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Ranking and tagging bursty features in text streams with context language models

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Frontiers of Computer Science 2017年第5期11卷 852-862页

作者： Wayne Xin ZHAO Chen LIU Ji-Rong WEN Xiaoming LI School of Information Renmin University of China Beijing 100872 China beijing key laboratory of Big data Management and analysis Methods Renmin University of China Beijing 100872 China beijing key laboratory on integration and analysis of large-scale stream data Beijing 100144 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

Detecting and using bursty pattems to analyze text streams has been one of the fundamental approaches in many temporal text mining applications. So far, most existing studies have focused on developing methods to detect bursty features based purely on term frequency changes. Few have taken the semantic contexts of bursty features into consideration, and as a result the detected bursty features may not always be interesting and can be hard to interpret. In this article, we propose to model the contexts of bursty features using a language modeling approach. We propose two methods to estimate the context language models based on sentence-level context and document-level context. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. We also use two example text mining applications to qualitatively demonstrate the usefulness of bursty feature ranking and tagging.

关键词： bursty features bursty features ranking bursty feature tagging context modeling

Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments

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Digital Communications and Networks 2022年第6期8卷 1014-1026页

作者： Shouli Zhang Chen Liu Xiaohong Li Yanbo Han Division of Intelligence and Computing Tianjin UniversityTianjin300072China beijing key laboratory on integration and analysis of large-scale stream data North China University of TechnologyBeijing10009China Cloud Computing Research Center North China University of TechnologyBeijing10009China

The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the *** this connection,a key issue is to determine when and where services should be *** service deployment strategies used to be static based on the rules defined at the design ***,dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation,where the static service deployment solutions are not *** this paper,we propose a dynamic service deployment mechanism based on the prediction of upcoming stream *** effectively predict upcoming workloads,we combine the online machine learning methods with an online optimization algorithm for service deployment.A simulation-based evaluation demonstrates that,compared with those state-of-the art approaches,the approach proposed in this paper has a lower latency of stream processing.

关键词： IoT stream processing Edge computing Out-of-Range stream fluctuation Dynamic service deployment

An Internet of Things based scalable framework for disaster data management

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Journal of Safety Science and Resilience 2022年第2期3卷 136-152页

作者： Zhiming Ding Shan Jiang Xinrun Xu Yanbo Han Institute of Software Chinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China North China University of Technology Beijing 100144China beijing key laboratory on integration and analysis of large-scale stream data BeijingChina

In recent years, undesirable disasters attacked the cities frequently, leaving heavy casualties and serious economiclosses. Meanwhile, disaster detection based on the Internet of Things(IoT) has become a hot spot that benefitedfrom the established development of smart city construction. And the IoT is visibly sensitive to the managementand monitoring of disasters, but massive amounts of monitoring data have brought huge challenges to datastorage and data analysis. This article develops a new and much more general framework for disaster emergencymanagement under the IoT environment. The framework is a bottom-up integration of highly scalable Raw dataStorages(RD-Stores) technology, hybrid indexing and queries technology, and machine learning technology foremergency disasters. Experimental results show that hybrid index and query technology have better performanceunder the condition of supporting multi-modal retrieval, and providing a better solution to offer real-time retrievalfor the massive sensor sampling data in the IoT. In addition, further works to evaluate the top-level sub-applicationsystem in this framework were performed based on the GPS trajectory data of 35,000 beijing taxis and thevolumetric ground truth data of 7,500 images. The results show that the framework has desirable scalability andhigher utility.

关键词： Disaster data management IoT Disaster detection Big data Artificial intelligence

Attack based on data: a novel perspective to attack sensitive points directly

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Cybersecurity 2024年第3期7卷 111-123页

作者： Yuyao Ge Zhongguo Yang Lizhe Chen Yiming Wang Chengyang Li School of Information Science and Technology North China University of TechnologyBeijingChina Bejing key laboratory on integration and analysis of large-scale stream data North China University of TechnologyBeijingChina School of Computer Science Peking UniversityBeijingChina

Adversarial attack for time-series classification model is widely explored and many attack methods are *** there is not a method of attack based on the data *** this paper,we innovatively proposed a black-box sparse attack method based on data *** method directly attack the sensitive points in the time-series data accord-ing to statistical features extract from the *** frst,we have validated the transferability of sensitive points among DNNs with different ***,we use the statistical features extract from the dataset and the sensi-tive rate of each point as the training set to train the predictive ***,predicting the sensitive rate of test set by predictive ***,perturbing according to the sensitive *** attack is limited by constraining the LO norm to achieve one-point *** conduct experiments on several datasets to validate the effectiveness of this method.

关键词： Black-box adversarial attack Time series classification data mining

Emergency logistics scheduling with multiple supply-demand points based on grey interval

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Journal of Safety Science and Resilience 2022年第2期3卷 179-188页

作者： Zhiming Ding Xinrun Xu Shan Jiang Jin Yan Yanbo Han University of Chinese Academy of Sciences Beijing100049China Institute of Software Chinese Academy of Sciences Beijing050052China beijing key laboratory on integration and analysis of large-scale stream data Beijing100124China North China University of Technology Beijing100144China

This study aimed to address the problem of post-disaster emergency material dispatching from multiple supply points to multiple demand *** large-scale natural disasters,it is very important for multiple emergency material supply points to serve as sources of materials for multiple disaster sites and to determine emergency material scheduling solutions ***,the quantity of emergency materials required at each disaster site is *** address this issue,in this study,we developed an emergency material scheduling model with multiple logistics supply points for multiple demand points based on the grey interval *** optimize the proposed multi-supply-point and multi-demand-point emergency material scheduling mode,a multi-objective optimization algorithm based on a genetic algorithm was *** results demonstrate that the multi-objective optimization method can solve the emergency logistics scheduling problem better than the particle swarm optimization multi-objective solution ***,the multi-supply point and multi-demand point emergency material dispatch model and optimization algorithm provides robust support for emergency management system decision-makers when they need to respond quickly to disaster relief activities.

