2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)最新文献_第2页

Regression analysis for Dependent current status data 相关电流状态数据的回归分析

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00111

H. Yan, Yuting Zhou, Xuemei Yang

{"title":"Regression analysis for Dependent current status data","authors":"H. Yan, Yuting Zhou, Xuemei Yang","doi":"10.1109/CACML55074.2022.00111","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00111","url":null,"abstract":"In the current state data, each individual is observed only once, and the only available information is whether the failure event of interest occured during the observation time. In other words, the current state data cannot observe any individual's specific survival time or the failure time, therefore, it is significant different from the normal right-censored data. In this paper, we use the Cox model to construct the model of interested failure time and observation time, because the model contains not only regression coefficient of finite dimension, but also the unknown function of infinite dimension, and there are covariables which cannot be observed, so it is difficult to directly maximize the likelihood function. Therefore, the non-observable latent variable is introduced to describe the dependence of two kinds of time, the step function is used to approximate the unknown function to reduce the difficulty of non-parametric part, further the parameter estimation is given by the EM algorithm, the consistency and asymptotic of the estimators are also certified. Some data simulations are performed, whose results show that the method presented here performed well under a limited sample. In the following paper, a group of mouse experiments demonstrating that the sterile environment has no significant effect on tumor inhibition. This paper only considered the current state data and the Cox model, In the futher, the statistical inference problem under other more general and more complex models can be further considered.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129877122","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

Lnnovation of Application Mode of Credit Data in E-commerce Platform 电子商务平台信用数据应用模式创新

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00095

Deng Feng

引用次数: 0

Cancer metastasis fast location based on coarse-to-fine network 基于粗到细网络的肿瘤转移快速定位

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00044

Rui-cang Wang, Yun Gu, Jie Yang

引用次数: 0

Semi-online Algorithms on Two Hierarchical Machines with Reassignment 两重分配层次机器的半在线算法

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00129

Shuliang Zhao

引用次数: 0

Secret Numerical Interval Decision Protocol for Protecting Private Information and Its Application 隐私信息保密数字区间决策协议及其应用

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00126

Shaofeng Lu, Chengzhe Lv, W. Wang, Changqing Xu, Huadan Fan, Yuefeng Lu, Yulong Hu, Wenxing Li

引用次数: 0

Detecting Water Quality Using KNN, Bayesian and Decision Tree 基于KNN、贝叶斯和决策树的水质检测

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00061

X. Jia

引用次数: 1

Intellegent Coal Gangue Sorting System Based on YOLOv5 基于YOLOv5的煤矸石智能分选系统

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00088

Pan Xin, Z. Dong

引用次数: 0

Adaptive Multi-tasking Framework for Video Action Proposal Localization 视频动作提案定位的自适应多任务框架

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00023

H. Jia

{"title":"Adaptive Multi-tasking Framework for Video Action Proposal Localization","authors":"H. Jia","doi":"10.1109/CACML55074.2022.00023","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00023","url":null,"abstract":"This paper focuses on the improvement of the accuracy of activity detection for multi-camera/extended video stream. Most existing methods typically sample frames from video, which is applied by sliding window method. Action localization in video can be divided into different phases: temporal proposal generation and action classification. In the part of proposal generation stage, most of the works choose the static sampling method, that is, in the evaluation stage, the same sampling rules are followed for any input videos. We figure that there is also guidance information for generating a proposal in the training data. In this paper, We propose a Adaptive Multi-tasking Framework, to deliver proposals according to the input video automatically. For each video, we can first establishes a mapping from visual signals to proposal bounding, the starting and ending frames for the proposal, and then combine the generated proposal with state-of-art model SlowFast to finish the action classification task. The framework in this practice was defined as Adaptive Proposal Generation Network(APGN). We train and test our model on the VIRAT dataset, which consists of real outdoor video with non-actors actions. We hope that the accuracy of activity detection will be enhanced by combining our model with some existing activity detection network which based on the old fashion methods. By testing with SlowFast network, we achieve the improvement of Mean Average Precision(mAP) by more than 10 percent. We believe that by replacing the typical sliding window framework with our proposed framework, other models can enhance accuracy and performance, which we will explore more in the future work.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114480167","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

Design of Automatic Recycling Robot Based on YOLO Target Detection 基于YOLO目标检测的自动回收机器人设计

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00059

Chuan Li, Manming Shu, Ling Du, Haoyue Tan, Lang Wei

引用次数: 0

ST-COVID: a Deep Multi-View Spatio-temporal Model for COVID-19 Forecasting ST-COVID:一种新型冠状病毒肺炎深度多视角时空预测模型

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00133

Chang Ju, Jingping Wang, Yingjun Zhang, Hui Yin, Hua Huang, Hongli Xu

引用次数: 0