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Feature Relationships Learning Incorporated Age Estimation Assisted by Cumulative Attribute Encoding

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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Coverless image steganography based on SIFT and BOF

Coverless image steganography based on SIFT and BOF

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作者： Yuan, Chengsheng Xia, Zhihua Sun, Xingming School of Computer and Software Nanjing University of Information Science and Technology China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology China

In order to realize secret communication, conventional steganography techniques can embed the secret message into carrier images by modifying the content and structure of carrier images. Although steganography techniques provide us with some more secure communication, the modification traces will be detected through using these brand-new steganalysis tools. Thereby, in order to eliminate the influence of modification traces, we propose a novel coverless image steganography scheme based on scale invarient feature transform and bag of feature. Different from conventional steganography, images have already contained the concealing information, so we can transmit natural images, whose features are the same with the secret information, to receivers. Firstly, the robust feature hash sequences are constructed with our protocol of feature. Then, the secret message is converted into bitstreams. Finally, these images with the seceret information are chosen and sent to receivers through using inverted index. Throughout the process, original images are never modified in our scheme, so our method can resist the analysis of existing steganalysis tools. Numerical experiments also indicate that our scheme has a desirable robustness to the common image attacks.

关键词： Steganography

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Short text classification technology based on KNN+hierarchy SVM 12th

Short text classification technology based on KNN+hierarchy ...

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12th International Conference on Future Information Technology, FutureTech 2017

作者： Yin, Chunyong Shi, Lingfeng Wang, Jin School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Information Engineering Yangzhou University Yangzhou China

ISBN: (纸本)9789811050404

A short text classification method based on combination of KNN and hierarchical SVM is proposed. First, the KNN algorithm is improved to get the K nearest neighbor class labels quickly, so as to effectively filter the candidate classes of documents. And then classify them from top to bottom using a multi-class sparse hierarchical SVM classifier. By this way, the document can be classified efficiently. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Classification (of information)

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Improved data stream clustering algorithm for anomaly detection 12th

Improved data stream clustering algorithm for anomaly detect...

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12th International Conference on Future Information Technology, FutureTech 2017

作者： Yin, Chunyong Zhang, Sun Wang, Jin School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Information Engineering Yangzhou University Yangzhou China

ISBN: (纸本)9789811050404

Intrusion detection provides important protection for network security and anomaly detection as a type of intrusion detection, can recognize the pattern of normal behaviors and label the behaviors which departure from normal pattern as abnormal behaviors. We think that the traditional methods based on dataset do not satisfy the needs of dynamic network environment. The network data stream is temporal and cannot be treated as static dataset. The concept and distribution of data objects is variety in different time stamps and the changing is unpredictable. Therefore, we propose an improved data stream clustering algorithm and design the frame of anomaly detection according to the improved algorithm. It can modify the established model with the changing of data stream and detect abnormal behaviors in time. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Intrusion detection

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Application of an improved data stream clustering algorithm in intrusion detection system 12th

Application of an improved data stream clustering algorithm ...

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12th International Conference on Future Information Technology, FutureTech 2017

作者： Yin, Chunyong Xia, Lian Wang, Jin School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Information Engineering Yangzhou University Yangzhou China

ISBN: (纸本)9789811050404

With the continuous development of computer network technology, traditional intrusion detection system is short of good adaptability. Aiming at the traditional intrusion detection system is difficult to adapt to the increasing amount of data demand for real-time processing capability, this paper proposes a clustering algorithm based on sliding window data streams, based on which we build the IDS network security defense model. The experiment results show that the model is able to adapt to the high-speed network intrusion detection requirements. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Data mining

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Access Control Policy Based on Friend Circle

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computers, Materials & Continua 2020年第3期62卷 1143-1159页

作者： Qin Liu Tinghuai Ma Fan Xing Yuan Tian Abdullah Al-Dhelaan Mohammed Al-Dhelaan School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China CICAEET Jiangsu Engineering Centre of Network MonitoringNanjing University of Information Science and TechnologyNanjing210044China Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia

Nowadays,the scale of the user’s personal social network(personal network,a network of the user and their friends,where the user we call“center user”)is becoming larger and more *** is difficult to find a suitable way to manage them *** order to solve this problem,we propose an access control model for social network to protect the privacy of the central users,which achieves the access control accurately and *** on the hybrid friend circle detection algorithm,we consider the aspects of direct judgment,indirect trust judgment and malicious users,a set of multi-angle control method which could be adapted to the social network environment is ***,we propose the solution to the possible conflict of rights in the right control,and assign the rights reasonably in the case of guaranteeing the privacy of the users.

