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Title: HOPF BIFURCATION ANALYSIS OF CHAOTIC CHEMICAL REACTOR MODEL.
Creator: Mandragona, Daniel, Choudhury, Roy, University of Central Florida
Abstract / Description: Bifurcations in Huang's chaotic chemical reactor system leading from simple dynamics into chaotic regimes are considered. Following the linear stability analysis, the periodic orbit resulting from a Hopf bifurcation of any of the six fixed points is constructed analytically by the method of multiple scales across successively slower time scales, and its stability is then determined by the resulting final secularity condition. Furthermore, we run numerical simulations of our chemical reactor...
Show moreBifurcations in Huang's chaotic chemical reactor system leading from simple dynamics into chaotic regimes are considered. Following the linear stability analysis, the periodic orbit resulting from a Hopf bifurcation of any of the six fixed points is constructed analytically by the method of multiple scales across successively slower time scales, and its stability is then determined by the resulting final secularity condition. Furthermore, we run numerical simulations of our chemical reactor at a particular fixed point of interest, alongside a set of parameter values that forces our system to undergo Hopf bifurcation. These numerical simulations then verify our analysis of the normal form.
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Date Issued: 2018
Identifier: CFH2000342, ucf:45831
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH2000342

Title: PARAMETER ESTIMATION IN LINEAR REGRESSION.
Creator: Ollikainen, Kati, Malone, Linda, University of Central Florida
Abstract / Description: Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known...
Show moreToday increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist with confidence intervals not including the true parameter (even though the variable was selected). The Elder/Pregibon rule was used for variable selection. A comparison of the bootstrap Percentile and BCa confidence interval was made as well as an investigation of adjustments to the usual 95% confidence intervals based on the Bonferroni and Scheffe multiple comparison principles. The results show that linear regression has problems in capturing the true parameters in the confidence intervals for the sample sizes considered, the bootstrap intervals perform no better than linear regression, and the Scheffe method is too wide for any application considered. The Bonferroni adjustment is recommended for larger sample sizes and when the t-value for a selected variable is about 3.35 or higher. For smaller sample sizes all methods show problems with type II errors resulting from confidence intervals being too wide.
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Date Issued: 2006
Identifier: CFE0001482, ucf:47081
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001482

Title: SCENE MONITORING WITH A FOREST OF COOPERATIVE SENSORS.
Creator: Javed, Omar, Shah, Mubarak, University of Central Florida
Abstract / Description: In this dissertation, we present vision based scene interpretation methods for monitoring of people and vehicles, in real-time, within a busy environment using a forest of co-operative electro-optical (EO) sensors. We have developed novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the...
Show moreIn this dissertation, we present vision based scene interpretation methods for monitoring of people and vehicles, in real-time, within a busy environment using a forest of co-operative electro-optical (EO) sensors. We have developed novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the need for extensive manual intervention, site models and camera calibration, and provides adaptability to changing environmental conditions. For object detection and categorization in the video stream, a two step detection procedure is used. First, regions of interest are determined using a novel hierarchical background subtraction algorithm that uses color and gradient information for interest region detection. Second, objects are located and classified from within these regions using a weakly supervised learning mechanism based on co-training that employs motion and appearance features. The main contribution of this approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The advantage of this approach is that it requires only a few initial training samples and can automatically adjust its parameters online to improve the detection and classification performance. Once objects are detected and classified they are tracked in individual cameras. Single camera tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence in individual cameras. The tracker has the capability to handle multiple occluded objects. Next, the objects are tracked across a forest of cameras with non-overlapping views. This is a hard problem because of two reasons. First, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, the system learns the inter-camera relationships to constrain track correspondences. These relationships are learned in the form of multivariate probability density of space-time variables (object entry and exit locations, velocities, and inter-camera transition times) using Parzen windows. To handle the appearance change of an object as it moves from one camera to another, we show that all color transfer functions from a given camera to another camera lie in a low dimensional subspace. The tracking algorithm learns this subspace by using probabilistic principal component analysis and uses it for appearance matching. The proposed system learns the camera topology and subspace of inter-camera color transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both the location and appearance cues. Extensive experiments and deployment of this system in realistic scenarios has demonstrated the robustness of the proposed methods. The proposed system was able to detect and classify targets, and seamlessly tracked them across multiple cameras. It also generated a summary in terms of key frames and textual description of trajectories to a monitoring officer for final analysis and response decision. This level of interpretation was the goal of our research effort, and we believe that it is a significant step forward in the development of intelligent systems that can deal with the complexities of real world scenarios.
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Date Issued: 2005
Identifier: CFE0000497, ucf:46362
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000497

