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2nd International Symposium on Application of Materials Science and Energy Materials, SAMSE 2018

作者： Zhao, Lili Li, Zhiming Zhang, Jincheng Lu, Ligen Guo, Yunqiu Feng, Lansheng Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan China State Key Discipline Laboratory of Wide Band-Gap Semiconductor Technology School of Microelectronics Xidian University Xi'an China School of Mechano-Electronic Engineering Xidian University Xi'an China

on the traditional heating structure of MOCVD reactor, the temperature distribution of substrate is nonuniform, which goes against the film growth. This paper focuses on the numerical calculation of the heating structure, by using SiC and graphite as the materials of the new susceptor and coupling with multifields. By adjusting the position and size of each of the ring body of graphite in the susceptor, the heat generation and heat transfer are changed, which leads to the approximately uniform heat conducted to the substrate, thus obtaining a more uniform temperature distribution. Compared to the traditional structure, under the same heating condition, the heating efficiency is improved about 9.1%, the uniformity of temperature distribution is improved more than 80%. This is very favorable for the growth of thin films. © 2019 Institute of Physics Publishing. All rights reserved.

关键词： Graphite

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Non-structured Pruning for Deep-learning based Steganalytic Frameworks

Non-structured Pruning for Deep-learning based Steganalytic ...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Qiushi Li Zilong Shao Shunquan Tan Jishen Zeng Bin Li Shenzhen Key Laboratory of Media Security Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Image steganalysis aims to discriminate innocent cover images and those suspected stego images embedded with secret message. Recently, increasing advanced deep neural networks have been proposed and used in image steganalysis. Though those deep learning models can gain superior performance, they also result in redundancy of computational resource and memory storage. In this paper, we apply a non-structured pruning method to prune XuNet2 and SRNet - the two state-of-the-art deep-learning framework in the field of JPEG image steganalysis. We obtain the priorities of the connections among neurons according to a certain criterion, then keep those significant weights and prune those nonsignificant ones in the meantime. We have conducted extensive experiments on BOSSBase and BOWS image dataset. The experimental results demonstrate that our proposed non-structured pruning method can significantly reduce the cost of computation and storage required by the original deep-learning frameworks without affecting their detection accuracy.

关键词： Transform coding Training Convolution Computer architecture Computational modeling Brain modeling Testing

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[Retracted] Defending Privacy Inference Attacks to Federated Learning for Intelligent IoT with Parameter Compression

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security and Communication networks 2023年第1期2023卷

作者： Yongsheng Zhu Hongbo Cao Yuange Ren Wanqi Wang Bin Wang Mingqing Hu Baigen Cai Wei Wang School of Electronic and Information Engineering Beijing Jiaotong University No. 3 Shangyuancun Beijing 100044 *** Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing 100081 *** Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University No. 3 Shangyuancun Beijing 100044 *** Zhejiang Key Laboratory of Multi-Dimensional Perception Technology Application and Cybersecurity Hangzhou 310053 China iFLYTEK Co. Ltd. Hefei China

Federated learning has been popularly studied with people’s increasing awareness of privacy protection. It solves the problem of privacy leakage by its ability that allows many clients to train a collaborative model without uploading local data collected by Internet of Things (IoT) devices. However, there are still threats of privacy leakage in federated learning. The privacy inference attacks can reconstruct the privacy data of other clients based on GAN from the parameters in the process of iterations for global models. In this work, we are motivated to prevent GAN-based privacy inference attacks in federated learning. Inspired by the idea of gradient compression, we propose a defense method called Federated Learning Parameter Compression (FLPC) which can reduce the sharing of information for privacy protection. It prevents attackers from recovering the private information of victims while maintaining the accuracy of the global model. Extensive experimental results demonstrated that our method is effective in the prevention of GAN-based privacy inferring attacks. In addition, based on the experimental results, we propose a norm-based metric to assess the performance of privacy-preserving.

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Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model

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Journal of Physics: Conference Series 2020年第1期1682卷

作者： Wei Wei Zhuo Wang Changlong Fu Robertas Damaševičius Rafal Scherer Marcin Wožniak School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 Shaanxi Key Laboratory for Network Computing and Security Technology China.

Nowadays, data plays a unique role in various fields. Based on the background of the era of big data, this paper collects user evaluations of certain commodities and labels the evaluations into positive emotions and negative emotions. We propose an intelligent recommendation algorithm based on Naive Bayes algorithm, which determines the user's preference for a product according to the user's opinion on a product, then uses the collaborative filtering algorithm to recommend other similar products according to the similarity between the product and other products, so as to realize the recommendation of other similar products. This can not only help users choose more goods in the environment, but also maximize the value of goods. In this paper, bayesian classifier was used for prediction, and the accuracy reached about 97%.

