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4th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2017

作者： Pan, Jeng-Shyang Meng, Zhenyu Chu, Shu-Chuan Roddick, John F. Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia

ISBN: (纸本)9783319685267

Optimization algorithm in swarm intelligence is getting more and more prevalent both in theoretical field and in real-world applications. Many nature-inspired algorithms in this domain have been proposed and employed in different applications. In this paper, a new QUATRE algorithm with sort strategy is proposed for global optimization. QUATRE algorithm is a simple but powerful stochastic optimization algorithm proposed in 2016 and it tackles the representational/positional bias existing in DE structure. Here a sort strategy is used for the enhancement of the canonical QUATRE algorithm. This advancement is verified on CEC2013 test suite for real-parameter optimization and also is contrasted with several state-of-the-art algorithms including Particle Swarm Optimization (PSO) variants, Differential Evolution (DE) variants on COCO framework under BBOB2009 benchmarks. Experiment results show that the proposed QUATRE algorithm with sort strategy is competitive with the contrasted algorithms. © 2018, Springer International Publishing AG.

关键词： Swarm intelligence

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Global variations of urban form: Characterization and quantification through intelligent remote sensing image analysis

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Sustainable Cities and Society 2025年 127卷

作者： Chen, Jie Sun, Geng Song, Yongze Cao, Yinxia Yoo, Cheolhee Guo, Ya Zhao, Chunhong Lu, Linlin Weng, Qihao School of Geosciences and Info-physics Central South University Changsha China School of Design and the Built Environment Curtin University PerthWA6845 Australia JC STEM Lab of Earth Observations Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hung Hom Hong Kong Research Centre for Artificial Intelligence in Geomatics The Hong Kong Polytechnic University Hung Hom Hong Kong Research Institute for Land and Space The Hong Kong Polytechnic University Hung Hom Hong Kong School of Geographical Sciences Northeast Normal University Changchun China International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China

Urban form serves as a crucial lens for visually discerning the complexities and evolving patterns of cities worldwide. Despite its importance, existing research lacks the capacity to effectively characterize global variations in urban form. To address this limitation, our study proposes a novel urban form typology—Modular Urban Local Form (MULF)—comprising three natural categories and eighteen artificial categories, aimed at modeling urban physical space. This typology is grounded in the identification of Modular Urban Form Prototype (MUFP) across global cities. The global variation analysis of MULF reveals two distinct patterns. As urban population size increases, the proportion of local form categories characterized by large buildings decreases from 0.50 to 0.24. As urban income levels rise, the proportion of compact building forms declines from 0.92 to 0.26. The MULF global mapping offers a valuable perspective for understanding the form variation and evolution of cities with different population sizes and income levels. The insights derived from the MULF system highlight its potential as a feasible and globally applicable framework for interdisciplinary research on urban sustainable development. © 2025 The Author(s)

关键词： Image correlation

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ABR-HIC: Attention Based Bidirectional RNN for Hierarchical Industry Classification

ABR-HIC: Attention Based Bidirectional RNN for Hierarchical ...

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IEEE International Conference on big data

作者： Rongzhe Wei Qinghua Zheng Bo Dong Kuanzheng Yang Huan He Jianfei Ruan Qian Xuesen College School of Mathematics and Statisitcs Xi’an Jiaotong University Xi’an China School of Computer Science and Technology SPKLSTN Lab Xi’an Jiaotong University Xi’an China School of Distance Education National Engineering Lab of Big Data Analytics Xi’an Jiaotong University Xi’an China SPKLSTN Lab Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Accurate industry classification of national economic activities as an important component in the construction of economic structure and as the basis of the formulation of economic policies and management of national economic activities has been gaining increasing attention. However, owing to the rapid growth in the number of industries, it is become increasingly difficult for tax bureaus to classify the registered taxpayers' industries. Conventional industrial classification methods only focus on the text features, which can not be analyzed and judged comprehensively according to the registration information, and can only carry on single-label classification since they neglect the primary and secondary relationships between the main and subsidiary industries, which can not meet application requirements. To better address these challenges, this paper proposes a model known as attention based bidirectional RNN for hierarchical industry classification (ABR-HIC), which is the first approach, to the best of our knowledge, to simultaneously address comprehensive registration information utilization and multi-label classification for the main and subsidiary industries. Our architecture establishes a bidirectional RNN using a word-attention mechanism, which is able to capture and fully utilize the text and non-text registration information for feature representation. By separating the taxpayer's primary and secondary multi-label classification problem corresponding to the main and subsidiary industries, respectively, into two subtasks and through multi-task learning, our model can provide comprehensive primary and secondary multi-industrial labels. Experiments were conducted on real tax data-sets of the Shaanxi Province, China and the results demonstrate the outstanding performance of our architecture in terms of both the classification effect and training time compared with those of state-of-the-art approaches.

