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Social sensing enhanced time estimation for bus service

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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Addressing the energy hole problem based on heterogeneous clustering size for wireless sensor networks

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Journal of Computational Information Systems 2014年第9期10卷 3973-3980页

作者： Song, Xiaoying Wen, Tao Guo, Quan Ma, Fengming Zhang, Dongqing Software Center Northeastern University Shenyang 110819 China Liaoning Key Lab. of Network Security and Computing Technology Dalian Neusoft Information Institute Dalian 116023 China School of Management Science Qufu Normal University Rizhao Shandong 276800 China

Clustering is one of the effective ways to solve energy hole problem for Wireless Sensor network. So far, the approaches of heterogeneous cluster size mainly have concentrated on the design of unequal cluster protocol rather than the optimal cluster radius theoretically. Furthermore, the existing protocols of data gathering fall short in the extra energy dissipation of the rotating cluster head frequently. To overcome these limitations, in this paper, we first present the theoretical analysis to obtain the optimal number of cluster when the energy dissipation is balancing in the whole network. Then, the optimal cluster radius of different rings is obtained by theoretical analysis as well. In succession, a simple and effective strategy is proposed to further prolong the network lifetime on basis of the optimal cluster radius obtained theoretically. Finally, the simulation results demonstrate the performance advantages of our approach. © 2014 Binary Information Press.

关键词： Wireless sensor networks

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Parametric local multimodal hashing for cross-view similarity search

Parametric local multimodal hashing for cross-view similarit...

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23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Zhai, Deming Chang, Hong Zhen, Yi Liu, Xianming Chen, Xilin Gao, Wen School of Computer Science and Technology Harbin Institute of Technology Harbin China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781577356332

Recent years have witnessed the growing popularity of hashing for efficient large-scale similarity search. It has been shown that the hashing quality could be boosted by hash function learning (HFL). In this paper, we study HFL in the context of multimodal data for cross-view similarity search. We present a novel multimodal HFL method, called Parametric Local Multimodal Hashing (PLMH), which learns a set of hash functions to locally adapt to the data structure of each modality. To balance locality and computational efficiency, the hashing projection matrix of each instance is parameterized, with guaranteed approximation error bound, as a linear combination of basis hashing projections of a small set of anchor points. A local optimal conjugate gradient algorithm is designed to learn the hash functions for each bit, and the overall hash codes are learned in a sequential manner to progressively minimize the bias. Experimental evaluations on cross-media retrieval tasks demonstrate that PLMH performs competitively against the state-of-the-art methods.

关键词： Hash functions

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Mining dynamical frequent itemsets based on ant colony algorithm

Mining dynamical frequent itemsets based on ant colony algor...

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IEEE International Conference on Computer science and Automation Engineering (CSAE)

作者： Chen, Shengbing Wang, Xiaofeng Wang, Xiaofang Key Lab of Network and Intelligent Information Processing Hefei University Hefei 230601 China Department of Computer Science and Technology Hefei University Hefei 230601 China Intelligent Computing Lab Institute of Intelligent Machines Chinese Academy of Sciences Hefei 230031 China Department of Computer Artillery Academy Hefei 230031 China

ISBN: (纸本)9781424487257

Mining frequent itemsets is a core problem in many data mining tasks, most existing works on mining frequent itemsets can only capture the long-term and static frequency itemsets, they do not suit the task whose frequent itemsets often change. Using the theory of ant colony algorithm, we proposed a new method for mining dynamical frequent itemset(called AC-MFI). The method considers the item of transaction as a node in the path, takes the itemset as a path, and takes each transaction as a foraging behavior. According to the pheromone updating policy of ant colony algorithm, AC-MFI mines dynamical frequent itemsets from transaction data stream. Experiment results show that the method is valid and practicable. © 2011 IEEE.

关键词： data mining

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Context-specific miRNA regulation network predicts cancer prognosis

Context-specific miRNA regulation network predicts cancer pr...

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IEEE International Conference on Systems Biology

作者： Xionghui Zhou Juan Liu Changning Liu Simon Rayner Fengji Liang Jingfang Ju Yinghui Li Shanguang Chen Jianghui Xiong School of Computer Science Wuhan University Wuhan China Bioinformatics Research Group Key Laboratory of Intelligent Information Processing Advanced Computing Research Laboratory Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Bioinformatics Group State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences (CAS) Wuhan China Bioinformatics Group and Data Coordination Center State Key Laboratory of Space Medicine Fundamentals and Application China Astronaut Research and Training Center Beijing China Department of Pathology Stony Brook University Medical Center New York USA

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context).

