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SecureShare: Blockchain based Secure and Verifiable Knowledge Sharing for AI-Generated Content (AIGC) services

IEEE Transactions on Vehicular technology 2025年

作者： Fan, Mochan Li, Zonghang Sun, Gang Du, Hongyang Yu, Hongfang Niyato, Dusit University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu610054 China Agile and Intelligent Computing Key Laboratory of Sichuan Province Chengdu China Department of Electrical and Electronic Engineering 999077 Hong Kong Peng Cheng Laboratory Shenzhen China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Benefiting from the rapidly expanding Internet of Things (IoT) data and powerful computing devices, AI-generated content (AIGC) trains models with vast knowledge to provide automated content generation services. Sharing knowledge through the ciphertext-policy attribute-based encryption (CP-ABE) algorithm is beneficial for training high-quality AIGC models to offer better services. However, existing CP-ABE sharing schemes often involve untrusted third parties, which can result in issues such as knowledge deletion, unverifiable access, and single points of failure. To address these challenges, some blockchain-based sharing schemes have been developed. However, they still face privacy leakage problems. In this paper, we propose SecureShare, a secure and verifiable knowledge sharing scheme based on a consortium blockchain for AIGC services. We begin by outlining a blockchain knowledge sharing architecture and optimizing the Delegated Proof of Stake (DPOS) committee node selection method to ensure that entities can achieve verifiable access control. Additionally, to achieve fine-grained access to knowledge ciphertext while preserving privacy, we propose a CP-ABE scheme with Policy Hiding, attribute privacy preservation, and Revocation, referred to as PHR-CP-ABE. PHR-CP-ABE ensures the privacy of access policies and attributes, and users whose attributes have been revoked cannot decrypt knowledge further. A case study on Dall-E clearly illustrates the operational mechanism of the proposed scheme. We provide theoretical analysis of the security of both the AIGC knowledge sharing scheme and PHR-CP-ABE. Through extensive performance analysis and comparisons with existing schemes, our approach demonstrates significant advantages in terms of computation and communication overhead. © 1967-2012 IEEE.

关键词： Ciphertext

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NR-Isomap: An Incremental Approach with Gaussian Process Kernels for Denoising

NR-Isomap: An Incremental Approach with Gaussian Process Ker...

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International Conference on Big Data and Artificial Intelligence (BDAI)

作者： Mahwish Yousaf Muhammad Saaadat Shakoor Khan Shamsher Ullah Shou Wang Li Jing School of Computer Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Materials Physics Institute of Solid-State Physics Chinese Academy of Sciences Hefei China Department Knowledge Unit of System and Technology University of Management and Technology Sialkot Pakistan

The Isomap is a popular nonlinear dimensionality reduction method often used for low-dimensional embedding space. When the big data is noisy, Isomap often fails because of the graph structure’ Short-Circuit Edges (SCE) problem. In this paper, an incremental Noise-Reduction Isomap (NR-Isomap) approach is to address the SCE problem in a graph structure. The GP kernels are combined with the manifold learning approach and provide significant results. NR-Isomap is based on the Hessian Locally Linear Embedding (HLLE) algorithm with Gaussian Process (GP) kernels. The HLLE algorithm provides the optimal solution to the SCE problem caused by an overly noisy dataset. NR-Isomap provides an answer to the Isomap SCE problem in noisy big data problems that cannot be handled using existing methods. Therefore, the GP kernel significantly improves and measures the performance of the NRI-somap accuracy. Our NR-Isomap method outperforms experiments on SK-Learn datasets and shows efficient results compared to Isomap. Our NR Isomap is much more efficient and faster compared to Isomap. The efficiency and accuracy of the NR-Isomap method are demonstrated theoretically and compared with other manifold learning methods.

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CW-YOLO: joint learning for mask wearing detection in low-light conditions

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Frontiers of computer science 2023年第6期17卷 191-193页

作者： Mingqiang GUO Hongting SHENG Zhizheng ZHANG Ying HUANG Xueye CHEN Cunjin WANG Jiaming ZHANG School of Computer Science China University of GeosciencesWuhan 430074China National Engineering Research Center of Geographic Information System Wuhan 430074China Wuhan Zondy Cyber Technology Co.Ltd. Wuhan 430074China Shenzhen Data Management Center of Planning and Natural Resources Shenzhen 518000China Key Laboratory of Urban Land Resources Monitoring and Simulation(Ministry of Natural Resources) Shenzhen 518000China College of Engineering Boston UniversityBoston 02215USA

1 Introduction With the rapid progress of Artificial Intelligence(AI)technology in object detection and face recognition,deep learning methods for face mask wearing detection have become increasingly mature and continuously take into account the needs of efficiency and ***,these conventional detection methods mostly ignore the complexity of real-world application scenarios,such as extremely darkness and gloomy *** unfavorable conditions lead to a series of image degradations that seriously hamper machine vision *** camera parameter adjustment,auxiliary lighting,or pre-processing enhancement[1]can weaken these negative effects to some extent to promote the detection,they will also result in increased hardware and time costs.

