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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong School of Computer Science and Technology Xidian University Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China School of Computer Science and Technology Xidian University Shaanxi Key Laboratory of Network and System Security Xi’an710071 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Concurrency control

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An Overview of Video Tampering Detection Techniques: State-of-the-Art and Future Directions

An Overview of Video Tampering Detection Techniques: State-o...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Saroj Kumar Pandey Lalan Kumar Gaurav Kumar Ankit Kumar Kamred Udham Singh Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

In the realm of video processing and forensics, finding video tampering is a critical challenge. The purposeful change or manipulation of video material to conceal or falsely depict the original information is referred to as “video tampering.” A significant demand for efficient video tampering detection methods has emerged in recent years as a result of the rise in false news, cyberbullying, and other types of criminal behavior. Modern approaches for detecting video manipulation may be generally divided into two categories: passive methods and aggressive methods. Whereas active methods entail putting digital watermarks or other identifiers on the video clip to aid tampering detection, passive methods rely on evaluating the video content for inconsistencies or artifacts generated during tampering. Techniques like sensor noise analysis, splicing detection, and forgery detection based on compression artifacts are examples of passive approaches. To find any discrepancies produced by manipulation, sensor noise analysis examines the noise patterns in the video. Splicing detection entails identifying the borders between several video segments and inspecting the content of each segment for irregularities. Analyzing the compressed video stream for irregularities caused during tampering is required for detecting forgeries based on compression artifacts. Techniques like digital watermarking, digital signatures, and fingerprinting are examples of active procedures. To detect any manipulation efforts, digital watermarking entails putting a digital watermark into the video clip. To confirm the legitimacy of the video clip, a digital signature is applied to it. To do fingerprinting, special characteristics from the video footage are extracted and saved in a database. In addition to the aforementioned methods, machine learning-based systems have also demonstrated promising outcomes in the identification of video manipulation. Using these methods, any tampering attempts in fresh mov

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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Instance by instance: An iterative framework for multi-instance 3d registration

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1117-1128页

作者： Yang, Jiaqi Cao, Xinyue Zhang, Xiyu Cheng, Yuxin Qi, Zhaoshuai Quan, Siwen National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China School of Electronic and Control Engineering Chang'an University Xi'an MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Shenzhen University Shenzhen China

Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. Pioneers followed a non-extensible one-shot framework, which prioritizes the registration of simple and isolated instances, often struggling to accurately register challenging or occluded instances. To address these challenges, we propose the first iterative framework for multi-instance 3D registration (MI-3DReg) in this work, termed instance-by-instance (IBI). It successively registers instances while systematically reducing outliers, starting from the easiest and progressing to more challenging ones. This enhances the likelihood of effectively registering instances that may have been initially overlooked, allowing for successful registration in subsequent iterations. Under the IBI framework, we further propose a sparse-to-dense correspondence-based multi-instance registration method (IBI-S2DC) to enhance the robustness of MI-3DReg. Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC, e.g., our mean registration F1 score is 12.02%/12.35% higher than the existing state-of-the-art on the synthetic/real datasets. The source codes are availableonline at https://***/caoxy01/IBI. © 2014 Chinese Association of Automation.

关键词： 3D modeling

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Overview of Information System Testing Technology Under the "CLOUD + MIcroservices" Mode 1

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2nd International Conference on computer and Communication engineering, CCCE 2022

作者： Zhang, Jianwei Jiang, Shan Wang, Kunlong Wang, Rui Liu, Qi Yuan, Xiaoguang Beijing Institute of Computer Technology and Application Beijing100854 China Graduate School of the Second Research Institute of China Aerospace Science and Industry Corporation Beijing100854 China College of Systems Engineering National University of Defense Technology Changsha410073 China

ISBN: (数字)9783031174223

ISBN: (纸本)9783031174216

With the continuous development of military theory, cloud computing platform and microservice technology are constantly applied to combat information platform. Under this trend, it has gradually become an urgent demand to ensure the quality of combat information system under the "Cloud + Microservice" mode. However, current studies mostly focus on a single technical point or aspect of the testing for military information system under the "Cloud + Microservice" mode, which lack of an overall summarization of its testing schema. Therefore, based on the relevant testing technologies of cloud platform, microservice and rapid environment construction in recent years, this paper summarizes and puts forward a framework for the testing of information system under the "Cloud + Microservice" mode, and systematically discusses the 3 important aspects of the framework: testing towards cloud platform, testing towards microservice applications and rapid construction of testing environment. The testing framework proposed in this paper can provide top-level guidance for the testing of such systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Testing

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Data-Sparse Deep Learning for Studying Diabetic Retinopathy Lesions

Data-Sparse Deep Learning for Studying Diabetic Retinopathy ...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Devendra Singh Narendra Bisht Dinesh C. Dobhal Janmejay Pant Saurabh Dhanik Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Management studies Graphic Era Hill University Bhimtal India Department of Computer Application Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Graphic Era Hill University Bhimtal Campus India School of Computing Graphic Era Hill University Bhimtal Campus Uttarakhand India

Diabetic retinopathy, a serious complication of diabetes, can result in permanent blindness if not promptly detected. The difficulty in assessing the stages of the disease using fundus images by experienced human specialists impedes early identification. This problem has prompted researchers to turn toward convolutional neural networks (CNNs) to simplify the evaluation process. The incorporation of CNNs offers hope for a more effective and accurate distinguishing of the stages of diabetic retinopathy, which could help prevent permanent loss of vision. The objective of such an effective screening mechanism is to simplify procedures, and thus allow for early intervention soon after onset so that the precious gift of sight will not be lost. We suggest a classification system below along these lines.