Title: IMAGE BASED VIEW SYNTHESIS.
Creator: Xiao, Jiangjian, Shah, Mubarak, University of Central Florida
Abstract / Description: This dissertation deals with the image-based approach to synthesize a virtual scene using sparse images or a video sequence without the use of 3D models. In our scenario, a real dynamic or static scene is captured by a set of un-calibrated images from different viewpoints. After automatically recovering the geometric transformations between these images, a series of photo-realistic virtual views can be rendered and a virtual environment covered by these several static cameras can be...
Show moreThis dissertation deals with the image-based approach to synthesize a virtual scene using sparse images or a video sequence without the use of 3D models. In our scenario, a real dynamic or static scene is captured by a set of un-calibrated images from different viewpoints. After automatically recovering the geometric transformations between these images, a series of photo-realistic virtual views can be rendered and a virtual environment covered by these several static cameras can be synthesized. This image-based approach has applications in object recognition, object transfer, video synthesis and video compression. In this dissertation, I have contributed to several sub-problems related to image based view synthesis. Before image-based view synthesis can be performed, images need to be segmented into individual objects. Assuming that a scene can approximately be described by multiple planar regions, I have developed a robust and novel approach to automatically extract a set of affine or projective transformations induced by these regions, correctly detect the occlusion pixels over multiple consecutive frames, and accurately segment the scene into several motion layers. First, a number of seed regions using correspondences in two frames are determined, and the seed regions are expanded and outliers are rejected employing the graph cuts method integrated with level set representation. Next, these initial regions are merged into several initial layers according to the motion similarity. Third, the occlusion order constraints on multiple frames are explored, which guarantee that the occlusion area increases with the temporal order in a short period and effectively maintains segmentation consistency over multiple consecutive frames. Then the correct layer segmentation is obtained by using a graph cuts algorithm, and the occlusions between the overlapping layers are explicitly determined. Several experimental results are demonstrated to show that our approach is effective and robust. Recovering the geometrical transformations among images of a scene is a prerequisite step for image-based view synthesis. I have developed a wide baseline matching algorithm to identify the correspondences between two un-calibrated images, and to further determine the geometric relationship between images, such as epipolar geometry or projective transformation. In our approach, a set of salient features, edge-corners, are detected to provide robust and consistent matching primitives. Then, based on the Singular Value Decomposition (SVD) of an affine matrix, we effectively quantize the search space into two independent subspaces for rotation angle and scaling factor, and then we use a two-stage affine matching algorithm to obtain robust matches between these two frames. The experimental results on a number of wide baseline images strongly demonstrate that our matching method outperforms the state-of-art algorithms even under the significant camera motion, illumination variation, occlusion, and self-similarity. Given the wide baseline matches among images I have developed a novel method for Dynamic view morphing. Dynamic view morphing deals with the scenes containing moving objects in presence of camera motion. The objects can be rigid or non-rigid, each of them can move in any orientation or direction. The proposed method can generate a series of continuous and physically accurate intermediate views from only two reference images without any knowledge about 3D. The procedure consists of three steps: segmentation, morphing and post-warping. Given a boundary connection constraint, the source and target scenes are segmented into several layers for morphing. Based on the decomposition of affine transformation between corresponding points, we uniquely determine a physically correct path for post-warping by the least distortion method. I have successfully generalized the dynamic scene synthesis problem from the simple scene with only rotation to the dynamic scene containing non-rigid objects. My method can handle dynamic rigid or non-rigid objects, including complicated objects such as humans. Finally, I have also developed a novel algorithm for tri-view morphing. This is an efficient image-based method to navigate a scene based on only three wide-baseline un-calibrated images without the explicit use of a 3D model. After automatically recovering corresponding points between each pair of images using our wide baseline matching method, an accurate trifocal plane is extracted from the trifocal tensor implied in these three images. Next, employing a trinocular-stereo algorithm and barycentric blending technique, we generate an arbitrary novel view to navigate the scene in a 2D space. Furthermore, after self-calibration of the cameras, a 3D model can also be correctly augmented into this virtual environment synthesized by the tri-view morphing algorithm. We have applied our view morphing framework to several interesting applications: 4D video synthesis, automatic target recognition, multi-view morphing.
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Date Issued: 2004
Identifier: CFE0000218, ucf:46276
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000218

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: RESOURCE ALLOCATION USING TOUCH AND AUDITION.
Creator: Mortimer, David, Gilson, Richard, University of Central Florida
Abstract / Description: When people multi-task with inputs that demand attention, processing, andencoding, sensory interference is possible at almost any level. Multiple Resource Theory (MRT) suggests that such interference may be avoided by drawing from separate pools of resources available when using different sensory channels, memory processes, and even different response modes. Thus, there should be advantages in dividing tasks among different sensory channels to tap independent pools of attentional resources....
Show moreWhen people multi-task with inputs that demand attention, processing, andencoding, sensory interference is possible at almost any level. Multiple Resource Theory (MRT) suggests that such interference may be avoided by drawing from separate pools of resources available when using different sensory channels, memory processes, and even different response modes. Thus, there should be advantages in dividing tasks among different sensory channels to tap independent pools of attentional resources. For example, people are better with two tasks using the eye and ear, than when using two auditory or two visual inputs. The majority of the research on MRT involves visual to auditory comparisons, i.e., the prime distance senses. The unstated implication is that the theory can be easily applied to other sensory systems, such as touch, but this is untested. This overlooks the fact that each sensory system has different characteristics that can influence how information processing is allocated in a multiple-task environment. For example, vision requires a directed gaze that is not required for sound or touch. Testing MRT with touch, not only eliminates competing theories, but helps establish its robustness across the senses. Three experiments compared the senses of touch and hearing to determine if the characteristics of those sensory modalities alter the allocation of processing resources. Specifically, it was hypothesized that differences in sensory characteristics would affect performance on a simple targeting task. All three experiments used auditory shadowing as the dual task load. In the first and third experiments a target was placed to the left or right of the participant and the targeting cue (either tactile, auditory, or combined) used to locate the target originated from the side on which the target was located. The only difference between experiments 1 and 3 was that in experiment 1 the auditory targeting cue was delivered by headphones, while in experiment 3 it was delivered by speakers. Experiment 2 was more difficult both in auditory perception and in processing. In this study the targeting cues came from in front of or behind the participant. Cues coming from in front of the participant meant the target was to the left, and conversely if the cue came from behind it meant that the target was to the right. The results of experiments 1 and 3 showed that when the signals originated from the sides, there was no difference in performance between the auditory and tactile targeting cues, whether by proximal or distal stimulation. However, in experiment 2, the participants were significantly slower to locate the target when using the auditory targeting cue than when using the tactile targeting cue, with nearly twice the losses when dual-tasking. No significant differences were found on performance of the shadowing task across the three experiments. The overall findings support the hypothesis that the characteristics of the sensory system itself influence the allocation of processing resources. For example, the differences in experiment 2 are likely due to front-back reversal, a common problem found with auditory stimuli located in front of or behind, but not with tactile stimuli.
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Date Issued: 2005
Identifier: CFE0000848, ucf:46657
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000848

