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检索条件"机构=In formation School and Key Laboratory of Data Engineering and Knowledge Engineering"
818 条 记 录,以下是651-660 订阅
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A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining
A Coarse-to-Fine Multi-stream Hybrid Deraining Network for S...
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IEEE International Conference on data Mining (ICDM)
作者: Yanyan Wei Zhao Zhang Haijun Zhang Richang Hong Meng Wang Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China
Single image deraining task is still a very challenging task due to its ill-posed nature in reality. Recently, researchers have tried to fix this issue by training the CNN-based end-to-end models, but they still canno...
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A better constant approximation for minimum 3-connected m-dominating set problem in unit disk graph using Tutte decomposition
A better constant approximation for minimum 3-connected m-do...
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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies
作者: Wei Wang Bei Liu Donghyun Kim Deying Li Jingyi Wang Yaolin Jiang School of Mathematics and Statistics Xi'an Jiaotong University Xi'an China Department of Mathematics and Physics North Carolina Central University Durham USA Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University) Renmin University of China Beijing P.R. China
Over years, virtual backbone has attracted lots of attentions as a promising approach to deal with the broadcasting storm problem in wireless networks. One popular way to construct a quality virtual backbone is to sol... 详细信息
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Generative Aspect Based Sentiment Analysis for Online Health Community with data Augmentation
SSRN
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SSRN 2023年
作者: Han, Pu Ye, Dongyu Gu, Liang Chen, Wenqi Wei, Hongchen Li, Xiaoyan School of Management Nanjing University of Posts & Telecommunications Nanjing210003 China Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China School of Basic Medical Sciences Nanjing Medical University Nanjing210029 China
Accurate analysis of patients' feedback on various medical aspects is of great importance for improving the quality of healthcare services. In this paper, we address the task of extracting aspects and opinions by ... 详细信息
来源: 评论
XTQA: Span-Level Explanations for Textbook Question Answering
arXiv
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arXiv 2020年
作者: Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States
Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainabl... 详细信息
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MDPL-net: Multi-layer Dictionary Learning Network with Added Skip Dense Connections
MDPL-net: Multi-layer Dictionary Learning Network with Added...
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IEEE International Conference on data Mining (ICDM)
作者: Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Lin Wu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China
Dictionary learning (DL) is powerful for representation learning, while it fails to capture the deep hierarchical information hidden in data. In this paper, we propose a new generalized end-to-end mulita-layer represe... 详细信息
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The K-Step H Indices in Citation Networks at the Paper, Author, and Institution Levels
SSRN
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SSRN 2023年
作者: Yang, Alex Jie Wu, Linwei Zhang, Qi Wang, Hao Deng, Sanhong School of Information Management Nanjing University 22 Hankou Rd. Nanjing210093 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing210093 China International Joint Informatics Laboratory Nanjing University China
From the viewpoint of high-generation citation networks, h indices can be extended to global metrics at both basic and aggregated levels. To accomplish this, three types of networks are designed: the paper-paper netwo... 详细信息
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GPTR: Gestalt-Perception Transformer for Diagram Object Detection
arXiv
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arXiv 2022年
作者: Hu, Xin Zhang, Lingling Liu, Jun Fan, Jinfu You, Yang Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University China Department of Control Science and Engineering Tongji University Shanghai China Department of Computer Science National University of Singapore Singapore Lenovo Research Beijing China
Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those... 详细信息
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Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery
arXiv
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arXiv 2024年
作者: Wang, Jiaxin Zhang, Lingling Liu, Jun Guo, Tianlin Wu, Wenjun The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an710049 China
We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. GRD aims to identify unlabeled instances in existing pre-defined relations or discover novel relations by ass... 详细信息
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Reconfigurable Digital Twin from the Perspective of Smart Manufacturing Systems in Industry 4.0/5.0
Reconfigurable Digital Twin from the Perspective of Smart Ma...
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2024 International Conference on Intelligent Systems and Robotics, CISR 2024
作者: Mo, Guangyu Sun, Yuan Huang, Sihan Gao, Feng Huang, Jintang Lai, Lijuan Wang, Guoxin Yan, Yan School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Beijing Aerospace Times Optical-Electronic Technology Co. Ltd. Beijing100094 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China China Academy of Aerospace Electronic Technology Beijing100094 China School of Industrial Automation Beijing Institute of Technology Zhuhai519088 China
In industry 4.0/5.0, Smart manufacturing systems are typically complex systems that can be adjusted to deal with demand fluctuation through reconfigurable software/hardware. The emergence of digital twin as enabler te... 详细信息
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Adaptive Loose Optimization for Robust Question Answering
arXiv
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arXiv 2023年
作者: Ma, Jie Wang, Pinghui Wang, Zewei Kong, Dechen Hu, Min Han, Ting Liu, Jun The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The China Mobile Research Institute China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China
Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Curre... 详细信息
来源: 评论