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FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

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Chinese Physics B 2022年第2期31卷 109-118页

作者： Fei Yu Zinan Zhang Hui Shen Yuanyuan Huang Shuo Cai Sichun Du School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410114China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China

A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation *** MHNN is simulated and dynamically analyzed,and implemented on ***,a new pseudo-random number generator(PRNG)based on MHNN is *** post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of *** experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming *** random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high ***,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).

关键词： memristive Hopfield neural network(MHNN) pseudo-random number generator(PRNG) FPGA image encryption decryption system

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Evaluation Model for Energy Efficiency of Factory Workshop Based on DSR and Fuzzy Borda

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Energy engineering 2025年第3期122卷 1073-1092页

作者： Shengjie Yang Zijian Zhu Xu Ouyang HDHL Electrical and Information Technology Co.Ltd.Changsha410205China College of Computer Science Hunan University of Technology and BusinessChangsha410205China College of Electrical and Information Engineering Hunan UniversityChangsha410082China

In the context of advancing towards dual carbon goals,numerous factories are actively engaging in energy efficiency upgrades and *** accurately pinpoint energy efficiency bottlenecks within factories and prioritize renovation sequences,it is crucial to conduct comprehensive evaluations of the energy performance across various ***,this paper proposes an evaluation model for workshop energy efficiency based on the drive-state-response(DSR)framework combined with the fuzzy BORDA ***,an in-depth analysis of the relationships between different energy efficiency indicators was *** on the DSR model,evaluation criteria were selected from three dimensions-drive factors,state characteristics,and response measures-to establish a robust energy efficiency indicator ***,three distinct assessment techniques were selected:Grey Relational Analysis(GRA),Entropy Weight Method(EWM),and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)forming a diversified set of evaluation ***,by introducing the fuzzy BORDA method,a comprehensive energy efficiency evaluation model was developed,aimed at quantitatively ranking the energy performance status of each *** a real-world factory as a case study,applying our proposed evaluationmodel yielded detailed scores and rankings for each ***,post hoc testing was performed using the Spearman correlation coefficient,revealing a statistic value of 10.209,which validates the effectiveness and reliability of the proposed evaluation *** model not only assists in identifying underperforming workshops within the factory but also provides solid data support and a decision-making basis for future energy efficiency optimization strategies.

关键词： DSR model fuzzy Borda method combined evaluation energy efficiency evaluation

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Ensemble Graph and Device Clustering Method based on Attention Mechanism for Decomposing Monolithic to Microservices 22

Ensemble Graph and Device Clustering Method based on Attenti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Deng, Qingyong Li, Qiuming Zuo, Qinghua Tian, Shujuan Long, Saiqin Guangxi Normal University School of Computer Science and Engineering The School of Software Guilin China Xiangtan University School of Computer Science Xiangtan China Jinan University College of Information Science and Technology Guangzhou China

ISBN: (纸本)9798331509712

Existing methods for decomposing monolithic applications into microservices in cloud environments primarily rely on the call relationships within itself. However, these methods are difficult to apply directly in resource-constrained and distributed edge network scenarios without considering the heterogeneity of the device. Therefore, this paper proposes a clustering method that ensembles graph structures and device features based on attention mechanism, which utilizes attention encoders to learn node embeddings and employs a spectral clustering algorithm to obtain decomposition results, optimizing the affinity and matching degree between microservices and devices. Experimental results demonstrate that the proposed method exhibits excellent performance in terms of functional independence, modularity, and adaptability of microservices. © 2024 IEEE.

