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Two kinds of average approximation accuracy

CAAI Transactions on Intelligence Technology 2024年第2期9卷 481-490页

作者： Qingzhao Kong Wanting Wang Dongxiao Zhang Wenbin Zhang Department of Science Jimei UniversityXiamenChina Digital Fujian Big Data Modeling and Intelligent Computing Institute Jimei UniversityXiamenChina Department of Computer Science Michigan Technological UniversityHoughtonMichiganUSA

Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target ***,traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set *** overcome this,two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is *** first is the relative average approximation accuracy,which is based on all sets in the universe and has several basic *** second is the absolute average approximation accuracy,which is based on undefinable sets and has yielded significant *** also explore the relationship between these two types of average approximation ***,the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.

关键词： rough sets rough set theory

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Rough set model based on variable universe

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CAAI Transactions on Intelligence Technology 2022年第3期7卷 503-511页

作者： Qingzhao Kong Xueer Chang Department of Science Jimei UniversityXiamenChina Digital Fujian Big Data Modeling and Intelligent Computing Institute XiamenChina

Rough set theory has a very good effect in information processing and knowledge *** an information table,the current scholars regard all objects as a universe,and then establish various rough set ***,in the analysis of many data problems,it is more reasonable to select parts of objects which are useful to us or can meet the actual needs as a ***,in order to make up for the deficiency of traditional models,a new model is introduced from the perspective of variable ***,some interesting properties of this model,such as approximation sets,reduct of attributes and maximum part of universe,are *** the study of this paper,it can be seen that the model developed in our paper is not only more accurate but also more effective in describing uncertain knowledge.

关键词： approximation universe describing

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Ship AIS Trajectory Outlier Identification Model under High-speed Sampling Conditions 4

Ship AIS Trajectory Outlier Identification Model under High-...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Wang, Han Chen, Qilong Jimei University College of Science Xiamen China Jimei University Digital Fujian Big Data Modeling and Intelligent Computing Institute College of Science Xiamen China

ISBN: (纸本)9798350354560

In order to efficiently identify the outliers of ship AIS trajectories under high-speed sampling conditions, this paper proposes an outlier identification model based on ship position distance distribution. Taking the ship AIS track data of Yangshan port as an example, the kernel density is used to estimate the daily ship position distance distribution, and then the threshold is determined and the outliers are screened out. In this paper, it is proved that the ship position distance distributions on two adjacent days are approximate, so the threshold value of yesterday can be substituted into today to identify today's outliers effectively. The experimental results show that in the application environments of Yangshan Port waters where the average rate of ship AIS track data writing into the database is 43 items/sec and the coastal waters of East China Sea where the average rate of ship AIS track data writing into the database is 287 items/sec, the model is able to efficiently identify the outliers of the ship AIS trajectories. © 2024 IEEE.

关键词： Trajectories

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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TransUNet for Image Forgery Localization 3

TransUNet for Image Forgery Localization

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3rd International Conference on Computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Li, Kedi Li, Lin Li, Qingyan School of Science Jimei University Xiamen361021 China Computer Engineering College Jimei University Xiamen361021 China Digital Fujian Big Data Modeling and Intelligent Computing Institute Jimei University Xiamen361021 China

ISBN: (纸本)9781510671720

Image tampering can easily be used in illegal activities such as false propaganda, fake news and falsifying evidence in court which may have a negative impact on society. Therefore, we need to constantly update and improve the image tampering detection technology. TransUNet is an efficient model for medical image segmentation. This paper modified and migrated TransUNet which to image forgery localization, and conducted experiments on standard datasets to demonstrate the effectiveness of TransUNet. The findings shows that the proposed framework is superior to several existing forged area localization techniques. © 2023 SPIE.

关键词： Image enhancement

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Ship AIS Trajectory Outlier Identification Model under High-speed Sampling Conditions

Ship AIS Trajectory Outlier Identification Model under High-...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Han Wang Qilong Chen College of Science Jimei University Xiamen China College of Science Jimei University Digital Fujian Big Data Modeling and Intelligent Computing Institute Xiamen China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In order to efficiently identify the outliers of ship AIS trajectories under high-speed sampling conditions, this paper proposes an outlier identification model based on ship position distance distribution. Taking the ship AIS track data of Yangshan port as an example, the kernel density is used to estimate the daily ship position distance distribution, and then the threshold is determined and the outliers are screened out. In this paper, it is proved that the ship position distance distributions on two adjacent days are approximate, so the threshold value of yesterday can be substituted into today to identify today’s outliers effectively. The experimental results show that in the application environments of Yangshan Port waters where the average rate of ship AIS track data writing into the database is 43 items/sec and the coastal waters of East China Sea where the average rate of ship AIS track data writing into the database is 287 items/sec, the model is able to efficiently identify the outliers of the ship AIS trajectories.

关键词： Adaptation models Information science databases Sea measurements Seaports Probability distribution Trajectory Marine vehicles Artificial intelligence Kernel

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Reversible data Hiding in Encrypted Images Based on Multi-Predictor and Quad-tree Block Encoding 2022

Reversible Data Hiding in Encrypted Images Based on Multi-Pr...

