporated to categorize the newly emerging faults. The clustered samples are then utilized to train the online sequential extreme learning machine (OS-ELM), facilitating the construction of a new fault diagnosis network tailored for diagnosing these faults in the target domain. The efficacy of the proposed methodology was rigorously verified through experiments executed on both the CWRU and XJTU-SY datasets. The outcomes conclusively demonstrated an accuracy of $\mathbf{9 8. 7 5 \%}$ on the CWRU dataset and $\mathbf{9 7. 9 2 \%}$ on the XJTU-SY dataset. This model portends immense potential in tackling fault diagnosis transfer scenarios characterized by the emergence of multiple novel faults within the target domain, thereby markedly broadening the applicability of deep transfer learning.

关键词： Fault diagnosis Accuracy Adaptive systems Extreme learning machines Transfer learning Clustering algorithms Feature extraction Classification algorithms Data mining Elbow

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FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI Segmentation in Incomplete Multi-Modal Scenarios 24

FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Yang, Rui Wang, Xiao Xu, Xin Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan China

ISBN: (纸本)9798400716720

Magnetic Resonance Imaging (MRI) is an invaluable tool for brain tumor segmentation. However, in clinical practice, certain modalities might be unavailable, leading to potential performance degradation in prediction tasks. According to current implementations, different modalities are treated as independent entities during the feature extraction phase, ignoring their inherent complementary nature. In this paper, inspired by knowledge distillation techniques, we introduce the Fusion-Inspired Modality Distillation (FIMD) framework. FIMD leverages the output from a fused multi-modal model as a teacher to guide the training of individual modalities. Each modality benefits from the collective knowledge of all modalities, enhancing its performance. Our FIMD method, devoid of specific architectural constraints, seamlessly integrates into existing multi-modal brain tumor segmentation frameworks. Notably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets indicate that FIMD can enhance the performance of current state-of-the-art (SOTA) algorithms on Dice by an average of 0.55-1.60%. Especially in the case of missing modalities, the effectiveness of the FIMD framework in enhancing the robustness and accuracy of brain tumor segmentation is demonstrated. © 2024 ACM.

关键词： Semantic Segmentation

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Forward and Backward Private Searchable Encryption with Fine-grained Access Control 2

Forward and Backward Private Searchable Encryption with Fine...

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2nd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2023

作者： Xu, Peiming Xu, Lingling Jin, Xiang Network Security Electric Power Research Institute CSG Guangdong Provincial Key Laboratory of Power System Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350331448

With the rapid development of cloud computing, more and more data are stored in the cloud server. However, due to the increasing demands for security and user privacy, sensitive data are expected to be stored in an encrypted manner. Then how to perform keyword search over the encrypted data becomes a key issue. The technology of searchable encryption has been proposed to address this issue, which allows the data owners share their encrypted data with users without leaking the queried keyword privacy. In this paper, we study forward and backward private searchable encryption with fine-grained access control. Our system can resist against the file injection attacks from malicious cloud and support forward and backward data privacy. In addition, it enables the data owners to enforce "AND""NOT"attribute-based access control on the data. We analyze the security and simulate the system. The experiment results show that our system can be applicable in the real-world scenarios. © 2023 IEEE.

关键词： Access control

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FLF-YOLO: an Efficient and Lightweight network for Small Target Detection in Aerial Photography 7

FLF-YOLO: an Efficient and Lightweight Network for Small Tar...

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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Jia, Bin Ao, Naixiang Wang, Xinyi Zhao, Xueyi Xiong, Bingbing School Of Computer Science And Technology Xinjiang University Urumqi China China Academy Of Electronics And Information Technology Beijing China Lianhaichuangzhi Information Technology Co. Ltd. Key Laboratory Of Big Data Of Xinjiang Social Security Risk And Xinjiang Urumqi China