关键词： Clustering algorithms

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Blockchain Scalability Technologies 7th

Blockchain Scalability Technologies

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Xu, Nengxiang Cai, Jiahong Gong, Yinyan Zhang, Huan Huang, Weihong Li, Kuan-ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9783031281235

As the underlying implementation technology of the current main-stream digital currency, blockchain can establish a trusted distributed system without relying on third-party trusted institutions or a privacy-protect system. The decentralized characteristics of blockchain have broad application scenarios, such as the Internet of Things, financial technology and other fields. However, the scalability of the current blockchain is seriously insufficient, such as limited throughput in performance, small storage capacity, and difficulty in scaling functions. This paper introduces the definition and technical classification of scalability, analyzes the current problems of scalability and briefly introduces the current main-stream expandable technologies such as sharding, on-chain and off-chain storage, off-chain payment channel and cross-chain technology from two aspects of performance and function, as well as the principles and ideas of these technologies. Finally, the research progress of current blockchain extension technology is summarized, and the problems faced by the current extension scheme are pointed out, which provides a direction for future research work. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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AKGIN: An API Knowledge Graph and Intent Network based Mashup-Oriented API Recommendation Method 26

AKGIN: An API Knowledge Graph and Intent Network based Mashu...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yu, Changyong Hu, Rong Wang, Bangjun Hunan University of Science and Technology Computing and Novel Software Technology Hnust School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350331684

With the continuous development of the Web API ecosystem, mashup-oriented API recommendation gets a lot of attention. Collaborative filtering, deep learning and their combination based methods are recently proposed for API recommendation. However, the recommendation results often perform poorly when data is sparse. Therefore, we propose an API knowledge graph (AKG) and intent network based mashup-oriented API recommendation method, whose main target is to recruit the semantic representation ability to improve the precision of API recommendation. Firstly, Latent Dirichlet Allocation (LDA) is used to extract API topics, which are important entities in the AKG. Then, the mashup intention is modeled based on the mashup-API interactions. The high-order relationship paths are recursively aggregated into the representation vector of Mashup and API. Finally, the inner product of their representations present the probability of the API recommendation for the mashup. The experimental results on real-world datasets show that our proposed method outperforms several baseline methods in mashup-oriented API recommendations. © 2023 IEEE.

关键词： Knowledge graph

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An Efficient Method for Solving a Least Square Problem over Mixed Constraints 2

An Efficient Method for Solving a Least Square Problem over ...

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2nd International Conference on Applied Statistics, Computational Mathematics, and Software engineering, ASCMSE 2023

作者： Luo, Jinzhi College of Mathematics and Computing Science Hunan University of Science and Technology Hunan Xiangtan411201 China

In this paper, we consider solving the generalized Sylvester equation AXB + CYD = E under mixed constraints {X T = X, L1 ≤ X ≤ U1,Y T = Y, L2 ≤ Y ≤ U2 }, where A, C ∈ Rm×n , B, D ∈ Rn×m, E ∈ Rm×m... 详细信息

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

In this paper, we consider solving the generalized Sylvester equation AXB + CYD = E under mixed constraints {X T = X, L1 ≤ X ≤ U1,Y T = Y, L2 ≤ Y ≤ U2 }, where A, C ∈ Rm×n , B, D ∈ Rn×m, E ∈ Rm×m and L1,U1, L2 ,U2 ∈ Rn×n are suitable given matrices, and the • is the Frobenius norm. An effective iterative algorithm is presented to solving the proposed problem and the relevant convergence properties of the algorithm are proved. Numerical examples are performed to demonstrate the algorithm is efficient. © 2023 SPIE.

关键词： Iterative methods

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CSCL-DTI: Predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: Predicting drug-target interaction through cross-v...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xuan Zhang, Xi Yu, Zu-Guo Long, Yahui Zeng, Xiangxiang Yu, Philip S. Xiangtan University College of Computer Science China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education China Singapore Hunan University College of Information Science and Engineering China University of Illinois Chicago Department of Computer Science United States

ISBN: (纸本)9798350386226

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI1 outperforms six state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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A novel shilling attack on black-box recommendation systems for multiple targets

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Neural Computing and Applications 2025年第5期37卷 3399-3417页

