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World Congress on Software Engineering

作者： Zhu, Liang Zhu, Yong Ma, Qin Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer Science Hebei University Baoding Hebei 071002 China Department of Foreign Language Teaching and Research Hebei University Baoding Hebei 071002 China

ISBN: (纸本)9780769543031

Based on a knowledge base, we propose a new method to realize free-style Chinese keyword search over relational databases. Firstly, an index (also called knowledge base) is built by extracting related information of Chinese tuple words in a database, then query words and tuple words are matched quickly each other by with the index to obtain some of candidate answers for a given query. Secondly, we present a new ranking strategy to compute similarities between the query and the candidate answers, and refine the candidate answers by a matching algorithm proposed in this paper. Finally, we rank refined candidate answers to obtain top-N results. The experiments are conducted using a real dataset, and the experimental results show that our method is efficient and effective. © 2010 IEEE.

关键词： Search engines

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Optimization methods for resources allocation in real-time strategy games

Optimization methods for resources allocation in real-time s...

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Tong, Xiao-Lei Li, Yan Li, Wen-Liang Zhang, Lei Key Lab. In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China Handan Hospital of Traditional Chinese Medicine Handan 056000 China

ISBN: (纸本)9781457703065

In order to meet the demands of the real time strategy (RTS) games, two learning methods are proposed based on genetic algorithm (GA) and Particle swarm optimization (PSO) to handle the problem of multi-team weapon target assignment (MT-WTA) and distribution of defensive position with restrictive limit of weapon resource. The goal is to take the greatest destruction on the targets. Firstly, we use GA to assign different types of weapons under limited resources. Secondly, we put the optimal results from the first stage into a random game's map to obtain final defensive locations. Then GA and PSO are used to achieve the best distribution of defensive positions respectively and their performance is compared in RTS Games. Both of these two methods have provided efficient, interesting AI to solve real-time strategy games problems, the experimental results can well support this point. © 2011 IEEE.

关键词： Genetic algorithms

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Second-Order Convolutional Neural Network Based on Cholesky Compression Strategy 21st

Second-Order Convolutional Neural Network Based on Cholesky ...

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21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Li, Yan Zhang, Jing Hua, Qiang Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Information Science Hebei University Baoding071002 China Research Center for Applied Mathematics and Interdisciplinary Sciences Beijing Normal University at Zhuhai Beijing519087 China

ISBN: (纸本)9783030692438

In the past few years, Convolution Neural Network (CNN) has been successfully applied to many computer vision tasks. Most of these networks can only extract first-order information from input images. The second-order statistical information refers to the second-order correlation obtained by calculating the covariance matrix, the fisher information matrix, or the vector outer product operation on the local feature group according to the channels. It has been shown that using second-order information on facial expression datasets can better capture the distortion of facial area features, while at the same time generate more parameters which may cause much more computational cost. In this article we propose a new CNN structure including layers which can (i) incorporate first-order information into the covariance matrix;(ii) use eigenvalue vectors to measure the importance of feature channels;(iii) reduce the bilinear dimensionality of the parameter matrix;and (iv) perform Cholesky decomposition on the positive definite matrix to complete the compression of the second-order information matrix. Due to the incorporation of both first-order and second-order information and the Cholesky compression strategy, our proposed method reduces the number of parameters by half of the SPDNet model, and simultaneously achieves better results in facial expression classification tasks than the corresponding first-order model and the reference second-order model. © 2021, Springer Nature Switzerland AG.

关键词： Covariance matrix

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TempCast: A Multi-modal Transformer for Short-Term Temperature Forecasting 8

TempCast: A Multi-modal Transformer for Short-Term Temperatu...

