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Physical Sciences and Mathematics Commons™
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- Fuzzy state aggregation (2)
- Policy hill-climbing (2)
- Pose estimation (2)
- Reinforcement learning (2)
- Algorithm (1)
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- Alignment (1)
- Amino acid properties (1)
- Camera (1)
- Classical machine learning (1)
- Clustering algorithms (1)
- Connectivity (1)
- Cooperation (1)
- Counter espionage (1)
- DNA sequencing (1)
- Digital library (1)
- Distance data (1)
- Dynamic joint action perception (1)
- Dynamic pricing (1)
- Family history (1)
- Feature extraction (1)
- Filtering news articles (1)
- Fuzzy equivalence (1)
- Gap penalties (1)
- Genealogy (1)
- Handwritten documents (1)
- Hebbian learning (1)
- Heuristic functions (1)
- Image acquisition (1)
- Image classification (1)
- Insider threats (1)
Articles 1 - 25 of 25
Full-Text Articles in Physical Sciences and Mathematics
Eliminating Redundant And Less-Informative Rss News Articles Based On Word Similarity And A Fuzzy Equivalence Relation, Ian Garcia, Yiu-Kai D. Ng
Eliminating Redundant And Less-Informative Rss News Articles Based On Word Similarity And A Fuzzy Equivalence Relation, Ian Garcia, Yiu-Kai D. Ng
Faculty Publications
The Internet has marked this era as the information age. There is no precedent in the amazing amount of information, especially network news, that can be accessed by Internet users these days. As a result, the problem of seeking information in online news articles is not the lack of them but being overwhelmed by them. This brings huge challenges in processing online news feeds, e.g., how to determine which news article is important, how to determine the quality of each news article, and how to filter irrelevant and redundant information. In this paper, we propose a method for filtering redundant …
An Improved Distance Heuristic Function For Directed Software Model Checking, Eric G. Mercer, Neha Rungta
An Improved Distance Heuristic Function For Directed Software Model Checking, Eric G. Mercer, Neha Rungta
Faculty Publications
State exploration in directed software model checking is guided using a heuristic function to move states near errors to the front of the search queue. Distance heuristic functions rank states based on the number of transitions needed to move the current program state into an error location. Lack of calling context information causes the heuristic function to underestimate the true distance to the error; however, inlining functions at call sites in the control flow graph to capture calling context leads to an exponential growth in the computation. This paper presents a new algorithm that implicitly inlines functions at call sites …
Steganalysis Embedding Percentage Determination With Learning Vector Quantization, Benjamin M. Rodriguez, Gilbert L. Peterson, Kenneth W. Bauer, Sos S. Agaian
Steganalysis Embedding Percentage Determination With Learning Vector Quantization, Benjamin M. Rodriguez, Gilbert L. Peterson, Kenneth W. Bauer, Sos S. Agaian
Faculty Publications
Steganography (stego) is used primarily when the very existence of a communication signal is to be kept covert. Detecting the presence of stego is a very difficult problem which is made even more difficult when the embedding technique is not known. This article presents an investigation of the process and necessary considerations inherent in the development of a new method applied for the detection of hidden data within digital images. We demonstrate the effectiveness of learning vector quantization (LVQ) as a clustering technique which assists in discerning clean or non-stego images from anomalous or stego images. This comparison is conducted …
Effects Of Gap Open And Gap Extension Penalties, Hyrum Carroll, Mark J. Clement, Perry Ridge, Quinn O. Snell
Effects Of Gap Open And Gap Extension Penalties, Hyrum Carroll, Mark J. Clement, Perry Ridge, Quinn O. Snell
Faculty Publications
Fundamental to multiple sequence alignment algorithms is modeling insertions and deletions (gaps). The most prevalent model is to use gap open and gap extension penalties. While gap open and gap extension penalties are well understood conceptually, their effects on multiple sequence alignment, and consequently on phylogeny scores are not as well understood. We use exhaustive phylogeny searching to explore the effects of varying the gap open and gap extension penalties for three nuclear ribosomal data sets. Particular attention is given to optimal phylogeny scores for 200 alignments of a range of gap open and gap extension penalties and their respective …
Large Grain Size Stochastic Optimization Alignment, Hyrum Carroll, Mark J. Clement, Perry Ridge, Dan Sneddon, Quinn O. Snell
Large Grain Size Stochastic Optimization Alignment, Hyrum Carroll, Mark J. Clement, Perry Ridge, Dan Sneddon, Quinn O. Snell
Faculty Publications
DNA sequence alignment is a critical step in identifying homology between organisms. The most widely used alignment program, ClustalW, is known to suffer from the local minima problem, where suboptimal guide trees produce incorrect gap insertions. The optimization alignment approach, has been shown to be effective in combining alignment and phylogenetic search in order to avoid the problems associated with poor guide trees. The optimization alignment algorithm operates at a small grain size, aligning each tree found, wasting time producing multiple sequence alignments for suboptimal trees. This research develops and analyzes a large grain size algorithm for optimization alignment that …
