2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)最新文献_第4页

Creating Cyber Deception Games 制作网络欺骗游戏

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00023

M. Major, Sunny Fugate, Justin Mauger, Kimberly J. Ferguson-Walter

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引用次数: 0

Discovery of Action Rules for Continuously Valued Data 连续值数据的动作规则发现

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00026

T. Johnsten, W. Green, Lowell Crook, R. Chan, Ryan G. Benton, David M. Bourrie

引用次数: 1

Deep Heterogeneous Social Network Alignment 深度异构社会网络对齐

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00015

Lin Meng, Yuxiang Ren, Jiawei Zhang, Fanghua Ye, Philip S. Yu

{"title":"Deep Heterogeneous Social Network Alignment","authors":"Lin Meng, Yuxiang Ren, Jiawei Zhang, Fanghua Ye, Philip S. Yu","doi":"10.1109/CogMI48466.2019.00015","DOIUrl":"https://doi.org/10.1109/CogMI48466.2019.00015","url":null,"abstract":"The online social network alignment problem aims at inferring the anchor links connecting the shared users across social networks, which are usually subject to the one-to-one cardinality constraint. Several existing social network alignment models have been proposed, many of which are based on the supervised learning setting. Given a set of labeled anchor links, a group of features can be extracted manually for the anchor links to build these models. Meanwhile, such methods may encounter great challenges in the application on real-world social network datasets, since manual feature extraction can be extremely expensive and tedious for the social networks involving heterogeneous information. In this paper, we propose to address the heterogeneous social network alignment problem with a deep learning model, namely DETA (Deep nETwork Alignment). Besides a small number of explicit features, DETA can automatically learn a set of latent features from the heterogeneous information. DETA models the anchor link one-to-one cardinality constraint as a mathematical constraint on the node degrees. Extensive experiments have been done on real-world aligned heterogeneous social network datasets, and the experimental results have demonstrated the effectiveness of the proposed model compared against the existing state-of-the-art baseline methods.","PeriodicalId":116160,"journal":{"name":"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116983727","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

Improved Res2Net Model for Person re-Identification 改进的Res2Net人物再识别模型

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-10-08 DOI: 10.1109/CogMI48466.2019.00041

Zongjing Cao, H. Lee

引用次数: 2