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Salix-Leaf: Find Main Veins of Signal Clusters for Practical Parallel Decoding

IEEE Transactions on Mobile Computing 2025年

作者： Li, Yajun Zhao, Jumin Li, Dengao Wu, Hejun Xu, Shuang Bai, Ruiqin Taiyuan University of Technology College of Electronic Information and Optical Engineering Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China Taiyuan University of Technology College of Computer Science and Technology College of Data Science Taiyuan China Sun Yat-sen University Department of Computer Science Guangzhou China

Parallel decoding of backscatter improves communication throughput by enabling concurrent transmission of backscatter tags. In practical applications of parallel decoding, it is extremely difficult to distinguish collided signals in superclusters where multiple signal clusters overlap. Existing methods are usually effective for superclusters with uniformly distributed signals. Nevertheless, there are many more scenarios in which signals in superclusters tend to gather unevenly, and existing methods cannot work. Such uneven clustering of signals occurs due to the following two possible causes: (1) signal-strengthdifferences (SSDs) among tags;or (2) cluster drifting (CD) driven by interferences from other objects within communication environments. This paper proposes a novel scheme called SalixLeaf, which aims to identify the main veins of signal clusters to address this problem of superclusters with unevenly distributed signals. Salix-Leaf identifies the main vein of each signal cluster for fine-grained clustering so that the direction of the main veins can be used to verify the accuracy of clustering. In addition, SalixLeaf employs a supercluster decomposer that divides signals into different segments for clustering analysis, enhancing robustness and practicability. Experimental results show that Salix-Leaf achieves a 1.2-fold increase in throughput and a 25% reduction in bit error rate (BER) compared to the state-of-the-art. © 2002-2012 IEEE.

关键词： Backscattering

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Small Object Detection via Precise Region-Based Fully Convolutional Networks

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Computers, Materials & Continua 2021年第11期69卷 1503-1517页

作者： Dengyong Zhang Jiawei Hu Feng Li Xiangling Ding Arun Kumar Sangaiah Victor SSheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

In the past several years,remarkable achievements have been made in the field of object *** performance is generally improving,the accuracy of small object detection remains low compared with that of large object *** addition,localization misalignment issues are common for small objects,as seen in GoogLeNets and residual networks(ResNets).To address this problem,we propose an improved region-based fully convolutional network(R-FCN).The presented technique improves detection accuracy and eliminates localization misalignment by replacing positionsensitive region of interest(PS-RoI)pooling with position-sensitive precise region of interest(PS-Pr-RoI)pooling,which avoids coordinate quantization and directly calculates two-order integrals for position-sensitive score maps,thus preventing a loss of spatial precision.A validation experiment was conducted in which the Microsoft common objects in context(MS COCO)training dataset was *** showed an accuracy improvement of 3.7%for object detection tasks and an increase of 6.0%for small objects.

关键词： Small object detection precise R-FCN PS-Pr-RoI pooling two-stage detector

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A Q-learning-based Multi-timescale Resilience Enhancement Approach for Power Grids with High Renewables

A Q-learning-based Multi-timescale Resilience Enhancement Ap...

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Power science and technology (ICPST), IEEE International Conference on

作者： Yanting Huang Qing Zhong Akang Wang Shunjiang Lin Chaoyi Peng Shunbo Lei School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen China School of Electric Power Engineering South China University of Technology Guangzhou China Shenzhen Research Institute of Big Data Shenzhen China Power Dispatch and Control Center China Southern Power Grid Guangzhou China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Power grid resilience is critically undermined by extreme weather events, especially when coupled with uncertain renewable energy generation, which can lead to a severe power outage. When facing such outages, it is essential for system operators to restore damaged lines in a sequence that maximizes the overall power supply. Identifying the optimal restoration sequence can be cast as a multi-stage problem where at each stage a single line is first restored in an hourly basis and then numerous power flow problems within the timescale of minutes are optimized under the resulting network topology. Evaluating the economic effect of restoring a specific line entails the optimization of power flow problems with time-varying renewables, lacking a closed-form expression. To address this, we employ the Q-learning algorithm, utilizing a well-designed Q-function to approximate the effect of restoring each line and embedding such a function into a training process. Simulations on the IEEE 33-Bus and IEEE 39-Bus Systems confirm the efficacy of the proposed approach, showcasing significant enhancements in resilience and recovery capabilities.

关键词： Renewable energy sources Q-learning Heuristic algorithms Power system dynamics Approximation algorithms Power grids Optimization

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A Probabilistic Multi-Model Framework for Efficient Search and Rescue of Lost Autonomous Underwater Vehicles 5

A Probabilistic Multi-Model Framework for Efficient Search a...

