Physical Sciences and Mathematics Commons^™

Sensitivity Of The Frozen/Melted Basal Boundary To Perturbations Of Basal Traction And Geothermal Heat Flux : Isunnguata Sermia, Western Greenland, Douglas John Brinkerhoff, Toby Warren Meierbachtol, Jesse Johnson, Joel T. Harper

Computer Science Faculty Publications

A full-stress, thermomechanically coupled, numerical model is used to explore the interaction between basal thermal conditions and motion of a terrestrially terminating section of the west Greenland ice sheet. The model domain is a two-dimensional flowline profile extending from the ice divide to the margin. We use data-assimilation techniques based on the adjoint model in order to optimize the basal traction field, minimizing the difference between modeled and observed surface velocities. We monitor the sensitivity of the frozen/melted boundary (FMB) to changes in prescribed geothermal heat flux and sliding speed by applying perturbations to each of these parameters. The FMB …

Modeling 5 Years Of Subglacial Lake Activity In The Macayeal Ice Stream (Antarctica) Catchment Through Assimilation Of Icesat Laser Altimetry, Sasha P. Carter, Helen A. Fricker, Donald D. Blankenship, Jesse V. Johnson, William Lipscomb, Stephen F. Price, Duncan A. Young

Computer Science Faculty Publications

Subglacial lakes beneath Antarctica’s fast-moving ice streams are known to undergo ~1km³ volume changes on annual timescales. Focusing on the MacAyeal Ice Stream (MacIS) lake system, we create a simple model for the response of subglacial water distribution to lake discharge events through assimilation of lake volume changes estimated from Ice, Cloud and land Elevation Satellite (ICESat) laser altimetry. We construct a steady-state water transport model in which known subglacial lakes are treated as either sinks or sources depending on the ICESat-derived filling or drainingrates. The modeled volume change rates of five large subglacial lakes in the downstream portion …

Stability And Classification Performance Of Feature Selection Techniques, Huanjing Wang, Taghi Khoshgoftaar, Qianhui Liang

Computer Science Faculty Publications

Feature selection techniques can be evaluated based on either model performance or the stability (robustness) of the technique. The ideal situation is to choose a feature selec- tion technique that is robust to change, while also ensuring that models built with the selected features perform well. One domain where feature selection is especially important is software defect prediction, where large numbers of met- rics collected from previous software projects are used to help engineers focus their efforts on the most faulty mod- ules. This study presents a comprehensive empirical ex- amination of seven filter-based feature ranking techniques (rankers) applied to …

The Power Of Fair Pricing Mechanisms, Christine Chung, Katrina Ligett, Aaron Roth, Kirk Pruhs

Computer Science Faculty Publications

We explore the revenue capabilities of truthful, monotone (“fair”) allocation and pricing functions for resource constrained auction mechanisms within a general framework that encompasses unlimited supply auctions, knapsack auctions, and auctions with general non-decreasing convex production cost functions. We study and compare the revenue obtainable in each fair pricing scheme to the profit obtained by the ideal omniscient multi-price auction. We show that for capacitated knapsack auctions, no constant pricing scheme can achieve any approximation to the optimal profit, but proportional pricing is as powerful as general monotone pricing. In addition, for auction settings with arbitrary bounded non-decreasing convex production …

Measuring Stability Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi Khoshgoftaar

Computer Science Faculty Publications

Feature selection has been applied in many domains, such as text mining and software engineering. Ideally a feature selection technique should produce consistent out- puts regardless of minor variations in the input data. Re- searchers have recently begun to examine the stability (robustness) of feature selection techniques. The stability of a feature selection method is defined as the degree of agreement between its outputs to randomly-selected subsets of the same input data. This study evaluated the stability of 11 threshold-based feature ranking techniques (rankers) when applied to 16 real-world software measurement datasets of different sizes. Experimental results demonstrate that AUC …

On Elementary Loops Of Logic Programs, Martin Gerber, Joohyung Lee, Yuliya Lierler

Computer Science Faculty Publications

Using the notion of an elementary loop, Gebser and Schaub (2005. Proceedings of the Eighth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'05), 53–65) refined the theorem on loop formulas attributable to Lin and Zhao (2004) by considering loop formulas of elementary loops only. In this paper, we reformulate the definition of an elementary loop, extend it to disjunctive programs, and study several properties of elementary loops, including how maximal elementary loops are related to minimal unfounded sets. The results provide useful insights into the stable model semantics in terms of elementary loops. For a nondisjunctive program, …

Measuring Terrain Distances Through Extracted Channel Networks, Christopher S. Stuetzle, Barbara Cutler, Zhongxian Chen, W. Randolph Franklin, Mehrad Kamalzare, Thomas F. Zimmie

