Proceedings of the 3rd International Conference on Advanced Information Science and System最新文献_第3页

Research on Calculation Method of Software Service Risk Occurrence Probability Based on Markov Chain Theory 基于马尔可夫链理论的软件服务风险发生概率计算方法研究

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503097

Xiaohui Jia, Shuyang Qu, Tilei Gao, Bingfu Mou

引用次数: 0

Attention-Based Sub-Word Network for Multilingual Short Text Classification 基于注意的多语言短文本分类子词网络

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503060

Yaru Sun, Ying Yang, Yongjian Wang

引用次数: 0

Comparison of the proportional hazard model and the accelerated failure model in the mixed cure model 混合固化模型中比例风险模型与加速破坏模型的比较

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503050

Yuting Zhou, Xuemei Yang, Xiaoying Wang, Junping Yin

{"title":"Comparison of the proportional hazard model and the accelerated failure model in the mixed cure model","authors":"Yuting Zhou, Xuemei Yang, Xiaoying Wang, Junping Yin","doi":"10.1145/3503047.3503050","DOIUrl":"https://doi.org/10.1145/3503047.3503050","url":null,"abstract":"Traditional survival analysis models such as the Cox model and the accelerated failure time model (AFT) assume that all individuals will eventually experience specified endpoint events, such as recurrence or death. However, in recent years, with the Advancement of science and technology and the improvement of medical standards, in many clinical trials, there are some individuals who will not experience terminal events after treatment, that is, they will not relapse or die. The researchers believe that these individuals have been cured and call them long-term survivors. In this case, using the traditional Cox model and the AFT model will cause large errors and affect the judgment. Therefore, we consider applying a mixed healing model to the data. In the previous period, we have compared the model of proportional risk function and proportional risk mixed healing model and accelerated failure function model with accelerated failure mixed healing model. In this paper, we want to compare the predicted effects of the PHMC model and the AFTMC model. Methods: We use Monte Carlo simulations to generate data that satisfy and do not satisfy proportional assumptions. Using the consistency probability, the average square error of regression coefficient and 95% confidence interval to cover the original parameter as the evaluation index, the discriminant precision and fitting effect of the same data are compared. Result: For the survival data based on the assumption of proportional risk, the fitting effect of PHMC model is more accurate than that of AFTMC model. For the survival data based on the assumption that the proportional risk is not satisfied, the fitting effect of AFTMC model is better than that of PHMC model. Conclusion: The PHMC model is recommended for survival data based on the assumption of proportional risk assumptions. The AFTMC model is recommended for survival data based on the assumption that the proportional risk is not met.","PeriodicalId":190604,"journal":{"name":"Proceedings of the 3rd International Conference on Advanced Information Science and System","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116265032","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

Combined Metapath Based Attention Network for Heterogenous Networks Node Classification 基于组合元路径的异构网络注意网络节点分类

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503109

Kang Chen, Dehong Qiu

{"title":"Combined Metapath Based Attention Network for Heterogenous Networks Node Classification","authors":"Kang Chen, Dehong Qiu","doi":"10.1145/3503047.3503109","DOIUrl":"https://doi.org/10.1145/3503047.3503109","url":null,"abstract":"In recent years, Graph Neural Networks(GNNs) have been widely used as representation learning methods on graphs especially homogeneous graphs, and demonstrated remarkable performance in various tasks. However, GNNs on Heterogeneous Graphs(HGs) haven’t been fully explored, and existing methods on HGs use either metapaths to extract semantics on generated graphs or construct attention mechanics to deal with the original graph directly. The former methods strongly depend on metapaths which makes their performance unstable, and the latter ones can hardly capture deep patterns on HGs as metapaths do. In this paper, we classify information between HG nodes into two parts, prior node information and direct node information, and propose a Combined metapath based Attention Network(CAN) to combine them that making up each one’s disadvantages. Moreover, any number of metapaths can be used in CAN which makes the proposed method more flexible. Based on metapaths we extract the prior node information, and with a novel attention mechanism, we extract the direct node information. Through additional semantic-level attention, we combine them into unique representations. Node classification experiments on real-world datasets demonstrate the performance of the proposed method.","PeriodicalId":190604,"journal":{"name":"Proceedings of the 3rd International Conference on Advanced Information Science and System","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131290351","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

A Residual Life Prediction model for Bivariate Degraded Products Based on Copula and Particle Filtering 基于Copula和粒子滤波的二元降解产物剩余寿命预测模型

