Digital Commons Network^™

Crafting A Research Paper, Ronan Fitzpatrick, Kevin O'Donnell

Conference papers

Writing research papers presents numerous different challenges, which vary from how to layout the various pages and format the text to what to include in the different sections that make up the paper. In order to clarify these issues this paper sets out guidelines and best practice approaches and demonstrates their use in the style of this paper.

Trading Agents Competing: Performance, Progress And Market Effectiveness, Michael P. Wellman, Shih-Fen Cheng, Daniel M. Reeves, Kevin M. Lochner

Research Collection School Of Computing and Information Systems

The annual trading agent competition offers agent designers a forum for evaluating programmed trading techniques in a challenging market scenario. TAC aims to spur research by enabling researchers to compare techniques on a common problem and build on each other's ideas. A fixed set of assumptions and environment settings facilitates communication of methods and results. As a multiyear event, TAC lets researchers observe trading agents' progress over time, in effect accelerating the evolution of an adapted population of traders. Given all the participant effort invested, it is incumbent on us to learn as much from the experience as possible. After …

Advances In Mobile Commerce Technologies, Ee Peng Lim, Keng Siau

Research Collection School Of Computing and Information Systems

No abstract provided.

Welcome To The Special Issue: The Best Of The Best, Donald C. Wunsch, Mike Hasselmo, De Liang Wang, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

Adaptive Critic Neural Network-Based Controller For Nonlinear Systems With Input Constraints, Pingan He, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel adaptive critic-Based multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of magnitude constraints on the input. Reinforcement learning scheme in discrete time is proposed for the NN controller, where the action generating NN learning is performed based on a certain performance measure, which is supplied from a critic. using the Lyapunov approach and with a novel weight update, the uniform ultimately boundedness (UUB) of the closed loop tracking error and weight estimates are shown. the adaptive critic NN does not require an …

Million City Traveling Salesman Problem Solution By Divide And Conquer Clustering With Adaptive Resonance Neural Networks, Samuel A. Mulder, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

The Traveling Salesman Problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-complete and is an often-used benchmark for new optimization techniques. One of the main challenges with this problem is that standard, non-AI heuristic approaches such as the Lin-Kernighan algorithm (LK) and the chained LK variant are currently very effective and in wide use for the common fully connected, Euclidean variant that is considered here. This paper presents an algorithm that uses adaptive resonance theory (ART) in combination with a variation of the Lin-Kernighan local optimization algorithm to …

Neuro Emission Controller For Minimizing Cyclic Dispersion In Spark Ignition Engines, Pingan He, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel neural network (NN) controller is developed to control spark ignition (SI) engines at extreme lean conditions. The purpose of neurocontroller is to reduce the cyclic dispersion at lean operation even when the engine dynamics are unknown. The stability analysis of the closed-loop control system is given and the boundedness of all signals is ensured. Results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller. The neuro controller can also be extended to minimize engine emissions with high EGR levels, where similar complex cyclic dynamics are observed. Further, the proposed approach can be applied to control …

Hybrid Data Mining Technique For Application In Geo-Chemical Data Analysis, Shipra Dutta

Masters Theses

"Data mining is a very active research area, which has gained a lot of attention over the past few years for its applications in various fields ranging from stock forecasting to detecting damages in the structures like aircrafts. The use of data mining for applications in science and business has evoked a lot of interest in the researchers to explore the area further. In this research, a novel hybrid clustering architecture will be presented as a possible solution to classify the geo-chemical data obtained by conducting various direct and indirect tests on different sites. The fact that no single learning …

Detecting And Representing Relevant Web Deltas In Whoweda, Sanjay Kumar Madria, Wee Keong Ng, Sourav S. Bhowmick

Computer Science Faculty Research & Creative Works

In this paper, we present a mechanism for detecting and representing changes, given the old and new versions of a set of interlinked Web documents, retrieved in response to a user''s query. In particular, we show how to detect and represent Web deltas, i.e., changes in the Web documents that are relevant to a user''s query in the context of our Web warehousing system called WHOWEDA (Warehouse of Web Data). In WHOWEDA, Web information is materialized views stored in Web tables in the form of Web tuples. These Web tuples, represented as directed graphs, can be manipulated using a set …

An Intelligent Early Warning System For Software Quality Improvement And Project Management, Xiaoqing Frank Liu, Gautam Kane, Monu Bambroo

