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Co‑packaged optics(CPO):status,challenges,and solutions

Frontiers of Optoelectronics 2023年第1期16卷 1-40页

作者： Min Tan Jiang Xu Siyang Liu Junbo Feng Hua Zhang Chaonan Yao Shixi Chen Hangyu Guo Gengshi Han Zhanhao Wen Bao Chen Yu He Xuqiang Zheng Da Ming Yaowen Tu Qiang Fu Nan Qi Dan Li Li Geng Song Wen Fenghe Yang Huimin He Fengman Liu Haiyun Xue Yuhang Wang Ciyuan Qiu Guangcan Mi Yanbo Li Tianhai Chang Mingche Lai Luo Zhang Qinfen Hao Mengyuan Qin School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina HKUST Fok Ying Tung Research Institute Guangzhou 511462China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511462China Chongqing United Micro-Electronics Center(CUMEC) Chongqing 401332China Hisense Broadband Multimedia Technologies Co. Ltd.Qingdao 266000China Institute of Microelectronics Chinese Academy of SciencesBeijing 100029China State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China School of Microelectronics Xi’an Jiaotong UniversityXi’an 710049China Zhangjiang Laboratory Shanghai 201210China The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Huawei Technologies Co. Ltd.Shenzhen 440307China College of Computer National University of Defense TechnologyChangsha 410073China Institute of Computing Technology Chinese Academy of SciencesBeijing 100086China

Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within *** conventional pluggable optics increases at a much slower rate than that of datacenter *** gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is *** optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and *** is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale *** international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and *** review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.

关键词： Co-packaged optics Silicon photonics High-performance computing Advanced packaging External laser Optical power delivery Co-simulation Standardization Transmitter Receiver

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An Incentive Mechanism for Privacy Preserved Data Trading with Verifiable Data Disturbance

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Man Li, Xinghua Luo, Bin Ren, Yanbing Miao, Yinbin Liu, Ximeng Deng, Robert H. State Key Laboratory of Integrated Services Networks the School of Cyber Engineering Xidian University Xi’an710071 China Engineering Research Center of Big data Security Ministry of Education Xi’an710071 China The College of Mathematics and Computer Science Fuzhou University Fujian350108 China The Department of Information Systems Singapore Management University 80 Stamford Road Singapore

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer’s (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs’s local disturbance’s privacy and verifiability with our proposed three-level verification algorithm. Subsequently, we model the trading as a game and disturbance verification results determine the compensation and punishment for trading bilateral utilities following Nash Equilibrium where DOs honestly disturb data. Theoretical analysis shows that DOs are motivated to honestly disturb data and their raw data privacy is preserved. Extensive experiments using the real-world dataset demonstrate that the deviating DOs in our scheme can be verified with a probability of more than 90% and the statistical result accuracy can be improved by more than 80% compared with the existing works. © 2004-2012 IEEE. All rights reserved.

关键词： Differential privacy

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Hybrid Deep Relation Matrix Bidirectional Approach for Relational Triple Extraction

Hybrid Deep Relation Matrix Bidirectional Approach for Relat...

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International Conference on computer Supported Cooperative Work in Design

作者： Yushuai Hu Jing Zhao Ming Li Lin Yuan Qingzhi Zou Ronghuan Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Relation extraction is a crucial task within information extraction, and numerous models have demonstrated impressive results. However, most of the tagging-based relation triple extraction methods employ unidirectional approaches to extract subjects, objects, and relations, which may overlook crucial information. In this paper, we introduce a novel deep matrix-based bidirectional relation extraction model. Firstly, we extract forward and backward entity pairs. During the bidirectional extraction process,there may be some redundant relationships,so we use a shared encoder to connect and enhance the extraction process. Secondly, we design a low-complexity relation extraction matrix to allocate all possible relations. We assess our model using diverse benchmark datasets, and comprehensive experiments show that our approach effectively addresses subsequent triple extraction issues stemming from entity extraction failures.

