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作者： Zheng, Xianghan An, Dongyun Chen, Xing Guo, Wenzhong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

Effective user interest prediction is significant for service providers in a set of application scenarios such as user behavior analysis and resource recommendation. However, existing approaches are either incomplete or proprietary. In this paper, user interest prediction based on the Markov chain modeling on clustered users is proposed with the following procedure: collect dataset from 4613 users and more than 16 million messages from Sina Weibo;obtain each user's interest eigenvalue sequence and establish single-Markov chain model;and implement user clustering algorithm for the multi-Markov chain construction in order to divide users into a set of predefined interest categories. The proposed solution is capable of predicting both long-term and short-term user interests based on a suitable selection of the initial state distribution, λ. The proposed solution also proves that short-term interests are consistent with long-term interests if the influences of social or user-related events that cause interruptions (e.g., earthquake and birthday) are not considered. Furthermore, experiments show that the proposed solution is feasible and efficient and can achieve a higher accuracy of prediction than that of the other approaches such as Support Vector Machine (SVM) and K-means. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： Clustering algorithms

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Failure Handling of E-Business Services in an Extensible Constraint Service Model

Failure Handling of E-Business Services in an Extensible Con...

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2013 IEEE 4th International Conference on Software Engineering and Service Science

作者： Ruoyu Chen Lejian Liao Zhi Fang Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications Beijing Key Laboratory of Intelligent Information TechnologySchool of Computer ScienceBeijing Institute of Technology

ISBN: (纸本)9781467349994

Failure handling of business transactions is essential in E-Business *** paper extends the service process specification proposed in a contract-centered constraint-based service modelling framework with failure handling *** failure in process model,generalized failure such as QoS violation can be modelled in *** semantics for failure handling in E-Business services as well as policies are discussed via a state transition system.A virtual machine is built for contracting and executing of services under this framework.

关键词： Services service Failure Handling of E-Business Services in an Extensible Constraint Service Model

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A Cell Condition-Sensitive Frequency Segmentation Method Based on the Sub-Band Instantaneous Energy Spectrum of Aluminum Electrolysis Cell Voltage

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Engineering 2021年第9期7卷 1282-1292页

作者： Zhaohui Zeng Weihua Gui Xiaofang Chen Yongfang Xie Hongliang Zhang Yubo Sun School of Automation Central South UniversityChangsha 410083China Key Laboratory of Intelligent Computing&Information Processing Ministry of EducationXiangtan UniversityXiangtan 411105China School of Metallurgy and Environment Central South UniversityChangsha 410083China

Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell.A variety of parameters for the analysis and control of industrial cells are calculated using the cell *** this paper,the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these *** on the qualitative analysis of the cell voltage,the sub-band instantaneous energy spectrum(SIEP)is first proposed,which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell ***,a cell condition-sensitive frequency segmentation method is *** proposed frequency segmentation method divides the effective frequency band into the[0,0.001]Hz band of lowfrequency signals and the[0.001,0.050]Hz band of low-frequency noise,and subdivides the lowfrequency noise into the[0.001,0.010]Hz band of metal pad abnormal rolling and the[0.01,0.05]Hz band of sub-low-frequency *** with the instantaneous energy spectrum based on empirical mode decomposition,the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell *** proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information,thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.

关键词： Sub-band instantaneous energy spectrum Cell condition-sensitive frequency band Frequency segmentation Metal pad abnormal rolling Aluminum electrolysis

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Parallel decision tree with application to water quality data analysis 1

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9th International Symposium on Neural Networks, ISNN 2012

作者： He, Qing Dong, Zhi Zhuang, Fuzhen Shang, Tianfeng Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China

ISBN: (数字)9783642313622

ISBN: (纸本)9783642313615

Decision tree is a popular classification technique in many applications, such as retail target marketing, fraud detection and design of telecommunication service plans. With the information exploration, the existing classification algorithms are not good enough to tackle large data set. In order to deal with the problem, many researchers try to design efficient parallel classification algorithms. Based on the current and powerful parallel programming framework - MapReduce, we propose a parallel ID3 classification algorithm(PID3 for short). We use water quality data monitoring the Changjiang River which contains 17 branches as experimental data. As the data are time series, we process the data to attribute data before using the decision tree. The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 Springer-Verlag.

关键词： MapReduce

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Lightweight spatial pyramid pooling network for real-time semantic segmentation

Lightweight spatial pyramid pooling network for real-time se...

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2021 International Symposium on intelligent Robotics and Systems, ISoIRS 2021

作者： Jiang, Peilong Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China

In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic segmentation model called LSPPNet which is specially designed for real-time application. We have done an exhaustive experiment to evaluate LSPPNet in the challenge urban street scenes datasets Cityscapes. Extensive experiment shows that LSPPNet gets a better trade-off between segmentation performance and inference speed. We test LSPPNet on an NVIDIA 2080 super graphics card and it can achieve 75.8% MIoU in Cityscapes test set in real-time speed. © Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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Frequent term based peer-to-peer text clustering

Frequent term based peer-to-peer text clustering

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2010 3rd International Symposium on Knowledge Acquisition and Modeling, KAM 2010

作者： He, Qing Li, Tingting Zhuang, Fuzhen Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University Chinese Academy of Sciences China

