检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Jiang, Lingyun Niu, Xiaofu Xu, Jia Yang, Dejun Xu, Lijie Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China Department of Computer Science Colorado School of Mines GoldenCO80401 United States

Crowdsourcing has become an efficient paradigm for performing large scale tasks. Truth discovery and incentive mechanism are fundamentally important for the crowdsourcing system. Many truth discovery methods and incentive mechanisms for crowdsourcing have been proposed. However, most of them cannot be applied to deal with the crowdsourcing with copiers. To address the issue, we formulate the problem of maximizing the social welfare such that all tasks can be completed with the least confidence for truth discovery. We design an incentive mechanism consisting truth discovery stage and reverse auction stage. In truth discovery stage, we estimate the truth for each task based on both the dependence and accuracy of workers. In reverse auction stage, we design a greedy algorithm to select the winners and determine the payment. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Moreover, our truth discovery method shows prominent advantage in terms of precision when there are copiers in the crowdsourcing systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Understanding the urban mobility community by taxi travel trajectory” [Commun. Nonlinear Sci. Numer. Simul. 101(2021) 105863/6]

引用

Communications in Nonlinear Science and Numerical Simulation 2022年 104卷

作者： Wei-Peng Nie Zhi-Dan Zhao Shi-Min Cai Tao Zhou Complex Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 610054 China Complexity Computation Laboratory Department of Computer Science School of Engineering Shantou University Shantou 515063 China Key Laboratory of Intelligent Manufacturing Technology (Ministry of Education) Shantou University Shantou 515063 China

来源：评论

学校读者我要写书评

暂无评论

Automatic Recognition of Business Process Images

Automatic Recognition of Business Process Images

引用

IEEE International Conference on Computer Science and Educational Informatization (CSEI)

作者： Qingyi Kang Yong Yu Fei Dai School of Software Yunnan University Kunming China School of Software Key Laboratory in Software Engineering of Yunnan Province Yunnan University Kunming China School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (纸本)9781728123097

The Business Process Modelling Notation (BPMN) is a defacto standard for modelling business processes in the early phases of systems development. The increased adoption of business process management approaches has led organizations to model large collections of BPMN business process models. The mix of constructs in BPMN business process models can lead to semantic error. The ability to take a picture of BPMN business process models and analyze the semantic correctness of BPMN business process images is thus a desirable feature. In this paper, we propose an automatic recognition method to transform BPMN business process images into Petri nets, for which efficient analysis techniques are available. The proposed recognition has been implemented as a tool, in conjunction with existing Petri net-based tools, that enables the soundness analysis of business process images. This transformation can led to the identification of deficiencies in the BPMN business process images.

关键词： Task analysis Petri nets Logic gates Image edge detection Semantics

来源：评论

学校读者我要写书评

暂无评论

Distributed Local Bayesian Network for Gene Regulatory Network Reconstruction

Distributed Local Bayesian Network for Gene Regulatory Netwo...

引用

International Conference on big data Computing and Communications (bigCOM)

作者： Luxuan Qu Zhiqiong Wang Yueyang Huo Yuezhou Zhou Junchang Xin Wei Qian College of Medicine and Biological Information Engineering Northeastern University Shenyang China College of Medicine and Biological Information Engineering Northeastern University Neusoft Research of Intelligent Healthcare Technology Co. Ltd. Shenyang China School of Computer Science and Engineering Key Laboratory of Big Data Management and Analytics (Liaoning Province) Northeastern University Shenyang China College of Engineering The University of Texas at El Paso El Paso USA

ISBN: (数字)9781728182759

ISBN: (纸本)9781728182766

Gene regulatory network provides an effective way to study functional genomes. Reconstruction of gene regulatory network can better help us to reveal the regulatory relationship and complex mechanism between genes, thus exploring the essence of biological systems and lives. At present, the modeling methods of gene regulatory network either solve the problem of accuracy or scale of gene regulatory network by optimizing the model or combining various models. However, with the continuous increase of network scale and the computational complexity, the reconstruction of the network has exceeded the ability of single machine. Thus, we presented the Distributed Local Bayesian Network (D-LBN) method based on MapReduce framework to solve this problem. D-LBN is based on LBN algorithm which uses mutual information method, k-nearest neighbor algorithm, Bayesian network model and conditional mutual information method to reconstruct gene regulatory network. D-LBN realizes distributed computing using MapReduce framework at the step of regulatory direction learning with Bayesian network model to achieve the purpose of reconstructing large-scale gene regulatory network. Experiments based on two scales of dataset is done to verify the performance of D-LBN in reconstructing gene regulatory network at different scales. The results show that D-LBN is consistent with LBN in evaluation index and much more efficient than LBN on large-scale network reconstruction. And the scale of the network that can be reconstructed is also greatly improved.

