检索结果-内蒙古大学图书馆

Interprovincial trade, economic development and the impact on air quality in China

RESOURCES CONSERVATION AND RECYCLING 2019年 142卷 204-214页

作者： Zeng, Bohan Wu, Tian Guo, Xiaoxian Renmin Univ China Sch Econ Beijing Peoples R China Chinese Acad Sci Acad Math & Syst Sci NCMIS Beijing Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China China Petr Technol & Dev Corp Beijing Peoples R China

Air pollution is becoming a serious problem for China and it has attracted great attention in recent years. This paper examines the relationship between interprovincial trade patterns, economic development and impacts on air quality. Using detailed data from 2004 to 2016 on interprovincial railway freight in China, the inverted u-shaped environmental Kuznets curve and the pollution haven hypothesis are assessed. The impact of interprovincial trade on the emissions of sulfur dioxide are estimated on the basis of controlling the industrial structure, regional foreign direct investment and motor vehicles retained. Evidence of pollution transfers is found, and such effects appear to be stronger between adjacent regions rather than nonadjacent regions, regardless of the roles of other explanatory variables. Our conclusions and policy implications will assist government decision-making regarding resource reallocation and sustainable development of green cities.

关键词： Air pollution Interprovincial trade Environmental Kuznets Curve Pollution haven hypothesis Resource reallocation

来源：评论

学校读者我要写书评

暂无评论

Survey on Classic and Latest Textual Sentiment Analysis Articles and Techniques

引用

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2019年第4期18卷 1243-1287页

作者： Shi, Yong Zhu, Luyao Li, Wei Gao, Kun Zheng, Yuanchun Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Text is a typical example of unstructured and heterogeneous data in which massive useful knowledge is embedded. Sentiment analysis is used to analyze and predict sentiment polarities of the text. This paper provides a survey and gives comparative analyses of the latest articles and techniques pertaining to lexicon-based, traditional machine learning-based, deep learning-based, and hybrid sentiment analysis approaches. These approaches have their own superiority and get the state-of-the-art results on diverse sentiment analysis tasks. Besides, a brief sentiment analysis example in the tourism domain is displayed, illustrating the entire process of sentiment analysis. Furthermore, we create a large table to compare the pros and cons of different types of approaches, and discuss some insights with respect to research trends. In addition, a lot of important sentiment analysis datasets are summarized in this survey.

关键词： Sentiment analysis sentiment lexicon hybrid approach deep learning

来源：评论

学校读者我要写书评

暂无评论

Multi-view transfer learning with privileged learning framework

引用

NEUROCOMPUTING 2019年 335卷 131-142页

作者： He, Yiwei Tian, Yingjie Liu, Dalian Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100090 Peoples R China Chinese Aacdemy Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Aacdemy Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing 100101 Peoples R China

In this paper, we present a multi-view transfer learning model named Multi-view Transfer Discriminative Model (MTDM) for both image and text classification tasks. Transfer learning, which aims to learn a robust classifier for the target domain using data from a different distribution, has been proved to be effective in many real-world applications. However, most of the existing transfer learning methods map across domain data into a high-dimension space which the distance between domains is closed. This strategy always fails in the multi-view scenario. On the contrary, the multi-view learning methods are also difficult to extend in the transfer learning settings. One of our goals in this paper is to develop a model which can perform better in both multi-view and transfer learning settings. On the one hand, the problem of multi-view is implemented by the paradigm of learning using privileged information (LUPI), which could guarantee the principle of complementary and consensus. On the other hand, the model adequately utilizes the source domain data to build a robust classifier for the target domain. We evaluate our model on both image and text classification tasks and show the effectiveness compared with other baseline approaches. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-view learning Transfer learning Learning using privileged information Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Coupling privileged kernel method for multi-view learning

引用

INFORMATION SCIENCES 2019年 481卷 110-127页

作者： Tang, Jingjing Tian, Yingjie Liu, Dalian Kou, Gang Southwestern Univ Finance & Econ Sch Business Adm Chengdu 611130 Sichuan Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing 100101 Peoples R China

