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2nd International Conference on Innovation in Artificial Intelligence, ICIAI 2018

作者： Huang, Zichao Li, Bo Yin, Jian School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9781450363457

Knowledge graphs are playing a crucial role in many machine learning applications. Since most of the knowledge graphs are far from complete, many knowledge graph completion models have been proposed. TransE and its extended models all model knowledge graphs with additive interaction. DistMult demonstrates that multiplicative interaction is more effective for modeling knowledge graphs. However, DistMult performs poorly on 1-to-N, N-to-1 and N-to-N relations. Besides, it does not consider the case that an entity could be a head entity or a tail entity which should be modeled separately. We propose a more fine-grained knowledge graph embedding model called MultE, which models knowledge graphs with multiplicative interaction. In MultE, an entity would have one representation when serving as head entity and another representation when serving as tail entity. For improving performance on 1-to-N, N-to-1 and N-to-N relations, MultE considers all valid tail(or head) entities during training and treats the prediction task as a classification problem. Experiment results show that MultE obtains consistent improvements compared with DistMult and achieves state-of-theart performance. © 2018 Association for Computing Machinery.

关键词： Knowledge graph

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Prediction model of physical activity level and hypertension based on artificial neural network

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Journal of Physics: Conference Series 2021年第1期1848卷

作者： Liang Hua Wang Yu Li Jinying Zhang Wanjun Wang Yiting school of Physical Education Henan University Kaifeng Henan 475001 China Department of Health Management People's Hospital of Henan Province People's Hospital of Henan University People's Hospital of Zhengzhou University Zhengzhou 450003 China Key Laboratory of Big Data Analysis and Processing of Henan province Henan University Kaifeng 475004 PR China school of Foreign Language Henan University Kaifeng Henan 475001 China

Purpose: Taking the population of henan Provincial People's Hospital Chronic Disease Control Center from July 1, 2020 to December 31, 2020 who came to the hospital for physical examination as the research object, analyze and explore the role and contribution of physical activity level in the prediction of hypertension by using the LSTM network model, which can provide references for the clinical diagnosis of hypertension. Methods: Randomly select 2000 physical examination data, remove missing and invalid data, and preprocess them. Finally, select factors such as gender, age, body mass index, weight grade, waist-to-hip ratio, physical activity level, body fat percentage and other factors to establish a neural network prediction model. Then test and study the model, focusing on exploring the contribution of physical activity level to prediction. Result: The level of physical activity has certain advantages in predicting the prevalence of hypertension, but the predictive ability in the later stage is insufficient

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An extragradient algorithm for fixed point problems and pseudomonotone equilibrium problems

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2020年第4期82卷 89-96页

作者： Yao, Zhangsong Liou, Yeong-Cheng Zhu, Li-Jun School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China Department of Healthcare Administration and Medical Informatics Research Center of Nonlinear Analysis and Optimization Kaohsiung Medical University Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province and Health Big Data Research Institute Advanced Intelligent Perception and Control Technology Innovative Team of NingXia North Minzu University Yinchuan750021 China

Iterative methods for solving fixed point problems and pseudomonotone equilibrium problems have been considered. An extragradient algorithm has been presnted for finding a common element of the fixed points of pseudocontractive operators and the solutions of pseudomonotone equilibrium problems in Hilbert spaces. Weak convergence result of the suggested algorithm is proved. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Iterative methods

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Comparison of Satellite-retrieved NO2 Vertical Column Density with Ground-level NO2 concentration in a provincial scale region

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IOP Conference Series: Earth and Environmental Science 2020年第1期615卷

作者： Fengbin Zheng Yu Xia Qiang Ge Kun Cai Henan Engineering Laboratory of Spatial Information Processing Henan University Henan 475004 PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan 475004 PR China School of Computer and Information Engineering Henan University Henan 475004 PR China

Satellite remote sensing will have certain errors in data retrieval accuracy, which requires further ground observation verification. This paper examines and verifies 2019 Jiangsu provincial Ground-level monitoring data and TROPOMI data to evaluate the applicability of TROPOMI tropospheric NO2 Vertical Column Density (VCD) data and compares the results to OMI data analysis verification results. The results show that the correlation coefficient r between the TROPOMI NO2 data and Ground-level monitoring data at the monthly mean scale is as high as 0.9, and consistent seasonal cyclic changes are observed. The correlation between Ground-level monitoring data and OMI NO2 data is lower than TROPOMI (r=0.78 < 0.9). Compared to OMI satellites, the TROPOMI attains a smaller deviation in tropospheric NO2 monitoring. Jiangsu Province had the highest monthly average concentration in January, which were 38.67 ug/m3 (Ground-level), 17.35 × 1015 molec/cm2 (TROPOMI) and 20.04 × 1015 molec/cm2 (OMI). The lowest concentration in August was 13.39 ug/m3 (Ground-level), 5.2 × 1015 molec/cm2 (TROPOMI) and 7.03 × 1015 molec/cm2 (OMI).

