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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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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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Power Analysis Attack on a Lightweight Block Cipher GIFT

Power Analysis Attack on a Lightweight Block Cipher GIFT

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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

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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CrimeSTC: A Deep Spatial-Temporal-Categorical Network for Citywide Crime Prediction 2020

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

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2020 The 3rd International Conference on Computational Intelligence and Intelligent Systems

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

ISBN: (纸本)9781450388085

关键词： Spatial-Temporal-Categorical Correlation Deep learning Crime Prediction Urban Computing

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Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Yang, Jian Qi, Guo-Jun Lin, Chia-Wen Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100080 China Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China Research Center for Industries of the Future the School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan

Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss of middle-layer features due to activation functions. To minimize the impact of intermediate feature loss on reconstruction quality, we propose a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone. Every third WDIB forms a Feature Shuffle Weighted Group (FSWG) by applying mutual information shuffle and fusion. Moreover, to mitigate the negative effects of intermediate feature loss, we introduce Wide Residual Weighting units within WDIB. These units effectively fuse features of varying levels of detail through a Wide-residual Distillation Connection (WRDC) and a Self-Calibrating Fusion (SCF). To compensate for global feature deficiencies, we incorporate a Transformer and explore a novel architecture to combine CNN and Transformer. We show that our FIWHN achieves a favorable balance between performance and efficiency through extensive experiments on low-level and high-level tasks. Codes will be available at https://***/IVIPLab/FIWHN. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Digital medical education empowered by intelligent fabric space

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National Science Open 2022年第1期1卷 68-77页

作者： Min Chen Rui Wang Rui Wang Yingting Zhou Zicheng He Xiaojuan Liu Muyao He Jiaxi Wang Chaolin Huang Huamin Zhou Ping Hong Chong Hou Ning Zhou Dingyu Zhang Guangming Tao Sport and Health Initiative Optical Valley Laboratory and Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Wuhan Jinyintan Hospital Wuhan 430048China State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Beijing Sport University Beijing 100091China School of Optics and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Tongji Medical College Huazhong University of Science and TechnologyWuhan 430074China Hubei Provincial Health and Health Committee Wuhan 430015China

Medical education plays an important role in promoting the development of global medical ***,the intrinsic gap existing between institutional medical teaching and practical clinical tasks causes low education efficiency and students’weak *** developments of sensing fabric and embedded computing,along with the advances in artificial intelligence(AI)and digital twin technology are paving the way for the transformation of medical research towards *** this work,we present an intelligent fabric space based on novel functional fabric materials and digital twin networking enabled by 5G and internet of things(IoT)*** this space,medical students can learn knowledge with collaborative mapping of the digital and real world,cyber-physical interaction and real-time tactile *** the proposed service system will evaluate and feedback students’operational behaviors to improve their experimental *** provide four typical applications of intelligent fabric space for medical education,including medical education training,health and behavior tracking,operation playback and reproduction,as well as medical knowledge *** proposed intelligent fabric space has the potential to promote innovative technologies for training cutting-edge medical students by effective and efficient ways.

关键词： medical education intelligent fabric digital twin tactile interaction

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Multi-Slot Distributed Measurement Selection: A Sparsity Learning Approach

Multi-Slot Distributed Measurement Selection: A Sparsity Lea...

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作者： Xia, Qian Wang, Wei Ran, Rong Gong, Yi Zhang, Zhaoyang College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China School of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China

When only a part of measurements are collected sequentially in large-scale Internet of Things (IoT) networks, it is motivated to extract significant information from the collected data for utilizing the communication resources over incoming slots more efficiently. In this article, we study the successive measurement selection problem for sparse parameter estimation and its distributed implementation in large-scale networks. Specifically, we transform the intractable multi-slot measurement selection problem into per-slot subproblems, and in each slot we select the uncollected measurements that are more 'useful' for parameter estimation based on sparse Bayesian learning. In order to deal with the combinatorial problem with distributed implementation, we relax the subproblem and propose an enhanced subgradient method to solve the Boolean constraint. The proposed distributed scheme needs only a common information broadcast by the centralized controller, where the communication overheads only depend on the number of sparse active sources rather than the number of sensors which is always huge. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes. When the total communication overheads are limited with the network scale, our scheme presents extreme practicability and applicability for the large-scale networks. © 2015 IEEE.

关键词： Wireless sensor networks

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