UNT Libraries - 46 Matching Results

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Content and Temporal Analysis of Communications to Predict Task Cohesion in Software Development Global Teams

Description: Virtual teams in industry are increasingly being used to develop software, create products, and accomplish tasks. However, analyzing those collaborations under same-time/different-place conditions is well-known to be difficult. In order to overcome some of these challenges, this research was concerned with the study of collaboration-based, content-based and temporal measures and their ability to predict cohesion within global software development projects. Messages were collected from three software development projects that involved students from two different countries. The similarities and quantities of these interactions were computed and analyzed at individual and group levels. Results of interaction-based metrics showed that the collaboration variables most related to Task Cohesion were Linguistic Style Matching and Information Exchange. The study also found that Information Exchange rate and Reply rate have a significant and positive correlation to Task Cohesion, a factor used to describe participants' engagement in the global software development process. This relation was also found at the Group level. All these results suggest that metrics based on rate can be very useful for predicting cohesion in virtual groups. Similarly, content features based on communication categories were used to improve the identification of Task Cohesion levels. This model showed mixed results, since only Work similarity and Social rate were found to be correlated with Task Cohesion. This result can be explained by how a group's cohesiveness is often associated with fairness and trust, and that these two factors are often achieved by increased social and work communications. Also, at a group-level, all models were found correlated to Task Cohesion, specifically, Similarity+Rate, which suggests that models that include social and work communication categories are also good predictors of team cohesiveness. Finally, temporal interaction similarity measures were calculated to assess their prediction capabilities in a global setting. Results showed a significant negative correlation between the Pacing Rate and ...
Date: May 2017
Creator: Castro Hernandez, Alberto

Influence of Underlying Random Walk Types in Population Models on Resulting Social Network Types and Epidemiological Dynamics

Description: Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.
Date: December 2016
Creator: Kolgushev, Oleg Mikhailovich

Infusing Automatic Question Generation with Natural Language Understanding

Description: Automatically generating questions from text for educational purposes is an active research area in natural language processing. The automatic question generation system accompanying this dissertation is MARGE, which is a recursive acronym for: MARGE automatically reads generates and evaluates. MARGE generates questions from both individual sentences and the passage as a whole, and is the first question generation system to successfully generate meaningful questions from textual units larger than a sentence. Prior work in automatic question generation from text treats a sentence as a string of constituents to be rearranged into as many questions as allowed by English grammar rules. Consequently, such systems overgenerate and create mainly trivial questions. Further, none of these systems to date has been able to automatically determine which questions are meaningful and which are trivial. This is because the research focus has been placed on NLG at the expense of NLU. In contrast, the work presented here infuses the questions generation process with natural language understanding. From the input text, MARGE creates a meaning analysis representation for each sentence in a passage via the DeconStructure algorithm presented in this work. Questions are generated from sentence meaning analysis representations using templates. The generated questions are automatically evaluated for question quality and importance via a ranking algorithm.
Date: December 2016
Creator: Mazidi, Karen

Real Time Assessment of a Video Game Player's State of Mind Using Off-the-Shelf Electroencephalography

Description: The focus of this research is on the development of a real time application that uses a low cost EEG headset to measure a player's state of mind while they play a video game. Using data collected using the Emotiv EPOC headset, various EEG processing techniques are tested to find ways of measuring a person's engagement and arousal levels. The ability to measure a person's engagement and arousal levels provide an opportunity to develop a model that monitor a person's flow while playing video games. Identifying when certain events occur, like when the player dies, will make it easier to identify when a player has left a state of flow. The real time application Brainwave captures data from the wireless Emotiv EPOC headset. Brainwave converts the raw EEG data into more meaningful brainwave band frequencies. Utilizing the brainwave frequencies the program trains multiple machine learning algorithms with data designed to identify when the player dies. Brainwave runs while the player plays through a video gaming monitoring their engagement and arousal levels for changes that cause the player to leave a state of flow. Brainwave reports to researchers and developers when the player dies along with the identification of the players exit of the state of flow.
Date: December 2016
Creator: McMahan, Timothy

