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An Ann-Based Scalable hashing algorithm for Computational Clouds with Schedulers

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2021年第4期31卷 697-712页

作者： Tchorzewski, Jacek Jakobik, Agnieszka Iacono, Mauro Cracow Univ Technol Fac Comp Sci & Telecommun Ul Warszawska 24 PL-31155 Krakow Poland AGH Univ Sci & Technol Fac Comp Sci Elect & Telecommun Al Mickiewicza 30 PL-30059 Krakow Poland Univ Campania Luigi Vanvitelli Dept Math & Phys Viale Lincoln 5 I-81100 Caserta Italy

The significant benefits of cloud computing (CC) resulted in an explosion of their usage in the last several years. From the security perspective, CC systems have to offer solutions that fulfil international standards and regulations. In this paper, we propose a model for a hash function having a scalable output. The model is based on an artificial neural network trained to mimic the chaotic behaviour of the Mackey-Glass time series. This hashing method can be used for data integrity checking and digital signature generation. It enables constructing cryptographic services according to the user requirements and time constraints due to scalable output. Extensive simulation experiments are conduced to prove its cryptographic strength, including three tests: a bit prediction test, a series test, and a Hamming distance test. Additionally, flexible hashing function performance tests are run using the CloudSim simulator mimicking a cloud with a global scheduler to investigate the possibility of idle time consumption of virtual machines that may be spent on the scalable hashing protocol. The results obtained show that the proposed hashing method can be used for building light cryptographic protocols. It also enables incorporating the integrity checking algorithm that lowers the idle time of virtual machines during batch task processing.

关键词： hashing algorithm artificial neural network scalable cryptography algorithm computational cloud task scheduler

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Deep adversarial multi-label cross-modal hashing algorithm

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INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2023年第2期12卷 16-16页

作者： Yang, Xiaohan Wang, Zhen Liu, Wenhao Chang, Xinyi Wu, Nannan Shandong Univ Technol Sch Comp Sci & Technol Zibo 255000 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Qianjin St Changchun 130012 Jilin Peoples R China

In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features' semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods.

关键词： Cross-modal retrieval Image-text retrieval Cross-modal similarity preserving hashing algorithm

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Coverless Image Steganography Using Histograms of Oriented Gradients-Based hashing algorithm

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JOURNAL OF INTERNET TECHNOLOGY 2017年第5期18卷 1177-1184页

作者： Zhou, Zhili Wu, Q. M. Jonathan Yang, Ching-Nung Sun, Xingming Pan, Zhaoqing Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON Canada Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Shoufeng Township Taiwan

The traditional image steganography approaches usually need a cover image so that the secret information can be imperceptibly embedded into it for secret communication. However, by utilizing the embedding traces left in the cover image, the existence of secret information could be successfully determined by steganalysis methods. To resist the existing steganalysis methods, a coverless image steganography approach is proposed using histograms of oriented gradients (HOGs)-based hashing algorithm. More specially, instead of designating a cover image for secret information embedding, the original images whose hash sequences equal to the secret information are directly selected from a large-scale database, which can be used as the stego-images for secret communication. In this approach, each image's hash sequences are obtained from its non-overlapping blocks by using the HOGs-based hashing algorithm. Both the theoretical analysis and experiments demonstrate that our coverless image steganography approach has good resistance to the existing steganalysis methods, and has good security and strong robustness to the common attacks such as resampling, brightness and contrast change, smoothing as well as addition of Gaussian noise.

关键词： Information hiding Coverless image steganography Watermarking Near-duplicate image retrieval hashing algorithm

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Coverless Image Steganography Using Histograms of Oriented Gradients-Based hashing algorithm

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网际网路技术学刊 2017年第5期18卷 1177-1184页

作者： Zhili Zhou Q. M. Jonathan Wu Ching-Nung Yang Xingming Sun Zhaoqing Pan

关键词： Information hiding Coverless image steganography Watermarking Near-duplicate image retrieval hashing algorithm

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GXHash: High-Performance Non-Cryptographic hashing 17

GXHash: High-Performance Non-Cryptographic Hashing

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17th International Conference on Advanced Computer Theory and Engineering

作者： Chawla, Varun

ISBN: (纸本)9798331539894;9798331539887

GXHash is a promising fast and consistent non-cryptographic hashing algorithm optimized for high throughput and efficiency by leveraging modern Central Processing Unit (CPU) capabilities like Single Instruction, Multiple Data (SIMD) and Instruction-Level Parallelism (ILP). Benchmarking against algorithms like Marvin, XxHash3, MD5, and SHA-256 shows GXHash's superior performance in terms of consistency, scalability and normalized performance. GXHash achieves up to 2 Gb/s throughput, especially excelling with larger input sizes. Despite some limitations in compiler dependencies, GXHash sets a good standard for non-cryptographic hashing, promising fast and energy-efficient processing.

