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Title: Online Neuro-Adaptive Learning For Power System Dynamic State Estimation.
Creator: Birari, Rahul, Zhou, Qun, Sun, Wei, Dimitrovski, Aleksandar, University of Central Florida
Abstract / Description: With the increased penetration of renewable generation in the smart grid , it is crucial to have knowledge of rapid changes of system states. The information of real-time electro-mechanical dynamic states of generators are essential to ensuring reliability and detecting instability of the grid. The conventional SCADA based Dynamic State Estimation (DSE) was limited by the slow sampling rates (2-4 Hz). With the advent of PMU based synchro-phasor technology in tandem with Wide Area Monitoring...
Show moreWith the increased penetration of renewable generation in the smart grid , it is crucial to have knowledge of rapid changes of system states. The information of real-time electro-mechanical dynamic states of generators are essential to ensuring reliability and detecting instability of the grid. The conventional SCADA based Dynamic State Estimation (DSE) was limited by the slow sampling rates (2-4 Hz). With the advent of PMU based synchro-phasor technology in tandem with Wide Area Monitoring System (WAMS), it has become possible to avail rapid real-time measurements at the network nodes. These measurements can be exploited for better estimates of system dynamic states. In this research, we have proposed a novel Artificial Intelligence (AI) based real-time neuro-adaptive algorithm for rotor angle and speed estimation of synchronous generators. Generator swing equations and power flow models are incorporated in the online learning. The algorithm learns and adapts in real-time to achieve accurate estimates. Simulation is carried out on 68 bus 16 generator NETS-NYPS model. The neuro-adaptive algorithm is compared with classical Kalman Filter based DSE. Applicability and accuracy of the proposed method is demonstrated under the influence of noise and faulty conditions.
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Date Issued: 2017
Identifier: CFE0006858, ucf:51747
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006858

Title: A SOFTWARE-BASED KNOWLEDGE MANAGEMENT SYSTEM USING NARRATIVE TEXTS.
Creator: McDaniel, Thomas Rudy, Dombrowski, Paul, University of Central Florida
Abstract / Description: Technical and professional communicators have in recent research been challenged to make significant contributions to the field of knowledge management, and to learn or create the new technologies allowing them to do so. The purpose of this dissertation is to make such a combined theoretical and applied contribution from the context of the emerging discipline of Texts and Technology. This dissertation explores the field of knowledge management (KM), particularly its relationship to the...
Show moreTechnical and professional communicators have in recent research been challenged to make significant contributions to the field of knowledge management, and to learn or create the new technologies allowing them to do so. The purpose of this dissertation is to make such a combined theoretical and applied contribution from the context of the emerging discipline of Texts and Technology. This dissertation explores the field of knowledge management (KM), particularly its relationship to the related study of artificial intelligence (AI), and then recommends a KM software application based on the principles of narratology and narrative information exchange. The focus of knowledge is shifted from the reductive approach of data and information to a holistic approach of meaning and the way people make sense of complex events as experiences expressed in stories. Such an analysis requires a discussion of the evolution of intelligent systems and narrative theory as well as an examination of existing computerized and non-computerized storytelling systems. After a thorough discussion of these issues, an original software program that is used to collect, analyze, and distribute thematic stories within any hierarchical organization is modeled, exemplified, and explained in detail.
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Date Issued: 2004
Identifier: CFE0000012, ucf:46117
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000012

Title: LEARNING HUMAN BEHAVIOR FROM OBSERVATION FOR GAMING APPLICATIONS.
Creator: Moriarty, Christopher, Gonzalez, Avelino, University of Central Florida
Abstract / Description: The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in....
Show moreThe gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in. This work attempts to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces Quake 2 agents that behave as a human player trains them to act. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO is a technique that offers the gaming community a mode of game play that has promising entertainment value.
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Date Issued: 2007
Identifier: CFE0001694, ucf:47201
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001694

