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作者： Xiaomei Tian Huihuang Zhao College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

Multiple-set membership query problem plays a very important role in many network systems, including packet forwarding architectures in big data centers, routing switching devices and internet firewall systems. Many solutions to multiple-set membership query problem are based on Bloom filters. And each of these solutions has its own merits. Some have high query speed and some have good accuracy. But generally, they cannot have both at the same time. In this paper, we propose a probabilistic data structure named constant-weight group coded Bloom Filter(CWGCBF) for fast and accurate multi-set membership query. The key technique in CWGCBF is encoding each element's set ID to constant weight code, and directly recording this constant-weight code in the bloom filter vector. Experimental results show that in terms of false positive ratio, CWGCBF, Shifting Bloom Filter and Combinatorial Bloom Filter have great advantages to the other compared Bloom filters, and in terms of time efficiency, CWGCBF and ID Bloom Filter are several times faster than the others.

关键词： Multiple-set membership query Constant-weight group coded bloom filter Data center

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Analysis and Design of Image Encryption Algorithms Based on Interlaced Chaos

Analysis and Design of Image Encryption Algorithms Based on ...

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作者： Kangman Li Qiuping Li College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

For chaotic encryption algorithm, the algorithm of generating chaotic sequence is complex, and the data is float, which directly affects the speed of encryption. In this paper, an interlaced chaotic encryption algorithm is proposed, In this algorithm, the pixel interval is encrypted by chaotic sequence,and the chaotic sequences of other pixels are obtained by XOR the chaotic values of the front and back points, Experiments show that the algorithm can encrypt and decrypt images quickly and has good effect.

关键词： Interlaced chaotic sequence XOR Encryption speed Encryption effect

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Design of a Real-Time and Reliable Multi-machine System

Design of a Real-Time and Reliable Multi-machine System

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作者： Jian Zhang Lang Li Qiuping Li Junxia Zhao Xiaoman Liang College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

An IoT(Internet of Things) system is a multi-machine system composed of a master controller and multiple slave controllers. In some automatic control systems such as petroleum and electric power, the main control uses UART(Universal Asynchronous Receiver/Transmitter) to communicate with the slave controller. However, the communication rate of the UART interface is limited and the delay time is long, which affects the system's response to external events. In order to improve the response speed of the system to external events, this paper designs a multi-machine system based on SPI(Serial Peripheral Interface) interface. First, we use the SPI interface of the microcontroller to build a communication network. The circuit structure of the system is simple, and the hardware resource consumption is low. Then, for the problem that the SPI interface cannot detect communication errors and there is no response mechanism, we designed a communication protocol to make up for these defects from the software level. Finally, we use STM32 F429 IGT6 as the master controller and STM32 F103 C8 T6 as the slave controller to build the experimental platform. The experimental results show that the system is safe and reliable, and can meet the requirements of real-time applications.

关键词： Real-Time Multi-Machine system SPI

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Design and Implementation of Handwritten Digit Recognition Based on K-Nearest Neighbor Algorithm

Design and Implementation of Handwritten Digit Recognition B...

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作者： Ying Wang Qingyun Liu Yaqi Sun Feng Zhang Yining Zhu College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

In this paper, a handwritten digit recognition system based on Knearest neighbor algorithm is designed and implemented. By analyzing the application principle and steps of k-nearest neighbor algorithm in machine learning classifier, we design handwritten digit recognition in Python *** order to improve the accuracy and accuracy of handwritten character recognition, the method of smooth denoising and character segmentation methods are used to preprocess the data, extract the structure and statistical features. The experiments had shown that the accuracy of the algorithm for handwritten character recognition can reach over 97%.

关键词： K-nearest neighbor algorithm Handwritten digit recognition Image preprocessing

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Gender Identification for Coloring Black and White Portrait with cGan

Gender Identification for Coloring Black and White Portrait ...

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作者： Qingyun Liu Mugang Lin Yaqi Sun Feng Zhang College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

This paper proposes a method to color black & white portrait images with gender recognition model and condition generation network(cGan). As a c Gan model essentially, a method named pix2 pix interprets image style transformation as a translation process from input pixels to target pixels. Lots of input to target image pairs should be prepared for model training for different application scenarios. When pix2 pix is applied to color black & white portraits, there are obvious differences in color and saturation between portraits of different genders while the ability of gender recognition isn't owned by pix2 pix in essence. Therefore, a gender recognition model is added to the network to produce more realistic and gender specific color portraits. The experiments show that our work has achieved better results than some other common methods.

关键词： Gender identification GANs Loss function Portrait style transfer

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A Novel AES Random Mask Scheme Against Correlation Power Analysis

A Novel AES Random Mask Scheme Against Correlation Power Ana...

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作者： Ge Jiao Lang Li Yi Zou College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

With the wide application of smart card,people's demand for the security of smart card is *** AES(Advanced Encryption Standard)algorithm in smart card itself is safe enough,but the encryption algorithm is still threatened by side channel attacks due to time,power consumption,electromagnetic radiation and other information leakage during *** at the shortcomings of existing mask schemes in security,a smart card AES encryption NARMS mask against side channel attack is *** scheme calculates the random mask according to the random hamming weight value and random integer,selects 3 rounds of random selection and NARMS random mask strategy for protection in the first,second and ninth rounds of AES algorithm,optimizes and improves the design of random mask and the overall process of mask *** mask scheme designed in this paper is comprehensively compared with ordinary AES algorithm,fixed value mask scheme and RSM(Rotating S-Box Masking) *** show that this scheme can effectively resist the first and second order CPA(Correlation Power Analysis) attacks and has high efficiency,thus ensuring the key security of AES algorithm and privacy security of smart card application.