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7th International Conference on intelligent Information Technology, ICIIT 2022

作者： Zhang, Huiqi Li, Lin Li, Qingyan School Of Science Jimei University Xiamen361021 China Computer Engineering College Jimei University Xiamen361021 China Digital Fujian Big Data Modeling And Intelligent Computing Institute Jimei University Xiamen361021 China

ISBN: (纸本)9781450396172

With the rapid development of the Internet, people's awareness of privacy protection is also gradually improved. Reversible data hiding of encrypted image is a technique that encrypts the original image and allows the embedding of secret data without knowing the original image information. This method can accurately extract the secret data and reconstruct the original image completely. In this paper, a reversible data hiding algorithm of encrypted image based on multi-predictor and quad-tree block encoding is proposed. First, the original image is divided into 8×8 non-overlapping blocks. Then, multi-predictor is used to predict each block to get the prediction error image, then block scrambling is used to encrypt it. Since the pixel values of the encrypted image are small integer closes to zero, the quad-tree block encoding method can be used to embed a large amount of data. Experimental results show that the embedding rate of this method is better than that of some new algorithms. © 2022 ACM.

关键词： Embeddings

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A Two-Stage Graph Computation Model with Communication Equilibrium 15th

A Two-Stage Graph Computation Model with Communication Equil...

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15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Dong, Yanmei Chen, Rongwang Guo, Kun College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China College of Mathematics and Computer Science Wuyi University Wuyishan Fujian354300 China Digital Fujian Tourism Big Data Institute Wuyishan Fujian354300 China

ISBN: (纸本)9789811625398

Distributed graph computing aims at performing in-depth analysis on large networks in a parallel manner. Iterative communication computation is an important model to perform graph analysis. Moreover, high-efficiency iterative communication computation is necessary for assuring the quality of graph partitioning. Many strategies to improve graph computing are usually based on the hypothesis of single communication iteration which focuses on the optimization of load balance in a single graph partitioning and the improvement of parallel granularity or communication manners. However, a graph in the real-world usually requires complex communication iterations to achieve good analysis results, which often have the problems of data and communication tilting and low analysis efficiency. In this paper, we propose a two-stage graph computing model with communication equilibrium (TSMCE). The model employs a communication-equilibrated graph partitioning strategy (CEGP) for load balancing and a two-stage graph computing mode (TS) to reduce the crossing-partition communication. With the change of graph density, various factors such as load balancing, communication balancing, cross-partition transmission traffic, communication delay, and convergence speed can always maintain an efficient balance. The experiments conducted on the real-world datasets show that the communication-equilibrated graph partitioning strategy can divide a graph with high quality compared with Hash and Metis. Besides, the overall performance of our two-stage graph computing model with communication equilibrium is higher than that of the BSP model. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Array optimization for MIMO radar based on harmony search mechanism

Array optimization for MIMO radar based on harmony search me...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Ling Jiang Yi Jiang Guimin Shi Zhongjie Xiao School of Mathematics and Computer Wuyi University Wuyishan China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan China Digital Fujian Tourism Big Data Institute Wuyishan China Big Data Development Administration of Anji County Huzhou China

ISBN: (纸本)9781728194202

In order to overcome the premarure risk of differential evolution algorithm, harmony search mechanism is introduced to optimize the pattern synthesis of multiple input and multiple output radar. Firstly, the memory of harmony search receives the optimizing results of differential evolution algorithm. And then it disturbs the local best to achieve better global optimization results. The advantage of novel method than standard differential evolution is tested with benchmark function while it can also maintain the diversity of population. What's more, several experiments are conducted to show that the optimal peak side lobe level and convergence performance have been achieved better through the proposed algorithm for multiple input and multiple output radar.

关键词： MIMO radar Sociology Education Radar Statistics Information technology Optimization

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Review Spam Detection Based on Multi-dimensional Features 9th

Review Spam Detection Based on Multi-dimensional Features

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Deng, Liming Wei, Jingjing Liang, Shaobin Wen, Yuhan Liao, Xiangwen College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Digital Fujian Institute of Financial Big Data Fuzhou350116 China College of Electronics and Information Science Fujian Jiangxia University Fuzhou350108 China

ISBN: (纸本)9783030596040

Review spam detection aims to detect the reviews with false information posted by the spammers on social media. The existing methods of review spam detection ignore the importance of the information hidden in the user interactive behaviors and fail to extract the indistinct contextual features caused by irregular writing style of reviews. In this paper, a new review spam detection method based on multi-dimensional features is proposed. The method utilizes the principal component analysis to get low-dimensional features to characterize the user-product relationship. Then, a neural network constructed with nested LSTM and capsule network is trained to extract textual context features and spatial structure features. Finally, the model combines the text and user behavioral features as the overall features, which are used as the input to the classification module to detect spam reviews. Experimental results show that the F1 value of our proposed method is 1.6%~3.5% higher than the existing methods, indicating the efficiency and effectiveness of our model, especially on the natural distribution datasets. © 2020, Springer Nature Switzerland AG.

关键词： Feature extraction

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