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Faculty Publications

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Articles 481 - 510 of 663

Full-Text Articles in Computer Sciences

Effectively Using Recurrently-Connected Spiking Neural Networks, Eric Goodman, Dan A. Ventura Jul 2005

Effectively Using Recurrently-Connected Spiking Neural Networks, Eric Goodman, Dan A. Ventura

Faculty Publications

Recurrently-connected spiking neural networks are difficult to use and understand because of the complex nonlinear dynamics of the system. Through empirical studies of spiking networks, we deduce several principles which are critical to success. Network parameters such as synaptic time delays and time constants and the connection probabilities can be adjusted to have a significant impact on accuracy. We show how to adjust these parameters to fit the type of problem.

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Time Invariance And Liquid State Machines, Eric Goodman, Dan A. Ventura Jul 2005

Time Invariance And Liquid State Machines, Eric Goodman, Dan A. Ventura

Faculty Publications

Time invariant recognition of spatiotemporal patterns is a common task of signal processing. Liquid state machines (LSMs) are a paradigm which robustly handle this type of classification. Using an artificial dataset with target pattern lengths ranging from 0.1 to 1.0 seconds, we train an LSM to find the start of the pattern with a mean absolute error of 0.18 seconds. Also, LSMs can be trained to identify spoken digits, 1-9, with an accuracy of 97.6%, even with scaling by factors ranging from 0.5 to 1.5.

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A Scenario-Based Performance Evaluation Of Multicast Routing Protocols For Ad Hoc Networks, Manoj Pandey, Daniel Zappala Jun 2005

A Scenario-Based Performance Evaluation Of Multicast Routing Protocols For Ad Hoc Networks, Manoj Pandey, Daniel Zappala

Faculty Publications

Current ad hoc multicast routing protocols have been designed to build and maintain a tree or mesh in the face of a mobile environment, with fast reaction to network changes in order to minimize packet loss. However, the performance of these protocols has not been adequately examined under realistic scenarios. Existing performance studies generally use a single, simple mobility model, with low density and often very low traffic rates. In this paper we explore the performance of ad hoc multicast routing protocols under scenarios that include realistic mobility patterns, high density and high traffic load. We use these scenarios to …

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Correspondence Expansion For Wide Baseline Stereo, Parris K. Egbert, Kevin L. Steele Jun 2005

Correspondence Expansion For Wide Baseline Stereo, Parris K. Egbert, Kevin L. Steele

Faculty Publications

We present a new method for generating large numbers of accurate point correspondences between two wide baseline images. This is important for structure-from-motion algorithms, which rely on many correct matches to reduce error in the derived geometric structure. Given a small initial correspondence set we iteratively expand the set with nearby points exhibiting strong affine correlation, and then we constrain the set to an epipolar geometry using RANSAC. A key point to our algorithm is to allow a high error tolerance in the constraint, allowing the correspondence set to expand into many areas of an image before applying a lower …

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An Evolutionary Algorithm To Generate Hyper-Ellipsoid Detectors For Negative Selection, Joseph M. Shapiro, Gary B. Lamont, Gilbert L. Peterson Jun 2005

An Evolutionary Algorithm To Generate Hyper-Ellipsoid Detectors For Negative Selection, Joseph M. Shapiro, Gary B. Lamont, Gilbert L. Peterson

Faculty Publications

This paper introduces hyper-ellipsoids as an improvement to hyper-spheres as intrusion detectors in a negative selection problem within an artificial immune system. Since hyper-spheres are a specialization of hyper-ellipsoids, hyper-ellipsoids retain the benefits of hyper-spheres. However, hyper-ellipsoids are much more flexible, mostly in that they can be stretched and reoriented. The viability of using hyper-ellipsoids is established using several pedagogical problems. We conjecture that fewer hyper-ellipsoids than hyper-spheres are needed to achieve similar coverage of nonself space in a negative selection problem. Experimentation validates this conjecture. In pedagogical benchmark problems, the number of hyper-ellipsoids to achieve good results is significantly …

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Active Contours Using A Constraint-Based Implicit Representation, Weiming Liu, Bryan S. Morse, Kalpathi Subramanian, Terry S. Yoo Jun 2005

Active Contours Using A Constraint-Based Implicit Representation, Weiming Liu, Bryan S. Morse, Kalpathi Subramanian, Terry S. Yoo