Evaluation Techniques and Graph-Based Algorithms for Automatic Summarization and Keyphrase Extraction

Description: Automatic text summarization and keyphrase extraction are two interesting areas of research which extend along natural language processing and information retrieval. They have recently become very popular because of their wide applicability. Devising generic techniques for these tasks is challenging due to several issues. Yet we have a good number of intelligent systems performing the tasks. As different systems are designed with different perspectives, evaluating their performances with a generic strategy is crucial. It has also become immensely important to evaluate the performances with minimal human effort. In our work, we focus on designing a relativized scale for evaluating different algorithms. This is our major contribution which challenges the traditional approach of working with an absolute scale. We consider the impact of some of the environment variables (length of the document, references, and system-generated outputs) on the performance. Instead of defining some rigid lengths, we show how to adjust to their variations. We prove a mathematically sound baseline that should work for all kinds of documents. We emphasize automatically determining the syntactic well-formedness of the structures (sentences). We also propose defining an equivalence class for each unit (e.g. word) instead of the exact string matching strategy. We show an evaluation approach that considers the weighted relatedness of multiple references to adjust to the degree of disagreements between the gold standards. We publish the proposed approach as a free tool so that other systems can use it. We have also accumulated a dataset (scientific articles) with a reference summary and keyphrases for each document. Our approach is applicable not only for evaluating single-document based tasks but also for evaluating multiple-document based tasks. We have tested our evaluation method for three intrinsic tasks (taken from DUC 2004 conference), and in all three cases, it correlates positively with ROUGE. Based on our experiments ...
Date: August 2016
Creator: Hamid, Fahmida

New Frameworks for Secure Image Communication in the Internet of Things (IoT)

Description: The continuous expansion of technology, broadband connectivity and the wide range of new devices in the IoT cause serious concerns regarding privacy and security. In addition, in the IoT a key challenge is the storage and management of massive data streams. For example, there is always the demand for acceptable size with the highest quality possible for images to meet the rapidly increasing number of multimedia applications. The effort in this dissertation contributes to the resolution of concerns related to the security and compression functions in image communications in the Internet of Thing (IoT), due to the fast of evolution of IoT. This dissertation proposes frameworks for a secure digital camera in the IoT. The objectives of this dissertation are twofold. On the one hand, the proposed framework architecture offers a double-layer of protection: encryption and watermarking that will address all issues related to security, privacy, and digital rights management (DRM) by applying a hardware architecture of the state-of-the-art image compression technique Better Portable Graphics (BPG), which achieves high compression ratio with small size. On the other hand, the proposed framework of SBPG is integrated with the Digital Camera. Thus, the proposed framework of SBPG integrated with SDC is suitable for high performance imaging in the IoT, such as Intelligent Traffic Surveillance (ITS) and Telemedicine. Due to power consumption, which has become a major concern in any portable application, a low-power design of SBPG is proposed to achieve an energy- efficient SBPG design. As the visual quality of the watermarked and compressed images improves with larger values of PSNR, the results show that the proposed SBPG substantially increases the quality of the watermarked compressed images. Higher value of PSNR also shows how robust the algorithm is to different types of attack. From the results obtained for the energy- efficient SBPG ...
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Date: August 2016
Creator: Albalawi, Umar Abdalah S

Sensing and Decoding Brain States for Predicting and Enhancing Human Behavior, Health, and Security

