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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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SCTAR: A Multi-Layer BiLSTM-Based Chinese Short Text Similarity Computation Model with Attention Mechanism

SCTAR: A Multi-Layer BiLSTM-Based Chinese Short Text Similar...

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International Conference on computer Supported Cooperative Work in Design

作者： Shuo Zhao Yiyun Xing Jianqiang Zhang Gongpeng Song Qin Lu 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)9798350349184

ISBN: (纸本)9798350349191

Text semantic similarity is a crucial research area in Natural Language Processing (NLP). However, traditional methods for calculating the similarity of short Chinese texts often fall short in accuracy and other crucial aspects. The Bidirectional Long Short-Term Memory Network (BiLSTM) has demonstrated remarkable performance in computing the similarity of short Chinese texts by effectively capturing long-range dependencies and semantic information. Additionally, the multi-head attention mechanism considers the interaction between different text locations and semantic information, enhancing the model’s representative capacity. Building upon this foundation, our paper proposes an enhanced model known as SCTbilstmAttRdrop (SCTAR). This model is constructed based on a multilayer BiLSTM architecture and incorporates an SE-gated convolutional module and a convolutional multi-head attention-aware model. We conducted extensive evaluations using two Chinese short text datasets, Chinese-SNLI and CCKS2018_Task3. The experimental results unequivocally demonstrate that our SCTAR model surpasses other common methods in terms of accuracy, precision, recall, and F1 scores when tasked with computing the similarity of Chinese short texts.

关键词： Accuracy Attention mechanisms Computational modeling Semantics Bidirectional control Feature extraction Nonhomogeneous media

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Research on long and short-term social recommendation based on convolutional and gated recurrent units

Research on long and short-term social recommendation based ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zihe Jia Peng Xue Zhiqiang Dai Qian Gao Xiaomeng 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

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users’ long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data; secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier; finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved.

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Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communications

arXiv

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arXiv 2024年

作者： Yue, Shaohua Zeng, Shuhao Liu, Liang Eldar, Yonina C. Di, Boya The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Holographic MIMO communications, enabled by large-scale antenna arrays with quasi-continuous apertures, is a potential technology for spectrum efficiency improvement. However, the increased antenna aperture size extends the range of the Fresnel region, leading to a hybrid near-far field communication mode. The users and scatterers randomly lie in near-field and far-field zones, and thus, conventional far-field-only and near-field-only channel estimation methods may not work. To tackle this challenge, we demonstrate the existence of the power diffusion (PD) effect, which leads to a mismatch between the hybrid-field channel and existing channel estimation methods. Specifically, in far-field and near-field transform domains, the power gain of one channel path may diffuse to other positions, thus generating fake paths. This renders the conventional techniques unable to detect those real paths. We propose a PD-aware orthogonal matching pursuit algorithm to eliminate the influence of the PD effect by identifying the PD range, within which paths diffuse to other positions. PD-OMP fits a general case without prior knowledge of near-field and far-field path numbers and the user’s location. The computational complexity of PD-OMP and the Cramér-Rao Lower Bound for the sparse-signal-recovery-based channel estimation are also derived. Simulation results show that PD-OMP outperforms state-of-the-art hybrid-field channel estimation methods. © 2024, CC BY-NC-ND.

关键词： Channel estimation

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ran Qiu Hu Liang Yuchen Liu Shengrong 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

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures.

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yuchen Liu Hu Liang Ran Qiu Shengrong 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

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network’s lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments.

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I Can See Your Secrets, A Way to Recognize User Pin Codes by Surveillance Camera

I Can See Your Secrets, A Way to Recognize User Pin Codes by...

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Smart World Congress (SWC), IEEE

作者： Zhenyang Guo Jin Cao Xiaoyong Zan Jiawei Yang Ang Li Ben Niu Zhe Sun Lihua Yin Hui Li School of Cyber Engineering Xidian University Xi’an China the State Key Laboratory of Integrated Services Networks Xi’an China ByteDance Ltd Beijing China Department of Electrical & Computer Engineering University of Maryland College Park U.S.A Chinese Academy of Sciences Institute of Information Engineering Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Surveillance cameras are currently widely used in public and private places to enhance area security and reduce crime rates. However, the extensive use of surveillance cameras also poses significant threats, particularly concerning the leakage of users’ private information. Surveillance cameras are often installed in the corners of areas such as hotel front desks, supermarket checkout areas, or ticket offices to observe user behavior, which can lead to shoulder-surfing attacks. In these public or private settings, when users input their personal identification number (PIN) for unlocking or payment, this process may be intentionally or unintentionally exposed and recorded. If the PIN is compromised, it can result in severe privacy breaches and financial losses for users. Existing work primarily focuses on dedicated handheld recording devices such as cameras and smartphones, lacking discussion on public surveillance cameras. In this paper, we first present an attack model for such scenarios. Based on this model, we design and implement an automated attack system for PIN recognition. Experimental results demonstrate that our proposed attack model is both feasible and effective, achieving $90 \%$ accuracy with our system. Finally, we urge users, smartphone manufacturers, and surveillance camera manufacturers to be aware of this attack vector to prevent further harm.

关键词： Codes Surveillance Side-channel attacks Cameras Privacy breach Vectors Pins Recording Security Smart phones

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