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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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Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning

arXiv

引用

arXiv 2023年

作者： Chen, Wei Wan, Huaiyu Wu, Yuting Zhao, Shuyuan Cheng, Jiayaqi Li, Yuxin Lin, Youfang School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China

Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline. The extrapolation of TKG is to predict unknowable facts happening in the future, holding significant practical value across diverse fields. Most extrapolation studies in TKGs focus on modeling global historical fact repeating and cyclic patterns, as well as local historical adjacent fact evolution patterns, showing promising performance in predicting future unknown facts. Yet, existing methods still face two major challenges: (1) They usually neglect the importance of historical information in KG snapshots related to the queries when encoding the local and global historical information;(2) They exhibit weak anti-noise capabilities, which hinders their performance when the inputs are contaminated with noise. To this end, we propose a novel Local-global history-aware Contrastive Learning model (LogCL) for TKG reasoning, which adopts contrastive learning to better guide the fusion of local and global historical information and enhance the ability to resist interference. Specifically, for the first challenge, LogCL proposes an entity-aware attention mechanism applied to the local and global historical facts encoder, which captures the key historical information related to queries. For the latter issue, LogCL designs a local-global query contrast module, effectively improving the robustness of the model. The experimental results on four benchmark datasets demonstrate that LogCL delivers better and more robust performance than the state-of-the-art baselines. © 2023, CC BY.

关键词： Knowledge graph

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Convolution without multiplication:A general speed up strategy for CNNs

引用

Science China(Technological Sciences) 2021年第12期64卷 2627-2639页

作者： CAI GuoRong YANG ShengMing DU Jing WANG ZongYue HUANG Bin GUAN Yin SU SongJian SU JinHe SU SongZhi College of Computer Engineering Jimei UniversityXiamen 361021China Department of Artificial Intelligence Xiamen UniversityXiamen 361005China Laboratory of Big Data and Artificial Intelligence NetDragon Websoft Inc.Fuzhou 350001China College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China The Third Research Institute RopeokInc.Xiamen 361008China

Convolutional Neural Networks(CNN)have achieved great success in many computer vision ***,it is difficult to deploy CNN models on low-cost devices with limited power budgets,because most existing CNN models are computationally ***,CNN model compression and acceleration have become a hot research topic in the deep learning *** schemes for speeding up the feed-forward process with a slight accuracy loss include parameter pruning and sharing,low-rank factorization,compact convolutional filters and knowledge *** this study,we propose a general acceleration scheme that replaces the floating-point multiplication with integer *** this end,we propose a general accelerate scheme,where the floating point multiplication is replaced by integer *** motivation is based on the fact that every floating point can be replaced by the summation of an exponential ***,the multiplication between two floating points can be converted to the addition among *** the experiment section,we directly apply the proposed scheme to AlexNet,VGG,ResNet for image classification,and Faster-RCNN for object *** results acquired from ImageNet and PASCAL VOC show that the proposed quantized scheme has a promising performance,even with only one item of ***,we analyzed the eciency of our method on mainstream *** experimental results show that the proposed quantized scheme can achieve acceleration on FPGA with a slight accuracy loss.

关键词： deep learning convolutional neural network network quantization network speed up

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Oil Spill Detection System in the Arabian Gulf Region: An Azure Machine-Learning Approach

Oil Spill Detection System in the Arabian Gulf Region: An Az...

引用

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Almeer, Shaima Albalooshi, Fatema Alhajeri, Aysha National Space Science Agency Space Data Analysis Laboratory Manama Bahrain Univeristy of Bahrain Computer Engineering Department Manama Bahrain

ISBN: (纸本)9781665440325

Locating oil spills is a crucial portion of an effective marine contamination administration. In this paper, we address the issue of oil spillage location exposure within the Arabian Gulf region, by leveraging a Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Custom Vision). Our workflow comprises of virtual machine, database, and four modules (Information Collection Module, Discovery Show, Application Module, and a Choice Module). The adequacy of the proposed workflow is assessed on Synthetic Aperture Radar (SAR) imagery of the targeted region. Qualitative and quantitative analysis show that the purposed algorithm can detect oil spill occurrence with an accuracy of 90.5%. © 2021 IEEE.

关键词： Synthetic aperture radar

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Reliability Analysis of Heterogeneous Sensor-Cloud Systems against Targeted Attacks

引用

China Communications 2022年第8期19卷 181-197页

作者： Hao Peng Zhen Qian Guangquan Xu Kejie Mao Kangtian Li Dandan Zhao Department of Computer Science and Engineering Zhejiang Normal UniversityJinhua Zhejiang 321004China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhua Zhejiang 321004China The Big Data School Qingdao Huanghai UniversityQingdao Shandong 266427China College of Intelligence and Computing Tianjin UniversityTianjin 300072China

Based on the wide application of cloud computing and wireless sensor networks in various fields,the Sensor-Cloud System(SCS)plays an indispensable role between the physical world and the network ***,due to the close connection and interdependence between the physical resource network and computing resource network,there are security problems such as cascading failures between systems in the *** this paper,we propose a model with two interdependent networks to represent a sensor-cloud ***,based on the percolation theory,we have carried out a formulaic theoretical analysis of the whole process of cascading *** the system’s subnetwork presents a steady state where there is no further collapse,we can obtain the largest remaining connected subgroup components and the penetration ***,this result is the critical maximum that the coupled SCS can *** verify the correctness of the theoretical results,we further carried out actual simulation *** results show that a scale-free network priority attack’s percolation threshold is always less than that of ER network which is priority ***,when the scale-free network is attacked first,adding the power law exponentλcan be more intuitive and more effective to improve the network’s reliability.

