Framework to Model User Request Access Patterns in the World Wide Web

In this paper, we present a novel approach to model user request patterns in the World Wide Web. Instead of focusing on the user traffic for web pages, we capture the user interaction at the object level of the web pages. Our framework model consists of three sub-models: one for user file access, one for web pages, and one for storage servers. Web pages are assumed to consist of different types and sizes of objects, which are characterized using several categories: articles, media, and mosaics. The model is implemented with a discrete event simulation and then used to investigate the performance of our system over a variety of parameters in our model. Our performance measure of choice is mean response time and by varying the composition of web pages through our categories, we find that our framework model is able to capture a wide range of conditions that serve as a basis for generating a variety of user request patterns. In addition, we are able to establish a set of parameters that can be used as base cases. One of the goals of this research is for the framework model to be general enough that the parameters can be varied such that it can serve as input for investigating other distributed applications that require the generation of user request access patterns.

Plasticity-induced characteristic changes of pattern dynamics and the related phase transitions in small-world neuronal networks

Phase transitions widely exist in nature and occur when some control parameters are changed. In neural systems, their macroscopic states are represented by the activity states of neuron populations, and phase transitions between different activity states are closely related to corresponding functions in the brain. In particular, phase transitions to some rhythmic synchronous firing states play significant roles on diverse brain functions and disfunctions, such as encoding rhythmical external stimuli, epileptic seizure, etc. However, in previous studies, phase transitions in neuronal networks are almost driven by network parameters （e.g., external stimuli）, and there has been no investigation about the transitions between typical activity states of neuronal networks in a self-organized way by applying plastic connection weights. In this paper, we discuss phase transitions in electrically coupled and lattice-based small-world neuronal networks （LBSW networks） under spike-timing-dependent plasticity （STDP）. By applying STDP on all electrical synapses, various known and novel phase transitions could emerge in LBSW networks, particularly, the phenomenon of self-organized phase transitions （SOPTs）： repeated transitions between synchronous and asynchronous firing states. We further explore the mechanics generating SOPTs on the basis of synaptic weight dynamics.

Reflections on the Transformation of World Pattern and Resoonsibility Adjustment

The year of 2000 is at the turn of the century as well as of the millennium. Themankind waves farewell to the 20th century as well as the second millenniumand welcomes the 21st century and the third millennium. At this special juncture,looking closely at the future based on understanding which was obtained from re-viewing and summarizing the past and present world politics from historical andphilosophical standpoints, we can find that it has become an unavoidable subject onhow to regard the trend of the world pattern toward multi-polarization and respon-sibility adjustment arising from it.

TPFTZ(Tianjin Port Free Trade Zone)——A New Pattern of China's Openning to the Outside World

Under the guidance of Deng Xiao-ping’s strategic think-ing of openning to the outside world, the structure of om-nibearing openness has basically taken shape in China. Follow-

Study on The Light of the World from the Perspective of Binary Oppositions

Structuralism is employed to analyze language, culture and society in 1900 s. It's an effective theory to anatomize literary works. The light of the world is a short story composed by Ernest Hemingway, the most important and outstanding author since the death of Shakespeare. The story is dissected from binary oppositions, a key element in Structuralism. Two binary oppositions of the Light of the World have been clarified: the lies and true heart of two whores, and the light of the world and the world without light.

Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.

A Remote Sensing-based Analysis on the Impact of Wenchuan Earthquake on the Core Value of World Nature Heritage Sichuan Giant Panda Sanctuary

In this study,we used remotely sensed data,GIS modeling,and statistical methods to evaluate the damage caused by the Wenchuan Earthquake (May 12,2008) to the giant panda (Ailuropoda melanoleuca) habitat in the World Nature Heritage Sichuan Giant Panda Sanctuary (WHSGPS) in China.A landscape ecological analysis found increases of landscape heterogeneity,complexity,and fragmentation in the giant panda habitat after the earthquake.A terrain analysis found that slope and elevation are directly associated with the distribution of the damaged areas.As slope and elevation increase,the size of the damaged area keeps increase until to a peak,and then starts to drop.The total area of the damaged vegetation in our study area is 114.26 km 2,accounting for 3.78% of the study area;30.46% of that 114.26 km 2 is located in the Core Zone of WHSGPS.There are 18.57km 2 of the damaged vegetation located in the identified suitable giant panda habitat,accounting for 1.75% of the total area of suitable giant panda habitats in the study area.Based on these findings,we conclude that the Wenchuan Earthquake does not have significant impact on the WHSGPS as a whole.

