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Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search

Journal of Computer Science & Technology 2021年第5期36卷 1167-1183页

作者： Dong-Hui Yang Zhen-Yu Li Xiao-Hui Wang Kavé Salamatian Gao-Gang Xie Network Technology Research Center Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Global Energy Interconnection Research Institute Co. Ltd.Beijing 102209China LISTIC Laboratory of Computer Science SystemsInformation and Knowledge ProcessingUniversitéSavoie Mont BlancChambéry 73011France Computer Network Information Center Chinese Academy of SciencesBeijing 100190China

Internet users heavily rely on web search engines for their intended *** major revenue of search engines is advertisements(or ads).However,the search advertising suffers from *** generate fake traffic which does not reach the intended audience,and increases the cost of the ***,it is critical to detect fraud in web *** studies solve this problem through fraudster detection(especially bots)by leveraging fraudsters'unique ***,they may fail to detect new means of fraud,such as crowdsourcing fraud,since crowd workers behave in part like normal *** this end,this paper proposes an approach to detecting fraud in web search from the perspective of fraudulent *** begin by using a unique dataset of 150 million web search logs to examine the discriminating features of fraudulent ***,we model the temporal correlation of fraudulent keywords as a graph,which reveals a very well-connected community ***,we design DFW(detection of fraudulent keywords)that mines the temporal correlations between candidate fraudulent keywords and a given list of *** particular,DFW leverages several refinements to filter out non-fraudulent keywords that co-occur with seeds *** evaluation using the search logs shows that DFW achieves high fraud detection precision(99%)and accuracy(93%).A further analysis reveals several typical temporal evolution patterns of fraudulent keywords and the co-existence of both bots and crowd workers as fraudsters for web search fraud.

关键词： community structure fraud analysis fraudulent keyword detection web search

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Prior knowledge and cognitive styles in personalized learning

Prior knowledge and cognitive styles in personalized learnin...

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19th International Conference on Computers in Education, ICCE 2011

作者： Huang, Pei-Ren Chang, Li-Ping Shih, Yu-Cheng Chen, Sherry Y. Graduate Institute of Network Learning Technology National Central University Taiwan School of Information Systems Computing and Mathematics Brunel University Uxbridge United Kingdom

ISBN: (纸本)9786161201883

In the past decade, a number of personalized learning systems have been developed. Prior knowledge has widely been considered in the development of personalized learning systems. On the other hand, previous research suggested that cognitive styles have great effects on student learning. To this end, this study examine how cognitive styles, affect learners' reactions to a personalized and non-personalized learning systems based on learners' priop knowledeg. Forty-four undergraduate and postgraduate students participated in this study. The results show that Serialists show positive reactions to the personalized learning system while Holists demonstrate equal reactions to the personalized learning system and the non-personalized learning system. The implications of these results for the design of personalized learning systems are discussed.

关键词： Students

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An Overview of Dynamic Spectrum Sharing: Ongoing Initiatives, Challenges, and a Roadmap for Future Research

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IEEE Transactions on Cognitive Communications and networking 2016年第2期2卷 110-128页

作者： Bhattarai, Sudeep Park, Jung-Min Gao, Bo Bian, Kaigui Lehr, William Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061-0131 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Network Computing and Information Systems Peking University Beijing100871 China Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

We are in the midst of a major paradigm shift in how we manage radio spectrum. This paradigm shift is necessitated by the growth of wireless services of all types and the demand pressure imposed on limited spectrum resources under legacy management regimes. The shift is feasible because of advances in radio and networking technologies that make it possible to share spectrum dynamically in all possible dimensions-i.e., across frequencies, time, location, users, uses, and networks. Realizing the full potential of this shift to Dynamic Spectrum Sharing will require the co-evolution of wireless technologies, markets, and regulatory policies;a process which is occurring on a global scale. This paper provides a current overview of major technological and regulatory reforms that are leading the way toward a global paradigm shift to more flexible, dynamic, market-based ways to manage and share radio spectrum resources. We focus on current efforts to implement database-driven approaches for managing the shared co-existence of users with heterogeneous access and interference protection rights, and discuss open research challenges. © 2015 IEEE.