关键词： sensor-cloud systems targeted attacks heterogeneous networks reliability analysis system security

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Towards Enhancing Relational Rules for Knowledge Graph Link Prediction

arXiv

引用

arXiv 2023年

作者： Wu, Shuhan Wan, Huaiyu Chen, Wei Wu, Yuting Shen, Junfeng Lin, Youfang School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China

Graph neural networks (GNNs) have shown promising performance for knowledge graph reasoning. A recent variant of GNN called progressive relational graph neural network (PRGNN), utilizes relational rules to infer missing knowledge in relational digraphs and achieves notable results. However, during reasoning with PRGNN, two important properties are often overlooked: (1) the sequentiality of relation composition, where the order of combining different relations affects the semantics of the relational rules, and (2) the lagged entity information propagation, where the transmission speed of required information lags behind the appearance speed of new entities. Ignoring these properties leads to incorrect relational rule learning and decreased reasoning accuracy. To address these issues, we propose a novel knowledge graph reasoning approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN). Specifically, RUN-GNN employs a query related fusion gate unit to model the sequentiality of relation composition and utilizes a buffering update mechanism to alleviate the negative effect of lagged entity information propagation, resulting in higher-quality relational rule learning. Experimental results on multiple datasets demonstrate the superiority of RUN-GNN is superior on both transductive and inductive link prediction tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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A Routing Optimization in Wireless Sensor Networks Based on Reverse Elite Sparrow Search Algorithm

Journal of Network Intelligence

引用

Journal of Network Intelligence 2021年第4期6卷 763-775页

作者： Ning, Chun-Yuan Liao, Lyu-Chao Nguyen, Trong-The Huang, Kuan-Chun Pan, Jeng-Shyang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Multimedia Communications Lab University of Information Technology VNU-HCM Viet Nam Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

This study suggests a solution to the routing optimization insufficiency based on the reverse elite sparrow search algorithm (RESSA) for energy balancing and network lifetime prolonging in wireless sensor networks (WSN). The node’s consumption and the remaining energy priority to balance consumption in the data forwarding to the cluster head are considered the optimal path evaluation function. The obtained results of the experimental the suggested scheme are compared with the other methods show that the proposed scheme increases data forwarding round numbers and consumes less energy, the remaining energy more, and higher efficiency of node energy consumption. © The Authors.

关键词： Energy utilization

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Dear-DIA^(XMBD): Deep Autoencoder Enables Deconvolution of data-Independent Acquisition Proteomics

引用

Research 2024年第1期6卷 707-720页

作者： Qingzu He Chuan-Qi Zhong Xiang Li Huan Guo Yiming Li Mingxuan Gao Rongshan Yu Xianming Liu Fangfei Zhang Donghui Guo Fangfu Ye Tiannan Guo Jianwei Shuai Jiahuai Han Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China School of Life Sciences Xiamen UniversityXiamen 361102China State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkXiamen 361102China Department of Computer Science Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine School of MedicineXiamen UniversityXiamen 361102China Bruker(Beijing)Scientific Technology Co.Ltd. BeijingChina Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024China Westlake Omics Ltd. Yunmeng Road 1HangzhouChina Department of Electronic Engineering Xiamen UniversityXiamen 361005China

data-independent acquisition(DIA)technology for protein identification from mass spectrometry and related algorithms is developing *** spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising *** this paper,we proposed an untargeted analysis method,Dear-DIA^(XMBD),for direct analysis of DIA ***-DIA^(XMBD) first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms,then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes,and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and *** show that Dear-DIA^(XMBD) performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms.

关键词： Deep Auto Independent

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Prediction of COVID-19 Transmission in the United States Using Google Search Trends

引用

computers, Materials & Continua 2022年第4期71卷 1751-1768页

作者： Meshrif Alruily Mohamed Ezz Ayman Mohamed Mostafa Nacim Yanes Mostafa Abbas Yasser El-Manzalawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Faculty of Engineering Al-Azhar UniversityCairo11651Egypt Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt RIADI Laboratory La Manouba UniversityManouba2010Tunisia Department of Translational Data Science and Informatics GeisingerDanvillePA17822USA

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health *** to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the *** accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access *** objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related ***,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms *** the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,*** the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,*** 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,*** found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 *** also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.

关键词： Forecasting COVID-19 transmission and mortality in the US stacked LSTM SARS-COV-2 and google COVID-19 search trends

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Efficient Asynchronous I/O with Request Merging

Efficient Asynchronous I/O with Request Merging

引用

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Md Kamal Hossain Chowdhury Houjun Tang Jean Luca Bez Purushotham V. Bangalore Suren Byna Department of Computer Science The University of Alabama Tuscaloosa USA Scientific Data Division Lawrence Berkeley National Laboratory Berkeley USA Department of Computer Science and Engineering The Ohio State University Columbus USA

With the advancement of exascale computing, the amount of scientific data is increasing day by day. Efficient data access is necessary for scientific discoveries. Unfortunately, the I/O performance is not improved, like the CPU and network speed. So, I/O operations take longer time than data generation or analysis. Asynchronous I/O has been proposed to extenuate the I/O bottleneck by overlapping I/O and computation time. However, multiple small write operations can diminish the benefits of asynchronous I/O, as the I/O time becomes significantly longer than the compute time, with little time to overlap with. To overcome these issues, we present an optimization technique to merge small contiguous write operations. We integrated our solution into the HDF5 asynchronous I/O VOL connector and demonstrated the effectiveness of merging HDF5 write operations automatically and transparently without requiring any code change from the application.

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