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The Improved Characteristics of Bionic Gabor Representations by Combining with SIFT key-points for Iris Recognition

Journal of Bionic engineering 2015年第3期12卷 504-517页

作者： Yuanning Liu Fei He Xiaodong Zhu Zhen Liu Ying Chen Ye Han Lijiao Yu College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Chanschun 130012 China Graduate School of Engineering Nagasaki Institute of Applied Science Japan College of Software Nanchang Hangkong University Nanchang 330063 China

Gabor filters are generally regarded as the most bionic filters corresponding to the visual perception of human. Their filtered coefficients thus are widely utilized to represent the texture information of irises. However, these wavelet-based iris representations are inevitably being misaligned in iris matching stage. In this paper, we try to improve the characteristics of bionic Gabor representations of each iris via combining the local Gabor features and the key-point descriptors of Scale Invariant Feature Transformation （SIFT）, which respectively simulate the process of visual object class recognition in frequency and spatial domains. A localized approach of Gabor features is used to avoid the blocking effect in the process of image division, meanwhile a SIFT key point selection strategy is provided to remove the noises and probable misaligned key points. For the combination of these iris features, we propose a support vector regression based fusion rule, which may fuse their matching scores to a scalar score to make classification decision. The experiments on three public and self-developed iris datasets validate the discriminative ability of our multiple bionic iris features, and also demonstrate that the fusion system outperforms some state-of-the-art methods.

关键词： iris recognition bionic Gabor features scale invariant feature transformation support vector regression score level fusion

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Entailment-based axiom pinpointing in debugging incoherent terminologies 1

Entailment-based axiom pinpointing in debugging incoherent t...

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1st International Workshop on Semantic Technologies, IWOST 2015

作者： Ye, Yuxin Ouyang, Dantong Su, Jing College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

One of the major problems of axiom pinpointing for incoherent terminologies is the precise positioning within the conflict axioms. In this paper we present a formal notion for the entailment-based axiom pinpointing of incoherent terminologies, where the parts of an axiom is defined by atomic entailment. Based on these concepts, we prove the one-to-many relationship between existing axiom pinpointing with the entailment-based axiom pinpointing. For its core task, calculating minimal unsatisfiable entailment, we provide algorithms for OWL DL terminologies using incremental strategy and Hitting Set Tree algorithm. The feasibility of our method is shown by case study and experiment evaluations.

关键词： Terminology

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Designment of E-R model based on RDF (S) 2

Designment of E-R model based on RDF (S)

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2nd International Workshop on Semantic Technologies, IWOST 2015

作者： Gao, Xiangbin Ouyang, Dantong Ye, Yuxin College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

Based on the vast domain resources of RDF (S) on the web and SPARQL's powerful query ability, this article presents a new method of designment of E-R model. The steps for this design are: (1) Formu- lating SPARQL rules (including resource query rules and schema query rules) by the analysis of RDF (S)'s structure. (2) Parsing the optimal resource obtained through the query sentences. (3) Completing the de- signment by taking advantages of the translation from RDF (S) model to entity-relationship model in accordance with the content queried. The re- sults indicate that, the designment of E-R model based on RDF (S) could restore user real requirements of great possibilities and help database de- signer to complete design in a strange area.

关键词： Syntactics

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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Location based surveillance for early detection of contagious outbreaks

Location based surveillance for early detection of contagiou...

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ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015

作者： Bai, Yuan Yang, Bo Du, Zhanwei Meyers, Lauren Ancel School of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China University of Texas at Austin AustinTX73301 United States

ISBN: (纸本)9781450335751

In previous studies, non-distance-dependent surveillance strategies have improved the performance of contagious outbreaks detection. In this paper, we propose a new distance-dependent strategy that does not require ascertainment of global or local network structure, namely, simply monitoring the relative significance difference of randomly selected individuals in school and workplace. To evaluate whether such two group could indeed provide early detection, we studied a flu outbreak in contact network simulation experiments. Our experimental results show that this method could provide significant additional time to react to epidemics, especially when the infection rate is not large.

关键词： Wearable technology

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The multilevel relational data model based on trust-label semantics

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Journal of computational Information Systems 2015年第11期11卷 3949-3956页

作者： Chen, Pan Wang, Limin Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin China State Key Laboratory of Computer Science Beijing China

As a new research direction in the field of database security, the technology of multilevel secure database is advancing by leaps and bounds. There are so many great multilevel secure relational models such as Bell-Lapadula model, SeaView model, Jajodia-Sandhu model and Smith-Winslett model in the last 40 years, which have solved or mitigated a lot of problems in this field with their own ways. But each of them still has one or several problems such as covert channels, semantic ambiguity, proliferation of tuples and poor expressiveness for complex situations. In this paper, we attempt to build a new model named TL model which could resolve as many problems as possible independently and retain the simpleness and expressiveness at the same time. We will clarify the TL model and present its simplicity, power and robustness. ©, 2015, Binary Information Press. All right reserved.

