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Faculty Publications

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Articles 451 - 480 of 663

Full-Text Articles in Computer Sciences

Cooperation-Based Clustering For Profit-Maximizing Organizational Design, Christophe G. Giraud-Carrier, Kevin Seppi, Nghia Tran, Sean C. Warnick Jul 2006

Cooperation-Based Clustering For Profit-Maximizing Organizational Design, Christophe G. Giraud-Carrier, Kevin Seppi, Nghia Tran, Sean C. Warnick

Faculty Publications

This paper shows how the notion of value of cooperation, a measure of the percentage of a firm’s profits due strictly to the cooperative effects among the goods it sells, can be used to analyze the relative economic advantage afforded by various organizational structures. The value of cooperation is computed from transactions data by solving a regression problem to fit the parameters of the consumer demand function, and then simulating the resulting profit-maximizing dynamic system under various organizational structures. A hierarchical agglomerative clustering algorithm can be applied to reveal the optimal organizational substructure.

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Particle Swarm Optimization In Dynamic Pricing, Christopher K. Monson, Patrick B. Mullen, Kevin Seppi, Sean C. Warnick Jul 2006

Particle Swarm Optimization In Dynamic Pricing, Christopher K. Monson, Patrick B. Mullen, Kevin Seppi, Sean C. Warnick

Faculty Publications

Dynamic pricing is a real-time machine learning problem with scarce prior data and a concrete learning cost. While the Kalman Filter can be employed to track hidden demand parameters and extensions to it can facilitate exploration for faster learning, the exploratory nature of Particle Swarm Optimization makes it a natural choice for the dynamic pricing problem. We compare both the Kalman Filter and existing particle swarm adaptations for dynamic and/or noisy environments with a novel approach that time-decays each particle's previous best value; this new strategy provides more graceful and effective transitions between exploitation and exploration, a necessity in the …

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Preparing More Effective Liquid State Machines Using Hebbian Learning, David Norton, Dan A. Ventura Jul 2006

Preparing More Effective Liquid State Machines Using Hebbian Learning, David Norton, Dan A. Ventura

Faculty Publications

In Liquid State Machines, separation is a critical attribute of the liquid—which is traditionally not trained. The effects of using Hebbian learning in the liquid to improve separation are investigated in this paper. When presented with random input, Hebbian learning does not dramatically change separation. However, Hebbian learning does improve separation when presented with real-world speech data.

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Learning A Rendezvous Task With Dynamic Joint Action Perception, Nancy Fulda, Dan A. Ventura Jul 2006

Learning A Rendezvous Task With Dynamic Joint Action Perception, Nancy Fulda, Dan A. Ventura

Faculty Publications

Groups of reinforcement learning agents interacting in a common environment often fail to learn optimal behaviors. Poor performance is particularly common in environments where agents must coordinate with each other to receive rewards and where failed coordination attempts are penalized. This paper studies the effectiveness of the Dynamic Joint Action Perception (DJAP) algorithm on a grid-world rendezvous task with this characteristic. The effects of learning rate, exploration strategy, and training time on algorithm effectiveness are discussed. An analysis of the types of tasks for which DJAP learning is appropriate is also presented.

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Spatiotemporal Pattern Recognition Via Liquid State Machines, Eric Goodman, Dan A. Ventura Jul 2006

Spatiotemporal Pattern Recognition Via Liquid State Machines, Eric Goodman, Dan A. Ventura

Faculty Publications

The applicability of complex networks of spiking neurons as a general purpose machine learning technique remains open. Building on previous work using macroscopic exploration of the parameter space of an (artificial) neural microcircuit, we investigate the possibility of using a liquid state machine to solve two real-world problems: stockpile surveillance signal alignment and spoken phoneme recognition.

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Learning Quantum Operators From Quantum State Pairs, Neil Toronto, Dan A. Ventura Jul 2006

Learning Quantum Operators From Quantum State Pairs, Neil Toronto, Dan A. Ventura

Faculty Publications

Developing quantum algorithms has proven to be very difficult. In this paper, the concept of using classical machine learning techniques to derive quantum operators from examples is presented. A gradient descent algorithm for learning unitary operators from quantum state pairs is developed as a starting point to aid in developing quantum algorithms. The algorithm is used to learn the quantum Fourier transform, an underconstrained two-bit function, and Grover’s iterate.

