Databases and Information Systems Commons^™

Demographic Prediction Of Mobile User From Phone Usage, Shahram Mohrehkesh, Shuiwang Ji, Tamer Nadeem, Michele C. Weigle

Computer Science Faculty Publications

In this paper, we describe how we use the mobile phone usage of users to predict their demographic attributes. Using call log, visited GSM cells information, visited Bluetooth devices, visited Wireless LAN devices, accelerometer data, and so on, we predict the gender, age, marital status, job and number of people in household of users. The accuracy of developed classifiers for these classification problems ranges from 45-87% depending upon the particular classification problem.

Warcreate - Create Wayback-Consumable Warc Files From Any Webpage, Mat Kelly, Michele C. Weigle, Michael L. Nelson

Computer Science Faculty Publications

[First Slide]

What is WARCreate?

• Google Chrome extension
• Creates WARC files
• Enables preservation by users from their browser
• First steps in bringing Institutional Archiving facilities to the PC

Modeling And Compressing 3-D Facial Expressions Using Geometry Videos, Jiazhi Xia, Dao T. P. Quynh, Ying He, Xiaoming Chen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In this paper, we present a novel geometry video (GV) framework to model and compress 3-D facial expressions. GV bridges the gap of 3-D motion data and 2-D video, and provides a natural way to apply the well-studied video processing techniques to motion data processing. Our framework includes a set of algorithms to construct GVs, such as hole filling, geodesic-based face segmentation, expression-invariant parameterization (EIP), and GV compression. Our EIP algorithm can guarantee the exact correspondence of the salient features (eyes, mouth, and nose) in different frames, which leads to GVs with better spatial and temporal coherence than that of …

Systems And Methods For Transaction Account Offerings, Clinton Allen, Michael Digregorio, Glade Erikson, Deepinder Gulati, Jacob Plammoottil Jacob, Sanjiv Khosla, Seema Chokshi

Research Collection School Of Computing and Information Systems

A method for receiving a user input for providing offerings is disclosed. Fields may be populated for creating a database query for matching a selection of offerings to a population of customers. Data may be received from the database, in response to interactively building the query and/or executing the query. Multiple rank ordered results may be produced for comparison, wherein the producing uses preprogrammed analytics, data from the database and user input, and wherein the results include customer lists linked to a distinct offering and a preferred delivery channel of the offering to a customer. A transaction account may be …

Tweets And Votes: A Study Of The 2011 Singapore General Election, Marko M. Skoric, Nathaniel D. Poor, Palakorn Achananuparp, Ee Peng Lim, Jing Jiang

Research Collection School Of Computing and Information Systems

This study focuses on the uses of Twitter during the elections, examining whether the messages posted online are reflective of the climate of public opinion. Using Twitter data obtained during the official campaign period of the 2011 Singapore General Election, we test the predictive power of tweets in forecasting the election results. In line with some previous studies, we find that during the elections the Twitter sphere represents a rich source of data for gauging public opinion and that the frequency of tweets mentioning names of political parties, political candidates and contested constituencies could be used to make predictions about …

Information Extraction From Text, Jing Jiang

Research Collection School Of Computing and Information Systems

Information extraction is the task of finding structured information from unstructured or semi-structured text. It is an important task in text mining and has been extensively studied in various research communities including natural language processing, information retrieval and Web mining. It has a wide range of applications in domains such as biomedical literature mining and business intelligence. Two fundamental tasks of information extraction are named entity recognition and relation extraction. The former refers to finding names of entities such as people, organizations and locations. The latter refers to finding the semantic relations such as FounderOf and HeadquarteredIn between entities. In …

Preface: Trends In Natural And Machine Intelligence, Jonathan H. Chan, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Trends in natural and machine intelligence are increasingly reflecting a convergence in these two well-established fields of study. The Third International Neural Network Society Winter Conference (INNS-WC 2012) was held in Bangkok, Thailand, on October 3-5, 2012. INNS-WC2012, with an aim to bring together scientists, practitioners, and students worldwide, to discuss the past, present, and future challenges and trends in the area of natural and machine intelligence. This event has been a bi-annual conference of the International Neural Network Society (INNS) to provide a forum for international researchers to exchange latest ideas and advances on neural networks and related discipline.