关键词： Spectroscopy

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Detection of Internet Water Army in Social network

Detection of Internet Water Army in Social Network

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2014 International Conference on Computer,Communications and information Technology(CCIT 2014)

作者： Kun Wang Yang Xiao Zhen Xiao Institute of Network Computing and Information Systems Peking University

ISBN: (纸本)9781629939865

As related works had a few research on Internet Water Army in social network, specifically on the Internet Water Army who trends to lead people's opinions, obscure the real voices and change public opinions in social network. To better understand what difference lie between Internet Water Army and legitimate user, we did some work about behaviour of them from real dataset in Sina microblogging service. We adopted some machine learning algorithms to classify the type of user with collected features through the measurement. At the same time we proposed an influence model and create a new online algorithm with linear complexity to reduce the water army's influence on legitimate users greatly.

关键词： Internet Water Army,Feature measurement,Machine learning,Influence model,MEIWA online algorithm

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Building a story tracer out of a web archive 08

Building a story tracer out of a web archive

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8th ACM/IEEE-CS Joint Conference on Digital Libraries 2008, JCDL'08

作者： Huang, LiaN'En Zhu, Jonathan J. H. Li, Xiaoming Institute of Network Computing and Information Systems Peking University Beijing China Dept of Media and Communication City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781595939982

There are quite a few web archives around the world, such as Internet Archive and Web InfoMall (http://***). Nevertheless, we have not seen substantial mechanism built on top of the archives to render the value of the data beyond what the Wayback machine offers. One of the reasons for this situation is the lack of a system vision and design which encompasses the oceanic data in a meaningful and cost-effective way. This paper describes an effort in this direction.

关键词： Cost effectiveness

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WordTopic-MultiRank: A New Method for Automatic Keyphrase Extraction 6

WordTopic-MultiRank: A New Method for Automatic Keyphrase Ex...

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6th International Joint Conference on Natural Language Processing, IJCNLP 2013

作者： Zhang, Fan Huang, Lian'en Peng, Bo Shenzhen Key Lab for Cloud Computing Technology and Applications Peking University Shenzhen Graduate School Shenzhen518055 China Institute of Network Computing and Information Systems Peking University Beijing100871 China

ISBN: (纸本)9784990734800

Automatic keyphrase extraction aims to pick out a set of terms as a representation of a document without manual assignment efforts. Supervised and unsupervised graph-based ranking methods have been studied for this task. However, previous methods usually computed importance scores of words under the assumption of single relation between words. In this work, we propose WordTopic-MultiRank as a new method for keyphrase extraction, based on the idea that words relate with each other via multiple relations. First we treat various latent topics in documents as heterogeneous relations between words and construct a multi-relational word network. Then, a novel ranking algorithm, named Biased-MultiRank, is applied to score the importance of words and topics simultaneously, as words and topics are considered to have mutual influence on each other. Experimental results on two different data sets show the outstanding performance and robustness of our proposed approach in automatic keyphrase extraction task. © IJCNLP *** right reserved.

关键词： Extraction

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HisTrace: Building a search engine of historical Events 08

HisTrace: Building a search engine of historical Events

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17th International Conference on World Wide Web 2008, WWW'08

作者： Huang, HuangLian'en Zhu, Jonathan J. H. Li, Xiaoming Institute of Network Computing and Information Systems Peking University Beijing China Dept of Media and Communication City University of Hong Kong Kowloon Hong Kong Hong Kong

ISBN: (纸本)9781605580852

In this paper, we describe an experimental search engine on our Chinese web archive since 2001. The original data set contains nearly 3 billion Chinese web pages crawled from past 5 years. From the collection, 430 million "article-like" pages are selected and then partitioned into 68 million sets of similar pages. The titles and publication dates are determined for the pages. An index is built. When searching, the system returns related pages in a chronological order. This way, if a user is interested in news reports or commentaries for certain previously happened event, he/she will be able to find a quite rich set of highly related pages in a convenient way.