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Histogram Matching For Camera Pose Neighbor Selection, Parris K. Egbert, Bryan S. Morse, Kevin L. Steele Jun 2006

Histogram Matching For Camera Pose Neighbor Selection, Parris K. Egbert, Bryan S. Morse, Kevin L. Steele

Faculty Publications

A prerequisite to calibrated camera pose estimation is the construction of a camera neighborhood adjacency graph, a connected graph defining the pose neighbors of the camera set. Pose neighbors to a camera C are images containing sufficient overlap in image content with the image from C that they can be used to correctly estimate the pose of C using structure-from-motion techniques. In a video stream, the camera neighborhood adjacency graph is often a simple connected path; frame poses are only estimated relative to their immediate neighbors. We propose a novel method to build more complex camera adjacency graphs that are …

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Minimum Spanning Tree Pose Estimation, Parris K. Egbert, Kevin L. Steele Jun 2006

Minimum Spanning Tree Pose Estimation, Parris K. Egbert, Kevin L. Steele

Faculty Publications

The extrinsic camera parameters from video stream images can be accurately estimated by tracking features through the image sequence and using these features to compute parameter estimates. The poses for long video sequences have been estimated in this manner. However, the poses of large sets of still images cannot be estimated using the same strategy because wide-baseline correspondences are not as robust as narrow-baseline feature tracks. Moreover, video pose estimation requires a linear or hierarchically-linear ordering on the images to be calibrated, reducing the image matches to the neighboring video frames. We propose a novel generalization to the linear ordering …

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A Multidiscipline Approach To Mitigating The Insider Threat, Jonathan W. Butts, Robert F. Mills, Gilbert L. Peterson Jun 2006

A Multidiscipline Approach To Mitigating The Insider Threat, Jonathan W. Butts, Robert F. Mills, Gilbert L. Peterson

Faculty Publications

Preventing and detecting the malicious insider is an inherently difficult problem that expands across many areas of expertise such as social, behavioral and technical disciplines. Unfortunately, current methodologies to combat the insider threat have had limited success primarily because techniques have focused on these areas in isolation. The technology community is searching for technical solutions such as anomaly detection systems, data mining and honeypots. The law enforcement and counterintelligence communities, however, have tended to focus on human behavioral characteristics to identify suspicious activities. These independent methods have limited effectiveness because of the unique dynamics associated with the insider threat. The …

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Fuzzy State Aggregation And Off-Policy Reinforcement Learning For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson May 2006

Fuzzy State Aggregation And Off-Policy Reinforcement Learning For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson

Faculty Publications

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the environment it is operating in changes. This ability to learn in an unsupervised manner in a changing environment is applicable in complex domains through the use of function approximation of the domain’s policy. The function approximation presented here is that of fuzzy state aggregation. This article presents the use of fuzzy state aggregation with the current policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF), exceeding the learning rate …

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Multiple Masks-Based Pixel Comparison Steganalysis Method For Mobile Imaging, Sos S. Agaian, Gilbert L. Peterson, Benjamin M. Rodriguez May 2006

Multiple Masks-Based Pixel Comparison Steganalysis Method For Mobile Imaging, Sos S. Agaian, Gilbert L. Peterson, Benjamin M. Rodriguez

Faculty Publications

No abstract provided.

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Axiomatic Multi-Transport Bargaining: A Quantitative Method For Dynamic Transport Selection In Heterogeneous Multi-Transport Wireless Environments, Qiuyi Duan, Michael A. Goodrich, Charles D. Knutson, Lei Wang Apr 2006

Axiomatic Multi-Transport Bargaining: A Quantitative Method For Dynamic Transport Selection In Heterogeneous Multi-Transport Wireless Environments, Qiuyi Duan, Michael A. Goodrich, Charles D. Knutson, Lei Wang

Faculty Publications

Transport selection mechanisms are designed to facilitate seamless connectivity in heterogeneous multi-transport environments, allowing access to the “best” available transport according to user requirements. Evaluating transport configurations dynamically according to the user’s preferences and Quality of Service (QoS) requirements is a challenging task. This paper describes a quantitative approach that applies the Utility Theorem and Nash’s Bargaining solution to heterogeneous wireless environments. The mathematical model presented generates and adjusts the transport preference list dynamically depending on the degree to which a transport satisfies user preferences and the application’s QoS requirements. We incorporate a negotiation engine using the Axiomatic Multi-Transport Bargaining …

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Separating Lines Of Text In Free-Form Handwritten Historical Documents, William A. Barrett, Douglas J. Kennard Apr 2006

Separating Lines Of Text In Free-Form Handwritten Historical Documents, William A. Barrett, Douglas J. Kennard

