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Letters to the editor: Comments on 舠An Improved hash code for Scatter Storage舡

Communications of the ACM 1968年第5期11卷 378-378页

作者： Beyer, J.D. Maurer, W.D. Bamberger, Frank K. Bell Telephone Lab Holmdel NJ United States Univ. of California Berkeley United States Univ. of Chicago Chicago IL United States

No abstract available.

关键词： hash code hash table scatter storage searching

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A novel image hashing with low-rank sparse matrix decomposition and feature distance

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VISUAL COMPUTER 2025年第3期41卷 1987-1998页

作者： Yu, Zixuan Tang, Zhenjun Liang, Xiaoping Zhang, Hanyun Sun, Ronghai Zhang, Xianquan Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

Image hashing is an efficient technique of image processing for various applications, such as retrieval, copy detection and authentication. In this paper, we design a novel image hashing algorithm using LRSMD (low-rank sparse matrix decomposition). Firstly, an input image is preprocessed by interpolation, Gaussian blur and color space conversion. Next, the preprocessed image is fed into the LRSMD for learning a low-rank matrix. Then, statistical features of non-overlapping blocks in the low-rank matrix are extracted. Finally, the hash code is obtained by calculating feature distances. Various experiments are done on public datasets to demonstrate the robustness and discrimination of the proposed algorithm. The results show that the proposed algorithm outperforms several advanced algorithms in balancing the performances of robustness and discrimination.

关键词： Low-rank sparse matrix decomposition hash code Statistical features Feature distance

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Cybersecurity analysis using the blockchain algorithm in nuclear power plants to enhance safe operations

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 782-792页

作者： Woo, Tae Ho Cyber Univ Korea Dept Mech & Control Engn 106 Bukchon Ro Seoul South Korea

The blockchain technology has been applied to nuclear power plants in which the cyber performance using the security analysis information is highly enhanced. Following this study, other kinds of information in the nuclear stuff could be applied using the blockchain technology. The simulations are performed as the conventional with system dynamics (SD) and blockchain modified modeling. The blockchain quantifications are done for conventional algorithm and SD as the highest value is 2.0. In addition, the encryption analysis is done, which is obtained by the hash code with the modulus algorithm that the remainder is calculated. For example, in the case of Long-term Conduction, the highest value is 0.6. After taking the information of hash code, the decoding could be accomplished. The networking bodies have same information and can take the secured information.

关键词： Nuclear security blockchain cyber hash code system

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Bilateral Cross hashing Image Retrieval Based on Principal Component Analysis

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-18页

作者： Yilmaz, Ahmet Karamanoglu Mehmetbey Univ Fac Engn Dept Comp Engn TR-70200 Karaman Turkiye

Image retrieval (IR) has become a crucial challenge in computer vision with the exponential growth of digital imagery. The existing methods employ a single hash source, which may overlook deep details in the image, and they struggle to handle the complexity and diversity of modern visual data. This study addresses this limitation by proposing a novel deep hashing-based IR method named bilateral cross hashing based on principal component analysis (BCHP). Bilateral cross hashing based on principal component analysis-image retrieval (BCHP-IR) employs the feature extraction capabilities of residual network-50 (ResNet-50) and the dimensionality reduction and information preservation properties of principal component analysis (PCA). The method extracts high-level features from query images using ResNet-50 and then compresses both features and class labels using PCA. The compressed data undergoes quantization to generate binary codes. These "bilateral" hash codes are combined to capture deep features and compared with image codes in the database. The BCHP-IR's effectiveness is demonstrated through extensive comparative analysis against reported methods, achieving superior performance metrics. On the MS-COCO dataset, BCHP-IR achieves mAP scores that are higher than the average of other benchmark algorithms by 6.3, 6.4, 6.2 and 5.0 at hash lengths of 16, 32, 48 and 64, respectively. These enhancements at those hash lengths are 4.6, 4.7, 4.8 and 4.3 for the NUS-WIDE dataset and 3.9, 2.9, 2.5 and 2.1 for the ImageNet dataset. Therefore, the proposed BCHP-IR method harnesses the power of ResNet-50 and PCA and offers a promising solution for efficient and effective image retrieval.

关键词： Deep learning Image retrieval hash code Principal component analysis Residual network

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Learning Binary hash codes for Fast Anchor Link Retrieval across Networks 19

Learning Binary Hash Codes for Fast Anchor Link Retrieval ac...