关键词： Breast cancer Context Correlation Biological processes Metastasis Tumors

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Rank internet service quality using multiple features: A machine learning approach

Rank internet service quality using multiple features: A mac...

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6th International Conference on Semantics, Knowledge and Grid, SKG 2010

作者： Tu, Dandan Shu, Chengchun Shi, Jingwei Zhu, Tao Wang, Shuang Yu, Haiyan Key Laboratory of Network Science and Technology ICT Institute of Computing Technology CAS P.O.Box 2704 100190 Beijing China NEC Labs China 11F Bldg Innovation Plaza Tsinghua Science Park Beijing 100084 China

ISBN: (纸本)9780769541891

This paper addresses the problem of Ranking Internet service quality by taking a machine learning approach using multiple service features. Ranking helps find good services for applications that use services as building blocks. Unlike other ranking problems, the goodness of Internet service qualities is dependent upon multiple key features. The key features vary across different service categories and have unequally discriminative natures. This paper divides the ranking problem into four subtasks including categorizing services according to the service functionalities, identifying key features that determine the quality, denoising for feature measurement values and computing global ranking scores with multiple key features, which are cast into machine learning problems and solved using techniques classification, feature selection, clustering, and regression respectively. In particular, we propose in this paper an efficient dense-block based denoising method for subjective features, and a Supported Vector Regression based method for computing global ranking scores. Experimental results on both synthetic and real data show that the proposed approach can quantitatively identify the key features across service categories, discard noisy measurement values in 10 times faster, and compute the global ranking scores using multiple features with low mean squared errors for both linear and nonlinear ranking functions. © 2010 IEEE.

关键词： Mean square error

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Rank Internet Service Quality Using Multiple Features: A Machine Learning Approach

Rank Internet Service Quality Using Multiple Features: A Mac...

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Dandan Tu Shuang Wang Haiyan Yu Tao Zhu Chengchun Shu Jingwei Shi Key Laboratory of Network Science and Technology ICT Institute of Computing Technology CAS P.O.Bo NEC Labs China 11F Bldg Innovation Plaza Tsinghua Science Park Bei jing 100084 China

关键词： Web and internet services Machine learning Computational modeling Training data Support vector machines Clustering algorithms Web services

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Virtual node based adaptive routing in wireless ad hoc networks

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中国高等学校学术文摘·计算机科学 2009年第4期3卷 535-542页

作者： Gang LI Hongmei SUN Key Lab of Network Science and Technology Institute of Computing TechnologyChinese Academy of SciencesBeijing 100080China Communications Technology Lab Intel China Research CenterBeijing 100080China

It is a challenge to make the routes quickly adapt to the changed network topology when nodes fail in a wireless ad hoc *** this paper,we propose an adaptive routing protocol,which groups the network nodes into virtual nodes according to their data transfer capabilities and creates virtual-node-based *** protocol can accommodate the routes to node failures by adaptively updating the virtual nodes and just-in-time using available nodes during data *** simulations indicate that the proposed protocol can keep the routes failed-node-free when the available virtual node members cover the failed nodes scattering area.

关键词： wireless ad hoc networks mobile ad hoc networks route protocols adaptability route repair

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A Multi-layers Neural network Model based on Characteristics of Ganglions' Receptive Fields in Retina and an Algorithm for Watchfulness-keeping

A Multi-layers Neural Network Model based on Characteristics...

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The Third Asian Conference on Vision (ACV2004)

作者：危辉陆汝钤 Department of Computer Science Lab of Algorithm for Cognitive Model Brain Science Research Center Fudan University Shanghai 200433 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100080 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100080 China

Machine vision is an active branch of Artificial Intelligence. An important problem in this area is the balance among efficiency, accuracy and huge computing. The visual system of human can keep watchfulness to the perimeter of visual field while at same time their central attention is focused to the center of visual field for fine information processing. This mechanism of computing resource assignment could ease the demand for huge and complex hardware structure. Therefore designing computer model based on biological visual

关键词： A Multi-layers Neural network Model based on Characteristics of Ganglions Receptive Fields in Retina and an Algorithm for Watchfulness-keeping Model

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