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Articles 331 - 360 of 663
Full-Text Articles in Computer Sciences
Malware Type Recognition And Cyber Situational Awareness, Thomas E. Dube, Richard A. Raines, Gilbert L. Peterson, Kenneth W. Bauer, Michael R. Grimaila, Steven K. Rogers
Malware Type Recognition And Cyber Situational Awareness, Thomas E. Dube, Richard A. Raines, Gilbert L. Peterson, Kenneth W. Bauer, Michael R. Grimaila, Steven K. Rogers
Faculty Publications
Current technologies for computer network and host defense do not provide suitable information to support strategic and tactical decision making processes. Although pattern-based malware detection is an active research area, the additional context of the type of malware can improve cyber situational awareness. This additional context is an indicator of threat capability thus allowing organizations to assess information losses and focus response actions appropriately. Malware Type Recognition (MaTR) is a research initiative extending detection technologies to provide the additional context of malware types using only static heuristics. Test results with MaTR demonstrate over a 99% accurate detection rate and 59% …
Simulating Windows-Based Cyber Attacks Using Live Virtual Machine Introspection, Dustyn A. Dodge, Barry E. Mullins, Gilbert L. Peterson, James S. Okolica
Simulating Windows-Based Cyber Attacks Using Live Virtual Machine Introspection, Dustyn A. Dodge, Barry E. Mullins, Gilbert L. Peterson, James S. Okolica
Faculty Publications
Static memory analysis has been proven a valuable technique for digital forensics. However, the memory capture technique halts the system causing the loss of important dynamic system data. As a result, live analysis techniques have emerged to complement static analysis. In this paper, a compiled memory analysis tool for virtualization (CMAT-V) is presented as a virtual machine introspection (VMI) utility to conduct live analysis during simulated cyber attacks. CMAT-V leverages static memory dump analysis techniques to provide live system state awareness. CMAT-V parses an arbitrary memory dump from a simulated guest operating system (OS) to extract user information, network usage, …
Supporting Wilderness Search And Rescue With Integrated Intelligence: Autonomy And Information At The Right Time And The Right Place, Michael A. Goodrich, Lanny Lin, Bryan S. Morse, Michael Roscheck
Supporting Wilderness Search And Rescue With Integrated Intelligence: Autonomy And Information At The Right Time And The Right Place, Michael A. Goodrich, Lanny Lin, Bryan S. Morse, Michael Roscheck
Faculty Publications
Current practice in Wilderness Search and Rescue (WiSAR) is analogous to an intelligent system designed to gather and analyze information to find missing persons in remote areas. The system consists of multiple parts — various tools for information management (maps, GPS, etc) distributed across personnel with different skills and responsibilities. Introducing a camera-equipped mini-UAV into this task requires autonomy and information technology that itself is an integrated intelligent system to be used by a sub-team that must be integrated into the overall intelligent system. In this paper, we identify key elements of the integration challenges along two dimensions: (a) attributes …
On The Use Of Cartographic Projections In Visualizing Phylogenetic Treespace, Mark J. Clement, Quinn O. Snell, Kenneth Sundberg
On The Use Of Cartographic Projections In Visualizing Phylogenetic Treespace, Mark J. Clement, Quinn O. Snell, Kenneth Sundberg
Faculty Publications
Phylogenetic analysis is becoming an increasingly important tool for biological research. Applications include epidemiological studies, drug development, and evolutionary analysis. Phylogenetic search is a known NP-Hard problem. The size of the data sets which can be analyzed is limited by the exponential growth in the number of trees that must be considered as the problem size increases. A better understanding of the problem space could lead to better methods, which in turn could lead to the feasible analysis of more data sets. We present a definition of phylogenetic tree space and a visualization of this space that shows significant exploitable …
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 …
Factors Affecting College Students’ Knowledge And Opinions Of Genetically Modified Foods, Chad Laux, Gretchen A. Mosher, Steven A. Freeman
Factors Affecting College Students’ Knowledge And Opinions Of Genetically Modified Foods, Chad Laux, Gretchen A. Mosher, Steven A. Freeman
Faculty Publications
