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2017

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Articles 151 - 180 of 196

Full-Text Articles in Numerical Analysis and Scientific Computing

Evaluating A Personal Stress Monitoring System, Sneha Gogineni May 2017

Evaluating A Personal Stress Monitoring System, Sneha Gogineni

Theses and Dissertations

Now-a-days, Life is generally much more stressful than in the past. "Stress" is the word that we use when we feel that we are overloaded mentally in our thoughts and wonder whether we can really cope with those placed upon us. Sometimes, stress gets us going and they are good for us but at other times, it could be the cause to undermine both our mental and physical health. The way we respond to a challenge can be considered as a kind of stress. Part of our response to a challenge is physiological and affects our own physical state. When …

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A Comparative Study Of Cognitive Systems For Learning, Praneetha Mandava May 2017

A Comparative Study Of Cognitive Systems For Learning, Praneetha Mandava

Theses and Dissertations

Learning is the modification of a behavioral tendency by experience. Memory and reasoning are the most important aspects for learning in humans; information is temporarily stored in the short-term memory and processed, compared with existing memories and stored in long-term memory, and can be re-used when needed. One way to describe an organized pattern of thought or behavior and the categories of information along with their relationships is by using schemas. A cognitive script is one form of a schema that evolves over multiple exposures to the same set of stimuli and/or repeated enactment of a particular behavior. This research …

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Continuous Top-K Monitoring On Document Streams, Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li May 2017

Continuous Top-K Monitoring On Document Streams, Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li

Research Collection School Of Computing and Information Systems

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. …

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A Neural Network Model For Semi-Supervised Review Aspect Identification, Ying Ding, Changlong Yu, Jing Jiang May 2017

A Neural Network Model For Semi-Supervised Review Aspect Identification, Ying Ding, Changlong Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Aspect identification is an important problem in opinion mining. It is usually solved in an unsupervised manner, and topic models have been widely used for the task. In this work, we propose a neural network model to identify aspects from reviews by learning their distributional vectors. A key difference of our neural network model from topic models is that we do not use multinomial word distributions but instead embedding vectors to generate words. Furthermore, to leverage review sentences labeled with aspect words, a sequence labeler based on Recurrent Neural Networks (RNNs) is incorporated into our neural network. The resulting model …

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Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen May 2017

Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We develop a methodology for persona generation using real time social media data for the distribution of products via online platforms. From a large social media account containing more than 30 million interactions from users from 181 countries engaging with more than 4,200 digital products produced by a global media corporation, we demonstrate that our methodology can first identify both distinct and impactful user segments and then create persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We validate our approach by implementing the methodology into an actual working system that leverages large scale online …

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A Parallelized Method For Solving Large Scale Integer Linear Optimization Problems Using Cut-And-Solve With Applications To Cgwas, John Brandenburg Apr 2017

A Parallelized Method For Solving Large Scale Integer Linear Optimization Problems Using Cut-And-Solve With Applications To Cgwas, John Brandenburg

Theses

The commercial solver CPLEX has been one of the top solvers of mixed-integer and purely integer linear problems for some time. Its method of solving, Branch-and-Cut, has been shown to be highly effective, but has its limits in terms of input sizes which are tractable, and cannot be effectively parallelized beyond a small number. Here we present a different method of solution, Cut-and-Solve, which utilizes the power of CPLEX to effectively parallelize any mixed-integer or integer linear problem. We have utilized Cut-and-Solve in a novel way to offer optimal solution guarantees more quickly. We will show comparisons of Cut-and-Solve to …

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Network Exploration Of Correlated Multivariate Protein Data For Alzheimer's Disease Association, Matthew J. Lane Apr 2017

Network Exploration Of Correlated Multivariate Protein Data For Alzheimer's Disease Association, Matthew J. Lane

Theses

Alzheimer Disease (AD) is difficult to diagnose by using genetic testing or other traditional methods. Unlike diseases with simple genetic risk components, there exists no single marker determining as to whether someone will develop AD. Furthermore, AD is highly heterogeneous and different subgroups of individuals develop the disease due to differing factors. Traditional diagnostic methods using perceivable cognitive deficiencies are often too little too late due to the brain having suffered damage from decades of disease progression. In order to observe AD at early stages prior to the observation of cognitive deficiencies, biomarkers with greater accuracy are required. By using …

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Inelastic Rate Coefficients For Collisions Of C₆Hˉ With H₂ And He, Kyle M. Walker, François Lique, Fabien Dumouchel, Richard Dawes Apr 2017

Inelastic Rate Coefficients For Collisions Of C₆Hˉ With H₂ And He, Kyle M. Walker, François Lique, Fabien Dumouchel, Richard Dawes