Title: LEARNING TECHNIQUES FOR INFORMATION RETRIEVAL AND MINING IN HIGH-DIMENSIONAL DATABASES.
Creator: Cheng, Hao, Hua, Kien A., University of Central Florida
Abstract / Description: The main focus of my research is to design effective learning techniques for information retrieval and mining in high-dimensional databases. There are two main aspects in the retrieval and mining research: accuracy and efficiency. The accuracy problem is how to return results which can better match the ground truth, and the efficiency problem is how to evaluate users' requests and execute learning algorithms as fast as possible. However, these problems are non-trivial because of the...
Show moreThe main focus of my research is to design effective learning techniques for information retrieval and mining in high-dimensional databases. There are two main aspects in the retrieval and mining research: accuracy and efficiency. The accuracy problem is how to return results which can better match the ground truth, and the efficiency problem is how to evaluate users' requests and execute learning algorithms as fast as possible. However, these problems are non-trivial because of the complexity of the high-level semantic concepts, the heterogeneous natures of the feature space, the high dimensionality of data representations and the size of the databases. My dissertation is dedicated to addressing these issues. Specifically, my work has five main contributions as follows. The first contribution is a novel manifold learning algorithm, Local and Global Structures Preserving Projection (LGSPP), which defines salient low-dimensional representations for the high-dimensional data. A small number of projection directions are sought in order to properly preserve the local and global structures for the original data. Specifically, two groups of points are extracted for each individual point in the dataset: the first group contains the nearest neighbors of the point, and the other set are a few sampled points far away from the point. These two point sets respectively characterize the local and global structures with regard to the data point. The objective of the embedding is to minimize the distances of the points in each local neighborhood and also to disperse the points far away from their respective remote points in the original space. In this way, the relationships between the data in the original space are well preserved with little distortions. The second contribution is a new constrained clustering algorithm. Conventionally, clustering is an unsupervised learning problem, which systematically partitions a dataset into a small set of clusters such that data in each cluster appear similar to each other compared with those in other clusters. In the proposal, the partial human knowledge is exploited to find better clustering results. Two kinds of constraints are integrated into the clustering algorithm. One is the must-link constraint, indicating that the involved two points belong to the same cluster. On the other hand, the cannot-link constraint denotes that two points are not within the same cluster. Given the input constraints, data points are arranged into small groups and a graph is constructed to preserve the semantic relations between these groups. The assignment procedure makes a best effort to assign each group to a feasible cluster without violating the constraints. The theoretical analysis reveals that the probability of data points being assigned to the true clusters is much higher by the new proposal, compared to conventional methods. In general, the new scheme can produce clusters which can better match the ground truth and respect the semantic relations between points inferred from the constraints. The third contribution is a unified framework for partition-based dimension reduction techniques, which allows efficient similarity retrieval in the high-dimensional data space. Recent similarity search techniques, such as Piecewise Aggregate Approximation (PAA), Segmented Means (SMEAN) and Mean-Standard deviation (MS), prove to be very effective in reducing data dimensionality by partitioning dimensions into subsets and extracting aggregate values from each dimension subset. These partition-based techniques have many advantages including very efficient multi-phased pruning while being simple to implement. They, however, are not adaptive to different characteristics of data in diverse applications. In this study, a unified framework for these partition-based techniques is proposed and the issue of dimension partitions is examined in this framework. An investigation of the relationships of query selectivity and the dimension partition schemes discovers indicators which can predict the performance of a partitioning setting. Accordingly, a greedy algorithm is designed to effectively determine a good partitioning of data dimensions so that the performance of the reduction technique is robust with regard to different datasets. The fourth contribution is an effective similarity search technique in the database of point sets. In the conventional model, an object corresponds to a single vector. In the proposed study, an object is represented by a set of points. In general, this new representation can be used in many real-world applications and carries much more local information, but the retrieval and learning problems become very challenging. The Hausdorff distance is the common distance function to measure the similarity between two point sets, however, this metric is sensitive to outliers in the data. To address this issue, a novel similarity function is defined to better capture the proximity of two objects, in which a one-to-one mapping is established between vectors of the two objects. The optimal mapping minimizes the sum of distances between each paired points. The overall distance of the optimal matching is robust and has high retrieval accuracy. The computation of the new distance function is formulated into the classical assignment problem. The lower-bounding techniques and early-stop mechanism are also proposed to significantly accelerate the expensive similarity search process. The classification problem over the point-set data is called Multiple Instance Learning (MIL) in the machine learning community in which a vector is an instance and an object is a bag of instances. The fifth contribution is to convert the MIL problem into a standard supervised learning in the conventional vector space. Specially, feature vectors of bags are grouped into clusters. Each object is then denoted as a bag of cluster labels, and common patterns of each category are discovered, each of which is further reconstructed into a bag of features. Accordingly, a bag is effectively mapped into a feature space defined by the distances from this bag to all the derived patterns. The standard supervised learning algorithms can be applied to classify objects into pre-defined categories. The results demonstrate that the proposal has better classification accuracy compared to other state-of-the-art techniques. In the future, I will continue to explore my research in large-scale data analysis algorithms, applications and system developments. Especially, I am interested in applications to analyze the massive volume of online data.
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Date Issued: 2009
Identifier: CFE0002882, ucf:48022
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002882

