21103781
Biomedical Informatics and Security Informatics Research in Digital Library
The Internet is changing the way we live and do business. It offers a tremendous opportunity for libraries, governments, and businesses to better deliver its contents and services and interact with its many constituents. After ten years of active research, there appears to be a need towards advancing the science of “informatics” in digital library, especially in several non-traditional but critical application areas. In this paper, we introduce two promising informatics research areas for digital library researchers, namely, Biomedical Informatics and Security Informatics. We discuss some common research elements between these two areas and present several case studies that aim to highlight the relevance and importance of such research in digital library.
19452291
Cross-Jurisdictional Activity Networks to Support Criminal Investigations
Border and transportation security is a critical part of the Department of Homeland Security's (DHS) national strategy. DHS strategy calls for the creation of "smart borders" where information from local, state, federal, and international sources can be combined to support risk-based management tools for border-management agencies. Sharing cross-jurisdictional law enforcement data is important because criminals take advantage of the fact that information sharing between law enforcement jurisdictions is very limited. Challenges to information integration in this important domain include policy and privacy concerns, data security requirements, semantic or schema-level matching of legacy system data, and identity matching. The BorderSafe project leverages the skills and experiences of a diverse group of researchers, law enforcement officials, and government agencies to address data sharing needs in the Southwest United States. Our demo presents a framework for combining law enforcement data across jurisdictions to support criminal investigation and describes how that framework was used in combining three large, overlapping datasets to produce an integrated system for visualizing criminal activity networks.
35410760
Collaborative Workflow Management for Interagency Crime Analysis
To strengthen homeland security, there is a critical need for new tools that can facilitate real time collaboration among various law enforcement agencies. Through a field study, we find that law enforcement work is knowledge intensive and involves complex collaborative processes interrelating a large number of disparate units in a loosely defined virtual organization. To support knowledge intensive collaboration, we propose a new workflow centric framework to seamlessly integrate previously separate techniques from the fields of information retrieval and workflow management. Specifically, we develop a collaborative workflow management framework for interagency crime analysis. The key contribution of our research is that by integrating various state-of-the-art techniques innovatively, the proposed system can support real time collaboration processes in a virtual organization that evolves dynamically.
38977435
Sharing and Visualizing Infectious Disease Datasets Using the WNV-BOT Portal System
Infectious disease outbreaks are critical threats to public health and national security. Information systems play a central role in developing an effective comprehensive approach to prevent, detect, respond to, and manage infectious disease outbreaks of plants, animals, and humans.
21266732
User Misconceptions of Information Retrieval Systems
In this paper, we report results of an investigation where thirty subjects were observedperforming subject-based search in an online catalog system. The observationshave revealed a range of misconceptions users have when performing subject-basedsearch. We have developed a taxonomy that characterizes these misconceptions andhypotheses about the causes of the misconceptions. Directions for improving searchperformance are also suggested.
40127439
Authorship Analysis in Cybercrime Investigation
Criminals have been using the Internet to distribute a wide range of illegal materials globally in an anonymous manner, making criminal identity tracing difficult in the cybercrime investigation process. In this study we propose to adopt the authorship analysis framework to automatically trace identities of cyber criminals through messages they post on the Internet. Under this framework, three types of message features, including style markers, structural features, and content-specific features, are extracted and inductive learning algorithms are used to build feature-based models to identify authorship of illegal messages. To evaluate the effectiveness of this framework, we conducted an experimental study on data sets of English and Chinese email and online newsgroup messages. We experimented with all three types of message features and three inductive learning algorithms. The results indicate that the proposed approach can discover real identities of authors of both English and Chinese Internet messages with relatively high accuracies.
6781537
Formal Models of Heavy-Tailed Behavior in Combinatorial Search
Recently, it has been found that the cost distributions of randomized backtrack search in combinatorial domains are often heavytailed. Such heavy-tailed distributions explain the high variability observed when using backtrack-style procedures. A good understanding of this phenomenon can lead to better search techniques. For example, restart strategies provide a good mechanism for eliminating the heavy-tailed behavior and boosting the overall search performance. Several state-of-the-art SAT solvers now incorporate such restart mechanisms. The study of heavy-tailed phenomena in combinatorial search has so far been been largely based on empirical data. We introduce several abstract tree search models, and show formally how heavy-tailed cost distribution can arise in backtrack search. We also discuss how these insights may facilitate the development of better combinatorial search methods.
5510684
Robust motion-based image segmentation using fusion
To support real-time tracking of objects in video sequences, there has been considerable effort directed at developing optical flow and general motion-based image segmentation algorithms. The goal is to segment multiple moving objects in the image based on their relative motion. This task can be complicated by the presence of lighting variations. Furthermore, a combination of multiple motions and complex lighting effects can lead to dramatic image variations that may not be adequately accounted for by a single motion-based segmentation algorithm. We propose to fuse the results of multiple motion segmentation algorithms to improve the system robustness. Our approach uses the expectation maximization (EM) algorithm as a fusion engine. It also uses principal components analysis (PCA) to perform dimensionality reduction to improve the performance of the EM algorithm and reduce the processing burden. The performance of the proposed fusion algorithm has been demonstrated in the "smart airbag" application of monitoring occupants in a moving automobile to determine if they are too close to the instrument panel (airbag). Through fusion of the outputs of multiple algorithms we are able to reduce the percentage of pixels missed on the target by 35.
31295380
Intelligent Agents for Mobile and Embedded Devices
The pervasive computing environments of the near future will involve the interactions, coordination and cooperation of numerous, casually accessible, and often invisible computing devices. These devices, whether carried on our person or embedded in our homes, businesses and classrooms, will connect via wireless and wired links to one another and to the global networking infrastructure. The result will be a networking milieu with a new level of openness. The localized and dynamic nature of their interactions raises many new issues that draw on and challenge the disciplines of agents, distributed systems, and security. This paper describes recent work by the UMBC Ebiquity research group which addresses some of these issues.
9059456
Model Checking One Million Lines of C Code
Implementation bugs in security-critical software are pervasive. Several authors have previously suggested model checking as a promising means to detect improper use of system interfaces and thereby detect a broad class of security vulnerabilities. In this paper, we report on our practical experience using MOPS, a tool for software model checking security-critical applications. As examples of security vulnerabilities that can be analyzed using model checking, we pick five important classes of vulnerabilities and show how to codify them as temporal safety properties, and then we describe the results of checking them on several significant Unix applications using MOPS. After analyzing over one million lines of code, we found more than a dozen new security weaknesses in important, widely-deployed applications. This demonstrates for the first time that model checking is practical and useful for detecting security weaknesses at large scale in real,
22537497
ACES: An efficient admission control scheme for QoS-aware web servers
The unpredictability of server response performance hinders the advance of new application on the Internet. In this paper, we present an efficient admission control algorithm, ACES, based on the server workload characteristics. The admission control algorithm ensures the bounded response time from a web server by periodical allocation of system resources according to the resource requirements of incoming tasks. By rejecting requests exceeding server capacity, the response performance of the server is well maintained even under high system utilization. The resource requirements of tasks are estimated based on their types. A double-queue structure is implemented to reduce the effects caused by estimation inaccuracy, and to exploit the spare capacity of the server, thus increasing the system throughput. The admission control algorithm can be used for server overload control and for QoS provisioning of service differentiating Internet servers. Response delays of accepted tasks are bounded by the desired predefined time period. Theoretical analyses and experimental studies show that the ACES algorithm provides desirable throughput and bounded response delay to the tasks, without any significant impact on the aggregate throughput performance of the system under various workload situations.