关键词： Search engines

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Analysis Model of Terrorist Attacks Based on Big Data 32

Analysis Model of Terrorist Attacks Based on Big Data

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第32届中国控制与决策会议

作者： LIN Zhenkai DOU Yimin LI Jinping School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing(University of Jinan) Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Institute of Pattern Recognition and Intelligent Systems University of Jinan

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

With the continuous deepening of international anti-terrorism movement,the anti-terrorism has entered a new stage,and it is facing new *** of the new challenges is to extract useful and valuable information from massive data *** anti-terrorism system model based on local shallow information is imperfect,which is not conducive to obtaining accurate prediction *** shortcomings of existing research are the lack of comprehensive analysis and deeper mining of *** order to improve the efficiency and accuracy of the present anti-terrorism system,we propose an effective method for risk assessment and prediction based on machine learning by using Global Terrorism Database(GTD).There are four basic steps:first,we reduce the data dimension through correlation calculation and Singular Value Decomposition(SVD),then,the function is established to rank the harmfulness of terrorist attacks;second,the cascaded network with attention mechanism is used to predict suspects;third,k-means is used to cluster the regions of terrorist attacks,and then we establish a generalized linear regression model to predict the situation of terrorist *** verify the feasibility of the model by comparing with the real *** experimental results show that the proposed method can analyze and predict the information related to terrorist attacks comprehensively and accurately.

关键词： anti-terrorism warning data modeling SVD cascade network with attention mechanism K-means

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Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation 26

Sequence to backward and forward sequences: A content-introd...

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26th International Conference on Computational Linguistics, COLING 2016

作者： Mou, Lili Song, Yiping Yan, Rui Li, Ge Zhang, Lu Jin, Zhi Key Laboratory of High Confidence Software Technologies MoE China Institute of Software Peking University China Institute of Network Computing and Information Systems Peking Univerity China Institute of Computer Science and Technology Peking University China

ISBN: (纸本)9784879747020

Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main GIST of the reply. We then propose seq2BF, a "sequence to backward and forward sequences" model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply. © 1963-2018 ACL.

关键词： Computational linguistics

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Searching for historical events on a large-scale web archive

Searching for historical events on a large-scale web archive

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6th International Conference on Semantics, Knowledge and Grid, SKG 2010

作者： Huang, Lian'En Lin, Wu Li, Xiaoming Internet Research and Engineering Center Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China Institute of Network Computing and Information Systems School of EECS Peking University Beijing 100871 China

ISBN: (纸本)9780769541891

Finding knowledge on the Web has long been a hot research issue. Today the Web has become a popular medium for publishing news and opinion articles, which are important carriers of human knowledge, especially of social knowledge. Developing techniques of automatically collecting and analysing these articles on a large scale is thus desirable. In this paper we propose techniques for searching for events on the Web, and our techniques have been tested on a large scale web archive. Given an event, or a news topic cared by many people, the purpose of this paper is to find out near-all news stories related to it. First, a novel domain-independent approach of extracting news stories from web pages is proposed which is based on anchor text and is applicable to most websites. Experiments show our approach performs good and is better than another approach we have found. Second, a domain-based method of representing events is proposed in which hundreds of keywords are used to represent an event and compose the query expression. This situation of retrieval is different from most search engines' in that the number of keywords is large. We then propose several retrieval algorithms based on BM25 for the method. Evaluation show that these algorithms perform better than unmodified BM25 in our situation and the best one is chosen as the algorithm of our system. Finally an experimental system has been built on a collection of 2 billion web pages and the running performance is reported, which shows the effectiveness of our approaches. © 2010 IEEE.

关键词： Websites

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