Description: The human brain acts as an intelligent sensor by helping in effective signal communication and execution of logical functions and instructions, thus, coordinating all functions of the human body. More importantly, it shows the potential to combine prior knowledge with adaptive learning, thus ensuring constant improvement. These qualities help the brain to interact efficiently with both, the body (brain-body) as well as the environment (brain-environment). This dissertation attempts to apply the brain-body-environment interactions (BBEI) to elevate human existence and enhance our day-to-day experiences. For instance, when one stepped out of the house in the past, one had to carry keys (for unlocking), money (for purchasing), and a phone (for communication). With the advent of smartphones, this scenario changed completely and today, it is often enough to carry just one's smartphone because all the above activities can be performed with a single device. In the future, with advanced research and progress in BBEI interactions, one will be able to perform many activities by dictating it in one's mind without any physical involvement. This dissertation aims to shift the paradigm of existing brain-computer-interfaces from just ‘control' to ‘monitor, control, enhance, and restore' in three main areas - healthcare, transportation safety, and cryptography. In healthcare, measures were developed for understanding brain-body interactions by correlating cerebral autoregulation with brain signals. The variation in estimated blood flow of brain (obtained through EEG) was detected with evoked change in blood pressure, thus, enabling EEG metrics to be used as a first hand screening tool to check impaired cerebral autoregulation. To enhance road safety, distracted drivers' behavior in various multitasking scenarios while driving was identified by significant changes in the time-frequency spectrum of the EEG signals. A distraction metric was calculated to rank the severity of a distraction task that can be used as an intuitive measure ...
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Date: August 2016
Creator: Bajwa, Garima

Analysis and Optimization of Graphene FET based Nanoelectronic Integrated Circuits

Description: Like cell to the human body, transistors are the basic building blocks of any electronics circuits. Silicon has been the industries obvious choice for making transistors. Transistors with large size occupy large chip area, consume lots of power and the number of functionalities will be limited due to area constraints. Thus to make the devices smaller, smarter and faster, the transistors are aggressively scaled down in each generation. Moore's law states that the transistors count in any electronic circuits doubles every 18 months. Following this Moore's law, the transistor has already been scaled down to 14 nm. However there are limitations to how much further these transistors can be scaled down. Particularly below 10 nm, these silicon based transistors hit the fundamental limits like loss of gate control, high leakage and various other short channel effects. Thus it is not possible to favor the silicon transistors for future electronics applications. As a result, the research has shifted to new device concepts and device materials alternative to silicon. Carbon is the next abundant element found in the Earth and one of such carbon based nanomaterial is graphene. Graphene when extracted from Graphite, the same material used as the lid in pencil, have a tremendous potential to take future electronics devices to new heights in terms of size, cost and efficiency. Thus after its first experimental discovery of graphene in 2004, graphene has been the leading research area for both academics as well as industries. This dissertation is focused on the analysis and optimization of graphene based circuits for future electronics. The first part of this dissertation considers graphene based transistors for analog/radio frequency (RF) circuits. In this section, a dual gate Graphene Field Effect Transistor (GFET) is considered to build the case study circuits like voltage controlled oscillator (VCO) and low ...
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Date: May 2016
Creator: Joshi, Shital

Adaptive Power Management for Autonomic Resource Configuration in Large-scale Computer Systems

Description: In order to run and manage resource-intensive high-performance applications, large-scale computing and storage platforms have been evolving rapidly in various domains in both academia and industry. The energy expenditure consumed to operate and maintain these cloud computing infrastructures is a major factor to influence the overall profit and efficiency for most cloud service providers. Moreover, considering the mitigation of environmental damage from excessive carbon dioxide emission, the amount of power consumed by enterprise-scale data centers should be constrained for protection of the environment.Generally speaking, there exists a trade-off between power consumption and application performance in large-scale computing systems and how to balance these two factors has become an important topic for researchers and engineers in cloud and HPC communities. Therefore, minimizing the power usage while satisfying the Service Level Agreements have become one of the most desirable objectives in cloud computing research and implementation. Since the fundamental feature of the cloud computing platform is hosting workloads with a variety of characteristics in a consolidated and on-demand manner, it is demanding to explore the inherent relationship between power usage and machine configurations. Subsequently, with an understanding of these inherent relationships, researchers are able to develop effective power management policies to optimize productivity by balancing power usage and system performance. In this dissertation, we develop an autonomic power-aware system management framework for large-scale computer systems. We propose a series of techniques including coarse-grain power profiling, VM power modelling, power-aware resource auto-configuration and full-system power usage simulator. These techniques help us to understand the characteristics of power consumption of various system components. Based on these techniques, we are able to test various job scheduling strategies and develop resource management approaches to enhance the systems' power efficiency.
Date: August 2015
Creator: Zhang, Ziming