关键词： Social network privacy protection circle of friends access control

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A duplicate data detection approach based on MapReduce and HDFS

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Recent Patents on computer Science 2017年第2期10卷 140-148页

作者： Wei, Fang Xue-Zhi, Wen Yu, Zheng 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 State Key Laboratory for Novel Software Technology Nanjing University Nanjing210066 China Department of Electrical & Computer Engineering University of Florida GainesvilleFL32611 United States

Background: With the surge in the volume of collected data, deduplication will undoubtedly become one of the problems faced by researchers. There is significant advantage for deduplication to reduce storage, network bandwidth, and system scalability of coarse-grained redundant data. Since the conventional methods of deleting duplicate data include hash comparison and binary differential incremental. They will lead to several bottlenecks for processing large scale data. And, the traditional Simhash similarity method has less consideration on the natural similarity of text in some specific fields and cannot run in parallel program with large scale text data processing efficiently. This paper examines several most important patents in the area of data detection. Then, this paper will focus on large scale of data deduplication based on MapReduce and HDFS. Methods: We propose a duplicate data detection approach based on MapReduce and HDFS, which uses the Simhash similarity computing algorithm and SSN algorithm, and explain our distributed duplicate detection workflow. The important technical advantages of the invention include generating a checksum for each processed record and comparing the generated checksum to detect duplicate record. It produces the fingerprints of short text with Simhash similarity algorithm. It clusters the fingerprint results using Shared Nearest Neighbor (SNN) algorithm. The whole parallel progress is implemented using MapReduce programming model. Results: From the experimental results, we conclude that our proposed approach obtains MapReduce job schedules with significantly less executing time, making it suitable for processing large scale datasets in real applications. The experimental results show the proposed approach has better performance and efficiency. Conclusion: In this patent, we propose a duplicate data detection approach based on MapReduce and HDFS, which uses the Simhash similarity computing algorithm and SSN algorithm. The results

关键词： MapReduce

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A density-based energy-efficient clustering heterogeneous algorithm for wireless sensor networks

International Journal of Control and Automation

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International Journal of Control and Automation 2014年第2期7卷 175-188页

作者： Xu, Zhanyang Yin, Yue Wang, Jin Kim, Jeong-Uk School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China Department of Energy Grid Sangmyung University Seoul 110-743 Korea Republic of

Clustering is an efficient method adopted in various routing algorithms for wireless sensor networks. However, most clustering algorithms are not suitable for heterogeneous networks. In this paper, we propose a Density-based Energy-efficient Clustering Heterogeneous Algorithm (DECHA). In DECHA, we define the density of a node and together with its energy condition to adjust the probability for the candidate cluster head selection dynamically. Candidate cluster heads further evaluate the energy level of its neighbors and adjust to find more proper cluster heads. Moreover, we design an intra-cluster algorithm as well as a multi-hop inter-cluster routing algorithm. Simulation results show that cluster heads are properly deployed in a heterogeneous wireless sensor network. Compared with some popular algorithms, in our DECHA, the stability period and network lifetime and prolonged and total energy consumption is prominently reduced. © 2014 SERSC.

关键词： Wireless sensor networks

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Non-Exchangeable Error Compensation for Strapdown Inertial Navigation System in High Dynamic Environment

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computers, Materials & Continua 2020年第8期64卷 973-986页

作者： Qi Wang Changsong Yang Shao’en Wu 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 Automation Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Science Ball State UniversityMuncie47306USA

Strapdown non-exchangeable error compensation technology in high dynamic environment is one of the key technologies of strapdown inertial navigation *** platform is used in strapdown inertial navigation system instead of physical platform in traditional platform inertial navigation system,which improves reliability and reduces cost and volume of *** maximum error source of attitude matrix solution is the non-exchangeable error of rotation due to the non-exchangeable of finite rotation of rigid *** rotation non-exchangeable error reaches the maximum in coning motion,although it can be reduced by shortening the correction period and increasing the real-time *** equivalent rotation vector method is used to modify the attitude to reduce the coning error in this *** experiments show that the equivalent rotation vector method can effectively suppress the non-exchangeable error and improve the accuracy of attitude calculation.

关键词： Strapdown error compensation coning motion equivalent rotation vector simulation experiments

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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...

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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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