Title: Categorical Change: Exploring the Effects of Concept Drift in Human Perceptual Category Learning.
Creator: Wismer, Andrew, Bohil, Corey, Szalma, James, Neider, Mark, Gluck, Kevin, University of Central Florida
Abstract / Description: Categorization is an essential survival skill that we engage in daily. A multitude of behavioral and neuropsychological evidence support the existence of multiple learning systems involved in category learning. COmpetition between Verbal and Implicit Systems (COVIS) theory provides a neuropsychological basis for the existence of an explicit and implicit learning system involved in the learning of category rules. COVIS provides a convincing account of asymptotic performance in human category...
Show moreCategorization is an essential survival skill that we engage in daily. A multitude of behavioral and neuropsychological evidence support the existence of multiple learning systems involved in category learning. COmpetition between Verbal and Implicit Systems (COVIS) theory provides a neuropsychological basis for the existence of an explicit and implicit learning system involved in the learning of category rules. COVIS provides a convincing account of asymptotic performance in human category learning. However, COVIS (-) and virtually all current theories of category learning (-) focus solely on categories and decision environments that remain stationary over time. However, our environment is dynamic, and we often need to adapt our decision making to account for environmental or categorical changes. Machine learning addresses this significant challenge through what is termed concept drift. Concept drift occurs any time a data distribution changes over time. This dissertation draws from two key characteristics of concept drift in machine learning known to impact the performance of learning models, and in-so-doing provides the first systematic exploration of concept drift (i.e., categorical change) in human perceptual category learning. Four experiments, each including one key change parameter (category base-rates, payoffs, or category structure [RB/II]), investigated the effect of rate of change (abrupt, gradual) and awareness of change (foretold or not) on decision criterion adaptation. Critically, Experiments 3 and 4 evaluated differences in categorical adaptation within explicit and implicit category learning tasks to determine if rate and awareness of change moderated any learning system differences. The results of these experiments inform current category learning theory and provide information for machine learning models of decision support in non-stationary environments.
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Date Issued: 2018
Identifier: CFE0007114, ucf:51947
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007114

Title: A phenomenological study of Black fifth grade students' perceptions of social studies and a discussion with secondary students.
Creator: Walker, Irenea, Russell, William, Hewitt, Randall, Hopp, Carolyn, Huff-Corzine, Lin, University of Central Florida
Abstract / Description: The problem I address in this study is the lack of Black elementary students' knowledge and interest of the social studies content. Black students who lack a true identity of self, fail to develop into productive citizens. Although previous studies have examined Black students' experiences in secondary social studies classrooms, few have thoroughly examined Black students' experiences in the elementary classrooms. For this study, I analyze Black fifth grade students' perceptions of the social...
Show moreThe problem I address in this study is the lack of Black elementary students' knowledge and interest of the social studies content. Black students who lack a true identity of self, fail to develop into productive citizens. Although previous studies have examined Black students' experiences in secondary social studies classrooms, few have thoroughly examined Black students' experiences in the elementary classrooms. For this study, I analyze Black fifth grade students' perceptions of the social studies content. Identifying these perceptions is imperative so educators can adjust their pedagogical practices based on what they deem as important for educational growth, and the experiences of Black students. Allowing Black students to share their experiences and express their thoughts is conducive to their knowledge and awareness of the subject (Scott, 2017). To grasp an authentic analysis of student understanding educators must start in the primary grades. Previous research highlights that curriculum and instruction fails to align with what students, especially Black students need to be successful in the classroom. Identifying these areas in elementary school will create a smooth transition for students as they advance to the next level.
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Date Issued: 2018
Identifier: CFE0007591, ucf:52547
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007591

$Influence of using context supportive of the area model on sixth grade students' performance when writing word problems for fraction subtraction and multiplication$

Title: Influence of using context supportive of the area model on sixth grade students' performance when writing word problems for fraction subtraction and multiplication.
Creator: Friske, Monica, Dixon, Juli, Andreasen, Janet, Ortiz, Enrique, University of Central Florida
Abstract / Description: The purpose of this action research study was to evaluate my own practice of teaching writing word problems with fraction subtraction and fraction multiplication using appropriate context. I wanted to see how focusing my instruction on the use of the area model and manipulatives could develop students' understanding of fractions when writing word problems. I chose this topic because Florida has adopted the Common Core State Standards and will be implementing them in the coming years. These...
Show moreThe purpose of this action research study was to evaluate my own practice of teaching writing word problems with fraction subtraction and fraction multiplication using appropriate context. I wanted to see how focusing my instruction on the use of the area model and manipulatives could develop students' understanding of fractions when writing word problems. I chose this topic because Florida has adopted the Common Core State Standards and will be implementing them in the coming years. These standards encourage the development of deeper understanding of mathematics, including fractions. I hoped this research would give my students the opportunity to make sense of fraction subtraction and fraction multiplication word problems on a deeper level, while giving me insight into my own practice in teaching context within word problems. Through this study, I learned that my students continued to switch the context of subtraction with multiplication within word problems. Students did make clear gains in their writing of fraction subtraction and fraction multiplication word problems. Although there is a limited amount of research on students mixing their context within fraction word problems, this study offers additional insight into a teacher's practice with writing fraction word problems.
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Date Issued: 2011
Identifier: CFE0004111, ucf:49112
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004111