Faculty Publications

We present an approach to finding (and separating) lines of text in free-form handwritten historical document images. After preprocessing, our method uses the count of foreground/background transitions in a binarized image to determine areas of the document that are likely to be text lines. Alternatively, an Adaptive Local Connectivity Map (ALCM) found in the literature can be used for this step of the process. We then use a min-cut/max-flow graph cut algorithm to split up text areas that appear to encompass more than one line of text. After removing text lines containing relatively little text information (or merging them with …

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Dial 2004 Working Group Report On Acquisition Quality Control, William A. Barrett, Henry Baird, Frank Le Bourgeois, Xiaofan Lin, George Nagy, Steve Simske, Elisa H. Barney Smith Apr 2006

Dial 2004 Working Group Report On Acquisition Quality Control, William A. Barrett, Henry Baird, Frank Le Bourgeois, Xiaofan Lin, George Nagy, Steve Simske, Elisa H. Barney Smith

Faculty Publications

This report summarizes the discussions of the Working Group on Acquisition Quality at the International Workshop on Document Image Analysis for Libraries, Palo Alto, CA, 23-24 January 2004. Acquisition of the image is one of the most time intensive components of forming a digital library, and the quality of the acquisition will affect all later stages of the digital library project. The current state of the art in acquisition is analyzed. Problems and suggested improvements for image acquisition and storage formats and the special problems associated with acquisition from microfilm follows. A list of general suggestions was developed which was …

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Learning Real-Time A* Path Planner For Unmanned Air Vehicle Target Sensing, Jason K. Howlett, Timothy W. Mclain, Michael A. Goodrich Mar 2006

Learning Real-Time A* Path Planner For Unmanned Air Vehicle Target Sensing, Jason K. Howlett, Timothy W. Mclain, Michael A. Goodrich

Faculty Publications

This paper presents a path planner for sensing closely-spaced targets from a fixed-wing unmanned air vehicle (UAV) having a specified sensor footprint. The planner is based on the learning real-time A* (LRTA*) search algorithm and produces dynamically feasible paths that accomplish the sensing objectives in the shortest possible distance. A tree of candidate paths that span the area of interest is created by assembling primitive turn and straight sections of a specified step size in a sequential fashion from the starting position of the UAV. An LRTA* search of the tree produces feasible paths any time during its execution and …

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Introducing Semantics In Web Personalization: The Role Of Ontologies, Magdalini Eirinaki, Dimitrios Mavroeidis, George Tsatsaronis, Michalis Vazirgiannis Jan 2006

Introducing Semantics In Web Personalization: The Role Of Ontologies, Magdalini Eirinaki, Dimitrios Mavroeidis, George Tsatsaronis, Michalis Vazirgiannis

Faculty Publications

Web personalization is the process of customizing a web site to the needs of each specific user or set of users. Personalization of a web site may be performed by the provision of recommendations to the users, high-lighting/adding links, creation of index pages, etc. The web personalization systems are mainly based on the exploitation of the navigational patterns of the web site’s visitors. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally recommended may be missed. The exploitation of the web pages’ semantics can considerably improve the results of web usage …

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Loss Aware Rate Allocations In H.263 Coded Video Transmissions, Xiao Su, Benjamin Wah Dec 2005

Loss Aware Rate Allocations In H.263 Coded Video Transmissions, Xiao Su, Benjamin Wah

Faculty Publications

For packet video, information loss and bandwidth limitation are two factors that affect video playback quality. Traditional rate allocation approaches have focused on optimizing video quality under bandwidth constraint alone. However, in the best-effort Internet, packets carrying video data are susceptible to losses, which need to be reconstructed at the receiver side. In this paper, we propose loss aware rate allocations in both group-of-block (GOB) level and macroblock level, given that certain packets are lost during transmissions and reconstructed using simple interpolation methods at the receiver side. Experimental results show that our proposed algorithms can produce videos of higher quality …

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A Context-Sensitive Structural Heuristic For Guided Search Model Checking, Eric G. Mercer, Neha Rungta Nov 2005

A Context-Sensitive Structural Heuristic For Guided Search Model Checking, Eric G. Mercer, Neha Rungta

Faculty Publications

Software verification using model checking often translates programs into corresponding transition systems that model the program behavior. As software systems continue to grow in complexity and size, exhaustively checking a property on a transition graph becomes difficult. The goal of guided search heuristics in model checking is to find a counterexample to the property being verified as quickly as possible in the transition graph. The FSM distance heuristic builds an interprocedural control flow graph of the program to estimate distance to a possible error state. It ignores calling context and underestimates the true distance to the error.

