Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic region is ported to a newer region with different demographic composition. Upon analysis for the newer region, the corresponding prevalence of influenza and pneumonia among the different demographic subgroups is inferred for the newer region. Bayesian reasoning coupled with disease timeline is used to reverse engineer an influenza outbreak for a given geographic and demographic setting. The temporal flow of the epidemic among the different sections of the population is analyzed to identify the corresponding risk levels. In comparison to spread vaccination, prioritizing the limited vaccination resources to the higher risk groups results in relatively lower influenza prevalence. HIV incidence in Texas from 1989-2002 is analyzed using demographic based epidemic curves. Dynamic Bayesian networks are integrated with …

The FrameNet database is being used in a variety of NLP research and applications such as word sense disambiguation, machine translation, information extraction and question answering. The database is currently available in XML format. The XML database though a wholesome way of distributing data in its entireness, is not practical for use unless converted to a more application friendly database. In light of this we have successfully converted the XML database to a relational MySQL™ database. This conversion reduced the amount of data storage amount to less than half. Most importantly the new database enables us to perform fast complex querying and facilitates use by applications and research. We show the steps taken to ensure relational integrity of the data during the refactoring process and a simple demo application demonstrating ease of use.

Extensive research has been conducted by the grid community to enable large-scale collaborations in pre-configured environments. grid collaborations can vary in scale and motivation resulting in a coarse classification of grids: national grid, project grid, enterprise grid, and volunteer grid. Despite the differences in scope and scale, all the traditional grids in practice share some common assumptions. They support mutually collaborative communities, adopt a centralized control for membership, and assume a well-defined non-changing collaboration. To support grid applications that do not confirm to these assumptions, we propose the concept of ad hoc grids. In the context of this research, we propose a novel architecture for ad hoc grids that integrates a suite of component frameworks. Specifically, our architecture combines the community management framework, security framework, abstraction framework, quality of service framework, and reputation framework. The overarching objective of our integrated architecture is to support a variety of grid applications in a self-controlled fashion with the help of a self-organizing ad hoc community. We introduce mechanisms in our architecture that successfully isolates malicious elements from the community, inherently improving the quality of grid services and extracting deterministic quality assurances from the underlying infrastructure. We also emphasize on the technology-independence of our …

Many of the contemporary routing algorithms use simple mechanisms such as flooding or broadcasting to disseminate the routing information available to them. Such routing algorithms cause significant network resource overhead due to the large number of messages generated at each host/router throughout the route update process. Many of these messages are wasteful since they do not contribute to the route discovery process. Reducing the resource overhead may allow for several algorithms to be deployed in a wide range of networks (wireless and ad-hoc) which require a simple routing protocol due to limited availability of resources (memory and bandwidth). Motivated by the need to reduce the resource overhead associated with routing algorithms a new implementation of distance vector routing algorithm using an agent-based paradigm known as Agent-based Distance Vector Routing (ADVR) has been proposed. In ADVR, the ability of route discovery and message passing shifts from the nodes to individual agents that traverse the network, co-ordinate with each other and successively update the routing tables of the nodes they visit.

Content-based image retrieval (CBIR) is the retrieval of images from a collection by means of internal feature measures of the information content of the images. In CBIR systems, text media is usually used only to retrieve exemplar images for further searching by image feature content. This research work describes a new method for integrating multimedia text and image content features to increase the retrieval performance of the system. I am exploring the content-based features of an image extracted from a video to build a storyboard for search retrieval of images. Metadata encoded multimedia features include extracting primitive features like color, shape and text from an image. Histograms are built for all the features extracted and stored in a database. Images are searched based on comparing these histogram values of the extracted image with the stored values. These histogram values are used for extraction of keyframes from a collection of images parsed from a video file. Individual shots of images are extracted from a video clip and run through processes that extract the features and build the histogram values. A keyframe extraction algorithm is run to get the keyframes from the collection of images to build a storyboard of images. In …

In this research, the application of intelligent mobile agents to the management of distributed network environments is investigated. Intelligent mobile agents are programs which can move about network systems in a deterministic manner in carrying their execution state. These agents can be considered an application of distributed artificial intelligence where the (usually small) agent code is moved to the data and executed locally. The mobile agent paradigm offers potential advantages over many conventional mechanisms which move (often large) data to the code, thereby wasting available network bandwidth. The performance of agents in network routing and knowledge acquisition has been investigated and simulated. A working mobile agent system has also been designed and implemented in JDK 1.2.