Faculty Publications

We present a new constraint-based implicit active contour, which shares desirable properties of both parametric and implicit active contours. Like parametric approaches, their representation is compact and can be manipulated interactively. Like other implicit approaches, they can naturally adapt to non-simple topologies. Unlike implicit approaches using level-set methods, representation of the contour does not require a dense mesh. Instead, it is based on specified on-curve and off-curve constraints, which are interpolated using radial basis functions. These constraints are evolved according to specified forces drawn from the relevant literature of both parametric and implicit approaches. This new type of active contour …

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Prioritized Multiplicative Schwarz Procedures For Solving Linear Systems, Nathaniel Powell, Kevin Seppi, Quinn O. Snell, David Wingate Apr 2005

Prioritized Multiplicative Schwarz Procedures For Solving Linear Systems, Nathaniel Powell, Kevin Seppi, Quinn O. Snell, David Wingate

Faculty Publications

We describe a new algorithm designed to quickly and robustly solve general linear problems of the form Ax = b. We describe both serial and parallel versions of the algorithm, which can be considered a prioritized version of an Alternating Multiplicative Schwarz procedure. We also adopt a general view of alternating Multiplicative Schwarz procedures which motivates their use on arbitrary problems (even which may not have arisen from problems that are naturally decomposable) by demonstrating that, even in a serial context, algorithms should use many, many partitions to accelerate convergence; having such an over-partitioned system also allows easy parallelization of …

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A New Blind Method For Detecting Novel Steganography, Brent T. Mcbride, Gilbert L. Peterson, Steven C. Gustafson Feb 2005

A New Blind Method For Detecting Novel Steganography, Brent T. Mcbride, Gilbert L. Peterson, Steven C. Gustafson

Faculty Publications

Steganography is the art of hiding a message in plain sight. Modern steganographic tools that conceal data in innocuous-looking digital image files are widely available. The use of such tools by terrorists, hostile states, criminal organizations, etc., to camouflage the planning and coordination of their illicit activities poses a serious challenge. Most steganography detection tools rely on signatures that describe particular steganography programs. Signature-based classifiers offer strong detection capabilities against known threats, but they suffer from an inability to detect previously unseen forms of steganography. Novel steganography detection requires an anomaly-based classifier. This paper describes and demonstrates a blind classification …

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Fast And Robust Incremental Action Prediction For Interactive Agents, Jonathan Dinerstein, Parris K. Egbert, Dan A. Ventura Feb 2005

Fast And Robust Incremental Action Prediction For Interactive Agents, Jonathan Dinerstein, Parris K. Egbert, Dan A. Ventura

Faculty Publications

The ability for a given agent to adapt on-line to better interact with another agent is a difficult and important problem. This problem becomes even more difficult when the agent to interact with is a human, since humans learn quickly and behave non-deterministically. In this paper we present a novel method whereby an agent can incrementally learn to predict the actions of another agent (even a human), and thereby can learn to better interact with that agent. We take a case-based approach, where the behavior of the other agent is learned in the form of state-action pairs. We generalize these …

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Ikum: An Integrated Web Personalization Platform Based On Content Structures And User Behavior, Magdalini Eirinaki, Joannis Vlachakis, Sarabjot Anand Jan 2005

Ikum: An Integrated Web Personalization Platform Based On Content Structures And User Behavior, Magdalini Eirinaki, Joannis Vlachakis, Sarabjot Anand

Faculty Publications

Web personalization is the process of customizing a web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the user’s navigational behavior. The objective of the I-KnowUMine project (IKUM) is to develop an integrated platform (referred to in the paper as the “IKUM system”) that uses state of the art technology and research results from different application domains in order to provide the basis for the development of online services in a wide range of application areas, presenting personalized content, services and applications to users in a …

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Prioritization Methods For Accelerating Mdp Solvers, Kevin Seppi, David Wingate Jan 2005

Prioritization Methods For Accelerating Mdp Solvers, Kevin Seppi, David Wingate

Faculty Publications

The performance of value and policy iteration can be dramatically improved by eliminating redundant or useless backups, and by backing up states in the right order. We study several methods designed to accelerate these iterative solvers, including prioritization, partitioning, and variable reordering. We generate a family of algorithms by combining several of the methods discussed, and present extensive empirical evidence demonstrating that performance can improve by several orders of magnitude for many problems, while preserving accuracy and convergence guarantees.