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5th International Conference on big data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Cao, Ying Zhang, Shuoning Kang, Zijian Fang, Shixin Guangdong University of Science & Technology Department of Management Guangdong Dongguan China Xiamen University Malaysia Department of Mathematics Selangor Sepang Malaysia Xiamen University Malaysia School of Computing and Data Science Selangor Sepang Malaysia Xiamen University Malaysia School of Humanities and Communication Selangor Sepang Malaysia

ISBN: (纸本)9798331506612

This research explores methods for locating a lost submersible in the ocean by developing several probabilistic and search models. The problem is based on the premise that a submersible sends signals every 5 minutes, and failure to receive signals for three consecutive intervals is an indication of a critical failure, such as loss of power. © 2024 IEEE.

关键词： Autonomous underwater vehicles

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Improved CP-ABE Algorithm Based on Identity and Access Control

Improved CP-ABE Algorithm Based on Identity and Access Contr...

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science and technology Innovation (IAECST), International Academic Exchange Conference on

作者： Dehua Zhang Xiaopeng Yang Ziyi Jia Hao Li Xiaobo Guo Qiang Wang Big Data Center of Hebei Province Shijiazhuang China Science and Technology on Communication Networks Laboratory Shijiazhuang China

To address the security vulnerabilities such as user privacy leakage and identity forgery of the traditional CP-ABE algorithm, we propose an improved CP-ABE algorithm in this paper. To achieve higher security, a hybrid signature based on ECDSA and Dilithium is introduced to provide strong non-repudiation of user identities, and a role/attribute-based access control model is designed. With this access control model, only users satisfying the access policy can access ciphertext data and thereby reducing the risk of data leakage. By performing permutation-based encryption on the attributes of the access policy, the enhanced algorithm effectively conceals the plaintext attributes, thereby enhancing the security of the access policy. The enhanced algorithm improves key generation efficiency by extracting the minimum attribute set, enhances encryption efficiency by encrypting the minimum policy satisfying the access policy, and improves decryption efficiency by having the authorization center evaluate access control attribute policies. Performance testing results demonstrate that the improved algorithm is more efficient than the traditional CP-ABE algorithm in key generation and data decryption. Security analysis indicates that compared to traditional CP-ABE algorithms, this enhanced method can effectively prevent user privacy data leakage, identity theft, and forgery attacks.

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Vision mamba mender 24

Vision mamba mender

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiacong Hu Anda Cao Zunlei Feng Shengxuming Zhang Yi Wang Lingxiang Jia Mingli Song College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. For more information, please visit https://***/.

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Word-of-Mouth Promotion: How to Attract Consistent Consumers as a Promoter for the B2C Model

Word-of-Mouth Promotion: How to Attract Consistent Consumers...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R. Gunavathi S. Vijayalakshmi M. Savithri S.B. Thamarai Selvi School of Data Science Christ University Computer Science and Engineering K.S.Rangasamy College of Technology

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The primary goal of this research article is to discover the consumers' behaviour while spending time on the e-commerce platform and to use the consumers who have positive Word-of-Mouth on the products to motivate them as promoters through positive Word of Mouth behaviour. The behavioural factors considered in this study are Relationship value, Trust value, Satisfaction level and Word of Mouth. The trial model includes the consumers who use an e-commerce platform for their online shopping in India. A proper questionnaire with four components was created and used to collect the sample data. Totally 300 responses have been received and analysed with the help of structured equation model and SPSS and AMOS software. The findings suggest that the “Word of Mouth” technique can be used as a tool to increase the number of consumers in an online platform, particularly e-commerce. We investigated how Relationship Value and Trust Value can be used as key factors to motivate consumers' positive WoM behaviour. This research would be more beneficial to the B2C model. The research has done only for Indian e-commerce portals for survey. There is a scope to do research for the global level e-commerce market. Future study focuses on dynamic attributes for relationship values. This research work will help the researchers who is working on B2C model and consumer behavioural models. This model would be used for any online transactions-based services. As best of the knowledge of the authors’ this study is the novel idea to understand the consumers’ behaviour for purchasing items through the positive WoM. This work can be adopted for any e-commerce platform.