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Ontologies In Web Personalization, Magdalini Eirinaki, Michalis Vazirgiannis Oct 2005

Ontologies In Web Personalization, Magdalini Eirinaki, Michalis Vazirgiannis

Faculty Publications

No abstract provided.

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Phylogenetic Analysis Of Large Sequence Data Sets, Hyrum Carroll, Mark J. Clement, Keith Crandall, Quinn O. Snell Oct 2005

Phylogenetic Analysis Of Large Sequence Data Sets, Hyrum Carroll, Mark J. Clement, Keith Crandall, Quinn O. Snell

Faculty Publications

Phylogenetic analysis is an integral part of biological research. As the number of sequenced genomes increases, available data sets are growing in number and size. Several algorithms have been proposed to handle these larger data sets. A family of algorithms known as disc covering methods (DCMs), have been selected by the NSF funded CIPRes project to boost the performance of existing phylogenetic algorithms. Recursive Iterative Disc Covering Method 3 (Rec-I-DCM3), recursively decomposes the guide tree into subtrees, executing a phylogenetic search on the subtree and merging the subtrees, for a set number of iterations. This paper presents a detailed analysis …

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Linear Equality Constraints And Homomorphous Mappings In Pso, Christopher K. Monson, Kevin Seppi Sep 2005

Linear Equality Constraints And Homomorphous Mappings In Pso, Christopher K. Monson, Kevin Seppi

Faculty Publications

We present a homomorphous mapping that converts problems with linear equality constraints into fully unconstrained and lower-dimensional problems for optimization with PSO. This approach, in contrast with feasibility preservation methods, allows any unconstrained optimization algorithm to be applied to a problem with linear equality constraints, making available tools that are known to be effective and simplifying the process of choosing an optimizer for these kinds of constrained problems. The application of some PSO algorithms to a problem that has undergone the mapping presented here is shown to be more effective and more consistent than other approaches to handling linear equality …

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Cooperative Reinforcement Learning Using An Expert-Measuring Weighted Strategy With Wolf, Kevin Cousin, Gilbert L. Peterson Sep 2005

Cooperative Reinforcement Learning Using An Expert-Measuring Weighted Strategy With Wolf, Kevin Cousin, Gilbert L. Peterson

Faculty Publications

Gradient descent learning algorithms have proven effective in solving mixed strategy games. The policy hill climbing (PHC) variants of WoLF (Win or Learn Fast) and PDWoLF (Policy Dynamics based WoLF) have both shown rapid convergence to equilibrium solutions by increasing the accuracy of their gradient parameters over standard Q-learning. Likewise, cooperative learning techniques using weighted strategy sharing (WSS) and expertness measurements improve agent performance when multiple agents are solving a common goal. By combining these cooperative techniques with fast gradient descent learning, an agent’s performance converges to a solution at an even faster rate. This statement is verified in a …

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Studies In The Dynamics Of Economic Systems, Christophe G. Giraud-Carrier, Kevin Seppi, Nghia Tran, Sean C. Warnick, W. Samuel Weyerman, R. Johnson Aug 2005

Studies In The Dynamics Of Economic Systems, Christophe G. Giraud-Carrier, Kevin Seppi, Nghia Tran, Sean C. Warnick, W. Samuel Weyerman, R. Johnson

Faculty Publications

This paper demonstrates the utility of systems and control theory in the analysis of economic systems. Two applications demonstrate how the analysis of simple dynamic models sheds light on important practical problems. The first problem considers the design of a retail laboratory, where the small gain theorem enables the falsification of pricing policies. The second problem explores industrial organization using the equilibria of profit-maximizing dynamics to quantify the percentage of a firm’s profits due strictly to the cooperative effects among its products. This ”Value of Cooperation” suggests an important measure for both organizational and antitrust applications.