Streaming multimedia content with UDP has become popular over distributed systems such as an Internet. This may encounter many losses due to dropped packets or late arrivals at destination since UDP can only provide best effort delivery. Even UDP doesn't have any self-recovery mechanism from congestion collapse or bursty loss to inform sender of the data to adjust future transmission rate of data like in TCP. So there is a need to incorporate various control schemes like forward error control, interleaving, and congestion control and error concealment into real-time transmission to prevent from effect of losses. Loss can be repaired by retransmission if roundtrip delay is allowed, otherwise error concealment techniques will be used based on the type and amount of loss. This paper implements the interleaving technique with packet spacing of varying interleaver block size for protecting real-time data from loss and its effect during transformation across the Internet. The packets are interleaved and maintain some time gap between two consecutive packets before being transmitted into the Internet. Thus loss of packets can be reduced from congestion and preventing loss of consecutive packets of information when a burst of several packets are lost. Several experiments have been conducted with …

I experimented with Hopfield networks in the context of a voice-based, query-answering system. Hopfield networks are used to store and retrieve patterns. I used this technique to store queries represented as natural language sentences and I evaluated the accuracy of the technique for error correction in a spoken question-answering dialog between a computer and a user. I show that the use of an auto-associative Hopfield network helps make the speech recognition system more fault tolerant. I also looked at the available encoding schemes to convert a natural language sentence into a pattern of zeroes and ones that can be stored in the Hopfield network reliably, and I suggest scalable data representations which allow storing a large number of queries.

Partial order planning is an important approach that solves planning problems without completely specifying the orderings between the actions in the plan. This property provides greater flexibility in executing plans; hence making the partial order planners a preferred choice over other planning methodologies. However, in order to find partially ordered plans, partial order planners perform a search in plan space rather than in space of world states and an uninformed search in plan space leads to poor efficiency. In this thesis, I discuss applying a reinforcement learning method, called First-visit Monte Carlo method, to partial order planning in order to design agents which do not need any training data or heuristics but are still able to make informed decisions in plan space based on experience. Communicating effectively with the agent is crucial in reinforcement learning. I address how this task was accomplished in plan space and the results from an evaluation of a blocks world test bed.

Natural language interfaces to databases (NLIDB) are systems that aim to bridge the gap between the languages used by humans and computers, and automatically translate natural language sentences to database queries. This thesis proposes a novel approach to NLIDB, using graph-based models. The system starts by collecting as much information as possible from existing databases and sentences, and transforms this information into a knowledge base for the system. Given a new question, the system will use this knowledge to analyze and translate the sentence into its corresponding database query statement. The graph-based NLIDB system uses English as the natural language, a relational database model, and SQL as the formal query language. In experiments performed with natural language questions ran against a large database containing information about U.S. geography, the system showed good performance compared to the state-of-the-art in the field.

Burrows-Wheeler compression is a three stage process in which the data is transformed with the Burrows-Wheeler Transform, then transformed with Move-To-Front, and finally encoded with an entropy coder. Move-To-Front, Transpose, and Frequency Count are some of the many algorithms used on the List Update problem. In 1985, Competitive Analysis first showed the superiority of Move-To-Front over Transpose and Frequency Count for the List Update problem with arbitrary data. Earlier studies due to Bitner assumed independent identically distributed data, and showed that while Move-To-Front adapts to a distribution faster, incurring less overwork, the asymptotic costs of Frequency Count and Transpose are less. The improvements to Burrows-Wheeler compression this work covers are increases in the amount, not speed, of compression. Best x of 2x-1 is a new family of algorithms created to improve on Move-To-Front's processing of the output of the Burrows-Wheeler Transform which is like piecewise independent identically distributed data. Other algorithms for both the middle stage of Burrows-Wheeler compression and the List Update problem for which overwork, asymptotic cost, and competitive ratios are also analyzed are several variations of Move One From Front and part of the randomized algorithm Timestamp. The Best x of 2x - 1 family includes Move-To-Front, …

The transmission of voice data over Internet Protocol (IP) networks is rapidly gaining acceptance in the field of networking. The major voice transmissions in the IP networks are involved in Internet telephony, which is also known as IP telephony or Voice Over IP (VoIP). VoIP is undergoing many enhancements to provide the end users with same quality as in the public switched telephone networks (PSTN). These enhancements are mostly required in quality of service (QoS) for the transmission of voice data over the IP networks. As with recent developments in the networking field, various protocols came into market to provide the QoS in IP networks - of them, multi protocol label switching (MPLS) is the most reliable and upcoming protocol for working on QoS. The problem of the thesis is to develop an IP-based virtual network, with end hosts and routers, implement MPLS on the network, and analyze its QoS for voice data transmission.

