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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the deep reinforcement learning method, the intrinsic curiosity model (ICM) guides the agent to obtain the opportunity to learn unknown strategies in the sparse reward environment, but the curiosity reward is a state difference value, which will make the agent pay too much attention to the exploration of new states, then could be the problem of blind exploration arises. To solve the above problem, this paper proposed an intrinsic curiosity model algorithm based on knowledge distillation (KD-ICM). Firstly, it introduced the method of knowledge distillation to make the agent acquire more abundant environmental information and strategy knowledge in a short time and accelerate the learning process. Secondly, by pre-training teachers' neural network model to guide the forward network to obtain a forward network model with higher accuracy and performance, reduced the blind exploration of agents. It designed two different simulation experiments on the Unity simulation platform for comparison. The experiments show that in the complex simulation task environment, the average reward of KDICM algorithm is 136% higher than that of ICM, and the optimal action probability is 13.47% higher than that of ICM. Both the exploration performance of the agent and the exploration quality can be improved, and it verifies the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent years, the motion deblurring algorithm based on deep learning has been widely concerned, while single defocus image deblurring is rarely studied. In order to specifically solve the defocus blur problem of single image, this paper proposed a defocus deblurring algorithm based on deep recurrent neural network. Firstly, the algorithm used two cascaded residual networks to estimate the defocus map and image deblurring, respectively. After that, to ensure that the depth features of defocus map and clear images could better propagate across stages and interact within stages, the algorithm introduced LSTM ( long short-term memory) as a recurrent layer in the residual network. Finally, the whole residual network underwent several iterations and reused the network parameters during the iterative stages. To train the network, this paper produced a synthetic defocus blur image dataset, where each defocus blurred image contained a corresponding clear image and defocus map. The experimental results show that, compared with existing defocus deblurring methods, the proposed algorithm has significant advantages in both the subjective and objective image quality evaluation, and can produce sharper edges and clear details in the restoration results. On the real defocus blur dual-pixel image dataset DPD, the proposed algorithm improves the peak signal-to-noise ratio ( PSNR) and structural similarity( SSIM ) by 0. 77 dB and 5. 6%, respectively, compared with DPDNet-Single. Therefore, the proposed method can effectively deal with defocus blur in real scenes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to factors such as illumination changes，camera jitter and dynamic background，existing foreground subtraction algorithms cannot achieve good segmentation results in complex scenes. To solve this kind of problems，this paper proposes a subtraction algorithm based on a fully convolutional siamese neural network，which can accurately segment the foreground with only two arbitrary frames. Specifically，the input two images are divided into the base image and the image to be segmented. The algorithm uses the fully- convolutional siamese network to get the similarity metric map of input frames. The similarity metric map contains information about changes in pixels of the image to be segmented relative to the base image. Then， the similarity metric map is fused with the image to be segmented，and the encoder-decoder network is used to achieve end-to-end foreground subtraction results. The paper evaluates the proposed algorithm on the CDnet2014 dataset to prove its effectiveness. [ABSTRACT FROM AUTHOR]

Single object tracking( SOT) is a technique to analyze the motion status and provide localization of the single target by using target appearance and context information in video. It has promising applications in intelligent surveillance, intelligent interaction, navigation and guidance, etc. However, problems such as occlusion, background interference and appearance variation led to slow progress in practical applications. With the rapid development of deep learning in recent years, the study of using deep learning techniques to optimize SOT algorithm has become one of the hot spots in computer vision. Around the SOT algorithm based on deep learning, this paper respectively provided outline and analysis of each of the six aspects of correlation filter, Siamese networks, meta-learning, attention, recurrent neural networks and generative adversarial networks according to the core algorithms, after analyzing the basic principles of SOT. In addition, this paper summarized the current state of research and proposed the development trend and optimization ideas of the algorithms. [ABSTRACT FROM AUTHOR]

