Numerical Analysis and Scientific Computing Commons^™

Covariance Selection By Thresholding The Sample Correlation Matrix, Binyan Jiang

Research Collection School Of Computing and Information Systems

This article shows that when the nonzero coefficients of the population correlation matrix are all greater in absolute value than (C₁logp/n)^1/2 for some constant C₁, we can obtain covariance selection consistency by thresholding the sample correlation matrix. Furthermore, the rate (logp/n)^1/2 is shown to be optimal.

Predicting User's Political Party Using Ideological Stances, Swapna Gottopati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang

Research Collection School Of Computing and Information Systems

Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties’ ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users’ ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task …

Information Vs Interaction: An Alternative User Ranking Model For Social Networks, Wei Xie, Ai Phuong Hoang, Feida Zhu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The recent years have seen an unprecedented boom of social network services, such as Twitter, which boasts over 200 million users. In such big social platforms, the influential users are ideal targets for viral marketing to potentially reach an audience of maximal size. Most proposed algorithms rely on the linkage structure of the respective underlying network to determine the information flow and hence indicate a users influence. From social interaction perspective, we built a model based on the dynamic user interactions constantly taking place on top of these linkage structures. In particular, in the Twitter setting we supposed a principle …

Using Micro-Reviews To Select An Efficient Set Of Reviews, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas

Research Collection School Of Computing and Information Systems

Online reviews are an invaluable resource for web users trying to make decisions regarding products or services. However, the abundance of review content, as well as the unstructured, lengthy, and verbose nature of reviews make it hard for users to locate the appropriate reviews, and distill the useful information. With the recent growth of social networking and micro-blogging services, we observe the emergence of a new type of online review content, consisting of bite-sized, 140 character-long reviews often posted reactively on the spot via mobile devices. These micro-reviews are short, concise, and focused, nicely complementing the lengthy, elaborate, and verbose …

Efficient Index-Based Approaches For Skyline Queries In Location-Based Applications, Ken C. K. Lee, Baihua Zheng, Cindy Chen, Chi-Yin Chow

Research Collection School Of Computing and Information Systems

Enriching many location-based applications, various new skyline queries are proposed and formulated based on the notion of locational dominance, which extends conventional one by taking objects' nearness to query positions into account additional to objects' nonspatial attributes. To answer a representative class of skyline queries for location-based applications efficiently, this paper presents two index-based approaches, namely, augmented R-tree and dominance diagram. Augmented R-tree extends R-tree by including aggregated nonspatial attributes in index nodes to enable dominance checks during index traversal. Dominance diagram is a solution-based approach, by which each object is associated with a precomputed nondominance scope wherein query points …

Modeling Interaction Features For Debate Side Clustering, Minghui Qiu, Liu Yang, Jing Jiang

Research Collection School Of Computing and Information Systems

Online discussion forums are popular social media platforms for users to express their opinions and discuss controversial issues with each other. To automatically identify the sides/stances of posts or users from textual content in forums is an important task to help mine online opinions. To tackle the task, it is important to exploit user posts that implicitly contain support and dispute (interaction) information. The challenge we face is how to mine such interaction information from the content of posts and how to use them to help identify stances. This paper proposes a two-stage solution based on latent variable models: an …

Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao

Research Collection School Of Computing and Information Systems

Recent years have witnessed extensive studies on distance metric learning (DML) for improving similarity search in multimedia information retrieval tasks. Despite their successes, most existing DML methods suffer from two critical limitations: (i) they typically attempt to learn a linear distance function on the input feature space, in which the assumption of linearity limits their capacity of measuring the similarity on complex patterns in real-world applications; (ii) they are often designed for learning distance metrics on uni-modal data, which may not effectively handle the similarity measures for multimedia objects with multimodal representations. To address these limitations, in this paper, we …

Online Multi-Task Collaborative Filtering For On-The-Fly Recommender Systems, Jialei Wang, Steven C. H. Hoi, Peilin Zhao, Zhi-Yong Liu

