Numerical Analysis and Scientific Computing Commons^™

Mining Direct Antagonistic Communities In Explicit Trust Networks, David Lo, Didi Surian, Zhang Kuan, Ee Peng Lim

Research Collection School Of Computing and Information Systems

There has been a recent increase of interest in analyzing trust and friendship networks to gain insights about relationship dynamics among users. Many sites such as Epinions, Facebook, and other social networking sites allow users to declare trusts or friendships between different members of the community. In this work, we are interested in extracting direct antagonistic communities (DACs) within a rich trust network involving trusts and distrusts. Each DAC is formed by two subcommunities with trust relationships among members of each sub-community but distrust relationships across the sub-communities. We develop an efficient algorithm that could analyze large trust networks leveraging …

Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Social networking has grown rapidly over the last few years, and social networks contain a huge amount of content. However, it can be not easy to navigate the social networks to find specific information. In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to retrieve the nearest node to the query node that matches the context specified. CANN considers both the structure of the social network, and the profile information of the nodes. We design ahyper-graph based index structure to support approximated CANN search efficiently.

Mining Top-K Large Structural Patterns In A Massive Network, Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu

Research Collection School Of Computing and Information Systems

With ever-growing popularity of social networks, web and bio-networks, mining large frequent patterns from a single huge network has become increasingly important. Yet the existing pattern mining methods cannot offer the efficiency desirable for large pattern discovery. We propose Spider- Mine, a novel algorithm to efficiently mine top-K largest frequent patterns from a single massive network with any user-specified probability of 1 − ϵ. Deviating from the existing edge-by-edge (i.e., incremental) pattern-growth framework, SpiderMine achieves its efficiency by unleashing the power of small patterns of a bounded diameter, which we call “spiders”. With the spider structure, our approach adopts a …

Quantitative Characterization Of Microstructure Features For 1st Generation Advanced High Strength Steels, Margarita Vidrio, Ellen Liu, Donsheng Li, Kyoo Sil Choi, Xin Sun

STAR Program Research Presentations

The role of Advanced High Strength Steels (AHSS) in the automotive industry is important because of its affordability and excellent mechanical properties. The 1st generation of AHSS achieves its preferred combination of strength and ductility by embedding harder martensite grains into softer ferritic matrix. Ductility and strength of these steels are important to safety, formability, application, and life. However, a noticeable degree of inconsistent forming behaviors has been observed in the 1st generation AHSS in production, which seems to be related to the microstructure-level inhomogeneity. The objective of this project is to grain fundamental understandings on how different microstructure level …

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. …

Solution Pluralism And Metaheuristics, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, Frederic H. Murphy, David Harlan Wood

Research Collection School Of Computing and Information Systems

Solution pluralism is an approach to problem solving and deliberation. It employs a plurality of distinct solutions for a decision problem for aiding decision making. The concept is well established in existing practice, although perhaps not recognized as such. This paper: (1) presents the concept as a generalization of established practice, (2) briefly describes successful uses of the concept in practice, and (3) presents several areas that appear would benefit from application of the concept. Throughout, the role of metaheuristics in finding the pluralities of solutions is emphasized.

Effects Of Mentoring On Player Performance In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Kuo-Wei Hsu, Jaideep Srivastava

Research Collection School Of Computing and Information Systems

Massively Multiplayer Online Role-Playing Games (MMORPGs) have become increasingly popular and have communities comprising millions of subscribers. With their increasing popularity, researchers are realizing that video games can be a means to fully observe an entire isolated universe. In this study, we examine and report our findings on the effects of mentoring activities on player performance in Ever Quest II, a popular MMORPG developed by Sony Online Entertainment.

Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayang Wang, Steven C. H. Hoi, Ying He

Research Collection School Of Computing and Information Systems

In this paper, we investigate a search-based face annotation framework by mining weakly labeled facial images that are freely available on the internet. A key component of such a search-based annotation paradigm is to build a database of facial images with accurate labels. This is however challenging since facial images on the WWW are often noisy and incomplete. To improve the label quality of raw web facial images, we propose an effective Unsupervised Label Refinement (ULR) approach for refining the labels of web facial images by exploring machine learning techniques. We develop effective optimization algorithms to solve the large-scale learning …

Trust Network Inference For Online Rating Data Using Generative Models, Freddy Tat Chua Chua, Ee Peng Lim

Research Collection School Of Computing and Information Systems

In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF)Model, a novel probabilistic model that generate ratings based on a number of rater’s and contributor’s factors. We demonstrate that parameters of the model can be learnt by Collapsed …

Unsupervised Information Extraction With Distributional Prior Knowledge, Cane Wing-Ki Leung, Jing Jiang, Kian Ming A. Chai, Hai Leong Chieu, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candidate slot fillers to identify meaningful template slots. We propose a generative model that incorporates distributional prior knowledge to help distribute candidates in a document into appropriate slots. Empirical results suggest that the proposed prior can bring substantial improvements to our task as compared to a K-means baseline and a Gaussian mixture model baseline. Specifically, the proposed prior has shown to be effective when coupled with discriminative features of the candidates.