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Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning

arXiv

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arXiv 2024年

作者： Tang, Hao Lu, Junhao Huang, Guoheng Li, Ming Chen, Xuhang Zhong, Guo Tan, Zhengguang Li, Zinuo School of Computer Science and Technology Guangdong University of Technology Guangdong China School of Computer Science and Engineering Huizhou University Huizhou China Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China School of Physics Maths and Computing University of Western Australia WA Australia

In Few-Shot Learning (FSL), traditional metric-based approaches often rely on global metrics to compute similarity. However, in natural scenes, the spatial arrangement of key instances is often inconsistent across images. This spatial misalignment can result in mismatched semantic pixels, leading to inaccurate similarity measurements. To address this issue, we propose a novel method called the Layer-Wise Features Metric of Semantic-Pixel Matching (LWFM-SPM) to make finer comparisons. Our method enhances model performance through two key modules: (1) the Layer-Wise Embedding (LWE) Module, which refines the cross-correlation of image pairs to generate well-focused feature maps for each layer;(2)the Semantic-Pixel Matching (SPM) Module, which aligns critical pixels based on semantic embeddings using an assignment algorithm. We conducted extensive experiments to evaluate our method on four widely used few-shot classification benchmarks: miniImageNet, tieredImageNet, CUB-200-2011, and CIFAR-FS. The results indicate that LWFM-SPM achieves competitive performance across these benchmarks. Our code will be publicly available on https://***/Halo2Tang/Code-for-LWFM-SPM. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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A High-capacity Information Hiding Scheme Based on Blakley Secret Sharing

A High-capacity Information Hiding Scheme Based on Blakley S...

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Jincheng Zhou Yue Li Yujie Shen Dan Tang Hongliang Cai Jing Gong Ningchao Bai School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Chengdu China School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

A high-capacity reversible information hiding scheme based on Blakley secret sharing is proposed to address the issues of low embedding rate and weak disaster recovery performance of images when using reversible information hiding to protect data in scenarios with large data transmission. The scheme employs Euclidean division and a two-dimensional modulus function to establish a robust mapping relationship between the secret information and the pixels of the cover image. Subsequently, the values derived from the preprocessing of the secret information are incorporated into the secret sharing method proposed by Blakley as the elements of the secret vector. The proposed scheme exploits the disaster-tolerance property of the secret sharing method and enhances the redundancy of the secret-carrying encrypted image, thereby improving the embedding rate and security of the algorithm while maintaining the security of the secret sharing.

关键词： Correlation Disasters Redundancy Reconstruction algorithms Vectors Manufacturing Cryptography Data mining Data communication Computational complexity

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Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction

arXiv

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arXiv 2023年

作者： Luo, Haoran Haihong, E. Yang, Yuhao Yao, Tianyu Guo, Yikai Tang, Zichen Zhang, Wentai Peng, Shiyao Wan, Kaiyang Song, Meina Lin, Wei Zhu, Yifan Tuan, Luu Anh School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application China Inspur Group Co. Ltd. China College of Computing and Data Science Nanyang Technological University Singapore

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging and output merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available1 © 2023, CC BY-NC-SA.

关键词： Knowledge graph

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Real-Time Connectivity with the Industrial Internet of Things

Real-Time Connectivity with the Industrial Internet of Thing...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sujayaraj Samuel Jayakumar Ashwini Kumar Vikas Kumar Kharbas Department of Forensic Science School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The economic net of things (Eliot) is an emerging era permitting the relationship and optimization of devices, machines, and belongings in industrial settings via statistics switch and exchange. Real-time connectivity with the Iota has revolutionized how business systems perform and has opened the door for full-size upgrades in productiveness, performance, and protection. With actual-time connectivity, commercial operations were capable of advantage near real-time perception into gadget performance, surroundings conditions, and the behavior of machines and property. It interprets into improved situational cognizance, higher decision-making main to optimized operational processes, and stepped forward usual operational visibility. Furthermore, actual-time connectivity with the Eliot is likewise allowing superior information analytics capabilities, taking into account the improvement of predictive analytics fashions, optimization of plant operations, and the gathering of critical information factors. All of those traits have improved the efficiency, scalability, and reliability of business operations and networks, permitting companies to higher-utilize their assets and become greater efficient.

关键词： Performance evaluation Scalability Switches Production Real-time systems Reliability Protection Predictive analytics Optimization Business

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