Title: ON ADVANCED TEMPLATE-BASED INTERPRETATION AS APPLIED TO INTENTION RECOGNITION IN A STRATEGIC ENVIRONMENT.
Creator: Akridge, Cameron, Gonzalez, Avelino, University of Central Florida
Abstract / Description: An area of study that has received much attention over the past few decades is simulations involving threat assessment in military scenarios. Recently, much research has emerged concerning the recognition of troop movements and formations in non-combat simulations. Additionally, there have been efforts towards the detection and assessment of various types of malicious intentions. One such work by Akridge addressed the issue of Strategic Intention Recognition, but fell short in the detection...
Show moreAn area of study that has received much attention over the past few decades is simulations involving threat assessment in military scenarios. Recently, much research has emerged concerning the recognition of troop movements and formations in non-combat simulations. Additionally, there have been efforts towards the detection and assessment of various types of malicious intentions. One such work by Akridge addressed the issue of Strategic Intention Recognition, but fell short in the detection of tactics that it could not detect without somehow manipulating the environment. Therefore, the aim of this thesis is to address the problem of recognizing an opponent's intent in a strategic environment where the system can think ahead in time to see the agent's plan. To approach the problem, a structured form of knowledge called Template-Based Interpretation is borrowed from the work of others and enhanced to reason in a temporally dynamic simulation.
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Date Issued: 2007
Identifier: CFE0001517, ucf:47146
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001517

Title: ROBUST DIALOG MANAGEMENT THROUGH A CONTEXT-CENTRIC ARCHITECTURE.
Creator: Hung, Victor, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user's goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an...
Show moreThis dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user's goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine's ability to communicate may be hindered by poor reception of utterances, caused by a user's inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by Context-Based Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user's assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users.
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Date Issued: 2010
Identifier: CFE0003230, ucf:48556
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003230

Title: SPATIO-TEMPORAL NEGOTIATION PROTOCOLS.
Creator: Luo, Yi, Boloni, Ladislau, University of Central Florida
Abstract / Description: Canonical problems are simplified representations of a class of real world problems. They allow researchers to compare algorithms in a standard setting which captures the most important challenges of the real world problems being modeled. In this dissertation, we focus on negotiating a collaboration in space and time, a problem with many important real world applications. Although technically a multi-issue negotiation, we show that the problem can not be represented in a satisfactory manner...
Show moreCanonical problems are simplified representations of a class of real world problems. They allow researchers to compare algorithms in a standard setting which captures the most important challenges of the real world problems being modeled. In this dissertation, we focus on negotiating a collaboration in space and time, a problem with many important real world applications. Although technically a multi-issue negotiation, we show that the problem can not be represented in a satisfactory manner by previous models. We propose the "Children in the Rectangular Forest" (CRF) model as a possible canonical problem for negotiating spatio-temporal collaboration. In the CRF problem, two embodied agents are negotiating the synchronization of their movement for a portion of the path from their respective sources to destinations. The negotiation setting is zero initial knowledge and it happens in physical time. As equilibrium strategies are not practically possible, we are interested in strategies with bounded rationality, which achieve good erformance in a wide range of practical negotiation scenarios. We design a number of negotiation protocols to allow agents to exchange their offers. The simple negotiation protocol can be enhanced by schemes in which the agents add additional information of the negotiation flow to aid the negotiation partner in offer formation. Naturally, the performance of a strategy is dependent on the strategy of the opponent and the characteristics of the scenario. Thus we develop a set of metrics for the negotiation scenario which formalizes our intuition of collaborative scenarios (where the agents' interests are closely aligned) versus competitive scenarios (where the gain of the utility for one agent is paid off with a loss of utility for the other agent). Finally, we further investigate the sophisticated strategies which allow agents to learn the opponents while negotiating. We find strategies can be augmented by collaborativeness analysis: the approximate collaborativeness metric can be used to cut short the negotiation. Then, we discover an approach to model the opponent through Bayesian learning. We assume the agents do not disclose their information voluntarily: the learning needs to rely on the study of the offers exchanged during normal negotiation. At last, we explore a setting where the agents are able to perform physical action (movement) while the negotiation is ongoing. We formalize a method to represent and update the beliefs about the valuation function, the current state of negotiation and strategy of the opponent agent using a particle filter. By exploring a number of different negotiation protocols and several peer-to-peer negotiation based strategies, we claim that the CRF problem captures the main challenges of the real world problems while allows us to simplify away some of the computationally demanding but semantically marginal features of real world problems.
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Date Issued: 2011
Identifier: CFE0003722, ucf:48782
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003722

