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International Conference on data engineering

作者： Yiming Xu Bin Shi Teng Ma Bo Dong Haoyi Zhou Qinghua Zheng Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG.

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Revealing the Defect-Dominated Electron Scattering in Mg_(3)Sb_(2)-Based Thermoelectric Materials

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Research 2023年第1期2022卷 379-388页

作者： Jucai Jia Yan Zhou Xiaoxi Chen Wenhua Xue Hulei Yu Jing Li Shizhen Zhi Chen Chen Jian Wang Shuaihang Hou Xingjun Liu Yumei Wang Feng Cao Yue Chen Jun Mao Qian Zhang School of Materials Science and Engineering and Institute of Materials Genome Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China Center for Device Thermography and Reliability(CDTR) H.H.Wills Physics LaboratoryUniversity of BristolTyndall AvenueBristol BS81TLUK Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of Sciences100190BeijingChina 'Department of Mechanical Engineering The University of Hong KongPokfulam RoadHong Kong SARChina School of Science Harbin Institute of Technology(Shenzhen)Shenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

The thermoelectric parameters are essentially governed by electron and phonon *** the carrier scattering mechanism plays a decisive role in electron transport,it is of great significance for the electrical properties of thermoelectric *** a typical example,the defect-dominated carrier scattering mechanism can significantly impact the room-temperature electron mobility of n-type Mg_(3)Sb_(2)-based ***,the origin of such a defect scattering mechanism is still ***,the existence of the Mg vacancies and Mg interstitials has been identified by synchrotron powder X-ray *** relationship among the point defects,chemical compositions,and synthesis conditions in Mg_(3)Sb_(2)-based materials has been *** further introducing the point defects without affecting the grain size via neutron irradiation,the thermally activated electrical conductivity can be *** results demonstrate that the point defects scattering of electrons is important in the n-type Mg_(3)Sb_(2)-based materials.

关键词： materials defect Electron

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Prediction of Hepatocellular Carcinoma Diseases Based on Methylation data and Screening of Hub Genes 5

Prediction of Hepatocellular Carcinoma Diseases Based on Met...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Zhang, Yawei Ren, Fangtao Liu, Xi Zhang, Fan School of Computer and Information Engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

DNA methylation is of great significance to the diagnosis, treatment and disease prediction of hepatocellular carcinoma (HCC). The commonly used DNA methylation microarrays have high data dimensions, and different CpG sites detected may map to the same gene. To extract more effective features for HCC disease prediction, this study uses a linear regression model that integrates TCGA database methylation data and gene expression data, based on the DNA methylation microarray data of the GEO database (GSE113017), the corresponding gene expression data was predicted and the differentially expressed genes (3766) with significant differences were screened out, which was used as the feature of the data set. Constructing an Artificial Neural Network (ANN) to train a machine learning model for HCC disease prediction and perform 10-fold cross-validation. The resulting model has an accuracy of 95.1% for HCC disease prediction based on DNA methylation microarray data. Compared with other HCC prediction methods, this method has better performance. Then analyze the differentially expressed genes with protein-protein interaction network (PPI network), and use the top five connected genes in the network as hub genes, namely: GNGT2, GNB4, FPR2, CDC20, NMUR1, which can be used as biomarkers for the diagnosis, treatment and prognosis of HCC. © 2021 ACM.

关键词： Machine learning

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Optimal location query based on k nearest neighbours

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Frontiers of Computer Science 2021年第2期15卷 105-117页

作者： Yubao LIU Zitong CHEN Ada Wai-Chee FU Raymond Chi-Wing WONG Genan DAI School of Data and Computer Science Sun Yat-Sen UniversityGuangzhou510006China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006China Department of Computer Science and Engineering Chinese University of Hong KongHong Kong999077China Department of Computer Science and Engineering The Hong Kong University of Science and TechnologyHong Kong999077China

Optimal location query in road networks is a basic operation in the location intelligence *** a set of clients and servers on a road network,the purpose of optimal location query is to obtain a location for a new server,so that a certain objective function calculated based on the locations of clients and servers is *** works assume no labels for servers and that a client only visits the nearest *** assumptions are not realistic and it renders the existing work not useful in many *** this paper,we relax these assumptions and consider the k nearest neighbours(KNN)of *** introduce the problem of KNN-based optimal location query(KOLQ)which considers the k nearest servers of clients and labeled *** also introduce a variant problem called relocation KOLQ(RKOLQ)which aims at relocating an existing server to an optimal *** main analysis algorithms are proposed for these *** experiments on the real road networks illustrate the efficiency of our proposed solutions.