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Autonomous Vehicle Technologies For Small Fixed-Wing Uavs, Randal Beard, Derek Kingston, Morgan Quigley, Deryl Snyder, Reed Christiansen, Walt Johnson, Timothy Mclain, Michael A. Goodrich Jan 2005

Autonomous Vehicle Technologies For Small Fixed-Wing Uavs, Randal Beard, Derek Kingston, Morgan Quigley, Deryl Snyder, Reed Christiansen, Walt Johnson, Timothy Mclain, Michael A. Goodrich

Faculty Publications

The objective of this paper is to describe the design and implementation of a small semi-autonomous fixed-wing unmanned air vehicle. In particular we describe the hardware and software architectures used in the design. We also describe a low weight, low cost autopilot developed at Brigham Young University and the algorithms associated with the autopilot. Novel PDA and voice interfaces to the UAV are described. In addition, we overview our approach to real-time path planning, trajectory generation, and trajectory tracking. The paper is augmented with movie files that demonstrate the functionality of the UAV and its control software.

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A Bayesian Technique For Task Localization In Multiple Goal Markov Decision Processes, James Carroll, Kevin Seppi Dec 2004

A Bayesian Technique For Task Localization In Multiple Goal Markov Decision Processes, James Carroll, Kevin Seppi

Faculty Publications

In a reinforcement learning task library system for Multiple Goal Markov Decision Process (MGMDP), localization in the task space allows the agent to determine whether a given task is already in its library in order to exploit previously learned experience. Task localization in MGMDPs can be accomplished through a Bayesian approach, however a trivial approach fails when the rewards are not distributed normally. This can be overcome through our Bayesian Task Localization Technique (BTLT).

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Variable Resolution Discretization In The Joint Space, Christopher K. Monson, Kevin Seppi, David Wingate, Todd S. Peterson Dec 2004

Variable Resolution Discretization In The Joint Space, Christopher K. Monson, Kevin Seppi, David Wingate, Todd S. Peterson

Faculty Publications

We present JoSTLe, an algorithm that performs value iteration on control problems with continuous actions, allowing this useful reinforcement learning technique to be applied to problems where a priori action discretization is inadequate. The algorithm is an extension of a variable resolution technique that works for problems with continuous states and discrete actions. Results are given that indicate that JoSTLe is a promising step toward reinforcement learning in a fully continuous domain.

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Jumpstarting Phylogenetic Analysis, Mark J. Clement, Keith A. Crandall, Kevin Seppi, Quinn O. Snell Sep 2004

Jumpstarting Phylogenetic Analysis, Mark J. Clement, Keith A. Crandall, Kevin Seppi, Quinn O. Snell

Faculty Publications

When a new epidemic strikes, it is often important to determine the relationship between the current organism and others that have been successfully treated previously. The phylogenetic analysis problem generates the most likely family tree for a group of organisms based on DNA sequence data. This process can take a prohibitively long period of time with current algorithms. If trees resulting from prior searches are used to seed the search, correct trees can be found much more quickly. This jumpstarting algorithm can generate superior phylogenetic solutions much more quickly than existing algorithms.

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T-Spline Simplification And Local Refinement, David L. Cardon, G. Thomas Finnigan, Nicholas S. North, Thomas W. Sederberg, Jianmin Zheng, Tom Lyche Aug 2004

T-Spline Simplification And Local Refinement, David L. Cardon, G. Thomas Finnigan, Nicholas S. North, Thomas W. Sederberg, Jianmin Zheng, Tom Lyche

Faculty Publications

A typical NURBS surface model has a large percentage of superfluous control points that significantly interfere with the design process. This paper presents an algorithm for eliminating such superfluous control points, producing a T-spline. The algorithm can remove substantially more control points than competing methods such as B-spline wavelet decomposition. The paper also presents a new T-spline local refinement algorithm and answers two fundamental open questions on T-spline theory.

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Using Permutations Instead Of Student’S T Distribution For P-Values In Paired-Difference Algorithm Comparisons, Tony R. Martinez, Joshua Menke Jul 2004

Using Permutations Instead Of Student’S T Distribution For P-Values In Paired-Difference Algorithm Comparisons, Tony R. Martinez, Joshua Menke

Faculty Publications

The paired-difference t-test is commonly used in the machine learning community to determine whether one learning algorithm is better than another on a given learning task. This paper suggests the use of the permutation test instead hecause it calculates the exact p-value instead of an estimate. The permutation test is also distribution free and the time complexity is trivial for the commonly used 10-fold cross-validation paired-difference test. Results of experiments on real-world problems suggest it is not uncommon to see the t-test estimate deviate up to 30-50% from the exact p-value.

