检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Wang, Nan Li, Jiwei Meng, Yuxian Sun, Xiaofei Qiu, Han Wang, Ziyao Wang, Guoyin He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China China Shannon.AI China Zhejiang University China Tsinghua University China University of Leeds United Kingdom Amazon Alexa AI United States

Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we propose to use the machine reading comprehension (MRC) framework to bridge this gap. We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense. The chosen predicate sense is then used to determine the semantic roles for that predicate, and these semantic roles are used to construct the query for another MRC model for argument labeling. In this way, we are able to leverage both the predicate semantics and the semantic role semantics for argument labeling. We also propose to select a subset of all the possible semantic roles for computational efficiency. Experiments show that the proposed framework achieves state-of-the-art or comparable results to previous work. Code is available at https://***/ShannonAI/MRC-SRL. © 2021, CC BY.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Research on government information sharing model using blockchain technology 10

Research on government information sharing model using block...

引用

10th International Conference on information Technology in Medicine and Education, ITME 2019

作者： Zhang, Yiwei Deng, Sanhong Zhang, Yue Kong, Jia School of Information Management Nanjing University Jiangsu Key Laboratory of Data Engineering and Knowledge Service. Nanjing China

ISBN: (纸本)9781728139173

Research Purpose: The distributed, traceable and security of blockchain technology are applicable to the construction of new government information resource models, which could eliminate the barn effect and trust in government information sharing, as well as promoting the transformation of government affairs from management to service, it is also of great significance to the sharing of government information and construction of service-oriented e-government. Propose Methods: By analyzing the current problems of government information sharing, combined with literature research, this paper proposes the theoretical framework and advantages of blockchain technology applied to government information management and sharing, expounds the blockchain-based solution, it also constructs a government information sharing model based on blockchain, and gives implementation strategies at the technical and management levels. Results and Conclusion: The government information sharing model based on the blockchain solution and the transparency of government information can be used as a research framework for information interaction analysis between the government and users. It can also promote the construction and development of information sharing for Chinese government, as well as providing unified information sharing solution at the departmental and regional levels for e-government. © 2019 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

I-mRMR: Incremental max-relevance, and min-redundancy feature selection 3rd

I-mRMR: Incremental max-relevance, and min-redundancy featur...

引用

3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 2019

作者： Xiu, Yeliang Zhao, Suyun Chen, Hong Li, Cuiping Information of School Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China

ISBN: (纸本)9783030260743

An incremental method of feature selection based on mutual information, called incremental Max-Relevance, and Min-Redundancy (I-mRMR), is presented. I-mRMR is an incremental version of Max-Relevance, and Min-Redundancy feature selection (mRMR), which is used to handle streaming data or large-scale data. First, Incremental Key Instance Set is proposed which composes of the non-distinguished instances by the historical selected features. Second, an incremental feature selection algorithm is designed in which the incremental key instance set, replacing of all the seen instances so far, is used in the process of adding representative features. Since the Incremental Key Instance Set is far less than the whole instances, the incremental feature selection by using this key set avoids redundant computation and save computation time and space. Finally, the experimental results show that I-mRMR could significantly or even dramatically reduce the time of feature selection with an acceptable classification accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

The minority matters: a diversity-promoting collaborative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

引用

Proceedings of the 36th International Conference on Neural information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Recommending courses in MOOCs for jobs: An auto weak supervision approach

arXiv

引用

arXiv 2020年

作者： Hao, Bowen Zhang, Jing Li, Cuiping Chen, Hong Yin, Hongzhi Key Laboratory of Data Engineering Knowledge Engineering of Ministry of Education School of Information Renmin University of China China School of Information Technology and Electrical Engineering The University of Queensland Australia

The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who take MOOCs to find new jobs. Despite the advances of supervised ranking models, the lack of enough supervised signals prevents us from directly learning a supervised ranking model. This paper proposes a general automated weak supervision framework (AutoWeakS) via reinforcement learning to solve the problem. On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models. On the other hand, the framework enables automatically searching the optimal combination of these supervised and unsupervised models. Systematically, we evaluate the proposed model on several datasets of jobs from different recruitment websites and courses from a MOOCs platform. Experiments show that our model significantly outperforms the classical unsupervised, supervised and weak supervision baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Stacked convolutional sparse auto-encoders for representation learning

引用

ACM Transactions on knowledge Discovery from data 2021年第2期15卷 31-31页

作者： Zhu, Yi Li, Lei Wu, Xindong School of Information Engineering Yangzhou University China School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

Deep learning seeks to achieve excellent performance for representation learning in image datasets. However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information. Meanwhile, the redundancy of image data incurs performance degradation on representation learning for aforementioned models. To address these problems, we propose a semi-supervised deep learning framework called stacked convolutional sparse auto-encoder, which can learn robust and sparse representations from image data with fewer labeled data records. More specifically, the framework is constructed by stacking layers. In each layer, higher layer feature representations are generated by features of lower layers in a convolutional way with kernels learned by a sparse auto-encoder. Meanwhile, to solve the data redundance problem, the algorithm of Reconstruction Independent Component Analysis is designed to train on patches for sphering the input data. The label information is encoded using a Softmax Regression model for semi-supervised learning. With this framework, higher level representations are learned by layers mapping from image data. It can boost the performance of the base subsequent classifiers such as support vector machines. Extensive experiments demonstrate the superior classification performance of our framework compared to several state-of-the-art representation learning methods. © 2021 ACM.

