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  Partner: UNT Libraries
 Degree Discipline: Computer Science
 Degree Level: Doctoral
 Collection: UNT Theses and Dissertations
Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos

Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos

Date: December 2015
Creator: Dahal, Ashok
Description: There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames ...
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Computational Methods for Discovering and Analyzing Causal Relationships in Health Data

Computational Methods for Discovering and Analyzing Causal Relationships in Health Data

Date: August 2015
Creator: Liang, Yiheng
Description: Publicly available datasets in health science are often large and observational, in contrast to experimental datasets where a small number of data are collected in controlled experiments. Variables' causal relationships in the observational dataset are yet to be determined. However, there is a significant interest in health science to discover and analyze causal relationships from health data since identified causal relationships will greatly facilitate medical professionals to prevent diseases or to mitigate the negative effects of the disease. Recent advances in Computer Science, particularly in Bayesian networks, has initiated a renewed interest for causality research. Causal relationships can be possibly discovered through learning the network structures from data. However, the number of candidate graphs grows in a more than exponential rate with the increase of variables. Exact learning for obtaining the optimal structure is thus computationally infeasible in practice. As a result, heuristic approaches are imperative to alleviate the difficulty of computations. This research provides effective and efficient learning tools for local causal discoveries and novel methods of learning causal structures with a combination of background knowledge. Specifically in the direction of constraint based structural learning, polynomial-time algorithms for constructing causal structures are designed with first-order conditional independence. Algorithms of ...
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Computational Methods for Vulnerability Analysis and Resource Allocation in Public Health Emergencies

Computational Methods for Vulnerability Analysis and Resource Allocation in Public Health Emergencies

Date: August 2015
Creator: Indrakanti, Saratchandra
Description: POD (Point of Dispensing)-based emergency response plans involving mass prophylaxis may seem feasible when considering the choice of dispensing points within a region, overall population density, and estimated traffic demands. However, the plan may fail to serve particular vulnerable sub-populations, resulting in access disparities during emergency response. Federal authorities emphasize on the need to identify sub-populations that cannot avail regular services during an emergency due to their special needs to ensure effective response. Vulnerable individuals require the targeted allocation of appropriate resources to serve their special needs. Devising schemes to address the needs of vulnerable sub-populations is essential for the effectiveness of response plans. This research focuses on data-driven computational methods to quantify and address vulnerabilities in response plans that require the allocation of targeted resources. Data-driven methods to identify and quantify vulnerabilities in response plans are developed as part of this research. Addressing vulnerabilities requires the targeted allocation of appropriate resources to PODs. The problem of resource allocation to PODs during public health emergencies is introduced and the variants of the resource allocation problem such as the spatial allocation, spatio-temporal allocation and optimal resource subset variants are formulated. Generating optimal resource allocation and scheduling solutions can be computationally hard ...
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Investigation on Segmentation, Recognition and 3D Reconstruction of Objects Based on Lidar Data Or Mri

Investigation on Segmentation, Recognition and 3D Reconstruction of Objects Based on Lidar Data Or Mri

Date: May 2015
Creator: Tang, Shijun
Description: Segmentation, recognition and 3D reconstruction of objects have been cutting-edge research topics, which have many applications ranging from environmental and medical to geographical applications as well as intelligent transportation. In this dissertation, I focus on the study of segmentation, recognition and 3D reconstruction of objects using LiDAR data/MRI. Three main works are that (I). Feature extraction algorithm based on sparse LiDAR data. A novel method has been proposed for feature extraction from sparse LiDAR data. The algorithm and the related principles have been described. Also, I have tested and discussed the choices and roles of parameters. By using correlation of neighboring points directly, statistic distribution of normal vectors at each point has been effectively used to determine the category of the selected point. (II). Segmentation and 3D reconstruction of objects based on LiDAR/MRI. The proposed method includes that the 3D LiDAR data are layered, that different categories are segmented, and that 3D canopy surfaces of individual tree crowns and clusters of trees are reconstructed from LiDAR point data based on a region active contour model. The proposed method allows for delineations of 3D forest canopy naturally from the contours of raw LiDAR point clouds. The proposed model is suitable not ...
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SEM Predicting Success of Student Global Software Development Teams

