PMID: 18255383;Abstract:
This paper describes a content-based image retrieval digital library that supports geographical image retrieval over a testbed of 800 aerial photographs, each 25 megabytes in size. In addition, this paper also introduces a methodology to evaluate the performance of the algorithms in the prototype system. The major contributions of this paper are two. 1) We suggest an approach that incorporates various image processing techniques including Gabor filters, image enhancement, and image compression, as well as information analysis technique such as self-organizing map (SOM) into an effective large-scale geographical image retrieval system. 2) We present two experiments that evaluate the performance of the Gabor-filter-extracted features along with the corresponding similarity measure against that of human perception, addressing the lack of studies in assessing the consistency between an image representation algorithm or an image categorization method and human mental model.
Abstract:
The ability to automatically detect fraudulent escrow websites is important in order to alleviate online auction fraud. Despite research on related topics, such as web spam and spoof site detection, fake escrow website categorization has received little attention. The authentic appearance of fake escrow websites makes it difficult for Internet users to differentiate legitimate sites from phonies; making systems for detecting such websites an important endeavor. In this study we evaluated the effectiveness of various features and techniques for detecting fake escrow websites. Our analysis included a rich set of fraud cues extracted from web page text, image, and link information. We also compared several machine learning algorithms, including support vector machines, neural networks, decision trees, naïve bayes, and principal component analysis. Experiments were conducted to assess the proposed fraud cues and techniques on a test bed encompassing nearly 90,000 web pages derived from 410 legitimate and fake escrow websites. The combination of an extended feature set and a support vector machines ensemble classifier enabled accuracies over 90 and 96% for page and site level classification, respectively, when differentiating fake pages from real ones. Deeper analysis revealed that an extended set of fraud cues is necessary due to the broad spectrum of tactics employed by fraudsters. The study confirms the feasibility of using automated methods for detecting fake escrow websites. The results may also be useful for informing existing online escrow fraud resources and communities of practice about the plethora of fraud cues pervasive in fake websites. © Springer Science+Business Media, LLC 2009.
Abstract:
Dynamic criminal network analysis is important for national security but also very challenging. However, little research has been done in this area. In this paper we propose to use several descriptive measures from social network analysis research to help detect and describe changes in criminal organizations. These measures include centrality for individuals, and density, cohesion, and stability for groups. We also employ visualization and animation methods to present the evolution process of criminal networks. We conducted a field study with several domain experts to validate our findings from the analysis of the dynamics of a narcotics network. The feedback from our domain experts showed that our approaches and the prototype system could be very helpful for capturing the dynamics of criminal organizations and assisting crime investigation and criminal prosecution. © Springer-Verlag Berlin Heidelberg 2004.
Abstract:
In a relatively short interval for an emerging technology, nanotechnology has made a significant economic impact in numerous sectors including semiconductor manufacturing, catalysts, medicine, agriculture, and energy production. A part of the United States (US) government investment in basic research has been realized in the last two decades through the National Science Foundation (NSF), beginning with the nanoparticle research initiative in 1991 and continuing with support from the National Nanotechnology Initiative after fiscal year 2001. This paper has two main goals: (a) present a longitudinal analysis of the global nanotechnology development as reflected in the United States Patent and Trade Office (USPTO) patents and Web of Science (WoS) publications in nanoscale science and engineering (NSE) for the interval 1991-2012; and (b) identify the effect of basic research funded by NSF on both indicators. The interval has been separated into three parts for comparison purposes: 1991-2000, 2001-2010, and 2011-2012. The global trends of patents and scientific publications are presented. Bibliometric analysis, topic analysis, and citation network analysis methods are used to rank countries, institutions, technology subfields, and inventors contributing to nanotechnology development. We then, examined how these entities were affected by NSF funding and how they evolved over the past two decades. Results show that dedicated NSF funding used to support nanotechnology R&D was followed by an increased number of relevant patents and scientific publications, a greater diversity of technology topics, and a significant increase of citations. The NSF played important roles in the inventor community and served as a major contributor to numerous nanotechnology subfields. © 2013 Springer Science+Business Media.
Abstract:
Expertise management systems are being widely adopted in organizations to manage tacit knowledge. These systems have successfully applied many information technologies developed for document management to support collection, processing, and distribution of expertise information. In this paper, we report a study on the potential of applying visualization techniques to support more effective and efficient exploration of the expertise information space. We implemented two widely applied dimensionality reduction visualization techniques, the self-organizing map (SOM) and multidimensional scaling (MDS), to generate compact but distorted (due to the dimensionality reduction) map visualizations for an expertise data set. We tested cognitive fit theory in our context by comparing the SOM and MDS displays with a standard table display for five tasks selected from a low-level, domain-independent visual task taxonomy. The experimental results based on a survey data set of research expertise of the business school professors suggested that using both SOM and MDS visualizations is more efficient than using the table display for the associate, compare, distinguish, and cluster tasks, but not the rank task. Users generally achieved comparable effectiveness for all tasks using the tabular and map displays in our study. © 2006 Elsevier B.V. All rights reserved.
