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Intelligent Intra- and Inter-Channel Nonlinearity Compensation for Terabit-Per-Lambda 16QAM Long-Haul Transmission

Intelligent Intra- and Inter-Channel Nonlinearity Compensati...

2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Fang, Xiansong Zhou, Lingjun Zhu, Yixiao Zheng, Jiayu Cai, Xiang Zhang, Fan School of Electronic and Computer Engineering Peking University Shenzhen China Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication System and Networks Shanghai China School of Electronics Peking University State Key Laboratory of Advanced Optical Communication System and Networks Beijing China Pengcheng Laboratory Shenzhen China

We experimentally demonstrate the combination of learned modified DBP and adaptive equalizer to compensate for intra- and inter-channel nonlinearity, in an 8× 125Gbaud 16QAM system over 1600km SSMF, showing a 97.... 详细信息

ISBN: (纸本)9798350312614

关键词： fiber nonlinearity machine learning XPM

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Cooperative Content Caching and Delivery in Vehicular networks: A Deep Neural Network Approach

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China Communications 2023年第3期20卷 43-54页

作者： Xuelian Cai Jing Zheng Yuchuan Fu Yao Zhang Weigang Wu State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China Research Institute of Smart Transportation Xidian UniversityXi’an 710071China Northwestern Polytechnical University Xi’an 710072China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China

The growing demand for low delay vehicular content has put tremendous strain on the backbone *** a promising alternative,cooperative content caching among different cache nodes can reduce content access ***,heterogeneous cache nodes have different communication modes and limited caching *** addition,the high mobility of vehicles renders the more complicated caching ***,performing efficient cooperative caching becomes a key *** this paper,we propose a cross-tier cooperative caching architecture for all contents,which allows the distributed cache nodes to ***,we devise the communication link and content caching model to facilitate timely content *** at minimizing transmission delay and cache cost,an optimization problem is ***,we use a multi-agent deep reinforcement learning(MADRL)approach to model the decision-making process for caching among heterogeneous cache nodes,where each agent interacts with the environment collectively,receives observations yet a common reward,and learns its own optimal *** simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

关键词： dynamic content delivery cooperative content caching deep neural network vehicular net-works

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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Extending the Reach of Optical Interconnects with Advanced Coded Modulation

Extending the Reach of Optical Interconnects with Advanced C...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Lei, Yi Wang, Xiang Lu, Qi Chen, Bin Hefei University of Technology School of Computer Science and Information Engineering Hefei China Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks China

An advanced coded modulation is proposed based on improved staircase codes using marked bits and geometrically-shaped constellations. Up to 0.95 dB SNR gain and 8.6% reach increase are achieved for beyond 800G data ce... 详细信息

ISBN: (纸本)9781665462136

关键词： Stairs

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A Review of Malware Detection Applications and Research 2nd

A Review of Malware Detection Applications and Research

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2nd International Conference on Network Simulation and Evaluation, NSE 2023

作者： Bai, Youming Li, Shudong Han, Weihong Wu, Xiaobo Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Department of New Networks Peng Cheng Laboratory Shenzhen518000 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819745210

Malware, as one of the main threats to computer systems and network security, is constantly evolving and growing, causing serious impacts on individual users, business organizations, and national security. This review summarizes and sorts out the current research status in the field of malware detection, focusing on the file structure, common types of malware, malware camouflage techniques, common analysis methods and techniques for malware detection, including feature based static analysis, dynamic analysis, machine learning methods, etc. Furthermore, this article discusses the methods and applications of malware detection, and analyzes some methods and ideas for detecting malware based on different features. Through the review of this article, readers can comprehensively understand the current status and development trends of malware detection, providing reference and guidance for improving network security protection capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

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Special Topic on Advancements in Web3 Infrastructure for the Metaverse

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中兴通讯技术（英文版） 2024年第2期22卷 1-2页