Research Collection School Of Computing and Information Systems

Traditional batch model-based Collaborative Filtering (CF) approaches typically assume a collection of users' rating data is given a priori for training the model. They suffer from a common yet critical drawback, i.e., the model has to be re-trained completely from scratch whenever new training data arrives, which is clearly non-scalable for large real recommender systems where users' rating data often arrives sequentially and frequently. In this paper, we investigate a novel efficient and scalable online collaborative filtering technique for on-the-fly recommender systems, which is able to effectively online update the recommendation model from a sequence of rating observations. Specifically, we …

Merged Aggregate Nearest Neighbor Query Processing In Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu

Research Collection School Of Computing and Information Systems

Aggregate nearest neighbor query, which returns a common interesting point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes an effective algorithm to process MANN query in road networks based on our pruning strategies. Extensive experiments are conducted …

A Unified Model For Topics, Events And Users On Twitter, Qiming Diao, Jing Jiang

Research Collection School Of Computing and Information Systems

With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also follow and tweet about them. Moreover, people’s posting behaviors on events are often closely tied to their personal interests. In this paper, we try to model topics, events and users on Twitter in a unified way. We propose a model which combines an LDA-like topic model and the Recurrent Chinese Restaurant Process to capture …

Learning Topics And Positions From Debatepedia, Swapna Gottopati, Minghui Qiu, Yanchuan Sim, Jing Jiang, Noah Smith

Research Collection School Of Computing and Information Systems

We explore Debatepedia, a communityauthored encyclopedia of sociopolitical debates, as evidence for inferring a lowdimensional, human-interpretable representation in the domain of issues and positions. We introduce a generative model positing latent topics and cross-cutting positions that gives special treatment to person mentions and opinion words. We evaluate the resulting representation’s usefulness in attaching opinionated documents to arguments and its consistency with human judgments about positions.

A Robust Rgbd Slam System For 3d Environment With Planar Surfaces, Po-Chang Su, Ju Shen, Sen-Ching S. Cheung

Computer Science Faculty Publications

With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth …

Generative Models For Item Adoptions Using Social Correlation, Freddy Chong Tat Chua, Hady Wirawan Lauw, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the homophily and social influence effects. In this paper, we focus on modeling social correlation on users’ item adoptions. Given a user-user social graph and an item-user adoption graph, our research seeks to answer the following questions: whether the items adopted by a user correlate to items adopted by her friends, and …

Oyster Sustainability Modeling As A Public Resource, Nathan A. Cooper

LSU New Orleans Theses and Dissertations

A simulation algorithm based on biological references points proposed by Powell and Klink (2007) is implemented for predicting the total allowable catch of eastern oysters (Crassostrea virginica) from Louisiana’s coast. The model accepts initial per-square-meter shell mass and oyster size distributions as input. Fishing effort is provided as fractions removed of each resource for each month of the season. The model outputs the expected remaining shell mass and harvests of sack and seed oysters after discrete fishing months. Oyster mortality credits the shell budget, while fishing fractions debit oyster and shell resources. Surviving oysters grow larger along a …

Flitecam Data Process Validation, Jesse K. Tsai, Sachindev S. Shenoy, Brent Cedric Nicklas, Zaheer Ali, William T. Reach

STAR Program Research Presentations

FLITECAM Data Processing Validation

Many of the challenges that come from working with astronomical imaging arise from the reduction of raw data into scientifically meaningful data. First Light Infrared Test CAMera (FLITECAM) is an infrared camera operating in the 1.0–5.5 μm waveband on board SOFIA (Stratospheric Observatory For Infrared Astronomy). Due to the significant noise from the atmosphere and the camera itself, astronomers have developed many methods to reduce the effects of atmospheric and instrumental emission. The FLITECAM Data Reduction Program (FDRP) is a program, developed at SOFIA Science Center, subtracts darks, removes flats, and dithers images.