Title: Autonomous Quadcopter Videographer.
Creator: Coaguila Quiquia, Rey, Sukthankar, Gita, Wu, Annie, Hughes, Charles, University of Central Florida
Abstract / Description: In recent years, the interest in quadcopters as a robotics platform for autonomous photography has increased. This is due to their small size and mobility, which allow them to reach places that are difficult or even impossible for humans. This thesis focuses on the design of an autonomous quadcopter videographer, i.e. a quadcopter capable of capturing good footage of a specific subject. In order to obtain this footage, the system needs to choose appropriate vantage points and control the...
Show moreIn recent years, the interest in quadcopters as a robotics platform for autonomous photography has increased. This is due to their small size and mobility, which allow them to reach places that are difficult or even impossible for humans. This thesis focuses on the design of an autonomous quadcopter videographer, i.e. a quadcopter capable of capturing good footage of a specific subject. In order to obtain this footage, the system needs to choose appropriate vantage points and control the quadcopter. Skilled human videographers can easily spot good filming locations where the subject and its actions can be seen clearly in the resulting video footage, but translating this knowledge to a robot can be complex. We present an autonomous system implemented on a commercially available quadcopter that achieves this using only the monocular information and an accelerometer. Our system has two vantage point selection strategies: 1) a reactive approach, which moves the robot to a fixed location with respect to the human and 2) the combination of the reactive approach and a POMDP planner that considers the target's movement intentions. We compare the behavior of these two approaches under different target movement scenarios. The results show that the POMDP planner obtains more stable footage with less quadcopter motion.
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Date Issued: 2015
Identifier: CFE0005592, ucf:50246
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005592

Title: Applications of Artificial Intelligence in Military Simulation.
Creator: Golovcsenko, Igor V., Biegel, John E., Engineering
Abstract / Description: University of Central Florida College of Engineering Thesis; This report is a survey of Artificial Intelligence (AI) technology contributions to military training. It provides an overview of military training simulation and a review of instructional problems and challenges which can be addressed by AI. The survey includes current as well as potential applications of AI, with particular emphasis on design and system integration issues. Applications include knowledge and skills training in...
Show moreUniversity of Central Florida College of Engineering Thesis; This report is a survey of Artificial Intelligence (AI) technology contributions to military training. It provides an overview of military training simulation and a review of instructional problems and challenges which can be addressed by AI. The survey includes current as well as potential applications of AI, with particular emphasis on design and system integration issues. Applications include knowledge and skills training in strategic planning and decision making, tactical warfare operations, electronics maintenance and repair, as well as computer-aided design of training systems. The report describes research contributions in the application of AI technology to the training world, and it concludes with an assessment of future research directions in this area.
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Date Issued: 1987
Identifier: CFR0011599, ucf:53044
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFR0011599

Title: Modeling Learner Mood in Realtime through Biosensors for Intelligent Tutoring Improvements.
Creator: Brawner, Keith, Gonzalez, Avelino, Boloni, Ladislau, Georgiopoulos, Michael, Proctor, Michael, Beidel, Deborah, University of Central Florida
Abstract / Description: Computer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person's cognitive and affective state vary over time of day and seasonally, individualized models have had differing...
Show moreComputer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person's cognitive and affective state vary over time of day and seasonally, individualized models have had differing difficulties. The simultaneous creation and execution of an individualized model, in real time, represents the last option for modeling such cognitive and affective states. This dissertation presents and evaluates four differing techniques for the creation of cognitive and affective models that are created on-line and in real time for each individual user as alternatives to generalized models. Each of these techniques involves making predictions and modifications to the model in real time, addressing the real time datastream problems of infinite length, detection of new concepts, and responding to how concepts change over time. Additionally, with the knowledge that a user is physically present, this work investigates the contribution that the occasional direct user query can add to the overall quality of such models. The research described in this dissertation finds that the creation of a reasonable quality affective model is possible with an infinitesimal amount of time and without (")ground truth(") knowledge of the user, which is shown across three different emotional states. Creation of a cognitive model in the same fashion, however, was not possible via direct AI modeling, even with all of the (")ground truth(") information available, which is shown across four different cognitive states.
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Date Issued: 2013
Identifier: CFE0004822, ucf:49734
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004822

