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检索条件"机构=The Key Laboratory for Computer Networks and Information Integration"

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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...

引用

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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A Global Task Scheduling Method Based on Network Measurement and Prediction in Computing Power networks 29

A Global Task Scheduling Method Based on Network Measurement...

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

作者： Zhang, Chuanfu Li, Wen Chen, Jing Ge, Jing Wang, Di Li, Na Sun, Hao Geng, Yudong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

To improve the task scheduling efficiency in computing power networks, this paper proposes a global task scheduling method based on network measurement and prediction in computing power networks (GTS-MP), which selects a more suitable computing power cluster and storage platform with a higher network availability score between them to implement tasks. The experiments are conducted on a test bench of urban area networks in 12 cities. The results show that this method greatly reduces the execution time of artificial intelligence model training tasks, with a maximum reduction of 40% compared to other plans. © 2023 IEEE.

关键词： computing power network global scheduling network availability score task offloading

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Self-Paced Self-Training Based on K-Nearest Neighbors 4

Self-Paced Self-Training Based on K-Nearest Neighbors

引用

4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Ma, Yongzheng Sun, Zhengkai Zhang, Yintong Guan, Hongjiao Qiao, Xinxiao 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798400709449

Self-training is a classical semi-supervised classification method, which iteratively trains labeled samples that are enhanced using unlabeled samples. However, traditional self-training selects pseudo-labeled examples without distinguishing between different classes. Furthermore, wrong pseudo-labeled samples accumulate, leading to an increase in learning bias. In this paper, we propose a self-paced self-training based on K-nearest neighbors (SPST-KNN). SPST-KNN is composed of three main components: sample difficulty calculation, sample type determination, and self-paced selection based on KNN. The types of examples, including safe, boundary, and outlying examples, are determined by KNN. According to the principle of self-paced learning, SPST-KNN preferentially selects easy pseudo-labeled examples in each sample type to be added into the labeled sample set. Experiments and ablation study on 20 real-world datasets have demonstrated the superiority and effectiveness of the proposed SPST-KNN. © 2023 ACM.

关键词： Iterative methods

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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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Knowledge Graph Link Prediction Model Based on Attention Graph Convolutional Network 9th

Knowledge Graph Link Prediction Model Based on Attention Gr...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Shi, Junkang Li, Ming Zhao, Jing 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: (纸本)9783031466731

The main purpose of knowledge graph embedding (KGE) is to complete the missing part of triplets. By learning from existing models, we found that adding graph convolutional networks (GCN) to KGE models achieved good performance. These models mainly use the local structural characteristics of data. However, their main goal is to learn an aggregator rather than a feature vector for each node. To address the problems with existing models, we proposed a knowledge graph link prediction model based on attentional relational graph convolutional networks (ARGCN). First, we capture the relationship attribute features between nodes by adding an attention mechanism and extract important node attribute features for visualization. Then, through a relational graph convolutional neural network, we capture the associated attributes between nodes and extract the feature information that can best represent nodes. Finally, we use DistMult as the scoring function. Our experiments show that our proposed is effective on standard FB15k-237 and WN18RR datasets. Compared with other models, it has relative improvements in Hits@1, Hits@3 and Hits@10. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Knowledge graph

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A Prediction-Based Fuzzy Method for Multi-objective Microservice Workflows Scheduling 18th

A Prediction-Based Fuzzy Method for Multi-objective Microse...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Xu, Jingwen Chen, Long Li, Xiaoping Wang, Shuang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China

ISBN: (纸本)9789819996391

Microservice workflows are widely used in real-time mobile computing scenarios such as face recognition and speech recognition. The key challenge is to develop efficient, stable, and robust algorithms capable of handling uncertain and fuzzy workflow tasks. In this paper, we consider the real-time microservice workflow fuzzy scheduling problem under VM-Container two-tier resources. A novel model based on triangle fuzzy numbers is formulated. The model encompasses two metrics: cost and degree of satisfaction. In response, this paper proposes an ARIMA prediction-based workflow fuzzy scheduling method (PFSM), comprising five key components: prediction of workflow arrival number, predistribution of task sub-deadlines, ordering of the task pool, task scheduling strategy, and resource management. To assess the performance of the proposed algorithms, several comparison algorithms are selected for analysis, and their performance differences are evaluated using ANOVA. The experimental results demonstrate the significant superiority of the proposed algorithms over the other compared algorithms in terms of overall performance. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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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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Deep Spatial Spectral Convolutional Sparse Coding for Spectral CT Image Reconstruction

Deep Spatial Spectral Convolutional Sparse Coding for Spectr...

