Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Engineering (16)
- Databases and Information Systems (14)
- Social and Behavioral Sciences (14)
- Artificial Intelligence and Robotics (11)
- Business (10)
-
- Accounting (8)
- Life Sciences (7)
- Medicine and Health Sciences (7)
- Programming Languages and Compilers (7)
- Education (6)
- Information Security (6)
- Linguistics (6)
- Mechanical Engineering (6)
- Bioinformatics (5)
- Civil and Environmental Engineering (5)
- Library and Information Science (5)
- Construction Engineering and Management (4)
- Medical Sciences (4)
- Software Engineering (4)
- Technology and Innovation (4)
- Arts and Humanities (3)
- Computational Biology (3)
- Data Science (3)
- Genetics and Genomics (3)
- Medical Pathology (3)
- Biochemistry, Biophysics, and Structural Biology (2)
- Biology (2)
- Biomedical Engineering and Bioengineering (2)
- Keyword
-
- Machine learning (45)
- Computer (21)
- Security (20)
- Model checking (13)
- Particle swarm optimization (12)
-
- Reinforcement learning (12)
- UAV (12)
- Generalization (11)
- Authentication (10)
- Clustering (10)
- Computer science (10)
- Database (10)
- Neural networks (10)
- Trust negotiation (10)
- Annotation (9)
- Computer graphics (9)
- Neural network (9)
- Privacy (9)
- Algorithm (8)
- Genealogy (8)
- Information retrieval (8)
- Interpolation (8)
- Ontology (8)
- Optimization (8)
- Simulation (8)
- DNA sequencing (7)
- Data extraction (7)
- Multicast (7)
- Robotics (7)
- Verification (7)
- Publication Year
- Publication
- Publication Type
Articles 361 - 390 of 823
Full-Text Articles in Computer Sciences
Parallel Active Learning: Eliminating Wait Time With Minimal Staleness, Paul Felt, Robbie Haertel, Eric K. Ringger, Kevin Seppi
Parallel Active Learning: Eliminating Wait Time With Minimal Staleness, Paul Felt, Robbie Haertel, Eric K. Ringger, Kevin Seppi
Faculty Publications
A practical concern for Active Learning (AL) is the amount of time human experts must wait for the next instance to label. We propose a method for eliminating this wait time independent of specific learning and scoring algorithms by making scores always available for all instances, using old (stale) scores when necessary. The time during which the expert is annotating is used to train models and score instances–in parallel–to maximize the recency of the scores. Our method can be seen as a parameterless, dynamic batch AL algorithm. We analyze the amount of staleness introduced by various AL schemes and then …
Geodesic Graph Cut For Interactive Image Segmentation, Bryan S. Morse, Brian L. Price, Scott Cohen
Geodesic Graph Cut For Interactive Image Segmentation, Bryan S. Morse, Brian L. Price, Scott Cohen
Faculty Publications
Interactive segmentation is useful for selecting objects of interest in images and continues to be a topic of much study. Methods that grow regions from foreground/background seeds, such as the recent geodesic segmentation approach, avoid the boundary-length bias of graph-cut methods but have their own bias towards minimizing paths to the seeds, resulting in increased sensitivity to seed placement. The lack of edge modeling in geodesic or similar approaches limits their ability to precisely localize object boundaries, something at which graph-cut methods generally excel. This paper presents a method for combining geodesicdistance information with edge information in a graphcut optimization …
Simultaneous Foreground, Background, And Alpha Estimation For Image Matting, Bryan S. Morse, Brian L. Price, Scott Cohen
Simultaneous Foreground, Background, And Alpha Estimation For Image Matting, Bryan S. Morse, Brian L. Price, Scott Cohen
Faculty Publications
Image matting is the process of extracting a soft segmentation of an object in an image as defined by the matting equation. Most current techniques focus largely on computing the alpha values of unknown pixels and treat computation of the foreground and background colors as an afterthought, if at all. However, for many applications, such as compositing an object into a new scene or deleting an object from the scene, the foreground and background colors are vital for an acceptable answer. We propose a method of solving for the foreground, background, and alpha of an unknown region in an image …