Chemistry Faculty Research & Creative Works

The recent detection of anions in the interstellar medium has shown that they exist in a variety of astrophysical environments -- circumstellar envelopes, cold dense molecular clouds and star-forming regions. Both radiative and collisional processes contribute to molecular excitation and de-excitation in these regions so that the ‘local thermodynamic equilibrium’ approximation, where collisions cause the gas to behave thermally, is not generally valid. Therefore, along with radiative coefficients, collisional excitation rate coefficients are needed to accurately model the anionic emission from these environments. We focus on the calculation of state-to-state rate coefficients of the C6H- molecule in …

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A Robust And Secure Video Steganography Method In Dwt-Dct Domains Based On Multiple Object Tracking And Ecc, Ramadhan J. Mstafa, Khaled M. Elleithy, Eman Abdelfattah Apr 2017

A Robust And Secure Video Steganography Method In Dwt-Dct Domains Based On Multiple Object Tracking And Ecc, Ramadhan J. Mstafa, Khaled M. Elleithy, Eman Abdelfattah

School of Computer Science & Engineering Faculty Publications

Over the past few decades, the art of secretly embedding and communicating digital data has gained enormous attention because of the technological development in both digital contents and communication. The imperceptibility, hiding capacity, and robustness against attacks are three main requirements that any video steganography method should take into consideration. In this paper, a robust and secure video steganographic algorithm in discrete wavelet transform (DWT) and discrete cosine transform (DCT) domains based on the multiple object tracking (MOT) algorithm and error correcting codes is proposed. The secret message is preprocessed by applying both Hamming and Bose, Chaudhuri, and Hocquenghem codes …

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Comparative Relation Generative Model, Maksim Tkachenko, Hady W. Lauw Apr 2017

Comparative Relation Generative Model, Maksim Tkachenko, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Online reviews are important decision aids to consumers. Other than helping users to evaluate individual products, reviews also support comparison shopping by comparing two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of authors. Therefore, we focus on comparative sentences, whereby two products are compared directly by a review author within a sentence. We study the problem of comparative relation mining. Given a set of comparative sentences, each relating a pair of entities, our objective is three-fold: …

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Predicting The Impact Of Software Engineering Topics: An Empirical Study, Santonu Sarkar, Rumana Lakdawala, Subhajit Datta Apr 2017

Predicting The Impact Of Software Engineering Topics: An Empirical Study, Santonu Sarkar, Rumana Lakdawala, Subhajit Datta

Research Collection School Of Computing and Information Systems

Predicting the future is hard, more so in active research areas. In this paper, we customize an established model for citation prediction of research papers and apply it on research topics. We argue that research topics, rather than individual publications, have wider relevance in the research ecosystem, for individuals as well as organizations. In this study, topics are extracted from a corpus of software engineering publications covering 55,000+ papers written by more than 70,000 authors across 56 publication venues, over a span of 38 years, using natural language processing techniques. We demonstrate how critical aspects of the original paper-based prediction …

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Characterizing Malicious Android Apps By Mining Topic-Specific Data Flow Signatures, Xinli Yang, David Lo, Li Li, Xin Xia, Tegawendé F. Bissyande, Jacques Klein Apr 2017

Characterizing Malicious Android Apps By Mining Topic-Specific Data Flow Signatures, Xinli Yang, David Lo, Li Li, Xin Xia, Tegawendé F. Bissyande, Jacques Klein

Research Collection School Of Computing and Information Systems

Context: State-of-the-art works on automated detection of Android malware have leveraged app descriptions to spot anomalies w.r.t the functionality implemented, or have used data flow information as a feature to discriminate malicious from benign apps. Although these works have yielded promising performance,we hypothesize that these performances can be improved by a better understanding of malicious behavior. Objective: To characterize malicious apps, we take into account both information on app descriptions,which are indicative of apps’ topics, and information on sensitive data flow, which can be relevant todiscriminate malware from benign apps. Method: In this paper, we propose a topic-specific approach to …

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Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim Apr 2017

Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user's dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content …

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Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim Apr 2017

Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study …

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Ten Simple Rules For Responsible Big Data Research, Matthew Zook, Solon Barocas, Danah Boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara A. Koenig, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, Frank Pasquale Mar 2017

Ten Simple Rules For Responsible Big Data Research, Matthew Zook, Solon Barocas, Danah Boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara A. Koenig, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, Frank Pasquale

Geography Faculty Publications

No abstract provided.

