2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)最新文献_第7页

An Efficient Parallel Stochastic Gradient Descent for Matrix Factorization On GPUS 基于gpu的矩阵分解的高效并行随机梯度下降算法

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00047

Tianyu Xing, Bin Wu, Bai Wang

引用次数: 0

DSC 2020 Index DSC 2020指数

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/dsc50466.2020.00069

引用次数: 0

A Low-Dimensional Representation Learning Method for Text Classification and Clustering 一种用于文本分类聚类的低维表示学习方法

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00039

Xiang Wang, Yunfan Liao, Junxing Zhu, Bin Zhou, Yan Jia

引用次数: 1

GeST: A Grid Embedding based Spatio-Temporal Correlation Model for Crime Prediction 基于网格嵌入的犯罪时空关联预测模型

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00009

Yiting Qian, Li Pan, Peng Wu, Zhengmin Xia

{"title":"GeST: A Grid Embedding based Spatio-Temporal Correlation Model for Crime Prediction","authors":"Yiting Qian, Li Pan, Peng Wu, Zhengmin Xia","doi":"10.1109/DSC50466.2020.00009","DOIUrl":"https://doi.org/10.1109/DSC50466.2020.00009","url":null,"abstract":"Crime prediction greatly contributes to improving public safety in urban cities. Recent studies have achieved effectiveness by considering spatio-temporal crime distribution correlations among regions. However, with developments of advanced telecommunications and intelligent transportation in urban cities, urban regions tend to be more interacted and integrated, which makes existing methods difficult to capture in-depth geographical and contextual inter-area spatial correlations. To solve the problem, we propose the Grid-embedding based Spatio-Temporal correlation (GeST) model, which consists of grid-embedding module and crime graph prediction module. In the grid-embedding module, the convolutional AutoEncoder can explore distance-based inter-area spatial correlations and decompose crime distributions into hidden crime spatial bases. The bases are regarded as the representation of decomposed crime distribution. The Graph Convolutional Network (GCN) in grid-embedding module can capture contextual spatial correlations among feature-similar regions. After combining two types of grid-embedding vectors, the crime graph prediction module utilizes Long Short-Term Memory (LSTM) neural network to learn temporal correlations of crime distribution. Experiments conducted on two real-world datasets show that the proposed model achieves better prediction performance than other methods.","PeriodicalId":423182,"journal":{"name":"2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125864469","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

Unsupervised Euclidean Distance Attack on Network Embedding 网络嵌入的无监督欧氏距离攻击

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2019-05-27 DOI: 10.1109/DSC50466.2020.00019

Qi Xuan, Jun Zheng, Lihong Chen, Shanqing Yu, Jinyin Chen, Dan Zhang, Qingpeng Zhang

{"title":"Unsupervised Euclidean Distance Attack on Network Embedding","authors":"Qi Xuan, Jun Zheng, Lihong Chen, Shanqing Yu, Jinyin Chen, Dan Zhang, Qingpeng Zhang","doi":"10.1109/DSC50466.2020.00019","DOIUrl":"https://doi.org/10.1109/DSC50466.2020.00019","url":null,"abstract":"Considering the wide application of network embedding methods in graph data mining, inspired by adversarial attacks in deep learning, a genetic algorithm-based Euclidean distance attack strategy is proposed to attack the network embedding method, thereby preventing structure information being discovered. EDA focuses on disturbing the Euclidean distance between a pair of nodes in the embedding space as much as possible through minimal modifications of the network structure. Since many downstream network algorithms, such as community detection and node classification, rely on the Euclidean distance between nodes to evaluate their similarity in the embedded space, EDA can be regarded as a general attack on various network algorithms. Different from traditional supervised attack strategies, EDA does not need labeling information, it is an unsupervised network embedding attack method. Experiments on a set of real networks demonstrate that the proposed EDA method can significantly reduce the performance of DeepWalk-based networking algorithms, i.e., community detection and node classification, and its performance is superior to several heuristic attack strategies.","PeriodicalId":423182,"journal":{"name":"2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131122066","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: 9