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arXiv 2022年

作者： Chen, Qian Wang, Zilong Zhou, Yilin Chen, Jiawei Xiao, Dan Lin, Xiaodong State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xidian University Xi’an710071 China School of Computer Science University of Guelph Guelph Canada

Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' local devices. However, the parameter server setting of FL not only has high bandwidth requirements, but also poses data privacy issues and a single point of failure. In this paper, we propose an efficient and privacy-preserving protocol, dubbed CFL, which is the first fine-grained global model training for FL in large-scale peer-to-peer (P2P) networks. Unlike previous FL in P2P networks, CFL aggregates local model update parameters hierarchically, which improves the communication efficiency facing large amounts of clients. Also, the aggregation in CFL is performed in a secure manner by introducing the authenticated encryption scheme, whose key is established through a random pairwise key scheme enhanced by a proposed voting-based key revocation mechanism. Rigorous analyses show that CFL guarantees the privacy and data integrity and authenticity of local model update parameters under two widespread threat models. More importantly, the proposed key revocation mechanism can effectively resist hijack attacks, thereby ensuring the confidentiality of the communication keys. Ingenious experiments on the Trec06p and Trec07 datasets show that the global model trained by CFL has good classification accuracy, model generalization, and rapid convergence rate, and the dropout-robustness of the system is achieved. Compared to the first global model training protocol for FL in P2P networks, PPT, CFL improves communication efficiency by 43.25%. Also, CFL outperforms PPT in terms of computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Peer to peer networks

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The influence of proton radiation on the performance of silicon Mach-Zehnder modulator

The influence of proton radiation on the performance of sili...

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2021 Asia Communications and Photonics Conference, ACP 2021

作者： Han, Changhao Hu, Zhaoyi Tao, Yuansheng Fu, Engang Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China

The influence of proton radiation on silicon Mach-Zehnder modulator performance was studied for space environment. The modulator was sensitive to proton radiation while still demonstrated a certain working ability aft... 详细信息

ISBN: (纸本)9781957171005

关键词： Silicon

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching

FERI: Feature Enhancement and Relational Interaction for Ima...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Shuo Zhao 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 Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.

关键词： computer vision Fuses Image matching Semantics Feature extraction Natural language processing

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Performance of Double-Stacked Intelligent Metasurface-Assisted Multiuser Massive MIMO Communications in the Wave Domain

arXiv

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arXiv 2024年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Chatzinotas, Symeon Kaklamani, Dimitra I. Venieris, Iakovos S. Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom SnT The University of Luxembourg Luxembourg Microwave and Fiber Optics Laboratory Greece Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece

Although reconfigurable intelligent surface (RIS) is a promising technology for shaping the propagation environment, it consists of a single-layer structure within inherent limitations regarding the number of beam steering patterns. Based on the recently revolutionary technology, denoted as stacked intelligent metasurface (SIM), we propose its implementation not only on the base station (BS) side in a massive multiple-input multiple-output (mMIMO) setup but also in the intermediate space between the base station and the users to adjust the environment further as needed. For the sake of convenience, we call the former BS SIM (BSIM), and the latter channel SIM (CSIM). To this end, we achieve hybrid wave-based combining at the BS and wave-based configuration at the intermediate space. Specifically, we propose a channel estimation method with reduced overhead, being crucial for SIM-assisted communications. Next, we derive the uplink sum spectral efficiency (SE) in closed form in terms of statistical channel state information (CSI). Notably, we optimize the phase shifts of both BSIM and CSIM simultaneously by using the projected gradient ascent method (PGAM). Compared to previous works on SIMs, we study the uplink transmission in a mMIMO setup, channel estimation in a single phase, a second SIM at the intermediate space, and simultaneous optimization of the two SIMs. Simulation results show the impact of various parameters on the sum SE, and demonstrate the superiority of our optimization approach compared to the alternating optimization (AO) method. © 2024, CC BY.