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Train energy-efficient speed profile generation under various track alignments: a quantum evolutionary algorithm with cosine annealing

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Swarm and Evolutionary Computation 2025年 97卷

作者： Qin, Yang Guo, Jingwei Xu, Peijuan Li, Duo Wang, Yonggang School of Transportation Engineering Chang'an University Shaanxi Xi'an710018 China Key Laboratory of Transport Industry of Management Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area Chang'an University Shaanxi Xi'an710018 China Faculty of Business City University of Macau 999078 China School of Engineering Newcastle University Newcastle Upon TyneNE1 7RU United Kingdom

The optimization of train speed profiles is an effective method for reducing energy consumption and operating costs for urban rail transit (URT). In recent years, numerous intelligent optimization algorithms have been employed to generate high-quality solutions for speed profile optimization models. However, most previous studies preset the train operation sequence, overlooking the complex impacts of track alignment diversity on train operation modes. This limitation hinders the applicability of these findings across multiple inter-stations within the URT network. Moreover, there has been insufficient validation of the proposed optimization algorithms under diverse train operation scenarios. This study reformulates the generation of train speed profiles as a multi-stage decision-making problem within a continuous speed space. By applying the law of conservation of energy, a dynamic model of the train operation process is established and the decision evolution of the train speed is mapped into a high-dimensional quantum space. Subsequently, an improved quantum evolutionary algorithm with adaptive rotation strategies is designed using the cosine annealing function (CA-QEA). Furthermore, a conditional quantum collapse mechanism is introduced to enhance the global search capability of the algorithm. Utilizing actual URT data from Tianjin, China, three test scenarios featuring various track alignments, including gentle slopes, energy-saving slopes, and multiple continuous steep slopes, are constructed. The results demonstrate that the proposed method can effectively reduce the traction energy consumption (TEC) in three different track alignment. The Friedman non-parametric test results indicate that the performance of CA-QEA surpasses that of several mature intelligent optimization algorithms. © 2025

关键词： Energy efficiency

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A security Reinforcement Model for Edge computing Facilities in 5G Power network

A Security Reinforcement Model for Edge Computing Facilities...

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第五届无线通信与应用国际会议

作者： Nige Li Peng Zhou Zhipeng Shao Xiaoxiao Chen Power Grid Digitizing Technology Department State Grid Smart Grid Research Institute Co. Ltd. State Grid Key Laboratory of Information and Network Security State Grid Zhejiang Electric Power Corporation Information and Telecommunication Branch

5G network has attracted much attention from the vertical industry since its commercial use. Compared with previous generations of mobile communication technologies, 5G bandwidth, delay and other communication KPI indicators have been greatly improved. 5G network has three typical application scenarios of large bandwidth, low delay and wide connection. In the power grid scenario, it can meet the needs of various power grid business access. As the key technology to realize 5G, multi-access edge computing(MEC) plays an important role in high-speed and low delay wireless data transmission network and has made indelible contributions to improving end-to-end service quality. In view of today's increasingly complex network security environment and changing attack means,security attacks against MEC systems and applications have become endless. In order to deal with large-scale and various attacks, this paper studies the micro application security reinforcement and independent application security reinforcement technology deployed in edge computing facilities in the environment of 5G power network, proposes the security reinforcement model of MEC, and strengthens the source code of edge computing facilities based on the so file confusion technology of OLLVM, so as to ensure the safe and stable operation of MEC.

关键词： Mobile edge computing Micro application security reinforcement Independent application security reinforcement

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STS-GAN: Can We Synthesize Solid Texture with High Fidelity from Arbitrary 2D Exemplar?

arXiv

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arXiv 2021年

作者： Zhao, Xin Guo, Jifeng Wang, Lin Li, Fanqi Li, Jiahao Zheng, Junteng Yang, Bo Shandong Provincial Key Laboratory of Preparation and Measurement of Building Materials University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Quan Cheng Laboratory Jinan250100 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China David R. Cheriton School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada Shandong Qiuqi Analysis Instrument Co. Ltd. Jinan250024 China

Solid texture synthesis (STS), an effective way to extend a 2D exemplar to a 3D solid volume, exhibits advantages in computational photography. However, existing methods generally fail to accurately learn arbitrary textures, which may result in the failure to synthesize solid textures with high fidelity. In this paper, we propose a novel generative adversarial nets-based framework (STS-GAN) to extend the given 2D exemplar to arbitrary 3D solid textures. In STS-GAN, multi-scale 2D texture discriminators evaluate the similarity between the given 2D exemplar and slices from the generated 3D texture, promoting the 3D texture generator synthesizing realistic solid textures. Finally, experiments demonstrate that the proposed method can generate high-fidelity solid textures with similar visual characteristics to the 2D exemplar. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

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A two-phase approach for locally differentially private fuzzy co-clustering

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Information Sciences 2025年 718卷

