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Towards property-preserving JPEG encryption with structured permutation and adaptive group differentiation

Visual Computer 2024年第9期40卷 6421-6447页

作者： Su, Guo-Dong Chang, Ching-Chun Lin, Chia-Chen Chang, Chin-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Fujian Province University Fuzhou China Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

The security and privacy of digital images are a major concern in cyberspace. JPEG is the most widely used image compression standard and yet there are problems with format compatibility and file size preservation in most of the modern encryption schemes for JPEG images. To address these problems, we propose a novel properties-preserving encryption scheme that obfuscates JPEG images by scrambling AC and DC coefficients. The key observation is that an innovative structured permutation has a remarkable effect on enciphering the coefficients and simultaneously prevents value overflow as well as size increment. On this basis, we first leverage the row-wise reordering and reversing technique to disorganize the DC coefficients. Afterward, a series of groups of consecutive DC differential values are produced using adaptive group differentiation and each of them is independently permuted inside. Finally, we perform block-wise ACCs permutation and intra-block ACCs shuffling to further confuse the outline of the original image. Moreover, invariant features regarding the coefficients are incorporated in the key generation process to resist various cryptanalytic attacks. Experimental results demonstrate a significant improvement in terms of format compatibility and file size preservation, and verify the security against various attacks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Cryptography

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Anole: A Lightweight and Verifiable Learned-Based Index for Time Range Query on Blockchain Systems 28th

Anole: A Lightweight and Verifiable Learned-Based Index for...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Chang, Jian Li, Binhong Xiao, Jiang Lin, Licheng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031306365

Time range query is essential to facilitate a wide range of blockchain applications such as data provenance in the supply chain. Existing blockchain systems adopt the storage-consuming tree-based index structure for better query performance, however, fail to efficiently work for most blockchain nodes with limited resources. In this paper, we propose Anole, a lightweight and verifiable time range query mechanism, to present the feasibility of building up a learned-based index to achieve high performance and low storage costs on blockchain systems. The key idea of Anole is to exploit the temporal characteristics of blockchain data distribution and design a tailored lightweight index to reduce storage costs. Moreover, it uses a digital signature to guarantee the correctness and completeness of query results by considering the learned index’s error bounds, and applies batch verification to further improve verification performance. Experimental results demonstrate that Anole improves the query performance by up to 10 × and reduces the storage overhead by 99.4 % compared with the state-of-the-art vChain+. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Improving service accessibility and equity for sustainable development goals without newly facilities by rural settlement reconstruction

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Geography and Sustainability 2025年第1期6卷 105-117页

作者： Caihui Cui Zhigang Han Feng Liu Jingru Ma Haiying Wang Xiang Chen College of Geographical Sciences Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou 450046China College of Remote Sensing and Geoinformatics Engineering Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou 450046China Henan Industrial Technology Academy of Spatiotemporal Big Data Henan UniversityZhengzhou 450046China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan UniversityZhengzhou 450046China Key Research Institute of Yellow River Civilization and Sustainable Development Henan UniversityKaifeng 475004China Department of Geography University of ConnecticutStorrsCT 06269USA

Ensuring the provision of accessible,affordable,and high-quality public services to all individuals aligns with one of the paramount aims of the United Nations’Sustainable Development Goals(SDGs).In the face of esca lating urbanization and a dwindling rural populace in China,reconstructing rural settlements to enhance public service accessibility has become a fundamental strategy for achieving the SDGs in rural ***,few stud ies have examined the optimal methods for rural settlement reconstruction that ensure accessible and equitable public services while considering multiple existing facilities and service *** paper focuses on rural settlement reconstruction in the context of the SDGs,employing an inverted MCLP-CC(maximal coverage loca tion problem for complementary coverage)model to identify optimal rural settlements and a rank-based method for their *** in Changyuan,a county-level city in Henan Province,China,this study observed significant enhancements in both accessibility and equity following rural settlement reconstruction by utilizing the MH3SFCA(modified Huff 3-step floating catchment area)and the spatial Lorenz curve ***,these improvements were achieved without the addition of new facilities,with the accessibility increasing by 44.21%,4.97%,and 3.11%;Gini coefficients decreasing by 19.53%,1.64%,and 3.18%;Ricci-Schutz coef-ficients decreasing by 21.09%,2.09%,and 4.33%for educational,medical,and cultural and sports facilities,*** indicated that rural settlement reconstruction can bolster the accessibility and equity of public ser-vices by leveraging existing *** paper provides a new framework for stakeholders to better reconstruct rural settlements and promote sustainable development in rural areas in China.

