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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： An, Qiulong Wu, Jianning Huang, Tianqiang Huang, Liqing Luo, Haifeng Ye, Feng College of Computer and Cyber Security Fujian Normal University Digital Fujian Institute of Big Data Security Technology Fujian Provincial Engineering Research Center of Big Data Analysis and Application Fuzhou China

ISBN: (纸本)9798350318401

Most existing deep clustering models attempt to group similar datas by using autoencoders to simultaneously minimize the clustering loss and reconstruction losses. However, these methods do not impose any constraints on the latent space, resulting in intermediate features that are not suitable for clustering. Moreover, they suffer from poor clustering performance when the number of clusters is large. To address this problem, this paper proposes an autoencoder network with preclustered center codebooks for clustering, two codebooks to represent the cluster centers and center offsets, respectively, making the network features more compatible with the clustering task. Finally, We conducted experiments on CIFAR-10 and STL-10 datasets, the results indicate the effectiveness and superiority of the proposed network model. © 2023 IEEE.

关键词： Network coding

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Interpretable text-to-sql generation with joint optimization 1

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17th International Conference on Web Information Systems and applications, WISA 2020

作者： Zhu, Mingdong Wang, Xianfang Zhang, Yang School of Computer Science and Technology Henan Institute of Technology Xinxiang China Intelligent Industrial Big Data Application Engineering Technology Research Center of Xinxiang Xinxiang China

ISBN: (数字)9783030600297

ISBN: (纸本)9783030600280

The purpose of Text-to-SQL is to obtain the correct answer for a textual question from the database, which can take advantage of advanced database system to provide reliable and efficient response. Existing Text-to-SQL methods generally focus on accuracy by designing complex deep neural network models, and hardly consider interpretability, which is very important for serious applications. To address this, in this paper we propose a novel framework for Interpretable Text-to-SQL Generation (ITSG) with joint optimization, which achieves state-of-the-art accuracy and possesses two-level interpretability at the same time. The framework mainly consists of three layers: a sequence encoder which encodes questions, table headers and significant table contents, an attention-based LSTM layer which generates SQL queries and a reinforcement learning layer which boosts the execution accuracy. Comparing with state-of-the-art methods on benchmark datasets, the experimental results show the effectiveness and interpretability of our ITSG framework. © Springer Nature Switzerland AG 2020.

关键词： Reinforcement learning

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Immobilized ZnO based nanostructures and their environmental applications

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Progress in Natural Science:Materials International 2021年第6期31卷 821-834页

作者： Wei Gaoa Yangsi Liu Junzhe Dong Department of Chemical and Materials Engineering The University of Auckland Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing Guyue New Materials Research Institute Beijing University of Technology

In the research of semiconductor metal oxides, zinc oxide(ZnO) nanomaterials have been committed to design and exploration because of their unique physical and chemical properties. ZnO has many structural characteristics and has considerable practical applications in the field of environmental technology. However, ZnO nanomaterials used for environmental functions are often presented in the form of powders. After use, they need to go through recovery processes such as centrifugation and filtration, which is also easy to cause secondary *** above problems can be avoided by fixing ZnO nanostructures on substrates to form easily movable ***, we present a review of our work and some others on the different synthetic strategies, formation mechanism, modification, performance and diversifications of immobilized ZnO based nanostructures, and highlight their photocatalytic related activities for environmental remediation. Various immobilized ZnO based nanostructures including nanorod arrays, 3D nanostructures, and composite heterostructures have been focused. We hope it may serve as a useful reference for researchers interested in immobilized semiconductor nanostructures and their photocatalysis and other environmental applications.

关键词： ZnO Nanostructure Immobilization Photocatalysis Environmental application

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A study of stock price analysis algorithms for new energy companies based on random forest and genetic algorithms 3

A study of stock price analysis algorithms for new energy co...

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3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Song, Shiyang Gao, Junhan Zhao, Anran Li, Yu Li, Chengyifan Zhu, Yuhang Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China College of Global Talents Beijing Institute of Technology Zhuhai China

ISBN: (纸本)9781510663473

China is the world's largest emerging energy market, but due to the rapid development of China's new energy industry special, the value of new energy companies are more susceptible to sharp increases and decreases brought about by external factors in the market compared to other *** this paper, we propose an algorithm that uses an effective cluster learning strategy (Random Forest) contrasted with a genetic algorithm. A new financial forecasting model GSRF is constructed by parameter optimization of random forest model through grid search algorithm,and the model is applied to short-term stock forecasting. This paper builds a stock price trend forecasting model based on both algorithms and uses the model to determine whether the cost of a stock will be higher than its cost at a given date. The experimental results show that the model built using GSRF stochastic forest has the highest return and the lowest risk compared to the return based on the traditional genetic algorithm trading strategy. © 2023 SPIE.

关键词： Genetic algorithms

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The Research on Clustering Ensembles Selection Algorithm based on Semi-supervised K-means Clustering 2

The Research on Clustering Ensembles Selection Algorithm bas...

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2nd International Seminar on Computer Science and Engineering technology, SCSET 2020

作者： Wu, Xiaoxuan Guo, Changjian Hu, Tanglei School of Artificial Intelligence and Big Data Hefei University Hefei China Anhui Engineering Lab of Big Data Technology Application for Urban Infrastructure Hefei University Hefei China Institute of Business Intelligence Hefei University of Technology Hefei China

Selective clustering ensemble algorithm can eliminate the inferior quality clustering member's influence and can achieve a better clustering solution relative to the clustering ensemble algorithm. For high dimensional data clustering, in this paper, a novel selective ensemble algorithm based on semi-supervised K-means clustering is proposed. In this paper, through a large number of experiments to verify the validity of the proposed algorithm for dealing with high dimensional data clustering. The new algorithm can achieve statistically significant performance improvement over other clustering algorithms. © Published under licence by IOP Publishing Ltd.

