Siddharth Kaza

Evaluating ontology mapping techniques: An experiment in public safety information sharing

The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless interoperability of systems in these agencies. Ontology-based interoperability approaches in the public safety domain need to rely on mapping between ontologies as each agency has its own representation of information. However, there has been little study of ontology mapping techniques in this domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. In addition, we implement an information theory based tool called MIMapper that uses WordNet and mutual information between data instances to map ontologies. We find that three tools: PROMPT, Chimaera, and LOM, have average F-measures of 0.46, 0.49, and 0.68 when matching pairs of ontologies with the number of classes ranging from 13-73. MIMapper performs better with an average F-measure of 0.84 in performing the same task. We conclude that the tools that use secondary sources (like WordNet) and data instances to establish mappings between ontologies are likely to perform better in this application domain.

Analyzing and visualizing criminal network dynamics: A case study

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.

Detecting Fake Medical Web Sites Using Recursive Trust Labeling

Fake medical Web sites have become increasingly prevalent. Consequently, much of the health-related information and advice available online is inaccurate and/or misleading. Scores of medical institution Web sites are for organizations that do not exist and more than 90% of online pharmacy Web sites are fraudulent. In addition to monetary losses exacted on unsuspecting users, these fake medical Web sites have severe public safety ramifications. According to a World Health Organization report, approximately half the drugs sold on the Web are counterfeit, resulting in thousands of deaths. In this study, we propose an adaptive learning algorithm called recursive trust labeling (RTL). RTL uses underlying content and graph-based classifiers, coupled with a recursive labeling mechanism, for enhanced detection of fake medical Web sites. The proposed method was evaluated on a test bed encompassing nearly 100 million links between 930,000 Web sites, including 1,000 known legitimate and fake medical sites. The experimental results revealed that RTL was able to significantly improve fake medical Web site detection performance over 19 comparison content and graph-based methods, various meta-learning techniques, and existing adaptive learning approaches, with an overall accuracy of over 94%. Moreover, RTL was able to attain high performance levels even when the training dataset composed of as little as 30 Web sites. With the increased popularity of eHealth and Health 2.0, the results have important implications for online trust, security, and public safety.

Security injections: modules to help students remember, understand, and apply secure coding techniques

With our global reliance on software, secure and robust programming has never been more important. Yet academic institutions have been slow to add secure coding to the curriculum. We present a model using checklist-based security injection modules to increase student awareness and ability to apply secure coding principles, specifically - identify, understand, and correct key security issues in code. The model is evaluated by mapping assessment questions to the cognitive dimension of the revised Blooms taxonomy. Experiments with students in four sections of CS0 and CS1 show that students using our modules perform significantly better at remembering, understanding and applying secure coding concepts. Students exposed to the modules also show increased ability to write code to address specific security issues.

A model for piloting pathways for computational thinking in a general education curriculum

Computational thinking has been identified as a necessary fundamental skill for all students. University curricula, however, are currently not designed to provide such knowledge to a broad student population. In this paper, we report on our experiences in the development of a model for incorporating computational thinking into the undergraduate, general education curriculum at Towson University. We discuss the model in terms of eliciting faculty interest, institutional support, and positive student response. In the first two years of this NSF-funded three-year project, we have developed, piloted and assessed five computational thinking general education courses - an Everyday Computational Thinking course, and four discipline-specific computational thinking general education courses. Initial assessments show promising and significant student, instructor and administration interest in computational thinking as a basis in courses covering multiple disciplines within the general education curriculum.

Visualization in law enforcement

Visualization techniques have proven to be critical in helping crime analysis. By interviewing and observing Criminal Intelligence Officers (CIO) and civilian crime analysts at the Tucson Police Department (TPD), we found that two types of tasks are important for crime analysis: crime pattern recognition and criminal association discovery. We developed two separate systems that provide automatic visual assistance on these tasks. To help identify crime patterns, a Spatial Temporal Visualization (STV) system was designed to integrate a synchronized view of three types of visualization techniques: a GIS view, a timeline view and a periodic pattern view. The Criminal Activities Network (CAN) system extracts, visualizes and analyzes criminal relationships using spring-embedded and blockmodeling algorithms. This paper discusses the design and functionality of these two systems and the lessons learned from the development process and interaction with law enforcement officers.

