Digital Commons Network^™

Learning The Unified Kernel Machines For Classification, Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang

Research Collection School Of Computing and Information Systems

Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel Machines (UKM) from both labeled and unlabeled data. Our proposed framework integrates supervised learning, semi-supervised kernel learning, and active learning in a unified solution. In the suggested framework, we particularly focus our attention on designing a new semi-supervised kernel learning method, i.e., Spectral Kernel Learning (SKL), which is built on the principles of kernel target alignment and unsupervised kernel design. Our algorithm is related to an equivalent quadratic programming problem that can be efficiently …

Development Of Dynamically-Generated Pages On A Website, Jodi Wagner

Regis University Student Publications (comprehensive collection)

The purpose of this paper is to examine the project management techniques involved in developing dynamically-generated pages for an existing website. The dynamically-generated pages will allow the editors of a website to add and remove content from pages without directly editing the pages themselves. The project will benefit editors, who will be able to spend more time on creating content for the website, which will lead to benefits for site visitors as well, in the form of uniformly designed pages and more content for the site.

Two Energy Efficient Algorithms For Tracking Objects In A Sensor Network, Arvind Rapaka, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

We propose two energy efficient algorithms for locating a target object moving in an area covered by a wireless ad hoc network. The first algorithm developed conserve energy by efficiently identifying sensor nodes, as Home Nodes, and use only local messages between neighboring nodes to follow the trail of the object. Since we avoid the long-range transmission and maximize the localization, the algorithms reduce the communication cost. The dynamic nature of the second algorithm exploits the predefined parameters such as the object velocity. Our algorithm represents query shipping against the conventional data shipping as a means to reduce the amount …

A Constructive Incremental Learning Algorithm For Binary Classification Tasks, Christophe G. Giraud-Carrier, Tony R. Martinez

Faculty Publications

This paper presents i-AA1*, a constructive, incremental learning algorithm for a special class of weightless, self-organizing networks. In i-AA1*, learning consists of adapting the nodes’ functions and the network’s overall topology as each new training pattern is presented. Provided the training data is consistent, computational complexity is low and prior factual knowledge may be used to “prime” the network and improve its predictive accuracy. Empirical generalization results on both toy problems and more realistic tasks demonstrate promise.

Vectorization Of Raster Images Using B-Spline Surfaces, Curtis A. Armstrong

Theses and Dissertations

A system has been developed for converting raster images into vector images. Raster images are made of pixels, while vector images are made of smoother shapes. The image is first segmented, and the segments are layered. The boundary of each segment is approximated with a periodic B-Spline curve. This curve is then used to create a B-Spline surface to approximate the interior of the segment. The algorithm fits each B-Spline to the colors of the image using least-squares approximation. The color and shape of each B-Spline surface are extrapolated into regions behind other segments. The result is a vector image …

Characterizing Dynamic Power And Data Rate Policies For Wirelessusb Networks, Jeffrey L. Barlow

Theses and Dissertations

Wireless communication is increasingly ubiquitous. However, mobility depends intrinsically on battery life. Power can be conserved at the Media Access Control (MAC) layer by intelligently adjusting transmission power level and data rate encoding. WirelessUSB is a low-power, low-latency wireless technology developed by Cypress Semiconductor Corporation for human interface devices such as keyboards and mice. WirelessUSB devices conserve power by employing power-efficient hardware, dynamic power level adjustment and dynamic data rate adjustment. We characterize the effects on power consumption of dynamically adjusting node power using two dynamic power negotiation techniques as well as two reactive techniques. We also characterize the effects …

Cooperation-Based Clustering For Profit-Maximizing Organizational Design, Christophe G. Giraud-Carrier, Kevin Seppi, Nghia Tran, Sean C. Warnick

Faculty Publications

This paper shows how the notion of value of cooperation, a measure of the percentage of a firm’s profits due strictly to the cooperative effects among the goods it sells, can be used to analyze the relative economic advantage afforded by various organizational structures. The value of cooperation is computed from transactions data by solving a regression problem to fit the parameters of the consumer demand function, and then simulating the resulting profit-maximizing dynamic system under various organizational structures. A hierarchical agglomerative clustering algorithm can be applied to reveal the optimal organizational substructure.