关键词： Channel state information

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Semantic-Enhanced Attention Network for Image-Text Matching

Semantic-Enhanced Attention Network for Image-Text Matching

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International Conference on computer Supported Cooperative Work in Design

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma 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 Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image-text matching is an important task in cross-modal information processing, which consists of evaluating the similarity between images and text. However, the data of the two modalities have different distributions and representations, which cannot be directly compared. Therefore, the two modalities need to be processed separately. Most existing image-text matching methods extract image fragments and text fragments separately, using an attention mechanism to establish relationships between regions in the image and words in the text. Subsequently, they aggregate the similarity of these images and text fragments. Regardless, not all fragment alignment is meaningful, and irrelevant fragment alignment will bring redundancy and reduce retrieval accuracy. In addition to that, there are contextual relationships between image regions and between text words, which should also be taken into account. In this paper, we simultaneously consider these two aspects and propose a Semantic-Enhanced Attention Network (SEAN). It first focuses on each modality, mining contextual relationships between fragments within each modality and aggregating contextual information into visual and textual embeddings. Then, we compute the similarity between all image regions and text fragments, focusing all attention on the region-word pairs with the highest similarity. Finally, we summarize these to infer the similarity between the image and text. Our method achieves competitive results on two generalized image text retrieval datasets, Flickr30K and MS-COCO.

关键词： Visualization Attention mechanisms Federated learning Computational modeling Aggregates Semantics Redundancy

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Image super resolution via multi-regularization combining hybrid Tikhonov-TV prior and deep denoiser prior

Image super resolution via multi-regularization combining hy...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiahao Zhang Shengrong Zhao Hu Liang Changchun Wen Chen Liang 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

In a real scenario, the image is often corrupted by complex degradation, and a lot of useful information is lost, which makes super-resolution (SR) reconstruction seriously ill-posed. To effectively solve such a problem, it is crucial to correctly exploit image prior knowledge. Although existing deep learning-based methods can obtain excellent results, they cannot deal with the complex degradation effectively, which would lead to the loss of texture details and the destruction of edge details. In this paper, an efficient multi-regularization method for SR is proposed, which can simultaneously exploit both internal and external image priors within a unified framework. The hybrid Tikhonov-TV prior and deep denoiser prior are introduced to constrain the reconstruction process. That is, the proposed model combines the superiority of the piecewise-smooth prior and deep prior. Moreover, an adaptive weight parameter is employed to make the hybrid component more detail-preserving. Experimental demonstrate that the proposed method achieves better performance in image detail protection than advanced methods.

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Saliency-Aware Regularized Graph Neural Network

arXiv

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arXiv 2024年

作者： Pei, Wenjie Xu, WeiNa Wu, Zongze Li, Weichao Wang, Jinfan Lu, Guangming Wang, Xiangrong Department of Computer Science Harbin Institute of Technology at Shenzhen Shenzhen518172 China Institute of Future Networks Southern University of Science and Technology Shenzhen518055 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518066 China

The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification is not explicitly modeled, which is crucial since different nodes may have different semantic relevance to graph classification;2) the graph representation directly aggregated from node features may have limited effectiveness to reflect graph-level information. In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone, which facilitates the message passing of features for salient nodes and suppresses the less relevant nodes. Thus, our model can learn more effective graph representation. We demonstrate the merits of SAR-GNN by extensive experiments on seven datasets across various types of graph data. Code will be released. © 2024, CC BY.

关键词： Graph neural networks

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50-km Weakly-Coupled Transmission with L-Band 4-LP-Mode FM-EDFA

50-km Weakly-Coupled Transmission with L-Band 4-LP-Mode FM-E...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shuailuo Huang Baolong Zhu Gang Qiao Yuyang Gao Mingqing Zuo Shikui Shen Zelin Wang Yongqi He Zhangyuan Chen Juhao Li China Unicom Research Institute Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Fundamental Network Technology China Mobile Research Institute Beijing China Pengcheng National Laboratory Shenzhen China

50-km weakly-coupled transmission based on a L-band 4-LP-mode FM-EDFA is experimentally demonstrated only adopting $\mathbf{2}\times \mathbf{2}$ or $\mathbf{4}\times \mathbf{4}$ MIMO-DSP.

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

50-km weakly-coupled transmission based on a L-band 4-LP-mode FM-EDFA is experimentally demonstrated only adopting $\mathbf{2}\times \mathbf{2}$ or $\mathbf{4}\times \mathbf{4}$ MIMO-DSP.

关键词： Asia Gain measurement Optical fiber communication MIMO L-band Photonics

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

Multilingual Knowledge Graph Completion via Multimodal Decou...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaojing Liu Xuesong Jiang Fengge Yi 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

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.

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

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

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ran Qiu Shengrong Zhao Hu Liang 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

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.

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