Title: Semiconductor Design and Manufacturing Interplay to Achieve Higher Yields at Reduced Costs using SMART Techniques.
Creator: Oberai, Ankush Bharati, Yuan, Jiann-Shiun, Abdolvand, Reza, Georgiopoulos, Michael, Sundaram, Kalpathy, Reilly, Charles, University of Central Florida
Abstract / Description: Since the outset of IC Semiconductor market there has been a gap between its design and manufacturing communities. This gap continued to grow as the device geometries started to shrink and the manufacturing processes and tools got more complex. This gap lowered the manufacturing yield, leading to higher cost of ICs and delay in their time to market. It also impacted performance of the ICs, impacting the overall functionality of the systems they were integrated in. However, in the recent years...
Show moreSince the outset of IC Semiconductor market there has been a gap between its design and manufacturing communities. This gap continued to grow as the device geometries started to shrink and the manufacturing processes and tools got more complex. This gap lowered the manufacturing yield, leading to higher cost of ICs and delay in their time to market. It also impacted performance of the ICs, impacting the overall functionality of the systems they were integrated in. However, in the recent years there have been major efforts to bridge the gap between design and manufacturing using software solutions by providing closer collaborations techniques between design and manufacturing communities. The root cause of this gap is inherited by the difference in the knowledge and skills required by the two communities. The IC design community is more microelectronics, electrical engineering and software driven whereas the IC manufacturing community is more driven by material science, mechanical engineering, physics and robotics. The cross training between the two is almost nonexistence and not even mandated. This gap is deemed to widen, with demand for more complex designs and miniaturization of electronic appliance-products. Growing need for MEMS, 3-D NANDS and IOTs are other drivers that could widen the gap between design and manufacturing. To bridge this gap, it is critical to have close loop solutions between design and manufacturing This could be achieved by SMART automation on both sides by using Artificial Intelligence, Machine Learning and Big Data algorithms. Lack of automation and predictive capabilities have even made the situation worse on the yield and total turnaround times. With the growing fabless and foundry business model, bridging the gap has become even more critical. Smart Manufacturing philosophy must be adapted to make this bridge possible. We need to understand the Fab-fabless collaboration requirements and the mechanism to bring design to the manufacturing floor for yield improvement. Additionally, design community must be educated with manufacturing process and tool knowledge, so they can design for improved manufacturability. This study will require understanding of elements impacting manufacturing on both ends of the design and manufacturing process. Additionally, we need to understand the process rules that need to be followed closely in the design phase. Best suited SMART automation techniques to bridge the gap need to be studied and analyzed for their effectiveness.
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Date Issued: 2018
Identifier: CFE0007351, ucf:52096
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007351

Title: AN ANALYSIS OF MISCLASSIFICATION RATES FOR DECISION TREES.
Creator: Zhong, Mingyu, Georgiopoulos, Michael, University of Central Florida
Abstract / Description: The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class...
Show moreThe decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by standard deviations). We also provide an explicit expression of the k-norm estimation for the tree's misclassification rate that combines both the expected value and the reliability. Furthermore, our proposed and proven theorem on k-norm estimation suggests an efficient pruning algorithm that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly that compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Finally, our work provides a deeper understanding of decision trees.
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Date Issued: 2007
Identifier: CFE0001774, ucf:47271
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001774

