A design based on Spark technology is proposed to solve the problem that the telecom operators' network bandwidth resources are resold. We use big data components such as Hadoop, Spark, Hive, HBase, and Zookeeper to build a domain name server logs analysis system and improve the generalized suffix automaton algorithm to match the logs of telecom operator customers with the logs for building a registration information database. Finally, we make an online log analysis platform based on Spring boot to discriminate data. Experiments show that based on Spark technology, the operator resources resale analysis system can accurately match operator telecom customer logs from massive logs. This design is of great significance to realize the operator's network bandwidth business clean, efficient, and high-quality operation with building a harmonious development network environment.

When taking pictures of electronic screens or objects with high-frequency textures, people often run across colorful rainbow patterns that are known as ''moire'', seriously affecting the image quality and subsequent processing.Current methods for removing moire patterns mostly extract multiscale information by downsampling pooling layers, which may inevitably cause information loss.To address this issue, this paper proposes a demoireing method in the wavelet domain.By employing both discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) instead of traditional downsampling and upsampling, this method can effectively increase the network receptive field without information loss.In addition, to further reconstruct more details of moire patterns, this paper proposes an efficient attention fusion module (EAFM).With a combination of efficient channel attention, spatial attention and local residual learning, this module can self-adaptively learn various weights of feature information at different levels and inspire the network to focus more on effective information such as moire details to improve learning and demoireing performance.Extensive experiments based on public datasets have shown that this suggested method can efficiently remove moire patterns and has a good quantitative and qualitative performance.

As the current trend of integrating technology in higher education worldwide, flipped learning is regard as an instructional strategy and a part of blended learning instruction, where the students understand and comprehend the instructional contents before attending class by online educational videos. In order to maximise learning, this study implements and re-designs the flipped learning based on the students' learning behaviour and academic performance. This paper adopts a real-word example of flipped classroom design in Computer Culture Foundation. The data used in the analyses are derived from both the students' learning behaviour and the academic performance for the course. The results led to the following conclusions: 1) the discussion is critical factors that can increase the students' learning interests; 2) the number of access, human factor and assignment may affect the students' academic performance. And survey data reinforced that the flipping learning model is an effective strategy to maximise learning.

In recent years, surface-enhanced Raman scattering (SERS) of a molecule/metal-semiconductor hybrid system has attracted considerable interest and regarded as the synergetic contribution of the electromagnetic and chemical enhancements from the incorporation of noble metal into semiconductor nanomaterials. However, the underlying mechanism is still to be revealed in detail. Herein, we report an irreversible accumulated SERS behavior induced by near-infrared (NIR) light irradiating on a 4-mercaptobenzoic acid linked with silver and silver-doped titanium dioxide (4MBA/Ag/Ag-doped TiO2) hybrid system. With increasing irradiation time, the SERS intensity of 4MBA shows an irreversible exponential increase, and the Raman signal of the Ag/Ag-doped TiO2 substrate displays an exponential decrease. A microscopic understanding of the time-dependent SERS behavior is derived based on the microanalysis of the Ag/Ag-doped TiO2 nanostructure and the molecular dynamics, which is attributed to three factors: (1) higher crystallinity of Ag/Ag-doped TiO2 substrate; (2) photo-induced charge transfer; (3) charge-induced molecular reorientation.

The purpose of this study is to analyse the relationship between online learning behaviour and learning achievement, and improve academic performance of learners in MOOCs. This paper analyses the learning behaviour of 1,388 undergraduates in the online advanced mathematics course of the online platform named 'Erya' with statistical analysis and clustering methods. The results show that: 1) the lack of positive interaction between teachers and learners can affect learners' enthusiasm for learning and learners' learning outcomes; 2) the academic performance related with the ethnic, the number of access and the completion of the after-school tasks, but the correlation with the discussion is small. In addition, we also made some suggestions based on the results of the learning behaviour analysis to improve academic performance in the massive open online courses.

The morphological evolution of polyhedral Cu2O crystals and the LSPR and SERS characteristics of the as-synthesized polyhedral Cu2O@Ag CMs with different structures.

Poisson noise removal is of significant importance for many applications such as spectral imaging, night vision and especially in medical imaging and astronomy. Gaussian scale mixture based methods have been widely used in image denoising. In this paper, we focus on the Poisson noise and propose a new strategy based on Bayesian least squares method for its removal. We begin with a method that removes Poisson noise by reducing it to an additive Gaussian noise with a Variance Stabilizing Transformation. Then we combine the localized version of BLS-GSM method to bring out a new denoising strategy for images corrupted by Poisson noise and experimentally show that it outperforms some of the best existing methods for Poisson noising removal both numerically and visually.