关键词： High Performance Computing Non-Cryptographic hashing hashing algorithm High Throughput Applications SIMD Instruction-Level Parallelism Performance Optimization Benchmarking Collision Resistance Avalanche Effect Computational efficiency

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Enhancing Logistics With the Internet of Things: A Secured and Efficient Distribution and Storage Model Utilizing Blockchain Innovations and Interplanetary File System

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IEEE ACCESS 2024年 12卷 4139-4152页

作者： Ugochukwu, Nwosu Anthony Goyal, S. B. Rajawat, Anand Singh Verma, Chaman Illes, Zoltan City Univ Fac Informat Technol Petaling Jaya 46100 Malaysia Sandip Univ Sch Comp Sci & Engn Nasik 422213 India Eotvos Lorand Univ Fac Informat Dept Media & Educ Informat H-1053 Budapest Hungary

Logistics transports, stores, and delivers goods from the producer to the final user. Logistics have become increasingly complex in today's globalized world, making it imperative to address data integrity, transparency, and secure storage challenges. IoT devices in logistics allow for real-time monitoring of goods, vehicles, and environmental conditions. However, this generates vast amounts of data, necessitating a reliable and secure data storage and management system. These issues above can be addressed by deploying a blockchain-based solution. Blockchain is an innovative technology that operates on a decentralized database system, and it has different applications, which include finance, healthcare, and so on. This research proposed a blockchain- IoT-based Model for enhancing the logistics process. The proposed model utilized the Interplanetary file system to secure and efficiently store logistics data on a distributed and decentralized network and the SHA-256 hashing algorithm to ensure users' private information anonymity. The model also establishes rules by using smart contracts, which increases efficiency. The performance evaluation of the proposed model was done based on the security transactions, latency, cost, and throughput. The experimental results and performance evaluation show that the proposed model is more efficient and secure than the existing blockchain-based systems. Additionally, the proposed model offers the real-time monitoring of goods while in transit. The proposed model solves the IoT logistics system's Security, storage, and interoperability challenges. It also provides recommendations for logistics stakeholders to adopt blockchain technology. Despite the implications, the limitation of this study is that it was tested in a controlled environment.

关键词： Blockchain efficiency hashing algorithm IoT IPFS logistics system smart contract and security

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Hash-Based Gaussian Mixture Model (HGMM) for Roadside LiDAR Smart Infrastructure Applications

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 12968-12979页

作者： Zhang, Tianya Terry Ge, Yi Chen, Anjiang Sartipi, Mina Jin, Peter J. Univ Tennessee Chattanooga UTC Comp Sci & Engn Dept Chattanooga TN 37403 USA Rutgers State Univ Dept Civil & Environm Engn CEE Piscataway NJ 08854 USA

This paper proposes a point cloud background filtering method and explores applications that integrate LiDAR technology into roadside sensor networks, addressing challenges in handling large volumes of sparse and unstructured 3D data. Traditional methods, which classify background points based on descriptive statistics over many frames, are computationally inefficient for roadside LiDAR surveillance. This study introduces a novel approach using hash-based transformation combined with probabilistic Gaussian Mixture Model (GMM) techniques, enhancing the efficiency of high-dimensional multivariate modeling of LiDAR data. This Hash-based Gaussian Mixture Modeling (HGMM) optimizes feature selection and Gaussian components using AIC and BIC scores, mitigating computational burdens and parameter sensitivity. Unlike Cartesian coordinate-based techniques, HGMM processes LiDAR data in Spherical coordinates, preserving meaningful patterns and structures. For infrastructure LiDAR, object detection only pertains to a small amount of data in a fixed environment, allowing background reduction modeling to significantly enhance data chain efficiency by transitioning only a tiny portion of the foreground LiDAR point clouds. The method was tested on diverse LiDAR datasets, including the New Brunswick DataCity Testbed dataset, Transportation Forecasting Competition (TRANSFOR 24) Dataset, DAIR-V2X, and A9-Dataset, demonstrating its adaptability and efficiency across different scenarios. This approach enhances LiDAR sensors' utility in supporting AI-enhanced decision-making processes and enriching the knowledge base of incorporating LiDAR sensors into current and prospective traffic management strategies.

关键词： Laser radar Three-dimensional displays Laser beams Point cloud compression Object detection Data models Solid modeling hashing algorithm Gaussian mixture model smart infrastructure application

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Retargeting HR Aerial Photos Under Contaminated Labels With Application in Smart Navigation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第1期25卷 349-358页

作者： Zhang, Luming Chen, Ming Tu, Bing Li, Yi Xia, Yingjie Jinhua Polytech Key Lab Crop Harvesting Equipment Technol Zhejiang Jinhua 321016 Peoples R China Hithink RoyalFlush Informat Network Co Ltd Hangzhou 310023 Peoples R China Nanjing Univ Informat Sci & Technol Inst Opt & Elect Jiangsu Key Lab Optoelect Detect Atmosphere & Ocea Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Int Joint Lab Meteorol Photon & Optoelect Nanjing 210044 Peoples R China Wenzhou Univ Coll Comp Sci Wenzhou 325035 Peoples R China Zhejiang Univ Coll Comp Sci Hangzhou 310027 Zhejiang Peoples R China