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8th IEEE International Conference on Cloud Computing and intelligence Systems, CCIS 2022

作者： Zhang, Wei Cao, Yang Zhai, Jun-Hai Mu, Ziyao Zhang, Shuai Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei071002 China Unit 78127 of the Chinese People's Liberation Army Chengdu610000 China

ISBN: (纸本)9781665477352

Accurate weather forecasting benefits us in a variety of ways, from scheduling flights to agricultural harvests. However, existing temperature forecasting models have two problems, one is the accumulation of errors caused by autoregressive models, and the other is difficult to predict complex and varying temperatures such as those in highlands. In this paper, we proposed TempCast, a multi-modal Transformer model for short-term temperature prediction. The model has two features: (i) Modeling the features entirely by self-attention, which can effectively capture the exact long-term dependent coupling between output and input. And multiple predictions are obtained at once using a generative decoder, (ii) The modeling of multi-source data through a decoupled multi-modal fusion mechanism can effectively come to cope with the drastic changes of weather in highlands and mountains, etc. The experimental results show that the method can well achieve short-term temperature prediction and significantly outperforms all traditional methods in several indicators. The method also provides a new solution idea for multi-modal temperature prediction. Our code and data are available at https://***/Adam618/Temp Cast. © 2022 IEEE.

关键词： Weather forecasting

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Instability and bifurcation analysis of a diffusive predator-prey model with fear effect and delay

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International Journal of Biomathematics 2024年第8期17卷 245-290页

作者： Hongyan Sun Jianzhi Cao Pengmiao Hao Lijie Zhang School of Management Hebei University Baoding 071002P.R.China College of Mathematics and Information Science Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding 071002P.R.China School of Mathematical Sciences Zhejiang Normal University Jinhua 321004P.R.China

In this paper,a delayed diffusive predator-prey model with fear effect under Neumann boundary conditions is *** the system without diffusion and delay,the conditions for the existence and local stability of equilibria are obtained by analyzing the ***,the instability induced by diffusion and delay-diffusion of the positive constant stationary solutions are discussed,***,the regions of instability and pattern formation can be achieved with respect to diffusion and delay ***,the existence and direction of Hopf bifurcation and the properties of the homogeneous/nonhomogeneous bifurcated periodic solutions are driven by using the center manifold theorem and the normal form ***,some numerical simulations are carried out to verify the theoretical results.

关键词： Delayed diffusive system Hopf bifurcation Turing instability pattern formation delay-diffusion-driven instability fear effect

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A fast positive-region reduction method based on dominance-equivalence relations

A fast positive-region reduction method based on dominance-e...

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2016 International Conference on machine learning and Cybernetics, ICMLC 2016

作者： Jin, Yongfei Li, Yan He, Qiang Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei Province071002 China School of Science Beijing University of Civil Engineering and Architecture Beijing102616 China

ISBN: (纸本)9781509003891

In this paper, we consider decision systems which consist of preference ordered conditional attributes and symbolic decision attributes. Thus, dominance relations and equivalence relations can be respectively defined on the conditional attribute set and decision attribute set. Based on this combined dominance-equivalence relations, a fast positive-region reduction (FPR) algorithm is developed by introducing the concept of positive approximation together with positive region reduction. The computation load of attribute reduction has been greatly reduced when compared with the original positive-region reduction algorithm (PR). Finally, the experimental results on 9 UCI data are demonstrated to make comparisons between the PR and the FPR method. © 2016 IEEE.

关键词： Approximation algorithms

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Rotation transformation-based selective ensemble of one-class extreme learning machines

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Advances in computational intelligence 2021年第1期2卷 1-10页

作者： Xing, Hong-Jie Bai, Yu-Wen Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

Extreme learning machine (ELM) possesses merits of rapid learning speed and good generalization ability. However, due to the random initialization of connection weights, the network outputs of ELM are usually unstable. Similar to ELM, one-class ELM (OCELM) also has the disadvantage of output instability. To enhance the stability and generalization performance of OCELM, a selective ensemble of OCELMs based on rotation transformation is proposed. First, principal component analysis (PCA)-based rotation transformation is utilized to construct different transformed training sets. Furthermore, several component OCELMs are trained independently on these training sets. Second, a dissimilarity measure based on angle cosine is used to evaluate the dissimilarity between each pair of OCELMs. The diversity of each component OCELM in the obtained ensemble can be further achieved. Thereafter, the component OCELMs with lower value of diversity are removed from the original ensemble. Finally, the voting strategy is utilized to determine that testing samples belong to the target class or the non-target class. Experimental results on 15 UCI benchmark data sets and one handwritten digit data set show that the proposed method is superior to its related approaches.