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An improved scan matching algorithm in SLAM

An improved scan matching algorithm in SLAM

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International Conference on Systems and Informatics (ICSAI)

作者： Hongkai Zhang Niansheng Chen Guangyu Fan Dingyu Yang Shanghai DianJi University School of Electronic Information Engineering Shanghai China Alibaba Group Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

Simultaneous localization and mapping (SLAM) technology has always been the research focus of robot navigation in unknown environment. Aiming at the problem of cumulative errors of robot pose in the localization process of SLAM algorithm based on particle filter, a loop detection algorithm based on graph-SLAM was proposed. The algorithm uses constraints to adjust the robot attitude at different moments. In this paper, the constraint refers to the scanning matching of lidar. In the process of drawing, when the robot returns to the known area, if the current laser scanning is successfully matched with the previous laser scanning, the robot's posture can be adjusted to eliminate the accumulated errors caused by the odometer. In the process of laser scanning matching, the method of grouping step threshold value judgment is proposed to match the laser point cloud, which can effectively reduce the computation. Experimental results show that the proposed algorithm can effectively eliminate the cumulative errors of positioning and achieve a better mapping effect.

关键词： Three-dimensional displays Lasers Jacobian matrices Simultaneous localization and mapping Mathematical model Laser radar

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An improved index based on mapreduce for path queries in public transportation networks 14

An improved index based on mapreduce for path queries in pub...

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14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Ning, Zhiqing Dai, Genan Liu, Yubao Ge, Youming Wu, Jiefeng School of Data and Computer Science Sun Yat-sun University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9781538680971

In this paper, we consider the path queries in public transportation networks which are widely used in path planning and location-based services. In general, path index is often adopted to speed up the path queries in public transportation networks. However, the pre-processing for building path index for the existing best-known query algorithms is often not efficient. Aimed at this shortcoming, we propose an improved index, which is based on MapReduce for path queries. Specifically, the proposed algorithm includes the map and reduce phases. In map phase, the public transportation network is divided into different subgraphs and generate key-value pairs of index for subgraphs. In reduce phase, the result of map function are aggregated by subgraphs' key-value pairs to obtain the index. The experimental results based on the real datasets show the effectiveness of the proposed algorithms. © 2018 IEEE.

关键词： MapReduce

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A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

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Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance

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A deep neural information fusion architecture for textual network embeddings

arXiv

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arXiv 2019年

作者： Xu, Zenan Su, Qinliang Quan, Xiaojun Zhang, Weijia School of Data and Computer Science Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Electronics and Information Technology Sun Yat-Sen University

Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations. Traditionally, the structural and textual embeddings were learned by models that rarely take the mutual influences between them into account. In this paper, a deep neural architecture is proposed to effectively fuse the two kinds of informations into one representation. The novelties of the proposed architecture are manifested in the aspects of a newly defined objective function, the complementary information fusion method for structural and textual features, and the mutual gate mechanism for textual feature extraction. Experimental results show that the proposed model outperforms the comparing methods on all three datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Network embeddings

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Parallelized Top-k Route Search with User's Preferences

Parallelized Top-k Route Search with User's Preferences

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Sen Zhang Youming Ge Qun Jiang Haihang Li Yubao Liu School of Data and Computer Science Sun Yat-Sen University Guangzhou P.R.China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R.China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

In this paper, we consider the top-k route search with user's preferences. Specifically, given a set of POIs, our problem is to find k different routes from a source POI to a target POI such that the constraint on route cost and the POIs covered by the route can optimally satisfy the user-defined weighted feature preference. It has been shown that the problem is NP-hard. The challenge is how to select from plenty of POIs and construct an optimal route especially when the size of candidate POIs is large. In order to support top-k route search on a large dataset or with a looser budget constraint, we propose a parallel method on a single machine to speed up the search. Moreover, we adopt further effective pruning strategies to reduce the search space. The experimental results on real-world datasets show that our proposed parallel method is much efficient, about 10 times faster than the existing serial algorithm at best.

关键词： Roads Search problems Parallel algorithms big data Task analysis Aggregates

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Top-k Nearest keyword Search in Public Transportation Networks

Top-k Nearest Keyword Search in Public Transportation Networ...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Wuwei Huang Genan Dai Youming Ge Yubao Liu School of Data and Computer Science Sun Yat-sen University Guangzhou P.R. China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China

ISBN: (数字)9781728158235

ISBN: (纸本)9781728158242

Top-k nearest keyword search is important for various applications. However, the existing methods are only applicable to static graphs, not public transportation networks. This is because unlike static graph, public transportation network is a temporal graph where the path in the temporal graph must satisfy the time constraint. Thus, the path which is reachable in the static graph, may not reachable in the temporal graph. Therefore, the methods applicable to static graphs cannot be applied to temporal graphs. In this paper, to solve the top-k nearest neighbor keyword search on public transportation networks, we propose two indexes and two algorithms called Temporal Forward Search (TFS) and Temporal Forward-Backward Search (TFBS) to improve the efficiency. Extensive experiments on the real-world datasets were conducted to show the efficiency of our proposed methods.

关键词： Indexes Public transportation keyword search Labeling Approximation algorithms Search problems Forestry

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