The automatic evaluation of Chinese character writing quality has a wide application prospect. Most of the existing evaluation methods of Chinese character writing quality are based on radical segmentation and feature...
The automatic evaluation of Chinese character writing quality has a wide application prospect. Most of the existing evaluation methods of Chinese character writing quality are based on radical segmentation and feature judgment, which require the high accuracy of Chinese character segmentation. However, there are many problems in the real handwriting, such as continuous writing, uneven strength of writing, personalized writing style and so on, which lead to the difficulty of segmentation in the ordinary handwriting. To solve the above problems, we propose an effective method based on image texture where the uniformity of writing lines and writing style is taken as an effective criterion. In our method, Gabor transform is used to extract the image features of writing samples, and finally the statistical learning method of support vector machine is used to effectively evaluate the writing quality. Experiments on multiple real datasets including CHAED show that our method is effective and accurate. The advantage of this method is that it does not need to segment fonts, and the cost of global feature extraction is small.
Based on the huge volumes of user check-in data in LBSNs,users’ intrinsic mobility patterns can be well explored,which is fundamental for predicting where a user will visit next given his/her historical check-in *** ...
Based on the huge volumes of user check-in data in LBSNs,users’ intrinsic mobility patterns can be well explored,which is fundamental for predicting where a user will visit next given his/her historical check-in *** there are various types of nodes and interactions in LBSNs,they can be treated as Heterogeneous Information network(HIN) where multiple semantic meta-paths can be *** by the recent success of meta-path context based embedding techniques in HIN,in this paper,we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location *** results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.
Cloud computing attracts users with its advantage of unlimited resource supply where resources can be elastically expanded on demand and balanced-load at the same time. This means that the application in the cloud env...
Cloud computing attracts users with its advantage of unlimited resource supply where resources can be elastically expanded on demand and balanced-load at the same time. This means that the application in the cloud environment should run in the way of elastic expansion cluster. At present, most of the elastic expansion and load balancing technologies provided by IaaS level are oriented to virtual machines (VM), with little consideration of application level, which does not fundamentally meet the needs of users. Based on this, starting from the motivation of cloud migration, we first analyse the cloud migration technology from three aspects: migration object, migration means and migration objectives, then explore the cloud migration criteria and four cloud migration strategies at the application level in the cloud environment, and point out that cloud migration should be optimized and maintained in terms of elastic expansion, load balancing, security, etc.
Traitor tracing scheme can be used to identify a decryption key is illegally used in public-key encryption. In CCS’13, Liu et al. proposed an attribute-based traitor tracing (ABTT) scheme with blackbox traceability w...
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Influential user evaluation is great importance in many application areas of online social *** order to identify influential users in a more adequate and practical way, we propose a Dynamic regional interaction model(...
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Influential user evaluation is great importance in many application areas of online social *** order to identify influential users in a more adequate and practical way, we propose a Dynamic regional interaction model(DRI) to evaluate user influence in online social networks. Influential users can be identified by the influence effect on different distance users based on dynamic regional interaction model. We have applied the influential user identification method to Sina Weibo and the experimental results show that compared with the existing methods the proposed method can identify the influence users in a more accuracy and efficiency way.
Ad-Hoc network which is one kind of self-organized networks is much more vulnerable than the infrastructural network with the properties of highly changeable linkage, dynamic structure, and wireless connections so tha...
Ad-Hoc network which is one kind of self-organized networks is much more vulnerable than the infrastructural network with the properties of highly changeable linkage, dynamic structure, and wireless connections so that the tradition intrusion detection system (IDS) should be improved to adapt in such network with limited computing resources and open channels. To ensure the security in Ad-Hoc network, the efficient anomaly detection methods should be probed. Over the past years, many studies have implemented anomaly detection methods (intrusion detection techniques) based on machine-learning methods in this field. This article analyzes the existing security problem in Ad-Hoc network, presents the basic theory of intrusion detection for Ad-Hoc network, and reviews the current and recent anomaly detection methods used machine learning techniques in the intrusion detection system.
Based on the huge volumes of user check-in data in LBSNs, users' intrinsic mobility patterns can be well explored, which is fundamental for predicting where a user will visit next given his/her historical check-in...
Based on the huge volumes of user check-in data in LBSNs, users' intrinsic mobility patterns can be well explored, which is fundamental for predicting where a user will visit next given his/her historical check-in records. As there are various types of nodes and interactions in LBSNs, they can be treated as Heterogeneous Information network (HIN) where multiple semantic meta-paths can be extracted. Inspired by the recent success of meta-path context based embedding techniques in HIN, in this paper, we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location prediction. Experimental results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.
This paper proposes an efficient, bi-convex, fuzzy, variational (BFV) method with teaching and learning based optimization (TLBO) for geometric image segmentation. Firstly, we adopt a bi-convex, object function to pro...
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Find a car in a large parking lot is a challenge in our daily life. In this paper, a novel car-searching approach based on smartphone for a large parking lot is presented. In the new approach, some QR codes are set up...
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ISBN:
(纸本)9781467386456
Find a car in a large parking lot is a challenge in our daily life. In this paper, a novel car-searching approach based on smartphone for a large parking lot is presented. In the new approach, some QR codes are set up in each area which can identify the parking spots. In addition, shortest path algorithm is used to plan the optimal car-searching path. Considering that some parkers with poor sense of direction may lose in the parking lot, a real-time navigation method is proposed for parker to search the car, which is based on the built-in sensors in smartphone and pedometer principle. The new approach is tested in a large indoor parking lot, and the experimental results show the efficiency of the proposed car-searching system.
K-means is a clustering algorithm which is used widely. Its clustering results heavily depend on the initial centroids. An adaptive method for disperse centroids is proposed to improve the stability and accuracy of th...
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ISBN:
(纸本)9781509048410
K-means is a clustering algorithm which is used widely. Its clustering results heavily depend on the initial centroids. An adaptive method for disperse centroids is proposed to improve the stability and accuracy of the clustering result. The Adaptively Disperse Centroids K-means Algorithm (ADC-K-means) is implemented using MapReduce model on hadoop platform, and it is compared with the k-means of Mahout which is a sub-project of hadoop. The experimental result shows that proposed algorithm is effective.
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