检索结果-内蒙古大学图书馆

A Practical Approach to Constructing a Geological Knowledge Graph:A Case Study of Mineral Exploration Data

Journal of Earth Science 2023年第5期34卷 1374-1389页

作者： Qinjun Qiu Bin Wang Kai Ma Hairong Lü Liufeng Tao Zhong Xie Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou 450046China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural ResourcesZhengzhou 450046China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Department of Automation Tsinghua UniversityBeijing 100084China

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for *** of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new *** knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or *** graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making *** this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling *** on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic ***,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph *** research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

关键词： mineral resource report geological knowledge knowledge graph ontology hierarchical knowledge representation model

来源：评论

学校读者我要写书评

暂无评论

CASSOR: Class-Aware Sample Selection for Ordinal Regression with Noisy Labels 20th

CASSOR: Class-Aware Sample Selection for Ordinal Regression...

引用

20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Yuan, Yue Wan, Sheng Zhang, Chuang Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Security Nanjing China

ISBN: (纸本)9789819970216

Ordinal regression aims at solving the classification problem, where the categories are related in a natural order. Due to the difficulty in distinguishing between highly relevant categories, label noise is frequently present in ordinal data. Moreover, the varying degrees of relevance between categories can lead to an inconsistent distribution of misclassification loss across categories, posing a challenge to select clean data consistently from all categories for training. To overcome this limitation, we develop a sample selection method termed ‘Class-Aware Sample Selection for Ordinal Regression’ (CASSOR). To be concrete, we devise a class-specific sample selection strategy in order to adaptively acquire sufficient clean examples for robust model training. Moreover, a label-ranking regularizer is designed to help guide the sample selection process via exploring the ordinal relationship between different examples. As a result, our proposed CASSOR is endowed with strong discrimination abilities on ordinal data. Intensive experiments have been performed on multiple real-world ordinal regression datasets, which firmly demonstrates the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

Automatic depression recognition by intelligent speech signal processing:A systematic survey

引用

CAAI Transactions on Intelligence technology 2023年第3期8卷 701-711页

作者： Pingping Wu Ruihao Wang Han Lin Fanlong Zhang Juan Tu Miao Sun Jiangsu Key Laboratory of Public Project Audit School of Engineering AuditNanjing Audit UniversityNanjingChina School of Information Engineering Nanjing Audit UniversityNanjingChina Key Laboratory of Modern Acoustics(MOE) School of PhysicsNanjing UniversityNanjingChina Faculty of Electrical Engineering Mathematics&Computer ScienceDelft University of TechnologyDelftThe Netherlands

Depression has become one of the most common mental illnesses in the *** better prediction and diagnosis,methods of automatic depression recognition based on speech signal are constantly proposed and updated,with a transition from the early traditional methods based on hand‐crafted features to the application of architectures of deep *** paper systematically and precisely outlines the most prominent and up‐to‐date research of automatic depression recognition by intelligent speech signal processing so ***,methods for acoustic feature extraction,algorithms for classification and regression,as well as end to end deep models are investigated and ***,general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.

关键词： acoustic signal processing deep learning feature extraction speech depression recognition

来源：评论

学校读者我要写书评

暂无评论

Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

引用

Chinese Journal of Aeronautics 2022年第5期35卷 40-52页

作者： Quanbo GE Zhongcheng MA Jinglan LI Qinmin YANG Zhenyu LU Hong LI School of Electronics and Information Engineering Tongji UniversityShanghai 201804China Institute of Systems Science and Control Engineering School of AutomationHangzhou Dianzi UniversityHangzhou 310018China State Key Laboratory of Industrial Control Technology and the College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and TechnologyNanjing 210044China Chinese Flight Test Establishment Xi’an 710089China

Mobile robots are often subject to multiplicative noise in the target tracking tasks,where the multiplicative measurement noise is correlated with additive measurement *** this paper,first,a correlation multiplicative measurement noise model is *** is able to more accurately represent the measurement error caused by the distance sensor dependence ***,the estimated performance mismatch problem of Cubature Kalman Filter(CKF)under multiplicative noise is *** improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative *** practice,the model parameters are unknown or inaccurate,especially the correlation of noise is difficult to obtain,which can lead to a decrease in filtering accuracy or even *** address this,an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is ***,the estimated performance is analyzed theoretically,and the simulation study is conducted to validate the effectiveness of the proposed algorithm.

关键词： Adaptive CKF Correlated noise Estimation accuracy Multiplicative noise Performance analysis Target tracking

来源：评论

学校读者我要写书评

暂无评论

A Near Lossless Learned Image Coding Network Quantization Approach for Cross-Platform Inference

A Near Lossless Learned Image Coding Network Quantization Ap...

引用

2023 IEEE International Conference on Visual Communications and Image processing, VCIP 2023

作者： Hang, Xinyu Jia, Chuanmin Ma, Siwei Gao, Wen Peking University National Engineering Research Center on Visual Technology School of Computer Science China Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9798350359855

This paper presents an innovative pipeline design for the quantization of neural image codecs, ensuring robust and consistent deployment across various platform inferences. Our approach addresses a significant challenge prevalent in neural image compression networks: the lack of quantization methods for entropy probability hyper-priors, such as Gaussian mixture models (GMMs), in the existing literature. Our method serves as a solution to prevent decoding errors stemming from float-point discrepancies between the encoder and decoder, especially when executed on different platforms. By implementing a quantization scheme based on look-up tables (LUTs), we achieve precise quantization of GMM probability models with minimal performance degradation, approaching near losslessness. Moreover, our contribution offers a comprehensive quantization pipeline that facilitates dependable data transmission and harmonious synchronization of arithmetic coding algorithms between the encoder and decoder components. Empirical evidence highlights the effectiveness of our method, demonstrating seamless codec matching on diverse platforms and substantiating its capacity to enhance the integrity and security of compressed data. This work enhances the practical applicability of neural compression techniques across real-world scenarios. © 2023 IEEE.

