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International Conference on Big data and Information Analytics (BigDIA)

作者： Hui Chen Lifei Chen Qingshan Jiang Shun Guo Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China College of Mathematics and Informatics Fujian Normal University Fuzhou China Shenzhen Key Lab for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781665422321

ISBN: (纸本)9781665446662

Classification is an essential technology in data mining, which has been widely used in real-world problems. A massive amount of information is pouring into our lives since the development of science and technology. The data size and dimension are growing rapidly in various fields and challenging data mining and machine learning approaches. Feature selection reduces the dimension of data and selects significant features, which is an important research direction. However, some norm measures of feature datasets may be imprecise. Many feature selection methods have the characteristics of high cardinality and high computational complexity. Therefore, there is a strong need to study effective feature selection methods. Naive Bayes is a simple and effective learning theory that does not need various parameters. However, its conditional independence assumption limits its classification results in practical applications. Meanwhile, the curse-of-dimensionality brings noises and messy interaction between features. In this paper, we propose a feature selection method named as the Prior Dirichlet Distribution of Naive Bayes (PDDNB) which optimizes the Bayesian theory based on Dirichlet priori distribution. This method simplifies the computational ways to an analytical solution, which significantly improve the calculation efficiency. The executive experiments on the ALL-AML datasets and the other five real-world datasets turn out our method to effectively increase classification accuracy.

关键词： Handheld computers Fuses Machine learning Feature extraction Bayes methods data mining Iterative methods

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Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

arXiv

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arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

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HAS-QA: Hierarchical answer spans model for open-domain question answering

arXiv

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arXiv 2019年

作者： Pang, Liang Lan, Yanyan Guo, Jiafeng Xu, Jun Su, Lixin Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Statistics University of California Berkeley

This paper is concerned with open-domain question answering (i.e., OpenQA). Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it. However, the performances of such models are not so good as that in the RC task. In our opinion, the perspective of RC ignores three characteristics in OpenQA task: 1) many paragraphs without the answer span are included in the data collection;2) multiple answer spans may exist within one given paragraph;3) the end position of an answer span is dependent with the start position. In this paper, we first propose a new probabilistic formulation of OpenQA, based on a three-level hierarchical structure, i.e., the question level, the paragraph level and the answer span level. Then a Hierarchical Answer Spans Model (HAS-QA) is designed to capture each probability. HAS-QA has the ability to tackle the above three problems, and experiments on public OpenQA datasets show that it significantly outperforms traditional RC baselines and recent OpenQA baselines. Copyright © 2019, The Authors. All rights reserved.

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Design of Target Behavior Intelligent Analysis System Based on Distributed Computing

Design of Target Behavior Intelligent Analysis System Based ...

引用

Signal, Information and data Processing (ICSIDP), IEEE International Conference on

作者： Xinlong Pan Xueqi Cheng Libo Yao Zhenqiu Zhu Biao Ding Tiantian Tang Minbo Li Chuanhui Liu CAS Key Laboratory of Network Data Science & Technology Inistitute of Computing Technology Beijing China Institute of Information Fusion Naval Aviation University Yantai China School of Software Fudan University Shanghai China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Through the fusion processing of target data obtained by radar, electronic reconnaissance, space reconnaissance, technical reconnaissance and civil cooperative system in early warning and surveillance system, multidimensional target data including explicit state information such as target attributes, type, position, speed and course could be obtained. The latent potential information, such as regular behavior, intention and threat degree, need to be mastered by human intelligence and experience analysis. It is urgent to use big data mining technology to realize intelligent analysis of target behavior. Based on the analysis of big data related technologies, this paper designs an intelligent target behavior analysis system based on distributed computing, which provides theoretical and technical support for intelligent upgrading of intelligence processing systems and command decision systems.

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Binary neural networks: A survey

arXiv

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arXiv 2020年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Bai, Xiao Song, Jingkuan Sebe, Nicu State Key Lab of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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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

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Erratum: Prototype of readout electronics for GAEA gamma spectrometer of Back-n facility at CSNS

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Journal of Instrumentation 2022年第8期17卷 E08002-E08002页

作者： L. Xie P. Cao T. Yu Z. Jiang Q. An J. Li C. Li X. Wu Z. Wang H. Bai J. Bai J. Bao Q. Chen Y. Chen Z. Chen Z. Cui A. Fan R. Fan C. Feng F. Feng K. Gao M. Gu C. Han Z. Han G. He Y. He Y. Hong Y. Hu H. Huang W. Jia H. Jiang W. Jiang Z. Jin L. Kang B. Li G. Li Q. Li X. Li Y. Li J. Liu R. Liu S. Liu G. Luan C. Ning B. Qi J. Ren Z. Ren X. Ruan Z. Song K. Sun Z. Tan J. Tang S. Tang L. Wang P. Wang Z. Wen Y. Yang H. Yi Y. Yu G. Zhang L. Zhang M. Zhang Q. Zhang X. Zhang Y. Zhang Z. Zhang M. Zhao L. Zhou Z. Zhou K. Zhu State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei China Department of Modern Physics University of Science and Technology of China Hefei China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing China Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics Mianyang China Institute of High Energy Physics Chinese Academy of Sciences (CAS) Beijing China Spallation Neutron Source Science Center Dongguan China USTC archaeometry lab University of Science and Technology of China Hefei China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of Sciences Beijing China Northwest Institute of Nuclear Technology Xi'an China University of Chinese Academy of Sciences Beijing China

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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

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On dynamic time division duplex transmissions for small cell networks

arXiv

引用

arXiv 2020年

作者： Ding, Ming López-Pérez, David Xue, Ruiqi Vasilakos, Athanasios V. Chen, Wen Data 61 Australia Bell Labs Alcatel- Lucent Ireland Shanghai Jiao Tong University China Department of Computer Science Electrical and Space Engineering Lule17University of Technology Sweden Shanghai Key Lab of Navigation and Location Based Services Shanghai Jiao Tong University School of Electronic Engineering and Automation Guilin University of Electronic Technology China

Motivated by the promising benefits of dynamic Time Division Duplex (TDD), in this paper, we use a unified framework to investigate both the technical issues of applying dynamic TDD in homogeneous small cell networks (HomSCNs), and the feasibility of introducing dynamic TDD into heterogeneous networks (HetNets). First, HomSCNs are analyzed, and a small cell BS scheduler that dynamically and independently schedules DL and UL subframes is presented, such that load balancing between the DL and the UL traffic can be achieved. Moreover, the effectiveness of various inter-link interference mitigation (ILIM) schemes as well as their combinations, is systematically investigated and compared. Besides, the interesting possibility of partial interference cancellation (IC) is also explored. Second, based on the proposed schemes, the joint operation of dynamic TDD together with cell range expansion (CRE) and almost blank subframe (ABS) in HetNets is studied. In this regard, scheduling polices in small cells and an algorithm to derive the appropriate macrocell traffic off-load and ABS duty cycle under dynamic TDD operation are proposed. Moreover, the full IC and the partial IC schemes are investigated for dynamic TDD in HetNets. The user equipment (UE) packet throughput performance of the proposed/discussed schemes is benchmarked using system-level simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Heterogeneous networks

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