作者： Zhang, Pengfei An, Jianlong Cheng, Xiang Zhu, Youwen Zhang, Ji Zhang, Zhikun State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining No. 168 Shungeng Road Tianjiaan District Huainan232001 China School of Computer Science and Engineering Anhui University of Science and Technology No. 168 Shungeng Road Tianjiaan District Huainan232001 China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics No. 237 Longquan Road Yunnan Kunming650221 China No. 3501 Daxue Road Shandong Jinan255020 China Key Laboratory of Equipment Data Security and Guarantee Technology Guilin University of Electronic Technology No. 1 Jinji Road Guangxi Guilin541010 China State Key Laboratory of Networking and Switching Technology No. 10 Xitucheng Road Haidian District Beijing100876 China Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District Beijing100876 China College of Computer and Data Science Nanjing University of Aeronautics and Astronautics No. 29 Yudao Street Qinhuai District Jiangsu Nanjing210016 China School of Mathematics Physics and Computing University of Southern Queensland West Street ToowoombaQLD4350 Australia College of Computer Science and Technology Zhejiang University No. 38 Zheda Road Xihu District Hangzhou310027 China

Clustering analysis is important for providing personalized services across various user experience optimization. As an advanced clustering technique, Fuzzy Co-Clustering (FCC) excels at handling high-dimensional data by simultaneously grouping both samples and attributes while allowing objects to belong to multiple clusters. To alleviate the potential privacy risks posed by directly using raw data, researchers often employ local differential privacy (LDP) techniques for protection. However, current clustering studies under LDP either directly perform clustering on the high-dimensional data, which necessitates splitting the privacy budget heavily, leading to excessive noise injection, or fail to adequately address the impact of Laplacian noise on both objective function modeling and distance metric selection, resulting in reduced clustering accuracy. To address these limitations, we present a two-phase approach SONY, which allocates part of the privacy budget to evaluate attribute importance and perform dimensionality reduction accordingly, and then utilizes the remaining budget to conduct noise-aware clustering on the pruned data. Our key technical contributions include: (1) a Noise-aware Similarity Calculation method (NSC) that replaces traditional Euclidean distance with a specialized metric which accounts for the statistical properties of Laplacian noise;(2) an importance-driven dimension reduction and budget allocation method (IDRBA) which optimizes both attribute selection and privacy budget allocation;and (3) an outlier-aware objective function calculation method (OOC) which enhances robustness against potential noise-induced outliers. We provide theoretical guarantees for SONY's convergence, privacy preservation, utility bounds, and computational complexity. Experimental evaluations conducted on two real-world datasets and one synthetic dataset, demonstrate that SONY outperforms the state-of-the-arts by at least 14% in F-Measure and 10% in Entropy while main

关键词： Data privacy

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Secrecy capacity bounds for visible light communications with signal-dependent noise

arXiv

引用

arXiv 2021年

作者： Wang, Jin-Yuan Fu, Xian-Tao Wang, Jun-Bo Lin, Min Cheng, Julian Alouini, Mohamed-Slim Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing210003 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China National Mobile Communications Research Laboratory Southeast University Nanjing211111 China School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In physical-layer security, one of the most fundamental issues is the secrecy capacity. The objective of this paper is to determine the secrecy capacity for an indoor visible light communication system consisting of a transmitter, a legitimate receiver and an eavesdropping receiver. In such a system, both signal-independent and signal-dependent Gaussian noises are considered. Under non-negativity and average optical intensity constraints, lower and upper secrecy capacity bounds are first derived by the variational method, the dual expression of the secrecy capacity, and the concept of "the optimal input distribution that escapes to infinity". Numerical results show that the secrecy capacity upper and lower bounds are tight. By an asymptotic analysis at large optical intensity, there is a small performance gap between the asymptotic upper and lower bounds. Then, by adding a peak optical intensity constraint, we further analyze the exact and asymptotic secrecy capacity bounds. Finally, the tightness of the derived bounds is verified by numerical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Asymptotic analysis

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Decoherence effects in a three-level system under Gaussian process

arXiv

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arXiv 2021年

作者： Zangi, Sultan Mahmood Rahman, Atta ur Ji, Zhao-Xo Ali, Hazrat Zhang, Huan-Guo School of Physics and Astronomy Yunnan University P.O. Box Kunming650500 China School of Physics University of Chinese Academy of Science Yuquan Road 19A Beijing100049 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Abbottabad University of Science and Technology P.O. Box 22500 KP Havellian Pakistan

When subjected to a classical fluctuating field characterized by a Gaussian process, we examine the purity and coherence protection in a three-level quantum system. This symmetry of the three-level system is examined when the local random field is investigated further in the noiseless and noisy regimes. In particular, we consider fractional Gaussian, Gaussian, Ornstein-Uhlenbeck, and power-law noisy regimes. We show that the destructive nature of the Ornstein-Uhlenbeck noise toward the symmetry of the qutrit to preserve purity and coherence encoded remains large. Our findings suggest that properly adjusting the noisy parameters to specifically provided values can facilitate optimal extended purity and coherence survival. Non-vanishing terms appear in the final density matrix of the single qutrit system, indicating that it is in a strong coherence regime. Because of all Gaussian noises, monotonic decay with no revivals has been observed in the single qutrit system. In terms of coherence and information preservation, we find that the current qutrit system outperforms systems with multiple qubits or qutrits using purity and von Neumann entropy. A comparison of noisy and noiseless situations shows that the fluctuating nature of the local random fields is ultimately lost when influenced by the classical Gaussian noises. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics

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