检索结果-内蒙古大学图书馆

International Joint Conference on Neural Networks (IJCNN)

作者： Xiulan Yuan Xuegang Hu Peipei Li Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei City China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei City China

Feature selection for multi-source multi-label data has attracted much attention, because there are a lot of scenarios that produce multiple source data with multi-labels in the real-world applications, which aggravates the problems of dimensional disaster and the label skewness. However, most of existing multi-source feature selection methods miss the multi-label issue while existing multi-label feature selection methods can not select the optimal feature set in the multi-source environment. Motivated by this, we propose a novel feature selection method, called MSMLFS. To be specific, the Inf-FS algorithm is firstly introduced to handle multi-label feature selection for each data source, which considers the label weight in the feature selection. Secondly, the over-sampling mechanism and the inter-source feature fusion method are used to handle the label skewness of multi-label data and the feature selection in multiple sources respectively. Finally, extensive experiments conducted on synthetic and real-world multi-source multi-label data sets demonstrate that the proposed method outperforms several state-of-the-art multi-source or multi-label feature selection methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims

arXiv

引用

arXiv 2024年

作者： Zhou, Shu Wang, Xin Zhou, Zhengda Yi, Haohan Zheng, Xuhui Wang, Hao School of Information Management Nanjing University China Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Provincial Universities Nanjing University China Baidu Inc. Beijing China

In order to solve the problem of insufficient generation quality caused by traditional patent text abstract generation models only originating from patent specifications, the problem of new terminology OOV caused by rapid patent updates, and the problem of information redundancy caused by insufficient consideration of the high professionalism, accuracy, and uniqueness of patent texts, we proposes a patent text abstract generation model (MSEA) based on a master-slave encoder architecture;Firstly, the MSEA model designs a master-slave encoder, which combines the instructions in the patent text with the claims as input, and fully explores the characteristics and details between the two through the master-slave encoder;Then, the model enhances the consideration of new technical terms in the input sequence based on the pointer network, and further enhances the correlation with the input text by re weighing the "remembered" and "for-gotten" parts of the input sequence from the encoder;Finally, an enhanced repetition suppression mechanism for patent text was introduced to ensure accurate and non redundant abstracts generated. On a publicly available patent text dataset, compared to the state-of-the-art model, Improved Multi-Head Attention Mechanism (IMHAM), the MSEA model achieves an improvement of 0.006, 0.005, and 0.005 in Rouge-1, Rouge-2, and Rouge-L scores, respectively. MSEA leverages the characteristics of patent texts to effectively enhance the quality of patent text generation, demonstrating its advancement and effectiveness in the experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Specifications

来源：评论

学校读者我要写书评

暂无评论

Autonomous Human-Robot Collaborative Assembly Method Driven by the Fusion of Large Language Model and Digital Twin

Autonomous Human-Robot Collaborative Assembly Method Driven ...

引用

2024 International Conference on Intelligent Systems and Robotics, CISR 2024

作者： Chen, Jianpeng Luo, Haiwei Huang, Sihan Zhang, Meidi Wang, Guoxin Yan, Yan Jing, Shikai School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Capital Aerospace Machinery Corporation Limited Beijing100076 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China China North Artificial Intelligence & Innovation Research Institute Beijing100071 China

Human-robot collaboration (HRC) plays an important role in human-centric manufacturing, which requires cooperative robots to have the ability of collaborate with human autonomously. It is very complex to understand the intention of human during the assembly process, therefore, we proposed a method of autonomous HRC assembly driven by the fusion of large language model (LLM) and digital twin in this paper. The assembly state is recognized from two perspectives, including the perception of key parts based on transfer learning and YOLO, and perceive operator actions based on LSTM and attention mechanism. In order to improve the autonomy of HRC, a collaborative task decision method driven by fine-tuning LLM based on assembly domain knowledge is proposed. A case study of reducer assembly is presented to verify the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative robots

