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IEEE Conference on Global Communications (GLOBECOM)

作者： Ya Li Guoquan Wu Jianhang Tang State Key Laboratory of Public Big Data Guizhou University China School of Information Science and Engineering Yanshan University China

Metaverse is envisaged as an evolving Internet paradigm that allows people to play, work, and socialize in a shared and virtual ecosystem with immersive and seamless experiences. However, multiple users will access the metaverse world for diverse scenes simultaneously due to its social property. How to provide high-quality and low-latency metaverse services for massive concurrent users is a crucial problem. In this work, a novel social-aware edge caching (SEC) framework is proposed for metaverse systems, where metaverse scenes are divided into massive environment panoramic frames and dynamic objects with different priorities. An unmanned aerial vehicle (UAV)-assisted edge server is deployed to cache the environment panoramic frames, while the dynamic objects are rendered on head-mounted displays (HM Ds). A synchronous advantage actor-critic (SA2C) algorithm is developed to generate caching solutions with low time complexity by considering the collective behaviors and social dynamics for requesting similar scenes. Finally, we provide some simulation experiments by leveraging a real-world dataset. The numerical results reveal that the proposed algorithm can reduce the service time and increase the cache hit rate significantly by comparing it with two benchmark caching algorithms.

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Mitigating Label-Noise for Facial Expression Recognition in the Wild

Mitigating Label-Noise for Facial Expression Recognition in ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Yan, Huan Gu, Yu Zhang, Xiang Wang, Yantong Ji, Yusheng Ren, Fuji School of Computer and Information Hefei University of Technology S2AC Lab China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China National Institute of Informatics Tokushima University Japan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Label-noise constitutes a major challenge for facial expression recognition in the wild due to the ambiguity of facial expressions worsened by low-quality images. To deal with this problem, we propose a simple but effective Label-noise Robust Network (LRN) which explores the inter-class correlations for mitigating ambiguity that usually happens between morphologically similar classes. Specifically, LRN leverages a multivariate normal distribution to model such correlations at the final hidden layer of the neural network to suppress the heteroscedastic uncertainty caused by inter-class label noise. Furthermore, LRN utilizes a confidence-based label-free loss to extract compact intra-class feature representations under label noise while preserving the intrinsic inter-class relationships. Experiments on three in-the-wild facial expression datasets demonstrates the superiority of our method. © 2022 IEEE.

关键词： Face recognition

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Roles of Multisourced Fluids in the Formation of Sandstone-Hosted Uranium Deposits in the SW Songliao Basin, NE China

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Journal of Earth Science 2023年第1期34卷 54-69页

作者： Zhibing Feng Fengjun Nie Li Jiang Fei Xia Jianfang Cai Bocheng Zhang Yayun Lu Liangliang Zhang State Key Laboratory of Nuclear Resources and Environment East China University of TechnologyNanchang330013China Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology East China University of TechnologyNanchang330013China Radioactive Geology and Exploration Technology Laboratory East China University of TechnologyNanchang330013China No.243 Geological Party China National Nuclear CorporationChifeng024000China Airborne Survey and Remote Sensing Center of Nuclear Industry China National Nuclear CorporationShijiazhuang050002China

The sandstone-hosted uranium deposits in the SW Songliao Basin differ from typical sandstone-hosted uranium deposits in terms of the geological features of the ore-deposits,including the geometry of the orebodies,mineral assemblage and *** drill core and microscopic observations,scanning electron microscopy(SEM),electron microprobe analysis(EMPA),heavy mineral concentrates,and fluid inclusion studies of the Upper Cretaceous Yaojia Formation,i.e.,the uranium-bearing layer,were integrated to investigate the roles of hydrothermal fluids in the formation of these uranium *** found that the kaolinite alteration is developed in the mineralized zones,but it is less common in the peripheral *** fluid inclusions are hydrothermal fluids with a medium-low temperature(67 to 179 ℃) and a high salinity(5.9 wt.% to 20.1 wt.%).According to the analyses,three kinds of hydrothermal fluids,i.e.,the acid fluid,the groundwater heated by the mafic magma,and the alkaline fluid rich in Ca^(2+) and CO_(3)^(2-),were *** fluids might have low U content,but they have participated in the formation of the uranium deposits *** formed by the acid-hydrothermal fluid absorbed large amounts of ***,the thermal energy from the hydrothermal fluids changed the intrastratal redox environment and increased the solubility of the uranium minerals in the *** alkaline-hydrothermal fluid rich in Ca^(2+) and C0_(3)^(2-) facilitated the formation of stable Ca-U(Ⅵ)-CO_(3) complex,which led to the enrichment of soluble uranium in solution,and final precipitation as pitchblende,brannerite and Ti-bearing uranium minerals in the uranium ores.