关键词： optimal location query k nearest neighbours road network

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Remaining Useful Life Prediction Method of Lithium-Ion Batteries is Based on Variational Modal Decomposition and Deep Learning Integrated Approach

SSRN

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SSRN 2023年

作者： Wang, Shuai Ma, Hongyan Zhang, Yingda Li, Shengyan He, Wei School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing10044 China Institute of Distributed Energy Storage Safety Big Data Beijing10044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing10044 China

A common method based on variational modal decomposition (VMD) and an integrated depth model is proposed to address the problem that it is difficult to precisely anticipate the remaining useful life (RUL) of lithium-ion batteries (LIBs). Initially, VMD is employed to decompose the LIBs capacity data in multiple scales to obtain the signal's global degradation tendency and local random fluctuation components. Then, the global degradation trend and each fluctuation component are modeled using an echo state network (ESN) and a Bayesian optimized long short-term memory (LSTM) network, respectively. The final LIBs RUL prediction results are obtained by integrating the prediction outcomes. On three public LIBs datasets with distinct degradation characteristics, the performance of the proposed model is tested, and alternative prediction algorithms are compared. The results of the experiments show that the proposed model's maximum average absolute percentage error does not exceed 0.43%. The average relative error not more than 0.5%, indicating great accuracy and stability in prediction. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Literature Review and Practice Comparison of Technology Foresight

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Procedia Computer Science 2022年 199卷 837-844页

作者： Zhao Minghui Ye Hanrui Peng Yao Zhang Lingling School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

With continuous breakthrough of key technologies, common technologies, emerging technologies, cross technologies, and subversive technologies, which have brought profound changes to politics, economy, society, culture, and environment. Under above-mentioned background, further exert the guiding role of technology foresight in technology planning formulation, further study the supporting role of technology foresight in emerging technology management, which is important for academic research, enterprise practice and government decision-making. This article can be divided into two parts, the literature review part mainly includes published articles, keyword network, citation network, which describes the historical context, current status, future trends, limitations and improvement directions of related research in detail. The practice comparison part mainly includes targets and experts, methods and processes, technologies and fields, which is also a feasible perspective for results evaluating.

关键词： Technology Foresight Literature Review Practice Comparison

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Identification of Plant Stomata Based on YOLO v5 Deep Learning Model 5

Identification of Plant Stomata Based on YOLO v5 Deep Learni...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Ren, Fangtao Zhang, Yawei Liu, Xi Zhang, Yingqi Liu, Ying Zhang, Fan School of Computer and Information Engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

Stomata is an important structure in all terrestrial plants and is very vital in controlling plant photosynthesis and transpiration flow. Precise detection of plant stomata is the basis for studying stomata characteristics. Traditional detection methods are mostly manual operations, which is a tedious and inefficient process. Manually extracting features requires high image quality. Choosing appropriate features depends on certain prior knowledge, especially for the object with large morphological changes such as plant stomata. With the widespread use of deep learning technology, efficient solutions to this task have become possible. This article combines the characteristics of the corn leaf stomatal data sets to improve the latest object detection model YOLO v5)You Only Look Once(. By introducing the attention mechanism, that is, adding the SE module to the backbone network, the precision and recall of stoma detection are improved. At the same time, The loss function has been improved from to for avoiding some problems that may occur when selecting the best prediction box. Experimental results show that the precision and recall rates of the improved model on the corn leaf stomata data sets have reached 94.8% and 98.7% respectively, lay the foundation for the measurement of stomatal parameters. In addition, this paper also can help agriculturists and botanists to build their own data sets for stomatal research by explaining the methods of acquiring, pre-processing, and annotating data sets. © 2021 ACM.

关键词： Object detection

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Large-Scale Network Adaptive Situation Awareness Method in Spatio-Temporal Dimension 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Hongbin Xu, Ying Liu, Bin Zhao, Dongmei Bai, Yikang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims

arXiv

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arXiv 2024年

作者： Zhou, Shu Wang, Xin Zhou, Zhengda Yi, Haohan Zheng, Xuhui Wang, Hao School of Information Management Nanjing University China Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Provincial Universities Nanjing University China Baidu Inc. Beijing China

In order to solve the problem of insufficient generation quality caused by traditional patent text abstract generation models only originating from patent specifications, the problem of new terminology OOV caused by rapid patent updates, and the problem of information redundancy caused by insufficient consideration of the high professionalism, accuracy, and uniqueness of patent texts, we proposes a patent text abstract generation model (MSEA) based on a master-slave encoder architecture;Firstly, the MSEA model designs a master-slave encoder, which combines the instructions in the patent text with the claims as input, and fully explores the characteristics and details between the two through the master-slave encoder;Then, the model enhances the consideration of new technical terms in the input sequence based on the pointer network, and further enhances the correlation with the input text by re weighing the "remembered" and "for-gotten" parts of the input sequence from the encoder;Finally, an enhanced repetition suppression mechanism for patent text was introduced to ensure accurate and non redundant abstracts generated. On a publicly available patent text dataset, compared to the state-of-the-art model, Improved Multi-Head Attention Mechanism (IMHAM), the MSEA model achieves an improvement of 0.006, 0.005, and 0.005 in Rouge-1, Rouge-2, and Rouge-L scores, respectively. MSEA leverages the characteristics of patent texts to effectively enhance the quality of patent text generation, demonstrating its advancement and effectiveness in the experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Specifications

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Literature Review and Practice Comparison of Technology Foresight 8

Literature Review and Practice Comparison of Technology Fore...

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8th International Conference on Information Technology and Quantitative Management, ITQM 2020 and 2021

作者： Minghui, Zhao Hanrui, Ye Yao, Peng Lingling, Zhang School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

关键词： Decision making

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