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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: The effect of jyoti meditation on student counselor emotional intelligence, stress, and daily spiritual experiences.
Creator: Gutierrez, Daniel, Young, Mark, Robinson, Edward, Conley, Abigail, Hagedorn, William, Ritz, Louis, University of Central Florida
Abstract / Description: Previous research has found meditation to be effective in reducing practitioner stress, improving emotional functioning, and increasing pro-social emotions, such as empathy and compassion. In addition, research examining the effects of meditation on student counselors has shown that it increases counselor self-efficacy, reduces distress, and increases cognitive empathy. Therefore, it behooves counselor educators to discover methods of integrating meditation into counselor training. The...
Show morePrevious research has found meditation to be effective in reducing practitioner stress, improving emotional functioning, and increasing pro-social emotions, such as empathy and compassion. In addition, research examining the effects of meditation on student counselors has shown that it increases counselor self-efficacy, reduces distress, and increases cognitive empathy. Therefore, it behooves counselor educators to discover methods of integrating meditation into counselor training. The meditation practice investigated in the current study is new to the counseling and psychology literature. The majority of the current research has examined transcendental and mindfulness-based practices. However, recent research has shown that spirituality has the ability to potentiate meditation. Jyoti mediation (JM), the practice used in this study, is a spiritually based practice used for spiritual and personal growth for over 500 years. This study examined whether student counselors, after participating in a JM group, would have a significantly different level of emotional intelligence, stress and daily spiritual experiences than a comparison group who received a psycho-educational curriculum. Moreover, I investigated if the frequency of meditation related to the treatment outcomes. I conducted a six week randomized controlled trial where participants (n = 60) completed self-report assessments on the first, third and sixth week of the intervention. In addition, the participants in the meditation condition were asked to complete a daily journal reporting their experiences with the meditation treatment and their frequency of practice. Participants were required to meditate once a week in the group, and requested to meditate at least ten additional minutes each day. In order to analyze the data, I conducted a repeated measures multivariate analysis of variance (RM-MANOVA). The RM-MANOVA revealed no significant difference between the two groups. However, because the range of time spent meditating was so wide, I conducted a second RM-MANOVA using only participants that meditated in group and an additional 60 minutes over the six weeks. The second RM-MANOVA approached significance in the main effects (p = .06); and revealed a significant univariate between group effect for stress. Likewise, I conducted two Pearson moment correlations to investigate the relationship between the study outcomes and meditation frequency. The first correlation revealed no significant relationship between meditation frequency and any of the independent. However, the second correlational analysis revealed a significant relationship between stress and meditation frequency. Also, both correlational analyses revealed a significant relationship between stress and emotional intelligence. In order to gain a better understanding of how the independent variables effected stress over time, I conducted a growth curve analysis (GCA). I used PROC Mixed in SAS and nested the measurement points into each individual. The GCA revealed significant non-trivial variance between individuals at initial status. In addition, the GCA revealed that emotional intelligence accounted for 27% of that variance, and when controlling for emotional intelligence there is a significant interaction between time and group. The implications and limitations of these findings are discussed.
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Date Issued: 2014
Identifier: CFE0005177, ucf:50666
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005177