Retargeting aims to shrink a photo wherein the perceptually prominent regions are appropriately kept. In practice, optimally shrinking a high resolution (HR) aerial photo is a useful tool for smart navigation. Nowadays, vehicle drivers' path planning is generally guided by an HR aerial photo recommended by a navigation App like Google Maps. Owing to the limited and various resolution of vehicle displays, we have to retarget each original HR aerial photo accordingly, wherein the navigation-aware regions can be well preserved. In practice, HR aerial photo retargeting is non-trivial due to three challenges: 1) the rich number of internal objects and their complex spatial layouts, 2) deriving the region-level semantics from potentially contaminated image labels, and 3) the inefficiency of retargeting each HR aerial photo with millions of pixels. To handle these problems, we propose a novel HR aerial photo retargeting pipeline that can intelligently avoid the negative effects from incorrect image labels. The key is a noise-tolerant hashing algorithm that converts image-level semantics into the hash codes corresponding to different regions, which guides the HR aerial photo shrinking. More specifically, for each HR aerial photo, we extract visually/semantically salient object patches inside it. To explicitly encode their spatial layout, we construct a graphlet by linking the spatially adjacent object patches into a small graph. Subsequently, a binary matrix factorization (MF) is designed to exploit the underlying semantics of these graphlets, wherein three attributes: i) binary hash codes learning, ii) noisy labels refinement, iii) deep image-level semantics, are collaboratively encoded. Such binary MF can be solved iteratively and each graphlet is subsequently converted into the binary hash codes. Finally, the hash codes corresponding to graphlets within each HR aerial photo are utilized to learn a Gaussian mixture model (GMM) that optimizes the HR aerial photo retargetin

关键词： Navigation Codes Semantics Visualization Correlation Pipelines Visual perception Aerial photo retargeting high resolution smart navigation hashing algorithm

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Effective image registration model using optimized KAZE algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 33959-33984页

作者： Zhang, Sheng Shen, Jie Zheng, Shengnan Tang, Jingjing Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Nanjing Inst Technol Sch Comp Engn Nanjing 211167 Peoples R China

The incompatible problem with velocity and accuracy has been restricting the application of the KAZE algorithm. In order to resolve this shortage, we propose the effective image registration model using the optimized KAZE algorithm. This effective image registration model consist of four stages. First of all, to reduce the input data of image registration, the original registration images are preprocessed by the fusion preprocessing method based on the average and the perceptual hashing algorithms. Second, to extract image features quickly, we utilize the FAST algorithm to extract image features instead of the local extremum based on the Hessian matrix and the Taylor principle. Third, in order to accelerate the velocity of image features matching, the compressed sensing principle is used to reduce the dimension of the image feature descriptors. Finally, the two-step strategy is adopted to ensure the accuracy of image registration, the step one is that the hybrid matching method based on the FLANN and the KNN algorithms is used to rough matching, and the step two is that adopt the RANSAC algorithm to further accurate matching. This paper utilizes two groups of the experiments to verify the effective model, the experiment results show that the effective model has velocity advantage compared with other current image registration methods, and also achieves the compatible with velocity and accuracy in the case of the highest matching score. This model provides an effective solution for the application of image registration, and also has great significance for the development of image registration.

关键词： Image registration KAZE algorithm hashing algorithm FAST algorithm RANSAC algorithm

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Revolutionizing Security in Land Management System with Integration of Efficient Consensus and Smart Contract Enabled Blockchain in Land Registry Procedure

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SN Computer Science 2025年第1期6卷 1-26页

作者： Shrivastava, Atul Lal Dwivedi, Rajendra Kumar Prakash, Shiva Department of ITCA Madan Mohan Malaviya University of Technology U.P. Gorakhpur 273010 India

The fourth industrial revolution is going to be propelled by blockchain technologies for establishing security. Due to this, many countries have begun to look into the possibility of applying the use of blockchain technology. The management of the land registry is one of the most inspiring sectors where blockchain finds its scope of application. The major issue with the current land registration system is to prevent fraud which can be achieved by using blockchain technology. This paper demonstrates a blockchain-based land registry system that can minimize the time taken in the selling and purchasing of property, prevent fraud, and provide the best form of ownership. Thus, by applying this method in land management, the government will receive help in providing services, taxes, and other aspects of governance. The results of the proposed model have been compared with the current model of the land registry system and are found to outperform the existing systems. The proposed model cuts the computation cost by 20% on ownership transfer in the land registry system. The results depict that it is a scalable and an accountable system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 4th industry revolution Blockchain Consensus algorithm hashing algorithm Information systems Land-registration Security Smart contract

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