关键词： Surveys Quantum computing Consumer behavior Computational modeling Oral communication Market research Mathematical models

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An SAV Method for Imaginary Time Gradient Flow Model in Density Functional Theory

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Advances in Applied Mathematics and Mechanics 2023年第3期15卷 719-736页

作者： Ting Wang Jie Zhou Guanghui Hu Department of Mathematics Faculty of Science and TechnologyUniversity of MacaoMacao SARChina School of Mathematics and Computational Science Xiangtan UniversityXiangtanHunan 411105China Zhuhai UM Science&Technology Research Institute ZhuhaiGuangdong 519030China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications University of MacaoMacao SARChina

In this paper,based on the imaginary time gradient flow model in the density functional theory,a scalar auxiliary variable(SAV)method is developed for the ground state calculation of a given electronic structure *** handle the orthonormality constraint on those wave functions,two kinds of penalty terms are introduced in designing the modified energy functional in SAV,i.e.,one for the norm preserving of each wave function,another for the orthogonality between each pair of different wave functions.A numerical method consisting of a designed scheme and a linear finite element method is used for the ***,the desired unconditional decay of modified energy can be obtained from our method,while computationally,both the original energy and modified energy decay behaviors can be observed successfully from a number of numerical *** importantly,numerical results show that the orthonormality among those wave functions can be automatically preserved,without explicitly preserving orthogonalization *** implies the potential of our method in large-scale simulations in density functional theory.

关键词： Density functional theory gradient flow scalar auxiliary variable unconditional energy stability orthonormalization free

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Provably Secure Symmetric-Key Privacy-Preserving authenticated key agreement protocol 23

Provably Secure Symmetric-Key Privacy-Preserving authenticat...

引用

Proceedings of the 2023 13th International Conference on Communication and Network Security

作者： Liang Ni Hengsheng Zhou Yawei Zhang Xiaoyan Liu Bingke Gu School of Computer Scienc Zhongyuan University of Technology China and Henan Development and Innovation Laboratory of Industrial Internet Security Big Data Zhongyuan University of Technology China School of Computer Science Zhongyuan University of Technology China School of Computer Science Zhongyuan University of Technology China and School of Computer Science Fujian Normal University China

ISBN: (纸本)9798400707964

In recent years, the rapid development of Quantum Computing technology has had a huge impact on the security of traditional Public Key Cryptography. However, the security of Hash functions and Symmetric Cryptographic algorithms is relatively less impacted by this security threaten and commonly have higher execution efficiency. This two primitives can offset the security impact by appropriately increasing the output scale of a Hash function and symmetric key respectively. Therefore, the Symmetric Key Authenticated Key Agreement(SAKA) protocol which constructed mainly by Hash functions and symmetric cryptographic algorithms has gradually attracted more attention. In this paper we proposed a provably secure SAKA scheme. We extend the security model used by Ferreira et al to formally prove the security of our protocol by introducing a criterion for indistinguishability of identities and Synchronization Robustness property. Our protocol not only captures basic security properties such as Perfect Forward Secrecy, Entity Authentication and Key Indistinguishability but also achieved Privacy Preservation and Full Synchronization Robustness. Furthermore, its implementation does not need to rely on a huge and complex Public key Infrastructure(PKI), so we can easily deploy it in a coucurrent environment. Comparison results indicate that the proposal consumes the least computation resource and reasonable communication cost among nine SAKA protocols while equiping 10 security properties.

关键词： perfect forward secrecy privacy preservation puncturable key encapsulation mechanism resistance to quantum computing symmetric key

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PDA-GCN: Predicting Piwi-interacting RNA-Disease Associations Based on Graph Convolution Network

PDA-GCN: Predicting Piwi-interacting RNA-Disease Association...

引用

IEEE International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Honglin Su Hui Gao Trusted Cloud Computing and Big Data Engineering Lab University of Electronic Science and Technology of China Sichuan China

Piwi-interacting RNAs (piRNAs) play a significant role in multiple complex human diseases. In order to understand disease pathogenesis at the molecular level, identifying potential associations between piRNAs and diseases accurately is very critical. The computational methods provide a cost-reducing strategy for biological wet experiments. Several computational methods have been proposed, however, most of them still adopted traditional machine learning methods. It is still a challenge for them to deal with the complex and non-linear data. Identifying associations between piRNA-disease is a link prediction task in the heterogeneous network constructed by piRNAs, diseases, and the existing verified associations between them. In this study, a computational method named PDA-GCN is proposed based on Graph Convolution Network (GCN). GCN performance well in aggregate complex information hidden in the heterogeneous network. PDA-GCN adopt 2-layer GCNs to extract features form three aspects. Experiment results show that PDA-GCN achieves superior performance comparing with state-of-the-art methods.

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