ISBN: (纸本)9781424480050

Text clustering is an important technology for automatically structuring large document collections. It is much more valuable in peer-to-peer networks. T he high dimensionality of documents means much more communication could be saved if each node could get the approximate clustering result by distributed algorithm instead of transferring them into a center and do the clustering. Most of the existing text clustering algorithms in unstructured peer-to-peer networks are based on K-means algorithm. A problem of those algorithms is that the clustering quality may decreased with the increase of the network size. In this paper, we propose a text clustering algorithm based on frequent term sets for peer-to-peer networks. It requires relatively lower communication volume while achieving a clustering result whose quality will not be affected by the size of the network. Moreover, it gives a term set describing each cluster, which makes it possible for people to have a clear comprehension for the clustering result, and facilitates the users to find resource in the network or manage the local documents in accordance with the whole network. © 2010 IEEE.

关键词： Peer to peer networks

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TINAC: A fast and effective web video topic detection framework

TINAC: A fast and effective web video topic detection framew...

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2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

作者： Ao, Xiang Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China

ISBN: (纸本)9781467300223

Most of the previous works for web video topic detection(e.g., graph-based co-clustering method) always encounter the problem of real-time topic detection, since they all suffer from the high computation complexity. Therefore, a fast topic detection is needed to meet users' or administrators' requirement in real-world scenarios. Along this line, we propose a fast and effective topic detection framework, in which video streams are first partitioned into buckets using a time-window function, and then an incremental hierarchical clustering algorithm is developed, finally a video-based fusion strategy is used to integrate information from multiple modalities. Furthermore, a series of novel similarity metrics are defined in the framework. The experimental results on three months' YouTube videos demonstrate the effectiveness and efficiency of the proposed method. © 2012 IEEE.

关键词： Graphic methods

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Theory for semantic relation computing and its application in semantic link network

Theory for semantic relation computing and its application i...

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2012 8th International Conference on Semantics, Knowledge and Grids, SKG 2012

作者： Sun, Yunchuan Lu, Cheng Yan, Hongli Bie, Rongfang Beijing Normal University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769547947

Semantic relation among different objects is one of the most important kinds of semantics which plays the primary role for people and intelligent systems in grasping the situation accurately in the context of connected systems. The semantic relations are viewed as the most important elements in most existing information models (such as ER model, RDF and SLN) during the mapping from the physical world into the cyber world, where efficient ways of representation for semantic relations are developed. However, all these models deal with the semantic relations in a simple and intuitive way. Few works go deep into the study of semantic relations from a mathematic view. This paper aims at exploring a mathematic theory of the semantic relations between objects including representing methods, normal forms of operations, semantic orthogonal basis of the semantic relations. We also discuss the reasoning mechanism based on the proposed theory. The applications of the developed theory in semantic link network are discussed finally. © 2012 IEEE.

关键词： intelligent systems

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A noise handling method for hyper surface classification

A noise handling method for hyper surface classification

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2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Li, Tingting Zhuang, Fuzhen He, Qing Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University Chinese Academy of Sciences China

ISBN: (纸本)9781424459346

Hyper surface classification (HSC) based on Jordan Curve Theorem is proven to be a simple and effective method to classify large datasets. Like most of classification algorithms, noise could also impact its accuracy even if the HSC algorithm limits the influence of noise in a local small region. In this paper, we propose a method that intuitively captures the primary goal of improving the accuracy of HSC when trained on noisy training datasets. The proposed method uses a separate pruning set to test whether the hyper surfaces covering few samples are assigned wrong labels due to the existence of noise. And then reassigns them appropriate labels if necessary. We compare the performance of HSC with and without the noise handling method. The promising experimental results indicate that the noise handling method can improve the accuracy of HSC when trained on noisy datasets, while keeping good performance when it is applied to datasets without noise. At the same time, it also reduces the model complexity of HSC to some extent. ©2010 IEEE.

关键词： Classification (of information)

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A New Network Intrusion Detection based on Semi-supervised Dimensionality Reduction and Tri-LightGBM 1

A New Network Intrusion Detection based on Semi-supervised D...

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1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Zhang, Hao Li, Jieling Fuzhou University College of Mathematics and Computer Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9781665404839

With the development of technology and threat forms, network intrusion detection has become a challenging task. The intrusion detection algorithm based on supervised learning requires a lot of manpower and material resources to obtain a large amount of labeled data. Besides, the accuracy of unsupervised learning can not meet the requirements of intrusion detection systems. We propose a semi-supervised network intrusion detection method in this paper. information Gain is employed to filter redundancy features. Then, we combine labeled samples with unlabeled samples and adopt Principal Component Analysis (PCA) to convert multiple features into comprehensive features. Finally, an Tri-Training strategy is adopted to integrate the basic LightGBM classifier, and make full use of unlabeled data to generate pseudo labels, thereby optimizing the basic LightGBM classifier. To verify the effectiveness of the proposed approach, a large number of experiments are performed on the UNSW-NB15 dataset. The experimental results fully show that the method is superior in improving detection efficiency and reducing label dependence, and has a lower false alarm rate and a higher detection rate. © 2020 IEEE.

关键词： Principal component analysis

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