Hengxun Li

First Research Institute of the Ministry of Public Security

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i-10 index

Research Trends

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Document Type

Co-Authors

Guangjun Hu

First Research Institute of the Ministry of Public Security

Chun Liao

Ministry of Agriculture and Rural Affairs

Ning Wang

Beijing Jiaotong University

Yan Xu

Faculty of 1000 (United States)

Ning Wang

Chongqing University

Wang Bin

Hunan First Normal University

Guoying Wu

Inner Mongolia People's Hospital

Weiqing Yang

Southwest Jiaotong University

Wei Guo

North China University of Technology

Dan Li

Wenzhou Polytechnic

Cooperative Institutions

Chinese Academy of Sciences

Institute of Information Engineering

Institute of Computing Technology

Beijing Language and Culture University

Chongqing University

First Research Institute of the Ministry of Public Security

University of Chinese Academy of Sciences

Beijing Institute of Technology

Harbin Institute of Technology

Faculty of 1000 (United States)

Author Statistics

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i-10 index

Research Field

A Cyclic Cascaded CRFs Model for Opinion Targets Identification Based on Rules and Statistics

IFIP advances in information and communication technology (2016)

Hengxun Li Chun Liao Guangjun Hu Ning Wang

CRFS

Identification

Microblogging

Sentiment Analysis

10.1007/978-3-319-48390-0_27

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Citations (1)

A RF-PSO Based Hybrid Feature Selection Model in Intrusion Detection System

Hengxun Li Wei Guo Guoying Wu Yanxia Li

Feature selection is an indispensable part in intrusion detection system to reduce irrelevant and redundant features and save the system from slow training and testing process. In general, wrapper and filter methods have been proposed for feature selection, but wrapper methods demand heavy computational resource for training and cross validation while filter methods lack the capability to minimize generalization error. In this paper we propose a hybrid feature selection model based on random forest and particle swarm optimization which makes use of both an independent measure and a learning algorithm to evaluate feature subsets. It uses the independent measure to decide the best subsets for a given cardinality and uses the learning algorithm to select the final best subset among the best subsets across different cardinalities. We utilized the KDD1999 dataset to evaluate the TPR and FPR of the proposed model, and compare them with CFS and SVM algorithms, the experimental evaluations demonstrate the capability of the proposed model.

Cardinality (data modeling)

Feature (linguistics)

10.1109/dsc.2018.00128

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Citations (25)

Tag-TextRank: A Webpage Keyword Extraction Method Based on Tags

Journal of Computer Research and Development (2012)

Peng Li Wang Bin Zhiwei Shi Cui Yachao Hengxun Li

Keyword extraction

Source

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Citations (18)

Opinion Targets Identification Based on Kernel Sentences Extraction and Candidates Selection

IFIP advances in information and communication technology (2016)

Hengxun Li Chun Liao Ning Wang Guangjun Hu

Identification

Sentiment Analysis

Kernel (algebra)

10.1007/978-3-319-48390-0_16

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Citations (0)

Design and Implentment of Secure Access Gateway of the Internet of Things based on FPGA

Xinxi wangluo anquan (2013)

Hengxun Li

For the presence of security threats in the process data exchange between perception network and core network designed Secure Access Gateway of the Internet of Things based on FPGA, Guarantee the network isolation between the different levels of security of perception network and core network, Achieved secure data exchange.

Gateway (web page)

Residential gateway

Isolation

H.248

Core network

Source

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Citations (0)

A Keyword Based Strategy for Spam Topic Discovery from the Internet

Yongqin Qiu Yan Xu Dan Li Hengxun Li

The increasing volume of spam has become a serious threat not only to the Internet, but also to the society. However, it's a great challenge to discover the spam from the Internet effectively and efficiently. Content-based filtering is one of the mainstream methods to solve the problem. This paper proposed a content based spam topic detection strategy through keyword extraction. In particular, spam topic is detected by using the topic model of multiple features with the keywords of clues, which integrate the corresponding feature of News, BBS and Blog. We get the min cost of 0.282 through TDT4 evaluating corpus and the satisfaction of 93.3% through the golaxy public opinion monitoring system of ICT, which is more effective than traditional method. The Experiments show that this algorithm is effective for spam topic detection.

Forum spam

Mainstream

10.1109/icgec.2010.71

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Citations (4)

PGM-WV: A context-aware hybrid model for heuristic and semantic question classification in question-answering system

Hengxun Li Ning Wang Guangjun Hu Weiqing Yang

In the field of information retrieval, with the rapid growth of the amount of questions and answers, automatic question-answering system comes up to be a hot research direction, which consists of three procedures: question classification, information retrieval and answer extraction. Question classification is the first and most important part of the whole task. Currently, two kinds of algorithms are employed, rule-based algorithms and statistical-model-based algorithms. Rule-based algorithms have good performance in accuracy and pertinence with the shortcoming of relying on professional knowledge and poor scalability. Statistical-model-based algorithms get classification models from training dataset, these methods extract syntax features heuristically and provide better scalability and thus most question classification algorithms are based on statistical-model. However, semantic features have largely been overlooked in existing statistical-model-based question classification algorithms. In this paper, we propose a context-aware hybrid model based on a statistical-model PGM and a semantic language model word2vec. The experimental evaluations demonstrate the capability of the proposed model.

Word2vec

10.1109/pic.2017.8359550

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Citations (3)