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503058

Tianyu Liu, Lei Yao, Bo Qiu, Pengqizi Huang

{"title":"A Residual Life Prediction model for Bivariate Degraded Products Based on Copula and Particle Filtering","authors":"Tianyu Liu, Lei Yao, Bo Qiu, Pengqizi Huang","doi":"10.1145/3503047.3503058","DOIUrl":"https://doi.org/10.1145/3503047.3503058","url":null,"abstract":"Abstract: Prognostics and health management (PHM) plays a significant role for products in field conditions. An accurate residual life (RL) prediction model is the core technique of PHM. Recently, most publications focus on the data-driven RL prediction methods based on degradation data. However, for some products with complex failure mechanisms, RL prediction is not a trivial task: (1) product fails due to degradation of more than one feature in its lifespan; (2) multi-features increase degradation model complexity and make RL updating intractable. This paper proposes a RL prediction method for bivariate degraded products. Two dependent features degrade over time and codetermine the failure time of a product. Evolution of each feature is modeled by a Wiener process, and their marginal cumulative distribution functions are connected by a Frank Copula function to characterize the dependence. To ensure accuracy of RL prediction, a particle filtering algorithm is used to update these parameters once new degradation data are available. Finally, some numerical examples and a real-world case of Lithium ion batteries are conducted to demonstrate the validation of the proposed method.","PeriodicalId":190604,"journal":{"name":"Proceedings of the 3rd International Conference on Advanced Information Science and System","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126594437","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

A pattern-spectrum-based AP method for classification of noised speech signals 一种基于模式谱的带噪语音信号分类方法

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503123

C. Wang

引用次数: 0

Automatic selection of ambient noise observation stations based on the clustering algorithm 基于聚类算法的环境噪声观测站自动选择

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503121

Xiaohua Zhou, Xinkai Meng, Guanghui Sun, Jainbin Zheng, Wenrui Ye

{"title":"Automatic selection of ambient noise observation stations based on the clustering algorithm","authors":"Xiaohua Zhou, Xinkai Meng, Guanghui Sun, Jainbin Zheng, Wenrui Ye","doi":"10.1145/3503047.3503121","DOIUrl":"https://doi.org/10.1145/3503047.3503121","url":null,"abstract":"In order to avoid increasing the workload of correlation function calculation for ambient noise tomography from intensive observation stations, a clustering method based on improved DBSCAN for ambient noise observation stations algorithm is proposed to improve data processing efficiency. According to the ambient noise tomography principle, the main influencing factors of Green's function retrieving are analyzed. Combined with the actual situation of ambient noise observation station arrangement, the selection method of main parameters in cluster algorithm is given. 155 seismic observatory stations in the North America are clustered to improve data processing efficiency. The results show that the overall efficiency of correlation function calculation and superposition is increased by 15.1%, the total time of extraction and screening of dispersion curve is reduced by 18.7%, and the average time of ambient noise tomography data processing is reduced by 12.6% compared with that before clustering, while the quality of ambient noise tomography is guaranteed by clustering processing of intensive ambient noise observation stations.","PeriodicalId":190604,"journal":{"name":"Proceedings of the 3rd International Conference on Advanced Information Science and System","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127312334","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

Groovy Pixels: Generating Drum Set Rhythms from Images Groovy像素:从图像生成鼓组节奏

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503119

Yanjia Zhang

引用次数: 1

Image-based Ransomware Classification with Classifier Combination 基于图像的分类器组合勒索软件分类

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503083

Bidong Wang, Hui Liu, X. Han, Dongliang Xuan

引用次数: 3

Network Embedding Method Based on Semantic Information 基于语义信息的网络嵌入方法

Proceedings of the 3rd International Conference on Advanced Information Science and System Pub Date : 2021-11-26 DOI: 10.1145/3503047.3503133

Dongjie Li, Dong Li, Chuanpeng Wang, Yinan Chen

{"title":"Network Embedding Method Based on Semantic Information","authors":"Dongjie Li, Dong Li, Chuanpeng Wang, Yinan Chen","doi":"10.1145/3503047.3503133","DOIUrl":"https://doi.org/10.1145/3503047.3503133","url":null,"abstract":"Graph embedding is the resultful method to map the graph in the low-dimensional vector space. Now most existing embedding methods to learn nodes representations mainly focus on obtaining nodes adjacent and feature information, but they ignore the state that there is also semantic information between nodes. Therefore, it is proposed a graph embedding method, which introduces point mutual information to compute the semantic similarity between nodes, the basic idea is to count the probability of two nodes appearing simultaneously in a sentence. And it learns representations by modeling the sum of the squares of the difference between point-wise mutual information and the inner product of node vectors, and theoretically shows that using point mutual information can also obtain a log-linear relationship between graph topological by leveraging the invariance property of difference between nodes. Finally, the study selects 5 social networks datasets for node classifications, clustering tasks, and compares them with 6 graph embedding methods and 4 methods based on graph neural network, the result demonstrates that the direct method does not negatively impact accuracy on many downstream applications, and outperforms all the baseline methods. In addition, the computation complexity of our method is lower than the worst-case.","PeriodicalId":190604,"journal":{"name":"Proceedings of the 3rd International Conference on Advanced Information Science and System","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124928545","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