Many human papilloma virus (HPV) types are sexually transmitted and HPV DNA types 16, 18, 31, and 45 account for more than 75% if all cervical dysplasia. Candidate vaccines are successfully completing US Federal Drug Agency (FDA) phase III testing and several drug companies are in licensing arbitration. Once this vaccine become available it is unlikely that 100% vaccination coverage will be probable; hence, the need for vaccination strategies that will have the greatest reduction on the endemic prevalence of HPV. This thesis introduces two discrete-time models for evaluating the effect of demographic-biased vaccination strategies: one model incorporates temporal demographics (i.e., age) in population compartments; the other non-temporal demographics (i.e., race, ethnicity). Also presented is an intuitive Web-based interface that was developed to allow the user to evaluate the effects on prevalence of a demographic-biased intervention by tailoring the model parameters to specific demographics and geographical region.

Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles is also guided by the ontology. The research conducted shows that a system can be created that utilizes statistical text classification methods to extract information from an ad-hoc generated information source like Wikipedia for use in a formal semantic ontology like Cyc. Benefits and limitations of the system are discussed along with future work.

Mobile agents require an appropriate platform that can facilitate their migration and execution. In particular, the design and implementation of such a system must balance several factors that will ensure that its constituent agents are executed without problems. Besides the basic requirements of migration and execution, an agent system must also provide mechanisms to ensure the security and survivability of an agent when it migrates between hosts. In addition, the system should be simple enough to facilitate its widespread use across large scale networks (i.e Internet). To address these issues, this thesis discusses the design and implementation of the Distributed Agent Delivery System (DADS). The DADS provides a de-coupled design that separates agent acceptance from agent execution. Using functional modules, the DADS provides services ranging from language execution and security to fault-tolerance and compression. Modules allow the administrator(s) of hosts to declare, at run-time, the services that they want to provide. Since each administrative domain is different, the DADS provides a platform that can be adapted to exchange heterogeneous blends of agents across large scale networks.

This research addresses the problem of automatic keyphrase extraction from large documents and back of the book indexing. The potential benefits of automating this process are far reaching, from improving information retrieval in digital libraries, to saving countless man-hours by helping professional indexers creating back of the book indexes. The dissertation introduces a new methodology to evaluate automated systems, which allows for a detailed, comparative analysis of several techniques for keyphrase extraction. We introduce and evaluate both supervised and unsupervised techniques, designed to balance the resource requirements of an automated system and the best achievable performance. Additionally, a number of novel features are proposed, including a statistical informativeness measure based on chi statistics; an encyclopedic feature that taps into the vast knowledge base of Wikipedia to establish the likelihood of a phrase referring to an informative concept; and a linguistic feature based on sophisticated semantic analysis of the text using current theories of discourse comprehension. The resulting keyphrase extraction system is shown to outperform the current state of the art in supervised keyphrase extraction by a large margin. Moreover, a fully automated back of the book indexing system based on the keyphrase extraction system was shown to lead to back …

The explosive demand for multimedia and fast transmission of continuous media on wireless networks means the simultaneous existence of traffic requiring different qualities of service (QoS). In this thesis, several efficient algorithms have been developed which offer several QoS to the end-user. We first look at a request TDMA/CDMA protocol for supporting wireless multimedia traffic, where CDMA is laid over TDMA. Then we look at a hybrid push-pull algorithm for wireless networks, and present a generalized performance analysis of the proposed protocol. Some of the QoS factors considered include customer retrial rates due to user impatience and system timeouts and different levels of priority and weights for mobile hosts. We have also looked at how customer impatience and system timeouts affect the QoS provided by several queuing and scheduling schemes such as FIFO, priority, weighted fair queuing, and the application of the stretch-optimal algorithm to scheduling.

In this thesis, I survey 11 approximation algorithms for unweighted set cover problem. I have also implemented the three algorithms and created a software library that stores the code I have written. The algorithms I survey are: 1. Johnson's standard greedy; 2. f-frequency greedy; 3. Goldsmidt, Hochbaum and Yu's modified greedy; 4. Halldorsson's local optimization; 5. Dur and Furer semi local optimization; 6. Asaf Levin's improvement to Dur and Furer; 7. Simple rounding; 8. Randomized rounding; 9. LP duality; 10. Primal-dual schema; and 11. Network flow technique. Most of the algorithms surveyed are refinements of standard greedy algorithm.

