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SSRN 2024年

作者： Cheng, Jinpeng Yang, Hao Cao, Xiaoyu Wu, Qiang Zhang, Na Yan, Zhiyu Wang, Hongbin Huang, Linsheng Ma, Xinming Xiong, Shuping Yang, Guijun College of Agronomy Henan Agricultural University Zhengzhou450046 China Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Zhuhai Orbita Aerospace Science &Technology Co. Ltd Zhuhai519080 China Sinochem Modern Agriculture Co. Ltd. Beijing100031 China College of Geological Engineering and Geomatics Chang’an University Xi’an710054 China

Grasping the planting information of seed maize is utterly important for strengthening the macro-control of the seed industry market and ensuring the safe production of conventional maize. At present, the traditional ground survey is the main method for surveying the planting distribution of seed maize, which is inefficient and expensive. And in the existing remote sensing classification methods of seed maize, most of them use texture information, and seldom use hyperspectral information. Here, we studied the "Zhuhai-1" satellite hyperspectral data to construct the spectral and spatial features of seed maize identification, and then used the Support Vector Machine (SVM) classifier to prepare the planting pattern map of seed maize, and finally an efficient and economical identification model for seed maize based on hyperspectral satellite images was proposed. The classification uses a multi-layer mask method to classify seed corn and conventional corn in the scene of corn planting distribution. The spectral feature extraction method of the classification model compares the two methods of Class Means Matrix Clustering Feature (CMMCF) and PCA-LDA (Principal Component Analysis -Linear Discriminant Analysis), and the extraction of spatial features uses Multiscale Extended Morphological Profile method (MEMP). Then, four feature combinations of PCA-LDA, CMMCF, PCA-LDA-MEMP and CMMCF-MEMP were constructed for classification. The classification results use overall accuracy (OA), mapping accuracy (PA), and user accuracy (UA) to evaluate the model accuracy;Fisher discriminant ratio (FR) evaluates feature separability. The results of the model show that the FR value of the texture feature in the model is higher than that of the spectral feature, and the texture feature plays an important role in the extraction of seed maize. CMMCF-MEMP-SVM model was the best, OA reached 94.10%, PA and UA extracted from seed maize were 91.28% and 92.43%, respectively. In addition, the CMMCF-MEMP

关键词： Morphology

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Blockchain-assisted Puncturable Signcryption for Cloud and Fog-Hosted Industrial Cyber-Physical Systems

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IEEE Network 2025年

作者： Ma, Qingru Shen, Jian Pandi, Vijayakumar Gupta, Brij B. Arya, Varsha Mahalinghum, Maheswari Zhejiang Sci-Tech University School of Computer Science and Technology School of Artificial Intelligence Hangzhou310018 China Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli620009 India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune412115 India School of Cybersecurity Korea University Seoul Korea Republic of Kyung Hee University Seoul02447 Korea Republic of Lebanese American University Beirut1102 Lebanon Center for Interdisciplinary Research Dehradun India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

The widespread adoption of emerging technologies, such as interconnected sensors and advanced automation systems, has led to rapid advancements in smart industrial environments. While industrial cyber-physical systems (ICPSs) bring efficiency and innovation to industrial processes, the security risks are also of great concern. The existing work provides many methods for data security and authentication for ICPSs. However, since the authentication status of ICPSs is changeable, the up-to-date methods can hardly meet the requirements for rapid authentication and secure data transmission. What’s more, largescale message revocation incurs significant communication and computational costs. This paper proposes a blockchain-assisted identity-based puncturable signcryption (BC-IBPSC) scheme for cloud and fog-hosted ICPSs. First, based on the concepts of puncturable encryption and signature, a novel identity-based puncturable signcryption method is presented to achieve efficient verifiable data encryption. Second, the blockchain is utilized to record all valid and punctured messages, to achieve evidence storage and traceability. The results of the analysis indicate that the scheme ensures security and operates efficiently. In addition, experiments show that the size of the signcrypted files does not affect the value of Gas consumption on the blockchain when uploading proofs. © 2025 IEEE. All rights reserved.