作者： Victor C.M.LEUNG CAI Wei Shenzhen MSU-BIT University China Shenzhen University China Wireless Networks and Mobile Systems(WiNMoS)Laboratory at UBC Canada IEEE Royal Society of Canada(Academy of Science) Canadian Academy of Engineer-ing and Engineering Institute of Canada Computer Engineering in the School of Science and Engineering at The Chinese University of Hong Kong ShenzhenChina Human-Crypto Society Laboratory and the CUHK(SZ)-White Matrix Joint Metaverse Laboratory IEEE ACM

Web3,also known as Web 3.0,has recently been at-tracting increasing attention from industry and *** the potential of blockchain tech-nologies,Web3 has emerged as a pivotal foundation in the realm of metaverse development,which is considered by many as the next-generation ***,Web3 tech-nologies such as smart contracts and protocols like non-fungible tokens(NFTs)have supported the immersive and content-rich experience of current Web3 metaverse *** addition,the decentralized autonomous organization(DAO)based on Web3 technologies has built the prototype of the future virtual ***,many advanced breakthroughs in Web3 infrastructure for the metaverse have become accessible to the general public.

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Bridging prediction and reality: Comprehensive analysis of experimental and AlphaFold 2 full-length nuclear receptor structures

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Computational and Structural Biotechnology Journal 2025年 27卷 1998-2013页

作者： Mazhibiyeva, Akerke Pham, Tri T. Pats, Karina Lukac, Martin Molnár, Ferdinand Laboratory of Computational Structural Biology Department of Biology Nazarbayev University Kabanbay Batyr 53 Astana010000 Kazakhstan Laboratory of Mechanobiology Department of Biology Nazarbayev University Kabanbay Batyr 53 Astana010000 Kazakhstan Institute of Applied Computer Sciences ITMO University 49 Kronverksky Ave. Saint Petersburg197101 Russia Department of Computer Networks and Engineering Hiroshima City University 3-4-1 Oozukahigashi Asaminami-ku Hiroshima-shi731–3166 Japan

AlphaFold 2 has revolutionized protein structure prediction, yet systematic evaluations of its performance against experimental structures for specific protein families remain limited. Here we present the first comprehensive analysis comparing AlphaFold 2-predicted and experimental nuclear receptor structures, examining root-mean-square deviations, secondary structure elements, domain organization, and ligand-binding pocket geometry. While AlphaFold2 achieves high accuracy in predicting stable conformations with proper stereochemistry, it shows limitations in capturing the full spectrum of biologically relevant states, particularly in flexible regions and ligand-binding pockets. Statistical analysis reveals significant domain-specific variations, with ligand-binding domains showing higher structural variability (CV = 29.3%) compared to DNA-binding domains (CV = 17.7%). Notably, Alphafold 2 systematically underestimates ligand-binding pocket volumes and captures only single conformational states in homodimeric receptors where experimental structures show functionally important asymmetry. These findings provide critical insights for structure-based drug design targeting nuclear receptors and establish a framework for evaluating Alphafold 2 predictions across other protein families. © 2025 The Author(s)

关键词： Structural analysis

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Privacy‐preserving remote sensing images recognition based on limited visual cryptography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1166-1177页

作者： Denghui Zhang Muhammad Shafiq Liguo Wang Gautam Srivastava Shoulin Yin Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhouChina Department of New Networks Peng Cheng LaboratoryShenzhenChina College of Information and Communications Engineering Dalian Minzu UniversityDalianChina Department of Mathematics and Computer Science Brandon UniversityBrandonManitobaCanada Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Math Lebanese American UniversityBeirutLebanon College of Information and Communications Engineering Harbin Engineering UniversityHarbinChina

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing *** fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image ***,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing *** is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational *** Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted *** stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in *** this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is *** diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is *** fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing *** experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

关键词： activity recognition feature extraction image classification KNN privacy protection remote monitoring remote sensing

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