SEM Predicting Success of Student Global Software Development Teams

Date: May 2015
Creator: Brooks, Ian Robert
Description: The extensive use of global teams to develop software has prompted researchers to investigate various factors that can enhance a team’s performance. While a significant body of research exists on global software teams, previous research has not fully explored the interrelationships and collective impact of various factors on team performance. This study explored a model that added the characteristics of a team’s culture, ability, communication frequencies, response rates, and linguistic categories to a central framework of team performance. Data was collected from two student software development projects that occurred between teams located in the United States, Panama, and Turkey. The data was obtained through online surveys and recorded postings of team activities that occurred throughout the global software development projects. Partial least squares path modeling (PLS-PM) was chosen as the analytic technique to test the model and identify the most influential factors. Individual factors associated with response rates and linguistic characteristics proved to significantly affect a team’s activity related to grade on the project, group cohesion, and the number of messages received and sent. Moreover, an examination of possible latent homogeneous segments in the model supported the existence of differences among groups based on leadership style. Teams with assigned leaders ...
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Video Analytics with Spatio-Temporal Characteristics of Activities

Video Analytics with Spatio-Temporal Characteristics of Activities

Date: May 2015
Creator: Cheng, Guangchun
Description: As video capturing devices become more ubiquitous from surveillance cameras to smart phones, the demand of automated video analysis is increasing as never before. One obstacle in this process is to efficiently locate where a human operator’s attention should be, and another is to determine the specific types of activities or actions without ambiguity. It is the special interest of this dissertation to locate spatial and temporal regions of interest in videos and to develop a better action representation for video-based activity analysis. This dissertation follows the scheme of “locating then recognizing” activities of interest in videos, i.e., locations of potentially interesting activities are estimated before performing in-depth analysis. Theoretical properties of regions of interest in videos are first exploited, based on which a unifying framework is proposed to locate both spatial and temporal regions of interest with the same settings of parameters. The approach estimates the distribution of motion based on 3D structure tensors, and locates regions of interest according to persistent occurrences of low probability. Two contributions are further made to better represent the actions. The first is to construct a unifying model of spatio-temporal relationships between reusable mid-level actions which bridge low-level pixels and high-level activities. Dense ...
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Autonomic Failure Identification and Diagnosis for Building Dependable Cloud Computing Systems

Autonomic Failure Identification and Diagnosis for Building Dependable Cloud Computing Systems

Date: May 2014
Creator: Guan, Qiang
Description: The increasingly popular cloud-computing paradigm provides on-demand access to computing and storage with the appearance of unlimited resources. Users are given access to a variety of data and software utilities to manage their work. Users rent virtual resources and pay for only what they use. In spite of the many benefits that cloud computing promises, the lack of dependability in shared virtualized infrastructures is a major obstacle for its wider adoption, especially for mission-critical applications. Virtualization and multi-tenancy increase system complexity and dynamicity. They introduce new sources of failure degrading the dependability of cloud computing systems. To assure cloud dependability, in my dissertation research, I develop autonomic failure identification and diagnosis techniques that are crucial for understanding emergent, cloud-wide phenomena and self-managing resource burdens for cloud availability and productivity enhancement. We study the runtime cloud performance data collected from a cloud test-bed and by using traces from production cloud systems. We define cloud signatures including those metrics that are most relevant to failure instances. We exploit profiled cloud performance data in both time and frequency domain to identify anomalous cloud behaviors and leverage cloud metric subspace analysis to automate the diagnosis of observed failures. We implement a prototype of the ...
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Performance Engineering of Software Web Services and Distributed Software Systems

Performance Engineering of Software Web Services and Distributed Software Systems