Early event prediction challenges most of existing modeling methods especially when dealing with complex spatio-temporal data. In this paper we propose a new method for predictive data modelling based on a new development of the recently proposed NeuCube spiking neural network architecture, called here NeuCube(ST). The NeuCube uses a Spiking Neural Network reservoir (SNNr) and dynamic evolving Spiking Neuron Network (deSNN) classifier. NeuCube(ST) is an integrated environment including data conversion into spike trains, input variable mapping, unsupervised learning in the SNNr, supervised classification learning, activity visualization and network structure analysis. A case study on a real world ecological data set is presented to demonstrate the validity of the proposed method.

In order to obtain a robust supervised model with good generalization ability, traditional supervised learning method has to be trained with sufficient well labeled and uniformly distributed samples. However, in many real applications, the cost of labeled samples is generally very expensive. How to make use of ample easily available unlabeled samples to remedy the insufficiency of labeled samples to train a supervised model is of great interest and practical significance. In this paper we propose a new supervised learning framework, Posterior Distribution Learning (PDL), which could train a robust supervised model with very a few labeled samples by including those unlabeled samples into training stage. Experimental results on both synthetic and real world data sets are presented to demonstrate the effectiveness of the proposed framework.

Semi-Supervised Learning (SSL) method has recently caught much attention in the fields of machine learning and computer vision owing to its superiority in classifying abundant unlabelled samples using a few labeled samples. The goal of this paper is to provide an experimental efficiency comparison between graph based sa algorithms and traditional supervised learning algorithms (e.g., support vector machines) for multispectral image classification. This research shows that SSL algorithms generally outperform supervised learning algorithms in both classification accuracy and anti-noise ability. In the experiments carried out on two data sets (hyperspectral image and Landsat image), the mean overall accuracies (OAs) of supervised learning algorithms are 15 percent and 86 percent, while the mean OAs of SSL algorithms are 26 percent and 99 percent. To overcome the polynomial complexity of SSL algorithms, we also developed a linear-complexity algorithm by employing multivariate Taylor Series Expansion (TSE) and Woodbury Formula.

This study is to investigate a new representation of a partition of an image domain into a number of regions using a level set method derived from a statistical framework. The proposed model is composed of evolving simple closed planar curves by a region-based force determined by maximizing the posterior image densities over all possible partitions of the image plane containing three terms: a Bayesian term based on the prior probability, a regularity term adopted to avoid the generation of excessively irregular and small segmented regions, and a term based on a region merging prior related to region area, which is applied to allow the number of regions to vary automatically during curve evolution and therefore can optimize the objective functional implicitly with respect to the number of regions. This formulation leads to a system of coupled curve evolution equations, which is easily amenable to a level set implementation, and an unambiguous segmentation because the evolving regions form a partition of the image domain at all times during curve evolution. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results on both synthetic and real images.

The purpose of this study is to propose a novel method of a partition of an image domain into an adaptive number of regions using a multilayer foreground-filled method. First, two coupled curves based on a three-region Chan-Vese model, which is built based on the techniques of evolving simple closed planar curves by an explicit correspondence to minimize energy functional containing a fitting term and a regularization term, evolve simultaneously to segment images containing two objects and one background region in each image layer. Second, a foreground-filled technique is used to generate a new image and the three-region Chan-Vese model is repeated to segment the new image for the next image layer. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented on the basis on the length change of an evolving curve. This iterative process is repeated until the background image layer is detected. Numerical experiments on some synthetic and real images have demonstrated the efficiency and robustness of our method.

This study is to investigate a new representation of a partition of an image domain into a fixed but arbitrary number of regions via active contours and level sets. The proposed algorithm is composed of simple closed evolving planar curves by an explicit correspondence to minimize the energy functional containing three terms: multiregion fitting energy, regularization related to the length of the curve, and the distance regularizing term to penalize the deviation of the level set function from a signed distance function. This formulation leads to a system of coupled curve evolution equations, which is easily amenable to a level set implementation, and an unambiguous segmentation because the evolving regions form a partition of the image domain at all times during curve evolution. In order to increase the robustness of the method to noise and to reduce the computational cost, a multiresolution level set schema is proposed, which can perform the evolution curves of the partitioned image at a different resolution. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results on both synthetic and real images. (C) 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI:10.1117/1.3582863]