Computer Sciences Commons^™

A First Look At Global News Coverage Of Disasters By Using The Gdelt Dataset, Haewoon Kwak, Jisun. An

Research Collection School Of Computing and Information Systems

In this work, we reveal the structure of global news coverage of disasters and its determinants by using a large-scale news coverage dataset collected by the GDELT (Global Data on Events, Location, and Tone) project that monitors news media in over 100 languages from the whole world. Significant variables in our hierarchical (mixed-effect) regression model, such as population, political stability, damage, and more, are well aligned with a series of previous research. However, we find strong regionalism in news geography, highlighting the necessity of comprehensive datasets for the study of global news coverage.

Large-Scale Mechanical Buckle Fold Development And The Initiation Of Tensile Fractures, Andreas Eckert, Peter Connolly, Xiaolong Liu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Tensile failure associated with buckle folding is commonly associated to the distribution of outer arc extension but has also been observed on fold limbs. This study investigates whether tensile stresses and associated failure can be explained by the process of buckling under realistic in situ stress conditions. A 2-D plane strain finite element modeling approach is used to study single-layer buckle folds with a Maxwell viscoelastic rheology. A variety of material parameters are considered and their influence on the initiation of tensile stresses during the various stages of deformation is analyzed. It is concluded that the buckling process determines the …

Modloc: Localizing Multiple Objects In Dynamic Indoor Environment, Xiaonan Guo, Dian Zhang, Kaishun Wu, Lionel M. Ni

Research Collection School Of Computing and Information Systems

Radio frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) can be easily measured by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive due to high accuracy and easy deployment. However, these technologies have not been extensively applied on real environment for two fatal limitations. First, it is hard to localize multiple objects. When the number of target objects is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb …

Linguistic Analysis Of Toxic Behavior In An Online Video Game, Haewoon Kwak, Telefonica

Research Collection School Of Computing and Information Systems

In this paper we explore the linguistic components of toxic behavior by using crowdsourced data from over 590 thousand cases of accused toxic players in a popular match-based competition game, League of Legends. We perform a series of linguistic analyses to gain a deeper understanding of the role communication plays in the expression of toxic behavior. We characterize linguistic behavior of toxic players and compare it with that of typical players in an online competition game. We also find empirical support describing how a player transitions from typical to toxic behavior. Our findings can be helpful to automatically detect and …

Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw

Research Collection School Of Computing and Information Systems

With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge. Multi-armed bandit (MAB) is a framework to balance this trade-off. Most of the previous work in MAB either models a single bandit for the whole population, or one bandit for each user. We propose an algorithm to divide the population of users into multiple clusters, and to customize the …

Online Passive Aggressive Active Learning And Its Applications, Jing Lu, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

We investigate online active learning techniques for classification tasks in data stream mining applications. Unlike traditional learning approaches (either batch or online learning) that often require to request the class label of each incoming instance, online active learning queries only a subset of informative incoming instances to update the classification model, which aims to maximize classification performance using minimal human labeling effort during the entire online stream data mining task. In this paper, we present a new family of algorithms for online active learning called Passive-Aggressive Active (PAA) learning algorithms by adapting the popular Passive-Aggressive algorithms in an online active …

Networked Employment Discrimination, Tamara Kneese

Media Studies

Employers often struggle to assess qualified applicants, particularly in contexts where they receive hundreds of applications for job openings. In an effort to increase efficiency and improve the process, many have begun employing new tools to sift through these applications, looking for signals that a candidate is “the best fit.” Some companies use tools that offer algorithmic assessments of workforce data to identify the variables that lead to stronger employee performance, or to high employee attrition rates, while others turn to third party ranking services to identify the top applicants in a labor pool. Still others eschew automated systems, but …

Ocena Wpływu Rozdzielczości Siatki Obliczeniowej Na Wyniki Modelowania Rozprzestrzeniania Się Zanieczyszczeń W Powietrzu, Mateusz Rzeszutek, Robert Oleniacz, Marian Mazur

Robert Oleniacz

In this study are presented the results of the assessment of the impact of grid resolution on the results of computational modeling of the dispersion of air pollutants. Calculations were performed using Gaussian, non-stationary puff model CALPUFF. Four different grids resolution were analyzed. Networks were established on the basis of SRTM3 terrain and land cover classes of CLC, 2006. Analysis of results of spatial distribution of concentrations of air pollutants obtained in a regular computing grid was performed using statistical indicators recommended by the U.S. EPA. The study shows the applicability of particular computational grids in the system modeling transport …

Partisan Sharing: Facebook Evidence And Societal Consequences, Jisun An, Daniele Quercia, Jon Crowcroft