This project contains …

Incremental And Accuracy-Aware Personalized Pagerank Through Scheduled Approximation, Fanwei Zhu, Yuan Fang, Kevin Chen-Chuan Chang, Jing Ying

Research Collection School Of Computing and Information Systems

As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an "organized" way, such that we can gradually improve our PPV estimation in an incremental manner, and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub based realization, where we adopt the …

Best Upgrade Plans For Large Road Networks, Yimin Lin, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

In this paper, we consider a new problem in the context of road network databases, named Resource Constrained Best Upgrade Plan computation (BUP, for short). Consider a transportation network (weighted graph) G where a subset of the edges are upgradable, i.e., for each such edge there is a cost, which if spent, the weight of the edge can be reduced to a specific new value. Given a source and a destination in G, and a budget (resource constraint) B, the BUP problem is to identify which upgradable edges should be upgraded so that the shortest path distance between source and …

Robust Median Reversion Strategy For On-Line Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Steven Hoi, Shuigeng Zhou

Research Collection School Of Computing and Information Systems

On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies. Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. To overcome the limitation, we propose to exploit the reversion phenomenon by robust L₁-median estimator, and design a novel on-line portfolio selection strategy named "Robust Median Reversion" (RMR), which makes optimal …

Learning To Name Faces: A Multimodal Learning Scheme For Search-Based Face Annotation, Dayong Wang, Steven C. H. Hoi, Pengcheng Wu, Jianke Zhu, Ying He, Chunyan Miao

Research Collection School Of Computing and Information Systems

Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, …

Large Scale Online Kernel Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi, Jinfeng Zhuang, Zhi-Yong Liu

Research Collection School Of Computing and Information Systems

In this work, we present a new framework for large scale online kernel classification, making kernel methods efficient and scalable for large-scale online learning tasks. Unlike the regular budget kernel online learning scheme that usually uses different strategies to bound the number of support vectors, our framework explores a functional approximation approach to approximating a kernel function/matrix in order to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) the Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) the …

Delayflow Centrality For Identifying Critical Nodes In Transportation Networks, Yew-Yih Cheng, Roy Ka Wei Lee, Ee-Peng Lim, Feida Zhu

Research Collection School Of Computing and Information Systems

In an urban city, its transportation network supports efficient flow of people between different parts of the city. Failures in the network can cause major disruptions to commuter and business activities which can result in both significant economic and time losses. In this paper, we investigate the use of centrality measures to determine critical nodes in a transportation network so as to improve the design of the network as well as to devise plans for coping with network failures. Most centrality measures in social network analysis research unfortunately consider only topological structure of the network and are oblivious of transportation …

Computing Immutable Regions For Subspace Top-K Queries, Kyriakos Mouratidis, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Given a high-dimensional dataset, a top-k query can be used to shortlist the k tuples that best match the user’s preferences. Typically, these preferences regard a subset of the available dimensions (i.e., attributes) whose relative significance is expressed by user-specified weights. Along with the query result, we propose to compute for each involved dimension the maximal deviation to the corresponding weight for which the query result remains valid. The derived weight ranges, called immutable regions, are useful for performing sensitivity analysis, for finetuning the query weights, etc. In this paper, we focus on top-k queries with linear preference functions over …

Politics, Sharing And Emotion In Microblogs, Tuan-Anh Hoang, William Cohen, Ee Peng Lim, Doug Pierce, David Redlawsk

Research Collection School Of Computing and Information Systems

In political contexts, it is known that people act as "motivated reasoners", i.e., information is evaluated first for emotional affect, and this emotional reaction influences later deliberative reasoning steps. As social media becomes a more and more prevalent way of receiving political information, it becomes important to understand more completely the interaction between information, emotion, social community, and information-sharing behavior. In this paper, we describe a high-precision classifier for politically-oriented tweets, and an accurate classifier of a Twitter user's political affiliation. Coupled with existing sentiment-analysis tools for microblogs, these methods enable us to systematically study the interaction of emotion and …

