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Exploiting Gabor Feature Extraction Method for Chinese Character Writing Quality Evaluation

Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhixiao Wang Wenyao Yan Mingtao Guo Jiulong Zhang School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 China Shaanxi Key Laboratory Xi'an of Network Computing and Security Technology China School of Data Science and Computer Xi'an innovation college of Yan'an University Xi'an 710100 China

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.

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Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

Exploiting Meta-Path with Attention Mechanism for Fine-Grain...

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2019 3rd International Conference on Data Mining, Communications and Information technology (DMCIT 2019)

作者： Zhixiao Wang Wenyao Yan Wendong Wang Ang Gao Lei Yu Shaobo Yang GaoYang Nie School of Computer Science and Engineering Xi'an University of Technology Telematics Group the University of Goettingen Shaanxi Key Laboratory of Network Computing and Security School of Electronic and Information Engineering Xi'an Jiaotong University College of Xi'an Innovation Yan'an University Yan'an University National Satellite Meteorological Center China Meteorological Administration

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.

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Revisiting Cloud Migration: Strategies and Methods

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhixiao Wang Wenyao Yan Wendong Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 China Shaanxi Key Laboratory Xi'an of Network Computing and Security Technology China School of Data Science and Computer Xi'an innovation college of Yan'an University Xi'an 710100 China Yan'an University Xi'an 716000 China

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.

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Efficient attribute-based encryption with blackbox traceability 12th

Efficient attribute-based encryption with blackbox traceabil...

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12th International Conference on Provable security, ProvSec 2018

作者： Xu, Shengmin Yang, Guomin Mu, Yi Liu, Ximeng Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Wollongong Australia School of Information Systems Singapore Management University Singapore Singapore College of Mathematics and Computer Science Fuzhou University FuzhouFujian China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China

ISBN: (纸本)9783030014452

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 which can trace decryption keys embedded in a decryption blackbox/device rather than tracing a well-formed decryption key. However, the existing ABTT schemes with blackbox traceability are based on composite order group and the size of the decryption key depends on the policies and the number of system users. In this paper, we revisit blackbox ABTT and introduce a new primitive called attribute-based set encryption (ABSE) based on key-policy ABE (KP-ABE) and identity-based set encryption (IBSE), which allows aggregation of multiple related policies and reduce the decryption key size in ABTT to be irrelevant to the number of system users. We present a generic construction of the ABTT scheme from our proposed ABSE scheme and fingerprint code based on the Boneh-Naor paradigm in CCS’08. We then give a concrete construction of the ABSE scheme which can be proven secure in the random oracle model under the decisional BDH assumption and a variant of q-BDHE assumption. © Springer Nature Switzerland AG 2018.

关键词： Public key cryptography

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A Study on Influential User Identification in Online Social networks

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Chinese Journal of Electronics 2016年第3期25卷 467-473页

作者： WANG Nan SUN Qindong ZHOU Yadong SHEN Si Shaanxi Key Laboratory of Network Computing and Security Xi'an University of Technology Ministry of Education Key Laboratory For Intelligent Network and Network Security Xi'an Jiaotong University

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.

关键词： Online social networks Influential user identification Dynamic regional interaction model(DRI) User influence evaluation

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Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc networks

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Journal of Physics: Conference Series 2019年第1期1288卷

作者： Zhixiao Wang Mingyu Chen Wenyao Yan Wendong Wang Ang Gao Gaoyang Nie Feng Wang Shaobo Yang Xi'an University of Technology Xi'an 710048 China Goettingen University Goettingen 37077 Germany Xi'an Jiaotong University Xi'an 710049 China Shaanxi Provincial Key Laboratory of Network Computing and Security Technology Xi'an 710048 China College of Xi'an Innovation Yan'an University Xi'an 710100 China Yan'an University Yan'an 716000 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

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.

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Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

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Journal of Physics: Conference Series 2019年第1期1284卷

作者： Zhixiao Wang Wenyao Yan Wendong Wang Ang Gao Lei Yu Shaobo Yang GaoYang Nie School of Computer Science and Engineering Xi'an University of Technology 710048 Xi'an China Telematics Group the University of Goettingen 37077 Goettingen Germany Shaanxi Key Laboratory of Network Computing and Security 710048 Xi'an China School of Electronic and Information Engineering Xi'an Jiaotong University 710048 Xi'an China College of Xi'an Innovation Yan'an University 710100 Xi'an China Yan'an University Yan'an 716000 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

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.

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A geometric image segmentation method based on a bi-convex, fuzzy, variational principle with teaching-learning optimization

A geometric image segmentation method based on a bi-convex, ...

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作者： Jin, Haiyan Li, Yaning Xing, Bei Wang, Lei Department of Computer Science and Engineering Xi'An University of Technology No. 5 South Jinhua Road Xi'an Shaanxi710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

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 process a geometric image. Then, we introduce TLBO to maximally optimize the length-penalty item, which will be changed under the teaching phase and the learner phase of the TLBO. This makes the length penalty item closer to the target boundary. Therefore, the length-penalty item can be automatically adjusted according to the fitness function, namely the evaluation standards of the image quality. At last, we combine the length-penalty item with the numerical remedy mechanism to achieve better results. Compared with existing methods, simulations show that our method is more effective. © 2016 - IOS Press and the authors. All rights reserved.

关键词： Image segmentation

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Smartphone Based Car-Searching System for Large Parking Lot

Smartphone Based Car-Searching System for Large Parking Lot

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IEEE Conference on Industrial Electronics and Applications

作者： Junhuai Li Yang An Rong Fei Huaijun Wang Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China

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.

关键词： Intelligent car-searching system QR code Smartphone Pedometer

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An Adaptively Disperse Centroids K-Means Algorithm Based on MapReduce Model

An Adaptively Disperse Centroids K-Means Algorithm Based on ...

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International Conference on Computational Intelligence and security

作者： Bin Wang Zheng Lv Jinwei Zhao Xiaofan Wang Tong Zhang Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Faculty of Computer Science and Engineering

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.

关键词： Clustering algorithms Algorithm design and analysis Iris Computational modeling Adaptation models Data models Computer science

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