Data distribution management (DDM) is a High Level Architecture/Run-time Infrastructure (HLA/RTI) service that manages the distribution of state updates and interaction information in large-scale distributed simulations. The key to efficient DDM is to limit and control the volume of data exchanged during the simulation, to relay data to only those hosts requiring the data. This thesis focuses upon different DDM implementations and strategies. This thesis includes analysis of three DDM methods including the fixed grid-based, dynamic grid-based, and region-based methods. Also included is the use of multi-resolution modeling with various DDM strategies and analysis of the performance effects of aggregation/disaggregation with these strategies. Running numerous federation executions, I simulate four different scenarios on a cluster of workstations with a mini-RTI Kit framework and propose a set of benchmarks for a comparison of the DDM schemes. The goals of this work are to determine the most efficient model for applying each DDM scheme, discover the limitations of the scalability of the various DDM methods, evaluate the effects of aggregation/disaggregation on performance and resource usage, and present accepted benchmarks for use in future research.

Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institution or a river shore. Finding the correct meaning of a word in a particular context is a task known as word sense disambiguation (WSD), which is essential for many natural language processing applications such as machine translation, information retrieval, and others. While most current WSD methods try to disambiguate a small number of words for which enough annotated examples are available, the method proposed in this thesis attempts to address all words in unrestricted text. The method is based on constraints imposed by syntactic dependencies and concept generalizations drawn from an external dictionary. The method was tested on standard benchmarks as used during the SENSEVAL-2 and SENSEVAL-3 WSD international evaluation exercises, and was found to be competitive.

I start with a brief introduction to the graphics processing unit (GPU) as well as general-purpose computation on modern graphics hardware (GPGPU). Next, I explore the motivations for GPGPU programming, and the capabilities of modern GPUs (including advantages and disadvantages). Also, I give the background required for further exploring GPU programming, including the terminology used and the resources available. Finally, I include a comprehensive survey of previous and current GPGPU work, and end with a look at the future of GPU programming.

Non-strict pure functional programming often requires redesigning algorithms and data structures to work more effectively under new constraints of non-strict evaluation and immutable state. Graph drawing algorithms, while numerous and broadly studied, have no presence in the non-strict pure functional programming model. Additionally, there is currently no freely licensed standalone toolkit used to quantitatively analyze aesthetics of graph drawings. This thesis addresses two previously unexplored questions. Can a force-directed graph drawing algorithm be implemented in a non-strict functional language, such as Haskell, and still be practically usable? Can an easily extensible aesthetic measuring tool be implemented in a language such as Haskell and still be practically usable? The focus of the thesis is on implementing one of the simplest force-directed algorithms, that of Fruchterman and Reingold, and comparing its resulting aesthetics to those of a well-known C++ implementation of the same algorithm.

Power consumption is an important issue for mobile computers since they rely on the short life of batteries. Conservation techniques are commonly used in hardware design of such systems but network interface is a significant consumer of power, which needs considerable research to be devoted towards designing a low-power design network protocol stack. Due to the dynamic nature of wireless networks, adaptations are necessary to achieve energy efficiency and a reasonable quality of service. This paper presents the application of energy-efficient techniques to each layer in the network protocol stack and a feedback is provided depending on the performance of this new design. And also a comparison of two existing MAC protocols is done showing a better suitability of E2MAC for higher power conservation. Multimedia applications can achieve an optimal performance if they are aware of the characteristics of the wireless link. Relying on the underlying operating system software and communication protocols to hide the anomalies of wireless channel needs efficient energy consumption methodology and fair quality of service like E2MAC. This report also focuses on some of the various concerns of energy efficiency in wireless communication and also looks into the definition of seven layers as defined by International …

Abstract This dissertation investigates authentication techniques in some emerging areas. Specifically, authentication schemes have been proposed that are well-suited for embedded systems, and privacy-respecting pay Web sites. With embedded systems, a person could own several devices which are capable of communication and interaction, but these devices use embedded processors whose computational capabilities are limited as compared to desktop computers. Examples of this scenario include entertainment devices or appliances owned by a consumer, multiple control and sensor systems in an automobile or airplane, and environmental controls in a building. An efficient public key cryptosystem has been devised, which provides a complete solution to an embedded system, including protocols for authentication, authenticated key exchange, encryption, and revocation. The new construction is especially suitable for the devices with constrained computing capabilities and resources. Compared with other available authentication schemes, such as X.509, identity-based encryption, etc, the new construction provides unique features such as simplicity, efficiency, forward secrecy, and an efficient re-keying mechanism. In the application scenario for a pay Web site, users may be sensitive about their privacy, and do not wish their behaviors to be tracked by Web sites. Thus, an anonymous authentication scheme is desirable in this case. That is, a …