关键词： hardware lighting weather

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Efficient Graph Neural Network Driven Recurrent Reinforcement Learning for GNSS Position Correction 36

Efficient Graph Neural Network Driven Recurrent Reinforcemen...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zhao, Haoli Tang, Jianhao Li, Zhenni Wu, Zhuoyu Xie, Shengli Wu, Zhaofeng Liu, Ming Kumara, Banage T.G.S. School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510000 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (纸本)9780936406350

With the wide applications of the Global Navigation Satellite system (GNSS) in autonomous driving scenarios, the demand for high-precision positioning of navigation systems has increased dramatically in complex multipath environments. Conventional model-based methods are constrained by strict assumptions about noise models and can hardly model complex environment errors. In contrast, approaches based on artificial intelligent learning have become an important direction to solving the problem of high-precision positioning because learning-based approaches only require simple assumptions. However, current learning-based approaches are facing the following issues. The existing Graph Neural Network-based (GNN) method could hardly adapt to dynamically changing driving environment scenarios since it considers positioning discretely. On the other hand, existing Reinforcement Learning-based (RL) approaches ignore the relationship between multi-constellation satellites, resulting in an inadequate description of the driving correction environment observations. In this paper, we construct a GNN-driven recurrent reinforcement learning method to consider the GNSS measurement of multi-constellation satellites and to learn real-time correction strategy in the dynamic driving environment. To establish a comprehensive positioning correction environment, we construct a multi-constellation graph observation, based on the feature vector concerning GNSS measurement of multi-constellation satellites and edges for satellites in and between constellations. To make more effective use of GNSS measurements, we employ the graph embedding module to deal with the multi-constellation graph inputs, to extract hidden topological features to form the brief states about relationships between multi-constellation satellites for the RL environment. Finally, we construct a recurrent actor-critic structured RL model with cumulative reward and continuous action space to exploit historical information and a

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Research and Application of Nuclear Reactor Computational Data Framework Based on HDF5 7th

Research and Application of Nuclear Reactor Computational Da...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Lu, Wei Feng, Jintao Yang, Hao Zeng, Hui Pan, Junjie Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China School of Computer Science Chengdu University of Information Technology Chengdu610225 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9783030786175

Nuclear reactor computing software need to process and maintain complex and massive data sets. In order to meet the requirement of mass data storage and processing in software, the data storage model and I/O method and programming interface of HDF5 (Hierarchical Data Format v5) are deeply studied. According to the data storage and processing requirement of typical nuclear reactor computing data such as assembly or core neutronics computing data and core thermal-hydraulic computing data, RCDF-H5 (Reactor Compute Data Framework based on HDF5) is proposed. KYMRES (KYlin-2 Main RESults databank) and COMRES (COre Main RESults databank) are designed and implemented based on RCDF-H5. The performance tests show that RCDF-H5 has a higher I/O efficiency than conventional storage solutions. A new data storage and management solution for nuclear reactor computing software is provided. © 2021, Springer Nature Switzerland AG.

关键词： Digital storage

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CostFM: A High Cost-Performance Fingerprint Management Mechanism for Shared SSDs

CostFM: A High Cost-Performance Fingerprint Management Mecha...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Hao Liu Mengting Lu Fang Wang Wenpeng He Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Wuhan China Cloud Intelligence Group Alibaba Co. Hangzhou China

As the storage and I/O demand of contemporary computer systems grow, SSDs with deduplication are widely deployed as shared storage devices to provide high performance in cloud platforms where diverse tenant workloads are collocated. However, existing global and fixed fingerprint management schemes are inefficient in the multi-tenant environment. Contention for in-memory fingerprint cache, embedded in DRAM to speed up fingerprint lookups, results in decreased cache utilization, and abundant fingerprint lookups into the backend flash memory degrade the system performance. Besides, a fixed cache replacement policy for all the tenants fails to capture the diverse characteristics, further increasing the lookup *** paper introduces CostFM, a novel fingerprint management mechanism highlighted by two notable features. First, it provides a benefit-aware cache allocation scheme, which estimates the cache hit gains of each tenant and uses dynamic programming to find the optimal cache allocation scheme, avoiding the cache contention for the fingerprint resource and improving cache utilization. Second, it applies a user-based policy model that captures access characteristics to select the suitable cache management policy for each tenant, boosting overall cache efficiency. Extensive experimental results show that CostFM decreases the average request latency by up to 30%, and it also reduces the fingerprint writes into the flash memory by up to 4.7x.