关键词： Random mask Advanced encryption standard Correlation power analysis Hamming weight

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Power Analysis Attack on a Lightweight Block Cipher GIFT

Power Analysis Attack on a Lightweight Block Cipher GIFT

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GIFT is a new lightweight block cipher with smaller area and higher efficiency,which is very suitable for the Internet of Things(IoT) devices with constrained *** power analysis attack is an efficient method to extract the key from the cryptographic ***,it's not easy to reveal the key by means of power analysis attack,when the cipher is implemented by *** this article,we present the method of power analysis attack against ***,we implemented GIFT on FPGA using the SAKURA-G *** we explored the impact of point of interest(POI) on power analysis *** proposed the method of power analysis attack against the diffusion layer of *** experimental results show that the result of power analysis attack is affected by POI and the key can be recovered when POI is *** can reveal the key using correlation power analysis,when targeting the diffusion layer of GIFT.

关键词： Power Analysis Attack Lightweight Block Cipher GIFT SAKURA-G

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Deep dual support vector data description for anomaly detection on attributed networks

arXiv

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arXiv 2021年

作者： Zhang, Fengbin Fan, Haoyi Wang, Ruidong Li, Zuoyong Liang, Tiancai School of Computer Science and Technology Harbin University of Science and Technology Harbin China School of Information Engineering Zhengzhou University Zhengzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China GRG Banking Technology Co. Ltd Guangzhou China

Networks are ubiquitous in the real world such as social networks and communication networks, and anomaly detection on networks aims at finding nodes whose structural or attributed patterns deviate significantly from the majority of reference nodes. However, most of the traditional anomaly detection methods neglect the relation structure information among data points and therefore cannot effectively generalize to the graph structure data. In this paper, we propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks. Specifically, Dual-SVDAE consists of a structure autoencoder and an attribute autoencoder to learn the latent representation of the node in the structure space and attribute space respectively. Then, a dual-hypersphere learning mechanism is imposed on them to learn two hyperspheres of normal nodes from the structure and attribute perspectives respectively. Moreover, to achieve joint learning between the structure and attribute of the network, we fuse the structure embedding and attribute embedding as the final input of the feature decoder to generate the node attribute. Finally, abnormal nodes can be detected by measuring the distance of nodes to the learned center of each hypersphere in the latent structure space and attribute space respectively. Extensive experiments on the real-world attributed networks show that Dual-SVDAE consistently outperforms the state-of-the-arts, which demonstrates the effectiveness of the proposed *** Codes 68T30 © 2021, CC BY.

关键词： Data description

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Analysis of Big Data Complex Network Structure Based on Fuzzy Clustering Algorithm

Analysis of Big Data Complex Network Structure Based on Fuzz...

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Networking, Communications and information technology (NetCIT), International Conference on

作者： Fu Shanshan Ruan Zhiqiang Center of Modem Education Technology and Information Management Minjiang University Fujian China College of Computer and Control Engineering Minjiang University Fujian China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fujian China Digital Fujian IoT Laboratory of Intelligent Production Fujian China

ISBN: (纸本)9781665400718

In the era of artificial intelligence with big data as the background, the statistical analysis method based on complex network has become a research hotspot and actively promoted the development of big data visualization. Complex network is a new discipline developed by cross research with other disciplines. Its network topology has obvious clustering characteristics, that is, complex network cluster structure. Together with small world and scale-free, it is called the three most important and basic attributes of complex network topology. The fundamental purpose of complex network clustering algorithm is to detect the actual network cluster structure in complex network, which has very important practical value for mining the cluster structure in network and understanding its characteristics, and is conducive to understanding the functions of network, such as predicting the development of complex network, analyzing the behavior of social network, discovering hot topics on network, etc. This paper combines the methods and ideas of complex network analysis, carries out the method design and research of organization analysis, and tries to solve the relationship and characteristics of inter departmental cooperation within the organization.

关键词： Visualization Data analysis Heuristic algorithms Data visualization Clustering algorithms Complex networks Organizations

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CrimeSTC: A Deep Spatial-Temporal-Categorical Network for Citywide Crime Prediction 3

CrimeSTC: A Deep Spatial-Temporal-Categorical Network for Ci...

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3rd International Conference on Computational Intelligence and Intelligent Systems, CIIS 2020

作者： Wei, Yue Liang, Weichao Wang, Youquan Cao, Jie College of Information Engineering Nanjing University of Finance and Economics Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of E-business Nanjing University of Finance and Economics Nanjing China

ISBN: (纸本)9781450388085

Crime is one of the most complex social problems around the world, posing a major threat to human life and property. Predicting crime incidents in advance can be a great help in fighting against crime and has drawn continuous attention from both academic and industrial communities. Although a plethora of methods have been proposed over the past decade, most of the algorithms either perform prediction by leveraging linear or other oversimplified models or fail to fully explore the dynamic patterns in the crime data. In this paper, we propose a novel deep learning based crime prediction framework called CrimeSTC to jointly learn the intricate spatial-temporal-categorical correlations hidden inside the crime and big urban data. Specifically, our framework consists of four parts: dynamic module (handling the data that change every day via local CNN and GRU), static module (handling the data that remain the same over time via fully connected layers), categorical module (capturing the categorical dependency via graph convolutional network) and joint training module (concatenating dynamic and static representations to forecast crime numbers). Extensive experiments on real world datasets validate the effectiveness of our framework. © 2020 ACM.

关键词： Crime

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