Mining Direct Antagonistic Communities In Signed Social Networks, David Lo, Didi Surian, Philips Kokoh Prasetyo, Zhang Kuan, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Social networks provide a wealth of data to study relationship dynamics among people. Most social networks such as Epinions and Facebook allow users to declare trusts or friendships with other users. Some of them also allow users to declare distrusts or negative relationships. When both positive and negative links co-exist in a network, some interesting community structures can be studied. In this work, we mine Direct Antagonistic Communities (DACs) within such signed networks. Each DAC consists of two sub-communities with positive relationships among members of each sub-community, and negative relationships among members of the other sub-community. Identifying direct antagonistic communities …

Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Multiple kernel learning (MKL) is a promising family of machine learning algorithms using multiple kernel functions for various challenging data mining tasks. Conventional MKL methods often formulate the problem as an optimization task of learning the optimal combinations of both kernels and classifiers, which usually results in some forms of challenging optimization tasks that are often difficult to be solved. Different from the existing MKL methods, in this paper, we investigate a boosting framework of MKL for classification tasks, i.e., we adopt boosting to solve a variant of MKL problem, which avoids solving the complicated optimization tasks. Specifically, we present …

Shortlisting Top-K Assignments, Yimin Lin, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

In this paper we identify a novel query type, the top-K assignment query (αTop-K). Consider a set of objects and a set of suppliers, where each object must be assigned to one supplier. Assume that there is a cost associated with every object-supplier pair. If we allocate each object to the server with the smallest cost (for the specific object), the derived overall assignment will have the minimum total cost. In many scenarios, however, runner-up assignments may be required too, like for example when a decision maker needs to make additional considerations, not captured by individual object-supplier costs. In this …

Active Learning With Expert Advice, Peilin Zhao, Steven C. H. Hoi, Jinfeng Zhuang

Research Collection School Of Computing and Information Systems

Conventional learning with expert advice methods assumes a learner is always receiving the outcome (e.g., class labels) of every incoming training instance at the end of each trial. In real applications, acquiring the outcome from oracle can be costly or time consuming. In this paper, we address a new problem of active learning with expert advice, where the outcome of an instance is disclosed only when it is requested by the online learner. Our goal is to learn an accurate prediction model by asking the oracle the number of questions as small as possible. To address this challenge, we propose …

Reviving Dormant Ties In An Online Social Network Experiment, Ee Peng Lim, Denzil Correa, David Lo, Michael Finegold, Feida Zhu

Research Collection School Of Computing and Information Systems

Social network users connect and interact with one another to fulfil different kinds of social and information needs. When interaction ceases between two users, we say that their tie becomes dormant. While there are different underlying reasons of dormant ties, it is important to find means to revive such ties so as to maintain vibrancy in the relationships. In this work, we thus focus on designing an online experiment to evaluate the effectiveness of personalized social messages to revive dormant ties. The experiment carefully selects users with dormant ties so that no user gets mixed treatments and be affected by …

Brovine: Mammary Gland Gene Database, Therin C. Irwin

Computer Science and Software Engineering

Brovine is used by the Animal Science department at Cal Poly to catalog and analyze genetic information. Brovine, or the Mammary Gland Gene Database, is a system used to store and categorize genetic information which is gathered through experimentation and through TESS, a web application that lets users search through catalogs of similar genetic information. This document describes the purpose, use, and maintenance of Brovine.

A Direct Mining Approach To Efficient Constrained Graph Pattern Discovery, Feida Zhu, Zequn Zhang, Qiang Qu

Research Collection School Of Computing and Information Systems

Despite the wealth of research on frequent graph pattern mining, how to efficiently mine the complete set of those with constraints still poses a huge challenge to the existing algorithms mainly due to the inherent bottleneck in the mining paradigm. In essence, mining requests with explicitly-specified constraints cannot be handled in a way that is direct and precise. In this paper, we propose a direct mining framework to solve the problem and illustrate our ideas in the context of a particular type of constrained frequent patterns — the “skinny” patterns, which are graph patterns with a long backbone from which …