检索结果-内蒙古大学图书馆

2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013

作者： Hu, Rong Dou, Wanchun Liu, Jianxun State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Sci and Tec Xiangtan Hunan China

ISBN: (纸本)9781479948970

Spurred by services computing and Web 2.0, more and more mashups are emerging on the Internet. The overwhelming mashups become too large to be effectively recommended by traditional methods. In view of this challenge, we propose a clustering-based collaborative filtering approach for mashup recommendation over big data. This approach mainly divided into two phases: clustering and collaborative filtering. By using clustering techniques, the data size is reduced so that the computation time of collaborative filtering algorithm is decreased significantly. Several experiments are done to verify the efficient of the proposed approach at the end of this paper. © 2013 IEEE.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Extracting Campus’Road Network from Walking GPS Trajectories

引用

Journal of Cyber Security 2020年第3期2卷 131-140页

作者： Yizhi Liu Rutian Qing Jianxun Liu Zhuhua Liao Yijiang Zhao Hong Ouyang Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Hunan University of Science and TechnologyXiangtan411201China

Road network extraction is vital to both vehicle navigation and road *** approaches focus on mining urban trunk roads from GPS trajectories of floating ***,path extraction,which plays an important role in earthquake relief and village tour,is always *** this issue,we propose a novel approach of extracting campus’road network from walking GPS *** consists of data preprocessing and road centerline *** patrolling GPS trajectories,collected at Hunan University of Science and Technology,were used as the experimental *** experimental evaluation results show that our approach is able to effectively and accurately extract both campus’trunk roads and *** coverage rate is 96.21%while the error rate is 3.26%.

关键词： Trajectory data mining Location-Based Services(LBS) road network extraction path extraction walking GPS trajectories

来源：评论

学校读者我要写书评

暂无评论

Discovering Batch processing Area from Workflow Logs

Discovering Batch Processing Area from Workflow Logs

引用

IEEE International Conference on Computational Science and Engineering, CSE

作者： Yiping Wen Jianxun Liu Zhigang Chen Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology China School of Information Science and Engineering Central South University Hunan China

Our previous work has preliminarily investigated the batch processing model and its implementation. To solve the problem of setting batch processing areas for the applications of batch processing in workflow, this paper proposes a method of discovering batch processing area from workflow logs. Its mining process includes phrases of mining initial workflow model, identifying batch processing feature of activity and discovering batch processing area in the initial workflow model. The result of simulation experiments shows that this method can work effectively for several different size of data sets.

关键词： Batch production systems Data mining Quality of service Algorithm design and analysis Data models Band pass filters knowledge engineering

来源：评论

学校读者我要写书评

暂无评论

Notice of Retraction: Design and implementation of a total quality management system for Graduation Project

Notice of Retraction: Design and implementation of a total q...

引用

International Conference on Educational and Information Technology (ICEIT)

作者： Li Zhang-bing Liu Jian-xun Zhao Yi-jiang Gong Bo Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan China

This article has been retracted by the publisher.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced personalized search using social data

Enhanced personalized search using social data

引用

2016 Conference on Empirical Methods in Natural Language processing, EMNLP 2016

作者： Zhou, Dong Lawless, Séamus Wu, Xuan Zhao, Wenyu Liu, Jianxun Key Laboratory of Knowledge Processing and Networked Manufacturing and School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan411201 China ADAPT Centre Knowledge and Date Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland

ISBN: (纸本)9781945626258

Search personalization that considers the social dimension of the web has attracted a significant volume of research in recent years. A user profile is usually needed to represent a user's interests in order to tailor future searches. Previous research has typically constructed a profile solely from a user's usage information. When the user has only limited activities in the system, the effect of the user profile on search is also constrained. This research addresses the setting where a user has only a limited amount of usage information. We build enhanced user profiles from a set of annotations and resources that users have marked, together with an external knowledge base constructed according to usage histories. We present two probabilistic latent topic models to simultaneously incorporate social annotations, documents and the external knowledge base. Our web search strategy is achieved using personalized social query expansion. We introduce a topical query expansion model to enhance the search by utilizing individual user profiles. The proposed approaches have been intensively evaluated on a large public social annotation dataset. Results show that our models significantly outperformed existing personalized query expansion methods which use user profiles solely built from past usage information in personalized search. © 2016 Association for Computational Linguistics

关键词： User profile

来源：评论

学校读者我要写书评

暂无评论

WSRank: A method for web service ranking in cloud environment

WSRank: A method for web service ranking in cloud environmen...

引用

9th IEEE Int. Conf. on Dependable, Autonomic and Secure Comput., DASC 2011, incl. 9th Int. Conf. on Pervasive Intelligence and Computing, PICom 2011, 9th Int. Symp. on Embedded Computing, EmbeddedCom 2011, 1st Int. Conf. on Cloud and Green Comput.CGC

作者： Hu, Rong Dou, Wanchun Liu, Xiaoqing Frank Liu, Jianxun State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Computer Science Missouri University of Science and Technology Rolla MO United States Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan Hunan China

ISBN: (纸本)9780769546124

For services that have similar functionalities, if they are published by different cloud platforms, it is a challenge to evaluate them, for satisfying different end users' personal preferences. In view of this challenge, a method for web service ranking, named WSRank, is investigated in cloud environment in this paper. It aims at ranking different Web services published by different cloud platforms, taking advantage of Page Rank principle. At last, a case study and experiment are presented to demonstrate the feasibility of the method. © 2011 IEEE.