Title: The Effects of Journaling and Vocabulary Strategies on Elementary Students' Attitudes Towards Mathematical Performance.
Creator: Janzen, Renee, Gresham, Regina, Haciomeroglu, Erhan, Roberts, Sherron, University of Central Florida
Abstract / Description: In an attempt to examine the effects of journaling and vocabulary strategies on elementary students' attitudes towards mathematical performance, I embedded reflective journaling and vocabulary strategies into my fourth grade mathematics curriculum. The mathematics content focused on whole number place value, multiplication, and division. My study revealed the positive effects these interventions can have on elementary students' attitudes towards mathematics.
Date Issued: 2012
Identifier: CFE0004266, ucf:49520
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004266

Title: LABELED SAMPLING CONSENSUS: A NOVEL ALGORITHM FOR ROBUSTLY FITTING MULTIPLE STRUCTURES USING COMPRESSED SAMPLING.
Creator: Messina, Carl, Foroosh, Hassan, University of Central Florida
Abstract / Description: The ability to robustly fit structures in datasets that contain outliers is a very important task in Image Processing, Pattern Recognition and Computer Vision. Random Sampling Consensus or RANSAC is a very popular method for this task, due to its ability to handle over 50% outliers. The problem with RANSAC is that it is only capable of finding a single structure. Therefore, if a dataset contains multiple structures, they must be found sequentially by finding the best fit, removing the points,...
Show moreThe ability to robustly fit structures in datasets that contain outliers is a very important task in Image Processing, Pattern Recognition and Computer Vision. Random Sampling Consensus or RANSAC is a very popular method for this task, due to its ability to handle over 50% outliers. The problem with RANSAC is that it is only capable of finding a single structure. Therefore, if a dataset contains multiple structures, they must be found sequentially by finding the best fit, removing the points, and repeating the process. However, removing incorrect points from the dataset could prove disastrous. This thesis offers a novel approach to sampling consensus that extends its ability to discover multiple structures in a single iteration through the dataset. The process introduced is an unsupervised method, requiring no previous knowledge to the distribution of the input data. It uniquely assigns labels to different instances of similar structures. The algorithm is thus called Labeled Sampling Consensus or L-SAC. These unique instances will tend to cluster around one another allowing the individual structures to be extracted using simple clustering techniques. Since divisions instead of modes are analyzed, only a single instance of a structure need be recovered. This ability of L-SAC allows a novel sampling procedure to be presented "compressing" the required samples needed compared to traditional sampling schemes while ensuring all structures have been found. L-SAC is a flexible framework that can be applied to many problem domains.
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Date Issued: 2011
Identifier: CFE0003893, ucf:48727
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003893

Title: UNCERTAINTY, IDENTIFICATION, AND PRIVACY: EXPERIMENTS IN INDIVIDUAL DECISION-MAKING.
Creator: Rivenbark, David, Harrison, Glenn, University of Central Florida
Abstract / Description: The alleged privacy paradox states that individuals report high values for personal privacy, while at the same time they report behavior that contradicts a high privacy value. This is a misconception. Reported privacy behaviors are explained by asymmetric subjective beliefs. Beliefs may or may not be uncertain, and non-neutral attitudes towards uncertainty are not necessary to explain behavior. This research was conducted in three related parts. Part one presents an experiment in individual...
Show moreThe alleged privacy paradox states that individuals report high values for personal privacy, while at the same time they report behavior that contradicts a high privacy value. This is a misconception. Reported privacy behaviors are explained by asymmetric subjective beliefs. Beliefs may or may not be uncertain, and non-neutral attitudes towards uncertainty are not necessary to explain behavior. This research was conducted in three related parts. Part one presents an experiment in individual decision making under uncertainty. EllsbergÃ‚Â's canonical two-color choice problem was used to estimate attitudes towards uncertainty. Subjects believed bets on the color ball drawn from EllsbergÃ‚Â's ambiguous urn were equally likely to pay. Estimated attitudes towards uncertainty were insignificant. Subjective expected utility explained subjectsÃ‚Â' choices better than uncertainty aversion and the uncertain priors model. A second treatment tested Vernon SmithÃ‚Â's conjecture that preferences in EllsbergÃ‚Â's problem would be unchanged when the ambiguous lottery is replaced by a compound objective lottery. The use of an objective compound lottery to induce uncertainty did not affect subjectsÃ‚Â' choices. The second part of this dissertation extended the concept of uncertainty to commodities where quality and accuracy of a quality report were potentially ambiguous. The uncertain priors model is naturally extended to allow for potentially different attitudes towards these two sources of uncertainty, quality and accuracy. As they relate to privacy, quality and accuracy of a quality report are seen as metaphors for online security and consumer trust in e-commerce, respectively. The results of parametric structural tests were mixed. Subjects made choices consistent with neutral attitudes towards uncertainty in both the quality and accuracy domains. However, allowing for uncertainty aversion in the quality domain and not the accuracy domain outperformed the alternative which only allowed for uncertainty aversion in the accuracy domain. Finally, part three integrated a public-goods game and punishment opportunities with the Becker-DeGroot-Marschak mechanism to elicit privacy values, replicating previously reported privacy behaviors. The procedures developed elicited punishment (consequence) beliefs and information confidentiality beliefs in the context of individual privacy decisions. Three contributions are made to the literature. First, by using cash rewards as a mechanism to map actions to consequences, the study eliminated hypothetical bias as a confounding behavioral factor which is pervasive in the privacy literature. Econometric results support the Ã‚Â"privacy paradoxÃ‚Â" at levels greater than 10 percent. Second, the roles of asymmetric beliefs and attitudes towards uncertainty were identified using parametric structural likelihood methods. Subjects were, in general, uncertainty neutral and believed Ã‚Â"badÃ‚Â" events were more likely to occur when their private information was not confidential. A third contribution is a partial test to determine which uncertain process, loss of privacy or the resolution of consequences, is of primary importance to individual decision-makers. Choices were consistent with uncertainty neutral preferences in both the privacy and consequences domains.
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Date Issued: 2010
Identifier: CFE0003251, ucf:48539
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003251