Computer Science Faculty Publications

This paper initiates a forensic analysis of the causes of levee failures by analyzing and extracting information from a sequence of elevation data. This is a crucial step in bettering the design and construction of levees and dams. (Fully diagnosing failures usually requires knowledge beyond the geometry of the levee, such as weather conditions and material properties). We use results from computer simulations of levee overtopping for training data. The simulations use smoothed particle hydrodynamics coupled with a well-known erodibility model. Using the sequential nature of our data, we extract important channel networks that form as the soil is scoured …

Computer Simulation Of Overtopping Of Levees, Mehrad Kamalzare, Zhongxian Chen, Christopher S. Stuetzle, Barbara Cutler, W. Randolph Franklin, Thomas F. Zimmie

Computer Science Faculty Publications

There have been many cases of earth embankment failures, for example, Hurricane Katrina in 2005, where breaching occurred and devastated the surrounding population. Levee failures are preventable by a better understanding of the ways in which these embankments are designed and fail. The objective of this research is to protect levees against future failures. This paper studies various overtopping quantities and durations to represent the same level of levee erosion hazard. This study is based on experimental results of steady flows on the land side of a levee. The effect of water flow has been investigated and a comparison has …

Measuring Robustness Of Feature Selection Techniques On Software Engineering Datasets, Huanjing Wang, Taghi Khoshgoftaar, Randall Wald

Computer Science Faculty Publications

Feature Selection is a process which identifies irrelevant and redundant features from a high-dimensional dataset (that is, a dataset with many features), and removes these before further analysis is performed. Recently, the robustness (e.g., stability) of feature selection techniques has been studied, to examine the sensitivity of these techniques to changes in their input data. In this study, we investigate the robustness of six commonly used feature selection techniques as the magnitude of change to the datasets and the size of the selected feature subsets are varied. All experiments were conducted on 16 datasets from three real-world software projects. The …

Centinela: A Human Activity Recognition System Based On Acceleration And Vital Sign Data, Óscar D. Lara, Alfredo J. Perez, Miguel A. Labrador, José D. Posada

Computer Science Faculty Publications

This paper presents Centinela, a system that combines acceleration data with vital signs to achieve highly accurate activity recognition. Centinela recognizes five activities: walking, running, sitting, ascending, and descending. The system includes a portable and unobtrusive real-time data collection platform, which only requires a single sensing device and a mobile phone. To extract features, both statistical and structural detectors are applied, and two new features are proposed to discriminate among activities during periods of vital sign stabilization. After evaluating eight different classifiers and three different time window sizes, our results show that Centinela achieves up to 95.7% overall accuracy, which …

Transition Systems For Model Generators — A Unifying Approach, Yuliya Lierler, Miroslaw Truszczyński

Computer Science Faculty Publications

A fundamental task for propositional logic is to compute models of propositional formulas. Programs developed for this task are called satisfiability solvers. We show that transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers can be adapted for solvers developed for two other propositional formalisms: logic programming under the answerset semantics, and the logic PC(ID). We show that in each case the task of computing models can be seen as “satisfiability modulo answer-set programming,” where the goal is to find a model of a theory that also is an answer set of a certain program. …

Automatic Content Generation For Video Self Modeling, Ju Shen, Anusha Raghunathan, Sen-Ching S. Cheung, Ravi R. Patel

Computer Science Faculty Publications

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him or herself. Its effectiveness in rehabilitation and education has been repeatedly demonstrated but technical challenges remain in creating video contents that depict previously unseen behaviors. In this paper, we propose a novel system that re-renders new talking-head sequences suitable to be used for VSM treatment of patients with voice disorder. After the raw footage is captured, a new speech track is either synthesized using text-to-speech or selected based on voice similarity from a database of clean speeches. …

Applications Of Hidden Markov Models In Microarray Gene Expression Data, Huimin Geng, Xutao Deng, Hesham Ali

Computer Science Faculty Publications

Hidden Markov models (HMMs) are well developed statistical models to capture hidden information from observable sequential symbols. They were first used in speech recognition in 1970s and have been successfully applied to the analysis of biological sequences since late 1980s as in finding protein secondary structure, CpG islands and families of related DNA or protein sequences [1]. In a HMM, the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the observable parameters. In this chapter, we described two applications using HMMs to predict gene functions …

Capability Information: A Cost-Effective Information Model For Multi-Hop Routing Of Wireless Ad Hoc Networks In The Real Environment, Zhen Jiang, Zhigang Li, Jie Wu, Nong Xiao

Computer Science Faculty Publications

No abstract provided.