Transformation Learning: Modeling Transferable Transformations In High-Dimensional Data, Christopher R. Wilson
Transformation Learning: Modeling Transferable Transformations In High-Dimensional Data, Christopher R. Wilson
Theses and Dissertations
The goal of learning transfer is to apply knowledge gained from one problem to a separate related problem. Transformation learning is a proposed approach to computational learning transfer that focuses on modeling high-level transformations that are well suited for transfer. By using a high-level representation of transferable data, transformation learning facilitates both shallow transfer (intra-domain) and deep transfer (inter-domain) scenarios. Transformations can be discovered in data using manifold learning to order data instances according to the transformations they represent. For high-dimensional data representable with coordinate systems, such as images and sounds, data instances can be decomposed into small sub-instances based …
Automatic Transition To Peer-To-Peer Download, Roger D. Pack
Automatic Transition To Peer-To-Peer Download, Roger D. Pack
Theses and Dissertations
For traditional web servers, available bandwidth decreases as the number of clients increases. This can cause servers to serve files slowly or to become completely overwhelmed when load grows too high. BitTorrent is a peer-to-peer solution to this problem, but it requires manual configuration for each file to be delivered this way. We develop a new system that integrates peer-to-peer file delivery with traditional client-server downloads. Clients initially attempt to download a file from a web server; if this is too slow, they transition to peer-to-peer delivery. Experiments with a prototype system show that it serves up to 30x faster …
The Role Of Algorithmic Decision Processes In Decision Automation: Three Case Studies, Blake Edward Durtschi
The Role Of Algorithmic Decision Processes In Decision Automation: Three Case Studies, Blake Edward Durtschi
Theses and Dissertations
This thesis develops a new abstraction for solving problems in decision automation. Decision automation is the process of creating algorithms which use data to make decisions without the need for human intervention. In this abstraction, four key ideas/problems are highlighted which must be considered when solving any decision problem. These four problems are the decision problem, the learning problem, the model reduction problem, and the verification problem. One of the benefits of this abstraction is that a wide range of decision problems from many different areas can be broken down into these four “key” sub-problems. By focusing on these key …
A Bayesian Decision Theoretical Approach To Supervised Learning, Selective Sampling, And Empirical Function Optimization, James Lamond Carroll
A Bayesian Decision Theoretical Approach To Supervised Learning, Selective Sampling, And Empirical Function Optimization, James Lamond Carroll
Theses and Dissertations
Many have used the principles of statistics and Bayesian decision theory to model specific learning problems. It is less common to see models of the processes of learning in general. One exception is the model of the supervised learning process known as the "Extended Bayesian Formalism" or EBF. This model is descriptive, in that it can describe and compare learning algorithms. Thus the EBF is capable of modeling both effective and ineffective learning algorithms. We extend the EBF to model un-supervised learning, semi-supervised learning, supervised learning, and empirical function optimization. We also generalize the utility model of the EBF to …
Convenient Decentralized Authentication Using Passwords, Timothy W. Van Der Horst
Convenient Decentralized Authentication Using Passwords, Timothy W. Van Der Horst
Theses and Dissertations
Passwords are a very convenient way to authenticate. In terms of simplicity and portability they are very difficult to match. Nevertheless, current password-based login mechanisms are vulnerable to phishing attacks and typically require users to create and manage a new password for each of their accounts. This research investigates the potential for indirect/decentralized approaches to improve password-based authentication. Adoption of a decentralized authentication mechanism requires the agreement between users and service providers on a trusted third party that vouches for users' identities. Email providers are the de facto trusted third parties on the Internet. Proof of email address ownership is …