Particle Swarm Optimization In Dynamic Pricing, Christopher K. Monson, Patrick B. Mullen, Kevin Seppi, Sean C. Warnick

Faculty Publications

Dynamic pricing is a real-time machine learning problem with scarce prior data and a concrete learning cost. While the Kalman Filter can be employed to track hidden demand parameters and extensions to it can facilitate exploration for faster learning, the exploratory nature of Particle Swarm Optimization makes it a natural choice for the dynamic pricing problem. We compare both the Kalman Filter and existing particle swarm adaptations for dynamic and/or noisy environments with a novel approach that time-decays each particle's previous best value; this new strategy provides more graceful and effective transitions between exploitation and exploration, a necessity in the …

A Microformatted Registry Alternative, Thomas R. Warne

Theses and Dissertations

To effectively use Web services, providers and consumers need to be connected by a registry. Several registry solutions exist today, including UDDI and WSIL. Also, many organizations simply use Web pages to list available Web services and their descriptions. This research describes a microformat for representing Web service description documents. These microformatted documents can be converted back to the original format for use by machines. They can also contain additional information, making them more useful to people. A registry, allowing indexing and searching of microformatted service descriptions, is also described. The benefits of this solution include: using existing standards; allowing …

Local Url Resolution Protocol, Joseph Clark Ekstrom

Theses and Dissertations

DOGMA is a resource management system designed to create a supercomputer like system from unused desktop computers. Scalability is one of the challenges faced by DOGMA because it uses a strict client/server architecture. Distributing large files over a client server architecture is problematic since available network bandwidth is limited. The Local URL Resolution Protocol(LURP) addresses this problem for environments where there are high node densities. LURP implements a locality aware Peer-to-Peer file distribution model to increase the speed of file distribution while reducing the overall network congestion.

Improving Record Linkage Through Pedigrees, Burdette N. Pixton

Theses and Dissertations

Record linkage, in a genealogical context, is the process of identifying individuals from multiple sources which refer to the same real-world entity. Current solutions focus on the individuals in question and on complex rules developed by human experts. Genealogical databases are highly-structured with relationships existing between the individuals and other instances. These relationships can be utilized and human involvement greatly minimized by using a filtered structured neural network. These neural networks, using traditional back-propagation methods, are biased in a way to make the network human readable. The results show an increase in precision and recall when pedigree data is available …

Jumpstarting Phylogenetic Searches, Jesse Lewis Mecham

Theses and Dissertations

Phylogenetic analysis is a central tool in studies of comparative genomics. When a new region of DNA is isolated and sequenced, researchers are often forced to throw away months of computation on an existing phylogeny of homologous sequences in order to incorporate this new sequence. The previously constructed trees are often discarded, and the researcher begins the search again from scratch. The jumpstarting algorithm uses trees from the prior search as a starting point for a new phylogenetic search. This technique drastically decreases search time for large data sets. This kind of analysis is necessary as researchers analyze tree of …

Markov Approximations: The Characterization Of Undermodeling Errors, Lei Lei

Theses and Dissertations

This thesis is concerned with characterizing the quality of Hidden Markov modeling when learning from limited data. It introduces a new perspective on different sources of errors to describe the impact of undermodeling. Our view is that modeling errors can be decomposed into two primary sources of errors: the approximation error and the estimation error. This thesis takes a first step towards exploring the approximation error of low order HMMs that best approximate the true system of a HMM. We introduce the notion minimality and show that best approximations of the true system with complexity greater or equal to the …

Reinforcement Programming: A New Technique In Automatic Algorithm Development, Spencer Kesson White

Theses and Dissertations

Reinforcement programming is a new technique for using computers to automatically create algorithms. By using the principles of reinforcement learning and Q-learning, reinforcement programming learns programs based on example inputs and outputs. State representations and actions are provided. A transition function and rewards are defined. The system is trained until the system converges on a policy that can be directly implemented as a computer program. The efficiency of reinforcement programming is demonstrated by comparing a generalized in-place iterative sort learned through genetic programming to a sorting algorithm of the same type created using reinforcement programming. The sort learned by reinforcement …

When Domains Require Modeling Adaptations, Krol Kevin Mathias, Cynthia Isenhour, Alex Dekhtyar, Judy Goldsmtih, Beth Goldstein

Computer Science and Software Engineering

The project described in this paper originated with an observation by the AI group at the University of Kentucky, that, individually, stochastic planning and constraint satisfaction are well-studied topics that resulted in efficient software, but stochastic planning in the presence of constraints on the domains and actions is an open area of investigation.