ISBN: (纸本)9798350361445

To address the issues related to the ineffective identification of small targets in aerial images and the absence of real-time detection, we propose a lightweight algorithm FLF-YOLO based on the improvement of ***, FasterNet, an efficient lightweight network, is used instead of the original backbone network, which reduces the computational complexity and improves the inference speed;then, a bottleneck structure combining with LSKNet is designed, which dynamically adjusts the spatial receptive field to improve the detection accuracy in limited resources;finally, Proposing the Focal- a EIoU1.5 loss for adaptive enhancement of bbox regression accuracy through Power transform. Experiments on the open dataset VisDrone 2019 show that in comparison with the baseline model YOLOv5s, the inference speed is improved by 3%, mAP@0.5 is unchanged, and mAP@0.5:0.95 is increased by 1% under the premise of reducing the number of parameters and calculation amount by 37.3% and 37.7% respectively. © 2024 IEEE.

关键词： Aerial photography

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Universal and General Quantum Simultaneous Secret Distribution with Dense Coding by Using One-Dimensional High-Level Cluster States

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Journal of computer Science & Technology 2021年第1期36卷 221-230页

作者： Zhi-Hao Liu Han-Wu Chen School of Computer Science and Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Nanjing 211189China

A universal and general quantum simultaneous secret distribution(QSSD)protocol is put forward based on the properties of the one-dimensional high-level cluster states,in which one sender dispatches different high-level classical secret messages to many users at the same *** to the idea of quantum dense coding,the sender can send different two-dit classical messages(two d-level classical numbers)to different receivers simultaneously by using a one-dimensional d-level cluster state,which means that the information capacity is up to the *** estimate the security of quantum channels,a new eavesdropping check strategy is put ***,a new attack model,the general individual attack is proposed and *** is shown that the new eavesdropping check strategy can effectively prevent the traditional attacks including the general individual *** addition,multiparty quantum secret report(MQSR,the same as quantum simultaneous secret submission(QSSS))in which different users submit their different messages to one user simultaneously can be gotten if the QSSD protocol is changed a little.

关键词： quantum simultaneous secret distribution(QSSD) multiparty quantum secret report(MQSR) quantum simultaneous secret submission(QSSS) quantum secret sharing quantum broadcast communication

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CL-TAP: An Efficient Certificateless Based Trusted Access Protocol for WLAN

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Chinese Journal of Electronics 2025年第1期23卷 142-146页

作者： Zhuo Ma Fenghua Li Jianfeng Ma Wenjiang Ji School of Computer Science and Technology Xidian University Xi'an Shannxi China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China

Based on the certificateless public key cryptography and the trusted computing technologies, a certificateless based trusted access protocol for WLAN (Wireless local area networks) is proposed. Such protocol realizes the mutual authentication and unicast session key agreement between STA and AP within 3 protocol rounds. In particular, the platform authentication and integrity verification are achieved during the authentication procedure. The security properties of the new protocol are examined using the Extended Canetti-Krawczyk security model. The analytic comparisons show that the new protocol is very efficient in both computing and communications.

关键词： Wireless LAN Trusted computing Unicast Computational modeling Authentication Access protocols Public key cryptography Delays security Research and development

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A robust and automatic method for the recognition of speech category in online learning discourse

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 3913-3924页

作者： Jiang, Dazhi He, Zhihui Chen, Yifei Xu, Linyan Lin, Jiali Guangdong Provincial Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510399 China Department of Computer Science Shantou University Shantou515063 China Department of Management Economics and Industrial Engineering Politecnico di Milano MI Milan20156 Italy School of Business Shantou University Shantou515063 China

It is a rapid and irresistible developing trend that the traditional classroom learning mode is transformed into an online learning mode. In the online learning process, it is important to achieve effective voice separation and provide a good lecture experience for students. In this paper, a robust and automatic method for the recognition of speech categories (teacher speech, student speech, silence speech and overlapping speech) is presented. The system, which is part of artificial intelligence in education (AIED), is a hybrid speech processing system that includes GMM modeling, GMM clustering, silence processing and overlapping processing. The online learning environment we are based on is not a customized environment that defines the roles of teachers or students, but a more casual, broader and more general online environment. The method proposed and designed based on this situation has better adaptability and scalability. The experimental results show that our automatic detection of speech categories fares well when compared to the results from professional coders. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Contrastive Learning

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