Bioclimatic Regularities of Change in the Density of Organic Carbon of the Steppe Soil in Different Regions of the World

The bioclimatic regularities between the average annual precipitation,average annual temperatures and the density of organic carbon in the soil layer of 0-30 cm of the steppes in the regions of the world are given.They are distinguished by a high certainty of quantization by asymmetric wave equations.It turned out that,due to the vibrational adaptation of organic carbon,precipitation and temperature are dependent on each other.For example,the model of the influence of precipitation on temperature includes the first term in the form of Laplace’s law(in mathematics),Mandelbrot’law(in physics),Zipf-Perl(in biology),and Pareto(in econometrics).The second term is the biotechnical law of the author of the article,which gives the maximum change in the indicator.Both components form a trend that makes it possible to divide the precipitation interval into three stages:(1)with an increase in precipitation from 0 to 60 mm,the temperature decreases according to Mandelbrot’s law from 23.25 to 0.50С;(2)from 60 to 2100 mm,the temperature rises to 24℃;(3)with a further increase in precipitation over 2100 mm,a slow decrease in temperature occurs.The third term is an asymmetric wavelet with a constant half-period of 367.8 mm.A positive sign shows that in the steppes there is a positive oscillatory adaptation of temperature to changes in precipitation.In the interval of precipitation 0-350 mm,an oscillatory decrease in temperature occurs.It turns out that the first oscillation at 0 mm precipitation begins with a very high temperature gradient of thermal energy.The first interval includes Mongolia and Inner Mongolia.In the second interval of 350-750 mm,an oscillatory increase in temperature occurs.Then,in the third interval 750-1050 mm,the temperature drops again.The second oscillation with a correlation coefficient of 0.9685 has clear precipitation boundaries in the range of 200-2000 mm.Due to the negative sign,the fluctuation is a crisis,inhibiting the rise in temperature.And the third fluctuation has a positive effect on the temperature.The mechanism of oscillatory adaptation in the steppe soil is so perfect that it changes for itself the conditions of the place where the grass grows.An amplitude-frequency analysis of each oscillation will make it possible to determine the specific particular effects of precipitation and temperature on each other and on the density of organic carbon.It was found that two-factor modeling of the change in the soil organic carbon density makes it possible to achieve an identification error even less than the absolute measurement error.

Possible Tendencies of World Pattern and Options for China

Possible Tendencies of World Pattern 1.'Eastward Power Shift'This is a matter of great concern for some of the people in American strategic circle.A decade and more after the'9/11'happens to witness the process ofrapid development of China in becoming one of the emerging economies of the world.Since 2012,the country has overtaken Japan in terms of GDP.

Fast Fourier single-pixel imaging based on Sierra–Lite dithering algorithm

The single-pixel imaging(SPI) technique is able to capture two-dimensional(2 D) images without conventional array sensors by using a photodiode. As a novel scheme, Fourier single-pixel imaging(FSI) has been proven capable of reconstructing high-quality images. Due to the fact that the Fourier basis patterns(also known as grayscale sinusoidal patterns)cannot be well displayed on the digital micromirror device(DMD), a fast FSI system is proposed to solve this problem by binarizing Fourier pattern through a dithering algorithm. However, the traditional dithering algorithm leads to low quality as the extra noise is inevitably induced in the reconstructed images. In this paper, we report a better dithering algorithm to binarize Fourier pattern, which utilizes the Sierra–Lite kernel function by a serpentine scanning method. Numerical simulation and experiment demonstrate that the proposed algorithm is able to achieve higher quality under different sampling ratios.

Facial Expression Recognition Based on the Q-shift DT-CWT and Rotation Invariant LBP

In this paper, a novel method based on dual-tree complex wavelet transform(DT-CWT) and rotation invariant local binary pattern(LBP) for facial expression recognition is proposed. The quarter sample shift (Q-shift) DT-CWT can provide a group delay of 1/4 of a sample period, and satisfy the usual 2-band filter bank constraints of no aliasing and perfect reconstruction. To resolve illumination variation in expression verification, low-frequency coefficients produced by DT-CWT are set zeroes, high-frequency coefficients are used for reconstructing the image, and basic LBP histogram is mapped on the reconstructed image by means of histogram specification. LBP is capable of encoding texture and shape information of the preprocessed images. The histogram graphs built from multi-scale rotation invariant LBPs are combined to serve as feature for further recognition. Template matching is adopted to classify facial expressions for its simplicity. The experimental results show that the proposed approach has good performance in efficiency and accuracy.