Date: May 2014
Creator: Lin, Chia-en
Description: The promise of service oriented computing, and the availability of Web services promote the delivery and creation of new services based on existing services, in order to meet new demands and new markets. As Web and internet based services move into Clouds, inter-dependency of services and their complexity will increase substantially. There are standards and frameworks for specifying and composing Web Services based on functional properties. However, mechanisms to individually address non-functional properties of services and their compositions have not been well established. Furthermore, the Cloud ontology depicts service layers from a high-level, such as Application and Software, to a low-level, such as Infrastructure and Platform. Each component that resides in one layer can be useful to another layer as a service. It hints at the amount of complexity resulting from not only horizontal but also vertical integrations in building and deploying a composite service. To meet the requirements and facilitate using Web services, we first propose a WSDL extension to permit specification of non-functional or Quality of Service (QoS) properties. On top of the foundation, the QoS-aware framework is established to adapt publicly available tools for Web services, augmented by ontology management tools, along with tools for performance modeling ...
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Boosting for Learning From Imbalanced, Multiclass Data Sets

Boosting for Learning From Imbalanced, Multiclass Data Sets

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Date: December 2013
Creator: Abouelenien, Mohamed
Description: In many real-world applications, it is common to have uneven number of examples among multiple classes. The data imbalance, however, usually complicates the learning process, especially for the minority classes, and results in deteriorated performance. Boosting methods were proposed to handle the imbalance problem. These methods need elongated training time and require diversity among the classifiers of the ensemble to achieve improved performance. Additionally, extending the boosting method to handle multi-class data sets is not straightforward. Examples of applications that suffer from imbalanced multi-class data can be found in face recognition, where tens of classes exist, and in capsule endoscopy, which suffers massive imbalance between the classes. This dissertation introduces RegBoost, a new boosting framework to address the imbalanced, multi-class problems. This method applies a weighted stratified sampling technique and incorporates a regularization term that accommodates multi-class data sets and automatically determines the error bound of each base classifier. The regularization parameter penalizes the classifier when it misclassifies instances that were correctly classified in the previous iteration. The parameter additionally reduces the bias towards majority classes. Experiments are conducted using 12 diverse data sets with moderate to high imbalance ratios. The results demonstrate superior performance of the proposed method compared ...
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Simulating the Spread of Infectious Diseases in Heterogeneous Populations with Diverse Interactions Characteristics

Simulating the Spread of Infectious Diseases in Heterogeneous Populations with Diverse Interactions Characteristics

Date: December 2013
Creator: Gomez-Lopez, Iris Nelly
Description: The spread of infectious diseases has been a public concern throughout human history. Historic recorded data has reported the severity of infectious disease epidemics in different ages. Ancient Greek physician Hippocrates was the first to analyze the correlation between diseases and their environment. Nowadays, health authorities are in charge of planning strategies that guarantee the welfare of citizens. The simulation of contagion scenarios contributes to the understanding of the epidemic behavior of diseases. Computational models facilitate the study of epidemics by integrating disease and population data to the simulation. The use of detailed demographic and geographic characteristics allows researchers to construct complex models that better resemble reality and the integration of these attributes permits us to understand the rules of interaction. The interaction of individuals with similar characteristics forms synthetic structures that depict clusters of interaction. The synthetic environments facilitate the study of the spread of infectious diseases in diverse scenarios. The characteristics of the population and the disease concurrently affect the local and global epidemic progression. Every cluster’ epidemic behavior constitutes the global epidemic for a clustered population. By understanding the correlation between structured populations and the spread of a disease, current dissertation research makes possible to identify risk ...
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Framework for Evaluating Dynamic Memory Allocators Including a New Equivalence Class Based Cache-conscious Allocator

Framework for Evaluating Dynamic Memory Allocators Including a New Equivalence Class Based Cache-conscious Allocator