Title: CONCEPT LEARNING BY EXAMPLE DECOMPOSITION.
Creator: Joshi, Sameer, Hughes, Charles, University of Central Florida
Abstract / Description: For efficient understanding and prediction in natural systems, even in artificially closed ones, we usually need to consider a number of factors that may combine in simple or complex ways. Additionally, many modern scientific disciplines face increasingly large datasets from which to extract knowledge (for example, genomics). Thus to learn all but the most trivial regularities in the natural world, we rely on different ways of simplifying the learning problem. One simplifying technique that...
Show moreFor efficient understanding and prediction in natural systems, even in artificially closed ones, we usually need to consider a number of factors that may combine in simple or complex ways. Additionally, many modern scientific disciplines face increasingly large datasets from which to extract knowledge (for example, genomics). Thus to learn all but the most trivial regularities in the natural world, we rely on different ways of simplifying the learning problem. One simplifying technique that is highly pervasive in nature is to break down a large learning problem into smaller ones; to learn the smaller, more manageable problems; and then to recombine them to obtain the larger picture. It is widely accepted in machine learning that it is easier to learn several smaller decomposed concepts than a single large one. Though many machine learning methods exploit it, the process of decomposition of a learning problem has not been studied adequately from a theoretical perspective. Typically such decomposition of concepts is achieved in highly constrained environments, or aided by human experts. In this work, we investigate concept learning by example decomposition in a general probably approximately correct (PAC) setting for Boolean learning. We develop sample complexity bounds for the different steps involved in the process. We formally show that if the cost of example partitioning is kept low then it is highly advantageous to learn by example decomposition. To demonstrate the efficacy of this framework, we interpret the theory in the context of feature extraction. We discover that many vague concepts in feature extraction, starting with what exactly a feature is, can be formalized unambiguously by this new theory of feature extraction. We analyze some existing feature learning algorithms in light of this theory, and finally demonstrate its constructive nature by generating a new learning algorithm from theoretical results.
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Date Issued: 2009
Identifier: CFE0002504, ucf:47694
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002504

Title: Enhancing Cognitive Algorithms for Optimal Performance of Adaptive Networks.
Creator: Lugo-Cordero, Hector, Guha, Ratan, Wu, Annie, Stanley, Kenneth, University of Central Florida
Abstract / Description: This research proposes to enhance some Evolutionary Algorithms in order to obtain optimal and adaptive network configurations. Due to the richness in technologies, low cost, and application usages, we consider Heterogeneous Wireless Mesh Networks. In particular, we evaluate the domains of Network Deployment, Smart Grids/Homes, and Intrusion Detection Systems. Having an adaptive network as one of the goals, we consider a robust noise tolerant methodology that can quickly react to changes in...
Show moreThis research proposes to enhance some Evolutionary Algorithms in order to obtain optimal and adaptive network configurations. Due to the richness in technologies, low cost, and application usages, we consider Heterogeneous Wireless Mesh Networks. In particular, we evaluate the domains of Network Deployment, Smart Grids/Homes, and Intrusion Detection Systems. Having an adaptive network as one of the goals, we consider a robust noise tolerant methodology that can quickly react to changes in the environment. Furthermore, the diversity of the performance objectives considered (e.g., power, coverage, anonymity, etc.) makes the objective function non-continuous and therefore not have a derivative. For these reasons, we enhance Particle Swarm Optimization (PSO) algorithm with elements that aid in exploring for better configurations to obtain optimal and sub-optimal configurations. According to results, the enhanced PSO promotes population diversity, leading to more unique optimal configurations for adapting to dynamic environments. The gradual complexification process demonstrated simpler optimal solutions than those obtained via trial and error without the enhancements.Configurations obtained by the modified PSO are further tuned in real-time upon environment changes. Such tuning occurs with a Fuzzy Logic Controller (FLC) which models human decision making by monitoring certain events in the algorithm. Example of such events include diversity and quality of solution in the environment. The FLC is able to adapt the enhanced PSO to changes in the environment, causing more exploration or exploitation as needed.By adding a Probabilistic Neural Network (PNN) classifier, the enhanced PSO is again used as a filter to aid in intrusion detection classification. This approach reduces miss classifications by consulting neighbors for classification in case of ambiguous samples. The performance of ambiguous votes via PSO filtering shows an improvement in classification, causing the simple classifier perform better the commonly used classifiers.
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Date Issued: 2018
Identifier: CFE0007046, ucf:52003
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007046