关键词： Emergency logistics scheduling Multi-objective programming Disaster relief

Text extraction method for historical Tibetan document images based on block projections

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Optoelectronics Letters 2017年第6期13卷 457-461页

作者：段立娟张西群马龙龙吴健 Faculty of Information Technology. beijing University of Technology Beijing 100124 China beijing key laboratory on integration and analysis of large-scale stream data Beijing University of Technology Beijing 100124 China beijing key laboratory of Trusted Computing Beijing University of Technology Beijing 100124 China Chinese Information Processing laboratory Institute of Software Chinese Academy of Sciences Beijing 100190 China

Text extraction is an important initial step in digitizing the historical documents. In this paper, we present a text extraction method for historical Tibetan document images based on block projections. The task of text extraction is considered as text area detection and location problem. The images are divided equally into blocks and the blocks are filtered by the information of the categories of connected components and corner point density. By analyzing the filtered blocks' projections, the approximate text areas can be located, and the text regions are extracted. Experiments on the dataset of historical Tibetan documents demonstrate the effectiveness of the proposed method.

关键词： historical Tibetan document filtered blocks bounding corner approximate projection coordinate

Industrial Internet of Things Multi-Protocol Convergence Gateway Research and Experiment

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Industrial Internet of Things Multi-Protocol Convergence Gat...

Big data challenge： a data management perspective

第三十九届中国控制会议

作者： Li Ma Zhaoyuan Xiu School of Information Science and Technology Beijing key laboratory of large-scale streaming data integration and analysis technologyNorth China University of Technology

IIOT（The Industrial Internet of Things） is an important infrastructure in the Industry 4.0 *** years of development,a number of independent Industrial Internet of Things standards have been *** different standards of the Industrial Internet of Things cannot communicate with each other,which brings difficulties to centralize monitoring and *** solve this problem,first,we proposed a multi-protocol converged gateway to convert various ⅡOT protocol ***,we created a software architecture and gateway data processing process for the conversion between different industrial Internet of things protocol data ***,we designed a converged gateway architecture and embedded a transformation layer in the existing protocols to form a converged Internet of things gateway and realize data intercommunication between different *** the same time,for industrial Internet of things nodes that do not have IPv6 addresses,we used the method of virtual IPv6 address to enable node messages to communicate with other IP networks or non-ip networks,and gave an addressing scheme of the IPv6 *** verified that the converged gateway solution is possible and the results are satisfactory through real node *** is a feasible solution to realize the conversion between the existing industrial Internet of Things standards.

关键词： converged gateway protocol conversion HOT

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Frontiers of Computer Science 2013年第2期7卷 157-164页

作者： Jinchuan CHEN Yueguo CHEN Xiaoyong DU Cuiping LI Jiaheng LU Suyun ZHAO Xuan ZHOU key laboratory of data Engineering and Knowledge Engineering School of Information Renmin University of China China Beijing 100872

There is a trend that, virtually everyone, rang- ing from big Web companies to traditional enterprisers to physical science researchers to social scientists, is either al- ready experiencing or anticipating unprecedented growth in the amount of data available in their world, as well as new op- portunities and great untapped value. This paper reviews big data challenges from a data management respective. In partic- ular, we discuss big data diversity, big data reduction, big data integration and cleaning, big data indexing and query, and fi- nally big data analysis and mining. Our survey gives a brief overview about big-data-oriented research and problems.

关键词： big data performance databases

Removal of hydrophobic volatile organic compounds with sodium hypochlorite and surfactant in a co-current rotating packed bed

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Journal of Environmental Sciences 2018年第2期30卷 190-196页

作者： Wenhui Li Haidi Liu Shuangde Li Weiman Li Yunfa Chen Jiajia Gao Yaqun Cao State key laboratory of Multi-phase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 China key laboratory of Healthy &Intelligent Kitchen System integration Zhejiang Ningbo 315336 China University of Chinese Academy of Sciences Beijing 100049 China

A co-current flow rotating packed bed was applied to remove volatile organic compounds（VOCs） by sodium hypochlorite（Na Cl O） and surfactant（sodium dodecyl benzene sulfonate,SDBS） from air stream. Xylene was used as a model VOC herein. The effect of p H,concentration of Na Cl O and SDBS solution, liquid flow rate, gas flow rate and rotational speed on xylene removal efficiency and overall mass transfer coefficient（KGa） were discussed. Then, a correlation for KGa of the co-current rotating packed bed was proposed by fitting the experimental data of KGa and independent variables of liquid/gas ratio,rotational speed, p H, Na Cl O concentration and treatment time, which was in good agreement with the experimental data（the deviation ≤ ± 30%）.

关键词： Co-current flow rotating packed bed VOCs Removal efficiency Overall mass transfer coefficient