Multi-view learning concentrates on fully using the data collected from diverse domains or obtained from various feature extractors to learn effectively. The consensus and complementarity principles provide significant guidance in multi-view modeling. Many support vector machine (SVM)-based multi-view learning models have been proposed, which mainly follow the consensus principle through exploiting the label correlation with regularization terms. In this paper, we propose a simple yet effective coupling privileged kernel method for multi-view learning, termed as MCPK. The coupling term included in the primal objective allows the combination of the errors from all views to be minimized, which guarantees the consensus principle. Similar to our previous work PSVM-2V, MCPK realizes the complementarity principle by applying the learning using privileged information (LUPI) paradigm. The proposed model not only fully integrates the information from all views in the learning process, but also maintains the characteristic of different views to some extent. We employ the standard quadratic programming solver to solve MCPK. Further more, we theoretically analyze the performance of MCPK from the viewpoints of the generalization capability and the PSVM-2V and SVM-2K models. Experimental results demonstrate that MCPK compares more favorably than other state-of-the-art multi-view algorithms in terms of classification accuracy and efficiency. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Multi-view learning Coupling term Privileged information Support vector machine Consensus and complementarity principles

来源：评论

学校读者我要写书评

暂无评论

Cascaded partial decoder for fast and accurate salient object detection

arXiv

引用

arXiv 2019年

作者： Wu, Zhe Su, Li Huang, Qingming Beijing China Key Lab of Big Data Mining and Knowledge Management UCAS Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Existing state-of-the-art salient object detection networks rely on aggregating multi-level features of pretrained convolutional neural networks (CNNs). Compared to high-level features, low-level features contribute less to performance but cost more computations because of their larger spatial resolutions. In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection. On the one hand, the framework constructs partial decoder which discards larger resolution features of shallower layers for acceleration. On the other hand, we observe that integrating features of deeper layers obtain relatively precise saliency map. Therefore we directly utilize generated saliency map to refine the features of backbone network. This strategy efficiently suppresses distractors in the features and significantly improves their representation ability. Experiments conducted on five benchmark datasets exhibit that the proposed model not only achieves state-of-the-art performance but also runs much faster than existing models. Besides, the proposed framework is further applied to improve existing multi-level feature aggregation models and significantly improve their efficiency and accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

A sensitive-eigenvector based global algorithm for quadratically constrained quadratic programming

引用

JOURNAL OF GLOBAL OPTIMIZATION 2019年第2期73卷 371-388页

作者： Lu, Cheng Deng, Zhibin Zhou, Jing Guo, Xiaoling North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Sch Econ & Management Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Zhejiang Univ Technol Dept Appl Math Coll Sci Hangzhou 310023 Zhejiang Peoples R China China Univ Min & Technol Dept Math Beijing 100083 Peoples R China

In this paper, we design an eigenvalue decomposition based branch-and-bound algorithm for finding global solutions of quadratically constrained quadratic programming (QCQP) problems. The hardness of nonconvex QCQP problems roots in the nonconvex components of quadratic terms, which are represented by the negative eigenvalues and the corresponding eigenvectors in the eigenvalue decomposition. For certain types of QCQP problems, only very few eigenvectors, defined as sensitive-eigenvectors, determine the relaxation gaps. We propose a semidefinite relaxation based branch-and-bound algorithm to solve QCQP. The proposed algorithm, which branches on the directions of the sensitive-eigenvectors, is very efficient for solving certain types of QCQP problems.

关键词： Quadratically constrained quadratic programming Semidefinite relaxation Branch-and-bound algorithm Global optimization

来源：评论

学校读者我要写书评

暂无评论

RETRACTED: Cross-Domain End-To-End Aspect-Based Sentiment Analysis with Domain-Dependent Embeddings (Retracted Article)

引用

COMPLEXITY 2021年第1期2021卷

作者： Tian, Yingjie Yang, Linrui Sun, Yunchuan Liu, Dalian Univ Chinese Acad Sci Sch Econ & Management 80 Zhongguancun East Rd Beijing 100190 Peoples R China Chinese Acad Sci China Res Ctr Fictitious Econ & Data Sci 80 Zhongguancun East Rd Beijing 100190 Peoples R China Chinese Acad Sci China Key Lab Big Data Min & Knowledge Management 80 Zhongguancun East Rd Beijing 100190 Peoples R China Beijing Normal Univ Business Sch Int Inst Big Data Finance Beijing 100875 Peoples R China Beijing Union Univ Inst Math & Phys Beijing 100101 Peoples R China Beijing Union Univ Inst Fundamental & Interdisciplinary Sci Beijing 100101 Peoples R China