关键词： K-means clustering

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Leaf area index estimation of winter pepper based on canopy spectral data and simulated bands of satellite 4th

Leaf area index estimation of winter pepper based on canopy ...

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4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Li, Dan Jiang, Hao Chen, Shuisen Wang, Chongyang Huang, Siyu Liu, Wei Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Key Laboratory of Remote Sensing & GIS Application & Engineer Technology Research Center for Remote Sensing Big Data Application of Guangdong Province Guangzhou Institute of Geography Guangzhou510070 China

ISBN: (纸本)9789811039652

Leaf area index (LAI) is an important indicator of crop growth status. In this paper, the relationships between canopy reflectance at 400–2500 nm and leaf area index (LAI) in pepper crop were studied. 102 pair of canopy reflectance and LAI of pepper were collected in 2014–2015. Reflectance of canopy were measured in the field over a spectral range of 400–2500 nm. Simultaneously, the LAI were collected by the LAI-2000. Estimation models of LAI were developed based on the whole spectrum range by partial least squares regression (PLSR) and support vector regression (SVR), respectively. Then the field canopy spectra were resampled according to the band response functions of seven satellite sensors. They were the Vegetation and environment monitoring on a new micro-satellite (VENlS), Worldview-2 (WV-2), RapidEye-1 (RE-1), HJ1/CCD1, Sentinel-2, Landsat 8/OLI and GaoFen (GF) 1/WFV1. The values of common used spectral indices were calculated based on the simulated sensor bands, respectively. Prediction models were also developed based on the spectral indices and simulated bands. The results showed that the PLSR model by whole spectrum had the good accuracy of LAI estimation with the R2c = 0.726, RMSEc = 0.462, R2cv = 0.635, RMSEcv = 0.538. For the simulated satellite datasets, the better LAI estimation were obtained by Sentinel-2 and Venls bands with the R2cv greater than 0.600 and RMSEcv less than 0.557. The Estimation model by simulated WV-2 bands, and RE-1 bands had the lowest performance with the R2cv between 0.50 and 0.55, and RMSEcv between 0.600 and 0.623. The inversion results demonstrated the potential of the multispectral remote sensing data to calibrate the LAI estimation model of winter pepper for the precision agriculture application. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Remote sensing

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Research on cross-project software defect prediction based on feature transfer method 4

Research on cross-project software defect prediction based o...

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4th International Conference on Advanced Information Science and System, AISS 2022

作者： Wang, Wennan Zhao, Hanxu Li, Yu Su, Junyu Lu, Jiadong Wang, Baoping Academy of Management Guangdong University of Science and Technology China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology China Institute of Data Science City University of Macau China

ISBN: (纸本)9781450397933

In this paper, the research and experimental analysis of cross-project application software defect prediction is carried out, and the TCA model is used to improve the application function of its prediction. The models pointed out in this paper usually include: normalization processing model and mathematical linear kernel mathematical statistics The difference between the functional SVM classifier and the extended migration component analysis TCA+ model is that the model pointed out in this paper not only satisfies the prediction of software defects within the project suitable for TCA, but also meets the prediction of software defects in the cross-project of TCA+, so the most appropriate normalization can be selected. Optimized processing options to improve cross-project software defect prediction capabilities. © 2022 ACM.

关键词： Forecasting

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Automatic Labeling for Gene-Disease Associations through Distant Supervision 14

Automatic Labeling for Gene-Disease Associations through Dis...

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14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019

作者： Teng, Fei Bai, Meng Li, Tianrui Institute of Artificial Intelligence National Engineering Laboratory Integrated Transportation Big Data Application Technology School of Information Science Technology Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781728123486

Associating genes with diseases is a fundamental challenge in human health with applications of understanding disease properties and developing precision medicine. Over the past decades, biomedical articles increase explosively, which contain a great number of gene-disease associations (GDAs). Association extraction requires annotated corpus of high accuracy, but manual labeling is time consuming and labor intensive. This paper proposes a distant supervision-based method, to automatically label corpus for GDAs extraction. Compared with the manually annotated gold corpus, the automatic labeled corpus has much larger scale and better quality. It improves the performance of state-of-the-art extraction models, with AUC of 0.96, and F1 of 90%. To the best of our knowledge, this is the first study of automatic labeling GDAs in the field of precision medicine. We extracted GDAs using new corpora from 115,261 PubMed abstracts about 29 lung cancers, and finally discovered 296 new genes/proteins related to lung cancers. These findings indicate new directions for drug design. © 2019 IEEE.

关键词： Genes

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MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

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Computers, Materials & Continua 2019年第9期61卷 1105-1115页

作者： Dongjie Zhu Yuhua Wang Chuiju You Jinming Qiu Ning Cao Chenjing Gong Guohua Yang Helen Min Zhou School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China College of Information Engineering Sanming University365004SanmingChina Fujian Province University Key Lab for Industry Big Data Analysis and Application FujianChina College of Information Engineering Qingdao Binhai UniversityQingdaoChina Jiangsu Province Wireless Sensing System Application Engneering Technology Research and Development Centre China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media *** to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation *** this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a ***,we design their own feature extraction models for multiple heterogeneous data ***,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task ***,we design each task’s own unique presentation layer for discriminant output of ***,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each *** experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.

关键词： User profiles multi-source multi-task learning social network

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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