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: Intelligent Selection Techniques For Virtual Environments.
Creator: Cashion, Jeffrey, Laviola II, Joseph, Bassiouni, Mostafa, Hughes, Charles, Bowman, Doug, University of Central Florida
Abstract / Description: Selection in 3D games and simulations is a well-studied problem. Many techniques have been created to address many of the typical scenarios a user could experience. For any single scenario with consistent conditions, there is likely a technique which is well suited. If there isn't, then there is an opportunity for one to be created to best suit the expected conditions of that new scenario. It is critical that the user be given an appropriate technique to interact with their environment....
Show moreSelection in 3D games and simulations is a well-studied problem. Many techniques have been created to address many of the typical scenarios a user could experience. For any single scenario with consistent conditions, there is likely a technique which is well suited. If there isn't, then there is an opportunity for one to be created to best suit the expected conditions of that new scenario. It is critical that the user be given an appropriate technique to interact with their environment. Without it, the entire experience is at risk of becoming burdensome and not enjoyable.With all of the different possible scenarios, it can become problematic when two or more are part of the same program. If they are put closely together, or even intertwined, then the developer is often forced to pick a single technique that works so-so in both, but is likely not optimal for either, or maybe optimal in just one of them. In this case, the user is left to perform selections with a technique that is lacking in one way or another, which can increase errors and frustration.In our research, we have outlined different selection scenarios, all of which were classified by their level of object density (number of objects in scene) and object velocity. We then performed an initial study on how it impacts performance of various selection techniques, including a new selection technique that we developed just for this test, called Expand. Our results showed, among other things, that a standard Raycast technique works well in slow moving and sparse environments, while revealing that our new Expand technique works well in denser environments.With the results from our first study, we sought to develop something that would bridge the gap in performance between those selection techniques tested. Our idea was a framework that could harvest several different selection techniques and determine which was the most optimal at any time. Each selection technique would report how effective it was, given the provided scenario conditions. The framework was responsible for activating the appropriate selection technique when the user made a selection attempt. With this framework in hand, we performed two additional user studies to determine how effective it could be in actual use, and to identify its strengths and weaknesses. Each study compared several selection techniques individually against the framework which utilized them collectively, picking the most suitable. Again, the same scenarios from our first study were reused. From these studies, we gained a deeper understanding of the many challenges associated with automatic selection technique determination. The results from these two studies showed that transitioning between techniques was potentially viable, but rife with design challenges that made its optimization quite difficult.In an effort to sidestep some of the issues surrounding the switching of discrete techniques, we sought to attack the problem from the other direction, and make a single technique act similarly to two techniques, adjusting dynamically to conditions. We performed a user study to analyze the performance of such a technique, with promising results. While the qualitative differences were small, the user feedback did indicate that users preferred this technique over the others, which were static in nature.Finally, we sought to gain a deeper understanding of existing selection techniques that were dynamic in nature, and study how they were designed, and how they could be improved. We scrutinized the attributes of each technique that were already being adjusted dynamically or that could be adjusted and innovated new ways in which the technique could be improved upon. Within this analysis, we also gave thought to how each technique could be best integrated into the Auto-Select framework we proposed earlier. This overall analysis of the latest selection techniques left us with an array of new variants that warrant being created and tested against their existing versions.Our overall research goal was to perform an analysis of selection techniques that intelligently adapt to their environment. We believe that we achieved this by performing several iterative development cycles, including user studies and ultimately leading to innovation in the field of selection. We conclude our research with yet more questions left to be answered. We intend to pursue further research regarding some of these questions, as time permits.
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Date Issued: 2014
Identifier: CFE0005469, ucf:50381
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005469

Title: Leveraging Help Requests in POMDP Intelligent Tutors.
Creator: Folsom-Kovarik, Jeremiah, Sukthankar, Gita, Schatz, Sarah, Gonzalez, Avelino, Shumaker, Randall, Schatz, Sarah, University of Central Florida
Abstract / Description: Intelligent tutoring systems (ITSs) are computer programs that model individual learners and adapt instruction to help each learner differently. One way ITSs differ from human tutors is that few ITSs give learners a way to ask questions. When learners can ask for help, their questions have the potential to improve learning directly and also act as a new source of model data to help the ITS personalize instruction. Inquiry modeling gives ITSs the ability to answer learner questions and refine...
Show moreIntelligent tutoring systems (ITSs) are computer programs that model individual learners and adapt instruction to help each learner differently. One way ITSs differ from human tutors is that few ITSs give learners a way to ask questions. When learners can ask for help, their questions have the potential to improve learning directly and also act as a new source of model data to help the ITS personalize instruction. Inquiry modeling gives ITSs the ability to answer learner questions and refine their learner models with an inexpensive new input channel.In order to support inquiry modeling, an advanced planning formalism is applied to ITS learner modeling. Partially observable Markov decision processes (POMDPs) differ from more widely used ITS architectures because they can plan complex action sequences in uncertain situations with machine learning. Tractability issues have previously precluded POMDP use in ITS models. This dissertation introduces two improvements, priority queues and observation chains, to make POMDPs scale well and encompass the large problem sizes that real-world ITSs must confront. A new ITS was created to support trainees practicing a military task in a virtual environment. The development of the Inquiry Modeling POMDP Adaptive Trainer (IMP) began with multiple formative studies on human and simulated learners that explored inquiry modeling and POMDPs in intelligent tutoring. The studies suggest the new POMDP representations will be effective in ITS domains having certain common characteristics.Finally, a summative study evaluated IMP's ability to train volunteers in specific practice scenarios. IMP users achieved post-training scores averaging up to 4.5 times higher than users who practiced without support and up to twice as high as trainees who used an ablated version of IMP with no inquiry modeling. IMP's implementation and evaluation helped explore questions about how inquiry modeling and POMDP ITSs work, while empirically demonstrating their efficacy.
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Date Issued: 2012
Identifier: CFE0004506, ucf:49262
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004506