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Feature Weighting Using Neural Networks, Tony R. Martinez, Xinchuan Zeng Jul 2004

Feature Weighting Using Neural Networks, Tony R. Martinez, Xinchuan Zeng

Faculty Publications

In this work we propose a feature weighting method for classification tasks by extracting relevant information from a trained neural network. This method weights an attribute based on strengths (weights) of related links in the neural network, in which an important feature is typically connected to strong links and has more impact on the outputs. This method is applied to feature weighting br the nearest neighbor classifier and is tested on 15 real-world classification tasks. The results show that it can improve the nearest neighbor classifier on 14 of the 15 tested tasks, and also outperforms the neural network on …

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Softprop: Softmax Neural Network Backpropagation Learning, Tony R. Martinez, Michael E. Rimer Jul 2004

Softprop: Softmax Neural Network Backpropagation Learning, Tony R. Martinez, Michael E. Rimer

Faculty Publications

Multi-layer backpropagation, like many learning algorithms that can create complex decision surfaces, is prone to overfitting. Softprop is a novel learning approach presented here that is reminiscent of the softmax explore-exploit Q-learning search heuristic It fits the problem while delaying settling into error minima to achieve better generalization and more robust learning. This is accomplished by blending standard SSE optimization with lazy training, a new objective function well suited to learning classification tasks, to form a more stable learning model. Over several machine learning data sets, softprop reduces classification error by 17.1 percent and the variance in results by 38.6 …

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Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting Jul 2004

Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting

Faculty Publications

Classification problems with output class overlap create problems for standard neural network approaches. We present a modification of a simple feed-forward neural network that is capable of learning problems with output overlap, including problems exhibiting hierarchical class structures in the output. Our method of applying weakened implicit negatives to address overlap and ambiguity allows the algorithm to learn a large portion of the hierarchical structure from very incomplete data. Our results show an improvement of approximately 58% over a standard backpropagation network on the hierarchical problem.

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Incremental Policy Learning: An Equilibrium Selection Algorithm For Reinforcement Learning Agents With Common Interests, Nancy Fulda, Dan A. Ventura Jul 2004

Incremental Policy Learning: An Equilibrium Selection Algorithm For Reinforcement Learning Agents With Common Interests, Nancy Fulda, Dan A. Ventura

Faculty Publications

We present an equilibrium selection algorithm for reinforcement learning agents that incrementally adjusts the probability of executing each action based on the desirability of the outcome obtained in the last time step. The algorithm assumes that at least one coordination equilibrium exists and requires that the agents have a heuristic for determining whether or not the equilibrium was obtained. In deterministic environments with one or more strict coordination equilibria, the algorithm will learn to play an optimal equilibrium as long as the heuristic is accurate. Empirical data demonstrate that the algorithm is also effective in stochastic environments and is able …

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Choosing A Starting Configuration For Particle Swarm Optimization, Mark Richards, Dan A. Ventura Jul 2004

Choosing A Starting Configuration For Particle Swarm Optimization, Mark Richards, Dan A. Ventura

Faculty Publications

The performance of Particle Swarm Optimization can be improved by strategically selecting the starting positions of the particles. This work suggests the use of generators from centroidal Voronoi tessellations as the starting points for the swarm. The performance of swarms initialized with this method is compared with the standard PSO algorithm on several standard test functions. Results suggest that CVT initialization improves PSO performance in high-dimensional spaces.

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Task-Focused Summarization Of Email, Eric K. Ringger, Richard Campbell, Simon Corston-Oliver, Michael Gamon Jul 2004

Task-Focused Summarization Of Email, Eric K. Ringger, Richard Campbell, Simon Corston-Oliver, Michael Gamon

Faculty Publications

We describe SmartMail, a prototype system for automatically identifying action items (tasks) in email messages. SmartMail presents the user with a task-focused summary of a message. The summary consists of a list of action items extracted from the message. The user can add these action items to their “to do” list.

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Responding To Policies At Runtime In Trustbuilder, Michael D. Jones, Kent E. Seamons, Bryan Smith Jun 2004

Responding To Policies At Runtime In Trustbuilder, Michael D. Jones, Kent E. Seamons, Bryan Smith

Faculty Publications

To preview my talk, I will first give a brief overview of trust negotiation and policy exchange. Third, I will discuss the limitations of current compliance checkers and adaptations needed for trust negotiation. Finally, I will outline the contributions of this research.