The use of biotechnology in food and agricultural applications has increased greatly during the past decade and is considered by many to be a controversial topic. Drawing upon a previous national study, a new survey was conducted of U.S. and international college students at a large, land-grant, Research University to determine factors that may affect opinions about genetically modified (GM) food products. Factors examined included nationality, discipline area of study, perceptions of safety, and awareness and levels of acceptance regarding GM food. Results indicated students born outside the United States had more negative opinions about genetically modified foods than did …
Developing Cyberspace Data Understanding Using Crisp-Dm For Host-Based Ids Feature Mining, Joseph R. Erskine, Gilbert L. Peterson, Barry E. Mullins, Michael R. Grimaila
Developing Cyberspace Data Understanding Using Crisp-Dm For Host-Based Ids Feature Mining, Joseph R. Erskine, Gilbert L. Peterson, Barry E. Mullins, Michael R. Grimaila
Faculty Publications
Current intrusion detection systems (IDS) generate a large number of specific alerts, but typically do not provide actionable information. Compounding this problem is the fact that many alerts are false positive alerts. This paper applies the Cross Industry Standard Process for Data Mining (CRISP-DM) to develop an understanding of a host environment under attack. Data is generated by launching scans and exploits at a machine outfitted with a set of host-based forensic data collectors. Through knowledge discovery, features are selected to project human understanding of the attack process into the IDS model. By discovering relationships between the data collected and …
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 …
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 …
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 …
Structured P2p Technologies For Distributed Command And Control, Daniel R. Karrels, Gilbert L. Peterson, Barry E. Mullins
Structured P2p Technologies For Distributed Command And Control, Daniel R. Karrels, Gilbert L. Peterson, Barry E. Mullins
Faculty Publications
The utility of Peer-to-Peer (P2P) systems extends far beyond traditional file sharing. This paper provides an overview of how P2P systems are capable of providing robust command and control for Distributed Multi-Agent Systems (DMASs). Specifically, this article presents the evolution of P2P architectures to date by discussing supporting technologies and applicability of each generation of P2P systems. It provides a detailed survey of fundamental design approaches found in modern large-scale P2P systems highlighting design considerations for building and deploying scalable P2P applications. The survey includes unstructured P2P systems, content retrieval systems, communications structured P2P systems, flat structured P2P systems and …
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 …
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 …
Dynamic Coalition Formation Under Uncertainty, Daylon J. Hooper, Gilbert L. Peterson, Brett J. Borghetti
Dynamic Coalition Formation Under Uncertainty, Daylon J. Hooper, Gilbert L. Peterson, Brett J. Borghetti
Faculty Publications
Coalition formation algorithms are generally not applicable to real-world robotic collectives since they lack mechanisms to handle uncertainty. Those mechanisms that do address uncertainty either deflect it by soliciting information from others or apply reinforcement learning to select an agent type from within a set. This paper presents a coalition formation mechanism that directly addresses uncertainty while allowing the agent types to fall outside of a known set. The agent types are captured through a novel agent modeling technique that handles uncertainty through a belief-based evaluation mechanism. This technique allows for uncertainty in environmental data, agent type, coalition value, and …
Rc-Chord: Resource Clustering In A Large-Scale Hierarchical Peer-To-Peer System, Daniel R. Karrels, Gilbert L. Peterson, Barry E. Mullins
Rc-Chord: Resource Clustering In A Large-Scale Hierarchical Peer-To-Peer System, Daniel R. Karrels, Gilbert L. Peterson, Barry E. Mullins
Faculty Publications
Conducting data fusion and Command and Control (C2) in large-scale systems requires more than the presently available Peer-to-Peer (P2P) technologies provide. Resource Clustered Chord (RC-Chord) is an extension to the Chord protocol that incorporates elements of a hierarchical peer-to-peer architecture to facilitate coalition formation algorithms in large-scale systems. Each cluster in this hierarchy represents a particular resource available for allocation, and RC-Chord provides the capabilities to locate agents of a particular resource. This approach improves upon other strategies by including support for abundant resources, or those resources that most or all agents in the system possess. This scenario exists in …
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 …
Reducing Source Load In Bittorrent, Brian Sanderson, Daniel Zappala