As a new type of integrity authentication technology, subject-sensitive Hash overcomes the shortcomings of existing technologies, and can realize subject-sensitive authentication of HRRS images. However, existing subject-sensitive Hash algorithms still have deficiencies in the aspects of robustness. This paper proposes a new subject-sensitive Hash algorithm for integrity authentication of HRRS images. The proposed algorithm consists of two steps: the first step is to use the trained Swin-Unet to extract the subject-sensitive features of the HRRS images, the second step is to compress and encode the extracted subject-sensitive features to obtain subject-sensitive Hash sequences of HRRS images. The experimental results show that, compared with subject-sensitive Hash algorithms based on existing deep neural network models such as Attention U-net and AAU-Net, the robustness of the proposed algorithm for JPEG compression and watermarking embedding is improved, and the tampering sensitivity of this algorithm is basically consistent with that of existing algorithms, and it has a high tampering sensitivity in subject-related tampering. The security of the proposed algorithm is the same as the existing algorithms. [ABSTRACT FROM AUTHOR]

To solve the problem that the influencing factors of the airport bus operation process are complicated and the operation time is difficult to predict, this paper established an airport bus operation time prediction model based on the subspace identification algorithm. Firstly, based on the multi-source big data generated during the operation process, considering the number of passengers, departure interval, road congestion and other factors at different times, it established the state space model of the airport bus operation. Then it extracted the characteristic variables suitable for describing the operation process of the airport bus as the input and output of the model,and solved the model by subspace identification method. Finally,this paper took an actual operating route of the capital airport bus as a case for simulation analysis. The calculation results show that the mean absolute percentage error and mean square error of the model are 2. 25% and 4. 77, respectively, which are better than the traditional BP neural network prediction model and the least square identification model. The model has good prediction accuracy and has certain practical application value. [ABSTRACT FROM AUTHOR]

It is a very meaningful direction to study data stream ensemble classification based on the condition of supervised or semi-supervised learning. This paper introduced three aspects including base classifiers, key technologies and ensemble strategies. The base classifiers mainly introduced decision trees, neural networks, support vector machines, etc. The key technologies were introduced from incremental and online aspects, and the ensemble strategies mainly introduced boosting, stacking, etc. This paper summarized and analyzed the advantages and disadvantages of different ensemble methods, comparison algorithms and experimental data sets. Finally, it gave the further research directions, including the handling of concept drift based on supervised and semi-supervised learning, the study of homogeneous integration and heterogeneous integration, and the classification of data stream ensemble based on unsupervised learning. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the knowledge overload in the process of online learning and the problems of data sparsity and cold start in the traditional recommendation algorithm, this paper proposed an improved learning resource recommendation algorithm based on MLP. The algorithm used the advantage of MLP in nonlinear data processing, converted the prediction mode of vector multiplication of learner characteristics and learning resource characteristics into the input mode of MLP, and improved the DN-CBR neural network recommendation model. In order to verify the effectiveness of the model, this paper built a dataset with the online learning platform data of iCourse as the sample. Compared with DN-CBR model, the improved model improves the normalized cumulative loss gain and hit rate by 1. 2 % and 3 % respectively on the dataset, which effectively improves the recommendation performance of the model. [ABSTRACT FROM AUTHOR]

Aiming at the problems of time-consuming and high error rate in handwriting test scores statistics a handwriting test paper recognition method based on improved convolution neural network algorithm was proposed. In order to simplify the classification of the score» the score column of the handwritten test paper was divided to obtain ten types of numbers from 0 to 9. In order to improve the efficiency of handwritten score recognition, a classification and recognition algorithm of convolution neural network and Bayesian was proposed. The constructed convolution neural network model was used to extract the characteristics of handwritten digits. The PCA algorithm was used to reduce the dimensionality of the features.The hayes classifier was used to distinguish ten kinds of numbers from 0 to 9. the auuracy and efficiency of the algorithm were verified in the MNIST database.The paper score summation model was established and automatic summation is performed after recognition. The experimental results show that for the recognition uf the handwritten scores of 1 188 lest papers in 3 courses♦ the algorithm in this paper has a recognition rate of 98. 23% compared with other algorithms, the average recognition time per test paper is 7. 5 s, and verified its practicality. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the large data environment, there are many problems in the parallel deep convolution neural network（DCNN） algorithm, such as excessive data redundancy, slow convolution layer operation and poor convergence of loss function. This paper proposed a parallel deep convolution neural network optimization algorithm based on the Im2 col method. First, the algorithm proposed a parallel feature extraction strategy based on Marr-Hildreth operator to extract target features from data as input of convolution neural network, which can effectively avoid the problem of excessive data redundancy. Secondly, the algorithm designed a parallel model training strategy based on the Im2 col method. The redundant convolution kernel is removed by designing the Mahalanobis distance center value, and the convolution layer operation speed is improved by combining the MapReduce and Im2 col methods. Finally, the algorithm proposed an improved small-batch gradient descent strategy, which eliminates the effect of abnormal data on the batch gradient and solves the problem of poor convergence of the loss function. The experimental results show that IA-PDCNNOA algorithm performs well in deep convolution neural network calculation under large data environment and is suitable for parallel DCNN model training of large datasets. [ABSTRACT FROM AUTHOR]