Computer Science Faculty Research & Creative Works

One of the main reasons behind unfruitful software development projects is that it is often too late to correct the problems by the time they are detected. It clearly indicates the need for early warning about the potential risks. In this paper, we discuss an intelligent software early warning system based on fuzzy logic using an integrated set of software metrics. It helps to assess risks associated with being behind schedule, over budget, and poor quality in software development and maintenance from multiple perspectives. It handles incomplete, inaccurate, and imprecise information, and resolve conflicts in an uncertain environment in its …

Instance Based Attribute Identification In Database Integration, Ee Peng Lim, Cecil Chua, Roger Hsiang-Li Chiang

Research Collection School Of Computing and Information Systems

Most research on attribute identification in database integration has focused on integrating attributes using schema and summary information derived from the attribute values. No research has attempted to fully explore the use of attribute values to perform attribute identification. We propose an attribute identification method that employs schema and summary instance information as well as properties of attributes derived from their instances. Unlike other attribute identification methods that match only single attributes, our method matches attribute groups for integration. Because our attribute identification method fully explores data instances, it can identify corresponding attributes to be integrated even when schema information …

Sensor Networks: An Overview, Malik Tubaishat, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

Advances in hardware and wireless network technologies have created low-cost, low-power, multifunctional miniature sensor devices. These devices make up hundreds or thousands of ad hoc tiny sensor nodes spread across a geographical area. These sensor nodes collaborate among themselves to establish a sensing network. A sensor network can provide access to information anytime, anywhere by collecting, processing, analyzing and disseminating data. Thus, the network actively participates in creating a smart environment.

Two Distributed Algorithms For E-Ticket Validation Protocols For Mobile Clients, H. Mohanty, Sanjay Kumar Madria, T. Suman Kumar Reddy, R. K. Ghosh

Computer Science Faculty Research & Creative Works

The e-ticket (electronic ticket) validation problem has relevance in mobile computing environment because of the multiple submission of a ticket that is possible due to intermittent disconnections and mobility of hosts. Here, we propose protocols that are not only sensitive to disconnection but also to location. One of the proposed protocols is the variant of the distributed protocol proposed by Pedone (2000) for Internet users. This shows that a distributed protocol for static network can be restructured for distributed computation in a mobile computing environment. We have also proposed another protocol that uses a hierarchical location database of mobile hosts …

Rate-Based End-To-End Congestion Control Of Multimedia Traffic In Packet Switched Networks, Mingsheng Peng, S. R. Subramanya, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes an explicit rate-based end-to-end congestion control mechanism to alleviate congestion of multimedia traffic in packet switched networks such as the Internet. The congestion is controlled by adjusting the transmission rates of the sources in response to the feedback information from destination such as the buffer occupancy, packet arrival rate and service rate at the outgoing link, so that a desired quality of service (QoS) can be met. The QoS is defined in terms of packet loss ratio, transmission delay, power, and network utilization. Comparison studies demonstrate the effectiveness of the proposed scheme over New-Reno TCP (a variant …

Machine Learning Techniques For Efficient Query Processing In Kowledge Base Systems, Kevin Paul Grant

LSU Doctoral Dissertations

In this dissertation we propose a new technique for efficient query processing in knowledge base systems. Query processing in knowledge base systems poses strong computational challenges because of the presence of combinatorial explosion. This arises because at any point during query processing there may be too many subqueries available for further exploration. Overcoming this difficulty requires effective mechanisms for choosing from among these subqueries good subqueries for further processing. Inspired by existing works on stochastic logic programs, compositional modeling and probabilistic heuristic estimates we create a new, nondeterministic method to accomplish the task of subquery selection for query processing. Specifically, …

Analyzing Erp Implementation At A Public University Using The Innovation Strategy Model, Keng Siau, J. Messersmith

Research Collection School Of Computing and Information Systems

Enterprise Resource Planning (ERP) systems have revolutionized the way companies are using information technology in their businesses. ERP was created in an effort to streamline business processes and has proven to be successful in many operations. Unfortunately, not all ERP implementations have met expectations. One way that businesses may be able to increase success rates is to embrace creativity and innovation in their ERP implementations. For businesses to do this, they must first understand how creativity originates and how that creativity can be integrated into business solutions. This article presents a case study that examines the ERP implementation at a …