关键词： SDGs Equity Accessibility Rural settlement reconstruction Spatial optimization

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Bioinspired polarized light compass in moonlit sky for heading determination based on probability density estimation

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Chinese Journal of Aeronautics 2022年第3期35卷 1-9页

作者： Yueting YANG Yan WANG Lei GUO Bo TIAN Jian YANG Wenshuo LI Taihang CHEN School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100083China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information TechnologyBeihang UniversityBeijing 100083China Hangzhou Innovation Institute Beihang UniversityBeijing 100083China School of Cyber Science and Technology Beihang UniversityBeijing 100083China

Bioinspired polarized skylight navigation,which can be used in unfamiliar territories,is an important alternative autonomous navigation technique in the absence of Global Navigation Satellite System(GNSS).However,the polarization pattern in night environment with noise effects and model uncertainties is a less explored *** several decades have passed since the first publication about the polarization of the moonlit night sky,the usefulness of nocturnal polarization navigation is only sporadic in previous *** study demonstrates that the nocturnal polarized light is capable of providing accurate and stable navigation information in dim light outdoor *** on the statistical characteristics of Angle of Polarization(Ao P)error,a probability density estimation method is proposed for heading *** illustrate the application potentials,the simulation and outdoor experiments are ***,the proposed method robustly models the distribution of Ao P error and gives accurate heading estimation evaluated by Standard Deviation(STD)which is 0.32°in a clear night sky and 0.47°in a cloudy night sky.

关键词： Nocturnal polarization Moonlit sky Angle of Polarization(AoP) Probability density estimation Navigation

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HIERARCHICAL REINFORCEMENT LEARNING WITH ADVANTAGE FUNCTION FOR ENTITY RELATION EXTRACTION

Journal of Applied and Numerical Optimization

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Journal of Applied and Numerical Optimization 2022年第3期4卷 393-404页

作者： Zhu, Xianchao Zhu, William School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu Chengdu610054 China

Unlike the traditional pipeline methods, the joint extraction approaches use a single model to distill the entities and semantic relations between entities from the unstructured texts and achieve better performances. A pioneering work, HRL-RE, uses a hierarchical reinforcement learning model to distill entities and relations that decompose the entire extraction process into a high-level relationship extraction and a low-level entity identification. HRL-RE makes the extraction of entities and relations more accurate while solving overlapped entities and relations to a certain extent. However, this method has not achieved satisfactory results in dealing with overlapped entities and relations in sentences. One reason is that learning a policy is usually inefficient, and the other one is the high variance of gradient estimators. In this paper, we propose a new method, Advantage Hierarchical Reinforcement Learning for Entity Relation Extraction (AHRL-ERE), which combines the HRL-RE model with a new advantage function to distill entities and relations from the structureless text. Specifically, based on the reference value of the policy function in the high-level subtask, we construct a new advantage function. Then, we combine this advantage function with the value function of the strategy in the low-level subtask to form a new value function. This new value function can immediately evaluate the current policy, so our AHR-ERE method can correct the direction of the policy gradient update in time, thereby making policy learning efficient. Moreover, our advantage function subtracts the reference value of the high-level policy value function from the low-level policy value function so that AHRL-ERE can decrease the variance of the gradient estimator. Thus our AHRL-ERE method is more effective for extracting overlapped entities and relations from the unstructured text. Experiments on the diffusely used datasets demonstrate that our proposed algorithm has better manifestation t

关键词： Extraction

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Predicting Gasoline RON Loss by Machine Learning 6

Predicting Gasoline RON Loss by Machine Learning

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6th IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022

作者： Liu, Limin Chen, Zhen Wang, Yongsheng Liu, Guangwen Inner Mongolia University of Technology Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service College of Data Science and Application Hohhot China Inner Mongolia University of Technology College of Data Science and Application Hohhot China

ISBN: (数字)9781665469869

ISBN: (纸本)9781665469869

In order to improve gasoline quality and reduce the environmental pollution caused by gasoline combustion, we aim to study the nonlinear relationship between octane loss and various gasoline components in gasoline refining process, further optimize the gasoline refining process, and reduce the content of sulfur and olefin in gasoline products while minimizing the octane loss. Firstly, we used the nonlinear data reduction algorithm t-SNE to preprocess the data of each component in gasoline and clarify each characteristic parameter;then we constructed the Multiple Nonlinear Regression (MNR) model to analyze the correlation and influence of each characteristic data on octane value;finally, we selected NSGA-II multi-objective genetic algorithm. The NSGA-II multi-objective genetic algorithm was selected for in-depth optimization of the model. The multiple linear regression model was also constructed for comparison experiments, and the advantages and disadvantages of the predicted results of the two models were analyzed, as well as the feasibility of extension. In the experiments with industrial data samples published in Sinopec Gaoqiao Petrochemical real-time database (Honeywell PHD) and LIMS experimental database, it was found that the multivariate nonlinear regression model has more superiority in achieving the purpose of the study, the value of harmful content in gasoline was reduced by 32.7%, and the predicted data of each component of gasoline was more in line with environmental protection requirements. Therefore, the multivariate nonlinear model has the highest reasonableness in predicting the octane loss values. © 2022 IEEE.

