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A Method for Improving CNN-Based Image Recognition Using DCGAN

computers, Materials & Continua 2018年第10期57卷 167-178页

作者： Wei Fang Feihong Zhang Victor S.Sheng Yewen Ding School of Computer&Software Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science&TechnologyNanjing210044China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing210023China Computer Science Department University of Central ArkansasConway AR72035USA

Image recognition has always been a hot research topic in the scientific community and *** emergence of convolutional neural networks(CNN)has made this technology turned into research focus on the field of computer vision,especially in image *** it makes the recognition result largely dependent on the number and quality of training ***,DCGAN has become a frontier method for generating images,sounds,and *** this paper,DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating *** combine DCGAN with CNN for the second *** DCGAN to generate samples and training in image recognition model,which based by *** method can enhance the classification model and effectively improve the accuracy of image *** the experiment,we used the radar profile as dataset for 4 categories and achieved satisfactory classification *** paper applies image recognition technology to the meteorological field.

关键词： DCGAN image recognition CNN samples

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Efficient sampling methods for characterizing POIs on maps based on road networks

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Frontiers of computer Science 2018年第3期12卷 582-592页

作者： Ziting ZHOU Pengpeng ZHAO Victor S. SHENG Jiajie XU Zhixu LI Jian WU Zhiming CUI School of Computer Science and Technology Soochow University Suzhou 215006 China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing 210044 China

With the rapid development of location-based services, a particularly important aspect of start-up marketing research is to explore and characterize points of interest （PoIs） such as restaurants and hotels on maps. However, due to the lack of direct access to PoI databases, it is necessary to rely on existing APIs to query Pols within a region and calculate PoI statistics. Unfortunately, public APIs generally im- pose a limit on the maximum number of queries. Therefore, we propose effective and efficient sampling methods based on road networks to sample PoIs on maps and provide unbiased estimators for calculating PoI statistics. In general, the more intense the roads, the denser the distribution of PoIs is within a region. Experimental results show that compared with state-of-the-art methods, our sampling methods improve the efficiency of aggregate statistical estimations.

关键词： sampling aggregate statistical estimation roadnetworks

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Full-blind delegating private quantum computation

arXiv

引用

arXiv 2020年

作者： Liu, Wen-Jie Chen, Zhen-Yu Liu, Jin-Suo Su, Zhao-Feng Chi, Lian-Hua Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China State Grid Electric Power Research Institute NARI Group Corporation Nanjing210003 China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Department of Computer Science and Information Technology La Trobe University VIC3086 Australia

The delegating private quantum computation (DQC) protocol with the universal quantum gate set {X, Z, H, P, R, CNOT} was firstly proposed by Broadbent et al., and then Tan et al. tried to put forward an half-blind DQC protocol (HDQC) with another universal set {H, P, CNOT, T}. However, the decryption circuit of Toffoli gate (i.e., T) is a little redundant, and Tan et al.'s protocol exists the information leak. In addition, both of these two protocols just focus on the blindness of data (i.e., the client's input and output), but do not consider the blindness of computation (i.e., the delegated quantum operation). For solving these problems, we propose a full-blind DQC protocol (FDQC) with quantum gate set {H, P, CNOT, T}, where the desirable delegated quantum operation, one of {H, P, CNOT, T}, is replaced by a fixed sequence (H, P, CZ, CNOT, T ) to make the computation blind, and the decryption circuit of Toffoli gate is also optimized. Analysis shows that our protocol can not only correctly perform any delegated quantum computation, but also holds the characteristics of data blindness and computation blindness. Copyright © 2020, The Authors. All rights reserved.

关键词： Eye protection

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Multiparty quantum sealed-bid auction using single photons as message carrier

arXiv

引用

arXiv 2020年

作者： Liu, Wen-Jie Wang, Hai-Bin Yuan, Gong-Lin Xu, Yong Chen, Zhen-Yu An, Xing-Xing Ji, Fu-Gao Gnitou, Gnim Tchalim Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China College of Mathematics and Information Science Guangxi University Nanning Guangxi China School of Electronics & Information Engineering Nanjing University of Information Science & Technology Nanjing210044 China

In this study, a novel multiparty quantum sealed-bid auction protocol using the single photons as the message carrier of bids is proposed, followed by an example of three-party auction. Compared with those protocols based on the entangled states (GHZ state, EPR pairs, etc.), the present protocol is more economic and feasible within present technology. In order to guarantee the security and the fairness of the auction, the decoy photon checking technique and an improved post-confirmation mechanism with EPR pairs are introduced respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

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Feature Relationships Learning Incorporated Age Estimation Assisted by Cumulative Attribute Encoding

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computers, Materials & Continua 2018年第9期56卷 467-482页

作者： Qing Tian Meng Cao Tinghuai Ma School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical and Electronic Engineering University of ManchesterManchesterM139PLUK

The research of human facial age estimation(AE)has attracted increasing attention for its wide *** to date,a number of models have been constructed or employed to perform *** the goal of AE can be achieved by either classification or regression,the latter based methods generally yield more promising results because the continuity and gradualness of human aging can naturally be preserved in age ***,the neighbor-similarity and ordinality of age labels are not taken into account *** overcome this issue,the cumulative attribute(CA)coding was *** such age label relationships can be parameterized via CA coding,the potential relationships behind age features are not incorporated to estimate *** this end,in this paper we propose to perform AE by encoding the potential age feature relationships with CA coding via an implicit modeling *** that,we further extend our model to gender-aware AE by taking into account gender variance in aging ***,we experimentally validate the superiority of the proposed methodology.

关键词： Age estimation cumulative attribute gender-aware age estimation correlation relationship learning

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Implementing real-time RCF-Retinex image enhancement method using CUDA

Implementing real-time RCF-Retinex image enhancement method ...