Pharmacogenomics: Analyzing Snps In The Cyp2d6 Gene Using Amino Acid Properties, Wesley A. Beckstead, Mark J. Clement, Mark Ebbert, David Mcclellan, Timothy O'Connor
Pharmacogenomics: Analyzing Snps In The Cyp2d6 Gene Using Amino Acid Properties, Wesley A. Beckstead, Mark J. Clement, Mark Ebbert, David Mcclellan, Timothy O'Connor
Faculty Publications
Each year people suffer from complications of adverse drug reactions, but with pharmacogenomics there is hope to prevent thousands of these people from suffering or dying needlessly. The CYP2D6 gene is responsible for metabolizing a large portion of these drugs. Because of the gene’s importance, various approaches have been taken to analyze CYP2D6 and single nucleotide polymorphisms (SNPs) throughout its sequence. This study introduces a novel method to analyze the effects of SNPs on encoded protein complexes by focusing on the biochemical properties of each nonsynonymous substitution using the program TreeSAAP. We apply this technique to SNPs found in the …
Fuzzy State Aggregation And Policy Hill Climbing For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
Fuzzy State Aggregation And Policy Hill Climbing 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 operating environment changes. Additionally, by applying reinforcement learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the fastest policy hill …
Digital Roots Of Human Relations: Enabling Technologies For Family History And Genealogical Research, William A. Barrett
Digital Roots Of Human Relations: Enabling Technologies For Family History And Genealogical Research, William A. Barrett
Faculty Publications
Flowing out of a Computer Science research lab on the third floor of the Talmage Building is a wellspring of enabling technologies for family history and genealogical research. Here, computer science students, working under the direction of Dr. Tom Sederberg and Dr. Bill Barrett are creating software tools to help individuals with their family history research so that people everywhere can seek out their ancestors and perform vital ordinances in their behalf, as desired. These tools include visualization of an entire pedigree on a single (large) sheet of paper, the ability to automatically calculate if and how two or more …
A Constructive Incremental Learning Algorithm For Binary Classification Tasks, Christophe G. Giraud-Carrier, Tony R. Martinez
A Constructive Incremental Learning Algorithm For Binary Classification Tasks, Christophe G. Giraud-Carrier, Tony R. Martinez
Faculty Publications
This paper presents i-AA1*, a constructive, incremental learning algorithm for a special class of weightless, self-organizing networks. In i-AA1*, learning consists of adapting the nodes’ functions and the network’s overall topology as each new training pattern is presented. Provided the training data is consistent, computational complexity is low and prior factual knowledge may be used to “prime” the network and improve its predictive accuracy. Empirical generalization results on both toy problems and more realistic tasks demonstrate promise.
Particle Swarm Optimization In Dynamic Pricing, Christopher K. Monson, Patrick B. Mullen, Kevin Seppi, Sean C. Warnick
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 …
Cooperation-Based Clustering For Profit-Maximizing Organizational Design, Christophe G. Giraud-Carrier, Kevin Seppi, Nghia Tran, Sean C. Warnick
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.
Preparing More Effective Liquid State Machines Using Hebbian Learning, David Norton, Dan A. Ventura
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.
Learning Quantum Operators From Quantum State Pairs, Neil Toronto, Dan A. Ventura
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.
Spatiotemporal Pattern Recognition Via Liquid State Machines, Eric Goodman, Dan A. Ventura
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.
Learning A Rendezvous Task With Dynamic Joint Action Perception, Nancy Fulda, Dan A. Ventura
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.
A Multidiscipline Approach To Mitigating The Insider Threat, Jonathan W. Butts, Robert F. Mills, Gilbert L. Peterson
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 …
Histogram Matching For Camera Pose Neighbor Selection, Parris K. Egbert, Bryan S. Morse, Kevin L. Steele
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 …
Minimum Spanning Tree Pose Estimation, Parris K. Egbert, Kevin L. Steele
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 …
Multiple Masks-Based Pixel Comparison Steganalysis Method For Mobile Imaging, Sos S. Agaian, Gilbert L. Peterson, Benjamin M. Rodriguez
Multiple Masks-Based Pixel Comparison Steganalysis Method For Mobile Imaging, Sos S. Agaian, Gilbert L. Peterson, Benjamin M. Rodriguez
Faculty Publications
No abstract provided.
Fuzzy State Aggregation And Off-Policy Reinforcement Learning For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
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 …
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
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 …
Separating Lines Of Text In Free-Form Handwritten Historical Documents, William A. Barrett, Douglas J. Kennard
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 …
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
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 …
Learning Real-Time A* Path Planner For Unmanned Air Vehicle Target Sensing, Jason K. Howlett, Timothy W. Mclain, Michael A. Goodrich
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 …
Introducing Semantics In Web Personalization: The Role Of Ontologies, Magdalini Eirinaki, Dimitrios Mavroeidis, George Tsatsaronis, Michalis Vazirgiannis
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 …