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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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An attribute-based encryption scheme with LSSS key-policy from lattices

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and computing 2018年 613卷 217-230页

作者： Liu, Lihua Wang, Shangping Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an710054 China School of Mathematics and Computer Science Shaanxi University of Technology HanzhongShaanxi723001 China

ISBN: (纸本)9783319607436

A new attribute-based encryption scheme (ABE) from lattices with Linear Secret Sharing Scheme (LSSS) key-policy is presented. In the new scheme, the key of an attribute under an access policy represented by LSSS is generated by the use of the sample-pre algorithm on lattices. Every attribute has its own authority, and the private key of each attribute under an access policy is produced by its own authority alone. The new scheme has the advantage of fine-grained access control, and also could support multi-authority attribute management, thus it facilitates the practical management of attribute encryption system. The security of the new scheme is proved in the selective-attribute attack model under learning with errors (LWE) assumption, and the security parameter selecting is discussed in detail. © 2018, Springer International Publishing AG.

关键词： Access control

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A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

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7th International Conference on Computational Data and Social networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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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 Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM networks

A Multi-label Classifier for Human Protein Subcellular Local...

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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

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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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An Improved Convolutional Neural network Algorithm for Multi-Label Classification

An Improved Convolutional Neural Network Algorithm for Multi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Xinsheng Wang Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Research Center of Big Data and Network Security Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Recent years conventional neural network(CNN) has been applied to different natural language processing(NLP) tasks such as sentence classification, sentence modeling, etc. Some researchers use CNN to do multi-label classification but their work mainly focus on image rather than text. In this paper, we propose an improved CNN via hierarchical dirichlet process(HDP) model to deal with the multi-label classification problem in NLP. We first apply an HDP model to discard some words which are less important semantically. Then we use word embedding methods to transform words to vectors. Finally, we train CNN based on word vectors. Experimental results demonstrate that our method is superior to most traditional multi-label classification methods and TextCNN in terms of performance.

关键词： Feature extraction Training Convolutional neural networks Natural language processing Vocabulary Transforms

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Analysis of SVEIR worm attack model with saturated incidence and partial immunization

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Journal of Communications and Information networks 2017年第4期1卷 105-115页

作者： Wang, Fangwei Huang, Wenyan Shen, Yulong Wang, Changguang College of Information Technology Hebei Normal University Shijiazhuang China Shannxi Key Laboratory of Network and System Security Xidian University Xi’an China College of Computers Xidian University Xi’an China

Internet worms can propagate across networks at terrifying speeds, reduce network security to a remarkable extent, and cause heavy economic losses. Thus, the rapid elimination of Internet worms using partial immunization becomes a significant matter for sustaining Internet infrastructure. This paper addresses this issue by presenting a novel worm susceptible-vaccinated-exposed-infectious-recovered model, named the SVEIR model. The SVEIR model extends the classical susceptible-exposed-infectious-recovered model (refer to SEIR model) through incorporating a saturated incidence rate and a partial immunization rate. The basic reproduction number in the SVEIR model is obtained. By virtue of the basic reproduction number, we prove the global stabilities of an infection-free equilibrium point and a unique endemic equilibrium point. Numerical methods are used to verify the proposed SVEIR model. Simulation results show that partial immunization is highly effective for eliminating worms, and the SVEIR model is viable for controlling and forecasting Internet worms.

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