Topological Analysis of Criminal Activity Networks: Enhancing Transportation Security

The security of border and transportation systems is a critical component of the national strategy for homeland security. The security concerns at the border are not independent of law enforcement in border-area jurisdictions because the information known by local law enforcement agencies may provide valuable leads that are useful for securing the border and transportation infrastructure. The combined analysis of law enforcement information and data generated by vehicle license plate readers at international borders can be used to identify suspicious vehicles and people at ports of entry. This not only generates better quality leads for border protection agents but may also serve to reduce wait times for commerce, vehicles, and people as they cross the border. This paper explores the use of criminal activity networks (CANs) to analyze information from law enforcement and other sources to provide value for transportation and border security. We analyze the topological characteristics of CAN of individuals and vehicles in a multiple jurisdiction scenario. The advantages of exploring the relationships of individuals and vehicles are shown. We find that large narcotic networks are small world with short average path lengths ranging from 4.5 to 8.5 and have scale-free degree distributions with power law exponents of 0.85-1.3. In addition, we find that utilizing information from multiple jurisdictions provides higher quality leads by reducing the average shortest-path lengths. The inclusion of vehicular relationships and border-crossing information generates more investigative leads that can aid in securing the border and transportation infrastructure.

Determining inventor status and its effect on knowledge diffusion: A study on nanotechnology literature from China, Russia, and India

In an increasingly global research landscape, it is important to identify the most prolific researchers in various institutions and their influence on the diffusion of knowledge. Knowledge diffusion within institutions is influenced by not just the status of individual researchers but also the collaborative culture that determines status. There are various methods to measure individual status, but few studies have compared them or explored the possible effects of different cultures on the status measures. In this article, we examine knowledge diffusion within science and technology-oriented research organizations. Using social network analysis metrics to measure individual status in large-scale coauthorship networks, we studied an individuals impact on the recombination of knowledge to produce innovation in nanotechnology. Data from the most productive and high-impact institutions in China (Chinese Academy of Sciences), Russia (Russian Academy of Sciences), and India (Indian Institutes of Technology) were used. We found that boundary-spanning individuals influenced knowledge diffusion in all countries. However, our results also indicate that cultural and institutional differences may influence knowledge diffusion.

Enhancing border security: Mutual information analysis to identify suspect vehicles

In recent years border safety has been identified as a critical part of homeland security. The Department of Homeland Security searches vehicles entering the country for drugs and other contraband. Customs and Border Protection (CBP) agents believe that such vehicles operate in groups and if the criminal links of one vehicle are known then their border crossing patterns can be used to identify other partner vehicles. We perform this association analysis by using mutual information (MI) to identify pairs of vehicles that may be involved in criminal activity. CBP agents also suggest that criminal vehicles may cross at certain times or ports to try and evade inspection. We propose to modify the MI formulation to include these heuristics by using law enforcement data from border-area jurisdictions. Statistical tests and selected cases judged by domain experts show that modified MI performs significantly better than classical MI in identifying potentially criminal vehicles.

Security in computer literacy: a model for design, dissemination, and assessment

While many colleges offer specialized security courses and tracks for students in computing majors, there are few offerings in information security for the non-computing majors. Information security is becoming increasingly critical in many fields, yet most computer literacy courses insufficiently address the security challenges faced by our graduates. This paper discusses the development and impact of a set of modules designed to integrate security into computer literacy across two universities and several community colleges in the state of Maryland. Results from our comparative analyses based on pre- and post- test analysis show significant improvements in post-test results.