Title: Balancing multiple roles: A re-examination of how work impacts academic performance for community college students.
Creator: Lue, Celena, Cintron Delgado, Rosa, Sivo, Stephen, Owens, J. Thomas, Penfold Navarro, Catherine, University of Central Florida
Abstract / Description: This study investigated how work impacts academic performance for community college students, from a multiple role balance perspective. Perna (2010) called for a re-examination of the role of work in college students' lives, especially regarding the exploration of benefits, rather than just the detriments of working while studying. According to Karp and Bork (2014), more research was also needed on community college students and how they balance multiple roles. Exploring the relationship...
Show moreThis study investigated how work impacts academic performance for community college students, from a multiple role balance perspective. Perna (2010) called for a re-examination of the role of work in college students' lives, especially regarding the exploration of benefits, rather than just the detriments of working while studying. According to Karp and Bork (2014), more research was also needed on community college students and how they balance multiple roles. Exploring the relationship between balancing multiple roles and academic performance may provide new insight into how community college students contend with demanding roles, while striving to achieve academic success. This study was framed by the theoretical understanding of Marks and MacDermid's (1996) role balance theory and the instrument used was the Role Balance scale. Four hundred and ten participants responded to the online survey containing role balance and demographic questions. Data from 403 respondents were used in the regression analysis to determine how work impacted role balance. Among the community college student respondents, hours worked for pay was a significant factor in predicting role balance when controlling for demographic and lifestyle variables. For every extra hour worked per week, the role balance score would decrease by 0.02. Demographic and lifestyle variables were not significant in predicting role balance. Data from the survey responses of all 410 respondents were used for the correlation analysis. There was no significant relationship found between role balance and academic performance.
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Date Issued: 2017
Identifier: CFE0006619, ucf:51277
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006619

Title: How does brief cognitive behavioral therapy work? Potential mechanisms of action for veterans with physical and psychological comorbidities.
Creator: Deavers, Frances, Cassisi, Jeffrey, Bowers, Clint, Eldridge, Gloria, University of Central Florida
Abstract / Description: Depression and anxiety are commonly comorbid among patients with chronic medical conditions. These comorbidities are associated with negative outcomes including poorer quality of life and worse physical functioning. Evidence that traditional cognitive behavioral therapy (CBT) is less effective for these populations has led to the development of brief CBT protocols that incorporate physical health self-management skills and are delivered in primary care. To continue refining treatment packages...
Show moreDepression and anxiety are commonly comorbid among patients with chronic medical conditions. These comorbidities are associated with negative outcomes including poorer quality of life and worse physical functioning. Evidence that traditional cognitive behavioral therapy (CBT) is less effective for these populations has led to the development of brief CBT protocols that incorporate physical health self-management skills and are delivered in primary care. To continue refining treatment packages, it is important to understand how brief CBT works. The present study used the transactional model of stress and coping as a framework for investigating potential mechanisms of action of brief CBT. Veterans with chronic obstructive pulmonary disease and/or heart failure and elevated symptoms of depression and/or anxiety were randomized to receive brief CBT (n =180) or enhanced usual care (EUC; n = 122). At 4-month follow-up, depression and anxiety symptoms were significantly lower in veterans who received brief CBT, compared to EUC. Multiple mediation analyses revealed that brief CBT was associated with higher self-efficacy and less avoidant coping at 4-month follow-up, which were in turn associated with less depression and anxiety symptoms. Illness intrusiveness was also a significant mediator of the relationship between brief CBT and anxiety symptoms, but not depression symptoms. In contrast, increases in active coping attributable to brief CBT were not associated with improvements in depression or anxiety symptoms. These results demonstrate the utility of the transactional model of stress and coping as a framework for understanding mechanisms of action of brief CBT in patients with comorbid physical and psychological conditions.
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Date Issued: 2017
Identifier: CFE0006733, ucf:51884
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006733