引用

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Jin Liu Jingjing Xie Kun Wang Jun Qiang College of Computer and Information Anhui Polytechnic University Wuhu China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Spectral computed tomography (CT) provides the potential to generate attenuation maps at varying spectral bins, which can subsequently be employed for the resolve of tissue materials. However, the majority of reconstruction algorithms employed for every projection energy bin typically exhibit a considerable degree of noise. Recently, a series of techniques have been designed in spectral CT reconstruction based on traditional iterative models or deep learning (DL) methods. However, these works are independent or simply coupled, often neglecting the dependency relationships of spatial and spectrum. Additionally, the interpretability and generalization capabilities remain as formidable challenges for current methods. To effectively address these challenges, we initially introduce a spatial-spectral convolutional sparse coding (SS-CSC) framework, which aims to jointly represent spatial and spectral features in a unified manner. Subsequently, we devise a novel deep unfolding network, inspired by SS-CSC, for spectral CT image representation. We refer to this component as the SS-CSC module. Furthermore, we expand the iterative reconstruction scheme for constructing an interpretable deep SS-CSC iterative reconstruction network (SS-CSC-Net) model for spectral CT imaging. The SS-CSC-Net is composed of several iteration blocks, with each block containing two modules: image reconstruction and SS-CSC modules. The spectral CT image is updated using a deep learning-based spatial-spectral prior constraint. Experimental results from both qualitative results and quantitative analyses indicate that the proposed SS-CSC-Net excels in noise reduction and in maintaining tissue edge integrity, delivering superior overall performance.

关键词： Convolutional codes Training Statistical analysis Computed tomography Noise reduction Training data Iterative methods Image reconstruction Unsupervised learning Testing

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Ultra-high definition video quality assessment method based on deep learning 9

Ultra-high definition video quality assessment method based ...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Li, Wei He, Can Zhou, MingLe Zhang, HengYu Liu, WenLong Hisense Video Technology Co. Ltd. Qing Dao China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

With the development of display technology, people's demand for high-definition video is increasing, and the requirements for video quality are also increasing. Nowadays, a large number of videos have emerged, and how to distinguish the quality of videos faces many challenges. Nowadays, deep learning is developing rapidly, and many video quality assessment (VQA) methods have emerged. These VQA methods can efficiently evaluate video quality and save a lot of manpower and material resources. To achieve the evaluation of UHD video quality, we propose an inter-frame downsampling UHD video quality assessment model (IFDU-VQA) based on deep learning. This model is a no-reference (NR) evaluation model that adopts a dual-branch structure: one branch uses a recursive network to deeply extract video inter-frame features, and combines inter-frame downsampling technology with a multi-information fusion (MIF) module to achieve different time and frequency information. Effective fusion;the other branch applies convolutional neural networks (CNN) to extract features from individual video frames. By fusing the outputs of the two branches, regression and pooling modules are used to calculate the final quality score of the video. Compared with multiple existing models, experimental results show that IFDU-VQA demonstrates superior performance on multiple standard data sets. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Iterative Reconstruction Network Based on Residual Constraint for Low-Dose CT Imaging 8

Deep Iterative Reconstruction Network Based on Residual Cons...

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8th International Conference on Systems and Informatics, ICSAI 2022

作者： Liu, Jin Kang, Yanqin Liu, Tao Zhang, TingYu Zhang, Yikun Anhui Polytechnic University School of Computer and Information Wuhu China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665474481

clinical low X-ray dose computed tomography (LDCT) scanner often induce high intensity strip artifact and spot nosie, compromising diagnoses and intervention plans. Recently, sparsely constrained and network learning-based frameworks have been shown to be efficient in mitigating such issue. In this work, we propose a deep iterative reconstruction network (DIRNet) model with a residual constraint to synergize the advantages of feature learning and image reconstruction to address the LDCT imaging problem. DIR-Net compose by few iteration units, and all iteration units include three different network modules: projection restoration, residual constraint and image update block. DIR-Net is a promising approach for building an end-to-reconstruction mapping strategy and directly obtaining high-quality CT images. Furthermore, LISTA is used to conFigure the network, and the whole network architecture yields improved interpretability. Qualitative and quantitative analysis in test data shown the promising imaging effects of DIR-Net in quantum noise reduction, block artifact removal and tissue detail texture mantian. © 2022 IEEE.

关键词： Image reconstruction

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