Local binary pattern-based reversible data hiding

A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.

A Novel Tracking-by-Detection Method with Local Binary Pattern and Kalman Filter

Tracking-Learning-Detection( TLD) is an adaptive tracking algorithm,which tracks by learning the appearance of the object as the video progresses and shows a good performance in long-term tracking task.But our experiments show that under some scenarios,such as non-uniform illumination changing,serious occlusion,or motion-blurred,it may fails to track the object. In this paper,to surmount some of these shortages,especially for the non-uniform illumination changing,and give full play to the performance of the tracking-learning-detection framework, we integrate the local binary pattern( LBP) with the cascade classifiers,and define a new classifier named ULBP( Uniform Local Binary Pattern) classifiers. When the object appearance has rich texture features,the ULBP classifier will work instead of the nearest neighbor classifier in TLD algorithm,and a recognition module is designed to choose the suitable classifier between the original nearest neighbor( NN) classifier and the ULBP classifier. To further decrease the computing load of the proposed tracking approach,Kalman filter is applied to predict the searching range of the tracking object.A comprehensive study has been conducted to confirm the effectiveness of the proposed algorithm (TLD _ULBP),and different multi-property datasets were used. The quantitative evaluations show a significant improvement over the original TLD,especially in various lighting case.

Vehicle detection algorithm based on codebook and local binary patterns algorithms

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.

Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30° on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

An Efficient Acoustic Scattering Model Based on Target Surface Statistical Descriptors for Synthetic Aperture Sonar Systems

Acoustic scattering as the perturbation of an incident acoustic field from an arbitrary object is a critical part of the targetrecognition process in synthetic aperture sonar(SAS)systems.The complexity of scattering models strongly depends on the size and structure of the scattered surface.In accurate scattering models including numerical models,the computational cost significantly increases with the object complexity.In this paper,an efficient model is proposed to calculate the acoustic scattering from underwater objects with less computational cost and time compared with numerical models,especially in 3D space.The proposed model,called texture element method(TEM),uses statistical and structural information of the target surface texture by employing non-uniform elements described with local binary pattern(LBP)descriptors by solving the Helmholtz integral equation.The proposed model is compared with two other well-known models,one numerical and other analytical,and the results show excellent agreement between them while the proposed model requires fewer elements.This demonstrates the ability of the proposed model to work with arbitrary targets in different SAS systems with better computational time and cost,enabling the proposed model to be applied in real environment.

Coal rock image recognition method based on improved CLBP and receptive field theory

Rapid coal-rock identification is one of the key technologies for intelligent and unmanned coal mining.Currently,the existing image recognition algorithms cannot satisfy practical needs in terms of recognition speed and accuracy.In view of the evident differences between coal and rock in visual attributes such as color,gloss and texture,the complete local binary pattern(CLBP)image feature descriptor is introduced for coal and rock image recognition.Given that the original algorithm oversimplifies local texture features by ignoring imaging information from higher-order pixels and the concave and convex areas between adjacent sampling points,this paper proposes a higher-order differential median CLBP image feature descriptor to replace the original CLBP center pixel gray with a local gray median,and replace the binary differential with a second-order differential.Meanwhile,for the high dimensionality of CLBP descriptor histogram and feature redundancy,deep learning perceptual field theory is introduced to realize data nonlinear dimensionality reduction and deep feature extraction.With relevant experiments conducted,the following conclusion can be drawn:(1)Compared with that of the original CLBP,the recognition accuracy of the improved CLBP algorithm is greatly improved and finally stabilized above 94.3%under strong noise interference;(2)Compared with that of the original CLBP model,the single image recognition time of the coal rock image recognition model fusing the improved CLBP and the receptive field theory is 0.0035 s,a reduction of 71.0%;compared with the improved CLBP model(without the fusion of receptive field theory),it can shorten the recognition time by 97.0%,but the accuracy rate still maintains more than 98.5%.The method offers a valuable technical reference for the fields of mineral development and deep mining.

Dendritic sidebranches of a binary system with enforced flow

In the present paper, the problem of sidebranches in the binary dendritic growth with enforced flow is studied. The positions of the first sidebranch and spacing of dendritic sidebranches are presented. For the neutral stable mode of dendritic growth, effects of various parameters on sidebranches are analysed. Our result shows that sidebranches are produced behind a critical point ξC′.

Novel similarity measures for face representation based on local binary pattern

The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.