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The multilevel relational data model based on trust-label semantics

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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A directional bi-dimensional empirical mode decomposition based image de-noising algorithm

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Journal of Information and Computational Science 2015年第17期12卷 6401-6408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, a new image de-noising algorithm based on directional bi-dimensional empirical mode decomposition. Attractive features of this algorithm include its data driven mechanism and its ability of capturing directional information. The framework contains three steps: the image to be denoised is first decomposed into several intrinsic mode functions, and a residue. Then the intrinsic mode functions are decomposed into various directional sub-bands. The directional sub-bands of image are smoothed by filters. Finally, the smoothed sub-bands of the image are combined with the image's original residue to get the final image. The proposed algorithm is tested on several natural images, and experimental results justify the superiority of the proposed algorithm subjectively and objectively. © 2015 by Binary Information Press.

关键词： Empirical mode decomposition

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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

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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PCNN based Otsu multi-threshold segmentation algorithm for noised images

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Journal of Computational Information Systems 2015年第21期11卷 7791-7798页

作者： Zhou, Fanghao Li, Xiongfei Zhang, Xiaoli Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

In this paper, a new image segmentation algorithm based on Otsu thresholding. One of attractive feature of this algorithm is its ability of processing noised images. The framework contains three steps: the image to be segmented is first denoised using PCNN (Pulse Coupled Neural Network);then find the optimal thresholds by maximizing the between-class variance;finally, the image can be segmented with the assistance of thresholds. We compared the proposed algorithm with other traditional ones on denoised images. The experimental results also justify the superiority of the proposed segmentation algorithm subjectively and objectively. Copyright © 2015 Binary Information Press.

关键词： Image segmentation

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Detect epistasis based on optimization of artificial neural network architecture and parameters

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ICIC Express Letters 2015年第5期9卷 1499-1506页

作者： Zhou, Zhihui Liu, Guixia Su, Lingta Tian, Yuan College of Computer Science and Technology China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Avenue Changchun China

Epistasis or the interaction of single nucleotide polymorphisms (SNPs) at different loci plays a significant role on the mechanisms and pathogenesis of many common complex, multifactorial diseases and may be responsible for the “missing” heredity in genetic epidemiology. A certain complex network of epistatic interactions may contribute to the disease susceptibility. Epistasis detection is a computational and statistical challenge for the absence of prior knowledge of biological data, intensive computational burden and inappropriate statistical tests. Here we investigate the application of a standard artificial neural network (ANN) to detect epistasis for genome-wide association studies (GWAS) for the ability of ANN to model nonlinear data. The pattern-recognition strategy of ANN is utilized in the hope that genotypes of SNP markers will successfully distinguish between cases and controls in a way leading to the ANN being able to classify the SNPs correctly. Using simulated data under a wide range of settings, we show that the proposed ANN can well model gene-gene interactions with good predictive ability. The performance of ANN shows considerable promise and further improvements could usefully be designed towards reducing the computing speed and optimizing implementation of such networks. © 2015, ICIC International.

关键词： Neural networks

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Metallogenic prediction based on GIS through automatic database building and extraction of regional gravity and magnetic structure

Metallogenic prediction based on GIS through automatic datab...

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International Conference on Electronics, InformationTechnology and Intellectualization, EITI 2014

作者： Ye, S.S. Wang, S.C. Ye, Y.X. Zhao, G.X. Teng, F. Institute of Mineral Resources Prediction with Synthetic Information Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China National Engineering Research Center of Geophysics Exploration Instruments Changchun Jilin University Changchun China Tianjin Institute of Geology and Mineral Resources Tianjin China

