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Computer Science Faculty Publications

2005

Articles 1 - 17 of 17

Full-Text Articles in Physical Sciences and Mathematics

On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Derek Leonard, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov Nov 2005

On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Derek Leonard, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov

Computer Science Faculty Publications

In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1-o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and therefore partitions, …

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Enhancing Undergraduate Ai Courses Through Machine Learning Projects, Ingrid Russell, Zdravko Markov, Todd W. Neller, Susan Coleman Oct 2005

Enhancing Undergraduate Ai Courses Through Machine Learning Projects, Ingrid Russell, Zdravko Markov, Todd W. Neller, Susan Coleman

Computer Science Faculty Publications

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a …

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Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao Jun 2005

Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao

Computer Science Faculty Publications

Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …

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Hierarchical Kohonenen Net For Anomaly Detection In Network Security, Suseela T. Sarasamma, Qiuming Zhu, Julie Huff Apr 2005

Hierarchical Kohonenen Net For Anomaly Detection In Network Security, Suseela T. Sarasamma, Qiuming Zhu, Julie Huff

Computer Science Faculty Publications

A novel multilevel hierarchicalKohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data …

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Personalization By Program Slicing, Saverio Perugini, Naren Ramakrishnan Apr 2005

Personalization By Program Slicing, Saverio Perugini, Naren Ramakrishnan

Computer Science Faculty Publications

Personalization involves customizing information access to the end-user. As any new area of computer science research it lacks formal models to guide the design of systems. In this paper, we present a modeling methodology, based on generative programming, for personalizing interactions with hierarchical websites. The methodology entails modeling a user’s interaction with a site in a program and applying program slicing to personalize the interaction. While preserving interactivity, this approach does not require the designer to anticipate all possible user interactions a priori and provide interfaces for each. Moreover, it provides a theoretical, systematic, and implementation-neutral way to design systems …

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A New Fault Information Model For Fault-Tolerant Adaptive And Minimal Routing In 3-D Meshes, Zhen Jiang, Jie Wu, Dajin Wang Jan 2005

A New Fault Information Model For Fault-Tolerant Adaptive And Minimal Routing In 3-D Meshes, Zhen Jiang, Jie Wu, Dajin Wang

Computer Science Faculty Publications

No abstract provided.

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Pigtail: A Pig Addendum, Todd W. Neller, Clifton G.M. Presser Jan 2005

Pigtail: A Pig Addendum, Todd W. Neller, Clifton G.M. Presser

Computer Science Faculty Publications

The object of the jeopardy dice game Pig is to be the first player to reach 100 points. Each turn, a player repeatedly rolls a die until either a 1 is rolled or the player holds and scores the sum of the rolls (i.e., the turn total). At any time during a player’s turn, the player is faced with two choices: roll or hold. If the player rolls a 1, the player scores nothing and it becomes the opponent’s turn. If the player rolls a number other than 1, the number is added to the player’s turn total …

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Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences, Ingrid Russell, Zdravko Markov, Todd W. Neller, Michael Georgiopoulos, Susan Coleman Jan 2005

Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences, Ingrid Russell, Zdravko Markov, Todd W. Neller, Michael Georgiopoulos, Susan Coleman

Computer Science Faculty Publications

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application …

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State Aggregation And Population Dynamics In Linear Systems, Jonathan E. Rowe, Michael D. Vose, Alden H. Wright Jan 2005

State Aggregation And Population Dynamics In Linear Systems, Jonathan E. Rowe, Michael D. Vose, Alden H. Wright

Computer Science Faculty Publications

We consider complex systems that are composed of many interacting elements, evolving under some dynamics. We are interested in characterizing the ways in which these elements may be grouped into higher-level, macroscopic states in a way that is compatible with those dynamics. Such groupings may then be thought of as naturally emergent properties of the system. We formalize this idea and, in the case that the dynamics are linear, prove necessary and sufficient conditions for this to happen. In cases where there is an underlying symmetry among the components of the system, group theory may be used to provide a …

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Protecting The Communication Structure In Sensor Networks, S. Olariu, Q. Xu, M. Eltoweissy, A. Wadaa Jan 2005

Protecting The Communication Structure In Sensor Networks, S. Olariu, Q. Xu, M. Eltoweissy, A. Wadaa

Computer Science Faculty Publications

In the near future wireless sensor networks will be employed in a wide variety of applications establishing ubiquitous networks that will pervade society. The inherent vulnerability of these massively deployed networks to a multitude of threats, including physical tampering with nodes exacerbates concerns about privacy and security. For example, denial of service attacks (DoS) that compromise or disrupt communications or target nodes serving key roles in the network, e.g. sink nodes, can easily undermine the functionality as well as the performance delivered by the network. Particularly vulnerable are the components of the communications or operation infrastructure. Although, by construction, most …

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A Generative Programming Approach To Interactive Information Retrieval: Insights And Experiences, Saverio Perugini, Naren Ramakrishnan Jan 2005

A Generative Programming Approach To Interactive Information Retrieval: Insights And Experiences, Saverio Perugini, Naren Ramakrishnan