An Improved K-Nearest-Neighbor Algorithm For Text Categorization, Shengyi Jiang, Guansong Pang, Meiling Wu, Limin Kuang

Research Collection School Of Computing and Information Systems

Text categorization is a significant tool to manage and organize the surging text data. Many text categorization algorithms have been explored in previous literatures, such as KNN, Naive Bayes and Support Vector Machine. KNN text categorization is an effective but less efficient classification method. In this paper, we propose an improved KNN algorithm for text categorization, which builds the classification model by combining constrained one pass clustering algorithm and KNN text categorization. Empirical results on three benchmark corpora show that our algorithm can reduce the text similarity computation substantially and outperform the-state-of-the-art KNN, Naive Bayes and Support Vector Machine classifiers. …

Mining Diversity On Social Media Networks, Lu Liu, Feida Zhu, Meng Jiang, Jiawei Han, Lifeng Sun, Shiqiang Yang

Research Collection School Of Computing and Information Systems

The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia and industry. However, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. …

Automatic Document Classification In Small Environments, Jonathan David Mcelroy

Master's Theses

Document classification is used to sort and label documents. This gives users quicker access to relevant data. Users that work with large inflow of documents spend time filing and categorizing them to allow for easier procurement. The Automatic Classification and Document Filing (ACDF) system proposed here is designed to allow users working with files or documents to rely on the system to classify and store them with little manual attention. By using a system built on Hidden Markov Models, the documents in a smaller desktop environment are categorized with better results than the traditional Naive Bayes implementation of classification.

A Data-Descriptive Feedback Framework For Data Stream Management Systems, Rafael J. Fernández Moctezuma

Dissertations and Theses

Data Stream Management Systems (DSMSs) provide support for continuous query evaluation over data streams. Data streams provide processing challenges due to their unbounded nature and varying characteristics, such as rate and density fluctuations. DSMSs need to adapt stream processing to these changes within certain constraints, such as available computational resources and minimum latency requirements in producing results. The proposed research develops an inter-operator feedback framework, where opportunities for run-time adaptation of stream processing are expressed in terms of descriptions of substreams and actions applicable to the substreams, called feedback punctuations. Both the discovery of adaptation opportunities and the exploitation of …

Networks - I: Pre-Coordination Mechanism For Self Configuration Of Neighborhood Cells In Mobile Wi-Max, Abdul Qadeer, Khalid Khan

International Conference on Information and Communication Technologies

WiMax broadband services successfully providing triple play (Voice, Video and Data) support with combating the challenges of better quality and interoperability. Support for smooth mobility in real time with no wired infrastructure and being surrounded by GSM waves demanding a comprehensive and powerful network. Covering large areas through number of base stations which not only require time to configure but also need resources for implementation with a recurring cost of functionality. Automation is everywhere and to provide novel wireless services it is necessary to come up with some distinct features like intelligent base stations which have the capability of doing …

User Choice Between Traditional And Computerized Methods: An Activity Perspective, Jun Sun

Information Systems Faculty Publications

Numerous computerized methods emerge to replace traditional methods in people’s personal, work and social lives, but many are hesitant to make the transition. This study examines the factors that influence human choice between different methods. According to Activity Theory, traditional and computerized methods are both tools that a person uses for a certain task. The situated experiences with various methods shape people’s attitude toward using them later in terms of tool readiness. The understanding leads to hypothesized relationships between user-, method- and task-specific factors and the dependent variable. The results from an empirical study support that method experiences have strong …

A Study Of Correlations Between The Definition And Application Of The Gene Ontology, Yuji Mo

Department of Computer Electronics and Engineering: Dissertations, Theses, and Student Research

