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The sound and complete R-calculus for revising propositional theories

Science China(information Sciences) 2015年第9期58卷 5-16页

作者： LI Wei SUI YueFei SUN MeiYing State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

The AGM postulates are for the belief revision(revision by a single belief), and the DP postulates for the iterated revision(revision by a finite sequence of beliefs). Li gave an R-calculus for R-configurations?|Γ,where ? is a set of atomic formulas or the negations of atomic formulas, and Γ is a finite set of formulas. In this paper, two deduction systems for the revision of a theory by another theory are given such that the systems are sound and complete, that is, if ?|Γ■Γ′ is provable then Γ′■ ? is consistent and Γ′- ? is a maximal subset of Γ such that(Γ′- ?) ∪ ? is consistent; and for any finite theories ? and Γ, there is a finite theoryΓ′ such that Γ′- ? is a maximal subset of Γ such that(Γ′- ?) ∪ ? is consistent, and ?|Γ ? Γ′ is ***, if?|Γ■Γ′ is provable then Γ′ satisfies the AGM and the DP postulates.

关键词： belief revision R-calculus minimal change the AGM postulates the DP postulates

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Tolerance Granulation Based Community Detection Algorithm

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Tsinghua Science and technology 2015年第6期20卷 620-626页

作者： Shu Zhao Wang Ke Jie Chen Feng Liu Menghan Huang Yanping Zhang Jie Tang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Center of Information Support & Assurance TechnologySchool of Computer Science and TechnologyAnhui University School of Computer Science and Technology Tsinghua University

Community structure is one of the most important features in real networks and reveals the internal organization of the vertices. Uncovering accurate community structure is effective for understanding and exploiting networks. Tolerance Granulation based Community Detection Algorithm(TGCDA) is proposed in this paper, which uses tolerance relation(namely tolerance granulation) to granulate a network hierarchically. Firstly, TGCDA relies on the tolerance relation among vertices to form an initial granule set. Then granules in this set which satisfied granulation coefficient are hierarchically merged by tolerance granulation operation. The process is finished till the granule set includes one granule. Finally, select a granule set with maximum granulation criterion to handle overlapping vertices among some granules. The overlapping vertices are merged into corresponding granules based on their degrees of affiliation to realize the community partition of complex networks. The final granules are regarded as communities so that the granulation for a network is actually the community partition of the *** on several datasets show our algorithm is effective and it can identify the community structure more accurately. On real world networks, TGCDA achieves Normalized Mutual information(NMI) accuracy 17.55% higher than NFA averagely and on synthetic random networks, the NMI accuracy is also improved. For some networks which have a clear community structure, TGCDA is more effective and can detect more accurate community structure than other algorithms.

关键词： tolerance relation community tolerance granulation

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A New Acquisition Method Based on Differential Correlation

A New Acquisition Method Based on Differential Correlation

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2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016)

作者： Sujuan Shang Yanjun Hu Jufeng Luo Yingguan Wang Key laboratory of Intelligent Computing & Signal Processing (Anhui University) Ministry of Education Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences

Acquisition is a key technology in DSSS *** differential correlation is usually employed to eliminate the effect of frequency ***,as the length of pseudo-code grows and the decrease of the SNR,the traditional differential acquisition algorithms will result in great loss of *** paper presents an improved differential acquisition method,named as M-orders Auto-Correlation based on Differential correlation（MAC-DF）.In the proposed method,the input signal is multiplied by the complex conjugate of the pseudo-code to eliminate its *** that,the product is applied to the M-orders Auto-Correlation to compensate the SNR loss caused by the differential *** means of mathematical model to analyze its acquisition *** compare MAC-DF acquisition algorithm with the differential coherent and non-coherent acquisition algorithms through *** simulation results indicate that this algorithm is approximately 5-6dB superior to the traditional differential acquisition algorithms in improving acquisition sensitivity and more adaptive to work under low SNR.

关键词： DSSS Pseudo-code acquisition low SNR differential correlation acquisition algorithm

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Yu-Hui Chang Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781509003914

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets.

关键词： Cybernetics Acceleration Time complexity Rough sets Heuristic algorithms Data mining Algorithm design and analysis

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Ranking Node Influence in Social Networks

Ranking Node Influence in Social Networks

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International Symposium on Parallel and Distributed computing

作者： Zheyi Chen Yuli Liu Weiping Zhu Department of Computer Science National Laboratory for Information Science and Technology Beijing China College of Mathematics and Computer Science Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9781509041534

In the study of social networks, analyzing node influence and identifying influential nodes in social networks is of great theoretical and practical significance. To effectively evaluate node influence, a novel concept of influence label is introduced, which can measure node influence through two label attributes called influence level and node degree. Then, a novel influence model called LDM (Level and Degree Model) is proposed. LDM updates the influence label of each node iteratively by the quality of neighbors and the number of neighbors. A gain function that conforms to power-law distribution is also introduced to further optimize the accuracy of LDM. In the experiment part, to prove the effectiveness of LDM, LDM is compared with other typical methods by employing IC model to simulate the information diffusion process on four real-world social networks. Experimental results show that LDM can rank node influence more accurately than other methods.