ISBN: (纸本)9781138027411

The application of intelligent geophysical interpreting, technology of mapping and database building based on GIS have been discussed under the guidance of the theory and the method of the mineral resources prediction of synthetic information in this paper. The automatic extraction of regional gravity and magnetic structures to build databases has been realized. With the prediction of the iron mine as a case, the whole process of the application of synthetic information in minerals’ prediction of GIS technology has been stated. Through the realization of the data process of regional gravity and magnetic data, and of line-ring structure extraction, geophysical spatial information and map databases have been automatically built. Through a combination of geological and geophysical interpretation, the spatial information and map databases of geophysical ore-finding (iron mine) have been built. Through model units, an ore-finding model of comprehensive information has been built. Through geological units, geophysical-geological spatial information and map databases of synthetic information of predictive models have been built. According to the requirement of different mathematical models for mineral prediction of synthetic information for the data models, the spatial information in the synthetic information databases of predictive models has been extracted, and the geological variables have been converted, and then converted mutually into a digital geological multivariate statistical variable matrix. Different statistic prediction models of the multivariate statistical methods have been established to complete the whole process of metallogenic prediction by the computer. © 2015 Taylor & Francis Group, London.

关键词： Forecasting

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Author Correction: Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets

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Nature communications 2021年第1期12卷 7260页

作者： Merel C Postema Daan van Rooij Evdokia Anagnostou Celso Arango Guillaume Auzias Marlene Behrmann Geraldo Busatto Filho Sara Calderoni Rosa Calvo Eileen Daly Christine Deruelle Adriana Di Martino Ilan Dinstein Fabio Luis S Duran Sarah Durston Christine Ecker Stefan Ehrlich Damien Fair Jennifer Fedor Xin Feng Jackie Fitzgerald Dorothea L Floris Christine M Freitag Louise Gallagher David C Glahn Ilaria Gori Shlomi Haar Liesbeth Hoekstra Neda Jahanshad Maria Jalbrzikowski Joost Janssen Joseph A King Xiang Zhen Kong Luisa Lazaro Jason P Lerch Beatriz Luna Mauricio M Martinho Jane McGrath Sarah E Medland Filippo Muratori Clodagh M Murphy Declan G M Murphy Kirsten O'Hearn Bob Oranje Mara Parellada Olga Puig Alessandra Retico Pedro Rosa Katya Rubia Devon Shook Margot J Taylor Michela Tosetti Gregory L Wallace Fengfeng Zhou Paul M Thompson Simon E Fisher Jan K Buitelaar Clyde Francks Department of Language & Genetics Max Planck Institute for Psycholinguistics Nijmegen The Netherlands. Department of Cognitive Neuroscience Donders Institute for Brain Cognition and Behaviour Donders Centre for Cognitive Neuroimaging Radboud University Medical Centre Nijmegen The Netherlands. Bloorview Research Institute Holland Bloorview Kids Rehabilitation Hospital and Department of Pediatrics University of Toronto Toronto ON USA. Child and Adolescent Psychiatry Department Gregorio Marañón General University Hospital School of Medicine Universidad Complutense IiSGM CIBERSAM Madrid Spain. Institut de Neurosciences de la Timone UMR 7289 Aix Marseille Université CNRS Marseille France. Department of Psychology Carnegie Mellon University Pittsburgh PA USA. Laboratory of Psychiatric Neuroimaging (LIM-21) Departamento e Instituto de Psiquiatria Hospital das Clinicas HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. IRCCS Stella Maris Foundation viale del Tirreno 331 56128 Pisa Italy. Department of Clinical and Experimental Medicine University of Pisa Pisa Italy. Department of Child and Adolescent Psychiatry and Psychology Hospital Clinic Psychiatry Unit Department of Medicine 2017SGR881 University of Barcelona IDIBAPS CIBERSAM Barcelona Spain. Department of Forensic and Neurodevelopmental Sciences Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Institute for Pediatric Neuroscience NYU Child Study Center New York NY USA. Department of Psychology Ben-Gurion University of the Negev Beer Sheva Israel. Brain Center Rudolf Magnus Department of Psychiatry University Medical Center Utrecht Utrecht The Netherlands. Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Goethe University Frankfurt am Main Frankfurt Germany. Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University o

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