Performance Measurement Framework For Hierarchical Text Classification, Ee Peng Lim, Aixin Sun, Wee-Keong Ng

Research Collection School Of Computing and Information Systems

Hierarchical text classification or simply hierarchical classification refers to assigning a document to one or more suitable categories from a hierarchical category space. In our literature survey, we have found that the existing hierarchical classification experiments used a variety of measures to evaluate performance. These performance measures often assume independence between categories and do not consider documents misclassified into categories that are similar or not far from the correct categories in the category tree. In this paper, we therefore propose new performance measures for hierarchical classification. The proposed performance measures consist of category similarity measures and distance-based measures that consider …

Under The Plastic: A Quantitative Look At Dvd Video Encoding And Its Impact On Video Modeling, Wu-Chi Feng, Jin Choi, Wu-Chang Feng, Jonathan Walpole

Computer Science Faculty Publications and Presentations

In this paper, we examine the DVD encoding process and the implications this process has video modeling and network traffic analysis. We have assembled a system that allows us to extract the video data from the DVDs as they were encoded for distribution. Analyzing the resulting video trace data, we describe how DVD encodings have evolved over time. In addition, our findings show that the underlying video content is fundamentally different than those produced by basic consumer video capture boards. We demonstrate how this affects current video modeling proposals and their affect on network traffic characterization. This research is based …

Metrics For Evaluating Human-Robot Interactions, Michael A. Goodrich, Dan R. Olsen Jr.

Faculty Publications

Metrics for evaluating the quality of a human-robot interface are introduced. The autonomy of a robot is measured by its neglect time. The robot attention demand metric measures how much of the user’s attention is involved with instructing a robot. The free-time and fan-out metrics are two ways to measure this demand. Each of them leads to estimates of the interaction effort. Reducing interaction effort without diminishing task effectiveness is the goal of human-robot interaction design.

Level Oriented Formal Model For Asynchronous Circuit Verification And Its Efficient Analysis Method, Eric G. Mercer, Tomoya Kitai, Chris Myers, Yusuke Oguro, Tomohiro Yoneda

Faculty Publications

Using a level-oriented model for verification of asynchronous circuits helps users to easily construct formal models with high readability or to naturally model datapath circuits. On the other hand, in order to use such a model on large circuits, techniques to avoid the state explosion problem must be developed. This paper first introduces a level-oriented formal model based on time Petri nets, and then proposes its partial order reduction algorithm that prunes unnecessary state generation while guaranteeing the correctness of the verification.

Coordinated Target Assignment And Intercept For Unmanned Air Vehicles, Erik P. Anderson, Randal W. Beard, Michael A. Goodrich, Timothy W. Mclain

Faculty Publications

This paper presents an end-to-end solution to the cooperative control problem represented by the scenario where unmanned air vehicles (UAVs) are assigned to transition through known target locations in the presence of dynamic threats. The problem is decomposed into the subproblems of: 1) cooperative target assignment; 2) coordinated UAV intercept; 3) path planning; 4) feasible trajectory generation; and 5) asymptotic trajectory following. The design technique is based on a hierarchical approach to coordinated control. Simulation results are presented to demonstrate the effectiveness of the approach.

A Performance Study Of Lam And Mpich On An Smp Cluster, Brian Patrick Kearns

Dissertations and Theses

Many universities and research laboratories have developed low cost clusters, built from Commodity-Off-The-Shelf (COTS) components and running mostly free software. Research has shown that these types of systems are well-equipped to handle many problems requiring parallel processing. The primary components of clusters are hardware, networking, and system software. An important system software consideration for clusters is the choice of the message passing library.

MPI (Message Passing Interface) has arguably become the most widely used message passing library on clusters and other parallel architectures, due in part to its existence as a standard. As a standard, MPI is open for anyone …

Resource Annotation Framework In A Georeferenced And Geospatial Digital Library, Zehua Liu, Ee Peng Lim, Dion Hoe-Lian Goh

Research Collection School Of Computing and Information Systems

G-Portal is a georeferenced and geospatial digital library that aims to identify, classify and organize geospatial and georeferenced resources on the web and to provide digital library services for these resources. Annotation service is supported in G-Portal to enable users to contribute content to the digital library. In this paper, we present a resource annotation framework for georeferenced and geospatial digital libraries and discuss its application in G-Portal. The framework is fiexible for managing annotations of heterogeneous web resources. It allows users to contribute not only the annotation content but also the schema of the annotations. Meanwhile, other digital library …