关键词： Table lookup

来源：评论

学校读者我要写书评

暂无评论

Graph Matching Based Graph Self-Supervised Learning for Molecular Property Prediction

Graph Matching Based Graph Self-Supervised Learning for Mole...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Hongxiang Zhou, Yixiao Hu, Huiying He, Zhicheng Wu, Runzhi Lyu, Xiaoqing Beijing Jiaotong University Weihai China Tianjin University Tianjin China Wangxuan Institute of Computer Technology Peking University Beijing China State Key Laboratory of Multimedia Information Processing Peking University Beijing China

ISBN: (纸本)9798350386226

In the field of graph representation learning for molecular property prediction, self-supervised learning is highly appreciated for its ability to mine potential connections in unlabeled data. However, existing approaches such as neighborhood prediction or contrastive learning often lack in fusing structural and semantic features of molecular graphs. To address this issue, we propose a Graph Matching Based Graph Self-Supervised Learning (GMSSL) framework, which aims to comprehensively learn the structure and semantics of molecular graphs. Specifically, utilizing intra-graph and inter-graph convolution and graph matching methods, GMSSL is able to encode complex information for efficient training without supervision. After pre-training and fine-tuning, GMSSL demonstrates powerful molecular property prediction on multiple datasets. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

ChatGPT: potential, prospects, and limitations

引用

Frontiers of information technology & Electronic Engineering 2024年第1期25卷 6-11页

作者： Jie ZHOU Pei KE Xipeng QIU Minlie HUANG Junping ZHANG School of Computer Science Fudan UniversityShanghai 200433China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433China

Recently,OpenAI released Chat Generative Pre-trained Transformer(ChatGPT)(Schulman et al.,2022)(https://***),which has attracted considerable attention from the industry and academia because of its impressive *** is the first time that such a variety of open tasks can be well solved within one large language *** better understand ChatGPT,we briefly introduce its history,discuss its advantages and disadvantages,and point out several potential ***,we analyze its impact on the development of trustworthy artificial intelligence,conversational search engine,and artificial general intelligence.

关键词： artificial limitations intelligence

来源：评论

学校读者我要写书评

暂无评论

Topic-BiGRU-U-Net for Document-level Relation Extraction from Biomedical Literature

Topic-BiGRU-U-Net for Document-level Relation Extraction fro...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Yali Yan, Rong Inner Mongolia University College of Computer Science Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (纸本)9798350337488

Document-level biomedical relation extraction refers to extract relationship facts from unstructured biomedical literature. Due to the fact that many relationship facts span multiple sentences and involve complex interactions between entities, it requires models with strong logical reasoning capabilities. However, previous methods have had limitations in terms of logical reasoning, such as lack logical reasoning and the entities merely based on a single granularity. Although other efforts are based on multiple granularity, including mention pairs and entity pairs, the inference mechanism is weak. In this paper, we propose a module to enhance the model's logical reasoning capabilities from four kinds of granularity (mention, entity, mention pairs, and entity pairs). Specifically, we encode the entities and mentions separately using BiGRU to capture contextual features. Simultaneously, we employ a U-Net network to model entity pairs and mention pairs, and enhance inter-sentence reasoning abilities. Additionally, to support the model's reasoning capabilities, we introduce topic nodes into the traditional heterogeneous graph to fully extract document information. Experimental results on CDR and GDA datasets in the biomedical field demonstrate the outstanding performance of the proposed model. © 2023 IEEE.

关键词： BiGRU document-level relation extraction information extraction U-Net

来源：评论

学校读者我要写书评

暂无评论

A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition 48

A Generalized Subspace Distribution Adaptation Framework for...

引用

48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Shaokai Song, Peng Ji, Liang Jin, Yun Zheng, Wenming Yantai University School of Computer and Control Engineering China Jiangsu Normal University School of Physics and Electronic Engineering China Research Center for Learning Science Southeast University Key Laboratory of Child Development and Learning Science Southeast University Ministry of Education China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9781728163277

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches. © 2023 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

A Spatio-Temporal Transformer for Enhancing the Coherence on 3D Human Motion Prediction 18th

A Spatio-Temporal Transformer for Enhancing the Coherence ...

引用

18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xie, Miner Wang, Lei Liu, Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9789819610709

Human motion prediction is of great importance for artificial intelligence systems, particularly in fields like autonomous driving and human-computer interaction. Existing methods have achieved good results in simple motions with small changes but still struggle with coherence in predicting complex motions with large changes in a short time. Regarding the issue in current methods, this paper proposes a novel network composed of three modules: spatial encoders that captures correlations between joints, temporal encoders that models the movement patterns of joints across frames, and a multi-frame temporal encoder that learns high-level temporal correlations to enhance prediction coherence. In addition, we propose to use Weighted Mean Per-Joint Position Error (WMPJPE) as the loss function by considering that different joints of the human skeleton have different effects on the entire action. We evaluate our method on the Human3.6M, AMASS, and 3DPW datasets. The quantitative results indicate that our method notably outperforms other methods in the prediction range of 160ms to 560ms. The qualitative results show that our method can improve the coherence of prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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：