作者： Liu, Shuangyu Yu, Siyang Li, Huan Yang, Zhibang Duan, Mingxing Liao, Xiangke School of Information Science and Engineering Hunan University Hunan Changsha410082 China College of Information Technology and Management Hunan University of Finance and Economics Hunan Changsha410000 China Hunan Province Key Laboratory of Industrial Internet Technology and Security Changsha University Hunan Changsha410022 China Shenzhen Institute Hunan University Guangdong Shenzhen518063 China School of Computer Science National University of Defense Technology Hunan Changsha410073 China

In the digital era, e-platforms ubiquitously deploy recommendation systems, utilizing machine learning paradigms to tailor content according to user preferences and needs. Yet, the integrity of these systems is often compromised by shilling attacks, where malicious entities inject fake user profiles to skew product exposure and sales. This vulnerability has catalyzed research efforts to bolster the robustness and security of recommendation systems, primarily through controlled attacks and reinforcement training. This paper introduces an innovative approach to combat the inefficiency of traditional single-target shilling attacks. We propose a novel multi-target shilling strategy for black-box recommendation systems, capable of generating convincing and aggressive fake user profiles. Given the complexity of the recommendation system algorithms, we employ surrogate models to replicate and understand the target system’s behavior. The surrogate model is then subjected to attacks using our proposed methodology. An attack is deemed successful when it achieves an 80% or higher success rate against the surrogate, preluding an attack on the actual recommendation system. Our model, RWA-GAN, stands out by integrating two components: a generative adversarial network (GAN) for crafting realistic fake user profiles, and a mechanism to enhance attack success rates. This dual approach not only expedites the attack process but also ensures the generation of more authentic and well-distributed fake profiles, making the model easier to migrate. Additionally, the paper delineates several defense strategies against such attacks, contributing significantly to the discourse on enhancing the robustness of recommender systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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Cryptography of Blockchain 7th

Cryptography of Blockchain

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Long, Ying Gong, Yinyan Huang, Weihong Cai, Jiahong Xu, Nengxiang Li, Kuan-ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9783031281235

With the development of digital currencies and 5G technology, blockchain has gained widespread attention and is being used in areas such as healthcare, industry and smart vehicles. Many security issues have also been exposed in the course of blockchain applications. Cryptography can ensure the security of data on the blockchain, the integrity and validity of data as well as the ability to authenticate users and anonymize them. This article therefore examines the cryptography underlying blockchain security issues, providing an overview of cryptographic homomorphic encryption, zero-knowledge proofs and secure multi-party computation commonly used in blockchains. At the same time, the development of quantum computing is bound to affect existing cryptographic systems, and blockchains applying these cryptographic systems are bound to be hit hard, so this article discusses four of the most promising post-quantum cryptography techniques available: hash-based public key cryptography, code-based public key cryptography, multivariate public key cryptography, and lattice-based public key cryptography. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Public key cryptography

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Network embedding for detecting protein complexes in attributed networks

Network embedding for detecting protein complexes in attribu...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Meng, Xiangmao Wang, Keming Xiang, Ju Wang, Wenkang School of Computer Science Xiangtan University Xiangtan China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350386226

Detecting protein complexes holds paramount importance in elucidating cellular organization and protein functionalities. Over the past decade, numerous approaches have centered their attention on the topological intricacies of protein-protein interaction (PPI) networks, yet these have often fallen short in harnessing the full spectrum of biological information inherent in proteins. To bridge this gap, we introduce a novel methodology, designated NE-DPC, which integrates both the topological landscape of PPI networks and the attribute profiles of proteins to facilitate the identification of protein complexes. Initially, we fuse the static PPI network with protein attributes through advanced network embedding techniques. Subsequently, we construct a cosine similarity matrix grounded on these embedded vectors, capturing the intricate relationships among proteins. Lastly, we employ a core-attachment strategy to pinpoint protein complexes. The results reveal that NE-DPC outperforms cutting-edge methods, demonstrating its effectiveness and potential in advancing protein complex detection. © 2024 IEEE.

关键词： attribute information network embedding protein complex protein-protein interaction network

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