关键词： social networking (online) CYP51A1 gene LDM Social Networks Line of nodes Social Capital

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A local-optimization refinement algorithm in single particle analysis for macromolecular complex with multiple rigid modules

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Protein & Cell 2016年第1期7卷 46-62页

作者： Hong Shan Zihao wang Fa Zhang Yong Xiong Chang-Cheng Yin Fei Sun Department of Biophysics College of Basic Medical Sciences Peking University Health Science Center Beijing 100191China National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Key Lab of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06511 USA Center for Biological Imaging Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China

Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macro- molecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate align- ments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orien- tations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimen- sional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large （60S/50S） and the small （40S/30S） subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolutionsingle particle analysis of macromolecular complexes with dynamic conformations.

关键词： cryo-electron microscopy single particleanalysis conformational heterogeneity rigid module Iocaoptimization refinement

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UNSUPERVISED PERSON RE-IDENTIFICATION WITH LOCALITY-CONSTRAINED EARTH MOVER'S DISTANCE

UNSUPERVISED PERSON RE-IDENTIFICATION WITH LOCALITY-CONSTRAI...

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IEEE International Conference on Image Processing

作者： Dan Wang Canxiang Yan Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Institute of Deep Learning Baidu Inc.

ISBN: (纸本)9781467399623

The difficult acquisition of labeled data and the misalignment of local matching are major obstacles to apply person re-identification in real scenarios. To alleviate these problems, we propose an unsupervised method, called locality-constrained Earth Mover's Distance (LC-EMD), to learn the optimal measure between image pairs. Specifically, Gaussian mixture models (GMMs) are learned as signatures. By imposing locality constraints, LC-EMD can naturally achieve partial matching between Gaussian components. Moreover, LC-EMD has the analytical solution which can be efficiently computed. Experiments on two public datasets demonstrate LC-EMD is robust to misalignment and performs better than other unsupervised methods.

关键词： Identification of persons Distance learning Gaussian mixture model Distance learning Misalignment Mixture models EARTHMOVER

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Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals

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Procedia Computer Science 2016年 88卷 176-184页

作者： Lijuan Duan Menghu Bao Jun Miao Yanhui Xu Juncheng Chen College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing 100124 China

Classification of motor imagery electroencephalogram (EEG) is one of the most important technologies for BCI. To improve the accuracy, this paper introduces a classification system based on Multilayer Extreme Learning Machine (ML-ELM). In the system, the combination of PCA and LDA is chosen as the method of feature extraction and the ML-ELM is used to classify. The ML-ELM has not only the advantage which ELM has but also better performance than ELM. In the experiment, our method is compared with the methods based on ELM, such as kernel-ELM, Constrained-ELM and V-ELM, and some state-of–the-art methods on the same dataset. The experimental results show that ML-ELM is much more suitable for motor imagery EEG data and has better performance than the others.

关键词： Electroencephalogram Classification Motor Imagery Extreme Learning Machine Multilayer Extreme Learning Machine

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Microblog Sentiment Analysis with Emoticon Space Model

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Journal of Computer Science & technology 2015年第5期30卷 1120-1129页

作者：姜飞刘奕群栾焕博孙甲申朱璇张敏马少平 State Key Laboratory of Intelligent Technology and Systems Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Beijing 100084 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Language Computing Laboratory Samsung Research & Development Institute of China Beijing 100028 China

Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model （ESM） that leverages more emotieons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus （NLP＆CC 2013） indicate that ESM effectively leverages emoticon signals best runs. and outperforms previous state-of-the-art strategies and benchmark

关键词： microblog sentiment analysis emoticon space polarity classification subjectivity classification emotion clas-sification

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Novel Detection Service Method for Moving Object in IOT

Novel Detection Service Method for Moving Object in IOT

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2016 3rd International Conference on Materials Engineering,Manufacturing technology and Control(ICMEMTC 2016)

作者： Jie Chen Degan Zhang School of Electronic and Information Engineering Tianjin Vocational Institute Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education

In embedded Internet of Things(IOT) environment, there are the troubles such as complex background, illumination changes, shadows and other factors for detecting moving object, so we put forward a new detection service method through mixing Gaussian Mixture Model(GMM), edge detection service method and continuous frame difference method in this paper. In time domain, the new method uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection service method which mixes edge detection service method, continuous frame difference method and GMM to get initial contour of moving object, and gets ultimate moving object. This method not only can well adapt to the illumination gradients and background disturbance occurred on scene, but also can well solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional method. As experimental result showing, this method holds better real-time and robustness. It is not only easily implemented, but also can accurately detect moving object.

关键词： GMM embedded IOT moving object detection service

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