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Knowledge Graph for China's Genealogy1

IEEE Transactions on Knowledge and data engineering 2023年第1期35卷 634-646页

作者： Wu, Xindong Jiang, Tingting Zhu, Yi Bu, Chenyang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei230009 China Hefei University of Technology Research Institute of Big Knowledge Anhui Hefei230009 China Mininglamp Technology Mininglamp Academy of Sciences Beijing100102 China Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei230009 China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230009 China

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descendant paths, and explore the origins of a family. However, the heterogenous, autonomous, complex, and evolving natures of genealogical data bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical data may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical datasets verify the feasibility and effectiveness of our proposed model. © 1989-2012 IEEE.

关键词： History

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BDLR:lncRNA identification using ensemble learning

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BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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Tropical cyclone detection by combining wavelet transform with deep learning using infrared satellite images (vol 44, pg 4617, 2023)

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第18期44卷 I-I页

作者： Zhang, Changjiang Zhang, Liu Ma, Leiming Lu, Xiaoqin School of Electronics and Information Engineering (School of Big Data Science) Taizhou University Taizhou China College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China Shanghai Central Meteorological Observatory Shanghai China Shanghai Typhoon Research Institute China Meteorological Administration Shanghai China

A tropical cyclone (TC) is a meteorological disaster that occurs over tropical or subtropical oceans. Automatic TC identification from a satellite image is essential to the subsequent automatic intelligent determination of TC positioning and intensity. Few recent studies have examined the automatic detection of TCs from satellite images. In this paper, wavelet (W) transformation, efficient (E) channel attention mechanism and YOLOX (Y) are combined to construct a deep learning model for TC detection, referred to as WEY-TCNet. WEY-TCNet automatically detects TCs from infrared satellite images. To improve the detection accuracy, a discrete wavelet transform is first used to decompose the infrared satellite image, after which the horizontal high-frequency components, including the TC rainband structure and inner core area, are extracted and fused with the original image. This step enhances the TC structure and brightness temperature gradient characterization. The fused image is then used as the input to WEY-TCNet. The ECANet channel attention mechanism was added to the original YOLOX-S network structure to focus on the key features of TC, such as structure and brightness temperature while suppressing invalid features and noise. This study constructed a satellite image dataset based on the infrared images of the Chinese Fengyun2D geostationary satellite. The dataset contains infrared satellite images of TCs in various life cycle stages, from generation to extinction. Our experiments showed that the WEY-TCNet model proposed in this paper achieves 91.49% detection mAP on the TC dataset, compared with the 86.69% detection mAP of the original YOLOX-S model, significantly improving it.

关键词： Tropical cyclone detection infrared satellite imagery YOLOX-S efficient channel attention discrete wavelet transform

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Livestock Recognition and Identification with Deep Convolutional Neural Networks: A Case Study of Pigs 23

Livestock Recognition and Identification with Deep Convoluti...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Zhao, Feiyang Tian, Maohong Hu, Sheng Liang, Jian Zhou, Longfu Li, Hualin ChongQing Institute of Engineering Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center ChongQing China ChongQing Institute of Engineering Shu Yi Xin Credit Management Co LTD ChongQing China ChongQing Institute of Engineering ChongQing China

ISBN: (纸本)9781450399029

Animal recognition with Object Detection (OD) method by using deep learning techniques has gained popularities in biodiversity preservation and "smart farm" program in recent years. Current research mainly focuses on model training and parameter optimization of different forms of livestock in a single scene. To meet the developmental needs of farms in modern agriculture, this paper proposes an image recognition method which preprocesses the morphological characteristics of livestock in different captivity scenarios, by combining Kennard Stone algorithm, K-means II algorithm and deep learning models for different scenes and live pigs of different behavior characteristics. Our results show that the F1-socre, mAP0.5, and mAP0.5-0.95 of the model were 98.48%, 99.27% and 73.03%, respectively. Our research shows significant improvements for promoting smart animal husbandry and saving labor costs for breeding enterprises, which can also speed up research of high accuracy livestock auto-weighing systems and subsequent applied in animal husbandry. © 2023 Copyright held by the owner/author(s).

关键词： Object detection

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Differentially Private Federated Stochastic Primal-Dual Learning for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17034-17050页

作者： Li, Yiwei Wang, Shuai Chang, Tsung-Hui Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

Federated learning (FL) has the potential to empower Internet-of-Vehicles (IoV) networks by enabling smart vehicles (SVs) to participate in the learning process under the orchestration of a vehicular service provider while keeping data locally. In this paper, we propose a novel federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP) to ensure robust privacy protection for FL based IoV (FL-IoV) systems. The FedSPD-DP algorithm leverages multiple steps of local stochastic gradient descent (SGD) and partial client participation (PCP) to improve communication efficiency while incorporating differential privacy (DP) to ensure privacy protection. Our theoretical analysis explores the impact of these strategies on learning performance. Specifically, we demonstrate that the data sampling strategy and PCP enhance data privacy, while a larger number of local SGD steps may increase privacy leakage, revealing a non-trivial tradeoff between communication efficiency and privacy protection. Extensive experiments on real-world data validate the effectiveness of the proposed algorithm, showing superior performance compared to state-of-the-art FL algorithms, and confirming the analytical results and properties. © 2014 IEEE.