Research Collection School Of Computing and Information Systems

The hypothesis of selective exposure assumes that people seek out information that supports their views and eschew information that conflicts with their beliefs, and that has negative consequences on our society. Few researchers have recently found counter evidence of selective exposure in social media: users are exposed to politically diverse articles. No work has looked at what happens after exposure, particularly how individuals react to such exposure, though. Users might well be exposed to diverse articles but share only the partisan ones. To test this, we study partisan sharing on Facebook: the tendency for users to predominantly share like-minded news …

Enhanced Rare-Region Effects In The Contact Process With Long-Range Correlated Disorder, Ahmed K. Ibrahim, Hatem Barghathi, Thomas Vojta

Physics Faculty Research & Creative Works

We investigate the nonequilibrium phase transition in the disordered contact process in the presence of long-range spatial disorder correlations. These correlations greatly increase the probability for finding rare regions that are locally in the active phase while the bulk system is still in the inactive phase. Specifically, if the correlations decay as a power of the distance, the rare-region probability is a stretched exponential of the rare-region size rather than a simple exponential as is the case for uncorrelated disorder. As a result, the Griffiths singularities are enhanced and take a non-power-law form. The critical point itself is of infinite-randomness …

Cost-Sensitive Online Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, “Cost-Sensitive Online Classification". In this paper, we formally study this problem, and propose a new framework for Cost-Sensitive Online Classification by directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of …

High Performance Computing Markov Models Using Hadoop Mapreduce, Matthew Shaffer

e-Research: A Journal of Undergraduate Work

In this paper, I will explain how I used the probability modeling tool, Markov Models, in combination with Hadoop MapReduce parallel programming platform in order to quickly and efficiently analyses documents and create a probability model of them. I will explain what Markov Models are, give a brief overview of what MapReduce is, explain why Markov models can be used for document analysis, explain my code of the modeling program, and examine the performance of various MapReduce platforms and techniques in analyzing documents.

Seeing Human Weight From A Single Rgb-D Image, Tam Nguyen, Jiashi Feng, Shuicheng Yan

Computer Science Faculty Publications

Human weight estimation is useful in a variety of potential applications, e.g., targeted advertisement, entertainment scenarios and forensic science. However, estimating weight only from color cues is particularly challenging since these cues are quite sensitive to lighting and imaging conditions. In this article, we propose a novel weight estimator based on a single RGB-D image, which utilizes the visual color cues and depth information. Our main contributions are three-fold.

First, we construct the W8-RGBD dataset including RGB-D images of different people with ground truth weight.

Second, the novel sideview shape feature and the feature fusion model are proposed to facilitate …

Phase Transitions On Random Lattices: How Random Is Topological Disorder?, Hatem Barghathi, Thomas Vojta

Physics Faculty Research & Creative Works

We study the effects of topological (connectivity) disorder on phase transitions. We identify a broad class of random lattices whose disorder fluctuations decay much faster with increasing length scale than those of generic random systems, yielding a wandering exponent of ω = (d−1)/(2d) in d dimensions. The stability of clean critical points is thus governed by the criterion (d+1)ν > 2 rather than the usual Harris criterion dν > 2, making topological disorder less relevant than generic randomness. The Imry-Ma criterion is also modified, allowing first-order transitions to survive in all dimensions d > 1. These results explain a host of puzzling violations …

Strong-Disorder Magnetic Quantum Phase Transitions: Status And New Developments, Thomas Vojta

Physics Faculty Research & Creative Works

This article reviews the unconventional effects of random disorder on magnetic quantum phase transitions, focusing on a number of new experimental and theoretical developments during the last three years. On the theory side, we address smeared quantum phase transitions tuned by changing the chemical composition, for example in alloys of the type A_1-xB_x. We also discuss how the interplay of order parameter conservation and overdamped dynamics leads to enhanced quantum Griffiths singularities in disordered metallic ferromagnets. Finally, we discuss a semiclassical theory of transport properties in quantum Griffiths phases. Experimental examples include the ruthenates Sr_{1-x …}

Online Probabilistic Learning For Fuzzy Inference System, Richard Jayadi Oentaryo, Meng Joo Er, San Linn, Xiang Li

Research Collection School Of Computing and Information Systems

Online learning is a key methodology for expert systems to gracefully cope with dynamic environments. In the context of neuro-fuzzy systems, research efforts have been directed toward developing online learning methods that can update both system structure and parameters on the fly. However, the current online learning approaches often rely on heuristic methods that lack a formal statistical basis and exhibit limited scalability in the face of large data stream. In light of these issues, we develop a new Sequential Probabilistic Learning for Adaptive Fuzzy Inference System (SPLAFIS) that synergizes the Bayesian Adaptive Resonance Theory (BART) and Rule-Wise Decoupled Extended …