关键词： Industries Economics Task analysis Feature extraction Computer architecture Machine learning

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Universal central extensions of Hom-Lie antialgebras

arXiv

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arXiv 2019年

作者： Zhang, Tao Zhong, Deshou College of Mathematics and Information Science Henan Engn Lab Big Data Stat Anal & Optimal Contr Henan Normal University Xinxiang453007 China School of Economics University of Chinese Academy of Social Sciences No.11 Changyu street Fangshan District Beijing102488 China

We develop a theory of universal central extensions for Hom-Lie antialgebras. It is proved that a Hom-Lie antialgebra admits a universal central extension if and only if it is perfect. Moreover, we show that the kernel of the universal central extension is equal to the second homology group with trivial *** Codes Primary 17D99, Secondary 18G60 Copyright © 2019, The Authors. All rights reserved.

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A survey on graph processing accelerators: Challenges and opportunities

arXiv

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arXiv 2019年

作者： Gui, Chuang-Yi Zheng, Long He, Bing-Sheng Liu, Cheng Chen, Xin-Yu Liao, Xiao-Fei Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computing National University of Singapore 117418 Singapore

Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures, dedicated hardware solutions, also referred to as graph processing accelerators, are essential and emerging to provide the benefits significantly beyond those pure software solutions can offer. In this paper, we conduct a systematical survey regarding the design and implementation of graph processing accelerator. Specifically, we review the relevant techniques in three core components toward a graph processing accelerator: preprocessing, parallel graph computation and runtime scheduling. We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator. Interestingly, we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and complexity of hardware configurations. We finially present to discuss several challenges in details, and to further explore the opportunities for the future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy efficiency

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Prospect of 5G Application in Civil Airport

Prospect of 5G Application in Civil Airport

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Ningning Le Yinger Zheng Qian Huang Yanqiu Pei Big Data Analysis and Application Center China Academy of Civil Aviation Science and Technology Beijing China Service Lab Huawei Research Institute Huawei Technology Co. Ltd Beijing China College of Geoscience and Surveying Engineering China University of Mining and Technology Beijing China

ISBN: (纸本)9781728125992

With the rapid growth of China's civil aviation transportation volume, the airport has entered the era of big data in which data is massive, diversified, and converted at high speeds. Achievements for intelligent application of big data based on 5G technology have become an indispensable component of smart airports. Based on the characteristics of 5G technology, this paper combines various state-of-art technologies including Discrete Global Grid Systems, Complex Network, Artificial Intelligence, to prospect the application scenarios of 5G in airport, as efficient and secure wide-area wireless communication system, IoT-based smart monitoring, airport unmanned driving, high-speed entertainment experience, intelligent passenger service.

关键词： 5G New data technology Application prospect Civil airport Airports big data 5G mobile communication PASSENGER SERVICES Grid systems Prospecting Wireless communication systems High speed

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Correction to: New theoretical ISM‑K2 Bayesian network model for evaluating vaccination effectiveness

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Journal of Ambient Intelligence and Humanized Computing 2022年第10期14卷 14383-14383页

作者： Xie, Xiaoliang Xie, Bingqi Xiong, Dan Hou, Muzhou Zuo, Jinxia Wei, Guo Chevallier, Julien School of Mathematics and Statistics Hunan University of Technology and Busin Ess Changsha China Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation Hunan University of Technology and Business Changsha China Institute of Big Data and Internet Innovation Hunan University of Technology and Business Changsha China School of Mathematics and Statistics Central South University Changsha China Department of Mathematics and Computer Science University of North Carolina at Pembroke Pembroke USA IPAG Business School (IPAG Lab) Paris France University Paris 8 (LED) Saint-Denis France

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Unsupervised Conditional Adversarial Networks for Tax Evasion Detection

Unsupervised Conditional Adversarial Networks for Tax Evasio...

