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IEEE Conference on Dependable and Secure Computing

作者： Jiasheng Li Zijian Zhang Meng Li Beijing Engineering Research Center of Massive Language Information Beijing Institute of Technology Beijing China School of Computer Science University of Auckland Aukland New Zealand Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei University of Technology Anhui China

Lottery is a game with many people's dreams. But corruptions of lottery centers make the lottery unfair. To address this unfair issue, fair lottery schemes have been studied for several years. In these schemes, delay functions or aggregation protocols can be used to generate the winning numbers fairly. However, to the best of our knowledge, none of the existing schemes can achieve the winning number generated randomly, while the randomness can be verified fairly. In this article, we first propose a [B]lockchain based sm[a]rt co[n]tract for [F]air and [E]fficient [L]ottery (BanFEL) scheme. We further present a winning number random generation smart contract, by which players submit the purchased numbers and the non-tampering property is protected. Security analysis and experiments show that the fairness of BanFEL is protected better than the existing works. Meanwhile, the verification cost of this scheme are at least 0.03s lower than the traditional Grumbach's scheme between 1000 and 10000 players.

关键词： Blockchain Smart contracts Security Mathematical model Privacy Information processing

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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View-based 3D model retrieval via convolutional neural networks

View-based 3D model retrieval via convolutional neural netwo...

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Chinese Intelligent Systems Conference, CISC 2017

作者： Du, Bowen Li, Haisheng Cai, Qiang School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China

ISBN: (纸本)9789811064982

In this paper, we propose a multi-view fusion 3D model retrieval using convolutional neural network to solve the problem of the local perception in feature descriptor. By view pooling, we combine information from multiple views of a 3D model to eliminate the position correlation caused by the viewing angle of camera. In addition, integrating pre-processed RGB view-feature with Binary view-feature in the same model is used to generate a single model descriptor. Experiments on ETH dataset demonstrate the superiority of the proposed method. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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A visualization method for hierarchical structure information of the food inspection data based on force-directed model

A visualization method for hierarchical structure informatio...

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Chinese Intelligent Systems Conference, CISC 2017

作者： Cai, Qiang Li, Qiang Li, Haisheng Mao, Dianhui School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China

ISBN: (纸本)9789811064982

Food safety supervision and management departments have accumulated a large number of food sampling data, and the data shows hierarchical and high-dimensional characteristics. Because of the lack of appropriate visualization methods, it is difficult to carry out efficient analysis. In this paper, we extracted a hierarchical and high-dimensional dataset from the food inspection data for visualization, and proposed a method based on the force-directed model. Besides, we applied an optimized method to design the interactive mode. Through the visual interaction, the information can be displayed much more efficiently and thus facilitate the understanding of information. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Visualization

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Quantitative Analysis of Protein and Polysaccharide in Lilium Lanzhou Based on Near Infrared Spectroscopy

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Xiaoqin Lian Shenmiao Tang Jingzhu Wu Yelan Wu Qun Chen Beijing Technology and Business University School of Computer and Information Engineering/Beijing Key Laboratory of Big Data Technology for Food Safety 100048 Beijing China

In order to quickly detect the nutritional components of Lilium Lanzhou, a national geographical indication product, a quantitative analysis model of protein and polysaccharide was established by near infrared spectroscopy and chemometrics. A total of 81 samples of Lilium Lanzhou were collected. SG smoothing + first derivative + MSc spectral preprocessing method was selected to establish the spectral model of protein and polysaccharide quantitative detection of Lilium Lanzhou Based on partial least square method. The correlation coefficient (R2) of protein model is 0.844, RMSEP is 0.268, the correlation coefficient (R2) of polysaccharide model is 0.715, RMSEP is 0.273. The experimental results show that NIRS technology is feasible for the rapid detection of key quality of Lilium Lanzhou, which is expected to provide a new method for the rapid evaluation of key nutritional quality of Lilium Lanzhou.

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Classification of Medical Consultation Text Using Mobile Agent System Based on Naïve Bayes Classifier 1st

Classification of Medical Consultation Text Using Mobile Age...

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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Chen, Xingyu Zeng, Guangping Zhang, Qingchuan Chen, Liu Wang, Zhuolin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Computer and Information Engineering Beijing Technology and Business University Beijing China School of Humanities and Social Science University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China

ISBN: (纸本)9783319728223

Aiming at the interaction model of the Internet medical website, a classifier of medical text data based on Naive Bayes was proposed and realized in this paper. Once a user posed questions on the websites, this classifier would instantly classify the user’s questions and enable accurate question delivery. Furthermore, a data service platform was realized by taking advantages of mobile agent technology. With the service platform, companies could avoid considering the security of data when conducting data analysis. Finally, experiments were conducted according to the process of data analysis in the service platform. The experimental results showed: the proposed service platform was feasible, and a medical consultation text classifier with high accuracy was realized to improve user experience of medical websites. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Websites

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Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

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China’s NTB Market: A Liquidity Dilemma

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Journal of Systems Science & Complexity 2018年第2期31卷 493-507页

作者： LI Yumeng MENG Xiangying ZHANG Yueyan WEI Xianhua School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laborutory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Sino-Danish Center for Education and Research University of Chinese Academy of Sciences Beijing 100190 China The State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing 100084 China Department of Automotive Engineering Tsinghua University Beijing 100084 China

This paper introduces China＇s New Third Board Market（the NTB market） and tests the market-making system in the role of improving market liquidity. The NTB Market is the main part of China＇s OTC market, established in 2006. In comparing with China＇s Main Board Market and the Second Board Market, it attracts a lot of start-up companies that need financing with less strict listing requirements. Meanwhile, it is full of opportunities and challenges that appeal to numerous securities traders and investors with the rapid development momentum. Since its establishment, the NTB Market has been stuck in the lack of liquidity. The authority of the NTB Market introduced market maker system, so as to solve the problem of liquidity. Using the difference-in-difference method（DID method）, the market maker system has been proved to make little contribution to improving the stock liquidity. Although the problem of liquidity is difficult to solve in short term, the NTB Market is full of opportunities for all kinds of participants definitely.

关键词： China＇s stock market DID method liquidity market makers.

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Few-shot deep adversarial learning for video-based person re-identification

arXiv

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arXiv 2019年

作者： Wu, Lin Wang, Yang Yin, Hongzhi Wang, Meng Shao, Ling Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China University of Queensland St Lucia4072 Australia School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China Abu Dhabi United Arab Emirates

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Unsupervised keyword Extraction Method Based on Chinese Patent Clustering

Unsupervised Keyword Extraction Method Based on Chinese Pate...

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IEEE International Conference on Big knowledge (ICBK)

作者： Yuxin Xie Xuegang Hu Yuhong Zhang Shi Li Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Connected Vehicle Research Institute (JAC Technical Center) Anhui Jianghuai Automobile Group Corp.Ltd Hefei China

Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic information. In this paper, we propose an Unsupervised keyword Extraction Method (UKEM) based on Chinese patent clustering. More specifically, we use a Skip-gram model to train word embeddings based on a Chinese patent corpus. Then each patent is represented as a vector called patent vector. These patent vectors are clustered to obtain several cluster centroids. Next, the distance between each word vector in patent abstract and cluster centroid is computed to indicate the semantic importance of this word. The experimental results on several Chinese patent datasets show that the performance of our proposed method is better than several competitive methods.

关键词： Patents Semantics data mining knowledge engineering Neural networks Big data Computer science

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