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World Wide Web Conference (WWW)

作者： Wang, Yongqing Shen, Huawei Gao, Jinhua Cheng, Xueqi Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing 100190 Peoples R China

ISBN: (纸本)9781450366748

Users are usually involved in multiple social networks, without explicit anchor links that reveal the correspondence among different accounts of the same user across networks. Anchor link prediction aims to identify the hidden anchor links, which is a fundamental problem for user profiling, information cascading, and cross-domain recommendation. Although existing methods perform well in the accuracy of anchor link prediction, the pairwise search manners on inferring anchor links suffer from big challenge when being deployed in practical systems. To combat the challenges, in this paper we propose a novel embedding and matching architecture to directly learn binary hash code for each node. hash codes offer us an efficient index to filter out the candidate node pairs for anchor link prediction. Extensive experiments on synthetic and real world large-scale datasets demonstrate that our proposed method has high time efficiency without loss of competitive prediction accuracy in anchor link prediction.

关键词： hash code anchor link prediction scalability

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Learning hash codes with Listwise Supervision

Learning Hash Codes with Listwise Supervision

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IEEE International Conference on Computer Vision (ICCV)

作者： Wang, Jun Liu, Wei Sun, Andy X. Jiang, Yu-Gang IBM TJ Watson Res Ctr Business Analyt & Math Sci Yorktown Hts NY 10598 USA IBM TJ Watson Res Ctr Multimedia Analyt Yorktown Hts NY USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9781479928392

hashing techniques have been intensively investigated in the design of highly efficient search engines for large-scale computer vision applications. Compared with prior approximate nearest neighbor search approaches like tree-based indexing, hashing-based search schemes have prominent advantages in terms of both storage and computational efficiencies. Moreover, the procedure of devising hash functions can be easily incorporated into sophisticated machine learning tools, leading to data-dependent and task-specific compact hash codes. Therefore, a number of learning paradigms, ranging from unsupervised to supervised, have been applied to compose appropriate hash functions. However, most of the existing hash function learning methods either treat hash function design as a classification problem or generate binary codes to satisfy pairwise supervision, and have not yet directly optimized the search accuracy. In this paper, we propose to leverage listwise supervision into a principled hash function learning framework. In particular, the ranking information is represented by a set of rank triplets that can be used to assess the quality of ranking. Simple linear projection-based hash functions are solved efficiently through maximizing the ranking quality over the training data. We carry out experiments on large image datasets with size up to one million and compare with the state-of-the-art hashing techniques. The extensive results corroborate that our learned hash codes via listwise supervision can provide superior search accuracy without incurring heavy computational overhead.

关键词： cryptography hash code hashing Regulated industries Supervision binary code Search engines

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LWDSA: light-weight digital signature algorithm for wireless sensor networks

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2017年第10期42卷 1629-1643页

作者： Lavanya, M. Natarajan, V. Anna Univ Dept Instrumentat Engn Madras 600025 Tamil Nadu India

The essential security mechanism in wireless sensor networks (WSNs) is authentication, where nodes can authenticate each other before transmitting a valid data to a sink. There are a number of public key authentication procedures available for WSN in recent years. Due to constraints in WSN environment there is a need for light-weight authentication procedure that consumes less power during computation. This proposed work aims at developing a light-weight authentication protocol using MBLAKE2b with elliptic curve digital signature algorithm (ECDSA). The proposed protocol is also tested using the protocol verification tool Scyther and found to be secure in all claims and roles. This proposed algorithm increases the network life time and reduces the computation time, which is essential for the constrained environment like WSNs.

关键词： Wireless sensor networks authentication elliptic curve cryptography sponge function hash code digital signature algorithm

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Fast image quality assessment via supervised iterative quantization method

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NEUROCOMPUTING 2016年 212卷 121-127页

作者： He, Lihuo Wang, Di Liu, Qi Lu, Wen Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

