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Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

Digital Communications and Networks 2025年第2期11卷 473-481页

作者： Qi Li Gaozhan Liu Qianqian Zhang Lidong Han Wei Chen Rui Li Jinbo Xiong School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal UniversityHangzhou 311121China Tongda College of Nanjing University of Posts and Telecommunications Yangzhou 225127China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi'an 710071China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal UniversityXining 810008China Faculty of Software Fujian Normal UniversityFuzhou 350108China

Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of *** existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute ***,resource-constrained IoT devices,especially those adopting wireless communication,frequently encounter affordability issues regarding decryption *** this paper,we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC).FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them.A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access *** attacks are thwarted by providing unauthorized users with invalid *** heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud ***,users can verify the correctness of multiple outsourced decryption results *** analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

关键词： Access control Policy hiding Verifiable outsourced computation Cloud IoT

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Prediction Model for Gas Outburst Intensity of Coal Mining Face Based on Improved PSO and LSSVM

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Energy Engineering 2021年第3期118卷 679-689页

作者： Haibo Liu Yujie Dong Fuzhong Wang School of Electric Engineering and Automation Henan Polytechnic UniversityJiaozuo454000China College of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

For the problems of nonlinearity,uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face,the least squares support vector machine(LSSVM)is proposed to establish the prediction ***,considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization(PSO),an improved PSO(IPSO)algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to ***,the penalty factor and kernel function parameter of LSSVM are searched automatically,and the regression accuracy and generalization performance is enhanced by applying ***,to verify the proposed prediction model,the model is applied for gas outburst prediction of Jiuli Hill coal mine in Jiaozuo City,and the results are compared with that of PSO-SVM model,IGA-LSSVM model and BP *** results show that the relative errors of the proposed model are not greater than 2.7%,and the prediction accuracy is higher than other three prediction *** IPSO-LSSVM model can be used to predict the intensity of gas outburst of coal mining face effectively.

关键词： Mining face gas outburst least squares support vector machine improved particle swarm optimization prediction

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Sedimentary elements,evolutions and controlling factors of the Miocene channel system:a case study of the deep-water Taranaki Basin in New Zealand

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Acta Oceanologica Sinica 2023年第11期42卷 44-58页

作者： Guangxu Wang Wei Wu Changsong Lin Quan Li Xiaoming Zhao Yongsheng Zhou Weiqing Liu Shiqin Liang Institute of Resources and Environment Henan Polytechnic UniversityJiaozuo 454003China School of Ocean Science China University of GeoscienceBeijing 100083China Institute of Exploration Technology CNOOC International Ltd.Beijing 100028China School of Geoscience and Technology Southwest Petroleum UniversityChengdu 610500China

Deep-water channel systems are important petroleum reservoirs,and many have been discovered *** deep-water channel sedimentary elements and evolution is helpful for deep-sea petroleum exploration and *** on high-resolution 3D seismic data,the Miocene channel system in the deep-water Taranaki Basin,New Zealand,was analyzed by using seismic interpretation techniques such as interlayer attribute extraction and strata *** channel system was divided into five composite channels(CC-I to CC-V)according to four secondary level channel boundaries,and sedimentary elements such as channels,slump deposits,inner levees,mass transport deposits,and hemipelagic drape deposits were identified in the channel *** morphological characteristics of several composite channels exhibited stark variances,and the overall morphology of the composite channels changed from relatively straight to highly sinuous to relatively *** evolution of the composite channels involved a gradual and repeated process of erosion and filling,and the composite channels could be divided into three evolutionary stages:initial erosion-filling,later erosion-filling(multistage),and channel *** middle Miocene channel system may have formed as a consequence of combined regional tectonic activity and global climatic change,and its intricate morphological alterations may have been influenced by the channel's ability to self-regulate and gravity flow *** studying the sedimentary evolution of a large-scale deep-water channel system in the Taranaki Basin during the Oligocene-Miocene,which transitioned from a passive margin to plate convergence,it can be understood how tectonic activity affected the channel and can also provide a theoretical reference for the evolution of the deepwater channels in areas with similar tectonic conversion environments around the world.