Date: August 2013
Creator: Janjusic, Tomislav
Description: Software applications’ performance is hindered by a variety of factors, but most notably by the well-known CPU-memory speed gap (often known as the memory wall). This results in the CPU sitting idle waiting for data to be brought from memory to processor caches. The addressing used by caches cause non-uniform accesses to various cache sets. The non-uniformity is due to several reasons, including how different objects are accessed by the code and how the data objects are located in memory. Memory allocators determine where dynamically created objects are placed, thus defining addresses and their mapping to cache locations. It is important to evaluate how different allocators behave with respect to the localities of the created objects. Most allocators use a single attribute, the size, of an object in making allocation decisions. Additional attributes such as the placement with respect to other objects, or specific cache area may lead to better use of cache memories. In this dissertation, we proposed and implemented a framework that allows for the development and evaluation of new memory allocation techniques. At the root of the framework is a memory tracing tool called Gleipnir, which provides very detailed information about every memory access, and relates it ...
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Modeling and Analysis of Next Generation 9-1-1 Emergency Medical Dispatch Protocols

Modeling and Analysis of Next Generation 9-1-1 Emergency Medical Dispatch Protocols

Date: August 2013
Creator: Gupta, Neeraj Kant
Description: Emergency Medical Dispatch Protocols are guidelines that a 9-1-1 dispatcher uses to evaluate the nature of emergency, resources to send and the nature of help provided to the 9-1-1 caller. The current Dispatch Protocols are based on voice only call. But the Next Generation 9-1-1 (NG9-1-1) architecture will allow multimedia emergency calls. In this thesis I analyze and model the Emergency Medical Dispatch Protocols for NG9-1-1 architecture. I have identified various technical aspects to improve the NG9-1-1 Dispatch Protocols. The devices (smartphone) at the caller end have advanced to a point where they can be used to send and receive video, pictures and text. There are sensors embedded in them that can be used for initial diagnosis of the injured person. There is a need to improve the human computer (smartphone) interface to take advantage of technology so that callers can easily make use of various features available to them. The dispatchers at the 9-1-1 call center can make use of these new protocols to improve the quality and the response time. They will have capability of multiple media streams to interact with the caller and the first responders.The specific contributions in this thesis include developing applications that use smartphone ...
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Multilingual Word Sense Disambiguation Using Wikipedia

Multilingual Word Sense Disambiguation Using Wikipedia

Date: August 2013
Creator: Dandala, Bharath
Description: Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three different approaches. Instead of using a predefined monolingual sense inventory such as WordNet, we use a language-independent framework where the word senses and sense-tagged data are derived automatically from Wikipedia. Using Wikipedia as a source of sense-annotations provides the much needed solution for knowledge acquisition bottleneck. In order to evaluate the viability of Wikipedia based sense-annotations, we cast the task of disambiguating polysemous nouns as a monolingual classification task and experimented on lexical samples from four different languages (viz. English, German, Italian and Spanish). The experiments confirm that the Wikipedia based sense annotations are reliable and can be used to construct accurate monolingual sense classifiers. ...
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Privacy Management for Online Social Networks

Privacy Management for Online Social Networks

Date: August 2013
Creator: Baatarjav, Enkh-Amgalan
Description: One in seven people in the world use online social networking for a variety of purposes -- to keep in touch with friends and family, to share special occasions, to broadcast announcements, and more. The majority of society has been bought into this new era of communication technology, which allows everyone on the internet to share information with friends. Since social networking has rapidly become a main form of communication, holes in privacy have become apparent. It has come to the point that the whole concept of sharing information requires restructuring. No longer are online social networks simply technology available for a niche market; they are in use by all of society. Thus it is important to not forget that a sense of privacy is inherent as an evolutionary by-product of social intelligence. In any context of society, privacy needs to be a part of the system in order to help users protect themselves from others. This dissertation attempts to address the lack of privacy management in online social networks by designing models which understand the social science behind how we form social groups and share information with each other. Social relationship strength was modeled using activity patterns, vocabulary usage, ...
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Real-time Rendering of Burning Objects in Video Games