Reducing Source Load In Bittorrent, Brian Sanderson, Daniel Zappala
Faculty Publications
One of the main goals of BitTorrent is to reduce load on web servers by encouraging clients to share content between themselves. However, BitTorrent’s current design relies heavily on the original source to serve a disproportionate amount of the file. We modify standard BitTorrent software so that a source determines the current popularity of each of the blocks of a file and tries to serve only those blocks that are rare. Using extensive PlanetLab experiments, we show that this modification can save a significant amount of the source’s upload bandwidth, with the tradeoff of some increased peer download time. In …
A Trust-Based Multiagent System, Richard S. Seymour, Gilbert L. Peterson
A Trust-Based Multiagent System, Richard S. Seymour, Gilbert L. Peterson
Faculty Publications
Cooperative agent systems often do not account for sneaky agents who are willing to cooperate when the stakes are low and take selfish, greedy actions when the rewards rise. Trust modeling often focuses on identifying the appropriate trust level for the other agents in the environment and then using these levels to determine how to interact with each agent. Adding trust to an interactive partially observable Markov decision process (I-POMDP) allows trust levels to be continuously monitored and corrected enabling agents to make better decisions. The addition of trust modeling increases the decision process calculations, and solves more complex trust …
Unified Behavior Framework For Reactive Robot Control, Brian G. Woolley, Gilbert L. Peterson
Unified Behavior Framework For Reactive Robot Control, Brian G. Woolley, Gilbert L. Peterson
Faculty Publications
Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of the existing reactive controllers. Additionally, the modular design of the behavior framework: (1) simplifies development and testing; (2) promotes the reuse of code; (3) supports designs that scale easily into large hierarchies while restricting code complexity; and (4) allows the behavior based system developer the freedom to use the behavior system they feel will function the best. …
A Sophisticated Library Search Strategy Using Folksonomies And Similarity Matching, William Lund, Yiu-Kai D. Ng, Maria Soledad Pera
A Sophisticated Library Search Strategy Using Folksonomies And Similarity Matching, William Lund, Yiu-Kai D. Ng, Maria Soledad Pera
Faculty Publications
Libraries, private and public, offer valuable resources to library patrons. As of today the only way to locate information archived exclusively in libraries is through their catalogs. Library patrons, however, often find it difficult to formulate a proper query, which requires using specific keywords assigned to different fields of desired library catalog records, to obtain relevant results. These improperly formulated queries often yield irrelevant results or no results at all. This negative experience in dealing with existing library systems turn library patrons away from library catalogs; instead, they rely on Web search engines to perform their searches first and upon …
Improving The Separability Of A Reservoir Facilitates Learning Transfer, David Norton, Dan A. Ventura
Improving The Separability Of A Reservoir Facilitates Learning Transfer, David Norton, Dan A. Ventura
Faculty Publications
We use a type of reservoir computing called the liquid state machine (LSM) to explore learning transfer. The Liquid State Machine (LSM) is a neural network model that uses a reservoir of recurrent spiking neurons as a filter for a readout function. We develop a method of training the reservoir, or liquid, that is not driven by residual error. Instead, the liquid is evaluated based on its ability to separate different classes of input into different spatial patterns of neural activity. Using this method, we train liquids on two qualitatively different types of artificial problems. Resulting liquids are shown to …
Music Recommendation And Query-By-Content Using Self-Organizing Maps, Kyle B. Dickerson, Dan A. Ventura
Music Recommendation And Query-By-Content Using Self-Organizing Maps, Kyle B. Dickerson, Dan A. Ventura
Faculty Publications
The ever-increasing density of computer storage devices has allowed the average user to store enormous quantities of multimedia content, and a large amount of this content is usually music. Current search techniques for musical content rely on meta-data tags which describe artist, album, year, genre, etc. Query-by-content systems allow users to search based upon the acoustical content of the songs. Recent systems have mainly depended upon textual representations of the queries and targets in order to apply common string-matching algorithms. However, these methods lose much of the information content of the song and limit the ways in which a user …