关键词： Learning systems Federated learning Computational modeling Benchmark testing Information retrieval Data mining Task analysis

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WiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation with Ultra-Low Sampling Rate by Diffusion Models

arXiv

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arXiv 2025年

作者： Liu, Zhiyuan Zhang, Shuhang Liu, Qingyu Zhang, Hongliang Song, Lingyang School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Pengcheng Laboratory Shenzhen518055 China Department of Electronics Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China

Fine-grained radio map presents communication parameters of interest, e.g., received signal strength, at every point across a large geographical region. It can be leveraged to improve the efficiency of spectrum utilization for a large area, particularly critical for the unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize radio samples collected by sparsely distributed sensors to infer the map. This problem is challenging due to the ultra-low sampling rate, where the number of available samples is far less than the fine-grained resolution required for radio map estimation. We propose WiFi-Diffusion – a novel generative framework for achieving fine-grained WiFi radio map estimation using diffusion models. WiFi-Diffusion employs the creative power of generative AI to address the ultra-low sampling rate challenge and consists of three blocks: 1) a boost block, using prior information such as the layout of obstacles to optimize the diffusion model;2) a generation block, leveraging the diffusion model to generate a candidate set of radio maps;and 3) an election block, utilizing the radio propagation model as a guide to find the best radio map from the candidate set. Extensive simulations demonstrate that 1) the fine-grained radio map generated by WiFi-Diffusion is ten times better than those produced by state-of-the-art (SOTA) when they use the same ultra-low sampling rate;and 2) WiFi-Diffusion achieves comparable fine-grained radio map quality with only one-fifth of the sampling rate required by SOTA. Copyright © 2025, The Authors. All rights reserved.

关键词： Wi-Fi

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Reversible Data Hiding in Encrypted Image via Joint Encoding of Multiple MSB and Pixel Difference

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Security and Communication networks 2023年第1期2023卷

作者： Kong, Ping Fu, Di Huang, Lin Zhou, Liang Li, Jian Qin, Chuan Shanghai Key Laboratory of Molecular Imaging Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences Shanghai201318 China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China

Reversible data hiding in encrypted image (RDHEI) has become a research hotspot, which can effectively protect image content privacy. An RDHEI scheme based on the joint encoding of multiple MSBs (most significant bits) and pixel difference is proposed in this paper. A block-based image encryption method is adopted on the content owner side, which can securely protect the image contents while retaining the spatial correlation within each block. By the joint encoding strategy of multiple MSB and pixel difference, the redundancy within the bit plane is sufficiently compressed to accommodate more additional data;thus, a high embedding rate can be achieved. According to different kinds of available keys, image decryption and data extraction can be separably conducted on the receiver side. Experimental results show that our scheme can achieve a higher embedding rate than some state-of-the-art schemes. © 2023 Ping Kong et al.

关键词： Pixels

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NQNR: News Recommendation Method Based on News Quality-Aware Modeling

NQNR: News Recommendation Method Based on News Quality-Aware...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Baojie Xu Zhenyu Yang Yan Huang Wenyue Hu Zhibo Zhang The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Personalized News Recommendation (PNR) can enhance user experience by alleviating information overload. Traditional news recommendation methods consider all clicking behaviors as user interests, resulting in biased user modeling that fails to accurately capture user interests. In addition, although there are methods to reduce the impact of low-quality news at the representation level by simply filtering it through the attention mechanism. However, this only implicitly models the news in the interaction sequence without specifically considering the quality of each news, and thus has very limited effect in identifying noise. To address these issues, this paper proposes News Recommendation method based on News Quality-aware modeling (NQNR). We attempt to explicitly model the news in the click sequence and candidate ranking one by one to visually assess the quality of each news. Specifically, we design a detection module to detect whether the input news is low-quality news. Then, by reducing the influence of low-quality news in user modeling and candidate ranking, user interests are modeled more accurately, while recommendations of such news are reduced for users. In addition, to capture the similarity of vectors more accurately, we also design a similarity computation method based on the multiple attention mechanism in the detection module. Experiments on a large real-world Microsoft News Dataset (MIND) show that our model significantly outperforms previous models. Our code is posted at the following URL: https://***/xxbbjj/NQNR-.

关键词： Uniform resource locators Attention mechanisms Codes Accuracy Filtering Computational modeling Noise Vectors User experience Cybernetics

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A Collaborative Heterogeneous Graph Neural Network for Personalized News Recommendation

A Collaborative Heterogeneous Graph Neural Network for Perso...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenglong Shi Haibin Geng Wenfeng Jiang Jinbao Li Chao Wu Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models.