关键词： sandstone-hosted uranium deposit hydrothermal fluid uranium mineralization Songliao Basin kaolinite carbonate minerals

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A Lightweight Processing Method Towards Integrated Multi-demand in BIM

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Wang, Yahui Huang, Limin Xu, Jian An, Xiaoya Li, Ziwei Chen, Jianhua Guo, Jianzhong Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Henan Zhengzhou450003 China State Key Laboratory of Geo-information Engineering Shaanxi Xi'an710000 China

In recent years, the rapid advancement of Building Information Modeling (BIM) technology has led to an increasing variety of application scenarios, necessitating diverse demands on the technology. Among these requirements, the lightweighting of BIM models is of paramount importance. However, conventional approaches primarily focus on the models themselves, neglecting the varying needs of users. This study presents a novel lightweighting method for BIM models, starting from user interests or specific scenario requirements. It employs optimized data structures and organization techniques to streamline source data, thereby solving the problem of lightweighting BIM models while catering to the diverse needs of users. A comparison of studies using apartment buildings, office buildings, and commercial complexes as examples show that this method works to greatly enhance the efficiency of visualizing different-sized BIM models in geospatial environments. This research provides a new approach to lightweighting BIM models and holds the promising potential to offer strong technical support for future advancements in BIM technology. © 2024 Yahui Wang et al., published by Sciendo.

关键词： Apartment houses

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A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media 28th

A Topic-Aware Graph-Based Neural Network for User Interest ...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Chen, Junyang Fan, Ge Gong, Zhiguo Li, Xueliang Leung, Victor C. M. Wang, Mengzhu Yang, Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Tencent Inc. Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9783031306716

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis

arXiv

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arXiv 2023年

作者： Haoran, Sun Yang, Wang Haipeng, Liu Biao, Qian Meng, Wang Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei China Department of Computer Science and Information Engineering Hefei University of Technology Hefei China

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel Fine-grained text-image Fusion based Generative Adversarial Networks, dubbed FF-GAN, which consists of two modules: Fine-grained text-image Fusion Block (FF-Block) and Global Semantic Refinement (GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given *** is available at htt s:// ithub com/haoranhfut/FF-GAN Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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TransD-based Multi-hop Meta Learning for Few-shot knowledge Graph Completion

TransD-based Multi-hop Meta Learning for Few-shot Knowledge ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jindi Li Kui Yu Yuling Li Yuhong Zhang Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Few-shot knowledge graph completion (FKGC), which aims to infer missing facts about a relation from only a few reference triples, has recently attracted great attention. The core of solving the FKGC task is to learn a vector representation for each few-shot relation using the corresponding entity represen-tations. To this end, existing models generally enhance entity representations with their direct neighbors. However, a large number of entities have few direct neighbors. Hence, encoding only direct neighborhood is insufficient to obtain satisfactory en-tity representations. In addition, current models typically utilize static embeddings to represent entities, ignoring their diverse semantics, i.e., an entity may show distinct semantics within different few-shot relations. To address these issues, we propose a new FKGC framework, namely TransD-based Multi-hop Meta Learning (TDML). TDML consists of three main components: a multi-hop neighbor encoder to enhance entity representations by aggregating heterogeneous multi-hop neighbors, a transformer encoder to generate the relation meta representations, and a TransD-based relation representation updater that allows each entity to exhibit relation-specific semantics and tune the relation meta representations. Extensive experiments on two public datasets demonstrate that our model outperforms state-of-the-art FKGC methods.