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: Cuba versus CIA.
Creator: Light, Robert E., Marzani, Carl
Date Issued: c1961
Identifier: 2683735, CFDT2683735, ucf:5122
Format: Document (PDF)
PURL: http://purl.flvc.org/FCLA/DT/2683735

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: 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: AN EXPLORATION OF SONG AS A STRATEGY TO ENGAGE ELEMENTARY STUDENTS DURING SOCIAL STUDIES LESSONS.
Creator: Rome, Morgan, Jennings-Towle, Kelly, University of Central Florida
Abstract / Description: The purpose of this thesis is to explore how curriculum-related songs provide an engaging atmosphere for elementary students learning social studies concepts. The investigation done for this thesis examines the resources available to teachers in terms of songs to be used for pedagogical engagement of social studies lessons. Through research and video analyzations it can be concluded that students are overall intrigued by the usage of songs in their social studies lessons. During the social...
Show moreThe purpose of this thesis is to explore how curriculum-related songs provide an engaging atmosphere for elementary students learning social studies concepts. The investigation done for this thesis examines the resources available to teachers in terms of songs to be used for pedagogical engagement of social studies lessons. Through research and video analyzations it can be concluded that students are overall intrigued by the usage of songs in their social studies lessons. During the social studies lessons observed in the video analyzations, the elementary students are focused, exhibit positive body language, participate, and have fun. Since engagement is documented within the analyzed videos and supported through others' research to be beneficial for students, this thesis researched and found a place for songs in elementary social studies lessons. Since there are a lack of current social studies resources that contain a musical element, eight social studies lesson plans were produced specifically for this thesis to demonstrate how songs can be implemented into the elementary curriculum to engage students.
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Date Issued: 2018
Identifier: CFH2000302, ucf:45792
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH2000302

Title: REAL LONELINESS AND ARTIFICIAL COMPANIONSHIP: LOOKING FOR SOCIAL CONNECTIONS IN TECHNOLOGY.
Creator: Montalvo, Fernando L, Smither, Janan, University of Central Florida
Abstract / Description: Loneliness among older adults is a problem with severe consequences to individual health, quality of life, cognitive capacity, and life-expectancy. Although approaches towards improving the quality and quantity of social relationships are the prevailing model of therapy, older adults may not always be able to form these relationships due to either personality factors, decreased mobility, or isolation. Intelligent personal assistants (IPAs), virtual agents, and social robotics offer an...
Show moreLoneliness among older adults is a problem with severe consequences to individual health, quality of life, cognitive capacity, and life-expectancy. Although approaches towards improving the quality and quantity of social relationships are the prevailing model of therapy, older adults may not always be able to form these relationships due to either personality factors, decreased mobility, or isolation. Intelligent personal assistants (IPAs), virtual agents, and social robotics offer an opportunity for the development of technology that could potentially serve as social companions to older adults. The present study explored whether an IPA could potentially be used as a social companion to older adults feeling lonely. Additionally, the research explored whether the device has the potential to generate social presence among both young and older adults. Results indicate that while the devices do show some social presence, participants rate the device low on some components of social presence, such as emotional contagion. This adversely affects the possibility of a social relationship between an older adult and the device. Analysis reveals ways to improve social presence in these devices.
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Date Issued: 2017
Identifier: CFH2000186, ucf:46005
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH2000186