Real-time Rendering of Burning Objects in Video Games

Date: August 2013
Creator: Amarasinghe, Dhanyu Eshaka
Description: In recent years there has been growing interest in limitless realism in computer graphics applications. Among those, my foremost concentration falls into the complex physical simulations and modeling with diverse applications for the gaming industry. Different simulations have been virtually successful by replicating the details of physical process. As a result, some were strong enough to lure the user into believable virtual worlds that could destroy any sense of attendance. In this research, I focus on fire simulations and its deformation process towards various virtual objects. In most game engines model loading takes place at the beginning of the game or when the game is transitioning between levels. Game models are stored in large data structures. Since changing or adjusting a large data structure while the game is proceeding may adversely affect the performance of the game. Therefore, developers may choose to avoid procedural simulations to save resources and avoid interruptions on performance. I introduce a process to implement a real-time model deformation while maintaining performance. It is a challenging task to achieve high quality simulation while utilizing minimum resources to represent multiple events in timely manner. Especially in video games, this overwhelming criterion would be robust enough to sustain ...
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Optimizing Non-pharmaceutical Interventions Using Multi-coaffiliation Networks

Optimizing Non-pharmaceutical Interventions Using Multi-coaffiliation Networks

Date: May 2013
Creator: Loza, Olivia G.
Description: Computational modeling is of fundamental significance in mapping possible disease spread, and designing strategies for its mitigation. Conventional contact networks implement the simulation of interactions as random occurrences, presenting public health bodies with a difficult trade off between a realistic model granularity and robust design of intervention strategies. Recently, researchers have been investigating the use of agent-based models (ABMs) to embrace the complexity of real world interactions. At the same time, theoretical approaches provide epidemiologists with general optimization models in which demographics are intrinsically simplified. The emerging study of affiliation networks and co-affiliation networks provide an alternative to such trade off. Co-affiliation networks maintain the realism innate to ABMs while reducing the complexity of contact networks into distinctively smaller k-partite graphs, were each partition represent a dimension of the social model. This dissertation studies the optimization of intervention strategies for infectious diseases, mainly distributed in school systems. First, concepts of synthetic populations and affiliation networks are extended to propose a modified algorithm for the synthetic reconstruction of populations. Second, the definition of multi-coaffiliation networks is presented as the main social model in which risk is quantified and evaluated, thereby obtaining vulnerability indications for each school in the system. Finally, maximization ...
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3D Reconstruction Using Lidar and Visual Images

3D Reconstruction Using Lidar and Visual Images

Date: December 2012
Creator: Duraisamy, Prakash
Description: In this research, multi-perspective image registration using LiDAR and visual images was considered. 2D-3D image registration is a difficult task because it requires the extraction of different semantic features from each modality. This problem is solved in three parts. The first step involves detection and extraction of common features from each of the data sets. The second step consists of associating the common features between two different modalities. Traditional methods use lines or orthogonal corners as common features. The third step consists of building the projection matrix. Many existing methods use global positing system (GPS) or inertial navigation system (INS) for an initial estimate of the camera pose. However, the approach discussed herein does not use GPS, INS, or any such devices for initial estimate; hence the model can be used in places like the lunar surface or Mars where GPS or INS are not available. A variation of the method is also described, which does not require strong features from both images but rather uses intensity gradients in the image. This can be useful when one image does not have strong features (such as lines) or there are too many extraneous features.
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Multi-perspective, Multi-modal Image Registration and Fusion