With the development of sentiment analysis, studies have been gradually classified based on different researched candidates. Among them, aspect-based sentiment analysis plays an important role in subtle opinion mining for online reviews. It used to be treated as a group of pipeline tasks but has been proved to be analysed well in an end-to-end model recently. Due to less labelled resources, the need for cross-domain aspect-based sentiment analysis has started to get attention. However, challenges exist when seeking domain-invariant features and keeping domain-dependent features to achieve domain adaptation within a fine-grained task. This paper utilizes the domain-dependent embeddings and designs the model CD-E2EABSA to achieve cross-domain aspect-based sentiment analysis in an end-to-end fashion. The proposed model utilizes the domain-dependent embeddings with a multitask learning strategy to capture both domain-invariant and domain-dependent knowledge. Various experiments are conducted and show the effectiveness of all components on two public datasets. Also, it is also proved that as a cross-domain model, CD-E2EABSA can perform better than most of the in-domain ABSA methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adaptive FH-SVM for Imbalanced Classification

引用

IEEE ACCESS 2019年 7卷 130410-130422页

作者： Wang, Qi Tian, Yingjie Liu, Dalian Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictit Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing 100101 Peoples R China

Support vector machines (SVMs), powerful learning methods, have been popular among machine learning researches due to their strong performance on both classification and regression problems. However, traditional SVM making use of Hinge Loss cannot deal with class imbalance problems, because it applies the same weight of loss to each class. Recently, Focal Loss has been widely used for deep learning to address the imbalanced datasets. The significant effectiveness of Focal loss attracts the attention in many fields, such as object detection, semantic segmentation. Inspired by Focal loss, we reconstructed Hinge Loss with the scaling factor of Focal loss, called FH Loss, which not only deals with the class imbalance problems but also preserve the distinctive property of Hinge loss. Owing to the difficulty of the trade-off between positive and negative accuracy in imbalanced classification, FH loss pays more attention on minority class and misclassified instances to improve the accuracy of each class, further to reduce the influence of imbalance. In addition, due to the difficulty of solving SVM with FH loss, we propose an improved model with modified FH loss, called Adaptive FH-SVM. The algorithm solves the optimization problem iteratively and adaptively updates the FH loss of each instance. Experimental results on 31 binary imbalanced datasets demonstrate the effectiveness of our proposed method.

关键词： Focal loss hinge loss class imbalance support vector machines (SVMs)

来源：评论

学校读者我要写书评

暂无评论

Feature selection with MCP² regularization

引用

NEURAL COMPUTING & APPLICATIONS 2019年第10期31卷 6699-6709页

作者： Shi, Yong Miao, Jianyu Niu, Lingfeng Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA Univ Chinese Acad Sci Sch Math Sci Beijing 100490 Peoples R China

Feature selection, as a fundamental component of building robust models, plays an important role in many machine learning and data mining tasks. Recently, with the development of sparsity research, both theoretical and empirical studies have suggested that the sparsity is one of the intrinsic properties of real world data and sparsity regularization has been applied into feature selection models successfully. In view of the remarkable performance of non-convex regularization, in this paper, we propose a novel non-convex yet Lipschitz continuous sparsity regularization term, named MCP2, and apply it into feature selection. To solve the resulting non-convex model, a new algorithm in the framework of the ConCave-Convex Procedure is given at the same time. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method.

关键词： Feature selection sparsity regularization MCP CCCP

来源：评论

学校读者我要写书评

暂无评论

Three IQs of AI systems and their testing methods

引用

JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 566-571页

作者： Liu, Feng Liu, Ying Shi, Yong Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

The rapid development of artificial intelligence (AI) has brought the AI threat theory as well as the problem about how to evaluate the intelligence level of intelligent products. Both need to find a quantitative method to evaluate the intelligence level of intelligence systems, including human intelligence. Based on the standard intelligence system and the extended Von Neumann architecture, this study proposes General intelligence quotient (IQ), Service IQ and Value IQ evaluation methods for intelligence systems, depending on different evaluation purposes. Among them, the General IQ of intelligence systems is to answer the question of whether 'the artificial intelligence can surpass the human intelligence', which is reflected in putting the intelligence systems on an equal status and conducting the unified evaluation. The Service IQ and Value IQ of intelligence systems are used to answer the question of 'how the intelligent products can better serve the human', reflecting the intelligence and required cost of each intelligence system as a product in the process of serving human.

关键词： artificial intelligence computer architecture extended Von Neumann architecture testing methods AI system intelligence level evaluation general intelligence quotient Value IQ evaluation methods Service IQ standard intelligence system human intelligence intelligent products artificial intelligence

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：