关键词： Representation learning Sparse auto-encoder

来源：评论

学校读者我要写书评

暂无评论

Access control of blockchain based on dual-policy attribute-based encryption

Access control of blockchain based on dual-policy attribute-...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Daojun Han Jinyu Chen Lei Zhang Yatian Shen Xueheng Wang Yihua Gao Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China

In scenarios where multiple parties such as the Internet of Things and Supply Chains participate in data sharing and computing, when accessing data, users not only need to accept the forward access control of the data owner, but also needs to perform reverse access control on the data, so as to realize two-way access control. In the traditional blockchain, all users can participate in accounting and view transaction data, and only protect user privacy through “pseudo-anonymity”. The access rights of different users cannot be distinguished, which cannot meet the user's two-way access control needs. However, most of the existing blockchain-based access control schemes are one-way access control, which cannot meet the needs of users for two-way access control in scenarios such as the Internet of Things and Supply Chains. Therefore, it is particularly important to design a two-way access control mechanism suitable for application in the blockchain. On this basis, this paper proposes a dual strategy attribute-based encryption (ABE) scheme for distributed outsourcing. This scheme combines two existing schemes, ciphertext-policy ABE and key-policy ABE, and proposes two access structures and a structure of attribute sets. The primary access structure and the secondary attributes are stored in the ciphertext, and the secondary access structure and the primary attributes are stored in the user's private key. Only when the primary attribute set satisfies the primary access structure and the secondary attribute set satisfies the secondary access structure can the user unlock the ciphertext. This scheme has no single authorization center; instead, blockchain nodes jointly participate in authorization. In addition, the proposed scheme outsources the encryption and decryption of the ciphertext to blockchain nodes to reduce the computing pressure on users and can adapt to the decentralized environment of the blockchain and provide users with two-way access control services. Finally

关键词： Authorization data privacy High performance computing Conferences Supply chains Blockchain Permission

来源：评论

学校读者我要写书评

暂无评论

Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous information Networks

引用

IEEE Transactions on Multimedia 2021年 23卷 2019-2032页

作者： Lei Sang Min Xu Shengsheng Qian Matt Martin Peter Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia Institute of Automation Chinese Academy of Sciences Beijing China INTERACT Technology Sydney NSW Australia Mininglamp Academy of Sciences Mininglamp Technology Beijing China

With the emergence of online social networks (OSNs), video recommendation has come to play a crucial role in mitigating the semantic gap between users and videos. Conventional approaches to video recommendation primarily focus on exploiting content features or simple user-video interactions to model the users’ preferences. Although these methods have achieved promising results, they fail to model the complex video context interdependency, which is obscure/hidden in heterogeneous auxiliary data from OSNs. In this paper, we study the problem of video recommendation in Heterogeneous information Networks (HINs) due to its excellence in characterizing heterogeneous and complex context information. We propose a Context-Dependent Propagating Recommendation network (CDPRec) to obtain accurate video embedding and capture global context cues among videos in HINs. The CDPRec can iteratively propagate the contexts of a video along links in a graph-structured HIN and explore multiple types of dependencies among the surrounding video nodes. Then, each video is represented as the composition of the multimodal content feature and global dependency structure information using an attention network. The learned video embedding with sequential based recommendation are jointly optimized for the final rating prediction. Experimental results on real-world YouTube video recommendation scenarios demonstrate the effectiveness of the proposed methods compared with strong baselines.

关键词： Semantics Collaboration YouTube Australia Visualization Context modeling

来源：评论

学校读者我要写书评

暂无评论

LadRa-Net: Locally-aware dynamic re-read attention net for sentence semantic matching

arXiv

引用

arXiv 2021年

作者： Zhang, Kun Lv, Guangyi Wu, Le Chen, Enhong Liu, Qi Wang, Meng Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology Anhui Hefei230029 China AI Lab at Lenovo Research Beijing100094 China University of Science and Technology of China Hefei230026 China

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Language Inference (NLI), Paraphrase Identification (PI), and so on. Much recent progress has been made in this area, especially attention-based methods and pre-trained language model based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of attention mechanism. In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations. Based on this attention variation, we develop a novel Dynamic Re-read Network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in dynamic re-read attention seems insufficient for sentence semantics, and employing pre-trained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRr-Net to Locally-Aware Dynamic Re-read Attention Net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of Byte-Pair Encoding (BPE) in pre-trained language models and boost the performance of dynamic reread attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research. © 2021, CC BY.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

knowledge Graph for China’s Genealogy

Knowledge Graph for China’s Genealogy

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Tingting Jiang Yi Zhu Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Information Engineering Yangzhou University Yangzhou China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical roots, and explore the origins of a family more easily. However, the multi-type, multisource dynamic changes and specialized nature of genealogical data bring challenges to the development of contemporary knowledge graph models. Applying existing methods to genealogical data can result in problems of overlooking certain specialized vocabulary and dynamic properties such as personal experiences. In this paper, we propose a genealogical knowledge graph model GKGM that combines HAO intelligence (h uman intelligence + a rtificial intelligence + o rganizational intelligence) and ontology granularity division technology to address the above problems. Furthermore, a method of applying the model to construct genealogical knowledge graphs is demonstrated, and an experiment conducted on a real-world genealogical dataset verifies the feasibility and effectiveness of the model.

关键词： Ontologies data models Blood data mining Biological system modeling History

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：