International Conference on Artificial Intelligence and Pattern Recognition最新文献_第5页

MirrorTrack MirrorTrack

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1109/AIPR.2008.4906442

Pak-Kiu Chung, Bing Fang, R. Ehrich, Francis K. H. Quek

引用次数: 0

Research on Improved Multi-Sensor Data Fusion Algorithm Based on D-S Evidence Theory 基于D-S证据理论的改进多传感器数据融合算法研究

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3488933.3488995

Junsuo Qu, Xing Cai, Haonan Shi

引用次数: 0

Robust Digital Watermarking Algorithm Based on DCT-SVD and QR Code 基于DCT-SVD和QR码的鲁棒数字水印算法

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3430199.3430244

Xiaolei Zhao, Guangqin Wang, Xibin Xu

引用次数: 1

Learning Resource Recommendation Method Based on Knowledge Graph and Neural Network 基于知识图和神经网络的学习资源推荐方法

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3488933.3488965

Chenyu Shi, Jinbao Teng, F. Guo, Wenwen Fu

{"title":"Learning Resource Recommendation Method Based on Knowledge Graph and Neural Network","authors":"Chenyu Shi, Jinbao Teng, F. Guo, Wenwen Fu","doi":"10.1145/3488933.3488965","DOIUrl":"https://doi.org/10.1145/3488933.3488965","url":null,"abstract":"In view of the traditional learning resource recommendation technology for different learners' learning style, knowledge level, learning mode and other characteristics of the difference, lack of personalized consideration, this paper proposes a individualization online learning resource recommendation method based on knowledge graph. Firstly, according to the knowledge structure and ability of students, the cognitive model of students is established, and the knowledge graph is used to describe the sequence relationship between knowledge points and learning resources; secondly, the word embedding technology is used to vectorize the information of learners' characteristics and learning behavior, and the learner characteristics are integrated and embedded into the recommendation model; finally, the bidirectional long short-term memory network is used Memory and attention mechanism are used for feature extraction to mine learners' implicit feedback information, so as to achieve the purpose of personalized recommendation for learners. The experimental results show that the proposed model is better than the traditional recommendation algorithm, and greatly improves the personalized learning efficiency of learners.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124115407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Rapid Pose Estimation of Mongolian Faces using Projective Geometry 基于投影几何的蒙古族人脸姿态快速估计

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1109/AIPR.2004.37

Huaming Li, Mingquan Zhou, Guohua Geng

引用次数: 2

A Study of Spectrum Management Schemes Based on Consortium Blockchain 基于联盟区块链的频谱管理方案研究

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3488933.3489005

Taotao Wang, Jianhua Liu

引用次数: 0

Research on Traffic Sign Detection Algorithm Based on YOLO 基于YOLO的交通标志检测算法研究

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3488933.3488955

Min Feng, Wen-Quing Ma, Zemin Hou

引用次数: 1

A Graph Embedding Method Based on Opinion Dynamics 一种基于意见动态的图嵌入方法

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3488933.3488975

Jinwei Du, Zhan Bu, Tao Yi

{"title":"A Graph Embedding Method Based on Opinion Dynamics","authors":"Jinwei Du, Zhan Bu, Tao Yi","doi":"10.1145/3488933.3488975","DOIUrl":"https://doi.org/10.1145/3488933.3488975","url":null,"abstract":"∗Graph (a.k.a. network), as an important form of data representation, exists widely in real scenes. Effective graph analysis helps users to understand the information hidden behind the data, which will benefit the following tasks (such as node classification, link prediction). Graph embedding is a very effective method to solve the problem of graph analysis. It maps the graph data into the low-dimensional space, and retains the structure and attribute information of the graph to the maximum extent. Graph embedding has become a hot topic in recent years.We propose a neural network graph embeddingmethod based onOpinionDynamics, called Graph Opinion Dynamics Networks (GODNs), which is a neural network architecture that adopts a new information aggregation strategy to process structured data, leveraging attentional mechanism and trusted neighours convergence to address the shortcomings of prior methods based on graph convolutions. Node embeddings are updated through multiple rounds of aggregation of trusted neighbors with different weights, and the rules are set to achieve convergence state. For transductive semi-supervised learning problems, we divide the datasets into sparse networks and dense networks. Our GODNs models have matched the results across the sparse networks (the Cora, Citeseer and Pubmed citation network datasets) and achieved a great improvement over dense networks (amazonphoto, amazon-computer and coauthor-cs electronic commerce datasets).","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126226400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Unbalanced Data Processing Algorithm Base Tomeklinks-Smote 基于tomeklinksmote的不平衡数据处理算法研究

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3430199.3430222

Ai-hua Li, Peng Zhang

{"title":"Research on Unbalanced Data Processing Algorithm Base Tomeklinks-Smote","authors":"Ai-hua Li, Peng Zhang","doi":"10.1145/3430199.3430222","DOIUrl":"https://doi.org/10.1145/3430199.3430222","url":null,"abstract":"Traditional machine learning algorithms tend to bias \"majority\" for classification of unbalanced data, which makes the classification accuracy of less-class samples lower. In order to improve the classification accuracy of less-class samples in the data set, this paper proposes a method based on imbalance TLS algorithm for data processing. This method first deletes duplicate samples in the original data set, and then deletes the boundary samples and noise samples that are Tomek links pairs' in the majority class and the minority class in the data set through the Tomek links undersampling algorithm and then oversampling the minority class samples with the smoth algorithm. After the obtained large-class sample and the obtained small-class sample are approximately balanced. On several commonly used data sets in the UCI database, compare with the original data set, the tradition al SMOTE oversampling only for small samples, and the traditional Tomek link under- sampling method for large samples only, Use SVM, logistic regression, Multilayer Perceptron (Neural Network) and random forest for classification. The experiment proves that the sampling method adopted in this paper can indeed improve the recognition of small class samples.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130428552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Dynamic Gesture Recognition Method Based on Encoded Video 基于编码视频的动态手势识别方法

International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3573942.3574084

Xi-Jiong Xie, Panyu Cao, Zhaozhe Zhang

{"title":"A Dynamic Gesture Recognition Method Based on Encoded Video","authors":"Xi-Jiong Xie, Panyu Cao, Zhaozhe Zhang","doi":"10.1145/3573942.3574084","DOIUrl":"https://doi.org/10.1145/3573942.3574084","url":null,"abstract":"Most of the video-based dynamic gesture recognition methods require decoding video into raw RGB images. The approved accuracy relies on multiple data patterns, such as depth map or optical flow, in specific scenario. So, the more complexity models, the huger calculation power and storage consumption. In this paper, a new characterized model for spatiotemporal data is proposed to represent the spatiotemporal features of dynamic gestures, take advantage of Intra-frames (I-frame), motion vectors, and residuals in encoded videos, so that the additional consumption of computation and storage caused by decoding videos are escaped. Furthermore, a key predicted frames (P-frame) selection (KPFS) module is proposed to filter those P-frames having no useful information, based on an image entropy estimated with the residuals. The more distinguished features are obtained. Comprehensively experiments are performed on two benchmark datasets, VIVA and SKIG. The results show that our method can achieve an average accuracy of 81.13% and 98.70% using lone RGB data, reduce the storage overhead by 88.5%. The result is similar to that of the state-of-the-art methods with the running speed of more than 4.3 times.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125017097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0