Interactive Television News, Derek L. Bunn
Interactive Television News, Derek L. Bunn
Theses and Dissertations
We design and evaluate a way to modify television news to make it interactive for viewers. We allow them to get more of what they want and less of what they don't want. This allows news to break constraints imposed by television broadcast schedules. Our solution is to augment the existing news broadcast structure in the following ways: add a video headlines menu, provide on-demand access to additional story content, provide interactive navigation controls between stories, and a control overlay. For news producers we create a video annotation program and process to help create the interactive news. We use the …
Uav Video Coverage Quality Maps And Prioritized Indexing For Wilderness Search And Rescue, Cameron Engh, Michael A. Goodrich, Bryan S. Morse
Uav Video Coverage Quality Maps And Prioritized Indexing For Wilderness Search And Rescue, Cameron Engh, Michael A. Goodrich, Bryan S. Morse
Faculty Publications
Video-equipped mini unmanned aerial vehicles (mini-UAVs) are becoming increasingly popular for surveillance, remote sensing, law enforcement, and search and rescue operations, all of which rely on thorough coverage of a target observation area. However, coverage is not simply a matter of seeing the area (visibility) but of seeing it well enough to allow detection of targets of interest, a quality we here call “see-ability”. Video flashlights, mosaics, or other geospatial compositions of the video may help place the video in context and convey that an area was observed, but not necessarily how well or how often. This paper presents a …
Studying The Performance Of Wireless Mesh Networks Using The Hxh Transport Control Protocol, Timothy Scott Larsen
Studying The Performance Of Wireless Mesh Networks Using The Hxh Transport Control Protocol, Timothy Scott Larsen
Theses and Dissertations
As the need to remain connected increases, more and more people are turning to wireless mesh networks because they reduce the need for network infrastructure. Unfortunately, TCP does not perform well in such networks. HxH, an alternate protocol, has shown great promise in simulations, but since it relies on exploiting passive feedback, real measurements are needed to determine how effective the protocol really is. This thesis uses a measurement study on a wireless mesh network to characterize the performance of the HxH protocol in real-world networks. Several aspects of the HxH protocol do in fact perform well on real networks, …
Graph-Based Global Illumination, Brian C. Ricks
Graph-Based Global Illumination, Brian C. Ricks
Theses and Dissertations
The slow render times of global illumination algorithms make them impractical in most commercial and academic settings. We propose a novel framework for calculating the computational complexity of global illumination algorithms and show that no other recent improvements have reduced this complexity. We further show that many algorithms use a tree as their rendering paradigm. We propose a new rendering algorithm, pipe casting, which calculates light paths using a graph instead of a tree. Pipe casting significantly reduces both computational complexity and actual render time of rendering. Using an L2 pixel-wise error comparison, on average our algorithm can render a …
Automatic Generation Of Music For Inducing Emotive Response, Tony R. Martinez, Kristine Monteith, Dan A. Ventura
Automatic Generation Of Music For Inducing Emotive Response, Tony R. Martinez, Kristine Monteith, Dan A. Ventura
Faculty Publications
We present a system that generates original music designed to match a target emotion. It creates n-gram models, Hidden Markov Models, and other statistical distributions based on musical selections from a corpus representing a given emotion and uses these models to probabilistically generate new musical selections with similar emotional content. This system produces unique and often remarkably musical selections that tend to match a target emotion, performing this task at a level that approaches human competency for the same task.