Interestingness-Driven Diffussion Process Summarization In Dynamic Networks, Qiang Qu, Siyuan Liu, Christian Jensen, Feida Zhu, Christos Faloutsos

Research Collection School Of Computing and Information Systems

The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which render existing techniques inapplicable. We study the topic of dynamic …

Sharing Political News: The Balancing Act Of Intimacy And Socialization In Selective Exposure, Jisun An, Daniele Quercia, Meeyoung Cha, Krishna Gummadi, Jon Crowcroft

Research Collection School Of Computing and Information Systems

One might think that, compared to traditional media, social media sites allow people to choose more freely what to read and what to share, especially for politically oriented news. However, reading and sharing habits originate from deeply ingrained behaviors that might be hard to change. To test the extent to which this is true, we propose a Political News Sharing (PoNS) model that holistically captures four key aspects of social psychology: gratification, selective exposure, socialization, and trust & intimacy. Using real instances of political news sharing in Twitter, we study the predictive power of these features. As one might expect, …

Automated Image Interpretation For Science Autonomy In Robotic Planetary Exploration, Raymond Francis

Electronic Thesis and Dissertation Repository

Advances in the capabilities of robotic planetary exploration missions have increased the wealth of scientific data they produce, presenting challenges for mission science and operations imposed by the limits of interplanetary radio communications. These data budget pressures can be relieved by increased robotic autonomy, both for onboard operations tasks and for decision- making in response to science data.

This thesis presents new techniques in automated image interpretation for natural scenes of relevance to planetary science and exploration, and elaborates autonomy scenarios under which they could be used to extend the reach and performance of exploration missions on planetary surfaces.

Two …

Bayesian Calibration Tool, Sveinn Palsson, Martin Hunt, Alejandro Strachan

The Summer Undergraduate Research Fellowship (SURF) Symposium

Fitting a model to data is common practice in many fields of science. The models may contain unknown parameters and often, the goal is to obtain good estimates of them. A variety of methods have been developed for this purpose. They often differ in complexity, efficiency and accuracy and some may have very limited applications. Bayesian inference methods have recently become popular for the purpose of calibrating model's parameters. The way they treat unknown quantities is completely different from any classical methods. Even though the unknown quantity is a constant, it is treated as a random variable and the desired …

Granular Matter: Microstructural Evolution And Mechanical Response, Aashish Ghimire, Ishan Srivastava, Timothy S. Fisher

The Summer Undergraduate Research Fellowship (SURF) Symposium

Heterogeneous (nano) composites, manufactured by the densification of variously sized grains, represent an important and ubiquitous class of technologically relevant materials. Typical grain sizes in such materials range from macroscopic to a few nanometers. The morphology exhibited by such disordered materials is complex and intricately connected with its thermal and electrical transport properties. It is important to quantify the geometric features of these materials and simulate the fabrication process. Additionally, granular materials exhibit complex structural and mechanical properties that crucially govern their reliability during industrial use. In this work, we simulate the densification of soft deformable grains from a low-density …

Mantle Transition Zone Discontinuities Beneath The Contiguous United States, Stephen S. Gao, Kelly H. Liu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Using over 310,000 high-quality radial receiver functions recorded by the USArray and other seismic stations in the contiguous United States, the depths of the 410 km and 660 km discontinuities (d410 and d660) are mapped in over 1,000 consecutive overlapping circles with a radius of 1⁰. The average mantle transition zone (MTZ) thickness for both the western and central/eastern U.S. is within 3 km from the global average of 250 km, suggesting an overall normal MTZ temperature beneath both areas. The Pacific Coast Ranges and the southern Basin and Range Province are underlain by a depressed d410, indicating higher-than-normal temperature …

Opinion Mining Of Sociopolitical Comments From Social Media, Swapna Gottipati

Dissertations and Theses Collection (Open Access)

Opinions are central to almost all human activities by influencing greatly the decision making process. In this thesis, we present the problems of mining issues, extracting entities and suggestive opinions towards the entities, detecting thoughtful comments, and extracting stances and ideological expressions from online comments in the sociopolitical domain. This study is essential for opinion mining applications that are beneficial for policy makers, government sectors and social organizations. Much work has been done to try to uncover consumer sentiments from online comments to help businesses improve their products and services. However, sociopolitical opinion mining poses new challenges due to complex …

