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arXiv 2025年

作者： Zhao, Chonghe Zhou, Yipeng Zhang, Shengli Sheng, Quan Z. Zhang, Yang Wen, Shiting College of Electronic and Information Engineering Shenzhen University Shenzhen China School of Computing Macquarie University Sydney Australia Anuradha and Vikas Sinha Department of Data Science University of North Texas DentonTX United States School of Computer and Data Engineering NingboTech University Ningbo China

Proof of Authority (PoA) plays a pivotal role in blockchains for reaching consensus. Clique, which selects consensus nodes to generate blocks with a pre-determined order, is the most popular implementation of PoA due to its low communication overhead and energy consumption. However, our study unveils that the speed to process transactions by Clique is severely restricted by 1) the long communication delay of full blocks (each containing a certain number of transactions) between consensus nodes;and 2) occurrences of no-turn blocks, generated by no-turn nodes if an in-turn block generation fails. Consequently, Clique struggles to support distributed applications requiring a high transaction processing speed, e.g., online gaming. To overcome this deficiency, we propose an Express Clique (ExClique) algorithm by improving Clique from two perspectives: compacting blocks for broadcasting to shorten communication delay and prohibiting the occurrences of no-turn blocks. For performance evaluation, we implement ExClique by modifying Geth of Ethereum, the software implementing Clique, and deploy a permissioned blockchain network by using container technology. The experimental results show that ExClique achieves a substantial enhancement in transactions per second (TPS). Specifically, it boosts TPS by 2.25× in a typical network with 21 consensus nodes and an impressive 7.01× in a large-scale network with 101 consensus nodes when compared to Clique. © 2025, CC BY.

关键词： Blockchain

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WMAJL: Watcher-Mediated Attention Joint Learning Model for Multimodal Relation Extraction

WMAJL: Watcher-Mediated Attention Joint Learning Model for M...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunrui Dong Guiduo Duan Tianxi Huang Yunhao Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Chengdu Textile College College of Humanities and General Education Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of Multimodal Relation Extraction (MRE), we present the $\color{Red}{\text{W}}$atcher-$\color{Red}{\text{M}}$ediated $\color{Red}{\text{A}}$ttention $\color{Red}{\text{J}}$oint $\color{Red}{\text{L}}$earning Model ($\color{Red}{\text{WMAJL}}$), a novel approach addressing the challenges of modality alignment noise, cross-modal fusion disparity, preservation of textual relative position information, and the distinctiveness of classification labels. WMAJL employs an integrative framework leveraging contrastive learning and variational autoencoder constraints to mitigate modality alignment noise by prioritizing relevant semantic data and effectively reducing extraneous noise that does not contribute to the task. The model’s innovative architecture includes a mediator watcher, which facilitates enhanced cross-modal fusion by enabling nuanced information exchange between textual and visual modalities while preserving the unique characteristics of each modality. Additionally, the design of auxiliary tasks, such as Named Entity Recognition (NER), and output supervision constructs loss functions that preserve relative position information, ensuring a precise depiction of entity relationships throughout the multilayer encoding processes. A key differentiator of WMAJL is its label-centric self-information loss technique, inspired by InfoNCE, which trains the model to cluster similar relation labels in semantically coherent areas, thereby optimizing classification label uniqueness by discerning subtle differences among relation types. The synergistic application of these strategies has led to a significant enhancement of WMAJL’s performance, as evidenced by its state-of-the-art F1 score of $\color{Red}{84.93\%}$ on the MNRE dataset. This achievement surpasses existing benchmarks and sets a new standard for multimodal knowledge extraction, underscoring WMAJL’s potential to revolutionize the MRE landscape.

关键词： Resistance Visualization Technological innovation Noise Autoencoders Semantics Contrastive learning Nonhomogeneous media Speech processing Standards

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Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization 19

Adaptive Latent Factor Analysis via Generalized Momentum-Inc...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Chen, Jiufang Yuan, Ye School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing400714 China Chongqing School University of Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781665472432

Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e., learning rate and regularization coefficient, self-adaptation. However, a standard PSO algorithm may suffer from accuracy loss caused by premature convergence. To address this issue, this paper incorporates more historical information into each particle's evolutionary process for avoiding premature convergence following the principle of a generalized-momentum (GM) method, thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a GM-PSO-based LFA (GMPL) model is further achieved to implement efficient self-adaptation of hyper-parameters. The experimental results on three HDI matrices demonstrate that the GMPL model achieves a higher prediction accuracy for missing data estimation in industrial applications. © 2022 IEEE.