Mobile edge computing is a method to solve the computing-intensive task requirements of robots. Traditional algorithms are based on intelligent algorithms or convex optimization methods, and the iteration time is long. Deep reinforcement learning can be solved in a single forward pass, but only for a fixed number of robots. Through the analysis and research of deep reinforcement learning, this paper performed input regularization before the input layer in the deep reinforcement learning neural network, and added a convolution layer after the output layer so that the network could adaptively meet the unloading requirements of the number of dynamic mobile robots. Finally, it carried out the simulation experiments to verify the effectiveness and feasibility of proposed algorithm, compared with the adaptive gen etic algorithm and reinforcement learning . [ABSTRACT FROM AUTHOR]

Most existing positive-unlabeled (PU) graph learning methods exact only node representations to infer node labels in dependently. This paper proposed a collective inference based method that exploited the correlations among nodes to assist in classification of unlabeled nodes. Firstly, it used the personalized PageRank algorithm to approximate correlation degrees between each node and observed positive nodes as a whole (positive correlation degree, PCD for short). Then, it built a local classifier to predict labels of unknown nodes by combining PCD with node representations that were captured via a graph neural network (CNN), and constructed a relational classifier to collectively up date labels of unknown nodes by combining PCD with local label dependency of nodes that were exacted via another CNN. Furtherly, it exploited the Markov CNN (CMNN ) frame-work to train these two classifiers, alternately and iteratively, to form a multi-hop collective inference procedure. Besides, it proposed a mixed non-negative unbiased risk estimator for the two classifiers to estimate empirical loss with only positive and unlabeled nodes. Finally, either of them could predict labels of unknown nodes. Experimental results on real-life datasets show that the proposed method remarkably out performs the state-of-the-art approaches in identifying both single-class target concept and multiple-classes-merged target concept, and performs quite robust against to error of the prior of positive nodes. [ABSTRACT FROM AUTHOR]

Object tracking is an important topic in computer visual directions, where scale changes, deformation and rotation are difficult to resolve in the field. For the challenging problems faced in the above track, based on existing Siamese network algorithms, this paper proposed multi-layer feature fusion and parallel self-attention Siamese networks (MPSiamRPN) for visual tracking. Firstly, MPSiamRPN used the modified ResNet50 to extract features from the template image and the search image. In order to deal with the loss of some features caused by the deep network, it proposed a multi-layer feature fusion module to fuse the features of the last three layers of ResNet. Secondly, it introduced the parallel self-attention module, which was composed of channel self-attention and spatial self-attention. The channel self-attention could selectively emphasize the beneficial channel features for tracking, and the spatial self-attention could learn the rich spatial information of the target. Finally, it proposed the region proposal network ( RPN) to perform classification and regression operations to determine the location and shape of the target. Experiments show that the MPSiamRPN can achieve competitive results on OTBlOO and VOT2018 test datasets. [ABSTRACT FROM AUTHOR]