Advanced Power Amplifiers Design for Modern Wireless Communication

Description: Modern wireless communication systems use spectrally efficient modulation schemes to reach high data rate transmission. These schemes are generally involved with signals with high peak-to-average power ratio (PAPR). Moreover, the development of next generation wireless communication systems requires the power amplifiers to operate over a wide frequency band or multiple frequency bands to support different applications. These wide-band and multi-band solutions will lead to reductions in both the size and cost of the whole system. This dissertation presents several advanced power amplifier solutions to provide wide-band and multi-band operations with efficiency improvement at power back-offs.
Date: August 2015
Creator: Shao, Jin

Predictive Modeling for Persuasive Ambient Technology

Description: Computer scientists are increasingly aware of the power of ubiquitous computing systems that can display information in and about the user's environment. One sub category of ubiquitous computing is persuasive ambient information systems that involve an informative display transitioning between the periphery and center of attention. The goal of this ambient technology is to produce a behavior change, implying that a display must be informative, unobtrusive, and persuasive. While a significant body of research exists on ambient technology, previous research has not fully explored the different measures to identify behavior change, evaluation techniques for linking design characteristics to visual effectiveness, nor the use of short-term goals to affect long-term behavior change. This study uses the unique context of noise-induced hearing loss (NIHL) among collegiate musicians to explore these issues through developing the MIHL Reduction Feedback System that collects real-time data, translates it into visuals for music classrooms, provides predictive outcomes for goalsetting persuasion, and provides statistical measures of behavior change.
Date: August 2015
Creator: Powell, Jason W.

The Procedural Generation of Interesting Sokoban Levels

Description: As video games continue to become larger, more complex, and more costly to produce, research into methods to make game creation easier and faster becomes more valuable. One such research topic is procedural generation, which allows the computer to assist in the creation of content. This dissertation presents a new algorithm for the generation of Sokoban levels. Sokoban is a grid-based transport puzzle which is computational interesting due to being PSPACE-complete. Beyond just generating levels, the question of whether or not the levels created by this algorithm are interesting to human players is explored. A study was carried out comparing player attention while playing hand made levels versus their attention during procedurally generated levels. An auditory Stroop test was used to measure attention without disrupting play.
Date: May 2015
Creator: Taylor, Joshua

Space and Spectrum Engineered High Frequency Components and Circuits

Description: With the increasing demand on wireless and portable devices, the radio frequency front end blocks are required to feature properties such as wideband, high frequency, multiple operating frequencies, low cost and compact size. However, the current radio frequency system blocks are designed by combining several individual frequency band blocks into one functional block, which increase the cost and size of devices. To address these issues, it is important to develop novel approaches to further advance the current design methodologies in both space and spectrum domains. In recent years, the concept of artificial materials has been proposed and studied intensively in RF/Microwave, Terahertz, and optical frequency range. It is a combination of conventional materials such as air, wood, metal and plastic. It can achieve the material properties that have not been found in nature. Therefore, the artificial material (i.e. meta-materials) provides design freedoms to control both the spectrum performance and geometrical structures of radio frequency front end blocks and other high frequency systems. In this dissertation, several artificial materials are proposed and designed by different methods, and their applications to different high frequency components and circuits are studied. First, quasi-conformal mapping (QCM) method is applied to design plasmonic wave-adapters and couplers working at the optical frequency range. Second, inverse QCM method is proposed to implement flattened Luneburg lens antennas and parabolic antennas in the microwave range. Third, a dual-band compact directional coupler is realized by applying artificial transmission lines. In addition, a fully symmetrical coupler with artificial lumped element structure is also implemented. Finally, a tunable on-chip inductor, compact CMOS transmission lines, and metamaterial-based interconnects are proposed using artificial metal structures. All the proposed designs are simulated in full-wave 3D electromagnetic solvers, and the measurement results agree well with the simulation results. These artificial material-based novel design methodologies pave the way ...
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Date: May 2015
Creator: Arigong, Bayaner