Title: A CORRELATIONAL STUDY OF EMOTIONAL INTELLIGENCE AND LANGUAGE STYLE MATCHING.
Creator: DePass, Deprise M., Whitten, Shannon, University of Central Florida
Abstract / Description: Individuals subconsciously convey emotions through language. The present study investigates the relationship between emotional intelligence (EQ) and language style matching (LSM). Emotional intelligence involves the ability to regulate, maintain, and express one's emotions and to perceive the emotion of others. LSM involves the phenomenon that when individuals talk they tend to mimic each other's word usage (Neiderhoffer and Pennebaker, 2002). The hypothesis of the present study is that...
Show moreIndividuals subconsciously convey emotions through language. The present study investigates the relationship between emotional intelligence (EQ) and language style matching (LSM). Emotional intelligence involves the ability to regulate, maintain, and express one's emotions and to perceive the emotion of others. LSM involves the phenomenon that when individuals talk they tend to mimic each other's word usage (Neiderhoffer and Pennebaker, 2002). The hypothesis of the present study is that individuals who are emotionally intelligent subconsciously match their language to their communication partner. Ten participants from the University of Central Florida's Psychology Department were given an emotional intelligence test. The participants were then asked to submit three text conversations stored in their phones, one in which they interpret as a positive encounter, another which they interpret as a negative encounter, and one interpreted as a neutral encounter. Bivariate correlations were used to analyze the data. The results did not support the hypothesis.
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Date Issued: 2017
Identifier: CFH2000214, ucf:46058
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH2000214

Title: PROFILING BY ANY OTHER NAME COULD BE THE FOREIGN INTELLIGENCE SURVEILLANCE ACT.
Creator: Malloy, Evan, Brown, Cynthia, University of Central Florida
Abstract / Description: The undergraduate thesis began with the research question of whether the Islamic community is being profiled by the use of the Foreign Intelligence Surveillance Act (FISA) following the terrorist attacks on September 11, 2001. At the beginning of the project, the researcher's hypothesis was that Muslim community had fallen victim to profiling through the use of electronic surveillance conducted by the American government. The research presented reveals a pattern of profiling and injustices...
Show moreThe undergraduate thesis began with the research question of whether the Islamic community is being profiled by the use of the Foreign Intelligence Surveillance Act (FISA) following the terrorist attacks on September 11, 2001. At the beginning of the project, the researcher's hypothesis was that Muslim community had fallen victim to profiling through the use of electronic surveillance conducted by the American government. The research presented reveals a pattern of profiling and injustices against many different groups of Americans throughout the history of United States surveillance laws starting with the illegal alcohol producers in the 1920's. Amendments to FISA have set necessary modern electronic surveillance regulations back 30 years. The researcher brings to light the injustices the Islamic community has endured out of the panic caused by the attacks on 9/11. The research presented was achieved by using empirical legal studies techniques of incorporating a mix-methods approach to utilize both quantitative and qualitative research components. The researcher developed a spreadsheet that included all published federal opinions of prosecutions involving FISA since its enactment in 1978. Statistical data was analyzed using frequency and average software, known as Stata, and the results of study suggest an extreme increase in the amount of prosecutions involving the Islamic community since 9/11 compared to prior.
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Date Issued: 2011
Identifier: CFH0003853, ucf:44694
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH0003853