Title: The conceptual field of proportional reasoning researched through the lived experiences of nurses.
Creator: Deichert, Deana, Dixon, Juli, Haciomeroglu, Erhan, Andreasen, Janet, Hunt, Debra, University of Central Florida
Abstract / Description: Proportional reasoning instruction is prevalent in elementary, secondary, and post-secondary schooling. The concept of proportional reasoning is used in a variety of contexts for solving real-world problems. One of these contexts is the solving of dosage calculation proportional problems in the healthcare field. On the job, nurses perform drug dosage calculations which carry fatal consequences. As a result, nursing students are required to meet minimum competencies in solving proportion...
Show moreProportional reasoning instruction is prevalent in elementary, secondary, and post-secondary schooling. The concept of proportional reasoning is used in a variety of contexts for solving real-world problems. One of these contexts is the solving of dosage calculation proportional problems in the healthcare field. On the job, nurses perform drug dosage calculations which carry fatal consequences. As a result, nursing students are required to meet minimum competencies in solving proportion problems. The goal of this research is to describe the lived experiences of nurses in connection to their use of proportional reasoning in order to impact instruction of the procedures used to solve these problems. The research begins by clarifying and defining the conceptual field of proportional reasoning. Utilizing Vergnaud's theory of conceptual fields and synthesizing the differing organizational frameworks used in the literature on proportional reasoning, the concept is organized and explicated into three components: concepts, procedures, and situations. Through the lens of this organizational structure, data from 44 registered nurses who completed a dosage calculation proportion survey were analyzed and connected to the framework of the conceptual field of proportional reasoning. Four nurses were chosen as a focus of in-depth study based upon their procedural strategies and ability to vividly describe their experiences. These qualitative results are synthesized to describe the lived experiences of nurses related to their education and use of proportional reasoning.Procedural strategies that are supported by textbooks, instruction, and practice are developed and defined. Descriptive statistics show the distribution of procedures used by nurses on a five question dosage calculation survey. The most common procedures used are the nursing formula, cross products, and dimensional analysis. These procedures correspond to the predominate procedures found in nursing dosage calculation texts. Instructional implications focus on the transition between elementary and secondary multiplicative structures, the confusion between equality and proportionality, and the difficulty that like quantities present in dealing with proportions.
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Date Issued: 2014
Identifier: CFE0005781, ucf:50058
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005781

Title: THE EFFECTS OF COMPUTER-ASSISTED REPEATED READINGS ON THE READING PERFORMANCE OF MIDDLE SCHOOL STUDENTS WITH MILD INTELLECTUAL DISABILITIES.
Creator: Cerasale, Mark, Martin, Suzanne, University of Central Florida
Abstract / Description: The No Child Left Behind Act of 2001 has mandated that all public school students will be reading at grade level by the 2013-2014 school year. Florida has embarked on an agenda to ensure that the kindergarten through high school student population is reading at or above grade level by 2014. Many of Florida's low-performing student population, including middle school students with high incidence disabilities, are reading below grade level. Using a multiple baseline across subjects design,...
Show moreThe No Child Left Behind Act of 2001 has mandated that all public school students will be reading at grade level by the 2013-2014 school year. Florida has embarked on an agenda to ensure that the kindergarten through high school student population is reading at or above grade level by 2014. Many of Florida's low-performing student population, including middle school students with high incidence disabilities, are reading below grade level. Using a multiple baseline across subjects design, this study examined the impact of computer-assisted repeated readings on the reading performance of three middle school students with mild intellectual disabilities over the course of 67 days. Results showed an improvement in reading fluency rate using instructional level text. The study was evaluated using quality indicators of single-subject research in special education. Future research is advocated to replicate this study across different grades and exceptionalities.
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Date Issued: 2009
Identifier: CFE0002855, ucf:48055
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002855

Title: Understanding images and videos using context.
Creator: Vaca Castano, Gonzalo, Da Vitoria Lobo, Niels, Shah, Mubarak, Mikhael, Wasfy, Jones, W Linwood, Wiegand, Rudolf, University of Central Florida
Abstract / Description: In computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also...
Show moreIn computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also investigate the influence of the semantic context, determined by objects, relationships, locations, and global composition, to achieve a general understanding of the image content as a whole. In our research, we also attempt to solve the related problem of finding the context associated with visual media. Given a set of visual elements (images), we want to extract the context that can be commonly associated with these images in order to remove ambiguity. The first part of this dissertation concentrates on achieving image understanding using semantic context.(&)nbsp; In spite of the recent success in tasks such as image classi?cation, object detection, image segmentation, and the progress on scene understanding, researchers still lack clarity about computer comprehension of the content of the image as a whole. Hence, we propose a Top-Down Visual Tree (TDVT) image representation that allows the encoding of the content of the image as a hierarchy of objects capturing their importance, co-occurrences, and type of relations. A novel Top-Down Tree LSTM network is presented to learn about the image composition from the training images and their TDVT representations. Given a test image, our algorithm detects objects and determine the hierarchical structure that they form, encoded as a TDVT representation of the image.A single image could have multiple interpretations that may lead to ambiguity about the intentionality of an image.(&)nbsp; What if instead of having only a single image to be interpreted, we have multiple images that represent the same topic. The second part of this dissertation covers how to extract the context information shared by multiple images. We present a method to determine the topic that these images represent. We accomplish this task by transferring tags from an image retrieval database, and by performing operations in the textual space of these tags. As an application, we also present a new image retrieval method that uses multiple images as input. Unlike earlier works that focus either on using just a single query image or using multiple query images with views of the same instance, the new image search paradigm retrieves images based on the underlying concepts that the input images represent.Finally, in the third part of this dissertation, we analyze the influence of context in videos. In this case, the temporal context is utilized to improve scene identification and object detection. We focus on egocentric videos, where agents require some time to change from one location to another. Therefore, we propose a Conditional Random Field (CRF) formulation, which penalizes short-term changes of the scene identity to improve the scene identity accuracy.(&)nbsp; We also show how to improve the object detection outcome by re-scoring the results based on the scene identity of the tested frame. We present a Support Vector Regression (SVR) formulation in the case that explicit knowledge of the scene identity is available during training time. In the case that explicit scene labeling is not available, we propose an LSTM formulation that considers the general appearance of the frame to re-score the object detectors.
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Date Issued: 2017
Identifier: CFE0006922, ucf:51703
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006922