关键词： Bayes methods Computational modeling Mutual information Mathematical model Biological system modeling Entropy

来源：评论

学校读者我要写书评

暂无评论

Dense residual network: Enhancing global dense feature flow for character recognition

arXiv

引用

arXiv 2020年

作者： Zhang, Zhao Tang, Zemin Wang, Yang Zhang, Zheng Zhan, Choujun Zha, Zhengjun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China School of Computer Science and Technology Soochow University Suzhou215006 China Shenzhen China School of Computer South China Normal University Guangzhou510631 China Deparmtment of Computer Science and Technology University of Science and Technology of China Hefei China

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Network (DenseNet), have achieved great success for image representation learning by capturing deep hierarchical features. However, most existing network architectures of simply stacking the convolutional layers fail to enable them to fully discover local and global feature information between layers. In this paper, we mainly investigate how to enhance the local and global feature learning abilities of DenseNet by fully exploiting the hierarchical features from all convolutional layers. Technically, we propose an effective convolutional deep model termed Dense Residual Network (DRN) for the task of optical character recognition. To define DRN, we propose a refined residual dense block (r-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts of inner layers at the same time. After fully capturing local residual dense features, we utilize the sum operation and several r-RDBs to construct a new block termed global dense block (GDB) by imitating the construction of dense blocks to adaptively learn global dense residual features in a holistic way. Finally, we use two convolutional layers to design a down-sampling block to reduce the global feature size and extract more informative deeper features. Extensive results show that our DRN can deliver enhanced results, compared with other related deep models. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Matching Biomedical Ontologies Through Compact Evolutionary Simulated Annealing Algorithm 12th

Matching Biomedical Ontologies Through Compact Evolutionary ...

引用

12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018

作者： Xue, Xingsi Chen, Jie Liu, Jianhua Chen, Dongxu College of Information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fujian350118 China Fujian Medical University Union Hospital Fuzhou Fujian350001 China

ISBN: (纸本)9789811358401

To overcome biomedical ontology’s heterogeneous problem, biomedical ontology matchers are developed to search for bridges of knowledge (or alignment) between heterogeneous biomedical ontologies. Since it is a complex problem, Evolutionary Algorithm (EA) can present a good methodology for matching biomedical ontologies. To improve the efficiency, in this paper, a Compact Evolutionary Simulated Annealing algorithm (CESA) is proposed to match the biomedical ontologies. In particular, CESA utilizes a Probability Vector (PV) to simulate the behavior of population-based EA, and introduces the Simulated Annealing algorithm (SA) as a local search in each generation. The experiment is conducted on the Large Biomed track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the experimental results show the effectiveness of our proposal. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

TRIPS: A System for translating raw indoor positioning data into visual mobility semantics 44th

TRIPS: A System for translating raw indoor positioning data ...

引用

44th International Conference on Very Large data Bases, VLDB 2018

作者： Li, Huan Lu, Hua Shi, Feichao Chen, Gang Chen, Ke Shou, Lidan Department of Computer Science Zhejiang University China Department of Computer Science Aalborg University Denmark Key Laboratory of Big Data Intelligent Computing of Zhejiang Province China CAD and CG State Key Lab Zhejiang University China

The rapid accumulation of indoor positioning data is increasingly booming the interest in indoor mobility analyses. As a fundamental analysis, it is highly relevant to translate raw indoor positioning data into mobility semantics that describe what, where and when in a more concise and semantics-oriented way. Such a translation is challenging as multiple data sources are involved, raw indoor positioning data is of low quality, and translation results are hard to assess. We demonstrate a system TRIPS that streamlines the entire translation process by three functional components. The Configurator provides a standard but concise means to configure multiple input sources, including the indoor positioning data, indoor space information, and relevant contexts. The Translator cleans the indoor positioning data and exports reliable mobility semantics without manual interventions. The Viewer offers a suite of flexible operations to trace the input, output and intermediate data involved in the translation. data analysts can interact with TRIPS to obtain the desired mobility semantics in a visual and convenient way. © 2018 VLDB Endowment.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Practical adaptive time-varying output formation tracking for high-order nonlinear multi-agent systems using neural networks

Practical adaptive time-varying output formation tracking fo...

引用

第三十八届中国控制会议

作者： Jianglong Yu Xiwang Dong Qingdong Li Zhang Ren Ming Ma School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Shananxi Key Laboratory of Integrated and Intelligent Navigation Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Xi'an Research Institute of Navigation Technology

Practical adaptive time-varying output formation tracking problems for high-order nonlinear strict-feedback multiagent systems are investigated in this paper. The time-varying output formation tracking requires the followers' output vectors to track the output vector of the leader while achieving a time-varying formation. The dynamics of the leader and the followers can have uncertain mismatched nonlinearities while the leader's control input is also unknown. To deal with the mismatched nonlinearities, the backstepping techniques are utilized to construct the protocols. Firstly, visual controllers are designed to overcome the effects of the uncertainties while guaranteeing the stability of each layer. The complicated visual controllers are estimated by the neural networks. A full adaptive protocol is constructed with adaptive gains to avoid using the global information of the interaction topology. Then, an algorithm is presented to design the protocol, whose parameters are much less. Besides, the stability of the algorithm is proved by Lyapunov theories. Finally, simulation results are presented to prove the effectiveness.

关键词： Practical time-varying formation tracking Adaptive control High-order nonlinear dynamics Multi-agent systems Neural networks

来源：评论

学校读者我要写书评

暂无评论

On privacy protection of latent dirichlet allocation model training

arXiv

引用

arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

来源：评论

学校读者我要写书评

暂无评论

A Survey on Machine Learning Algorithms in Little-Labeled data for Motor Imagery-Based Brain-Computer Interfaces

引用

Journal of Information Hiding and Privacy Protection 2019年第1期1卷 11-21页

作者： Yuxi Jia Feng Li Fei Wang Yan Gui School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore639798Singapore

The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for ***,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the *** classified these algorithms into two categories,namely,and enhancing the representation and expanding the training ***,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so *** methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG *** result of these techniques showed the resolution of the challenges to some *** a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.

关键词： Brain-Computer interface electroencephalography(EEG) machine learning

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：