The R Journal (June 2011) 3(1): Complete Issue, The R Foundation

The R Journal

Contributed Research Articles

testthat: Get Started with Testing, Hadley Wickham

Content-Based Social Network Analysis of Mailing Lists, Angela Bohn, Ingo Feinerer, Kurt Hornik, and Patrick Mair

Rmetrics: timeDate Package, Yohan Chalabi, Martin Mächler, and Diethelm Würtz

The digitize Package: Extracting Numerical Data from Scatterplots, Timothée Poisot

Differential Evolution with DEoptim, David Ardia, Kris Boudt, Peter Carl, Katharine M. Mullen, and Brian G. Peterson

rworldmap: A New R Package for Mapping Global Data, Andy South

Cryptographic Boolean Functions with R, Frédéric Lafitte, Dirk Van Heule and Julien Van hamme

Raster Images in R Graphics, Paul Murrell

Probabilistic Weather Forecasting in R, …

Raster Images In R Graphics, Paul Murrell

The R Journal

The R graphics engine has new support for rendering raster images via the functions rasterImage() and grid.raster(). This leads to better scaling of raster images, faster rendering to screen, and smaller graphics files. Several examples of possible applications of these new features are described.

Cryptographic Boolean Functions With R, Frédéric Lafitte, Dirk Van Heule, Julien Van Hamme

The R Journal

A new package called boolfun is avail able for R users. The package provides tools to handle Boolean functions, in particular for cryptographic purposes. This document guides the user through some (code) examples and gives a feel of what can be done with the package.

Rmetrics: Timedate Package, Yohan Chalabi, Martin Mächler, Diethelm Würtz

The R Journal

The management of time and holidays can prove crucial in applications that rely on historical data. Atypical example is the aggregation of a data set recorded in different time zones and under different daylight saving time rules. Be sides the time zone conversion function, which is well supported by default classes in R,one might need functions to handle special days or holidays. In this respect, the package timeDate enhances default date-time classes in R and brings new functionalities to time zone management and the creation of holiday calendars

The Digitize Package: Extracting Numerical Data From Scatterplots, Timothée Poisot

The R Journal

I present the small R package digitize, designed to extract data from scatter plots with a simple method and suited to small datasets. I present an application of this method to the ex traction of data from a graph whose source is not available.

Analyzing An Electronic Limit Order Book, David Kane, Andrew Liu, Khanh Nguyen

The R Journal

The orderbook package provides facilities for exploring and visualizing the data associated with an order book: the electronic collection of the outstanding limit orders for a financial instrument. This article provides an overview of the orderbook package and examples of its use.

Testthat: Get Started With Testing, Hadley Wickham

The R Journal

Software testing is important, but many of us don’t do it because it is frustrating and boring. testthat is a new testing framework for R that is easy learn and use, and integrates with your existing workflow. This paper shows how, with illustrations from existing packages.

Rworldmap: A New R Package For Mapping Global Data, Andy South

The R Journal

rworldmap is a relatively new package available on CRAN for the mapping and visualisation of global data. The vision is to make the display of global data easier, to facilitate understanding and communication. The initial focus is on data referenced by country or grid due to the frequency of use of such data in global assessments. Tools to link data referenced by country (either name or code) to a map, and then to display the map are provided as are functions to map global gridded data. Country and gridded functions accept the same arguments to specify the nature of categories …

Differential Evolution With Deoptim, David Ardia, Kris Boudt, Peter Carl, Katharine M. Mullen, Brian G. Peterson

The R Journal

The R package DEoptim implements the Differential Evolution algorithm. This algorithm is an evolutionary technique similar to classic genetic algorithms that is useful for the solution of global optimization problems. In this note we provide an introduction to the package and demonstrate its utility for financial applications by solving a non-convex portfolio optimization problem.

Probabilistic Weather Forecasting In R, Chris Fraley, Adrian Raftery, Tilmann Gneiting, Mclean Sloughter, Veronica Berrocol

The R Journal

This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble post-processing via Bayesian model averaging (BMA), and Prob ForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the component distribution depends on the weather parameter (temperature, quantitative precipitation or wind speed). The model parameters are estimated from training data. The GOP technique uses geostatistical methods to produce probabilistic fore casts of entire weather fields for temperature or pressure, based on a single numerical forecast on …