Using spikes to represent and convey information, SNN is more biologically plausible than traditional artificial neural networks. However, a classical SNN has limited feature extraction ability due to the shallow network structure, leading to inferior classification performance to CNN especially on multi-class classification tasks such as object categorization. Inspired by the powerful convolutional structure of CNN, this paper proposed a SCSNN. By exploiting convolutional structures and dynamic impulse triggered property of biological neurons, the proposed SCSNN organized integrate-and-fire models in a convolutional fashion, and trained by a new surrogate gradient back-propagation algorithm directly. It validated the proposed SCSNN on the MNIST and Fashion-MNIST dataset respectively, obtaining superior performance to state-or-the-art SNN on both datasets. The classification accuracy reaches 99. 62% on the MNIST dataset, and 93. 52% on the Fashion-MNIST dataset, which verifies the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Mainstream target tracking algorithms only use visible light（RGB） images for tracking tasks. When the illumination conditions of the tracking scene are poor, visible light images algorithm of representing color and texture characteristics will seriously limit the tracking performance of the tracker. Aiming at the problem of missing single mode target information, based on the SiamFC network model and the idea of infrared and visible image fusion, this paper proposed a target tracking algorithm based on dual mode weight self-updating Siamese network. This algorithm effectively utilized the complementary advantages of infrared and visible image in the field of target tracking since the infrared image could collect the thermal information of moving target. It used the AlexNet model to extract the moving object with the robust characteristics, which guaranteed the tracking accuracy and improved the tracking speed of tracking model. Finally experimental results on OTB2015 datasets and infrared-visible light datasets RGB-T234 show that the proposed algorithm of target tracking under various tracking scenario has good tracking effect. [ABSTRACT FROM AUTHOR]

Considering federated learning under mobile edge computing, where a global server connects a large number of mobile devices through the network to jointly train a deep neural network model. The data distribution shift caused by global class imbalance and device local class imbalance leads to the performance degradation of the standard federated averaging ( FedAvg) algorithm. This paper proposed a device selection algorithm based on the combinatorial multi-armed bandit ( CMAB) as an online learning algorithm framework, and designed a class estimation scheme to form a nonlinear reward function. Based on class estimation scheme and CMAB, in each round of communication, the global server selected the device subset with class imbalance that could be best complementary with test performance deviation across classes of global model from last communication round. Therefore, the current aggregated global model could achieve more balance and better test performance per class, and also faster convergence and more stable training dynamics. Extensive numerical results demonstrate the influence of different parameters on FedAvg under class imbalance, and verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presented a novel discriminative K-SVD network ( DKSVDN) for face recognition. It embedded discriminative information into traditional K-SVD algorithm by special design of dictionary as well as sparse representation coefficients on the dictionary. The dictionary consisted of label specific atoms and descriptive atoms, while sparse codes contained one-hot label vectors and descriptive codes. In addition, as sparse representation algorithms were time-consuming, DKSVDN attached a co-trained feed-forward neural network to discriminative dictionary learning model to predict sparse codes. Moreover, with generative module in DKSVDN, this method also designed a new dreaming training phase to improve the robustness of DKSVDN for unknown pattern in known class. The experiment results on public face image datasets verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]

On the basis of the principle of SIFT algorithm, this paper constructed a completely new algorithm framework. The algorithm used FCN and BP neural network to comprehensively consider the semantic segmentation and image gray level co-occurrence matrix of the main target of image to achieve adaptive feature point detection range and quantity adjustment. And it used the offset stability of camera position to eliminate false matching during feature point matching. In the meantime, it optimized the pose recovery results by using graph-based optimization nonlinearity and obtained a more accurate camera pose. Finally, comparing with the existing mainstream algorithms, the experimental results verify the effectiveness of the proposed algorithm, the improvement of the scene-adaptive degree of feature points detection, the matching precision of feature points, the precision of posture recovery, and the better efficiency of three-dimensional reconstruction. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) as a novel intelligent optimization algorithm has been used successfully in many fields, but its application to flood hazard risk assessment is a new research topic. This paper introduces the theory and flow of application of particle swarm optimization rule mining (PSO-Miner) algorithm to flood damage risk assessment. This paper selected Beijiang River Basin, China, as study area for flood damage risk assessment based on PSO-Miner algorithm and BPANN method. The results of a case study indicate that the advantages of PSO-Miner algorithm can be summarized as follows: It does not assume an implicit assumption for processing dataset and has strong robustness; it can mine very simple assessment rules; it can have a better performance than BPANN model. So the PSO-Miner algorithm provides a new approach for flood risk assessment. [ABSTRACT FROM AUTHOR]