Trajectory Analytics

Description: The numerous surveillance videos recorded by a single stationary wide-angle-view camera persuade the use of a moving point as the representation of each small-size object in wide video scene. The sequence of the positions of each moving point can be used to generate a trajectory containing both spatial and temporal information of object's movement. In this study, we investigate how the relationship between two trajectories can be used to recognize multi-agent interactions. For this purpose, we present a simple set of qualitative atomic disjoint trajectory-segment relations which can be utilized to represent the relationships between two trajectories. Given a pair of adjacent concurrent trajectories, we segment the trajectory pair to get the ordered sequence of related trajectory-segments. Each pair of corresponding trajectory-segments then is assigned a token associated with the trajectory-segment relation, which leads to the generation of a string called a pairwise trajectory-segment relationship sequence. From a group of pairwise trajectory-segment relationship sequences, we utilize an unsupervised learning algorithm, particularly the k-medians clustering, to detect interesting patterns that can be used to classify lower-level multi-agent activities. We evaluate the effectiveness of the proposed approach by comparing the activity classes predicted by our method to the actual classes from the ground-truth set obtained using the crowdsourcing technique. The results show that the relationships between a pair of trajectories can signify the low-level multi-agent activities.
Date: May 2015
Creator: Santiteerakul, Wasana

Exploration of Visual, Acoustic, and Physiological Modalities to Complement Linguistic Representations for Sentiment Analysis

Description: This research is concerned with the identification of sentiment in multimodal content. This is of particular interest given the increasing presence of subjective multimodal content on the web and other sources, which contains a rich and vast source of people's opinions, feelings, and experiences. Despite the need for tools that can identify opinions in the presence of diverse modalities, most of current methods for sentiment analysis are designed for textual data only, and few attempts have been made to address this problem. The dissertation investigates techniques for augmenting linguistic representations with acoustic, visual, and physiological features. The potential benefits of using these modalities include linguistic disambiguation, visual grounding, and the integration of information about people's internal states. The main goal of this work is to build computational resources and tools that allow sentiment analysis to be applied to multimodal data. This thesis makes three important contributions. First, it shows that modalities such as audio, video, and physiological data can be successfully used to improve existing linguistic representations for sentiment analysis. We present a method that integrates linguistic features with features extracted from these modalities. Features are derived from verbal statements, audiovisual recordings, thermal recordings, and physiological sensors signals. The resulting multimodal sentiment analysis system is shown to significantly outperform the use of language alone. Using this system, we were able to predict the sentiment expressed in video reviews and also the sentiment experienced by viewers while exposed to emotionally loaded content. Second, the thesis provides evidence of the portability of the developed strategies to other affect recognition problems. We provided support for this by studying the deception detection problem. Third, this thesis contributes several multimodal datasets that will enable further research in sentiment and deception detection.
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Date: December 2014
Creator: Pérez-Rosas, Verónica

Modeling and Analysis of Intentional And Unintentional Security Vulnerabilities in a Mobile Platform