An Architecture For Global Ubiquitous Sensing, Alfredo J. Perez

Computer Science Faculty Publications

A new class of wireless sensor networks has recently appeared due to the pervasiness of cellular phones with embedded sensors, mobile Internet connectivity, and location technologies. This mobile wireless sensor network has the potential to address large-scale societal problems and improve the people’s quality of life in a better, faster and less expensive fashion than current solutions based on static wireless sensor networks. Ubiquitous Sensing is the umbrella term used in this dissertation that encompasses location-based services, human-centric, and participatory sensing applications. At the same time, ubiquitous sensing applications are bringing a new series of challenging problems. This dissertation proposes …

Abstract Answer Set Solvers With Backjumping And Learning, Yuliya Lierler

Computer Science Faculty Publications

Nieuwenhuis et al. (2006. Solving SAT and SAT modulo theories: From an abstract Davis-Putnam-Logemann-Loveland procedure to DPLL(T). Journal of the ACM 53(6), 937977 showed how to describe enhancements of the Davis–Putnam–Logemann–Loveland algorithm using transition systems, instead of pseudocode. We design a similar framework for several algorithms that generate answer sets for logic programs: SMODELS, SMODELS_cc, asp-sat with Learning (CMODELS), and a newly designed and implemented algorithm sup. This approach to describe answer set solvers makes it easier to prove their correctness, to compare them, and to design new systems.

Analyses, Simulations, And Physical Modeling Validation Of Levee And Embankment Erosion, Zhongxian Chen, Christopher S. Stuetzle, Barbara Cutler, Jared Gross, W. Randolph Franklin, Thomas F. Zimmie

Computer Science Faculty Publications

We present a computer simulation of hydraulic erosion on levees, dams, and earth embankments, with emphasis on rill and gully initiation and propagation. We focus on erosion features that occur after an earthen structure is overtopped. We have developed a 3D fluid and hydraulic erosion simulation engine using Smoothed Particle Hydrodynamics (SPH). We present the results of digital simulations for different soil types. Furthermore, small-scale physical models of levees composed of different soils were constructed and tested experimentally. The digital simulations are compared to physical experimental results to validate the computer models.

P2n: A Pedagogical Pattern For Teaching Computer Programming To Non-Cs Majors, Zhen Jiang, Eduardo B. Fernandez, Liang Cheng

Computer Science Faculty Publications

No abstract provided.

Fast Mobility Control Protocols With Sink Location Protection In Wireless Sensor Networks, Xiao Chen, Qijun Gu, Zhen Jiang, Jie Wu

Computer Science Faculty Publications

No abstract provided.

Automatic Code Homework Grading Based On Concept Extraction, I. Alhami, Izzat Alsmadi

Computer Science Faculty Publications

E-learning is taking more roles in the current methods of education. The automatic grading and assessment play a major role in both e-learning and traditional education as a method to reduce educational expenses and relief instructors from some of the lengthy tasks such as grading. In this paper, automatic grading for software code assignments or homework is described. A tool is developed to automatically grade students' code assignments. Concepts or code from Students' answers are first parsed. Key abstractions and keywords are extracted from students' assignments and compared with typical or expected answers. Weights are given to code keywords by …

Activities And Trends In Testing Graphical User Interfaces Automatically, Izzat M. Alsmadi

Computer Science Faculty Publications

This study introduced some new approaches for software test automation in general and testing graphical user interfaces in particular. The study presented ideas in the different stages of the test automation framework. Test automation framework main activities include test case generation, execution and verification. Other umbrella activities include modeling, critical paths selection and some others. In modeling, a methodology is presented to transform the user interface of applications into XML (i.e., extensible Markup Language) files. The purpose of this intermediate transformation is to enable producing test automation components in a format that is easier to deal with (in terms of …

Integration Of Breast Cancer Gene Signatures Based On Graph Centrality, Jianxin Wang, Gang Chen, Min Li, Yi Pan

Computer Science Faculty Publications

Background: Various gene-expression signatures for breast cancer are available for the prediction of clinical outcome. However due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene expression and clinical outcome.

Results: In this paper, we propose a method to integrate different breast cancer gene signatures by using graph centrality in a context-constrained protein interaction network (PIN). The context-constrained PIN for breast cancer is built by integrating complete PIN and various gene signatures reported in literatures. Then, we use graph centralities to quantify the importance of …

Biological Network Motif Detection And Evaluation, Wooyoung Kim, Min Li, Jianxin Wang, Yi Pan

Computer Science Faculty Publications

Background: Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over- represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important.