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Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance

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International Journal of computer Vision 2025年 1-21页

作者： Zhang, Yingkai Lai, Zeqiang Zhang, Tao Fu, Ying Zhou, Chenghu School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China College of Resources and Environment University of Chinese Academy of Sciences Beijing China

Hyperspectral images (HSIs) super-resolution (SR) aims to improve the spatial resolution, yet its performance is often limited at high-resolution ratios. The recent adoption of high-resolution reference images for super-resolution is driven by the poor spatial detail found in low-resolution HSIs, presenting it as a favorable method. However, these approaches cannot effectively utilize information from the reference image, due to the inaccuracy of alignment and its inadequate interaction between alignment and fusion modules. In this paper, we introduce a Spatial-Spectral Concordance Hyperspectral Super-Resolution (SSC-HSR) framework for unaligned reference RGB guided HSI SR to address the issues of inaccurate alignment and poor interactivity of the previous approaches. Specifically, to ensure spatial concordance, i.e., align images more accurately across resolutions and refine textures, we construct a Two-Stage Image Alignment (TSIA) with a synthetic generation pipeline in the image alignment module, where the fine-tuned optical flow model can produce a more accurate optical flow in the first stage and warp model can refine damaged textures in the second stage. To enhance the interaction between alignment and fusion modules and ensure spectral concordance during reconstruction, we propose a Feature Aggregation (FA) module and an Attention Fusion (AF) module. In the feature aggregation module, we introduce an Iterative Deformable Feature Aggregation (IDFA) block to achieve significant feature matching and texture aggregation with the fusion multi-scale results guidance, iteratively generating learnable offset. Besides, we introduce two basic spectral-wise attention blocks in the attention fusion module to model the inter-spectra interactions. Extensive experiments on three natural or remote-sensing datasets show that our method outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code is publicly available to the community ( ht

关键词： Hyperspectral image super-resolution Unaligned RGB guidance Spatial-spectral concordance Two-stage image alignment Feature aggregation Attention fusion

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BDLR:lncRNA identification using ensemble learning

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BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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Securing Sdn/Nfv-Enabled Campus Networks with Software-Defined Perimeter-Based Zero-Trust Architecture

SSRN

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SSRN 2023年

作者： Ruambo, Francis A. Zou, Deqing Lopes, Ivandro O. Yuan, Bin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on Big Data Security Wuhan China National Engineering Research Center for Big Data Technology and System Wuhan China Services Computing Technology and System Lab Wuhan China Cluster and Grid Computing Lab Wuhan China Mbeya university of Science and Technology Mbeya131 Tanzania United Republic of Nucleo Operacional para a Sociedade de Informacao Cape Verde Songshan Laboratory Zhengzhou China

Network softwarization is a breakthrough in designing modern networks and providing numerous new network operations and services. This change is exemplified by Software Defined Networks (SDN) and Network Function Virtualization (NFV). As a result, the combination of SDN and NFV (SDN/NFV) frameworks and individual SDN and NFV frameworks have gained substantial traction in network security over the past decade, significantly supporting on-demand protection measures. However, the inherent security shortcomings of SDN, NFV, and SDN/NFV frameworks provide the fundamental issue in implementing security procedures via network softwarization. To solve this issue, we present a Software-Defined Perimeter (SDP)-powered Zero-Trust Architecture (ZTA) to improve security in the SDN/NFV-enabled campus network holistically. In addition to this, we also propose a threat model and quantitatively assess the suggested approach's performance and security. Furthermore, we highlight potential zero-trust network security function implementations in SDN/NFV-enabled infrastructure to increase protection for endpoint devices, network resources, and flows for detecting threats and responding effectively with minimal latency and overhead. © 2023, The Authors. All rights reserved.

关键词： Access control

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A Survey of Fault Tolerance in Cloud computing

A Survey of Fault Tolerance in Cloud Computing

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systems, Advanced Technologies and Knowledge (ISSATK), International Symposium of

作者： Om Kolthoum Elhajjej Nouha Alyaoui Samia Dardouri Karim Chabir Research Laboratory Modeling Aanalysis Control of System (MACS) LR16ES22 National engineering school of Gabes (ENIG) University of Gabes Tunisia Computer Science and Telecommunications Department Laboratory of Modeling Analysis and Control of Systems (MACS) Higher Institute of Applied Sciences and Technology Zarroug Gafsa Gafsa University Tunisia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV’COM Laboratory-Sup’Com University of Carthage Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance.

关键词： Measurement Surveys Cloud computing Fault tolerance Technological innovation Scalability Fault tolerant systems computer architecture Software Software reliability

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