Description: Mobile phones are one of the essential parts of modern life. Making a phone call is not the main purpose of a smart phone anymore, but merely one of many other features. Online social networking, chatting, short messaging, web browsing, navigating, and photography are some of the other features users enjoy in modern smartphones, most of which are provided by mobile apps. However, with this advancement, many security vulnerabilities have opened up in these devices. Malicious apps are a major threat for modern smartphones. According to Symantec Corp., by the middle of 2013, about 273,000 Android malware apps were identified. It is a complex issue to protect everyday users of mobile devices from the attacks of technologically competent hackers, illegitimate users, trolls, and eavesdroppers. This dissertation emphasizes the concept of intention identification. Then it looks into ways to utilize this intention identification concept to enforce security in a mobile phone platform. For instance, a battery monitoring app requiring SMS permissions indicates suspicious intention as battery monitoring usually does not need SMS permissions. Intention could be either the user's intention or the intention of an app. These intentions can be identified using their behavior or by using their source code. Regardless of the intention type, identifying it, evaluating it, and taking actions by using it to prevent any malicious intentions are the main goals of this research. The following four different security vulnerabilities are identified in this research: Malicious apps, spammers and lurkers in social networks, eavesdroppers in phone conversations, and compromised authentication. These four vulnerabilities are solved by detecting malware applications, identifying malicious users in a social network, enhancing the encryption system of a phone communication, and identifying user activities using electroencephalogram (EEG) for authentication. Each of these solutions are constructed using the idea of intention identification. Furthermore, many of ...
Date: December 2014
Creator: Fazeen, Mohamed & Issadeen, Mohamed

A New Look at Retargetable Compilers

Description: Consumers demand new and innovative personal computing devices every 2 years when their cellular phone service contracts are renewed. Yet, a 2 year development cycle for the concurrent development of both hardware and software is nearly impossible. As more components and features are added to the devices, maintaining this 2 year cycle with current tools will become commensurately harder. This dissertation delves into the feasibility of simplifying the development of such systems by employing heterogeneous systems on a chip in conjunction with a retargetable compiler such as the hybrid computer retargetable compiler (Hy-C). An example of a simple architecture description of sufficient detail for use with a retargetable compiler like Hy-C is provided. As a software engineer with 30 years of experience, I have witnessed numerous system failures. A plethora of software development paradigms and tools have been employed to prevent software errors, but none have been completely successful. Much discussion centers on software development in the military contracting market, as that is my background. The dissertation reviews those tools, as well as some existing retargetable compilers, in an attempt to determine how those errors occurred and how a system like Hy-C could assist in reducing future software errors. In the end, the potential for a simple retargetable solution like Hy-C is shown to be very simple, yet powerful enough to provide a very capable product in a very fast-growing market.
Date: December 2014
Creator: Burke, Patrick William

Trajectories As a Unifying Cross Domain Feature for Surveillance Systems

Description: Manual video analysis is apparently a tedious task. An efficient solution is of highly importance to automate the process and to assist operators. A major goal of video analysis is understanding and recognizing human activities captured by surveillance cameras, a very challenging problem; the activities can be either individual or interactional among multiple objects. It involves extraction of relevant spatial and temporal information from visual images. Most video analytics systems are constrained by specific environmental situations. Different domains may require different specific knowledge to express characteristics of interesting events. Spatial-temporal trajectories have been utilized to capture motion characteristics of activities. The focus of this dissertation is on how trajectories are utilized in assist in developing video analytic system in the context of surveillance. The research as reported in this dissertation begins real-time highway traffic monitoring and dynamic traffic pattern analysis and in the end generalize the knowledge to event and activity analysis in a broader context. The main contributions are: the use of the graph-theoretic dominant set approach to the classification of traffic trajectories; the ability to first partition the trajectory clusters using entry and exit point awareness to significantly improve the clustering effectiveness and to reduce the computational time and complexity in the on-line processing of new trajectories; A novel tracking method that uses the extended 3-D Hungarian algorithm with a Kalman filter to preserve the smoothness of motion; a novel camera calibration method to determine the second vanishing point with no operator assistance; and a logic reasoning framework together with a new set of context free LLEs which could be utilized across different domains. Additional efforts have been made for three comprehensive surveillance systems together with main contributions mentioned above.
Date: December 2014
Creator: Wan, Yiwen