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Task Similarity Measures For Transfer In Reinforcement Learning Task Libraries, James Carroll, Kevin Seppi Aug 2005

Task Similarity Measures For Transfer In Reinforcement Learning Task Libraries, James Carroll, Kevin Seppi

Faculty Publications

Recent research in task transfer and task clustering has necessitated the need for task similarity measures in reinforcement learning. Determining task similarity is necessary for selective transfer where only information from relevant tasks and portions of a task are transferred. Which task similarity measure to use is not immediately obvious. It can be shown that no single task similarity measure is uniformly superior. The optimal task similarity measure is dependent upon the task transfer method being employed. We define similarity in terms of tasks, and propose several possible task similarity measures, dT, dp, dQ, and dR which are based on …

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Edge Inference For Image Interpolation, Bryan S. Morse, Neil Toronto, Dan A. Ventura Aug 2005

Edge Inference For Image Interpolation, Bryan S. Morse, Neil Toronto, Dan A. Ventura

Faculty Publications

Image interpolation algorithms try to fit a function to a matrix of samples in a "natural-looking" way. This paper presents edge inference, an algorithm that does this by mixing neural network regression with standard image interpolation techniques. Results on gray level images are presented, and it is demonstrated that edge inference is capable of producing sharp, natural-looking results. A technique for reintroducing noise is given, and it is shown that, with noise added using a bicubic interpolant, edge inference can be regarded as a generalization of bicubic interpolation. Extension into RGB color space and additional applications of the algorithm are …

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A Comparison Of Generalizability For Anomaly Detection, Gilbert L. Peterson, Robert F. Mills, Brent T. Mcbride, Wesley T. Allred Aug 2005

A Comparison Of Generalizability For Anomaly Detection, Gilbert L. Peterson, Robert F. Mills, Brent T. Mcbride, Wesley T. Allred

Faculty Publications

In security-related areas there is concern over the novel “zeroday” attack that penetrates system defenses and wreaks havoc. The best methods for countering these threats are recognizing “non-self” as in an Artificial Immune System or recognizing “self” through clustering. For either case, the concern remains that something that looks similar to self could be missed. Given this situation one could logically assume that a tighter fit to self rather than generalizability is important for false positive reduction in this type of learning problem. This article shows that a tight fit, although important, does not supersede having some model generality. This …

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Categorizing And Extracting Information From Multilingual Html Documents, Yiu-Kai D. Ng, Seungjin Lim Jul 2005

Categorizing And Extracting Information From Multilingual Html Documents, Yiu-Kai D. Ng, Seungjin Lim

Faculty Publications

The amount of online information written in different natural languages and the number of non-English speaking Internet users have been increasing tremendously during the past decade. In order to provide high-performance access of multilingual information on the Internet, we have developed a data analysis and querying system (DatAQs) that (i) analyzes, identifies, and categorizes languages used in HTML documents, (ii) extracts information from HTML documents of interest written in different languages, (iii) allows the user to submit queries for retrieving extracted information in the same natural language provided by the query engine of DatAQs using a menu-driven user interface, and …

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Detecting Similar Html Documents Using A Fuzzy Set Information Retrieval Approach, Yiu-Kai D. Ng, Rajiv Yerra Jul 2005

Detecting Similar Html Documents Using A Fuzzy Set Information Retrieval Approach, Yiu-Kai D. Ng, Rajiv Yerra

Faculty Publications

Web documents that are either partially or completely duplicated in content are easily found on the Internet these days. Not only do these documents create redundant information on the Web, which take longer to filter unique information and cause additional storage space, but also they degrade the efficiency of Web information retrieval. In this paper, we present a new approach for detecting similar Web documents, especially HTML documents. Our detection approach determines the odd ratio of any two documents, which makes use of the degrees of resemblance of the documents, and graphically displays the locations of similar (not necessarily the …

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Comparing High-Order Boolean Features, Adam Drake, Dan A. Ventura Jul 2005

Comparing High-Order Boolean Features, Adam Drake, Dan A. Ventura

Faculty Publications

Many learning algorithms attempt, either explicitly or implicitly, to discover useful high-order features. When considering all possible functions that could be encountered, no particular type of high-order feature should be more useful than any other. However, this paper presents arguments and empirical results that suggest that for the learning problems typically encountered in practice, some high-order features may be more useful than others.

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Validating Human–Robot Interaction Schemes In Multitasking Environments, Jacob W. Crandall, Michael A. Goodrich, Curtis W. Nielsen, Dan R. Olsen Jr. Jul 2005

Validating Human–Robot Interaction Schemes In Multitasking Environments, Jacob W. Crandall, Michael A. Goodrich, Curtis W. Nielsen, Dan R. Olsen Jr.

Faculty Publications

The ability of robots to autonomously perform tasks is increasing. More autonomy in robots means that the human managing the robot may have available free time. It is desirable to use this free time productively, and a current trend is to use this available free time to manage multiple robots. We present the notion of neglect tolerance as a means for determining how robot autonomy and interface design determine how free time can be used to support multitasking, in general, and multirobot teams, in particular. We use neglect tolerance to 1) identify the maximum number of robots that can be …

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