关键词： deep-water channel system channel geomorphology sedimentary evolution climate and region tectonic activities deep-water Taranaki Basin

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Adaptive weighted broad learning system for imbalanced classification 6

Adaptive weighted broad learning system for imbalanced class...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Li, Yanting Gao, Yiping Jin, Junwei Chen, C. L. Philip Zhengzhou University of Light Industry College of Computer Science and Technology Zhengzhou China Henan University of Technology College of Artificial Intelligence and Big Data Zhengzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350316520

Broad Learning System (BLS) perform well in classification tasks with good computational efficiency. However, its effectiveness decreases when faced with imbalanced data distribution. The traditional BLS cannot solve this problem effectively. In this paper, we introduce an adaptive weighted method for BLS, aiming to improve its classification performance on imbalanced data. This method can assign different weights according to the distribution characteristics of samples. There exists a closed-form solution to the problem and the transformation matrix can be solved efficiently by pseudo-inverse. Experimental results on several imbalanced datasets demonstrate that the proposed method surpasses standard BLS and many advanced imbalanced classification methods. © 2024 IEEE.

关键词： Classification (of information)

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A Deep Learning Approach for Rectifying Fisheye Image 2

A Deep Learning Approach for Rectifying Fisheye Image

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Gai, Shuli Cui, Wenhua Liang, Bodong Tao, Ye University of Science and Technology Liaoning School of Computer and Information Engineering Anshan China Shenzhen Polytechnic University School of Automotive and Transportation Engineering Shenzhen China

ISBN: (纸本)9798350368888

Fisheye lens cameras offer a significantly wider field of view compared to conventional cameras. However, they also introduce geometric distortions and distorted shapes in captured images. This paper presents a self-supervised fisheye correction method based on convolutional neural networks. The method is trained using a readily available urban landscape roadway dataset and leverages the inherent stability of pixel coordinate relationships after uniform distortion to design an effective correction model. During the training process, distorted images are synthesized from the original undistorted images, and the predicted parameters are utilized to correct fisheye images. Furthermore, the loss function is constructed using the geometric mean of the Hough transform, and the entire model is trained with the assistance of the slope loss of the straight line, thus achieving an end-to-end framework. Experimental results demonstrate that our method provides superior performance in correcting real-world in-vehicle images, outperforming similar approaches in terms of correction accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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A NOA-AP based Typical Process Route Automatic Discovery Approach for Intelligent Manufacturing 7

A NOA-AP based Typical Process Route Automatic Discovery App...

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7th International Conference on Robotics, Control and Automation Engineering, RCAE 2024

作者： Cao, Xinyu Xu, Binzi Huang, Dengchao Wang, Chun Anhui Polytechnic University School of Electrical Engineering Wuhu241000 China Huaibei Normal University School of Computer Science and Technology Huaibei235000 China

ISBN: (纸本)9798350355642

With the continuous advancement of intelligent manufacturing, the efficient extraction of reusable typical process routes based on artificial intelligence for computer-aided process planning (CAPP) has emerged as a critical necessity. Because a good process route can not only effectively improve the efficiency of process planning, but also promote the transformation of traditional manufacturing to intelligent manufacturing. To address this situation, this paper proposes a Nutcracker Optimization Algorithm (NOA) enhanced Affinity Propagation (AP) algorithm under the consideration of clustering effectiveness to automatic discover the typical process routes from the massive historical data. First, two soft constraints (i.e., quantity constraint and size constraint) are introduced simultaneously in the clustering process to ensure the quality and validity of the obtained typical process routes according to the actual demand. In addition, previous research has emphasized that the selection of preference value and damping factor in the AP algorithm significantly influences clustering outcomes. Hence, NOA is implemented to identify the optimal individual within the search space to determine suitable preference values and damping factors, thereby enhancing the clustering quality of the AP algorithm. The numerical illustration shows that the proposed NOA-AP could improve the clustering quality and discover more representative and effective typical process routes. © 2024 IEEE.