Uncertainty Evaluation in Large-scale Dynamical Systems: Theory and Applications

Description: Significant research efforts have been devoted to large-scale dynamical systems, with the aim of understanding their complicated behaviors and managing their responses in real-time. One pivotal technological obstacle in this process is the existence of uncertainty. Although many of these large-scale dynamical systems function well in the design stage, they may easily fail when operating in realistic environment, where environmental uncertainties modulate system dynamics and complicate real-time predication and management tasks. This dissertation aims to develop systematic methodologies to evaluate the performance of large-scale dynamical systems under uncertainty, as a step toward real-time decision support. Two uncertainty evaluation approaches are pursued: the analytical approach and the effective simulation approach. The analytical approach abstracts the dynamics of original stochastic systems, and develops tractable analysis (e.g., jump-linear analysis) for the approximated systems. Despite the potential bias introduced in the approximation process, the analytical approach provides rich insights valuable for evaluating and managing the performance of large-scale dynamical systems under uncertainty. When a system’s complexity and scale are beyond tractable analysis, the effective simulation approach becomes very useful. The effective simulation approach aims to use a few smartly selected simulations to quickly evaluate a complex system’s statistical performance. This approach was originally developed to evaluate a single uncertain variable. This dissertation extends the approach to be scalable and effective for evaluating large-scale systems under a large-number of uncertain variables. While a large portion of this dissertation focuses on the development of generic methods and theoretical analysis that are applicable to broad large-scale dynamical systems, many results are illustrated through a representative large-scale system application on strategic air traffic management application, which is concerned with designing robust management plans subject to a wide range of weather possibilities at 2-15 hours look-ahead time.
Date: December 2014
Creator: Zhou, Yi (Software engineer)

A Computational Methodology for Addressing Differentiated Access of Vulnerable Populations During Biological Emergencies

Description: Mitigation response plans must be created to protect affected populations during biological emergencies resulting from the release of harmful biochemical substances. Medical countermeasures have been stockpiled by the federal government for such emergencies. However, it is the responsibility of local governments to maintain solid, functional plans to apply these countermeasures to the entire target population within short, mandated time frames. Further, vulnerabilities in the population may serve as barriers preventing certain individuals from participating in mitigation activities. Therefore, functional response plans must be capable of reaching vulnerable populations.Transportation vulnerability results from lack of access to transportation. Transportation vulnerable populations located too far from mitigation resources are at-risk of not being able to participate in mitigation activities. Quantification of these populations requires the development of computational methods to integrate spatial demographic data and transportation resource data from disparate sources into the context of planned mitigation efforts. Research described in this dissertation focuses on quantifying transportation vulnerable populations and maximizing participation in response efforts. Algorithms developed as part of this research are integrated into a computational framework to promote a transition from research and development to deployment and use by biological emergency planners.
Date: August 2014
Creator: O’Neill II, Martin Joseph

Modeling Epidemics on Structured Populations: Effects of Socio-demographic Characteristics and Immune Response Quality

Description: Epidemiologists engage in the study of the distribution and determinants of health-related states or events in human populations. Eventually, they will apply that study to prevent and control problems and contingencies associated with the health of the population. Due to the spread of new pathogens and the emergence of new bio-terrorism threats, it has become imperative to develop new and expand existing techniques to equip public health providers with robust tools to predict and control health-related crises. In this dissertation, I explore the effects caused in the disease dynamics by the differences in individuals’ physiology and social/behavioral characteristics. Multiple computational and mathematical models were developed to quantify the effect of those factors on spatial and temporal variations of the disease epidemics. I developed statistical methods to measure the effects caused in the outbreak dynamics by the incorporation of heterogeneous demographics and social interactions to the individuals of the population. Specifically, I studied the relationship between demographics and the physiological characteristics of an individual when preparing for an infectious disease epidemic.
Date: August 2014
Creator: Reyes Silveyra, Jorge A.