Title: Automated Synthesis of Memristor Crossbar Networks.
Creator: Chakraborty, Dwaipayan, Jha, Sumit Kumar, Leavens, Gary, Ewetz, Rickard, Valliyil Thankachan, Sharma, Xu, Mengyu, University of Central Florida
Abstract / Description: The advancement of semiconductor device technology over the past decades has enabled the design of increasingly complex electrical and computational machines. Electronic design automation (EDA) has played a significant role in the design and implementation of transistor-based machines. However, as transistors move closer toward their physical limits, the speed-up provided by Moore's law will grind to a halt. Once again, we find ourselves on the verge of a paradigm shift in the computational...
Show moreThe advancement of semiconductor device technology over the past decades has enabled the design of increasingly complex electrical and computational machines. Electronic design automation (EDA) has played a significant role in the design and implementation of transistor-based machines. However, as transistors move closer toward their physical limits, the speed-up provided by Moore's law will grind to a halt. Once again, we find ourselves on the verge of a paradigm shift in the computational sciences as newer devices pave the way for novel approaches to computing. One of such devices is the memristor -- a resistor with non-volatile memory.Memristors can be used as junctional switches in crossbar circuits, which comprise of intersecting sets of vertical and horizontal nanowires. The major contribution of this dissertation lies in automating the design of such crossbar circuits -- doing a new kind of EDA for a new kind of computational machinery. In general, this dissertation attempts to answer the following questions:a. How can we synthesize crossbars for computing large Boolean formulas, up to 128-bit?b. How can we synthesize more compact crossbars for small Boolean formulas, up to 8-bit?c. For a given loop-free C program doing integer arithmetic, is it possible to synthesize an equivalent crossbar circuit?We have presented novel solutions to each of the above problems. Our new, proposed solutions resolve a number of significant bottlenecks in existing research, via the usage of innovative logic representation and artificial intelligence techniques. For large Boolean formulas (up to 128-bit), we have utilized Reduced Ordered Binary Decision Diagrams (ROBDDs) to automatically synthesize linearly growing crossbar circuits that compute them. This cutting edge approach towards flow-based computing has yielded state-of-the-art results. It is worth noting that this approach is scalable to n-bit Boolean formulas. We have made significant original contributions by leveraging artificial intelligence for automatic synthesis of compact crossbar circuits. This inventive method has been expanded to encompass crossbar networks with 1D1M (1-diode-1-memristor) switches, as well. The resultant circuits satisfy the tight constraints of the Feynman Grand Prize challenge and are able to perform 8-bit binary addition. A leading edge development for end-to-end computation with flow-based crossbars has been implemented, which involves methodical translation of loop-free C programs into crossbar circuits via automated synthesis. The original contributions described in this dissertation reflect the substantial progress we have made in the area of electronic design automation for synthesis of memristor crossbar networks.
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Date Issued: 2019
Identifier: CFE0007609, ucf:52528
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007609

Title: An intelligent editor for natural language processing of unrestricted text.
Creator: Glinos, Demetrios George, Gomez, Fernando, Arts and Sciences
Abstract / Description: University of Central Florida College of Arts and Sciences Thesis; The understanding of natural language by computational methods has been a continuing and elusive problem in artificial intelligence. In recent years there has been a resurgence in natural language processing research. Much of this work has been on empirical or corpus-based methods which use a data-driven approach to train systems on large amounts of real language data. Using corpus-based methods, the performance of part-of...
Show moreUniversity of Central Florida College of Arts and Sciences Thesis; The understanding of natural language by computational methods has been a continuing and elusive problem in artificial intelligence. In recent years there has been a resurgence in natural language processing research. Much of this work has been on empirical or corpus-based methods which use a data-driven approach to train systems on large amounts of real language data. Using corpus-based methods, the performance of part-of-speech (POS) taggers, which assign to the individual words of a sentence their appropriate part of speech category (e.g., noun, verb, preposition), now rivals human performance levels, achieving accuracies exceeding 95%. Such taggers have proved useful as preprocessors for such tasks as parsing, speech synthesis, and information retrieval. Parsing remains, however, a difficult problem, even with the benefit of POS tagging. Moveover, as sentence length increases, there is a corresponding combinatorial explosing of alternative possible parses. Consider the following sentence from a New York Times online article: After Salinas was arrested for murder in 1995 and lawyers for the bank had begun monitoring his accounts, his personal banker in New York quietly advised Salinas' wife to move the money elsewhere, apparently without the consent of the legal department. To facilitate the parsing and other tasks, we would like to decompose this sentence into the following three shorter sentences which, taken together, convey the same meaning as the original: 1. Salinas was arrested for murder in 1995. 2. Lawyers for the bank had begun monitoring his accounts. 3. His personal banker in New York quietly advised Salinas' wife to move the money elsewhere, apparently without the consent of the legal department. This study investigates the development of heuristics for decomposing such long sentences into sets of shorter sentences without affecting the meaning of the original sentences. Without parsing or semantic analysis, heuristic rules were developed based on: (1) the output of a POS tagger (Brill's tagger); (2) the punctuation contained in the input sentences; and (3) the words themselves. The heuristic algorithms were implemented in an intelligent editor program which first augmented the POS tags and assigned tags to punctuation, and then tested the rules against a corpus of 25 New York Times online articles containing approximately 1,200 sentences and over 32,000 words, with good results. Recommendations are made for improving the algorithms and for continuing this line of research.
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Date Issued: 1999
Identifier: CFR0008181, ucf:53055
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFR0008181