Title: THE EFFECT OF IMMEDIATE FEEDBACK AND AFTER ACTION REVIEWS (AARS) ON LEARNING, RETENTION AND TRANSFER.
Creator: Sanders, Michael, Williams, Kent, University of Central Florida
Abstract / Description: An After Action Review (AAR) is the Army training system's performance feedback mechanism. The purpose of the AAR is to improve team (unit) and individual performance in order to increase organizational readiness. While a large body of knowledge exists that discusses instructional strategies, feedback and training systems, neither the AAR process nor the AAR systems have been examined in terms of learning effectiveness and efficiency for embedded trainers as part of a holistic training system...
Show moreAn After Action Review (AAR) is the Army training system's performance feedback mechanism. The purpose of the AAR is to improve team (unit) and individual performance in order to increase organizational readiness. While a large body of knowledge exists that discusses instructional strategies, feedback and training systems, neither the AAR process nor the AAR systems have been examined in terms of learning effectiveness and efficiency for embedded trainers as part of a holistic training system. In this thesis, different feedback methods for embedded training are evaluated based on the timing and type of feedback used during and after training exercises. Those feedback methodologies include: providing Immediate Directive Feedback (IDF) only, the IDF Only feedback condition group; using Immediate Direct Feedback and delayed feedback with open ended prompts to elicit self-elaboration during the AAR, the IDF with AAR feedback condition group; and delaying feedback using opened ended prompts without any IDF, the AAR Only feedback condition group. The results of the experiment support the hypothesis that feedback timing and type do effect skill acquisition, retention and transfer in different ways. Immediate directive feedback has a significant effect in reducing the number of errors committed while acquiring new procedural skills during training. Delayed feedback, in the form of an AAR, has a significant effect on the acquisition, retention and transfer of higher order conceptual knowledge as well as procedural knowledge about a task. The combination of Immediate Directive Feedback with an After Action Review demonstrated the greatest degree of transfer on a transfer task.
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Date Issued: 2005
Identifier: CFE0000441, ucf:46411
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000441

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: SUPPLEMENTAL INSTRUCTION IN A COMMUNITY COLLEGE DEVELOPMENTAL MATHEMATICS CURRICULUM: A PHENOMENOLOGICAL STUDY OF LEARNING EXPERIENCES.
Creator: Phelps, Julie, Evans, Ruby, University of Central Florida
Abstract / Description: Mirroring the changing demographics of the nation, the community college student population continues to grow in size and in diversity. Almost half of all students who enter these institutions need at least one remedial course, which is often developmental mathematics. Developed in 1973, Supplemental Instruction (SI) has quickly gained recognition as an academic support program that is used to aid student performance, retention, and academic success. This dissertation used a phenomenological...
Show moreMirroring the changing demographics of the nation, the community college student population continues to grow in size and in diversity. Almost half of all students who enter these institutions need at least one remedial course, which is often developmental mathematics. Developed in 1973, Supplemental Instruction (SI) has quickly gained recognition as an academic support program that is used to aid student performance, retention, and academic success. This dissertation used a phenomenological approach to identify factors that motivated students' attendance and subsequent learning experiences in SI sessions associated with developmental mathematics. Sources of data included five rounds of interviews (three with SI learners and two with SI leaders), a Multiple Intelligence Inventory, and statistical information from the referent community college. Study findings revealed eight themes that characterized motivating factors for attending these optional instructional sessions. Moreover, nine themes emerged from the data regarding types of activities learners experienced in SI. Findings suggest that SI helps create a climate of achievement for learners taking developmental mathematics in a community college setting.
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Date Issued: 2005
Identifier: CFE0000661, ucf:46512
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000661

