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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： An, Qiulong Wu, Jianning Huang, Tianqiang Huang, Liqing Luo, Haifeng Ye, Feng College of Computer and Cyber Security Fujian Normal University Digital Fujian Institute of Big Data Security Technology Fujian Provincial Engineering Research Center of Big Data Analysis and Application Fuzhou China

ISBN: (纸本)9798350318401

Most existing deep clustering models attempt to group similar datas by using autoencoders to simultaneously minimize the clustering loss and reconstruction losses. However, these methods do not impose any constraints on the latent space, resulting in intermediate features that are not suitable for clustering. Moreover, they suffer from poor clustering performance when the number of clusters is large. To address this problem, this paper proposes an autoencoder network with preclustered center codebooks for clustering, two codebooks to represent the cluster centers and center offsets, respectively, making the network features more compatible with the clustering task. Finally, We conducted experiments on CIFAR-10 and STL-10 datasets, the results indicate the effectiveness and superiority of the proposed network model. © 2023 IEEE.

关键词： Network coding

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Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR 13

Towards Context-aware Support for Color Vision Deficiency: A...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Morita, Shogo Zhang, Yan Yamauchi, Takuto Chen, Sinan Li, Jialong Tei, Kenji Tokyo Institute of Technology School of Computing Tokyo Japan University of Tokyo International Research Center for Neurointelligence Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan Kobe University Center of Mathematical and Data Sciences Kobe Japan

ISBN: (纸本)9798350355079

People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on presentation-based aids, like the color vision modes found in iPhone accessibility settings. However, offering context-aware support, like indicating the doneness of meat, remains a challenge since task-specific solutions are not cost-effective for all possible scenarios. To address this, our paper proposes an application that provides contextual and autonomous assistance. This application is mainly composed of: (i) an augmented reality interface that efficiently captures context;and (ii) a multi-modal large language model-based reasoner that serves to cognitize the context and then reason about the appropriate support contents. Preliminary user experiments with two color vision deficient users across five different scenarios have demonstrated the effectiveness and universality of our application. © 2024 IEEE.

关键词： Color vision

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FedPerturb: Covert Poisoning Attack on Federated Learning via Partial Perturbation 26

FedPerturb: Covert Poisoning Attack on Federated Learning vi...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Jin, Tongsai Fu, Zhihui Meng, Dan Wang, Jun Qi, Yue Cao, Guitao MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Oppo Research Institute China

ISBN: (纸本)9781643684369

Federated learning breaks through the barrier of data owners by allowing them to collaboratively train a federated machine learning model without compromising the privacy of their own data. However, Federation Learning also faces the threat of poisoning attacks, especially from the client model updates, which may impair the accuracy of the global model. To defend against the poisoning attacks, previous work aims to identify the malicious updates in high dimensional spaces. However, we find that the distances in high dimensional spaces cannot identify the changes in a small subset of dimensions, and the small changes may affect the global models severely. Based on this finding, we propose an untargeted poisoning attack under the federated learning setting via the partial perturbations on a small subset of the carefully selected model parameters, and present two attack object selection strategies. We experimentally demonstrate that the proposed attack scheme achieves high attack success rate on five state-of-the-art defense schemes. Furthermore, the proposed attack scheme remains effective at low malicious client ratios and still circumvents three defense schemes with a malicious client ratio as low as 2%. © 2023 The Authors.