Results: We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and …

Parallel Progressive Multiple Sequence Alignment On Reconfigurable Meshes, Ken Nguyen, Yi Pan, Ge Nong

Computer Science Faculty Publications

Background: One of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing extensive task. In addition, the advancement in DNA/RNA and Protein sequencing techniques has created a vast amount of sequences to be analyzed that exceeding the capability of traditional computing models. Therefore, an effective parallel multiple sequence alignment model capable of resolving these issues is in a great demand.

Results: We design O(1) run-time solutions …

A Comparison Of The Functional Modules Identified From Time Course And Static Ppi Network Data, Xiwei Tang, Jianxin Wang, Binbin Liu, Min Li, Gang Chen, Yi Pan

Computer Science Faculty Publications

Background: Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems.

Results: In this paper, Time Course Protein Interaction Networks (TC- PINs) are reconstructed by incorporating time series gene expression into PPI networks. Then, a clustering algorithm is used to create functional modules from three …

Inferring Viral Quasispecies Spectra From 454 Pyrosequencing Reads, Irina Astrovskaya, Bassam Tork, Serghei Mangul, Kelly Westbrooks, Ion Măndoiu, Peter Balfe, Alexander Zelikovskiy

Computer Science Faculty Publications

Background: RNA viruses infecting a host usually exist as a set of closely related sequences, referred to as quasispecies. The genomic diversity of viral quasispecies is a subject of great interest, particularly for chronic infections, since it can lead to resistance to existing therapies. High-throughput sequencing is a promising approach to characterizing viral diversity, but unfortunately standard assembly software was originally designed for single genome assembly and cannot be used to simultaneously assemble and estimate the abundance of multiple closely related quasispecies sequences.

Results: In this paper, we introduce a new Viral Spectrum Assembler (ViSpA) method for quasispecies spectrum reconstruction …

Estimation Of Alternative Splicing Isoform Frequencies From Rna-Seq Data, Marius Nicolae, Serghei Mangul, Ion I. Măndoiu, Alexander Zelikovskiy

Computer Science Faculty Publications

Background: Massively parallel whole transcriptome sequencing, commonly referred as RNA-Seq, is quickly becoming the technology of choice for gene expression profiling. However, due to the short read length delivered by current sequencing technologies, estimation of expression levels for alternative splicing gene isoforms remains challenging.

Results: In this paper we present a novel expectation-maximization algorithm for inference of isoform- and genespecific expression levels from RNA-Seq data. Our algorithm, referred to as IsoEM, is based on disambiguating information provided by the distribution of insert sizes generated during sequencing library preparation, and takes advantage of base quality scores, strand and read pairing information …

In-Degree Dynamics Of Large-Scale P2p Systems, Zhongmei Yao, Daren B. H. Cline, Dmitri Loguinov

Computer Science Faculty Publications

This paper builds a complete modeling framework for understanding user churn and in-degree dynamics in unstructured P2P systems in which each user can be viewed as a stationary alternating renewal process. While the classical Poisson result on the superposition of n stationary renewal processes for n→∞ requires that each point process become sparser as n increases, it is often difficult to rigorously show this condition in practice. In this paper, we first prove that despite user heterogeneity and non-Poisson arrival dynamics, a superposition of edge-arrival processes to a live user under uniform selection converges to a Poisson process when …

Tracking And Recognizing Multiple Faces Using Kalman Filter And Modularpca, Jacob Foytik, Praveen Sankaran, Vijayan Asari

Computer Science Faculty Publications

Real-time tracking and recognizing multiple faces in complex environments has the ability to provide efficient security automation to large areas. Previous research has shown that Kalman filter techniques paired with the traditional face detection methods can be used to track one or more faces in a viewing region, but prove unreliable under variant conditions due to the inability to reliably distinguish between multiple trackers. A real-time face tracking and recognition system is presented that is capable of processing multiple faces simultaneously. The proposed system utilizes the Kalman filter for tracking and uses a low-level recognition system to properly distinguish between …

From Isotropic To Anisotropic Side Chain Representations: Comparison Of Three Models For Residue Contact Estimation, Weitao Sun, Jing He

Computer Science Faculty Publications

The criterion to determine residue contact is a fundamental problem in deriving knowledge-based mean-force potential energy calculations for protein structures. A frequently used criterion is to require the side chain center-to-center distance or the C-alpha-to-C-alpha atom distance to be within a pre-determined cutoff distance. However, the spatially anisotropic nature of the side chain determines that it is challenging to identify the contact pairs. This study compares three side chain contact models: the Atom Distance criteria (ADC) model, the Isotropic Sphere Side chain (ISS) model and the Anisotropic Ellipsoid Side chain (AES) model using 424 high resolution protein structures in the …