Evaluating Models Of Latent Document Semantics In The Presence Of Ocr Errors, Daniel D. Walker, William B. Lund, Eric K. Ringger
Evaluating Models Of Latent Document Semantics In The Presence Of Ocr Errors, Daniel D. Walker, William B. Lund, Eric K. Ringger
Faculty Publications
Models of latent document semantics such as the mixture of multinomials model and Latent Dirichlet Allocation have received substantial attention for their ability to discover topical semantics in large collections of text. In an effort to apply such models to noisy optical character recognition (OCR) text output, we endeavor to understand the effect that character-level noise can have on unsupervised topic modeling. We show the effects both with document-level topic analysis (document clustering) and with word-level topic analysis (LDA) on both synthetic and real-world OCR data. As expected, experimental results show that performance declines as word error rates increase. Common …
Directable Weathering Of Concave Rock Using Curvature Estimation, Matthew Beardall, Joseph Butler, Mckay Farley, Michael D. Jones
Directable Weathering Of Concave Rock Using Curvature Estimation, Matthew Beardall, Joseph Butler, Mckay Farley, Michael D. Jones
Faculty Publications
We address the problem of directable weathering of exposed concave rock for use in computer-generated animation or games. Previous weathering models that admit concave surfaces are computationally inefficient and difficult to control. In nature, the spheroidal and cavernous weathering rates depend on the surface curvature. Spheroidal weathering is fastest in areas with large positive mean curvature and cavernous weathering is fastest in areas with large negative mean curvature. We simulate both processes using an approximation of mean curvature on a voxel grid. Both weathering rates are also influenced by rock durability. The user controls rock durability by editing a durability …
Interactive Football Summarization, Brandon B. Moon
Interactive Football Summarization, Brandon B. Moon
Theses and Dissertations
Football fans do not have the time to watch every game in its entirety and need an effective solution that summarizes them the story of the game. Human-generated summaries are often too short, requiring time and resources to create. We utilize the advantages of Interactive TV to create an automatic football summarization service that is cohesive, provides context, covers the necessary plays, and is concise. First, we construct a degree of interest function that ranks each play based on detailed, play-by-play game events as well as viewing statistics collected from an interactive viewing environment. This allows us to select the …
Bisecting Document Clustering Using Model-Based Methods, Aaron Samuel Davis
Bisecting Document Clustering Using Model-Based Methods, Aaron Samuel Davis
Theses and Dissertations
We all have access to large collections of digital text documents, which are useful only if we can make sense of them all and distill important information from them. Good document clustering algorithms that organize such information automatically in meaningful ways can make a difference in how effective we are at using that information. In this paper we use model-based document clustering algorithms as a base for bisecting methods in order to identify increasingly cohesive clusters from larger, more diverse clusters. We specifically use the EM algorithm and Gibbs Sampling on a mixture of multinomials as the base clustering algorithms …
Noninvasive Estimation Of Pulmonary Artery Pressure Using Heart Sound Analysis, Aaron W. Dennis
Noninvasive Estimation Of Pulmonary Artery Pressure Using Heart Sound Analysis, Aaron W. Dennis
Theses and Dissertations
Right-heart catheterization is the most accurate method for estimating pulmonary artery pressure (PAP). Because it is an invasive procedure it is expensive, exposes patients to the risk of infection, and is not suited for long-term monitoring situations. Medical researchers have shown that PAP influences the characteristics of heart sounds. This suggests that heart sound analysis is a potential noninvasive solution to the PAP estimation problem. This thesis describes the development of a prototype system, called PAPEr, which estimates PAP noninvasively using heart sound analysis. PAPEr uses patient data with machine learning algorithms to build models of how PAP affects heart …
Fused Visible And Infrared Video For Use In Wilderness Search And Rescue, Dennis Eggett, Michael A. Goodrich, Bryan S. Morse, Nathan Rasmussen
Fused Visible And Infrared Video For Use In Wilderness Search And Rescue, Dennis Eggett, Michael A. Goodrich, Bryan S. Morse, Nathan Rasmussen
Faculty Publications
Mini Unmanned Aerial Vehicles (mUAVs) have the potential to assist Wilderness Search and Rescue groups by providing a bird’s eye view of the search area. This paper proposes a method for augmenting visible-spectrum searching with infrared sensing in order to make use of thermal search clues. It details a method for combining the color and heat information from these two modalities into a single fused display to reduce needed screen space for remote field use. To align the video frames for fusion, a method for simultaneously pre-calibrating the intrinsic and extrinsic parameters of the cameras and their mount using a …
Open Access Fiber To The Home Networking, Roger E. Timmerman
Open Access Fiber To The Home Networking, Roger E. Timmerman
Theses and Dissertations