关键词： Momentum

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Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks 19

Graph Regularized Nonnegative Latent Factor Analysis Model f...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Zhou, Yue Liu, ZhiGang Yuan, Ye Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The Chongqing School University of Chinese Academy of Sciences Chongqing400714 China Southwest University College of Computer and Information Science Chongqing400715 China

ISBN: (纸本)9781665472432

With the development of blockchain technology, a cryptocurrency based on blockchain technology is becoming more and more popular. The huge cryptocurrency transaction network has therefore received widespread attention. The link prediction learning structure of the network is supportive to understand the mechanism of networks, so it also has been widely studied in the cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in past studies. In this study, therefore, we use a graph-regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, nonnegative, multiplicative, and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose a graph regularized nonnegative latent factor analysis (GrNLFA) model. Eventually, the experimental results on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and computational efficiency. © 2022 IEEE.

关键词： Forecasting

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Real-Time Interactive Face Beautification

SSRN

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SSRN 2024年

作者： Lin, Luojun Chen, Wang Chen, Peizhen Zheng, Xiawu Jin, Lianwen College of Computer and Data Science Fuzhou University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University China School of Electronic and Information Engineering South China University of Technology China

The increasing popularity of sharing visually appealing self-portraits on social media platforms accentuates the demand for advanced face beautification technologies that can provide immediate results tailored to individual aesthetic preferences. To this end, we develop a Real-time Interactive Face Beautification task that can adaptively and promptly enhance facial images while preserving identity information. Specifically, we propose an aesthetic hyperplane by using a standard facial beauty prediction model, and subsequently develop an optimization-free Latent Space Beauty Interpolation method to manipulate faces through the aesthetic hyperplane. This method involves encoding facial representations into latent codes, which are then deftly manipulated within the latent space to align with a user-specified aesthetic score, effectively drawing them closer to or further from the aesthetic hyperplane as desired. These tailored latent codes are subsequently employed to generate the refined facial images via a pre-trained generative network (e.g., StyleGAN), facilitating swift and precise adherence to user-prescribed aesthetic standards. We conduct extensive experiments on the CelebA-HQ dataset with various attributes, calculating the time required to retouch each face and compare it with state-of-the-arts. The experimental results demonstrate that our method achieves very promising performance in terms of both effectiveness and visual quality. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Early Detection of Breast Cancer with Microcalcifications on Mammography Using Deep Learning

Early Detection of Breast Cancer with Microcalcifications on...

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computing and information Technology (ICCIT-1441), 2020 International Conference on

作者： Ashraf Osman Ibrahim Hafia Mamoun Ismail Abuharaz Mohammed A Saleh Razan Alharith Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Depeartment of Computer Science Al Neelain University Khartoum Sudan DaTA Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia School of Computing and Artificial Intelligence Southwest Jiaotong University

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Breast cancer is among the leading causes of death in women globally, making early detection critical for improving survival rates. Microcalcifications (MCs) on mammographic images are important markers for early detection of breast cancer; yet, identifying and interpreting them can be difficult. Conventional diagnostic procedures sometimes face obstacles due to the complexity and nuance of MC patterns, resulting in higher percentages of missed diagnoses and false positives. This paper develops a deep convolutional neural network (CNN) model to increase the detection and classification accuracy of microcalcifications (MCs) in mammographic images. dataset of 1,093 mammography images used, the proposed model reaches a remarkable training accuracy of 99.98% and testing accuracy of 90.37%. The model's excellent accuracy and low overfitting highlight its potential to assist radiologists in the early detect and diagnose of breast cancer, thereby improving patient outcomes. This study's contribution is the innovative use of advanced deep learning algorithms to a major issue in medical imaging, which represents a significant improvement over current diagnostic approaches.