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Empirical Analysis Of Computational And Accuracy Tradeoffs Using Compactly Supported Radial Basis Functions For Surface Reconstruction, Weiming Liu, Bryan S. Morse, Lauralea Otis Jun 2004

Empirical Analysis Of Computational And Accuracy Tradeoffs Using Compactly Supported Radial Basis Functions For Surface Reconstruction, Weiming Liu, Bryan S. Morse, Lauralea Otis

Faculty Publications

Implicit surfaces can be constructed from scattered surface points using radial basis functions (RBFs) to interpolate the surface’s embedding function. Many researchers have used thin-plate spline RBFs for this because of their desirable smoothness properties. Others have used compactly supported RBFs, leading to a sparse matrix solution with lower computational complexity and better conditioning. However, the limited radius of support introduces a free parameter that leads to varying solutions as well as varying computational requirements: a larger radius of support leads to smoother and more accurate solutions but requires more computation. This paper presents an empirical analysis of this radius …

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Aggressive Telecommunications Overbooking Ratios, Robert Ball, Mark J. Clement, Casey T. Deccio, Feng Huang, Quinn O. Snell Apr 2004

Aggressive Telecommunications Overbooking Ratios, Robert Ball, Mark J. Clement, Casey T. Deccio, Feng Huang, Quinn O. Snell

Faculty Publications

The Internet is comprised of vast networks of wires and fiber. A common misconception is that there is an unlimited amount of bandwidth; in reality there exists only a finite amount. Each length of wire and fiber is owned by a company, and every company wants to maximize its profit. One means of improving profit is to overbook existing transmission lines in order to increase income without increasing expenses. If too much overbooking is performed, the Quality of Service (QoS) seen by customers will decline. This paper explains a process to achieve an optimal Overbooking Ratio (OR) for admission control …

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Cognitive Robot Mapping With Polylines And An Absolute Space Representation, Kennard R. Laviers, Gilbert L. Peterson Apr 2004

Cognitive Robot Mapping With Polylines And An Absolute Space Representation, Kennard R. Laviers, Gilbert L. Peterson

Faculty Publications

Robot mapping even today is one of the most challenging problems in robot programming. Most successful methods use some form of occupancy grid to represent a mapped region. This approach becomes problematic if the robot is mapping a large environment, the map quickly becomes too large for processing and storage. Rather than storing the map as an occupancy grid, our robot (equipped with sonars) sees the world as a series of connected spaces. These spaces are initially mapped as an occupancy grid in a room by room fashion. As the robot leaves a space, denoted by passing through a doorway, …

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A Piecewise Linear Approach To Overbooking, Robert Ball, Mark J. Clement, Casey T. Deccio, Feng Huang, Quinn O. Snell Apr 2004

A Piecewise Linear Approach To Overbooking, Robert Ball, Mark J. Clement, Casey T. Deccio, Feng Huang, Quinn O. Snell

Faculty Publications

Overbooking is frequently used to increase the revenue generated by a network infrastructure without incurring additional costs. If the overbooking factor is chosen appropriately, additional virtual circuits can be admitted without degrading quality of service for existing customers. Most implementations use a single factor to accept a linear fraction of traffic requests. If a piecewise linear approach is used in admissions, additional traffic can be accepted without causing proportional increases in loss rate and utilization. This additional accepted traffic can significantly improve the profit margin for network service providers.

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Dynamic Autonomous Transport Selection In Heterogeneous Wireless Environments, Jeffrey M. Brown, Heidi R. Duffin, Charles D. Knutson, Shannon B. Barnes, Ryan W. Woodings Mar 2004

Dynamic Autonomous Transport Selection In Heterogeneous Wireless Environments, Jeffrey M. Brown, Heidi R. Duffin, Charles D. Knutson, Shannon B. Barnes, Ryan W. Woodings

Faculty Publications

In this paper, we introduce Quality of Transport (QoT), an architecture for synergistically and autonomously managing session-layer protocol access to multiple transports in heterogeneous wireless environments. We present an overview of the QoT architecture including: 1) transport discovery, 2) service discovery, 3) object exchange, 4) transport switching, and 5) intelligent transport selection. Preliminary successes with our design and implementation of QoT suggest that dynamic intelligent autonomous transport switching can help to optimize user experience and session layer performance in multi-transport environments.

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Prioritized Soft Constraint Satisfaction: A Qualitative Method For Dynamic Transport Selection In Heterogeneous Wireless Environments, Heidi R. Duffin, Michael A. Goodrich, Charles D. Knutson Mar 2004

Prioritized Soft Constraint Satisfaction: A Qualitative Method For Dynamic Transport Selection In Heterogeneous Wireless Environments, Heidi R. Duffin, Michael A. Goodrich, Charles D. Knutson

Faculty Publications

This paper presents Prioritized Soft Constraint Satisfaction (PSCS), a novel approach to selecting the “best” transport in dynamic wireless transport switching systems. PSCS maintains a satisfying connection to another endpoint by choosing transports based on a user-established range of preferences and priority for criteria such as speed, power, range and cost. Additionally, feedback is provided regarding tradeoffs among the criteria, thus enabling the user to adjust inputs according to the capabilities of the system. We also recommend guidelines for setting preferences and priorities.

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