关键词： Learning systems

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Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China Key Lab of Mountain Hazards and Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and Information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture *** the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation *** this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter *** proposed model highlights the residual region with considerable information and constructs color ***,we incorporate the content-based color saliency as spatial information in the Gaussian mixture *** segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum ***,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic *** experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft research in Cambridge *** the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram

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Voxelization Algorithm Based on Octree Spatial Averaging 13

Voxelization Algorithm Based on Octree Spatial Averaging

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13th International Conference of Information and Communication technology, ICTech 2024

作者： Lv, HongChao Wang, Jun Wang, HongLiang Zhou, XingYu Shenyang University of Chemical Technology Shandong China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350376258

This paper proposes a high-precision and efficient voxelization algorithm for the development of voxelization in the preprocessing stage of industrial simulation. The main idea is to establish an octree data structure to evenly divide the three-dimensional mesh model space into voxel sets. By using the separation axis method and the improved scan line method, the position relationship between voxels and triangles can be directly determined by skipping the calculation of intersection between voxels and triangles. The innovation of this article lies in the use of the separation axis algorithm and the improved scanning line method for voxelization, and the simplification of the algorithm's operations through the octree data structure, thereby reducing the time and spatial complexity of the algorithm. ©2024 IEEE.

关键词： Consensus algorithm

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AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer 29

AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algori...

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

作者： Tian, Min Liu, Qi Li, Yanlong Du, Wei Ren, Zenghui Liu, Yue Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. Based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the Parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%. © 2023 IEEE.

关键词： adaptive heterogeneous algorithm MPI+Athread Sunway supercomputer three-dimensional heat conduction problem tridiagonal linear systems

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hcaPCG: A Heterogeneous and communication-avoid PCG with Jacobi preconditioner on SW26010-Pro architecture 29

hcaPCG: A Heterogeneous and communication-avoid PCG with Jac...

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

作者： Tian, Min Liu, Yue Wang, Li Chen, Yanyan Liu, Qi Pan, Jingshan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor's storage structure;combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency;optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication;employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation. © 2023 IEEE.

关键词： Communication-avoid Heterogeneous System Jacobi preconditioner PCG algorithm SW26010-Pro

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Medical CT Image Super-resolution Algorithm based on GAN

Medical CT Image Super-resolution Algorithm based on GAN

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computer, Vision and Intelligent technology (ICCVIT), International Conference on

作者： Saihu Tian Taile Peng Zhongan Wang Zhengfeng Li School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei Normal University Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Computed Tomography (CT) images are essential for visualizing the internal structures of the human body and are widely applied in the diagnosis of various diseases. However, during the processes of generating, storing, and transmitting medical CT images, the clarity of these images can be degraded by non-human factors, resulting in issues such as reduced resolution and blurred texture details, thereby compromising image fidelity. In this study, we present an approach utilizing Generative Adversarial Networks (GANs). that incorporates a grayscale value loss as an additional constraint while maintaining the original loss function constraints. We conduct experiments for 4x super-resolution reconstruction. To evaluate the reconstruction performance, we compare the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) of the reconstructed images. The experimental results demonstrate ,which the proposed method exhibits promising performance in both quantitative metrics and visual quality.

关键词： Visualization PSNR Computed tomography Computational modeling Superresolution Noise Gray-scale Generative adversarial networks Image reconstruction Medical diagnostic imaging

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research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

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computers, Materials & Continua 2024年第10期81卷 1409-1432页

作者： Hongjun Zhang Zeyu Zhang Yilong Ruan Hao Ye Peng Li Desheng Shi School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210023China Ministry of Science and Technology Innovation China Communications Services Corporation LimitedBeijing100071China School of Artificial Intelligence The University of ManchesterManchesterM139PLUK Ministry of Technology Innovation China Telecom Artificial Intelligence Technology(Beijing)Corporation LimitedBeijing100032China Ministry of Science and Technology Innovation Zhongbo Information Technology Research Institute Corporation LimitedNanjing210012China Jiangsu Postal Big Data Technology and Application Engineering Research Center Nanjing University of Posts and TelecommunicationsNanjing210003China

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering *** address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering ***,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing ***,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary *** new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in ***,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic *** experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most ***,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering *** not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through *** method shows great potential in real-world applications where dynamic data growth is common,and it is of significant imp

关键词： Tensor incremental update distributed clustering processing big data

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Zero Trust Networks: Evolution and application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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