引用

IEEE International Conference on big data

作者： Rongzhe Wei Bo Dong Qinghua Zheng Xulyu Zhu Jianfei Ruan Huan He Qian Xuesen College School of Mathematics and Statisitcs Xi’an Jiaotong University Xi’an China National Engineering Lab of Big Data Analytics School of Distance Education Xi’an Jiaotong University Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China SPKLSTN Lab Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

The identification of tax evasion plays an important role in ensuring tax order, promoting the level of tax collection and management, and reducing tax losses. With the advancements in data mining technology, many machine learning techniques have yielded results in identifying tax evasion. However, to realize satisfactory performance, these models require large amounts of human annotated data. In the tax field, unlabeled tax data are abundant, data annotation in a single region is expensive, and the distributions of characteristics differ among regions; these factors pose substantial difficulties in the development of an identification model. Existing tax evasion detection methods are either trained for single-region tasks, in which case they perform poorly on inter-region tax evasion identification due to the discrepancies in feature distributions, or utilize labeled data from both the target-task field and different but related auxiliary fields to reuse and transfer knowledge of the target domain data, in which case they cannot deal with scenarios in which there are no labeled data in target audit tasks. Although current unsupervised transfer learning techniques can train models in labeled regions for unlabeled regions, large intra-class distribution discrepancies cannot be perfectly minimized in tax evasion detection scenarios. To better address the above challenges, this paper proposes a general architecture, namely, the unsupervised conditional adversarial networks (UCAN) for tax evasion detection, which is the first approach to solve audit tasks in unlabeled target domains via inter-region transfer. Our architecture establishes an adversarial neural network adding label information in the distribution adapter, which can granularly adapt the joint probability distribution (JPD) of the data. We introduce a constraint that is based on the conditional maximum mean discrepancy (CMMD) of the extracted features to align the conditional probability distribution (CPD)

关键词： Finance Feature extraction Task analysis Adaptation models Computer architecture data models data mining

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Recommending investors for new startups by integrating network diffusion and investors’ domain preference

arXiv

引用

arXiv 2019年

作者： Xu, Shuqi Zhang, Qianming Lü, Linyuan Mariani, Manuel Sebastian Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu China CompleX Lab Big Data Research Center University of Electronic Science and Technology of China Chengdu China Alibaba Research Center for Complexity Sciences Hangzhou Normal University Hangzhou China URPP Social Networks Universität Zürich Zürich Switzerland

Over the past decade, many startups have sprung up, which create a huge demand for financial support from venture investors. However, due to the information asymmetry between investors and companies, the financing process is usually challenging and time-consuming, especially for the startups that have not yet obtained any investment. Because of this, effective data-driven techniques to automatically match startups with potentially relevant investors would be highly desirable. Here, we analyze 34, 469 valid investment events collected from *** and consider the cold-start problem of recommending investors for new startups. We address this problem by constructing different tripartite network representations of the data where nodes represent investors, companies, and companies’ domains. First, we find that investors have strong domain preferences when investing, which motivates us to introduce virtual links between investors and investment domains in the tripartite network construction. Our analysis of the recommendation performance of diffusion-based algorithms applied to various network representations indicates that prospective investors for new startups are effectively revealed by integrating network diffusion processes with investors’ domain preference. Copyright © 2019, The Authors. All rights reserved.

关键词： Investments

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Erratum: “Spin wave propagation in a ferrimagnetic thin film with perpendicular magnetic anisotropy” [Appl. Phys. Lett. 117, 232407 (2020)]

引用

Applied Physics Letters 2021年第2期118卷

作者： Lutong Sheng Yawen Liu Jilei Chen Hanchen Wang Jianyu Zhang Mingfeng Chen Ji Ma Chuanpu Liu Sa Tu Ce-Wen Nan Haiming Yu 1Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China 2Department of Physics and Astronomy University of California Riverside California 92521 USA 3State Key Lab of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing 100084 China

In the original article,1 the line spectrum in Fig. 2(b) is missing. The correct Fig. 2(b), given here, shows how we get the frequency difference from this spec

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