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Advances in intelligent Systems and computing 2014年 215卷 603-616页

作者： Chen, Guang Song, Dandan Liao, Lejian Beijing Eng. Research Center of High Volume Language Inf. Processing and Cloud Computing App. Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing China

Brain-computer interface (BCI) is a new way for man-machine interaction with wide applications, in which steady-state visual evoked potentials (SSVEP) is a promising option. However, many characteristics of SSVEP show great user variation. So parameter optimization and channel selection for each subject were applied to improve the performance of BCI. These optimizations limit the practical applicability of the SSVEP-based BCI. The use of a canonical correlation analysis (CCA) method for multi-channel SSVEP in the time domain detection showed highly increased detection accuracy, but it is sensitive to the noise when the stimulate frequency is low. In this paper, a method of CCA in the frequency domain is presented for classifying multi-channel SSVEPs. First overlapping average is conducted on the original training signals. Then fast Fourier transform (FFT) is used to transform the signals from time domain to frequency domain to produce the reference data. Finally, according to the correlation coefficients of the new data and the references in the frequency domain, the SSVEP is classified. The experimental results show the enhanced accuracy of our method when applied to low stimulate frequencies. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Brain computer interface

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Ratable Aspects over Sentiments: Predicting Ratings for Unrated Reviews

Ratable Aspects over Sentiments: Predicting Ratings for Unra...

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IEEE International Conference on Data Mining (ICDM)

作者： Wenjuan Luo Fuzhen Zhuang Xiaohu Cheng Qing He Zhongzhi Shi Renren Inc Beijing China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing CAS China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479943012

Most existing rat able aspect generating methods for aspect mining focus on identifying and rating aspects of reviews with overall ratings, while huge amount of unrated reviews are beyond their ability. This drawback motivates the research problem in this paper: predicting aspect ratings and overall ratings for unrated reviews. To solve this problem, we novelly propose a topic model based on Latent Dirichlet Allocation with indirect supervision. Compared with the previous bag-of-words representation of review documents, we utilize the quad-tuples of (head, modifier, rating, entity) to explicitly model the associations between modifiers and ratings. Specifically, our solution for aspect mining in unrated reviews is decomposed into three steps. Firstly, rat able aspects are generated over sentiments from training reviews with overall ratings. Afterwards, inference of aspect identification and rating for unrated reviews are provided. Finally, overall ratings are predicted for unrated reviews. Under this framework, aspect and sentiment associations are captured in the form of joint probabilities through a generative process. The effectiveness of our approach is testified on a real-world dataset crawled from Trip Advisor http://***/, and extensive experiments show that our method significantly outperforms state-of-the-art methods.

关键词： Training Feature extraction Gaussian distribution Inference algorithms Equations Mathematical model Numerical models

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Primal least squares twin support vector regression

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第9期14卷 722-732页

作者： Hua-juan HUANG Shi-fei DING Zhong-zhi SHI School of Computer Science and Technology China University of Mining and Technology Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual ***,this solution is affected by time and memory constraints when dealing with large *** this paper,we present a least squares version for TSVR in the primal space,termed primal least squares TSVR (PLSTSVR).By introducing the least squares method,the inequality constraints of TSVR are transformed into equality ***,we attempt to directly solve the two QPPs with equality constraints in the primal space instead of the dual space;thus,we need only to solve two systems of linear equations instead of two *** results on artificial and benchmark datasets show that PLSTSVR has comparable accuracy to TSVR but with considerably less computational *** further investigate its validity in predicting the opening price of stock.

关键词： Twin support vector regression Least squares method Primal space Stock prediction

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Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks

Energy-Efficient Flow Scheduling and Routing with Hard Deadl...

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International Conference on Distributed computing Systems

作者： Lin Wang Fa Zhang Kai Zheng Athanasios V. Vasilakos Shaolei Ren Zhiyong Liu University of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences IBM China Research Lab University of Western Macedonia Greece Florida International University USA Institute of Computing Technology Chinese Academy of Sciences

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.