Title: ADAPTIVE INTELLIGENT USER INTERFACES WITH EMOTION RECOGNITION.
Creator: NASOZ, FATMA, Christine Lisetti, Dr L., University of Central Florida
Abstract / Description: The focus of this dissertation is on creating Adaptive Intelligent User Interfaces to facilitate enhanced natural communication during the Human-Computer Interaction by recognizing users' affective states (i.e., emotions experienced by the users) and responding to those emotions by adapting to the current situation via an affective user model created for each user. Controlled experiments were designed and conducted in a laboratory environment and in a Virtual Reality environment to collect...
Show moreThe focus of this dissertation is on creating Adaptive Intelligent User Interfaces to facilitate enhanced natural communication during the Human-Computer Interaction by recognizing users' affective states (i.e., emotions experienced by the users) and responding to those emotions by adapting to the current situation via an affective user model created for each user. Controlled experiments were designed and conducted in a laboratory environment and in a Virtual Reality environment to collect physiological data signals from participants experiencing specific emotions. Algorithms (k-Nearest Neighbor [KNN], Discriminant Function Analysis [DFA], Marquardt-Backpropagation [MBP], and Resilient Backpropagation [RBP]) were implemented to analyze the collected data signals and to find unique physiological patterns of emotions. Emotion Elicitation with Movie Clips Experiment was conducted to elicit Sadness, Anger, Surprise, Fear, Frustration, and Amusement from participants. Overall, the three algorithms: KNN, DFA, and MBP, could recognize emotions with 72.3%, 75.0%, and 84.1% accuracy, respectively. Driving Simulator experiment was conducted to elicit driving-related emotions and states (panic/fear, frustration/anger, and boredom/sleepiness). The KNN, MBP and RBP Algorithms were used to classify the physiological signals by corresponding emotions. Overall, KNN could classify these three emotions with 66.3%, MBP could classify them with 76.7% and RBP could classify them with 91.9% accuracy. Adaptation of the interface was designed to provide multi-modal feedback to the users about their current affective state and to respond to users' negative emotional states in order to decrease the possible negative impacts of those emotions. Bayesian Belief Networks formalization was employed to develop the User Model to enable the intelligent system to appropriately adapt to the current context and situation by considering user-dependent factors, such as: personality traits and preferences.
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Date Issued: 2004
Identifier: CFE0000126, ucf:46201
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000126

Title: A GENRE OF COLLECTIVE INTELLIGENCE: BLOGS AS INTERTEXTUAL, RECIPROCAL, AND PEDAGOGICAL.
Creator: Gramer, Rachel, Bell, Kathleen, University of Central Florida
Abstract / Description: This thesis investigates the rhetorical features of blogs that lend them dialogic strength as an online genre through the lens of Mikhail Bakhtin's theories of speech genres, utterances, and dialogism. As a relatively new online genre, blogs stem from previous genres (in print and online as well as verbal), but their emergence as a popular form of expression in our current culture demands attention to how blogs also offer us different rhetorical opportunities to meet our changing social...
Show moreThis thesis investigates the rhetorical features of blogs that lend them dialogic strength as an online genre through the lens of Mikhail Bakhtin's theories of speech genres, utterances, and dialogism. As a relatively new online genre, blogs stem from previous genres (in print and online as well as verbal), but their emergence as a popular form of expression in our current culture demands attention to how blogs also offer us different rhetorical opportunities to meet our changing social exigencies as online subjects in the 21st century. This thesis was inspired by questions about how blogs redefine the rhetorical situation to alter our textual roles as readers, writers, and respondents in the new generic circumstances we encounter--and reproduce--online. Applying the framework of Henry Jenkins' Convergence Culture and Pierre Levy's Collective Intelligence, this thesis analyzes how blogs enable us as online subjects to add our utterances to our textual collective intelligence, which benefits from our personal experience and the epistemic conversations of blogs as online texts. In addition, it is also an inquiry into how the rhetorical circumstances of blogs as textual sites of collective intelligence can create a reciprocal learning environment in the writing classroom. I ultimately examine blogs through the lenses of alternative pedagogy--informed by David Wallace and Helen Rothschild Ewald's Mutuality in the Rhetoric and Composition Classroom and Xin Liu Gale's Teachers, Discourses, and Authority in the Postmodern Composition Classroom--to suggest the potential consequences of a writing education that includes how we are currently writing--and being written by--our culture's online generic practice of blogs.
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Date Issued: 2008
Identifier: CFE0002402, ucf:47770
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002402

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

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