Computer Science Faculty Publications

We describe the application of generative programming to a problem in interactive information retrieval. The particular interactive information retrieval problem we study is the support for "out-of-turn interaction" with a website – how a user can communicate input to a website when the site is not soliciting such information on the current page, but will do so on a subsequent page. Our solution approach makes generous use of program transformations (partial evaluation, currying, and slicing) to delay the site’s current solicitation for input until after the user’s out-of-turn input is processed. We illustrate how studying out-of-turn interaction through a generative …

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The Good, Bad And The Indifferent: Explorations In Recommender System Health, Benjamin J. Keller, Sun-Mi Kim, N. Srinivas Vemuri, Naren Ramakrishnan, Saverio Perugini Jan 2005

The Good, Bad And The Indifferent: Explorations In Recommender System Health, Benjamin J. Keller, Sun-Mi Kim, N. Srinivas Vemuri, Naren Ramakrishnan, Saverio Perugini

Computer Science Faculty Publications

Our work is based on the premise that analysis of the connections exploited by a recommender algorithm can provide insight into the algorithm that could be useful to predict its performance in a fielded system. We use the jumping connections model defined by Mirza et al. [6], which describes the recommendation process in terms of graphs. Here we discuss our work that has come out of trying to understand algorithm behavior in terms of these graphs. We start by describing a natural extension of the jumping connections model of Mirza et al., and then discuss observations that have come from …

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Recommender Systems Research, Saverio Perugini Jan 2005

Recommender Systems Research, Saverio Perugini

Computer Science Faculty Publications

We outline the history of recommender systems from their roots in information retrieval and filtering to their role in today’s Internet economy. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. Research in recommender systems lies at the intersection of several areas of computer science, such as artificial intelligence and human-computer interaction, and has progressed to an important research area of its own. It is important to note that recommendations are not delivered within a vacuum, but rather cast within an informal community of users …

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Introduction: Data Communication And Topology Algorithms For Sensor Networks, Stephan Olariu, David Simplot-Ryl, Ivan Stojmenovic Jan 2005

Introduction: Data Communication And Topology Algorithms For Sensor Networks, Stephan Olariu, David Simplot-Ryl, Ivan Stojmenovic

Computer Science Faculty Publications

(First paragraph) We are very proud and honored to have been entrusted to be Guest Editors for this special issue. Papers were sought to comprehensively cover the algorithmic issues in the “hot” area of sensor networking. The concentration was on network layer problems, which can be divided into two groups: data communication problems and topology control problems. We wish to briefly introduce the five papers appearing in this special issue. They cover specific problems such as time division for reduced collision, fault tolerant clustering, self-stabilizing graph optimization algorithms, key pre-distribution for secure communication, and distributed storage based on spanning trees …

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Lessons Learned With Arc, An Oai-Pmh Service Provider, Xiaoming Liu, Kurt Maly, Michael L. Nelson Jan 2005

Lessons Learned With Arc, An Oai-Pmh Service Provider, Xiaoming Liu, Kurt Maly, Michael L. Nelson

Computer Science Faculty Publications

Web-based digital libraries have historically been built in isolation utilizing different technologies, protocols, and metadata. These differences hindered the development of digital library services that enable users to discover information from multiple libraries through a single unified interface. The Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) is a major, international effort to address technical interoperability among distributed repositories. Arc debuted in 2000 as the first end-user OAI-PMH service provider. Since that time, Arc has grown to include nearly 7,000,000 metadata records. Arc has been deployed in a number of environments and has served as the basis for many other …

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Final Report For The Development Of The Nasa Technical Report Server (Ntrs), Michael L. Nelson Jan 2005

Final Report For The Development Of The Nasa Technical Report Server (Ntrs), Michael L. Nelson

Computer Science Faculty Publications

The author performed a variety of research, development and consulting tasks for NASA Langley Research Center in the area of digital libraries (DLs) and supporting technologies, such as the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). In particular, the development focused on the NASA Technical Report Server (NTRS) and its transition from a distributed searching model to one that uses the OAI-PMH. The Open Archives Initiative (OAI) is an international consortium focused on furthering the interoperability of DLs through the use of "metadata harvesting". The OAI-PMH version of NTRS went into public production on April 28, 2003. Since that …

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Archive Ingest And Handling Test, Michael L. Nelson, Johan Bollen, Giridhar Manepalli, Rabia Haq Jan 2005

Archive Ingest And Handling Test, Michael L. Nelson, Johan Bollen, Giridhar Manepalli, Rabia Haq

Computer Science Faculty Publications

The Archive Ingest and Handling Test (AIHT) was a Library of Congress (LC) sponsored research project administered by Information Systems and Support Inc. (ISS). The project featured five participants: Old Dominion University Computer Science Department; Harvard University Library; Johns Hopkins University Library; Stanford University Library; Library of Congress. All five participants received identical disk drives containing copies of the 911.gmu.edu web site, a collection of 9/11 materials maintained by George Mason University (GMU). The purpose of the AIHT experiment was to perform archival forensics to determine the nature of the archive, ingest it, simulate at least one of the file …

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