Multi-perspective, Multi-modal Image Registration and Fusion

Date: August 2012
Creator: Belkhouche, Mohammed Yassine
Description: Multi-modal image fusion is an active research area with many civilian and military applications. Fusion is defined as strategic combination of information collected by various sensors from different locations or different types in order to obtain a better understanding of an observed scene or situation. Fusion of multi-modal images cannot be completed unless these two modalities are spatially aligned. In this research, I consider two important problems. Multi-modal, multi-perspective image registration and decision level fusion of multi-modal images. In particular, LiDAR and visual imagery. Multi-modal image registration is a difficult task due to the different semantic interpretation of features extracted from each modality. This problem is decoupled into three sub-problems. The first step is identification and extraction of common features. The second step is the determination of corresponding points. The third step consists of determining the registration transformation parameters. Traditional registration methods use low level features such as lines and corners. Using these features require an extensive optimization search in order to determine the corresponding points. Many methods use global positioning systems (GPS), and a calibrated camera in order to obtain an initial estimate of the camera parameters. The advantages of our work over the previous works are the following. ...
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GPS CaPPture: a System for GPS Trajectory Collection, Processing, and Destination Prediction

GPS CaPPture: a System for GPS Trajectory Collection, Processing, and Destination Prediction

Date: May 2012
Creator: Griffin, Terry W.
Description: In the United States, smartphone ownership surpassed 69.5 million in February 2011 with a large portion of those users (20%) downloading applications (apps) that enhance the usability of a device by adding additional functionality. a large percentage of apps are written specifically to utilize the geographical position of a mobile device. One of the prime factors in developing location prediction models is the use of historical data to train such a model. with larger sets of training data, prediction algorithms become more accurate; however, the use of historical data can quickly become a downfall if the GPS stream is not collected or processed correctly. Inaccurate or incomplete or even improperly interpreted historical data can lead to the inability to develop accurately performing prediction algorithms. As GPS chipsets become the standard in the ever increasing number of mobile devices, the opportunity for the collection of GPS data increases remarkably. the goal of this study is to build a comprehensive system that addresses the following challenges: (1) collection of GPS data streams in a manner such that the data is highly usable and has a reduction in errors; (2) processing and reduction of the collected data in order to prepare it and ...
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Investigating the Extractive Summarization of Literary Novels

Investigating the Extractive Summarization of Literary Novels

Date: December 2011
Creator: Ceylan, Hakan
Description: Abstract Due to the vast amount of information we are faced with, summarization has become a critical necessity of everyday human life. Given that a large fraction of the electronic documents available online and elsewhere consist of short texts such as Web pages, news articles, scientific reports, and others, the focus of natural language processing techniques to date has been on the automation of methods targeting short documents. We are witnessing however a change: an increasingly larger number of books become available in electronic format. This means that the need for language processing techniques able to handle very large documents such as books is becoming increasingly important. This thesis addresses the problem of summarization of novels, which are long and complex literary narratives. While there is a significant body of research that has been carried out on the task of automatic text summarization, most of this work has been concerned with the summarization of short documents, with a particular focus on news stories. However, novels are different in both length and genre, and consequently different summarization techniques are required. This thesis attempts to close this gap by analyzing a new domain for summarization, and by building unsupervised and supervised systems ...
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Measuring Semantic Relatedness Using Salient Encyclopedic Concepts

Measuring Semantic Relatedness Using Salient Encyclopedic Concepts

Date: August 2011
Creator: Hassan, Samer
Description: While pragmatics, through its integration of situational awareness and real world relevant knowledge, offers a high level of analysis that is suitable for real interpretation of natural dialogue, semantics, on the other end, represents a lower yet more tractable and affordable linguistic level of analysis using current technologies. Generally, the understanding of semantic meaning in literature has revolved around the famous quote ``You shall know a word by the company it keeps''. In this thesis we investigate the role of context constituents in decoding the semantic meaning of the engulfing context; specifically we probe the role of salient concepts, defined as content-bearing expressions which afford encyclopedic definitions, as a suitable source of semantic clues to an unambiguous interpretation of context. Furthermore, we integrate this world knowledge in building a new and robust unsupervised semantic model and apply it to entail semantic relatedness between textual pairs, whether they are words, sentences or paragraphs. Moreover, we explore the abstraction of semantics across languages and utilize our findings into building a novel multi-lingual semantic relatedness model exploiting information acquired from various languages. We demonstrate the effectiveness and the superiority of our mono-lingual and multi-lingual models through a comprehensive set of evaluations on specialized ...
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Toward a Data-Type-Based Real Time Geospatial Data Stream Management System