关键词： Schedules Routing Power demand Switches Optimal scheduling Data models Approximation algorithms

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The Construction of Flight Bank in Large Hub Airports

The Construction of Flight Bank in Large Hub Airports

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2012 International Conference on Convergence Computer Technology(IC3T 2012)

作者： Jian-li Ding Lian Zhao College of Computer Science and Technology And Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Information Technology Research Base Civil Aviation Administration of China

In this paper,the development and construction of hub airport with hub-and-spoke system as the starting point to analyze the operating conditions of flight bank in domestic and international typical hub airports,and indicate the importance of improving efficiency of flight schedules by the use of flight ***,this paper expatiates on the conception and operating features of flight bank,puts forward the construct process around several important aspects including quantity definition,time distribution and configuration,and then makes time to optimize the time distribution and design the structure *** has great guiding significance for reasonable designation and operation of flight bank to advance the operation efficiency of large hub airports with hub-and-spoke system.

关键词： Flight bank Hub airport Time distribute optimization Structure designation.

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The Model and Optimization of Search Engine Based on Hadoop

The Model and Optimization of Search Engine Based on Hadoop

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2012 International Conference on Convergence Computer Technology(IC3T 2012)

作者： Jian-li Ding Bo Yang College of Computer Science and Technology And Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Information Technology Research Base Civil Aviation Administration of China

With the rapid increase of websites and internet users,the traditional search engine will face great challenge in the real-time search,response speed and mass storage *** cloud computing with two major advantages in massive data processing and mass data storage,so the search engine deployed in the cloud can solve these *** analyzing the open-source cloud computing system Hadoop,cloud platform search engine model is constructed and search engine algorithm is optimized to improve the overall performance of search engines.

关键词： Cloud computing,Hadoop,Search Engine,Model,Algorithm Optimization

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Efficiently Finding Top-K Items from Evolving Distributed Data Streams

Efficiently Finding Top-K Items from Evolving Distributed Da...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Baoyuan Qi Gang Ma Zhongzhi Shi Wei Wang University of Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Cas Beijing China Key Lab. of Intell. Inf. Process. Institute of Computing Technology Chinese Academy of Sciences Beijing CN Beijing Lexo Technologies Co. Ltd. Beijing China

ISBN: (纸本)9781479967162

The problem of efficiently finding top-k frequent items has attracted much attention in recent years. Storage constraints in the processing node and intrinsic evolving feature of the data streams are two main challenges. In this paper, we propose a method to tackle these two challenges based on space-saving and gossip-based algorithms respectively. Our method is implemented on SAMOA, a scalable advanced massive online analysis machine learning framework. The experimental results show its effectiveness and scalability.

关键词： Peer-to-peer computing Data mining Distributed databases Protocols Machine learning algorithms Monitoring Radiation detectors

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Corrigendum to “Fast and scalable lock methods for video coding on many-core architecture” [J. Vis. Commun. Image R. 25(7) (2014) 1758–1762]

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Journal of Visual Communication and Image Representation 2015年 27卷 57-57页

作者： Weizhi Xu Hui Yu Dianjie Lu Fenglong Song Da Wang Xiaochun Ye Songwei Pei Dongrui Fan Hongtao Xie Institute of Microelectronics Tsinghua University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Information Science and Engineering Shandong Normal University Jinan China Department of Computer Science and Technology Beijing University of Chemical Technology Beijing China Institute of Information Engineering Chinese Academy of Sciences National Engineering Laboratory for Information Security Technologies Beijing China

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Corrigendum to “Fast and scalable lock methods for video coding on many-core architecture” [J. Vis. Commun. 25 (7) (2014) 1758–1762]

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Journal of Visual Communication and Image Representation 2015年 26卷 350-350页

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Social sensing enhanced time estimation for bus service

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Concurrency and Computation: Practice and Experience 2015年第15期27卷

作者： Jin Liu Juan Li Xiaohui Cui Xiaoguang Niu Xiaoping Sun Jing Zhou State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China International School of Software Wuhan University Wuhan China Computer School Wuhan University Wuhan China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science Communication University of China China

The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social factors may include heavy traffic cost, traffic congestion, poor air quality and so forth. Existing prediction techniques rarely consider social sensing when predicting the bus arrival time. Accordingly, this paper proposes a social sensing enhanced service for predicting bus routes, which integrates sensing ability and social networks to understand and measure the influence between social events and vehicle velocity. We focus on the analysis of two different attributions: PT service quality attributions PEA s and road condition attributions PRCA s. Both of them synthesize the social sensing in their evaluation of bus routes. PEA represents individual preferences and PRCA represents physical factors that significantly influence vehicle velocity. Bus relevant social events were further categorized into PEA events or PRCA events. PEA s of buses were scored according to the tendency of bus conditions reflected in social events. Furthermore, an artificial neural network prediction model is established to estimate the bus travel time. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： social sensing in-time bus service bus route arrival time estimation

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