来源：评论

学校读者我要写书评

暂无评论

Image manipulation detection by multi-view multi-scale supervision

arXiv

引用

arXiv 2021年

作者： Chen, Xinru Dong, Chengbo Ji, Jiaqi Cao, Juan Li, Xirong MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AIMC Lab School of Information Renmin University of China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Media Convergence Production Technology and Systems

The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images. Current research emphasizes the sensitivity, with the specificity overlooked. In this paper we address both aspects by multi-view feature learning and multi-scale supervision. By exploiting noise distribution and boundary artifact surrounding tampered regions, the former aims to learn semantic-agnostic and thus more generalizable features. The latter allows us to learn from authentic images which are nontrivial to be taken into account by current semantic segmentation network based methods. Our thoughts are realized by a new network which we term MVSS-Net. Extensive experiments on five benchmark sets justify the viability of MVSS-Net for both pixel-level and image-level manipulation detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

arXiv

引用

arXiv 2022年

作者： Fan, Zhaoxin Song, Zhenbo Xu, Jian Wang, Zhicheng Wu, Kejian Liu, Hongyan He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Management Science and Engineering Tsinghua University Beijing100084 China Nreal China

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Image Manipulation Detection by Multi-View Multi-Scale Supervision

Image Manipulation Detection by Multi-View Multi-Scale Super...

引用

International Conference on Computer Vision (ICCV)

作者： Xinru Chen Chengbo Dong Jiaqi Ji Juan Cao Xirong Li MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AIMC Lab School of Information Renmin University of China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Media Convergence Production Technology and Systems

ISBN: (纸本)9781665428132

关键词： Representation learning Image segmentation Computer vision Sensitivity Costs Image edge detection Semantics

来源：评论

学校读者我要写书评

暂无评论

A property of reductions in Fuzzy Variable Precision Rough Set model

A property of reductions in Fuzzy Variable Precision Rough S...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Eric C. C. Tsang Su-Yun Zhao Cai-Li Zhou Faculty of Information Technology Macau University of Science and Technology Taipa Macao China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China School of Mathematics and Computer Sciences Hebei University China

In this paper, we use strict mathematics reasoning to discover the relation between the threshold and reduction in Fuzzy Variable Precision Rough Sets (FVPRS), i.e., the reductions act as a nested structure with the monotonously increasing threshold. By using the nested structure of reductions, we could design algorithms to quickly find different reductions when a reduction is required. Here `different' means the reductions obtained using different thresholds.

关键词： Rough sets Approximation methods Fuzzy sets Educational institutions Computational modeling Machine learning Cybernetics

来源：评论

学校读者我要写书评

暂无评论

Review of graph construction and graph learning in stock price prediction

引用

Procedia Computer Science 2022年 214卷 771-778页

作者： Yunong Wang Yi Qu Zhensong Chen Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Management and Engineering Capital University of Economics and Business Beijing 100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks.

关键词： Stock price prediction Graph construction Graph learning data structure

来源：评论

学校读者我要写书评

暂无评论

Chinese integral word segmentation recognition based on reliability

引用

Journal of Information and Computational Science 2009年第1期6卷 533-542页

作者： Wan, Changxuan Wang, Fang School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China School of Humanities Nanchang University Nanchang 330013 China

As one part of preprocessing, automatic word segmentation is an key issue in Chinese information retrieval. Since integral words are put wholly together to compose into the more meaningful words and more express users' query minds in information retrieval, we combine the calculation of mutual information, prefix- and-suffix information of integral word with the reliability of composing into the integral word, and put forward three Chinese integral word segmentation recognition method based on reliability according to the characteristics of the integral words. Then, we design and realize the prototype system of three Chinese integral word segmentation recognition methods based on reliability. Finally, tests and analysis on 2nd SIGHAN (2005) PKU test corpus show that the recognition performance of the system is good, and can satisfy different needs at the same time. © 2009 Binary Information Press.

关键词： Reliability

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：