In order to identify the emotion types in speech signals, this paper proposed an improved genetic algorithm optimized BP neural network(IAGA-BP). This algorithm enhanced the optimization performance of genetic algorithm by improving the selection operator and the adaptive crossover and mutation probability formula, such that optimizing the initial weights and thresholds of the BP neural network. The experimental results show that the IAGA-BP network can effectively improve the recognition rate and speed up the convergence compared with the BP, GA-BP and AGA-BP networks. [ABSTRACT FROM AUTHOR]

A fault identification technique for vehicle automatic transmission based on support vector machine (SVM) is presented in this paper. Firstly, a virtual testing system designed is used to collect, process and screen the operation data of an automatic transmission under three statuses with appropriate data extracted as training samples. Then, a SVM-based multi-class classifier is designed to identify faults. Finally, the outcomes are compared with that using BP neural-network diagnostics. The results show that the fault identification based on SVM converges faster with stronger classification ability, suitable for real-time fault identification of vehicle automatic transmission. [ABSTRACT FROM AUTHOR]

This paper aims at improving the performance of the SLIQ decision tree algorithm (Mehta et. al, 1996) for classification in data mining. An improvement over the SLIQ algorithm has been proposed to reduce the computational complexity. It was observed that the decision tree constructed using the proposed decision tree algorithm gave far better classification accuracy than the classification accuracy obtained using the SLIQ algorithm and the neural network classification technique. [ABSTRACT FROM AUTHOR]

A shape recognition system based on ART2 neural network is developed in this paper to realize the reliable recognition of workpiece. A new type of features, orthogonal Fourier-Mellin moments, is extracted after image preprocessing. This type of features is advantage of being invariant to dimension, orientation and intensity variance. The algorithm of ART2 nerual networks is modified and employed to recognize the pattern. Experimental results verify the effectiveness of this system. Further research according to analysis of the OFFM's computation is put forward as well. [ABSTRACT FROM AUTHOR]

Because the detect recognition characteristic extracted from the welding image bas been seriously interfered by noises, and the accuracy of existing recognition algorithms is low, a defect recognition algorithm integrating rough set (RS) with fuzzy neural network (FNN) is presented in this paper. Firstly, the fuzzy C-mean (FCM) clustering algorithm was adopted to discretize the attributes of samples, and RS was used to reduce the attributes of sample data and obtain decision rules, then π function was used to fuzzified the attributes of samples according to the center and radius of clustering to overcome the problem that RS is sensitive to noises. Then, the obtained reduced fuzzy decision rules and fuzzy logical inference were used to ascertain the structure of FNN, and dependent factors together with antecedent coverage factors were employed to determine the initial parameters of network. In consideration of the reliability of the data in the samples, the weighted cost function was used to adjust model parameters. The simulation result shows that this algorithm can solve the problems, such as the uncertainty of sample data caused by noise interference in the process of classification and the difficulty in determining the structure of FNN, and can greatly improve the recognition capability of welding image defects. [ABSTRACT FROM AUTHOR]

Based on the spatial resource index of R-tree, concerning about the request of resource ability guide line, this paper used the road networks model for computing the spatial distance, and proposed a spatial resource index that contained the information of ability dimensionality and the top-k query algorithm. In the end, it implemented the algorithm in the contrast of traditional traversing algorithm in the experiment, and validated the impact of the algorithm. [ABSTRACT FROM AUTHOR]

Discovery of influential people is a very important research topic in microblog. Betweenness centrality is an efficient method to find important nodes in networks. However, traditional betweenness centrality algorithms are only fit in small size of networks, and not efficient in microblog of massive messages. This paper proposed a random walk based betweenness centrality algorithm, and this algorithm could deal with massive microblog messages, and was more efficient than related work. [ABSTRACT FROM AUTHOR]

Copyright of Applied Mathematics & Mechanics (1000-0887) is the property of Applied Mathematics & Mechanics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)