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作者： Yang, Xiaomin Jian, Lihua Wu, Wei Liu, Kai Yan, Binyu Zhou, Zhili Peng, Jian College of Electronics and Information Engineering Sichuan University No. 24 South Section 1 Yihuan Road Chengdu China College of Computer Science Sichuan University No. 24 South Section 1 Yihuan Road Chengdu China School of Electrical Engineering and Information Sichuan University No. 24 South Section 1 Yihuan Road Chengdu China Jiangsu Engineering Center of Network Monitoring and School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

RCF-Retinex is a novel Retinex-based image enhancement method which can improve contrast, eliminate noise, and enhance details simultaneously. It utilizes region covariance filter (RCF) to estimate the illumination. However, RCF-Retinex encounters time-consuming problem, since the region covariance filter is computationally intensive, which restricts the practical application in real-time systems. Therefore, it is necessary to decrease the computational complexity by parallelization. This paper proposes a GPU-based RCF-Retinex, which can accelerate region covariance filter using CUDA. It is feasible to use CUDA to parallel the region covariance filter due to its consecutive convolution operations, thus we can obtain the illumination image fast. Experiments have proved the improvement of running time and the enhancement results are similar with those using the unaccelerated RCF-Retinex method. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image enhancement

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A Novel Steganography Scheme Combining Coverless Information Hiding and Steganography

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Journal of Information Hiding and Privacy Protection 2019年第1期1卷 43-48页

作者： Ruohan Meng Zhili Zhou Qi Cui Xingming Sun Chengsheng Yuan School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Engineering Centre of Network Monitoring Nanjing210044China Department of Electrical and computer Engineering University of WindsorWindsorONCanada N9B 3P4

At present,the coverless information hiding has been ***,due to the limited mapping relationship between secret information and feature selection,it is challenging to further enhance the hiding capacity of coverless information *** the same time,the steganography algorithm based on object detection only hides secret information in foreground objects,which contribute to the steganography capacity is *** object recognition contains multiple objects and location,secret information can be mapped to object categories,the relationship of location and so ***,this paper proposes a new steganography algorithm based on object detection and relationship mapping,which integrates coverless information hiding and *** this method,the coverless information hiding is realized by mapping the object type,color and secret information in object detection *** the same time,the object detection method is used to find the safe area to hide secret *** proposed algorithm can not only improve the steganographic capacity of the two information hiding methods but also make the coverless information hiding more secure and robust.

关键词： Steganography faster R-CNN coverless information hiding

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Big data trip classification on the New York City Taxi and uber sensor network

Big data trip classification on the New York City Taxi and u...

引用

作者： Sun, Huiyu Hu, Siyuan McIntosh, Suzanne Cao, Yi Department of Computer Science New York University United States Jiangsu Engineering Centre of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology China

Millions of trips are made every day by taxis and Uber in New York City. We first employ big data technologies to analyze this vast dataset: Apache Spark is used for data processing and classification, Apache Hive is used for data storage, and MapReduce is used for data profiling. Since taxis and Uber are equipped with GPS sensors, we then visualize a mobile sensor network over New York City separated into fine-sized regions each acting as a mobile sensing node. Each location on the network falls into a region and is classified into one of three categories based on which service dominates the particular region: Yellow taxi, Green taxi, or Uber. We utilize logistic regression to classify a region into one of the three categories. Our classification algorithm is then used to analyze the interaction between taxi and Uber, for example to quantify the expansion of Uber. Experiments run on the Spark cluster show our classifier achieves an accuracy of over 85% scored on the 2014 taxi and Uber dataset. Finally, we propose a trip recommendation system for users using classification results together with a web service application. © 2018 Taiwan Academic network Management Committee. All rights reserved.

关键词： Classification (of information)

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A novel convolution neural network for background segmentation recognition 4th

A novel convolution neural network for background segmentati...

引用

4th International Conference on Cloud Computing and Security, ICCCS 2018

作者： Fang, Wei Ding, Yewen Zhang, Feihong School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030000059

The convolution neural network for image classification is an application of deep learning on image processing. Convolutional neural networks have the advantage of being able to convolve directly with image pixels and extract image features from image pixels. This approach is closer to the treatment of the human brain’s visual system. However, up to now, it is impossible to achieve 100% classification accuracy regardless of any kind of convolutional neural network models. At the same time, we also found that sometimes the picture background will also affect the recognition effect of the neural network on the picture, and after removing the picture background, it can correctly recognize the same picture. Therefore, we design a model based on the convolutional neural network to remove the image background for some specific images (such as selfies, animals, flowers, etc.) here, and then the image after processing will be identified and classified. Experiments show that the proposed method can maintain a high level of integrity of the target to be detected in the image after removing the background (here the indicator is Intersection Over Union, IOU);moreover, through multiple classification models verify that the classification accuracy of some background-removed pictures is significantly higher than that of pictures without any treatment. © Springer Nature Switzerland AG 2018.

关键词： Image classification

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Identifying Materials of Photographic Images and Photorealistic computer Generated Graphics Based on Deep CNNs

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computers, Materials & Continua 2018年第5期55卷 229-241页

作者： Qi Cui Suzanne McIntosh Huiyu Sun School of Computer and Software Nanjing University of Information Science and TechnologyNing Liu RoadNo.219Nanjing210044China Jiangsu Engineering Centre of Network Monitoring Ning Liu RoadNo.219Nanjing210044China Computer Science Department New York UniversityNew YorkNY 10012USA

Currently,some photorealistic computer graphics are very similar to photographic *** computer generated graphics can be forged as photographic images,causing serious security *** aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime *** paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network *** results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.

关键词： Image identification CNN DNN DCNNs computer generated graphics

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