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Efficient Deep learning-based Real-time Migration Strategies for Containers in SDN

Efficient Deep learning-based Real-time Migration Strategies...

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Next Generation data-driven Networks (NGDN), International Conference on

作者： Shan Huang Yanan Liu Yanuo Li Feili He Xiaodong Duan School of Computer Science and Engineering Dalian Minzu University Key Laboratory of Big Data Application Technology of the State Ethnic Affairs Commission Dalian Ethnic Culture Digital Technology Key Laboratory Dalian China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Container technology is widely used and improve the efficiency of container real-time migration has become an important research topic. Existing studies mainly focus on optimizing iterative dumping of containers without fully considering the impact of migration node selection on migration efficiency. Software-defined networking (SDN), achieves centralized control of the network by separating the control plane and the forwarding plane. Applying deep learning to SDN makes tasks such as traffic prediction and network optimization more intelligent. This paper proposes Prediction Model based on CLCB (CNN-LSTM-CBAM) uses real-time collected load data as input, extracts feature components of load data using one-dimensional convolutional neural networks, and then predicts using LSTM. To improve prediction accuracy, a lightweight CBAM attention module is introduced, and the prediction results are output through fully connected layers. The Network Traffic Prediction Model based on GCGRU-Conv (GCN-GRU-CNN) considers both spatial and temporal features of network traffic. GCN and GRU are used to extract spatial and temporal features of network traffic, respectively, and then these features are fused together using convolutional neural networks. To address the gradient problem caused by increasing network depth, a residual network structure is introduced. Experimental results show that the method proposed can effectively reduce the container migration time and has better prediction results.

关键词： Deep learning Costs Telecommunication traffic Containers Predictive models Feature extraction Real-time systems Convolutional neural networks Software defined networking Load modeling

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knowledge Representation and Reasoning for Complex Time Expression in Clinical Text

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data Intelligence 2022年第3期4卷 573-598页

作者： Danyang Hu Meng Wang Feng Gao Fangfang Xu Jinguang Gu School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration of the People’s Republic of ChinaBeijing 10038China Institute of Big Data Science and Engineering Wuhan University of Science and TechnologyWuhan 430065China

Temporal information is pervasive and crucial in medical records and other clinical text,as it formulates the development process of medical conditions and is vital for clinical decision ***,providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is *** order to capture complex temporal semantics in clinical text,we propose a novel Clinical Time Ontology(CTO)as an extension from OWL *** specifically,we identified eight timerelated problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time,cyclic time,irregular time,negations and other complex aspects of clinical ***,we extended Allen’s and TEO’s temporal relations and defined the relation concept description between complex and simple ***,we provided a formulaic and graphical presentation of complex time and complex time *** carried out empirical study on the expressiveness and usability of CTO using real-world healthcare ***,experiment results demonstrate that CTO could faithfully represent and reason over 93%of the temporal expressions,and it can cover a wider range of time-related classes in clinical domain.

关键词： Clinical text Temporal ontology Temporal relations OWL Negation of temporal relation

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Transparent Four-Port MIMO DRAs Decoupled by Conformal Strips

Transparent Four-Port MIMO DRAs Decoupled by Conformal Strip...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： He, Chao Yang, Nan Leung, Kwok Wa Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Kowloon Hong Kong CityU Shenzhen Research Institute Information and Communication Technology Centre Shenzhen China

ISBN: (纸本)9798350355581

A four-port multi-input multi-output (MIMO) dielectric resonator (DR) antenna (DRA) is presented in this paper. Combing four rectangular DRs together can form a single DR element. With shape modification and conformal strips, the four ports can be decoupled. To verify the design idea, a transparent prototype can be designed and fabricated for 2.4-GHz band applications. The measured results show an impedance bandwidth of 5.8% and an isolation level of 24 dB. © 2024 IEEE.

关键词： Microwave antennas

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