Hsinchun Chen

Hsinchun Chen

Professor, Management Information Systems
Regents Professor
Member of the Graduate Faculty
Professor, BIO5 Institute
Primary Department
Contact
(520) 621-4153

Research Interest

Dr Chen's areas of expertise include:Security informatics, security big data; smart and connected health, health analytics; data, text, web mining.Digital library, intelligent information retrieval, automatic categorization and classification, machine learning for IR, large-scale information analysis and visualization.Internet resource discovery, digital libraries, IR for large-scale scientific and business databases, customized IR, multilingual IR.Knowledge-based systems design, knowledge discovery in databases, hypertext systems, machine learning, neural networks computing, genetic algorithms, simulated annealing.Cognitive modeling, human-computer interactions, IR behaviors, human problem-solving process.

Publications

Daning, H. u., Kaza, S., & Chen, H. (2009). Identifying significant facilitators of dark network evolution. Journal of the American Society for Information Science and Technology, 60(4), 655-665.

Abstract:

Social networks evolve over time with the addition and removal of nodes and links to survive and thrive in their environments. Previous studies have shown that the linkformation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the important facilitators. In a research partnership with law enforcement agencies, we used dynamic social-network analysis methods to examine several plausible facilitators of co-offending relationships in a large-scale narcotics network consisting of individuals and vehicles. Multivariate Cox regression and a two-proportion z-test on cyclic and focal closures of the network showed that mutual acquaintance and vehicle affiliations were significant facilitators for the network under study. We also found that homophily with respect to age, race, and gender were not good predictors of future link formation in these networks. Moreover, we examined the social causes and policy implications for the significance and insignificance of various facilitators including common jails on future co-offending.These findings provide important insights into the link-formation processes and the resilience of social networks. In addition, they can be used to aid in the prediction of future links. The methods described can also help in understanding the driving forces behind the formation and evolution of social networks facilitated by mobile and Web technologies.

Mielke, C., & Chen, H. (2008). Mapping dark web geolocation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5376 LNCS, 97-107.

Abstract:

In this paper we first provide a brief review of the Dark Web project of the University of Arizona Artificial Intelligence Lab. We then report our research design and case study that aim to identify the geolocation of the countries, cities, and ISPs that host selected international Jihadist web sites. We provide an overview of key relevant Internet functionality and architecture and present techniques for exploiting networking technologies for locating servers and resources. Significant findings from our case study and suggestion for future research are also presented. © 2008 Springer Berlin Heidelberg.

Chen, H., Roco, M. C., & Son, J. (2013). Nanotechnology public funding and impact analysis: A tale of two decades (1991-2010). IEEE Nanotechnology Magazine, 7(1), 9-14.

Abstract:

Nanotechnology?s economic and societal benefits have continued to attract significant research and development (R&D) attention from governments and industries worldwide. Over the past two decades, nanotechnology has seen quasi-exponential growth in the numbers of scientific papers and patent publications produced. New research topics and application areas are continually emerging, and investment from government, industry, and academia [1], [2] has expanded at substantial levels. But what is the impact of public funding on nanotechnology? How important is its role in driving innovation, invention, and knowledge transfer? © 2007-2011 IEEE.

McDonald, D. M., Chen, H., Hua, S. u., & Marshall, B. B. (2004). Extracting gene pathway relations using a hybrid grammar: The Arizona Relation Parser. Bioinformatics, 20(18), 3370-3378.

PMID: 15256411;Abstract:

Motivation: Text-mining research in the biomedical domain has been motivated by the rapid growth of new research findings. Improving the accessibility of findings has potential to speed hypothesis generation. Results: We present the Arizona Relation Parser that differs from other parsers in its use of a broad coverage syntaxsemantic hybrid grammar. While syntax grammars have generally been tested over more documents, semantic grammars have outperformed them in precision and recall. We combined access to syntax and semantic information from a single grammar. The parser was trained using 40 PubMed abstracts and then tested using 100 unseen abstracts, half for precision and half for recall. Expert evaluation showed that the parser extracted biologically relevant relations with 89% precision. Recall of expert identified relations with semantic filtering was 35 and 61% before semantic filtering. Such results approach the higher-performing semantic parsers. However, the AZ parser was tested over a greater variety of writing styles and semantic content. © Oxford University Press 2004; all rights reserved.

Tianjun, F., Huang, C., & Hsinchun, C. (2009). Identification of extremist videos in online video sharing sites. 2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009, 179-181.

Abstract:

Web 2.0 has become an effective grassroots communication platform for extremists to promote their ideas, share resources, and communicate among each other. As an important component of Web 2.0, online video sharing sites such as YouTube and Google video have also been utilized by extremist groups to distribute videos. This study presented a framework for identifying extremist videos in online video sharing sites by using user-generated text content such as comments, video descriptions, and titles without downloading the videos. Text features including lexical features, syntactic features and content specific features were first extracted. Then Information Gain was used for feature selection, and Support Vector Machine was deployed for classification. The exploratory experiment showed that our proposed framework is effective for identifying online extremist videos, with the F-measure as high as 82%. ©2009 IEEE.