关键词： Smart manufacturing

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Aligning Language Models with Human Preferences via a Bayesian Approach 37

Aligning Language Models with Human Preferences via a Bayesi...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Wang, Jiashuo Wang, Haozhao Sun, Shichao Li, Wenjie Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781713899921

In the quest to advance human-centric natural language generation (NLG) systems, ensuring alignment between NLG models and human preferences is crucial. For this alignment, current popular methods leverage a reinforcement learning (RL) approach with a reward model trained on feedback from humans. However, inherent disagreements due to the subjective nature of human preferences pose a significant challenge for training the reward model, resulting in a deterioration of the NLG performance. To tackle this issue, previous approaches typically rely on majority voting or averaging to consolidate multiple inconsistent preferences into a merged one. Although straightforward to understand and execute, such methods suffer from an inability to capture the nuanced degrees of disaggregation among humans and may only represent a specialized subset of individuals, thereby lacking the ability to quantitatively disclose the universality of human preferences. To address this challenge, this paper proposes a novel approach, which employs a Bayesian framework to account for the distribution of disagreements among human preferences as training a preference model, and names it as d-PM. Besides, considering the RL strategy's inefficient and complex training process over the training efficiency, we further propose utilizing the contrastive learning strategy to train the NLG model with the preference scores derived from the d-PM model. Extensive experiments on two human-centric NLG tasks, i.e., emotional support conversation and integrity "Rule-of-Thumb" generation, show that our method consistently exceeds previous SOTA models in both automatic and human evaluations. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Contrastive Learning

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Effect of long reaction distance on gas composition from organic-rich shale pyrolysis under high-temperature steam environment

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International Journal of Coal science & technology 2024年第3期11卷 102-119页

作者： Lei Wang Rui Zhang Guoying Wang Jing Zhao Dong Yang Zhiqin Kang Yangsheng Zhao Key Laboratory of In-Situ Property Improving Mining of Ministry of Education Taiyuan University of TechnologyTaiyuan030024China The In-Situ Steam Injection Branch of State Center for Research and Development of Organic-Rich Shale Exploitation Taiyuan University of TechnologyTaiyuan030024China College of Mining Engineering Taiyuan University of TechnologyTaiyuan030024China School of Energy Science and Engineering Henan Polytechnic UniversityJiaozuo454002China

When high-temperature steam is used as a medium to pyrolyze organic-rich shale,water steam not only acts as heat transfer but also participates in the chemical reaction of organic matter pyrolysis,thus affecting the generation law and release characteristics of gas *** this study,based on a long-distance reaction system of organic-rich shale pyrolysis via steam injection,the effects of steam temperature and reaction distance on gas product composition are analyzed in depth and compared with other pyrolysis *** advantages of organic-rich shale pyrolysis via steam injection are then *** volume concentration of hydrogen in the gas product obtained via the steam injection pyrolysis of organic-rich shale is the highest,which is more than 60%.The hydrogen content increases as the reaction distance is extended;however,the rate of increase changes *** the reaction distance from 800 to 4000 mm increases the hydrogen content from 34.91%to 69.68%and from 63.13%to 78.61%when the steam temperature is 500℃ and 555℃,***,the higher the heat injection temperature,the smaller the reaction distance required to form a high concentration hydrogen pyrolysis environment(hydrogen concentration>60%).When the steam pyrolysis temperature is increased from 500℃ to 555℃,the reaction distance required to form a high concentration of hydrogen is reduced from 3800 to 800 *** with the direct retorting process,the volume concentration of hydrogen obtained from high-temperature steam pyrolysis of organic-rich shale is 8.82 and 10.72 times that of the commonly used Fushun and Kivite furnaces,*** pyrolysis of organic-rich shale via steam injection is a pyrolysis process in a hydrogen-rich environment.

