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Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence最新文献

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Anomaly Detection of Storage Battery Based on Isolation Forest and Hyperparameter Tuning 基于隔离森林和超参数整定的蓄电池异常检测
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395271
Chun-Hsiang Lee, Xu Lu, X. Lin, Hongfeng Tao, Yaolei Xue, Chao Wu
{"title":"Anomaly Detection of Storage Battery Based on Isolation Forest and Hyperparameter Tuning","authors":"Chun-Hsiang Lee, Xu Lu, X. Lin, Hongfeng Tao, Yaolei Xue, Chao Wu","doi":"10.1145/3395260.3395271","DOIUrl":"https://doi.org/10.1145/3395260.3395271","url":null,"abstract":"The safety of an uninterruptible power supply (UPS) unit is very important in the operation of a telecommunication room. It is necessary to identify and replace abnormal electrical batteries of the UPS to ensure the normal operation of the equipment. In this paper, a single-model method based on isolation forest and hyperparameter tuning is proposed for detecting abnormal batteries. Experimental results show that the proposed method is efficient in offline situations. A multi-model method is also proposed to deal with the online anomaly detection problem, which is found performing well.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124070907","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: 3
Rank-IDF: A Statistical and Network Based Feature Words Selection in Big Data Text Analysis Rank-IDF:基于统计和网络的大数据文本分析特征词选择
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395291
S. Long, Li Yan
{"title":"Rank-IDF: A Statistical and Network Based Feature Words Selection in Big Data Text Analysis","authors":"S. Long, Li Yan","doi":"10.1145/3395260.3395291","DOIUrl":"https://doi.org/10.1145/3395260.3395291","url":null,"abstract":"As big data for text has been one of the core data types in the era of artificial intelligence, feature words selection technique become increasingly important in big data text analysis. The traditional statistical TF-IDF feature words selection algorithm lacks the semantic information extraction ability of text, while the network model Textrank applies the sentence semantic features to feature calculation between words. Network model such as Textrank is very suitable for text feature selection, but it does not take influencing factors of the relationship between documents into consideration, so common words appearing frequently in feature words selected result. Based on the analysis of both feature words selection method, this paper raises a combination of statistical and network model integrated the advantages of Textrank and TF-IDF, and proposes a text feature selection method based on Rank-IDF. The Rank-IDF algorithm has better feature selection and common word filtering effects.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124198071","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: 1
The Concrete Complexity of LWE without Gaussian Sampling 无高斯抽样LWE的具体复杂度
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395281
Xi Qian, Jiu-fen Liu, Chunxiang Gu, Yonghui Zheng
{"title":"The Concrete Complexity of LWE without Gaussian Sampling","authors":"Xi Qian, Jiu-fen Liu, Chunxiang Gu, Yonghui Zheng","doi":"10.1145/3395260.3395281","DOIUrl":"https://doi.org/10.1145/3395260.3395281","url":null,"abstract":"The LWE problem is a widely used tool in cryptography because of the well hardness and cryptographic versatility. In consideration of efficiency, a variant of LWE---LWE uniform errors, is proposed with some related work. However, no conclusion has been given to the complexity analysis for the LWE without Gaussian sampling of restricted samples. By combining the attack methods of standard LWE problem, we expand them to the discussion of the variant successfully. As a result, the complexity analysis results are given, and the estimations of runtime are also provided.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125706922","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
Pruning algorithm of convolutional neural network based on optimal threshold 基于最优阈值的卷积神经网络剪枝算法
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395300
Jianjun Wang, Leshan Liu, Ximeng Pan
{"title":"Pruning algorithm of convolutional neural network based on optimal threshold","authors":"Jianjun Wang, Leshan Liu, Ximeng Pan","doi":"10.1145/3395260.3395300","DOIUrl":"https://doi.org/10.1145/3395260.3395300","url":null,"abstract":"In the process of pruning, in order to automatically obtain an optimal pruning threshold that can balance the maximum sparse rate and the minimum error. This paper proposes a convolutional neural network pruning algorithm based on the optimal threshold. The algorithm uses the optimization ability of the greedy algorithm to select an optimal threshold, and uses the sensitivity and correlation of the node as factors to determine whether the node is important. Then by deleting the nodes whose importance is below the optimal threshold, the purpose of pruning the network is achieved. Experiments show that under the premise of loss accuracy within 2%, the algorithm can test the Lenet-5 network pruning on the M-NIST data set, which can accelerate 36.62%. This algorithm tests the VggNet network pruning on the CIFAR-10 dataset, which can speed up 43.86%. Experiments show that the algorithm effectively reduces network parameters and reduces running time.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128444071","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
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence 2020年第五届国际数学与人工智能会议论文集
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.1145/3395260
{"title":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","authors":"","doi":"10.1145/3395260","DOIUrl":"https://doi.org/10.1145/3395260","url":null,"abstract":"","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"44 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120866802","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
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