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Designing Novel Nanostructured Permanent Magnets, Ali Al Kadhim Mar 2017

Designing Novel Nanostructured Permanent Magnets, Ali Al Kadhim

UNO Student Research and Creative Activity Fair

Rare earth element based alloys have been the source of high performance magnetic alloys, and have played a paramount role in the development of various technologies, including: memory devices (such as credit cards, random-access memory), sensors, and various biomedical applications. However, there is a tremendous need to replace rare earth metals with material with powerful magnetic properties. Our group recently found CrTe-based materials that show very promising magnetic properties in nanostructured form. The magnetic modeling of such material in nanostructured form prior to their fabrication demonstrates their magnetic properties in bulk form. In this project, we investigate the behavior of …

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Optimizing Campus Mobility With A Focus On Sustainability: A Graph Theory Approach To Intra-Campus Transportation Networks, Quinn M. Nelson Mar 2017

Optimizing Campus Mobility With A Focus On Sustainability: A Graph Theory Approach To Intra-Campus Transportation Networks, Quinn M. Nelson

UNO Student Research and Creative Activity Fair

The idea of public transportation is supported by most in theory but often heavily criticized by users when put into application. There are common tensions that are related to public transportation, as described by frequent users: unreliable, too crowded, and slow. The University of Nebraska-Omaha (UNO) is a growing metropolitan institution that uses a shuttle system to transport students among their three campuses daily. As of 2015, the current total student enrollment is approximately 16,000; UNO plans to enroll 20,000 students by 2020. The expected student growth is also reflected by the current construction of new buildings and expansion of …

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Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu Mar 2017

Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu

Research Collection School Of Computing and Information Systems

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly …

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Discovering Burst Patterns Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang Feb 2017

Discovering Burst Patterns Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang

Research Collection School Of Computing and Information Systems

Twitter has become one of largest social networks for users to broadcast burst topics. There have been many studies on how to detect burst topics. However, mining burst patterns in burst topics has not been solved by the existing works. In this paper, we investigate the problem of mining burst patterns of burst topic in Twitter. A burst topic user graph model is proposed, which can represent the topology structure of burst topic propagation across a large number of Twitter users. Based on the model, hierarchical clustering is applied to cluster burst topics and reveal burst patterns from the macro …

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Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang Feb 2017

Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for …

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Seapot-Rl: Selective Exploration Algorithm For Policy Transfer In Rl, Akshay Narayan, Zhuoru Li, Tze-Yun Leong Feb 2017

Seapot-Rl: Selective Exploration Algorithm For Policy Transfer In Rl, Akshay Narayan, Zhuoru Li, Tze-Yun Leong

Research Collection School Of Computing and Information Systems

We propose a new method for transferring a policy from a source task to a target task in model-based reinforcement learning. Our work is motivated by scenarios where a robotic agent operates in similar but challenging environments, such as hospital wards, differentiated by structural arrangements or obstacles, such as furniture. We address problems that require fast responses adapted from incomplete, prior knowledge of the agent in new scenarios. We present an efficient selective exploration strategy that maximally reuses the source task policy. Reuse efficiency is effected through identifying sub-spaces that are different in the target environment, thus limiting the exploration …

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Transcription Through The Eye Of A Needle: Daily And Annual Cyclic Gene Expression Variation In Douglas-Fir Needles, Peter Dolan Jan 2017

Transcription Through The Eye Of A Needle: Daily And Annual Cyclic Gene Expression Variation In Douglas-Fir Needles, Peter Dolan

Computer Science Publications

Background: Perennial growth in plants is the product of interdependent cycles of daily and annual stimuli that induce cycles of growth and dormancy. In conifers, needles are the key perennial organ that integrates daily and seasonal signals from light, temperature, and water availability. To understand the relationship between seasonal cycles and seasonal gene expression responses in conifers, we examined diurnal and circannual needle mRNA accumulation in Douglas-fir (Pseudotsuga menziesii) needles at diurnal and circannual scales. Using mRNA sequencing, we sampled 6.1 × 109 reads from 19 trees and constructed a de novo pan-transcriptome reference that includes 173,882 tree-derived transcripts. Using …

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A Robust Framework For Mining Youtube Data, Zifeng Tian Jan 2017

A Robust Framework For Mining Youtube Data, Zifeng Tian

Theses, Dissertations and Capstones

YouTube is currently the most popular and successful video sharing website. As YouTube has broad and profound social impact, YouTube analytics has become a hot research area. The videos on YouTube have become a treasure of data. However, getting access to the immense and massive YouTube data is a challenge. Previous research, studies, and analysis so far, are only conducted on very small volumes of YouTube video data. To date, there exists no mechanism to systematically and continuously collect, process and store the rich set of YouTube data. This thesis presents a methodology to systematically and continuously mine and store …

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High Performance Techniques Applied In Partial Differential Equations Library, Shilei Lin Jan 2017

High Performance Techniques Applied In Partial Differential Equations Library, Shilei Lin