Toward a Data-Type-Based Real Time Geospatial Data Stream Management System

Date: May 2011
Creator: Zhang, Chengyang
Description: The advent of sensory and communication technologies enables the generation and consumption of large volumes of streaming data. Many of these data streams are geo-referenced. Existing spatio-temporal databases and data stream management systems are not capable of handling real time queries on spatial extents. In this thesis, we investigated several fundamental research issues toward building a data-type-based real time geospatial data stream management system. The thesis makes contributions in the following areas: geo-stream data models, aggregation, window-based nearest neighbor operators, and query optimization strategies. The proposed geo-stream data model is based on second-order logic and multi-typed algebra. Both abstract and discrete data models are proposed and exemplified. I further propose two useful geo-stream operators, namely Region By and WNN, which abstract common aggregation and nearest neighbor queries as generalized data model constructs. Finally, I propose three query optimization algorithms based on spatial, temporal, and spatio-temporal constraints of geo-streams. I show the effectiveness of the data model through many query examples. The effectiveness and the efficiency of the algorithms are validated through extensive experiments on both synthetic and real data sets. This work established the fundamental building blocks toward a full-fledged geo-stream database management system and has potential impact in many ...
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A Framework for Analyzing and Optimizing Regional Bio-Emergency Response Plans

A Framework for Analyzing and Optimizing Regional Bio-Emergency Response Plans

Date: December 2010
Creator: Schneider, Tamara
Description: The presence of naturally occurring and man-made public health threats necessitate the design and implementation of mitigation strategies, such that adequate response is provided in a timely manner. Since multiple variables, such as geographic properties, resource constraints, and government mandated time-frames must be accounted for, computational methods provide the necessary tools to develop contingency response plans while respecting underlying data and assumptions. A typical response scenario involves the placement of points of dispensing (PODs) in the affected geographic region to supply vaccines or medications to the general public. Computational tools aid in the analysis of such response plans, as well as in the strategic placement of PODs, such that feasible response scenarios can be developed. Due to the sensitivity of bio-emergency response plans, geographic information, such as POD locations, must be kept confidential. The generation of synthetic geographic regions allows for the development of emergency response plans on non-sensitive data, as well as for the study of the effects of single geographic parameters. Further, synthetic representations of geographic regions allow for results to be published and evaluated by the scientific community. This dissertation presents methodology for the analysis of bio-emergency response plans, methods for plan optimization, as well as methodology ...
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Socioscope: Human Relationship and Behavior Analysis in Mobile Social Networks

Socioscope: Human Relationship and Behavior Analysis in Mobile Social Networks

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Date: August 2010
Creator: Zhang, Huiqi
Description: The widely used mobile phone, as well as its related technologies had opened opportunities for a complete change on how people interact and build relationship across geographic and time considerations. The convenience of instant communication by mobile phones that broke the barrier of space and time is evidently the key motivational point on why such technologies so important in people's life and daily activities. Mobile phones have become the most popular communication tools. Mobile phone technology is apparently changing our relationship to each other in our work and lives. The impact of new technologies on people's lives in social spaces gives us the chance to rethink the possibilities of technologies in social interaction. Accordingly, mobile phones are basically changing social relations in ways that are intricate to measure with any precision. In this dissertation I propose a socioscope model for social network, relationship and human behavior analysis based on mobile phone call detail records. Because of the diversities and complexities of human social behavior, one technique cannot detect different features of human social behaviors. Therefore I use multiple probability and statistical methods for quantifying social groups, relationships and communication patterns, for predicting social tie strengths and for detecting human behavior ...
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