Crystalclear: Active Visualization Of Association Rules, Hian-Huat Ong, Kok-Leong Ong, Wee-Keong Ng, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Effective visualization is an important aspect of active data mining. In the context of association rules, this need has been driven by the large amount of rules produced from a run of the algorithm. To be able to address real user needs, the rules need to be summarized and organized so that it can be interpreted and applied in a timely manner. In this paper, we propose two visualization techniques that is an improvement over those used by existing data mining packages. In particular, we address the visualization of "differences" in the set of rules due to incremental changes in …

Vdl: A Language For Active Mining Variants Of Association Rules, Kok-Leong Ong, Wee-Keong Ng, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The popularity of association rules has resulted in several variations being proposed. In each case, additional attributes in the data are considered so as to produce more informative rules. In the context of active mining, different types of rules may be required over a period of time due to knowledge needs or the availability of new attributes. The present approach is the ad-hoc development of algorithms for each variant of rules. This is time consuming and costly, and is a stumping block to the vision of active mining. We argue that knowledge needs and the changing characteristics of the data …

Intergenerational Partnerships In The Design Of A Digital Library Of Geography Examination Resources, Yin-Leng Theng, Dion Hoe-Lian Goh, Ee Peng Lim, Zehua Liu, Natalie Lee-San Pang, Patricia Bao-Bao Wong, Lian-Heong Chua

Research Collection School Of Computing and Information Systems

This paper describes the engagement of intergenerational partners in the design of a digital library of geographical resources (GeogDL) to help prepare Singapore students for a national examination in geography. GeogDL is built on top of G-Portal, a digital library providing services over geospatial and georeferenced Web content. Scenario-based design and claims analysis were employed as a means of refinement to the initial design of the GeogDL prototype.

A Data Mining Approach To Library New Book Recommendations, San-Yih Hwang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

In this paper, we propose a data mining approach to recommending new library books that have never been rated or borrowed by users. In our problem context, users are characterized by their demographic attributes, and concept hierarchies can be defined for some of these demographic attributes. Books are assigned to the base categories of a taxonomy. Our goal is therefore to identify the type of users interested in some specific type of books. We call such knowledge generalized profile association rules. In this paper, we propose a new definition of rule interestingness to prune away rules that are redundant and …

Semantic Exploration Of Lecture Videos, Edward Altman, Yu Chen, Wai Chong Low

Research Collection School Of Computing and Information Systems

E-Learning is rapidly changing the way that universities and corporations offer education and training. In recent years, the acquisition and distribution of rich media content has been largely automated, However, existing applications are functionally and visually static and remain organized around the delivery media, rather than the knowledge representation and learning tasks of the student. The innovative approach of this system is the extraction of semantically meaningful structures in the lecture combined with text analysis to support task based queries. In this demonstration we will show this combination of pedagogical and content descriptions leads to novel forms of visualization and …

Load-Sensitive Transmission Power Control In Wireless Ad-Hoc Networks, Seung Jong Park, Raghupathy Sivakumar

Computer Science Faculty Research & Creative Works

Transmission power control in ad-hoc networks has hitherto been used only for achieving connectivity of networks. It has been implicitly assumed that the optimal throughput performance in ad-hoc networks can be achieved when using the minimum transmission power required to keep the network connected. However, in this paper we argue that such an assumption remains valid only under high node densities, which is not the characteristic of typical ad-hoc networks. Using both throughput and throughput per unit energy as the optimization criteria, we demonstrate that the optimal transmission power depends on several network characteristics such as the number of stations, …

On Clustering And Retrieval Of Video Shots Through Temporal Slices Analysis, Chong-Wah Ngo, Ting-Chuen Pong, Hong-Jiang Zhang

Research Collection School Of Computing and Information Systems

Based on the analysis of temporal slices, we propose novel approaches for clustering and retrieval of video shots. Temporal slices are a set of two-dimensional (2-D) images extracted along the time dimension of an image volume. They encode rich set of visual patterns for similarity measure. In this paper, we first demonstrate that tensor histogram features extracted from temporal slices are suitable for motion retrieval. Subsequently, we integrate both tensor and color histograms for constructing a two-level hierarchical clustering structure. Each cluster in the top level contains shots with similar color while each cluster in bottom level consists of shots …