关键词： Semantics

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Protein-protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer 14

Protein-protein interaction network constructing based on te...

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14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015

作者： Zhu, Fei Liu, Quan Zhang, Xiaofang Shen, Bairong School of Computer Science and Technology Soochow University Suzhou215006 China Center for Systems Biology Soochow University Suzhou215006 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9781467379519

As a notoriously lethal human disease, cancer has obtained much concern for a long time. There have accumulated huge amounts of literature and experimental data on cancer-related research. It is impossible for people to deal with these texts manually to discover novel information and knowledge. However, text mining has an advantage of extracting previously unknown and understandable knowledge from large amounts of texts, and forming well-defined knowledge, providing the possibility to fully taking use of the existed texts. With the proceeding of biomedical research, people have gradually realized that complex biological functions and the phenomenon of life are the results of complex interactions among a variety of biological entities, such as protein. Deeply studying protein interaction network is essential to understand life. We, adopting reinforcement learning idea, put forward an algorithm for protein interaction network constructing. With the algorithm, nodes are used to represent proteins and edges denote interactions. During the evolutionary process, a node selects with which nodes in the network it tends to interact. Keep selecting and carrying on iteration, until eventually attaining an optimal network. The network is the result of the dynamic nature of learning behavior. As a malignancy, prostate cancer has been concerned for a long time. We attain biological texts from PubMed and establish a prostate cancer protein interaction networks by the proposed methods. The results show that our proposed method is pretty good. Network topology analysis results also show that the network node degree distribution is scale-free. © 2015 IEEE.

关键词： Reinforcement learning

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On-demand tile preload for large-scale seismic data 3D-visualization

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Journal of computational Information Systems 2015年第4期11卷 1513-1520页

作者： Wei, Xiaohui Bai, Xin Bai, Sen Li, Hongliang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China

Real-time three-dimensional visualization for seismic data is difficult when seismic data are large-scale and usually exceed the limitation of host memories. This paper proposed a dynamic caching framework based on OCTree data structure to dynamically load data blocks (Tiles) within ROI (Region of Interest) into memory for render in runtime. In order to improve the hit rate of the cache and ensure the smoothness of interaction, an On-Demand tile Preload (ODP) mechanism and algorithm is proposed and implemented. It preloads data tiles based on tile hot zones and ROI track prediction when loading threads are idle. Experiments show that ODP achieved 21% rendering speedup on average in real-time interaction while causing none additional costs. 1553-9105/Copyright © 2015 Binary Information Press

关键词： Visualization

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Intelligent Model Learning Based on Variance for Bayesian Reinforcement Learning

Intelligent Model Learning Based on Variance for Bayesian Re...

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International Conference on Tools with Artificial Intelligence

作者： Shuhua You Quan Liu Zongzhang Zhang Hui Wang Xiaofang Zhang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

ISBN: (纸本)9781509001644

We consider a modular method to reinforcement learning that represents uncertainty of model parameters by maintaining probability distributions over them. The algorithm we call MBDP (model-based Bayesian dynamic programming) can be decomposed into two parallel types of inference: model learning and policy learning. During learning a model, we update posterior distributions of a model over observations after taking an action in each state. During learning a policy, we solve MDPs by dynamic programming with greedy approximation to make an agent choose behaviors which maximize return under the estimated model. Furthermore, we propose a principled method which utilizes the variance of Dirichlet distributions for determining when to learn and relearn the model. We demonstrate that MBDP can find near optimal policies with high probability by sufficient model learning and experimental results show that MBDP performs better compared with current state-of-the-art methods in reinforcement learning.

关键词： reinforcement learning Bayesian dynamic programming model learning policy learning Dirichlet distributions

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Image de-nosing with morphological component analysis

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Journal of Information and computational Science 2015年第5期12卷 1875-1882页

作者： Yang, Chengjia Li, Xiongfei Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun China Network Information Center Jilin Jianzhu University Changchun China

In this paper, we investigate the problem of image de-noising. Here, the theory of morphological component analysis is employed to separate the image to be de-noised into some layers with different morphological components. In this paper, images are decomposed into two parts: smooth and textural parts. As noise only exists in the textural parts, we utilize bilateral filter to smooth textural parts. Finally, the smooth parts and the filtered textual parts are combined to get the image free of noise. The algorithm is tested experimentally, and the results show that it is superior to other state-of-art algorithms. Copyright © 2015 Binary Information Press.

关键词： Image denoising

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