No-reference/Blind image quality assessment (NR-IQA/BIQA) is significant for image processing and yet very challenging, especially for real-time application and big image data processing. Traditional NR-IQA metrics usually train complex models such as support vector machine, neural network, and probability graph model, which result in long computational time and poor robustness. To overcome these weaknesses, the paper proposes a fast no-reference image quality assessment via hash coding method, named NRHC. First, the image is divided into overlapped patches to extract the spatial statistical features of natural scene images. Then the features are encoded to produce binary hash codes via supervised iterative quantization (SITQ) method. Finally, the Hamming distances between the hash code of the test image and those of original undistorted images are calculated to obtain the final image quality. Thorough experiments on benchmark databases demonstrate that the proposed approach achieves comparable performance and has higher computational efficiency and stronger robustness compared with the state-of-the-art NR-IQA methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： No-reference Image quality assessment Supervised iterative quantization hash code

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A secure data transmission and efficient data balancing approach for 5G-based IoT data using UUDIS-ECC and LSRHS-CNN algorithms

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IET COMMUNICATIONS 2022年第5期16卷 571-583页

作者： Yadav, Kusum Jain, Anurag Alharbi, Yasser Alferaidi, Ali Alkwai, Lulwah M. Ahmed, Nada Mohamed Osman Sid Hamad, Sawsan Ali Saad Univ Hail Coll Comp Sci & Engn Hail Saudi Arabia Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttarakhand India

Due to the realization of 5G technology, the Internet of things (IoT) has made remarkable advancements in recent years. However, security along with data balancing issues is proffered owing to IoT data's growth. The universally unique identifier short input pseudo-random (SiP) hash-based elliptic curve cryptography (UUDIS-ECC) centred secure data transfer (DT) and linear scaling Rock Hyraxes swarm-based convolutional neural network (LSRHS-CNN) centred 5G IoT data balancing are proposed here to address those issues. Authentication, destination selection, validation, secure DT, and also load balancing are the proposed method's five phases. Initially, during the registration phase, the Length Nano ID (LNanoID) is created in the authentication. The user is permitted to further communicate if the LNanoID is matched with the already saved LNanoID. The destination is selected if the user is authorized. Utilizing the sender and the receiver's public key, the hash code is produced in the validation centre by the SiP hash function after destination selection. After that, by employing the UUDIS-ECC algorithm, the IoT data is safely transmitted towards the destination. The 5G IoT data is balanced by using the LSRHS-CNN algorithm during DT. Superior results are attained by the proposed methods analogized to existing research methods.

关键词： Internet of Things destination selection UUDIS-ECC algorithm 5G technology efficient data balancing hash code secure data transmission SiP universally unique identifier short input pseudorandom hash Length Nano ID LNanoID secure DT validation centre computer network security convolutional neural nets linear scaling Rock Hyraxes swarm load balancing Internet of things 5G mobile communication LSRHS-CNN algorithm 5G-based IoT data elliptic curve cryptography public key cryptography

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Deep Semantic Multimodal hashing Network for Scalable Image-Text and Video-Text Retrievals

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第4期34卷 1838-1851页

作者： Jin, Lu Li, Zechao Tang, Jinhui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

hashing has been widely applied to multimodal retrieval on large-scale multimedia data due to its efficiency in computation and storage. In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable image-text and video-text retrieval. The proposed deep hashing framework leverages 2-D convolutional neural networks (CNN) as the backbone network to capture the spatial information for image-text retrieval, while the 3-D CNN as the backbone network to capture the spatial and temporal information for video-text retrieval. In the DSMHN, two sets of modality-specific hash functions are jointly learned by explicitly preserving both intermodality similarities and intramodality semantic labels. Specifically, with the assumption that the learned hash codes should be optimal for the classification task, two stream networks are jointly trained to learn the hash functions by embedding the semantic labels on the resultant hash codes. Moreover, a unified deep multimodal hashing framework is proposed to learn compact and high-quality hash codes by exploiting the feature representation learning, intermodality similarity-preserving learning, semantic label-preserving learning, and hash function learning with different types of loss functions simultaneously. The proposed DSMHN method is a generic and scalable deep hashing framework for both image-text and video-text retrievals, which can be flexibly integrated with different types of loss functions. We conduct extensive experiments for both single-modal- and cross-modal-retrieval tasks on four widely used multimodal-retrieval data sets. Experimental results on both image-text- and video-text-retrieval tasks demonstrate that the DSMHN significantly outperforms the state-of-the-art methods.

关键词： Semantics hash functions Correlation Task analysis Videos Learning systems Sparse matrices Deep hashing hash code image-text retrieval semantic information similarity preserving video-text retrieval

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