Semantic Visualization For Spherical Representation, Tuan M. V. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Visualization of high-dimensional data such as text documents is widely applicable. The traditional means is to find an appropriate embedding of the high-dimensional representation in a low-dimensional visualizable space. As topic modeling is a useful form of dimensionality reduction that preserves the semantics in documents, recent approaches aim for a visualization that is consistent with both the original word space, as well as the semantic topic space. In this paper, we address the semantic visualization problem. Given a corpus of documents, the objective is to simultaneously learn the topic distributions as well as the visualization coordinates of documents. We propose …

Theoretical/Experimental Comparison Of Deep Tunneling Decay Of Quasi-Bound H(D)Oco To H(D) + Co₂, Albert F. Wagner, Richard Dawes, Robert Continetti, Hua Guo

Chemistry Faculty Research & Creative Works

The measured H(D)OCO survival fractions of the photoelectron-photofragment coincidence experiments by the Continetti group are qualitatively reproduced by tunneling calculations to H(D) + CO₂ on several recent ab initio potential energy surfaces for the HOCO system. the tunneling calculations involve effective one-dimensional barriers based on steepest descent paths computed on each potential energy surface. the resulting tunneling probabilities are converted into H(D)OCO survival fractions using a model developed by the Continetti group in which every oscillation of the H(D)-OCO stretch provides an opportunity to tunnel. Four different potential energy surfaces are examined with the best qualitative agreement with experiment …

Software Porting Of A 3d Reconstruction Algorithm To Razorcam Embedded System On Chip, Kevin Curtis Gunn

Graduate Theses and Dissertations

A method is presented to calculate depth information for a UAV navigation system from Keypoints in two consecutive image frames using a monocular camera sensor as input and the OpenCV library. This method was first implemented in software and run on a general-purpose Intel CPU, then ported to the RazorCam Embedded Smart-Camera System and run on an ARM CPU onboard the Xilinx Zynq-7000. The results of performance and accuracy testing of the software implementation are then shown and analyzed, demonstrating a successful port of the software to the RazorCam embedded system on chip that could potentially be used onboard a …

Effect Of The Center-Of-Mass Approximation On The Scaling Of Electron-Capture Fully Differential Cross Sections, A. L. Harris, Don H. Madison

Physics Faculty Research & Creative Works

We present results for p+He single electron capture and transfer with target excitation using the first Born approximation. The effect of approximating the center of mass of the helium atom and outgoing hydrogen atom at the respective nuclei is explored. Semianalytical results are compared for the calculations with and without the approximation, and it is shown that one must properly account for the center of mass of the atoms. It is also shown that this approximation is the result of the apparent v⁴ scaling that was previously observed with the four-body transfer with target excitation model.

Communication: Rigorous Quantum Dynamics Of O + O₂ Exchange Reactions On An Ab Initio Potential Energy Surface Substantiate The Negative Temperature Dependence Of Rate Coefficients, Yaqin Li, Zhigang Sun, Bin Jiang, Daiqian Xie, Richard Dawes, Hua Guo

Chemistry Faculty Research & Creative Works

The kinetics and dynamics of several O + O₂ isotope exchange reactions have been investigated on a recently determined accurate global O₃ potential energy surface using a time-dependent wave packet method. The agreement between calculated and measured rate coefficients is significantly improved over previous work. More importantly, the experimentally observed negative temperature dependence of the rate coefficients is for the first time rigorously reproduced theoretically. This negative temperature dependence can be attributed to the absence in the new potential energy surface of a submerged "reef" structure, which was present in all previous potential energy surfaces. In addition, contributions …

Seismic Imaging Of Mantle Transition Zone Discontinuities Beneath The Northern Red Sea And Adjacent Areas, A. A. Mohamed, Stephen S. Gao, A. A. Elsheikh, Kelly H. Liu, Youqiang Yu, R. E. Fat-Helbary

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The dramatic asymmetry in terms of surface elevation, Cenozoic volcanisms and earthquake activity across the Red Sea is an enigmatic issue in global tectonics, partially due to the unavailability of broad-band seismic data on the African Plate adjacent to the Red Sea. Here, we report the first comprehensive image of the mantle transition zone (MTZ) discontinuities using data from the Egyptian National Seismic Network, and compare the resulting depths of the 410 and 660-km discontinuities with those observed on the Arabian side. Our results show that when a standard earth model is used for time-to-depth conversion, the resulting depth of …

Interpretable Machine Learning And Sparse Coding For Computer Vision, Will Landecker

Dissertations and Theses

Machine learning offers many powerful tools for prediction. One of these tools, the binary classifier, is often considered a black box. Although its predictions may be accurate, we might never know why the classifier made a particular prediction. In the first half of this dissertation, I review the state of the art of interpretable methods (methods for explaining why); after noting where the existing methods fall short, I propose a new method for a particular type of black box called additive networks. I offer a proof of trustworthiness for this new method (meaning a proof that my method does not …