关键词： Authentication

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Progressively guide to attend: An iterative alignment framework for temporal sentence grounding

arXiv

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arXiv 2021年

作者： Liu, Daizong Qu, Xiaoye Zhou, Pan The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Electronic Information and Communication Huazhong University of Science and Technology Huawei Cloud

A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description. Existing methods mainly leverage vanilla soft attention to perform the alignment in a single-step process. However, such single-step attention is insufficient in practice, since complicated relations between inter- and intra-modality are usually obtained through multi-step reasoning. In this paper, we propose an Iterative Alignment Network (IA-Net) for TSG task, which iteratively interacts inter- and intra-modal features within multiple steps for more accurate grounding. Specifically, during the iterative reasoning process, we pad multi-modal features with learnable parameters to alleviate the nowhere-to-attend problem of non-matched frame-word pairs, and enhance the basic co-attention mechanism in a parallel manner. To further calibrate the misaligned attention caused by each reasoning step, we also devise a calibration module following each attention module to refine the alignment knowledge. With such iterative alignment scheme, our IA-Net can robustly capture the fine-grained relations between vision and language domains step-by-step for progressively reasoning the temporal boundaries. Extensive experiments conducted on three challenging benchmarks demonstrate that our proposed model performs better than the state-of-the-arts. Copyright © 2021, The Authors. All rights reserved.

关键词： Alignment

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

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Visible Light Positioning Method for Indoor Line-of-Sight Scene Based on Time Series Coding

Visible Light Positioning Method for Indoor Line-of-Sight Sc...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Yonghao Yu Yongde Guo Dawei Zhao Kexue Fu Yongwei Tang Qinyuan Wang Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning.

关键词： Location awareness Deep learning Meters Training Accuracy Attention mechanisms Time series analysis Training data Encoding data models

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Boosting the Electrochemical Performance of Li-and Mn-Rich Cathodes by a Three-in-One Strategy

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Nano-Micro Letters 2021年第12期13卷 311-321页

作者： Wei He Fangjun Ye Jie Lin Qian Wang Qingshui Xie Fei Pei Chenying Zhang Pengfei Liu Xiuwan Li Laisen Wang Baihua Qu Dong-Liang Peng State Key Lab of Physical Chemistry of Solid Surface Collaborative Innovation Center of Chemistry for Energy MaterialsCollege of Materials and Pen-Tung Sah Institute of Micro-Nano Science and TechnologyXiamen UniversityXiamen 361005People’s Republic of China College of Chemistry and Chemical Engineering Xiamen UniversityXiamen 361005People’s Republic of China Zhengzhou Key Laboratory of Big Data Analysis and Application Henan Academy of Big DataZhengzhou UniversityZhengzhou 450002People’s Republic of China Fujian Provincial Key Laboratory of Light Propagation and Transformation College of Information Science and EngineeringHuaqiao UniversityXiamen 361021People’s Republic of China Shenzhen Research Institute of Xiamen University Shenzhen 518000People’s Republic of China

There are plenty of issues need to be solved before the practi-cal application of Li-and Mn-rich cathodes,including the detrimental voltage decay and mediocre rate capability,*** doping can e ectively solve the above problems,but cause the loss of *** introduction of appropriate defects can compensate the capacity loss;however,it will lead to structural mismatch and stress ***,a three-in-one method that combines cation–polyanion co-doping,defect construction,and stress engineering is *** co-doped Na^(+)/SO_(4)^(2-)can stabilize the layer framework and enhance the capacity and voltage *** induced defects would activate more reac-tion sites and promote the electrochemical ***,the unique alternately distributed defect bands and crystal bands structure can alleviate the stress accumulation caused by changes of cell parameters upon ***,the modified sample retains a capacity of 273 mAh g^(-1)with a high-capacity retention of 94.1%after 100 cycles at 0.2 C,and 152 mAh g^(-1)after 1000 cycles at 2 C,the corresponding voltage attenuation is less than 0.907 mV per cycle.

关键词： Li-and Mn-rich cathodes Cation–polyanion co-doping Defect and stress engineering Good structure stability Electrochemical performance

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Optimizing Multi-DNN Inference on Mobile Devices through Heterogeneous Processor Co-Execution

arXiv

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arXiv 2025年

作者： Gao, Yunquan Zhang, Zhiguo Donta, Praveen Kumar Dehury, Chinmaya Kumar Wang, Xiujun Niyato, Dusit Zhang, Qiyang School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Computer Science and Technology Anhui University of Technology Anhui Ma’anshan243032 China Department of Computer and Systems Sciences Stockholm University Stockholm106 91 Sweden Institute of Computer Science University of Tartu IISER Berhampur India College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Computer Science School Peking University Beijing100876 China

Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving a growing demand to enable their capabilities on mobile devices. However, existing mobile inference frameworks are often rely on a single processor to handle each model’s inference, limiting hardware utilization and leading to suboptimal performance and energy efficiency. Expanding DNNs accessibility on mobile platforms requires more adaptive and resource-efficient solutions to meet increasing computational demands without compromising device functionality. Nevertheless, parallel inference of multiple DNNs on heterogeneous processors remains a significant challenge. Several works have explored partitioning DNN operations into subgraphs to enable parallel execution across heterogeneous processors. However, these approaches typically generate excessive subgraphs based solely on hardware compatibility, increasing scheduling complexity and memory management overhead. To address these limitations, we propose an Advanced Multi-DNN Model Scheduling (ADMS) strategy that optimizes multi-DNN inference across heterogeneous processors on mobile devices. ADMS constructs an optimal subgraph partitioning strategy offline, considering both hardware support of operations and scheduling granularity, while employing a processor-state-aware scheduling algorithm that dynamically balances workloads based on real-time operational conditions. This ensures efficient workload distribution and maximizes the utilization of available processors. Experimental results show that, compared to vanilla inference frameworks, ADMS reduced multi-DNN inference latency by 4.04×.MSC Codes 68T07, 68W40 Copyright © 2025, The Authors. All rights reserved.

关键词： Parallel processing systems

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Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval

arXiv

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arXiv 2022年

作者： Fang, Xiang Liu, Daizong Zhou, Pan Hu, Yuchong Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Wangxuan Institute of Computer Technology Peking University No. 128 Zhongguancun North Street Beijing China School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China

As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. Most previous methods depend heavily on numerous manual annotations (i.e., moment boundaries), which are extremely expensive to acquire in practice. In addition, due to the domain gap between different datasets, directly applying these pre-trained models to an unseen domain leads to a significant performance drop. In this paper, we focus on a novel task: cross-domain VMR, where fully-annotated datasets are available in one domain ("source domain"), but the domain of interest ("target domain") only contains unannotated datasets. As far as we know, we present the first study on cross-domain VMR. To address this new task, we propose a novel Multi-Modal Cross-Domain Alignment (MMCDA) network to transfer the annotation knowledge from the source domain to the target domain. However, due to the domain discrepancy between the source and target domains and the semantic gap between videos and queries, directly applying trained models to the target domain generally leads to a performance drop. To solve this problem, we develop three novel modules: (i) a domain alignment module is designed to align the feature distributions between different domains of each modality;(ii) a cross-modal alignment module aims to map both video and query features into a joint embedding space and to align the feature distributions between different modalities in the target domain;(iii) a specific alignment module tries to obtain the fine-grained similarity between a specific frame and the given query for optimal localization. By jointly training these three modules, our MMCDA can learn domain-invariant and semantic-aligned cross-modal representations. Extensive experiments on three challenging benchmarks (ActivityNet Captions, Charades-STA and TACoS) illustrate that our cross-domain method MMCDA outperforms a

关键词： Alignment

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Auction Theory and Game Theory Based Pricing of Edge Computing Resources: A Survey

引用

IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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A Novel Gardenia-Shaped Structure Materials with Variable Stiffness Under Elastic Tension

SSRN

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SSRN 2023年

作者： Feng, Ning Tie, Yuanhao Wang, Shangbin Huang, Chongfu Lv, Liwen Xie, Weibo Wang, Jingze School of Intelligent Manufacturing and Transportation Chongqing Vocational Institute of Engineering Chongqing402260 China Henan Province Engineering Research Center of Ultrasonic Technology Application Pingdingshan University Pingdingshan467000 China College of Mechanical and Electrical Engineering Xinjiang Agricultural University Urumchi830052 China School of Mechanical and Transportation Engineering Guangxi University of Science and Technology Liuzhou545006 China School of Resources and Security Chongqing Vocational Institute of Engineering Chongqing402260 China Key Laboratory of Coastal Environment and Resources of Zhejiang Province School of Engineering Westlake University Hangzhou310024 China

Nature's creatures often have unique behaviors that have inspired humans to create many bionic structures to meet engineering needs. In previous work, we reported a Gardenia-shaped structure (GSS) materials. Then an optimal GSS materials is developed in this paper. The optimal GSS materials shows its easily available variable stiffness characteristics from the testing in the homemade experimental bench. The variable stiffness characteristics of the optimal GSS materials were investigated by using both experimental and finite element methods in this paper. The experimental results show good agreement with the numerical results. The excellent performance of the optimal GSS materials has the potential for engineering applications to meet multi-stiffness requirements. © 2023, The Authors. All rights reserved.

关键词： Stiffness

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