We were interested in an advising scenario, and chose the US social welfare system, a.k.a. “Welfare to Work” as our test domain. This required computer scientists to learn more than expected about social science as well as the local welfare system. This paper discusses the discipline specific …

Implementation Of Microsoft's Virtual Pc In Networking Curriculum, Jim Yulga

Regis University Student Publications (comprehensive collection)

Using Microsoft's Virtual PC software product as a virtual technology in the implementation of Network Specialist curriculum allows increased versatility and considerable hardware cost savings. Rather than purchasing individual computers or removable hard drives, using boot manager programs, or simulation software (including Computer Based Training programs), for student use in learning the administration of an operating system, one computer with hard drive, sufficient processor power, and RAM can be used to implement the effective hands-on learning approach of plan, implement and test, and then review. In addition, this software allows a non-dedicated (production) computer lab to be used. This is …

A Relational Database Approach For Frequent Subgraph Mining, Subhesh Kumar Pradhan

Computer Science and Engineering Theses - Archive

Data mining aims at discovering interesting and previously unknown patterns from data sets. Further more, graph-based data mining represents a collection of techniques for mining the relational aspects of data represented as a graph. Complex relationships in data can be represented using graphs and hence graph mining is appropriate for analyzing data that is rich in structural relationships. Database mining of graphs, on the other hand, aims at directly mining graphs stored in a database using SQL queries. Several SQL-based mining algorithms have been developed successfully and their efficiency and scalability have been established. One of them is HDB-Subdue which …

Ontosearch: A Full-Text Search Engine For The Semantic Web, Xing Jiang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

OntoSearch, a full-text search engine that exploits ontological knowledge for document retrieval, is presented in this paper. Different from other ontology based search engines, OntoSearch does not require a user to specify the associated concepts of his/her queries. Domain ontology in OntoSearch is in the form of a semantic network. Given a keyword based query, OntoSearch infers the related concepts through a spreading activation process in the domain ontology. To provide personalized information access, we further develop algorithms to learn and exploit user ontology model based on a customized view of the domain ontology. The proposed system has been applied …

Keyframe Retrieval By Keypoints: Can Point-To-Point Matching Help?, Wanlei Zhao, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Bag-of-words representation with visual keypoints has recently emerged as an attractive approach for video search. In this paper, we study the degree of improvement when point-to-point (P2P) constraint is imposed on the bag-of-words. We conduct investigation on two tasks: near-duplicate keyframe (NDK) retrieval, and high-level concept classification, covering parts of TRECVID 2003 and 2005 datasets. In P2P matching, we propose a one-to-one symmetric keypoint matching strategy to diminish the noise effect during keyframe comparison. In addition, a new multi-dimensional index structure is proposed to speed up the matching process with keypoint filtering. Through experiments, we demonstrate that P2P constraint can …

Cs 415: Social Implications Of Computing, Leo Finkelstein

Computer Science & Engineering Syllabi

CS 415 is a communication skills course using as its subject matter current salient issues associated with the social implications of computing. In addition to the course text, you will need to use certain reading materials in the library and elsewhere, and you will be responsible for using concepts and theories provided in class lectures and discussions.

A Novel Privacy Preserving Authentication And Access Control Scheme For Pervasive Computing Environments, K. Ren, Wenjing Lou, K. Kim, Robert H. Deng

Research Collection School Of Computing and Information Systems

Privacy and security are two important but seemingly contradictory objectives in a pervasive computing environment (PCE). On one hand, service providers want to authenticate legitimate users and make sure they are accessing their authorized services in a legal way. On the other hand, users want to maintain the necessary privacy without being tracked down for wherever they are and whatever they are doing. In this paper, a novel privacy preserving authentication and access control scheme to secure the interactions between mobile users and services in PCEs is proposed. The proposed scheme seamlessly integrates two underlying cryptographic primitives, namely blind signature …

Image Interpolation By Two-Dimensional Parametric Cubic Convolution, Jiazheng Shi, Stephen E. Reichenbach

School of Computing: Faculty Publications

Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2, 2] x [-2, 2] with constraints for biaxial symmetry, diagonal (or 90 rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-5PCC), relaxes the …

Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a neural network (NN) based decentralized excitation controller design for large scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem controllers can be guaranteed. NNs are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded (UUB). Simulation results with a 3-machine power system demonstrate the …

Extraction Of Coherent Relevant Passages Using Hidden Markov Models, Jing Jiang, Chengxiang Zhai

