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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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Non-invasive estimation of pulmonary hypertension and clinical deterioration risk in pediatric congenital heart disease:Development and validation of predictive tools

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Chinese Medical Journal 2024年第11期137卷 1384-1386页

作者： Ting Wang Dansha Zhou Yuqin Chen Suhua Kuang Yue Xing Qijian Yi Zhengxia Pan Weibin Xu Jiao Rao Yunqi Liu Guoliang Lu Ziying Lin Xiang Li Yi Xie Yulong Wu Peng An Xiaoxiao Deng Jiayue He Jiayi Xie Chenxi Li Gang Geng Daiyin Tian Enmei Liu Jingsi Huang Zhou Fu Jian Wang Department of Respiratory Chongqing Higher Institution Engineering Research Center of Children’s Medical Big Data Intelligent ApplicationNational Clinical Research Center for Child Health and DisordersMinistry of Education Key Laboratory of Child Development and DisordersChildren’s Hospital of Chongqing Medical UniversityChongqing 400014China State Key Laboratory of Respiratory Diseases National Center for Respiratory MedicineGuangdong Key Laboratory of Vascular DiseasesNational Clinical Research Center for Respiratory DiseasesGuangzhou Institute of Respiratory Healththe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou Medical UniversityGuangzhouGuangdong 510120China Department of Cardiac Surgery The First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou Medical UniversityGuangzhouGuangdong 510120China Department of Cardiovascular Medicine Children’s Hospital of Chongqing Medical University National Clinical Research Center for Child Health and DisordersMinistry of Education Key Laboratory of Child Development and DisordersChildren’s Hospital of Chongqing Medical UniversityChongqing 400014China Department of Thoracic and Cardiac Surgery Children’s Hospital of Chongqing Medical University National Clinical Research Center for Child Health and DisordersMinistry of Education Key Laboratory of Child Development and DisordersChildren’s Hospital of Chongqing Medical UniversityChongqing 400014China Department of Cardiac Center of Guangdong Women and Children Hospital Guangzhou GuangzhouGuangdong 511400China Department of Guangzhou Laboratory Guangzhou International Bio IslandGuangzhouGuangdong 510005China

To the Editor:Owing to the heterogeneity of congenital heart disease-associated pulmonary hypertension(CHDPH)disease and the development of the pulmonary vascular system in pediatric patients,the management of CHD-PH in children is signicantly different from that in adults.[1]Early identication and intervention of CHD-PH are of great signicance for disease management in children with CHD,and non-invasive,accurate clinical tools are urgently needed to predict PH risk in children with ***,the applicability of the current risk-scoring system to pediatric CHD-PH remains ***,there are presently no user-friendly,non-invasive online tools available for predicting CHD-PH,and there is a scarcity of related studies in Asian *** study aimed to develop and validate the prediction tools for estimating the risk of pulmonary hypertension and clinical deterioration in children with congenital heart disease.

关键词： pediatric congenital hypertension

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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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Gsgm: A Group-Sequence-Aware Graph Model for Personalized News Recommender System

SSRN

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SSRN 2024年

作者： Lv, Pengtao Zhang, Qinghui Guan, Zhenhan Deveci, Muhammet Shi, Lei Zhong, Kaiyang Wang, Jingping Xu, Heliang Zhang, Yana Ministry of Education Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China Henan Grain Big Data Analysis and Application Engineering Research Center Henan University of Technology Henan Zhengzhou450001 China The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul4940 Turkey State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages Minzu University of China Beijing100081 China Southwestern University of Finance and Economics China

News recommender system is an important method of helping readers identify interesting stories. Although human behaviors exhibit sequential patterns, most current studies on news recommendations ignore the influence of reading sequences. Leveraging sequential patterns in news recommendation is, however, very challenging, as the capture of reading behavior sequence at the individual level may be impossible due to severity of sparsity in the field of news. To address this challenge, we proposed a group-sequence-aware graph model (GSGM) to learn representative reading sequences of groups instead of individuals, where groups are viewed as latent variables and automatically learnt from users' reading behaviours. Besides, when readers select different groups, they are likely to have distinct reading preferences. This finding is used to learn group-dependent personal reading preferences in GSGM. A wide range of experiments are conducted to verify the efficiency and effectiveness of the GSGM on real datasets. Experimental results demonstrate that GSGM outperforms the state-of-the-art competing methods. © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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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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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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How to train your agent to read and write

arXiv

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arXiv 2021年

作者： Liu, Li He, Mengge Xu, Guanghui Tan, Mingkui Wu, Qi School of Software Engineering South China University of Technology China Pazhou Laboratory China University of Adelaide Australia Key Laboratory of Big Data and Intelligent Robot Ministry of Education United Kingdom

Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (e.g., students) to fully grasp this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. Although there have been existing works focusing on summarizing (i.e., reading) the knowledge in a given text or generating (i.e., writing) a text based on the given knowledge, the ability of simultaneously reading and writing is still under development. Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice. In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a Reader that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text Writer that generates a novel paragraph, and a Reviewer that reviews the generated paragraph from three different aspects. Extensive experiments show that our DRAW network outperforms considered baselines and several state-of-the-art methods on AGENDA and M-AGENDA datasets. Our code and supplementary are released at https://***/menggehe/DRAW. Copyright © 2021, The Authors. All rights reserved.

关键词： knowledge graph

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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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Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

arXiv

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arXiv 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Mei, Fang Kang, Jiawen Du, Hongyang Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn36830 United States

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://***/GAI-enhanced-DRL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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High power non-electrolytic capacitor LED driver with three ports based on energy distribution 5

High power non-electrolytic capacitor LED driver with three ...

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2022 5th International Conference on Power Electronics and Control engineering, ICPECE 2022

作者： Li, Zejun Zhang, Junzhao Xu, Bingshe Han, Xiaoqing Li, Fan Lu, Xiaofeng College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Laboratory of Interface Science and Engineering of New Materials Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Intelligent City Lighting Data Sharing and Pub. Serv. Platform Eng. Res. Ctr. of 1331 Proj. in Shanxi Prov. Based on Big Data Shanxi Taiyuan030024 China Shanxi Intelligent Lighting Joint Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Culture and Tourism Industry Research Institute Shanxi Taiyuan030024 China

To eliminate the influence of electrolytic capacitors on the driving life of high-power LED, a three-port high-power LED without electrolytic capacitor driving power supply based on energy distribution is proposed. The energy difference between the input terminal and the output terminal is balanced by the auxiliary DC-DC circuit to reduce the ripple of the output current. The working principle of the topology and switching mode is analyzed in detail. According to the proposed circuit topology, the mechanism of the output current ripple of the low-frequency circuit is analyzed, and a low-frequency ripple control strategy suitable for the circuit topology is proposed. The specific implementation scheme and the key parameters of the circuit design are given, and the model is verified, which proves that the proposed model is feasible. © Published under licence by IOP Publishing Ltd.

关键词： Light emitting diodes

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