The concept of open-access networks appeals to communities that want to invest in and improve their access to modern telecommunications services. By investing in, or building their own open-access telecommunications networks, communities can create an environment where several telecommunications service providers can co-exist on a common open-access infrastructure. This model promotes innovation and competition among several smaller service providers rather than having a monopoly or oligopoly from those companies that can afford the investment of infrastructure in the community. This research provides an analysis of two large open-access fiber-to-the-home networks in Utah to determine a set of recommendations and best-practices …
Gpu-Accelerated Hierarchical Dense Correspondence For Real-Time Aerial Video Processing, Stephen Cluff, Bryan S. Morse, Jonathan D. Cohen, Mark Duchaineau
Gpu-Accelerated Hierarchical Dense Correspondence For Real-Time Aerial Video Processing, Stephen Cluff, Bryan S. Morse, Jonathan D. Cohen, Mark Duchaineau
Faculty Publications
Video from aerial surveillance can provide a rich source of data for many applications and can be enhanced for display and analysis through such methods as mosaic construction, super-resolution, and mover detection. All of these methods require accurate frame-to-frame registration, which for live use must be performed in real time. In many situations, scene parallax may make alignment using global transformations impossible or error-prone, limiting the performance of subsequent processing and applications. For these cases, dense (per-pixel) correspondence is required, but this can be computationally prohibitive. This paper presents a hierarchical dense correspondence algorithm designed for implementation on graphics processing …
Using Operator Teams For Supervisory Control, Jonathan M. Whetten
Using Operator Teams For Supervisory Control, Jonathan M. Whetten
Theses and Dissertations
Robots and other automated systems have potential use in many different fields. As the scope of robot applications that robots are used for increases, there is a growing desire to have human operators manage multiple robots. Typical methods of enabling operators to multi-task in this way involve some combination of user interfaces that support human cognition and advanced robot autonomy. Our research explores a complementary method of managing multiple robots by utilizing operator teams. The evidence suggests that for appropriate task scenarios, two cooperating operators can be more than twice as effective as one operator working alone.
An Empirical Study Of Instance Hardness, Michael Reed Smith
An Empirical Study Of Instance Hardness, Michael Reed Smith
Theses and Dissertations
Most widely accepted measures of performance for learning algorithms, such as accuracy and area under the ROC curve, provide information about behavior at the data set level. They say nothing about which instances are misclassified, whether two learning algorithms with the same classification accuracy on a data set misclassify the same instances, or whether there are instances misclassified by all learning algorithms. These questions about behavior at the instance level motivate our empirical analysis of instance hardness, a measure of expected classification accuracy for an instance. We analyze the classification of 57 data sets using 9 learning algorithms. Of …
Feature-Based Interactive Terrain Sketching, Daniel B. Adams
Feature-Based Interactive Terrain Sketching, Daniel B. Adams
Theses and Dissertations
Procedural generation techniques are able to quickly and cheaply produce large areas of terrain. However, these techniques produce results that are not easily directable and often require artists to edit the results by hand to achieve the desired layout. This paper proposes a sketch-based system for controlling fractal terrain that allows for a wide variety of terrain feature types. Artists sketch features rather than constrained points or elevations. The system is interactive, provides quick on-demand previews of the terrain, and allows for iterative design modifications. Interaction between features is handled in a realistic fashion. An arbitrary vertex insertion order midpoint …
Classifying Sentence-Based Summaries Of Web Documents, Yiu-Kai D. Ng, Maria Soledad Pera
Classifying Sentence-Based Summaries Of Web Documents, Yiu-Kai D. Ng, Maria Soledad Pera
Faculty Publications
Text classification categorizes Web documents in large collections into predefined classes based on their contents. Unfortunately, the classification process can be time-consuming and users are still required to spend considerable amount of time scanning through the classified Web documents to identify the ones that satisfy their information needs. In solving this problem, we first introduce CorSum, an extractive single-document summarization approach, which is simple and effective in performing the summarization task, since it only relies on word similarity to generate high-quality summaries. Hereafter, we train a Naïve Bayes classifier on CorSum-generated summaries and verify the classification accuracy using the summaries …
Chemalign: Biologically Relevant Multiple Sequence Alignment Using Physicochemical Properties, Hyrum Carroll, Mark J. Clement, Quinn O. Snell, David Mcclellan
Chemalign: Biologically Relevant Multiple Sequence Alignment Using Physicochemical Properties, Hyrum Carroll, Mark J. Clement, Quinn O. Snell, David Mcclellan
Faculty Publications
We present a new algorithm, ChemAlign, that uses physicochemical properties and secondary structure elements to create biologically relevant multiple sequence alignments (MSAs). Additionally, we introduce the Physicochemical Property Difference (PPD) score for the evaluation of MSAs. This score is the normalized difference of physicochemical property values between a calculated and a reference alignment. It takes a step beyond sequence similarity and measures characteristics of the amino acids to provide a more biologically relevant metric. ChemAlign is able to produce more biologically correct alignments and can help to identify potential drug docking sites.