Title: Modeling User Transportation Patterns Using Mobile Devices.
Creator: Davami, Erfan, Sukthankar, Gita, Gonzalez, Avelino, Foroosh, Hassan, Sukthankar, Rahul, University of Central Florida
Abstract / Description: Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts...
Show moreParticipatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data.Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing system capable of achieving tasks practically impossible for conventional sensing platforms. One of the advantages of participatory sensing platforms is their mobility, since human users are often in motion. This dissertation presents a set of techniques for modeling and predicting user transportation patterns from cell-phone and social media check-ins. To study large-scale transportation patterns, I created a mobile phone app, Kpark, for estimating parking lot occupancy on the UCF campus. Kpark aggregates individual user reports on parking space availability to produce a global picture across all the campus lots using crowdsourcing. An issue with crowdsourcing is the possibility of receiving inaccurate information from users, either through error or malicious motivations. One method of combating this problem is to model the trustworthiness of individual participants to use that information to selectively include or discard data.This dissertation presents a comprehensive study of the performance of different worker quality and data fusion models with plausible simulated user populations, as well as an evaluation of their performance on the real data obtained from a full release of the Kpark app on the UCF Orlando campus. To evaluate individual trust prediction methods, an algorithm selection portfolio was introduced to take advantage of the strengths of each method and maximize the overall prediction performance.Like many other crowdsourced applications, user incentivization is an important aspect of creating a successful crowdsourcing workflow. For this project a form of non-monetized incentivization called gamification was used in order to create competition among users with the aim of increasing the quantity and quality of data submitted to the project. This dissertation reports on the performance of Kpark at predicting parking occupancy, increasing user app usage, and predicting worker quality.
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Date Issued: 2015
Identifier: CFE0005597, ucf:50258
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005597

Title: EPISODIC MEMORY MODEL FOR EMBODIED CONVERSATIONAL AGENTS.
Creator: Elvir, Miguel, Gonzalez, Avelino, University of Central Florida
Abstract / Description: Embodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into...
Show moreEmbodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into dialog management tools for ECAs. In our work, we propose to take a closer look at the shared characteristics of episodic memory models in recent examples from the field. Additionally, we propose several enhancements to these existing models through a unified episodic memory model for ECAÃ‚Â's. As part of our research into episodic memory models, we present a process for determining the prevalent contexts in the conversations obtained from the aforementioned interactions. The process presented demonstrates the use of statistical and machine learning services, as well as Natural Language Processing techniques to extract relevant snippets from conversations. Finally, mechanisms to store, retrieve, and recall episodes from previous conversations are discussed. A primary contribution of this research is in the context of contemporary memory models for conversational agents and cognitive architectures. To the best of our knowledge, this is the first attempt at providing a comparative summary of existing works. As implementations of ECAs become more complex and encompass more realistic conversation engines, we expect that episodic memory models will continue to evolve and further enhance the naturalness of conversations.
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Date Issued: 2010
Identifier: CFE0003353, ucf:48443
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003353