When using the Gene Ontology (GO), nucleotide and amino acid sequences are annotated by terms in a structured and controlled vocabulary organized into relational graphs. The usage of the vocabulary (GO terms) in the annotation of these sequences may diverge from the relations defined in the ontology. We measure the consistency of the use of GO terms by comparing GO's defined structure to the terms' application. To do this, we first use synthetic data with different characteristics to understand how these characteristics influence the correlation values determined by various similarity measures. Using these results as a baseline, we found that …

Overview Of Contrast Data Mining As A Field And Preview Of An Upcoming Book, Guozhu Dong, James Bailey

Kno.e.sis Publications

This report provides an overview of the field of contrast data mining and its applications, and offers a preview of an upcoming book on the topic. The importance of contrasting is discussed and a brief survey is given covering the following topics: general definitions and terminology for contrast patterns, representative contrast pattern mining algorithms, applications of contrast mining for fundamental data mining tasks such as classification and clustering, applications of contrast mining in bioinformatics, medicine, blog analysis, image analysis and subgroup mining, results on contrast based dataset similarity measure, and on analyzing item interaction in contrast patterns, and open research …

Computing Inconsistency Measure Based On Paraconsistent Semantics, Pascal Hitzler, Yue Ma, Guilin Qi

Computer Science and Engineering Faculty Publications

Measuring inconsistency in knowledge bases has been recognized as an important problem in several research areas. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. However, existing methods suffer from two limitations: (i) they are mostly restricted to propositional knowledge bases; (ii) very few of them discuss computational aspects of computing inconsistency measures. In this article, we try to solve these two limitations by exploring algorithms for computing an inconsistency measure of first-order knowledge bases. After introducing a four-valued semantics for first-order logic, we define an …

Quantifying Computer Network Security, Ian Burchett

Masters Theses & Specialist Projects

Simplifying network security data to the point that it is readily accessible and usable by a wider audience is increasingly becoming important, as networks become larger and security conditions and threats become more dynamic and complex, requiring a broader and more varied security staff makeup. With the need for a simple metric to quantify the security level on a network, this thesis proposes: simplify a network’s security risk level into a simple metric. Methods for this simplification of an entire network’s security level are conducted on several characteristic networks. Identification of computer network port vulnerabilities from NIST’s Network Vulnerability Database …

Vireo@Trecvid 2011: Instance Search, Semantic Indexing, Multimedia Event Detection And Known-Item Search, Chong-Wah Ngo, Shi-Ai Zhu, Wei Zhang, Chun-Chet Tan, Ting Yao, Lei Pang, Hung-Khoon Tan

Research Collection School Of Computing and Information Systems

The vireo group participated in four tasks: instance search, semantic indexing, multimedia event detection and known-item search. In this paper,we will present our approaches and discuss the evaluation results.

Study Of Feature Selection Algorithms For Text-Categorization, Kandarp Dave

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis will discuss feature selection algorithms for text-categorization. Feature selection algorithms are very important, as they can make-or-break a categorization engine. The feature selection algorithms that will be discussed in this thesis are Document Frequency, Information Gain, Chi Squared, Mutual Information, NGL (Ng-Goh-Low) coefficient, and GSS (Galavotti-Sebastiani-Simi) coefficient . The general idea of any feature selection algorithm is to determine importance of words using some measure that can keep informative words, and remove non-informative words, which can then help the text-categorization engine categorize a document, D , into some category, C . These feature selection methods are explained, implemented, …

The Valuation Of User-Generated Content: A Structural, Stylistic And Semantic Analysis Of Online Reviews, Noi Sian Koh

Dissertations and Theses Collection (Open Access)

The ability and ease for users to create and publish content has provided vast amount of online product reviews. However, the amount of data is overwhelmingly large and unstructured, making information difficult to quantify. This creates challenge in understanding how online reviews affect consumers’ purchase decisions. In my dissertation, I explore the structural, stylistic and semantic content of online reviews. Firstly, I present a measurement that quantifies sentiments with respect to a multi-point scale and conduct a systematic study on the impact of online reviews on product sales. Using the sentiment metrics generated, I estimate the weight that customers place …