关键词： Web services

来源：评论

学校读者我要写书评

暂无评论

Personalized searching for web service using user interests

Personalized searching for web service using user interests

引用

作者： Hu, Rong Dou, Wanchun Liu, Xiaoqing Frank Liu, Jianxun State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Department of Computer Science Missouri University of Science and Technology Rolla MO United States Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan Hunan China

ISBN: (纸本)9780769546124

Users usually have different prospective even they input a same keyword to search Web services. It is a challenge to personalize web service search engine as more and more keyword-like Web services becoming available on Internet. User interest plays an important role in personalizing search result. Therefore, through interest extraction, Web service search engine is personalized. At last, an experiment is presented to demonstrate the feasibility of the method. © 2011 IEEE.

关键词： Web services

来源：评论

学校读者我要写书评

暂无评论

Implementation of a Visual Modeling Tool for Defining Instance Aspect in Workflow

Implementation of a Visual Modeling Tool for Defining Instan...

引用

International Symposium on Parallel and Distributed processing with Applications, ISPA

作者： Jianxun Liu Zefeng Zhu Yiping Wen Jinjun Chen Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Xiangtan Hunan China Faculty of Information and Communication Technologies Swinburne University of Technology Melbourne Australia

The instance-aspect oriented workflow management system is to vertically combine multiple workflow activity instances and submit them for execution as a whole according to some batch or combination logics. It is inspired by the idea of aspect-oriented programming methodology and aims at improving the execution efficiency of business processes. Traditional workflow systems do not support workflow model with instance aspects. In our previous work, we have studied workflow instance modeling technology. This paper makes a research on the principles, methods and implementation of a workflow visual GUI tool for modeling instance aspects in workflow. It is based on an open source GUI tool, together workflow editor, and makes some expansion in instance aspect functionality.

关键词： Logic Graphical user interfaces Packaging Workflow management software XML Engines Information systems Distributed processing Manufacturing processes Virtual manufacturing

来源：评论

学校读者我要写书评

暂无评论

Kernel Ridge Regression Classification

Kernel Ridge Regression Classification

引用

International Joint Conference on Neural Networks

作者： Jinrong He Lixin Ding Lei Jiang Ling Ma School of Computer State Key Laboratory of Software Engineering Wuhan University State Key Laboratory of Software Engineering Wuhan University Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Second Artillery Equipment Academy

ISBN: (纸本)9781479914821

We present a nearest nonlinear subspace classifier that extends ridge regression classification method to kernel version which is called Kernel Ridge Regression Classification (KRRC). Kernel method is usually considered effective in discovering the nonlinear structure of the data manifold. The basic idea of KRRC is to implicitly map the observed data into potentially much higher dimensional feature space by using kernel trick and perform ridge regression classification in feature space. In this new feature space, samples from a single-object class may lie on a linear subspace, such that a new test sample can be represented as a linear combination of class-specific galleries, then the minimum distance between the new test sample and class specific subspace is used for classification. Our experimental studies on synthetic data sets and some UCI benchmark datasets confirm the effectiveness of the proposed method.

关键词： Kernel Classification algorithms Educational institutions Linear regression Training Face recognition Robustness

来源：评论

学校读者我要写书评

暂无评论

CA-NCF: A Category Assisted Neural Collaborative Filtering Approach for Personalized Recommendation

CA-NCF: A Category Assisted Neural Collaborative Filtering A...

引用

Symposium of Image, Signal processing, and Artificial Vision (STSIVA)

作者： Yimin Peng Rong Hu Yiping Wen Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing & Networked Manufacturing Xiangtan China

ISBN: (纸本)9781665426565

In the big data environment, the sparsity problem of collaborative filtering recommendation algorithm becomes increasingly serious, which has a great impact on the accuracy of recommendation. In some recent researches, item categories were input into neural networks to enrich the embedded information in the process of training. However, these methods generally simultaneously use item categories and items as embedded information, which may weaken the importance of item categories. Therefore, this paper proposes a neural collaborative filtering method based on category assistance. In this method, the interaction between item category and user is first modeled by Neural Matrix Factorization ((Neu-MF)), which raises the impact of item category in the relationship extraction between items and users. Then, only the items in the trained results of categories are used in an optimized Neural Collaborative Filtering (NCF) framework for item recommendation. Based on the real ecommerce data set from Alibaba, experimental results show that this method obtains better result in the Hit Rate (HR) and the Normalized Discounted Cumulative Gain (NDCG) compared with other baseline methods.

关键词： Training Collaborative filtering Conferences Neural networks Collaboration Big Data Electronic commerce

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：