关键词： Machine learning

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Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

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Digital Communications and Networks 2024年第1期10卷 109-116页

作者： Xiaoming He Yingchi Mao Yinqiu Liu Ping Ping Yan Hong Han Hu The College of Computer and Information Hohai UniversityNanjingChina The Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjing China The School of Computer Science and Engineering Nanyang Technological UniversitySingapore The College of Textile and Clothing Engineering Soochow UniversitySuzhouChina The College of Internet of Things Nanjing University of Posts and TelecomurunicationsNanjingChina

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference ***,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable *** the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign ***,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power ***,agents in the collection stage slow down,which hinders the learning of other ***,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler *** experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

关键词： B5G Heterogeneous edge networks PPO Channel assignment Power allocation Throughput

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A mobile energy trading scheme based on Lightning Network

A mobile energy trading scheme based on Lightning Network

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作者： Tan, Chaoyue Chen, Yuling Ren, Xiaojun Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology GuiZhou University Guiyang China Block Chain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shouguang China

Current blockchain-based energy trading models raise serious concerns regarding the high and capped transaction latency and expensive service charges. To solve these problems, In this article, we combine Lightning Network (LN) and smart contract to present a mobile energy trading scheme based on LN. The focal point of the scheme lies in transfer of value occurs off-blockchain, which addresses the problem of transaction latency. Next, to solve the security of proposed scheme, we design a mechanism to delivery secret R. Participants exchange some parameters to calculate R instead of delivery it directly. The found security is guaranteed by committing funds into a multi-signature address in proposed scheme. Then, we conduct a comprehensive experiment to evaluate the proposed scheme. The simulation and analysis results show that the proposed scheme is efficient than traditional trading. In addition, we analyze our entire scheme and conclude that it defenses anti-tampering attacks and replay attack capacity effectively. © 2021 John Wiley & Sons, Ltd.

关键词： Blockchain

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PDDD-PreTrain:A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

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Plant Phenomics 2023年第2期5卷 313-332页

作者： Xinyu Dong Qi Wang Qianding Huang Qinglong Ge Kejun Zhao Xingcai Wu Xue Wu Liang Lei Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China The School of Physics and Optoelectronic Engineering Guangdong University of TechnologyGuangzhou 510006China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationGuizhou UniversityGuiyang 550025China

Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural *** intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming,costly,inefficient,and subjective *** a mainstream AI method,deep learning has substantially improved plant disease detection and diagnosis for precision *** the meantime,most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased ***,the commonly used pre-trained models are from the computer vision dataset,not the botany dataset,which barely provides the pre-trained models sufficient domain knowledge about plant ***,this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic *** address this issue,we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease *** addition,we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification,plant disease detection,plant disease segmentation,and other *** extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time,thereby supporting the better diagnosis of plant *** addition,our pre-trained models will be open-sourced at https://***/and Zenodo platform https://***/10.5281/zenodo.7856293.

关键词： diagnosis Plant thereby

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ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models 41

ReMax: A Simple, Effective, and Efficient Reinforcement Lear...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Ziniu Xu, Tian Zhang, Yushun Lin, Zhihang Yu, Yang Sun, Ruoyu Luo, Zhi-Quan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China National Key Laboratory for Novel Software Technology Nanjing University China Polixir.ai China China Shenzhen International Center For Industrial and Applied Mathematics China

Reinforcement Learning from Human Feedback (RLHF) is key to aligning Large Language Models (LLMs), typically paired with the Proximal Policy Optimization (PPO) algorithm. While PPO is a powerful method designed for general reinforcement learning tasks, it is overly sophisticated for LLMs, leading to laborious hyper-parameter tuning and significant computation burdens. To make RLHF efficient, we present ReMax, which leverages 3 properties of RLHF: fast simulation, deterministic transitions, and trajectory-level rewards. These properties are not exploited in PPO, making it less suitable for RLHF. Building on the renowned REINFORCE algorithm, ReMax does not require training an additional value model as in PPO and is further enhanced with a new variance reduction technique. ReMax offers several benefits over PPO: it is simpler to implement, eliminates more than 4 hyper-parameters in PPO, reduces GPU memory usage, and shortens training time. ReMax can save about 46% GPU memory than PPO when training a 7B model and enables training on A800-80GB GPUs without the memory-saving offloading technique needed by PPO. Applying ReMax to a Mistral-7B model resulted in a 94.78% win rate on the AlpacaEval leaderboard and a 7.739 score on MT-bench, setting a new SOTA for open-source 7B models. These results show the effectiveness of ReMax while addressing the limitations of PPO in LLMs. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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