Title: The Effects of Synchronous Online Cognitive Strategy Instruction in Writing for Students with Learning Disabilities.
Creator: Straub, Carrie, Vasquez, Eleazar, Wienke, Wilfred, Dieker, Lisa, Kaplan, Jeffrey, University of Central Florida
Abstract / Description: This study investigates the effects of self-regulated strategy development (Harris, Graham, (&) Mason, 2009) for cognitive strategy instruction in persuasive writing (POW+TREE) using a synchronous online learning environment for special education students. Participants are four adolescent students with learning disabilities (LD) with low achievement in writing. One undergraduate research assistant delivered instruction using a synchronous online platform (e.g., Adobe Connect) in conjunction...
Show moreThis study investigates the effects of self-regulated strategy development (Harris, Graham, (&) Mason, 2009) for cognitive strategy instruction in persuasive writing (POW+TREE) using a synchronous online learning environment for special education students. Participants are four adolescent students with learning disabilities (LD) with low achievement in writing. One undergraduate research assistant delivered instruction using a synchronous online platform (e.g., Adobe Connect) in conjunction with collaborative writing software (e.g., Google Docs word processing). A multiple probe across participants design was used to demonstrate a functional relationship between instruction and number of essay elements (EE). Number of correct minus incorrect word sequences (CIWS) was used as a secondary dependent measure. A non-experimental pre-post design was used to compare the mean performance of holistic writing quality scores and standard scores from the TOWL-3. All four participants gained EE and CIWS from baseline to treatment and demonstrated standard score changes from pre to post-test on the TOWL-3. Implications for writing instruction for students with LD using online learning environments are discussed.
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Date Issued: 2012
Identifier: CFE0004606, ucf:49937
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004606

Title: INTEGRATED INP PHOTONIC SWITCHES.
Creator: May-Arrioja, Daniel, LiKamWa, Patrick, University of Central Florida
Abstract / Description: Photonic switches are becoming key components in advanced optical networks because of the large variety of applications that they can perform. One of the key advantages of photonic switches is that they redirect or convert light without having to make any optical to electronic conversions and vice versa, thus allowing networking functions to be lowered into the optical layer. InP-based switches are particularly attractive because of their small size, low electrical power consumption, and...
Show morePhotonic switches are becoming key components in advanced optical networks because of the large variety of applications that they can perform. One of the key advantages of photonic switches is that they redirect or convert light without having to make any optical to electronic conversions and vice versa, thus allowing networking functions to be lowered into the optical layer. InP-based switches are particularly attractive because of their small size, low electrical power consumption, and compatibility with integration of laser sources, photo-detectors, and electronic components. In this dissertation the development of integrated InP photonic switches using an area-selective zinc diffusion process has been investigated. The zinc diffusion process is implemented using a semi-sealed open-tube diffusion technique. The process has proven to be highly controllable and reproducible by carefully monitoring of the diffusion parameters. Using this technique, isolated p-n junctions exhibiting good I-V characteristics and breakdown voltages greater than 10 V can be selectively defined across a semiconductor wafer. A series of Mach-Zehnder interferometric (MZI) switches/modulators have been designed and fabricated. Monolithic integration of 1x2 and 2x2 MZI switches has been demonstrated. The diffusion process circumvents the need for isolation trenches, and hence optical losses can be significantly reduced. An efficient optical beam steering device based on InGaAsP multiple quantum wells is also demonstrated. The degree of lateral current spreading is easily regulated by controlling the zinc depth, allowing optimization of the injected currents. Beam steering over a 21 microns lateral distance with electrical current values as low as 12.5 mA are demonstrated. Using this principle, a reconfigurable 1x3 switch has been implemented with crosstalk levels better than -17 dB over a 50 nm wavelength range. At these low electrical current levels, uncooled and d.c. bias operation is made feasible. The use of multimode interference (MMI) structures as active devices have also been investigated. These devices operate by selective refractive index perturbation on very specific areas within the MMI structure, and this is again realized using zinc diffusion. Several variants such as a compact MMI modulator that is as short as 350 µm, a robust 2x2 photonic switch and a tunable MMI coupler have been demonstrated.
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Date Issued: 2006
Identifier: CFE0001368, ucf:47007
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001368

workload (3)	+ -
Elementary (2)	+ -
Multiple Linear Regression (2)	+ -
Multiple Resource Theory (2)	+ -
multiple group membership (2)	+ -

multiple regression (2)	+ -
multiple resource theory (2)	+ -
Activities of Daily Living (1)	+ -
Altruism (1)	+ -
Audition (1)	+ -
Aurora (1)	+ -
Autism (1)	+ -
Before-After Analysis (1)	+ -
Bifurcation (1)	+ -
Black students (1)	+ -
Bootstrap (1)	+ -
COVIS (1)	+ -
Camera Calibration (1)	+ -
Carthage (1)	+ -
Christine Hippler (1)	+ -
Christine Lynn Hippler (1)	+ -
Cluster Analysis (1)	+ -
Coefficient Estimation (1)	+ -
Cognitive Styles (1)	+ -
Compressed Sampling (1)	+ -
Computer Vision (1)	+ -
ConnectED Initiative (1)	+ -
Cost flow diagram (1)	+ -
Crash Modification Factors (1)	+ -
Crash Modification Functions (1)	+ -

ETD Collection (49)	+ -
Honors Collection (4)	+ -

University of Central Florida (53)	+ -
Shah, Mubarak (4)	+ -
Szalma, James (4)	+ -
Dixon, Juli (3)	+ -
Hancock, Peter (3)	+ -

Mouloua, Mustapha (3)	+ -
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Chow, Louis (2)	+ -
Dieker, Lisa (2)	+ -
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Haciomeroglu, Erhan (2)	+ -
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Taylor, Grant (2)	+ -
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