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Instance selection for big data based on locally sensitive hashing and double-voting mechanism

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Advances in computational intelligence 2022年第2期2卷 1-10页

作者： Zhai, Junhai Huang, Yajie Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

The increasing data volumes impose unprecedented challenges to traditional data mining in data preprocessing, learning, and analyzing, it has attracted much attention in designing efficient compressing, indexing and searching methods recently. Inspired by locally sensitive hashing (LSH), divide-and-conquer strategy, and double-voting mechanism, we proposed an iterative instance selection algorithm, which needs to run p rounds iteratively to reduce or eliminate the unwanted bias of the optimal solution by double-voting. In each iteration, the proposed algorithm partitions the big dataset into several subsets and distributes them to different computing nodes. In each node, the instances in local data subset are transformed into Hamming space by l hash function in parallel, and each instance is assigned to one of the l hash tables by the corresponding hash code, the instances with the same hash code are put into the same bucket. And then, a proportion of instances are randomly selected from each hash bucket in each hash table, and a subset is obtained. Thus, totally l subsets are obtained, which are used for voting to select the locally optimal instance subset. The process is repeated p times to obtain p subsets. Finally, the globally optimal instance subset is obtained by voting with the p subsets. The proposed algorithm is implemented with two open source big data platforms, Hadoop and Spark, and experimentally compared with three state-of-the-art methods on testing accuracy, compression ratio, and running time. The experimental results demonstrate that the proposed algorithm provides excellent performance and outperforms three baseline methods.

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Layout identification of printed mathematical formula for recognition

Layout identification of printed mathematical formula for re...

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International Conference on Information Engineering and Computer Science

作者： Tian, Xue-Dong Zhao, Yan Wang, Hui Wang, Qiang-Jun College of Mathematics and Computer Science Hebei University Baoding China Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding China Baoding Senior Technical College Baoding China College of Literature Hebei University Baoding China

ISBN: (纸本)9781424479412

Printed mathematical formulas edited by different soft wares have some obvious differences. To distinguish it before recognition is beneficial to the formula recognition. Based on the statistical analysis to the characteristics of Latex and Word typesetting, an algorithm is designed to identify Latex and Word layout of printed formulas. The experiment indicates that this algorithm can achieve a relative high accuracy to standard printed documents. ©2010 IEEE.

关键词： Latexes

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A maintenance algorithm of FDS based mathematical expression index

A maintenance algorithm of FDS based mathematical expression...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Song-Qiang Yang Xue-Dong Tian Hebei key laboratory of Machine Learning and Computational Intelligence Hebei University Baoding China

ISBN: (纸本)9781479942145

Mathematical expressions have complex two dimensions structure which could not be processed properly by traditional full-text retrieval method. It is necessary for us to research and develop special retrieval theory and technology including the indexing and matching methods of mathematical expressions which fully considers the characteristics of them. In this paper, a maintenance algorithm is designed for the mathematical expression index based on FDS which we given previously. Firstly, the existing achievements about mathematical expression recognition and retrieval are introduced and discussed. Then, the FDS is defined based on formula structural tree and the corresponding mathematical expression index is constructed. Finally, the index maintenance algorithms including the searching, inserting, and deleting operations of index items corresponding to the FDS based index are designed. The experimental result shows that the proposed method is helpful for the mathematical resources management and retrieval.

关键词： Maintenance engineering Indexes Abstracts Information management

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