关键词： Steam temperature Pyrolysis gas Hydrogen-rich Reaction distance Direct retorting

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Effectively enhanced catalytic effect of sulfur doped Ti_(3)C_(2)on the kinetics and cyclic stability of hydrogen storage in MgH_(2)

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Journal of Magnesium and Alloys 2025年第4期13卷 1843-1853页

作者： Zhenluo Yuan Xiuxiu Zhang Yitian Wu Shuyan Guan Shiqian Zhao Liqiang Ji Qiuming Peng Shumin Han Yanping Fan Baozhong Liu College of Chemistry and Chemical Engineering Henan Polytechnic UniversityJiaozuo 454000China Henan Famous Diamond Co. Ltd.Mengzhou 454763China School of Chemical Engineering Zhengzhou UniversityZhengzhou 450001China Central Iron and Steel Research Institute Co. Ltd.Beijing 100081China State Key Laboratory of Metastable Materials Science and Technology Yanshan UniversityQinhuangdao 066004China

Designing catalysts with high catalytic activity and stability is the key to achieve the commercial application of MgH_(2).Herein,the sulfur doped Ti_(3)C_(2)(S-Ti_(3)C_(2))was successfully prepared by heat treatment of Ti_(3)C_(2)MXene under Ar/H_(2)S atmosphere to facilitate the hydrogen release and uptake from MgH_(2).The S-Ti_(3)C_(2)exhibited pleasant catalytic effect on the hydriding/dehydriding kinetics and cyclic stability of MgH_(2).The addition of 5 wt%S-Ti_(3)C_(2)into MgH_(2)resulted in a reduction of 114℃in the starting dehydriding temperature compared to pure MgH_(2).MgH_(2)+5 wt%S-Ti_(3)C_(2)sample could quickly release 6.6 wt%hydrogen in 17 min at 220℃,and 6.8 wt%H_(2)was absorbed in 25 min at 200℃.Cyclic testing revealed that MgH_(2)+5 wt%S-Ti_(3)C_(2)system achieved a reversible hydrogen capacity of 6.5 wt%.Characterization analysis demonstrated that Ti-species(Ti0,Ti^(2+),Ti-S,and Ti^(3+))as active species significantly lowered the dehydrogenation temperature and promoted the re-/dehydrogenation kinetics of MgH_(2),and sulfur doping can effectively improve the stability of Ti0 and Ti^(3+),contributing to the improvement of cyclic stability of MgH_(2).This study provides strategy for the construction of catalysts for hydrogen storage materials.

关键词： Hydrogen storage MgH_(2) Kinetics Cyclic stability

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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

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BMEMat(BioMedical Engineering Materials) 2025年第1期3卷 158-181页

作者： Suparna Das Hirak Mazumdar Kamil Reza Khondakar Ajeet Kaushik Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for WomenHyderabadIndia School of Engineering and Technology Department of Computer Science and EngineeringAdamas UniversityKolkataIndia School of Technology Woxsen UniversityHyderabadIndia NanoBiotech Laboratory Department of Environmental EngineeringFlorida Polytechnic UniversityLakelandUSA

Machine learning(ML)and nanotechnology interfacing are exploring opportunities for cancer treatment *** improve cancer therapy,this article investigates the synergistic combination of Graphene Oxide(GO)‐based devices with ML *** production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the *** is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal ***,complicated biological data are analyzed using ML algorithms,which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular *** study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using *** modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery ***,tracking treatment outcomes in real time is made possible by ML algorithms,which permit adaptive modifications to therapy *** optimizing medication doses and delivery settings,the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

关键词： artificial intelligence cancer diagnosis graphene oxide machine learning real‐time monitoring sensing

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