All College Thesis Program, 2016-2019

This thesis explores various Trilinos packages to determine a method for updating the deal.ii library, which specializes in solving partial differential equations by finite element methods. It begins with introducing related concepts and general goals, followed by exploring computational and mathematical methods which are analytical solutions of one-dimensional Boussinesq equations and developing newer prototypes for solvers in deal.ii based on Trilinos packages. After demonstrating the methods, it indicates the reducing solving time in newer prototypes. Based on results from the prototype, similar methods are applied to update the deal.ii library. In the end, a testing program is exploited to demonstrate …

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Comparison Of Two-Pass Algorithms For Dynamic Topic Modelling Based On Matrix Decompositions, John Cardiff, Gabriella Skitalinskaya, Mikhail Alexandrov Jan 2017

Comparison Of Two-Pass Algorithms For Dynamic Topic Modelling Based On Matrix Decompositions, John Cardiff, Gabriella Skitalinskaya, Mikhail Alexandrov

Conference Papers

In this paper we present a two-pass algorithm based on different matrix decompositions, such as LSI, PCA, ICA and NMF, which allows tracking of the evolution of topics over time. The proposed dynamic topic models as output give an easily interpreted overview of topics found in a sequentially organized set of documents that does not require further processing. Each topic is presented by a user-specified number of top-terms. Such an approach to topic modeling if applied to, for example, a news article data set, can be convenient and useful for economists, sociologists, political scientists. The proposed approach allows to achieve …

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An Out-Of-Core Gpu Based Dimensionality Reduction Algorithm For Big Mass Spectrometry Data And Its Application In Bottom-Up Proteomics, Muaaz Awan, Fahad Saeed Jan 2017

An Out-Of-Core Gpu Based Dimensionality Reduction Algorithm For Big Mass Spectrometry Data And Its Application In Bottom-Up Proteomics, Muaaz Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and …

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Discovery And Characterization Of Small Molecule Rac1 Inhibitors, Jamie L. Arnst, Ashley L. Hein, Margaret A. Taylor, Nick Y. Palermo, Jacob I. Contreras, Yogesh A. Sonawane, Andrw O. Wahl, Michel M. Ouellette, Amarnath Natarajan, Ying Yan Jan 2017

Discovery And Characterization Of Small Molecule Rac1 Inhibitors, Jamie L. Arnst, Ashley L. Hein, Margaret A. Taylor, Nick Y. Palermo, Jacob I. Contreras, Yogesh A. Sonawane, Andrw O. Wahl, Michel M. Ouellette, Amarnath Natarajan, Ying Yan

Holland Computing Center: Faculty Publications

Aberrant activation of Rho GTPase Rac1 has been observed in various tumor types, including pancreatic cancer. Rac1 activates multiple signaling pathways that lead to uncontrolled proliferation, invasion and metastasis. Thus, inhibition of Rac1 activity is a viable therapeutic strategy for proliferative disorders such as cancer. Here we identified small molecule inhibitors that target the nucleotide-binding site of Rac1 through in silico screening. Follow up in vitro studies demonstrated that two compounds blocked active Rac1 from binding to its effector PAK1. Fluorescence polarization studies indicate that these compounds target the nucleotide-binding site of Rac1. In cells, both compounds blocked Rac1 binding …

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Exploring Population Change Detection By Monitoring Effective Number Of Breeders, Brian Trethewey Jan 2017

Exploring Population Change Detection By Monitoring Effective Number Of Breeders, Brian Trethewey

Graduate Student Theses, Dissertations, & Professional Papers

Detecting if a population is in decline is an important objective for biologists and conservationists who are monitoring threatened populations. As genetic methods improve effective population size (Ne) and effective number of breeders (Nb) continue to gain popularity as a way to monitor species. Using simulated populations and linkage disequilibrium, we explored detecting population decline through Nb in age structured populations. Through comparisons of sensitivity (1 – false negatives) and specificity (1- false positives) over 1000 replicates, we explored how factors such as starting Nb, number of SNPs, number of individuals …

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K-Mer Analysis Pipeline For Classification Of Dna Sequences From Metagenomic Samples, Russell Kaehler Jan 2017

K-Mer Analysis Pipeline For Classification Of Dna Sequences From Metagenomic Samples, Russell Kaehler

Graduate Student Theses, Dissertations, & Professional Papers

Biological sequence datasets are increasing at a prodigious rate. The volume of data in these datasets surpasses what is observed in many other fields of science. New developments wherein metagenomic DNA from complex bacterial communities is recovered and sequenced are producing a new kind of data known as metagenomic data, which is comprised of DNA fragments from many genomes. Developing a utility to analyze such metagenomic data and predict the sample class from which it originated has many possible implications for ecological and medical applications. Within this document is a description of a series of analytical techniques used to process …

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Xic Clustering By Baseyian Network, Kyle J. Handy Jan 2017

Xic Clustering By Baseyian Network, Kyle J. Handy

Graduate Student Theses, Dissertations, & Professional Papers

No abstract provided.

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