关键词： Deep learning Training Accuracy Biological system modeling Breast cancer Mammography data models Convolutional neural networks Testing Overfitting

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Machine Learning for Drug Discovery: Predicting Drug-Protein Binding Affinities using Graph Convolutional Networks 5

Machine Learning for Drug Discovery: Predicting Drug-Protein...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Radhamahendran, S. Dogra, Akriti Mendhe, Dinesh Tilak Babu, S.B.G. Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology and Advanced Studies School of Life Sciences Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India State University of New Jersey Computer and Information Research Scientist Office of Research Computing Rutgers New BrunswickNJ United States Aditya Engineering College Department of ECE Surampalem India Vellore Institute of Technology School of Computer Science Engineering Tamil Nadu Vellore India UPUMS Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350351378

Determining the potential interactions between ligands and proteins is a crucial first step in developing new drugs. Graph BAR, our novel prediction model, makes accurate guesses about the strength of ligand-protein bindings by means of a graph convolutional neural network. When compared to graph convolutional neural networks, standard convolutional neural network models are more resource and time intensive to manage. The interaction between proteins and ligands is shown via a network consisting of many adjacency meshes. The atomic distances dictate the alterations to these matrices. Additionally, the atomic properties can be shown in a feature matrix. Using the PDBbind datasets, we verified the functionality of graph convolution and evaluated Graph BAR's ability to forecast the binding strengths of proteins and ligands. In order to maximize The model's performance was improved by utilizing ways for adding additional input, which took advantage of the processing capability of graph convolutional neural networks. Graph BAR's predictions appear to have potential for use in drug creation due to its great accuracy and quickness. The results of our research suggest that Graph Convolutional Networks have the potential to expedite the discovery of new medications by properly predicting the interactions between pharmaceuticals and proteins. © 2024 IEEE.

关键词： Proteins

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Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

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computers, Materials & Continua 2022年第1期70卷 715-738页

作者： Shidrokh Goudarzi Seyed Ahmad Soleymani Mohammad Hossein Anisi Domenico Ciuonzo Nazri Kama Salwani Abdullah Mohammad Abdollahi Azgomi Zenon Chaczko Azri Azmi Centre for Artificial Intelligent(CAIT) Universiti Kebangsaan MalaysiaBangi43600Malaysia School of Computer Engineering Iran University of Science and TechnologyResalat Sq.16846-13114TehranIran School of Computing Faculty of Engineering Universiti Teknologi MalaysiaJohorMalaysia School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Department of Electrical Engineering and Information Technologies University of Naples“Federico II”Naples80125Italy Razak Faculty of Technology and Informatics Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network *** address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood *** light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river *** framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a ***,an algorithm hybridized with Group Method data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative *** proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River *** performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

关键词： Unmanned aerial vehicles wireless sensor networks group method data handling particle swarm optimization river flow prediction

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Intelligent Fast Cell Association Scheme Based on Deep Q-Learning in Ultra-Dense Cellular Networks

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China Communications 2021年第2期18卷 259-270页

作者： Jinhua Pan Lusheng Wang Hai Lin Zhiheng Zha Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

To support dramatically increased traffic loads,communication networks become *** cell association(CA)schemes are timeconsuming,forcing researchers to seek fast *** paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is *** the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until *** the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user *** demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional ***,performance metrics,such as capacity and fairness,can be guaranteed.

关键词： ultra-dense cellular networks(UDCN) cell association(CA) deep Q-learning proportional fairness Q-learning

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Feature Rotation Invariance Learning for Point Cloud Analysis 10

Feature Rotation Invariance Learning for Point Cloud Analysi...

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10th IEEE Smart World Congress, SWC 2024

作者： Shi, Lu Cao, Qi Zhang, Guoqing Yi, Jin Huang, Yansen Cen, Yigang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer Science and Technology Visual Intellgence +X International Cooperation Joint Laboratory of Moe Beijing100044 China University of Glasgow The School of Computing Science 567739 Singapore Beijing Jiaotong University With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education The State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer and Information Technology Beijing100044 China Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. Ltd The College of Civil Engineering Guizhou550025 China

ISBN: (纸本)9798331520861

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks. © 2024 IEEE.

关键词： Contrastive Learning

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