关键词： Correlation Message passing Semantics Collaboration Logic gates Graph neural networks Topology History Data mining Cybernetics

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An Entity Relation Extraction Framework Based on Large Language Model and Multi-Tasks Iterative Prompt engineering

An Entity Relation Extraction Framework Based on Large Langu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haibin Geng Chenglong Shi Xuesong Jiang Zan Kong Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Document-level entity relation extraction is an important task in the field of natural language processing, which plays an important role in semantic understanding and knowledge graph construction. However, existing deep neural networks and graph neural networks models are limited by their performance and parameters number, which can not capture global semantics and have poor generalization ability. Furthermore, existing methods employing large language model for entity relation extraction do not establish good relationships among multi-tasks of entity relation extraction task, resulting in more information can not be effectively shared and transmitted between tasks. In addition, previous approaches can not effectively eliminate false entities and relationships. To solve these problems, we propose an entity relation extraction framework based on large language model and multi-tasks iterative prompt engineering. In our model, we design an iterative prompt engineering, which can better establish the relationship among multi-tasks, and ensure every task to obtain the optimal results. Moreover, we design semantic merging, group disambiguation and self-verification modules to eliminate the false entity relations and noise nodes. Additionally, we design summary prompts to provide sufficient global semantics for better text segmentation. Finally, we evaluated our model on wikiann, wikineural, ACE2005, CoNLL2003, CoNLL2004, and SciERC datasets and compared it with other baseline models.

关键词： Large language models Semantics Noise Merging Knowledge graphs Multitasking Natural language processing Iterative methods Prompt engineering Data mining

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Large Language Models and Artificial Intelligence Generated Content Technologies Meet Communication networks

arXiv

引用

arXiv 2024年

作者： Guo, Jie Wang, Meiting Yin, Hang Song, Bin Chi, Yuhao Yu, Fei Richard Yuen, Chau State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Department of Systems and Computer Engineering Carleton University OttawaK1S 5B6 Canada School of Electrical and Electronics Engineering Nanyang Technological University 487372 Singapore

Artificial intelligence generated content (AIGC) technologies, with a predominance of large language models (LLMs), have demonstrated remarkable performance improvements in various applications, which have attracted great interests from both academia and industry. Although some noteworthy advancements have been made in this area, a comprehensive exploration of the intricate relationship between AIGC and communication networks remains relatively limited. To address this issue, this paper conducts an exhaustive survey from dual standpoints: firstly, it scrutinizes the integration of LLMs and AIGC technologies within the domain of communication networks;secondly, it investigates how the communication networks can further bolster the capabilities of LLMs and AIGC. Additionally, this research explores the promising applications along with the challenges encountered during the incorporation of these AI technologies into communication networks. Through these detailed analyses, our work aims to deepen the understanding of how LLMs and AIGC can synergize with and enhance the development of advanced intelligent communication networks, contributing to a more profound comprehension of next-generation intelligent communication networks. © 2024, CC BY-NC-SA.

关键词： Generative adversarial networks

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FEST: Feature Enhancement Swin Transformer for Remote Sensing Image Semantic Segmentation

FEST: Feature Enhancement Swin Transformer for Remote Sensin...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The global context is crucial for the precise segmentation of remote sensing images. However, the large volumes and high spatial resolutions of remote sensing images make efficient analysis of the entire scene challenging for most convolutional neural network (CNN)-based methods. To address this issue, we propose to design an innovative framework for semantic segmentation of remote sensing images called Feature Enhancement Swin Transformer (FEST). Firstly, we utilize the Swin Transformer as the encoder and incorporates a Global Information Enhancement Model (GIEM) within each Swin Transformer block to reduce information loss and enable encoding of more accurate spatial information. Secondly, we introduce an enhanced decoding structure called Enhanced Feature Fusion Module (EFFM) with added enhanced channel and spatial attention modules to retain localized information while obtaining extensive contextual information. Finally, for loss calculation, we utilize the dice and cross-entropy loss to jointly supervise the model, aiming to achieve a competitive performance. We comprehensively evaluated FEST on the ISPRS-Vaihingen and Potsdam datasets. The results indicate that our approach has achieved significant improvements in semantic segmentation tasks compared to existing methods.

关键词： Deep learning Federated learning Semantic segmentation Semantics Transformers Information retrieval Encoding

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