Mcc: A Runtime Verification Tool For Mcapi User Applications, Eric G. Mercer, Ganesh Gopalakrishnan, Jim Holt, Subodh Sharma
Mcc: A Runtime Verification Tool For Mcapi User Applications, Eric G. Mercer, Ganesh Gopalakrishnan, Jim Holt, Subodh Sharma
Faculty Publications
We present a dynamic verification tool MCC for Multicore Communication API applications – a new API for communication among cores. MCC systematically explores all relevant interleavings of an MCAPI application using a tailormade dynamic partial order reduction algorithm (DPOR). Our contributions are (i) a way to model the non-overtaking message matching relation underlying MCAPI calls with a high level algorithm to effect DPOR for MCAPI that controls the lower level details so that the intended executions happen at runtime; and (ii) a list of default safety properties that can be utilized in the process of verification. To our knowledge, this …
Uav Intelligent Path Planning For Wilderness Search And Rescue, Michael A. Goodrich, Lanny Lin
Uav Intelligent Path Planning For Wilderness Search And Rescue, Michael A. Goodrich, Lanny Lin
Faculty Publications
In the priority search phase of Wilderness Search and Rescue, a probability distribution map is created. Areas with higher probabilities are searched first in order to find the missing person in the shortest expected time. When using a UAV to support search, the onboard video camera should cover as much of the important areas as possible within a set time. We explore several algorithms (with and without set destination) and describe some novel techniques in solving this problem and compare their performances against typical WiSAR scenarios. This problem is NP-hard, but our algorithms yield high quality solutions that approximate the …
Livecut: Learning-Based Interactive Video Segmentation By Evaluation Of Multiple Propagated Cues, Bryan S. Morse, Brian L. Price, Scott Cohen
Livecut: Learning-Based Interactive Video Segmentation By Evaluation Of Multiple Propagated Cues, Bryan S. Morse, Brian L. Price, Scott Cohen
Faculty Publications
Video sequences contain many cues that may be used to segment objects in them, such as color, gradient, color adjacency, shape, temporal coherence, camera and object motion, and easily-trackable points. This paper introduces LIVEcut, a novel method for interactively selecting objects in video sequences by extracting and leveraging as much of this information as possible. Using a graph-cut optimization framework, LIVEcut propagates the selection forward frame by frame, allowing the user to correct any mistakes along the way if needed. Enhanced methods of extracting many of the features are provided. In order to use the most accurate information from the …
Versatile Reactive Navigation, Robert P. Burton, Luther A. Tychonievich, Louis P. Tychonievich
Versatile Reactive Navigation, Robert P. Burton, Luther A. Tychonievich, Louis P. Tychonievich
Faculty Publications
Most autonomous mobile agents operate in a highly constrained environment. Despite significant research, existing solutions are limited in their ability to handle heterogeneous constraints within highly dynamic or uncertain environments. This paper presents a novel maneuver selection technique suited for both 2D and 3D environments with highly dynamic maneuvering constraints and multiple mobile obstacles. Agents may have any arbitrary set of nonholonomic control variables; maneuvers can be constrained by a broad class of function inequalities, including time-dependent constraints involving nonlinear relationships between controlled and agent-state variables. The resulting algorithm has been implemented to run in real time using only a …