2016 13th Web Information Systems and Applications Conference (WISA)最新文献

Research on Optimization of Clustering by Fast Search and Find of Density Peaks 基于快速搜索和发现密度峰的聚类优化研究

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.35

Jinlong Zheng, Hao Sun, Mingxin Zhang, Guohai Zhang

引用次数: 8

Research on Android Access Control Based on Isolation Mechanism 基于隔离机制的Android访问控制研究

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.53

Ying Peng, Mingxin Zhang, Jinlong Zheng, Z. Qian

引用次数: 3

Research on Multi-Objective Dynamic Cloud Service Strategy 多目标动态云服务策略研究

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.25

Chun-Yan Liu, Cheng-Ming Zou

引用次数: 1

Differential Evolution-Based Fusion and Its Properties for Web Search 基于差分进化的网络搜索融合及其特性研究

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.23

Jinsong Xia, Chunlin Xu, Shengli Wu

引用次数: 1

A Hybrid Method Based on Cuckoo Search and Krill Herd Optimization with Differential Evolution 基于布谷鸟搜索和差分进化磷虾群优化的混合方法

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.37

Zichen Zhang, Yongquan Dong, Tongfei Gao

引用次数: 1

Group Detection for Specific Topic on Micro-Blog 微博特定话题分组检测

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.36

Xiaolei Fu, Mingwang Gao, Qiang Liu, Hongmei Liu

引用次数: 1

A Method to Identify Exercises from Web Resources 从网络资源中识别练习的方法

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.47

Xiangdong Che, Yukun Li, Xinzeng Zhang

引用次数: 0

Combining Influence and Sensitivity to Factorize Matrix for Multi-Context Recommendation 结合因子矩阵的影响和敏感性的多上下文推荐

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.24

Qingna Zhao, Yue Kou, Derong Shen, Tiezheng Nie, Ge Yu

{"title":"Combining Influence and Sensitivity to Factorize Matrix for Multi-Context Recommendation","authors":"Qingna Zhao, Yue Kou, Derong Shen, Tiezheng Nie, Ge Yu","doi":"10.1109/WISA.2016.24","DOIUrl":"https://doi.org/10.1109/WISA.2016.24","url":null,"abstract":"With the growing amount of information available online, context-aware recommender systems have emerged to improve the precision of recommendation. Matrix factorization models are the state-of-the-art in these systems, especially for multi-context recommendation. However, existing models ignored either context influence or entity sensitivity. That is, they assume that one entity (user or item) shares the same factors across different contexts, or one context shares the same influence across different entities. In fact, for one context (or entity), its influence (or sensitivity) may be different with the changes of entities (or contexts). In this paper, we present a matrix factorization model for multi-context recommendation (namely ISMF). Unlike traditional models, ISMF considers both context influence and entity sensitivity. Instead of enforcing the same factors for each entity, we detail the factors as entity-intrinsic factors and entity-specific factors to represent entity-itself and context influence respectively. Meanwhile, we use some parameters acting on these factors to represent entity sensitivity. Also a matrix factorization algorithm for ISMF is proposed. We iteratively determine the factors and relevant parameters to maintain the precision for recommendation. The experiments demonstrate the feasibility and effectiveness of our method.","PeriodicalId":308797,"journal":{"name":"2016 13th Web Information Systems and Applications Conference (WISA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127802612","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

A Multilayer Collaborative Filtering Recommendation Method in Electricity Market 电力市场中一种多层协同过滤推荐方法

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.20

L. Tao, Ming-hai Jiao, Yong Dai, Chuncheng Gao

引用次数: 2

Hybrid Intrusion Detection Method Based on Improved Bisecting K-Means in Cloud Computing 云计算中基于改进等分k均值的混合入侵检测方法

2016 13th Web Information Systems and Applications Conference (WISA) Pub Date : 2016-09-01 DOI: 10.1109/WISA.2016.52

Xinlong Zhao, Weishi Zhang

{"title":"Hybrid Intrusion Detection Method Based on Improved Bisecting K-Means in Cloud Computing","authors":"Xinlong Zhao, Weishi Zhang","doi":"10.1109/WISA.2016.52","DOIUrl":"https://doi.org/10.1109/WISA.2016.52","url":null,"abstract":"Compared with traditional IT infrastructure, cloud computing system has many new features, such as virtualization and data outsourcing. The new architecture brings about higher resource utilization, and also introduces a number of new security risks. Therefore we need to study new intrusion detection technology suitable to cloud computing environment to address these risks. Aiming at characteristics and security challenges of cloud computing, and based on silhouette coefficient and partitioned clustering subspace method to improve the bisecting K-means unsupervised learning method, this paper proposes a hybrid distributed intrusion detection method capable of detecting both known and unknown attacks simultaneously. The method can obtain the value of initial cluster center more stably, and generate cluster center table of intrusion detection engine, reduce false positive rates and false negative rates of known attacks, achieve unknown attack detection, and accelerate the speed of intrusion detection. At last, it has simulation experiments aimed at this method, and analyzes test results compared with standard K-means algorithm, and shows good practical value of this method.","PeriodicalId":308797,"journal":{"name":"2016 13th Web Information Systems and Applications Conference (WISA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132721201","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: 5