Procedural Generation of Content for Online Role Playing Games

Description: Video game players demand a volume of content far in excess of the ability of game designers to create it. For example, a single quest might take a week to develop and test, which means that companies such as Blizzard are spending millions of dollars each month on new content for their games. As a result, both players and developers are frustrated with the inability to meet the demand for new content. By generating content on-demand, it is possible to create custom content for each player based on player preferences. It is also possible to make use of the current world state during generation, something which cannot be done with current techniques. Using developers to create rules and assets for a content generator instead of creating content directly will lower development costs as well as reduce the development time for new game content to seconds rather than days. This work is part of the field of computational creativity, and involves the use of computers to create aesthetically pleasing game content, such as terrain, characters, and quests. I demonstrate agent-based terrain generation, and economic modeling of game spaces. I also demonstrate the autonomous generation of quests for online role playing games, and the ability to play these quests using an emulated Everquest server.
Date: August 2014
Creator: Doran, Jonathon

Geostatistical Inspired Metamodeling and Optimization of Nanoscale Analog Circuits

Description: The current trend towards miniaturization of modern consumer electronic devices significantly affects their design. The demand for efficient all-in-one appliances leads to smaller, yet more complex and powerful nanoelectronic devices. The increasing complexity in the design of such nanoscale Analog/Mixed-Signal Systems-on-Chip (AMS-SoCs) presents difficult challenges to designers. One promising design method used to mitigate the burden of this design effort is the use of metamodeling (surrogate) modeling techniques. Their use significantly reduces the time for computer simulation and design space exploration and optimization. This dissertation addresses several issues of metamodeling based nanoelectronic based AMS design exploration. A surrogate modeling technique which uses geostatistical based Kriging prediction methods in creating metamodels is proposed. Kriging prediction techniques take into account the correlation effects between input parameters for performance point prediction. We propose the use of Kriging to utilize this property for the accurate modeling of process variation effects of designs in the deep nanometer region. Different Kriging methods have been explored for this work such as simple and ordinary Kriging. We also propose another metamodeling technique Kriging-Bootstrapped Neural Network that combines the accuracy and process variation awareness of Kriging with artificial neural network models for ultra-fast and accurate process aware metamodeling design. The proposed methodologies combine Kriging metamodels with selected algorithms for ultra-fast layout optimization. The selected algorithms explored are: Gravitational Search Algorithm (GSA), Simulated Annealing Optimization (SAO), and Ant Colony Optimization (ACO). Experimental results demonstrate that the proposed Kriging metamodel based methodologies can perform the optimizations with minimal computational burden compared to traditional (SPICE-based) design flows.
Date: May 2014
Creator: Okobiah, Oghenekarho

Monitoring Dengue Outbreaks Using Online Data

Description: Internet technology has affected humans' lives in many disciplines. The search engine is one of the most important Internet tools in that it allows people to search for what they want. Search queries entered in a web search engine can be used to predict dengue incidence. This vector borne disease causes severe illness and kills a large number of people every year. This dissertation utilizes the capabilities of search queries related to dengue and climate to forecast the number of dengue cases. Several machine learning techniques are applied for data analysis, including Multiple Linear Regression, Artificial Neural Networks, and the Seasonal Autoregressive Integrated Moving Average. Predictive models produced from these machine learning methods are measured for their performance to find which technique generates the best model for dengue prediction. The results of experiments presented in this dissertation indicate that search query data related to dengue and climate can be used to forecast the number of dengue cases. The performance measurement of predictive models shows that Artificial Neural Networks outperform the others. These results will help public health officials in planning to deal with the outbreaks.
Date: May 2014
Creator: Chartree, Jedsada