Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence最新文献

Joint bottleneck feature and attention model for speech recognition 语音识别的联合瓶颈特征与注意模型

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208798

Long Xingyan, Qu Dan

引用次数: 5

Interchange of criminal rules between CLRL and LKIF cll与LKIF之间的刑事规则互换

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208802

Xing Wang, Yixing Sun, Xiaoliang Tang, Ji Chen, Jiuxiang Jin

{"title":"Interchange of criminal rules between CLRL and LKIF","authors":"Xing Wang, Yixing Sun, Xiaoliang Tang, Ji Chen, Jiuxiang Jin","doi":"10.1145/3208788.3208802","DOIUrl":"https://doi.org/10.1145/3208788.3208802","url":null,"abstract":"There is much fuzzy and non-monotonic knowledge in the Semantic Web Criminal Law Area. In recent years, the problem of fuzzy rules interchange has become one of the most important problems in the Semantic Web. Aiming at the problem of heterogeneous fuzzy rule interchange in the Semantic Web, which is based on the proposed Semantic Web Criminal Law Rule Language (CLRL), and based on the rules and norms of XML. We construct the rule mapping between the CLRL and Legal Knowledge Interchange Format (LKIF), and propose a heterogeneous fuzzy criminal law rules interchange architecture (CRIAXS), which supports the bidirectional rule interchange between legal rules. We also analyze the problem of information loss caused by the different language expression ability in the process of legal knowledge interchange, and put forward to the solution. Based on the above description, the prototype system CRIAXS which is based on the JavaScript language has been achieved on the HBuilder platform. We also verify the correctness and stability of the system through multiple conversion examples. The results show that the architecture and the implemented system which laysa solid foundation for the rule-based reasoning, has a good solution to the communication problem between heterogeneous systems, and it has a wide range of application prospects.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128991213","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

Background subtraction via online box constrained RPCA 基于在线框约束RPCA的背景减法

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208797

Hang Li, Zhuang Miao, Yang Li, Jiabao Wang, Yafei Zhang

引用次数: 3

A user-satisfaction-based clustering method 基于用户满意度的聚类方法

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208789

Wenjun Quan, Qing Zhou, Hai Nan, Yanbin Chen, Ping Wang

{"title":"A user-satisfaction-based clustering method","authors":"Wenjun Quan, Qing Zhou, Hai Nan, Yanbin Chen, Ping Wang","doi":"10.1145/3208788.3208789","DOIUrl":"https://doi.org/10.1145/3208788.3208789","url":null,"abstract":"Clustering is a common method for data analysis where a good clustering helps users to better understand the data. As for clustering quality measurement, the mainly used are some objective measures, while some researchers also paid attention to users' goals and they proposed methods to get users involved in clustering. However, a good clustering must meet the satisfaction of the users. Apart from these objective measures and users' goals, whether the clustering is easy to understand is also important for clustering quality measurement, especially in high-dimensional data clustering, if the data points in the final clusters are with high dimensions, it will hinder users' understanding of the clustering results. With all these concerns considered, we proposed an index of users' satisfaction with high-dimensional data clustering. According to this index, we further put forward a user-satisfaction-based clustering method to better serve users' satisfaction. We first developed an optimization model about users' satisfaction, then we used genetic algorithm to solve this model and obtained some high-quality clusterings, after reclustering of the clusterings obtained in previous steps, a few representative high-quality clusterings are provided for users to select. The experiment results suggest that our method is effective to provide some representative clusterings with the clustering quality, users' goals and the interpretability of clustering results being well considered.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127352036","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

The Merrifield-Simmons index of two classes of lexicographic product graphs of corona graphs 日冕图的两类词典积图的Merrifield-Simmons索引

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208790

Min Guo, Wen-wen Tian, N. Li

引用次数: 0

Extreme learning machine for regression based on condition number and variance decomposition ratio 基于条件数和方差分解比的极值学习机回归

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208794

Meiyi Li, Weibiao Cai, Qingshuai Sun

{"title":"Extreme learning machine for regression based on condition number and variance decomposition ratio","authors":"Meiyi Li, Weibiao Cai, Qingshuai Sun","doi":"10.1145/3208788.3208794","DOIUrl":"https://doi.org/10.1145/3208788.3208794","url":null,"abstract":"The extreme learning machine (ELM) is a novel single hidden layer feedforward neural network. Compared with traditional neural network algorithm, ELM has the advantages of fast learning speed and good generalization performance. However, there are still some shortages that restrict the further development of ELM, such as the perturbation and multicollinearity in the linear model. To the adverse effects caused by the perturbation and the multicollinearity, this paper proposes ELM based on condition number and variance decomposition ratio (CVELM) for regression, which separates the interference terms in the model by condition number and variance decomposition ratio, and then manipulate the interference items with weighted. Finally, the output layer weight is calculated by the least square method. The proposed algorithm can not only get good stability of the algorithm, but also reduce the impact on the non-interference items when dealing with the interference terms. The regression experiments on several datasets show that the proposed method owns a good generalization performance and stability.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123246222","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

Bayesian analysis for multivariate skew-normal reproductive dispersion random effects models 多元偏正态生殖分散随机效应模型的贝叶斯分析

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208805

Yuanying Zhao, Xingde Duan, De-Wang Li

引用次数: 0

Deep learning based classification for paddy pests & diseases recognition 基于深度学习的水稻病虫害分类识别

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208795

Ahmad Arib Alfarisy, Quan Chen, M. Guo

引用次数: 54

Credit risk assessment of high-tech enterprises based on RSNCL-ANN ensemble model 基于RSNCL-ANN集成模型的高新技术企业信用风险评估

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208801

Maoguang Wang, Jiayu Yu, Zijian Ji

引用次数: 1

Prescription fraud detection through statistic modeling 基于统计建模的处方欺诈检测

Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence Pub Date : 2018-04-20 DOI: 10.1145/3208788.3208803

Hongxiang Zhang, Lizhen Wang

引用次数: 3