Title: Context-Centric Affect Recognition From Paralinguistic Features of Speech.
Creator: Marpaung, Andreas, Gonzalez, Avelino, DeMara, Ronald, Sukthankar, Gita, Wu, Annie, Lisetti, Christine, University of Central Florida
Abstract / Description: As the field of affect recognition has progressed, many researchers have shifted from having unimodal approaches to multimodal ones. In particular, the trends in paralinguistic speech affect recognition domain have been to integrate other modalities such as facial expression, body posture, gait, and linguistic speech. Our work focuses on integrating contextual knowledge into paralinguistic speech affect recognition. We hypothesize that a framework to recognize affect through paralinguistic...
Show moreAs the field of affect recognition has progressed, many researchers have shifted from having unimodal approaches to multimodal ones. In particular, the trends in paralinguistic speech affect recognition domain have been to integrate other modalities such as facial expression, body posture, gait, and linguistic speech. Our work focuses on integrating contextual knowledge into paralinguistic speech affect recognition. We hypothesize that a framework to recognize affect through paralinguistic features of speech can improve its performance by integrating relevant contextual knowledge. This dissertation describes our research to integrate contextual knowledge into the paralinguistic affect recognition process from acoustic features of speech. We conceived, built, and tested a two-phased system called the Context-Based Paralinguistic Affect Recognition System (CxBPARS). The first phase of this system is context-free and uses the AdaBoost classifier that applies data on the acoustic pitch, jitter, shimmer, Harmonics-to-Noise Ratio (HNR), and the Noise-to-Harmonics Ratio (NHR) to make an initial judgment about the emotion most likely exhibited by the human elicitor. The second phase then adds context modeling to improve upon the context-free classifications from phase I. CxBPARS was inspired by a human subject study performed as part of this work where test subjects were asked to classify an elicitor's emotion strictly from paralinguistic sounds, and then subsequently provided with contextual information to improve their selections. CxBPARS was rigorously tested and found to, at the worst case, improve the success rate from the state-of-the-art's 42% to 53%.
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Date Issued: 2019
Identifier: CFE0007836, ucf:52831
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007836

Title: EXPLOITING OPPONENT MODELING FOR LEARNING IN MULTI-AGENT ADVERSARIAL GAMES.
Creator: Laviers, Kennard, Sukthankar, Gita, University of Central Florida
Abstract / Description: An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent's actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of...
Show moreAn issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent's actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players' physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator. Simultaneously predicting the movement trajectories, future reward, and play strategies of multiple players in real-time is a daunting task but we illustrate how it is possible to divide and conquer this problem with an assortment of data-driven models. By leveraging spatio-temporal traces of player movements, we learn discriminative models of defensive play for opponent modeling. With the reward information from previous play matchups, we use a modified version of UCT (Upper Conference Bounds applied to Trees) to create new offensive plays and to learn play repairs to counter predicted opponent actions. In team games, players must coordinate effectively to accomplish tasks while foiling their opponents either in a preplanned or emergent manner. An effective team policy must generate the necessary coordination, yet considering all possibilities for creating coordinating subgroups is computationally infeasible. Automatically identifying and preserving the coordination between key subgroups of teammates can make search more productive by pruning policies that disrupt these relationships. We demonstrate that combining opponent modeling with automatic subgroup identification can be used to create team policies with a higher average yardage than either the baseline game or domain-specific heuristics.
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Date Issued: 2011
Identifier: CFE0003914, ucf:48720
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003914

Title: CONTEXTUALIZING OBSERVATIONAL DATA FOR MODELING HUMAN PERFORMANCE.
Creator: Trinh, Viet, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when...
Show moreThis research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when performing a mission to facilitate the learning of such CxBR models. This research is derived from the contextualization problem left behind in Fernlund's research on using the Genetic Context Learner (GenCL) to model CxBR agents from observed human performance [Fernlund, 2004]. To accomplish the process of context discovery, this research proposes two contextualization algorithms: Contextualized Fuzzy ART (CFA) and Context Partitioning and Clustering (COPAC). The former is a more naive approach utilizing the well known Fuzzy ART strategy while the latter is a robust algorithm developed on the principles of CxBR. Using Fernlund's original five drivers, the CFA and COPAC algorithms were tested and evaluated on their ability to effectively contextualize each driver's individualized set of behaviors into well-formed and meaningful context bases as well as generating high-fidelity agents through the integration with Fernlund's GenCL algorithm. The resultant set of agents was able to capture and generalized each driver's individualized behaviors.
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Date Issued: 2009
Identifier: CFE0002563, ucf:48253
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002563

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