Research Collection School Of Computing and Information Systems

In information retrieval, retrieving relevant passages, as opposed to whole documents, not only directly benefits the end user by filtering out the irrelevant information within a long relevant document, but also improves retrieval accuracy in general. A critical problem in passage retrieval is to extract coherent relevant passages accurately from a document, which we refer to as passage extraction. While much work has been done on passage retrieval, the passage extraction problem has not been seriously studied. Most existing work tends to rely on presegmenting documents into fixed-length passages which are unlikely optimal because the length of a relevant passage …

Private Information Retrieval Using Trusted Hardware, Shuhong Wang, Xuhua Ding, Robert H. Deng, Feng Bao

Research Collection School Of Computing and Information Systems

Many theoretical PIR (Private Information Retrieval) constructions have been proposed in the past years. Though information theoretically secure, most of them are impractical to deploy due to the prohibitively high communication and computation complexity. The recent trend in outsourcing databases fuels the research on practical PIR schemes. In this paper, we propose a new PIR system by making use of trusted hardware. Our system is proven to be information theoretically secure. Furthermore, we derive the computation complexity lower bound for hardware-based PIR schemes and show that our construction meets the lower bounds for both the communication and computation costs, respectively.

Autonomic Management Of Stream Processing Applications Via Adaptive Bandwidth Control, Dimitrios Pendarakis, Jeremy Silber, Laura Wynter

Research Collection School Of Computing and Information Systems

We present a novel autonomic control system for high performance stream processing systems. The system uses bandwidth controls on incoming or outgoing streams to achieve a desired resource utilization balance among a set of concurrently executing stream processing tasks. We show that CPU prioritization and allocation mechanisms in schedulers and virtual machine managers are not sufficient to control such I/O-centric applications, and present an autonomic bandwidth control system that adaptively adjusts incoming and outgoing traffic rates to achieve system management goals. The system dynamically learns the bandwidth rate necessary to meet the system management goals using stochastic nonlinear optimization, and …

Mobile Healthcare Informatics, Keng Siau, Zixing Shen

Research Collection School Of Computing and Information Systems

Advances in wireless technology give pace to the rapid development of mobile applications. The coming mobile revolution will bring dramatic and fundamental changes to our daily life. It will influence the way we live, the way we do things, and the way we take care of our health. For the healthcare industry, mobile applications provide a new frontier in offering better care and services to patients, and a more flexible and mobile way of communicating with suppliers and patients. Mobile applications will provide important real time data for patients, physicians, insurers, and suppliers. In addition, it will revolutionalize the way …

Generalized On-Demand Service Architecture For Interactive Applications [Us 2006/0168107 A1], Rajesh K. Balan, Paul C. Castro, Maria R. Ebling, Archan Misra

Research Collection School Of Computing and Information Systems

A method, system, and computer program for executing a network-based distributed application. An infrastructure is described that includes application containers configured to execute an application instance of the distributed application and measure quality of service metrics for the application instance. Workload management elements form a decentralized workload management layer. The workload management layer is configured to assign a workload to application containers based on the quality of service metrics received by the application containers.

Hierarchical Hidden Markov Model For Rushes Structuring And Indexing, Chong-Wah Ngo, Zailiang Pan, Xiaoyong Wei

Research Collection School Of Computing and Information Systems

Rushes footage are considered as cheap gold mine with the potential for reuse in broadcasting and filmmaking industries. However, it is difficult to mine the "gold" from the rushes since usually only minimum metadata is available. This paper focuses on the structuring and indexing of the rushes to facilitate mining and retrieval of "gold". We present a new approach for rushes structuring and indexing based on motion feature. We model the problem by a two-level Hierarchical Hidden Markov Model (HHMM). The HHMM, on one hand, represents the semantic concepts in its higher level to provide simultaneous structuring and indexing, on …

Prediction-Based Gesture Detection In Lecture Videos By Combining Visual, Speech And Electronic Slides, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong

Research Collection School Of Computing and Information Systems

This paper presents an efficient algorithm for gesture detection in lecture videos by combining visual, speech and electronic slides. Besides accuracy, response time is also considered to cope with the efficiency requirements of real-time applications. Candidate gestures are first detected by visual cue. Then we modifity HMM models for complete gestures to predict and recognize incomplete gestures before the whole gestures paths are observed. Gesture recognition is used to verify the results of gesture detection. The relations between visual, speech and slides are analyzed. The correspondence between speech and gesture is employed to improve the accuracy and the responsiveness of …