关键词： Federated learning

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Self-Transriber: Few-Shot Lyrics Transcription With Self-Training 48

Self-Transriber: Few-Shot Lyrics Transcription With Self-Tra...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gao, Xiaoxue Yue, Xianghu Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The current lyrics transcription approaches heavily rely on supervised learning with labeled data, but such data are scarce and manual labeling of singing is expensive. How to benefit from unlabeled data and alleviate limited data problem have not been explored for lyrics transcription. We propose the first semi-supervised lyrics transcription paradigm, Self-Transcriber, by leveraging on unlabeled data using selftraining with noisy student augmentation. We attempt to demonstrate the possibility of lyrics transcription with a few amount of labeled data. Self-Transcriber generates pseudo labels of the unlabeled singing using teacher model, and augments pseudo-labels to the labeled data for student model update with both self-training and supervised training losses. This work closes the gap between supervised and semi- supervised learning as well as opens doors for few-shot learning of lyrics transcription. Our experiments show that our approach using only 12.7 hours of labeled data achieves competitive performance compared with the supervised approaches trained on 149.1 hours of labeled data for lyrics transcription. © 2023 IEEE.

关键词： Lyrics transcription music information retrieval singing processing and self-training

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Fast Fractional Programming for Multi-Cell Integrated Sensing and Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Chen, Yannan Feng, Yi Li, Xiaoyang Zhao, Licheng Shen, Kaiming School of Science and Engineering Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

This paper concerns the coordinate multi-cell beamforming design for integrated sensing and communications (ISAC). In particular, we assume that each base station (BS) has massive antennas. The optimization objective is to maximize a weighted sum of the data rates (for communications) and the Fisher information (for sensing). We first show that the conventional beamforming method for the multiple-input multiple-output (MIMO) transmission, i.e., the weighted minimum mean square error (WMMSE) algorithm, works for the ISAC problem case from a fractional programming (FP) perspective. However, the WMMSE algorithm frequently requires computing the N×N matrix inverse, where N is the number of transmit or receive antennas, so the algorithm becomes quite costly when antennas are massively deployed. To address this issue, we develop a nonhomogeneous bound and use it in conjunction with the FP technique to solve the ISAC beamforming problem without the need to invert any large matrices. It is further shown that the resulting new FP algorithm has an intimate connection with gradient projection, based on which we can accelerate the convergence via Nesterov’s gradient extrapolation. © 2002-2012 IEEE.

关键词： Mean square error

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Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF 1-WFV satellite data

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Journal of Oceanology and Limnology 2024年第4期42卷 1048-1060页

作者： Chang CAO Junsheng LI Xiaodong JIA Shenglei WANG Bo WAN Liaoning Earthquake Agency Shenyang 11000China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China

Rapidly monitoring regional water quality and the changing trend is of great practical and scientific significance,especially for the Beijing-Tianjin-Hebei(BTH)region of China where water resources are relatively scarce and inland water bodies are generally *** remote sensing data of the GF 1 satellite launched in 2013 have characteristics of high spatial and temporal resolution,which can be used for the dynamic monitoring of the water environment in small lakes and ***,the water quality remote sensing monitoring model based on the GF 1 satellite data for lakes and reservoirs in BTH is still lacking because of the considerable differences in the optical characteristics of the lakes and *** this paper,the typical reservoirs in BTH-Guanting Reservoir,Yuqiao Reservoir,Panjiakou Reservoir,and Daheiting Reservoir are taken as the study *** the atmospheric correction of GF 1-WFV,the relative radiation normalized atmospheric correction was adopted after comparing it with other methods,such as 6 S and *** the water clarity retrieval,a water color hue angle based model was proposed and outperformed other available published models,with the R 2 of 0.74 and MRE of 31.7%.The clarity products of the four typical reservoirs in the BTH region in 2013-2019 were produced using the GF 1-WFV *** on the products,temporal and spatial changes in clarity were analyzed,and the main influencing factors for each water body were *** was found that the clarity of Guanting,Daheiting,and Panjiakou reservoirs showed an upward trend during this period,while that of Yuqiao Reservoir showed a downward *** the influencing factors,the water level of the water bodies can be an important factor related to the water clarity changes in this region.

关键词： GF 1 satellite atmospheric correction clarity Beijing-Tianjin-Hebei spatial and temporal change analysis

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Optimal Structure Exploration of Bayesian Network Based on Dealing with the Route Choice Model 6

Optimal Structure Exploration of Bayesian Network Based on D...

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6th International Conference on Computer Information science and Application Technology, CISAT 2023

作者： Wang, Ziyi Zeng, Yufei Wang, Rui College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Harbin Institute of Technology Yichun China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510668546

Route selection in residents' travel is a key link in travel decision-making in transportation systems. In existing studies, discrete choice models are often used to describe residents' travel path choices. However， with today's highly developed big data, the traditional discrete choice model is too simple to represent residents' travel path choices, ignoring the connection between various factors. Therefore, a big data-driven approach is proposed to solve this problem in this paper. Driven by research data and with the introduction of the Bayesian network model, the paper adopts a hill-climbing search strategy to improve the search efficiency, uses a variety of scoring functions to train data, which optimizes the Bayesian network structure. The experimental results show that the model fully considers the causation between the independent variables, which is more logical and makes the construction of the resident travel route selection model more accurate and efficient. © 2023 SPIE.

关键词： Bayesian networks

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design 13

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Hong, Wenjing Chen, Cheng Zhu, Zexuan Tang, Ke Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design. © 2024 IEEE.

关键词： Vaccines

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