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Title: A COMPARISON OF COMPUTER AND TRADITIONAL FACE-TO-FACE CLASSROOM ORIENTATION FOR BEGINNING CRITICAL CARE NURSES.
Creator: Anzalone, Patricia, Sole, Mary Lou, University of Central Florida
Abstract / Description: Purpose: Education of the novice critical care nurse has traditionally been conducted by critical care educators in face-to-face classes in an orientation or internship. A shortage of qualified educators and growth in electronic modes of course delivery has led organizations to explore electronic learning (e-learning) to provide orientation to critical care nursing concepts. Equivalence of e-learning versus traditional critical care orientation has not been studied. The primary aim of this...
Show morePurpose: Education of the novice critical care nurse has traditionally been conducted by critical care educators in face-to-face classes in an orientation or internship. A shortage of qualified educators and growth in electronic modes of course delivery has led organizations to explore electronic learning (e-learning) to provide orientation to critical care nursing concepts. Equivalence of e-learning versus traditional critical care orientation has not been studied. The primary aim of this study was to examine the equivalency of knowledge attainment in the cardiovascular module of the Essentials of Critical Care Orientation (ECCO) e-learning program to traditional face-to-face critical care orientation classes covering the same content. Additional aims were to determine if learning style is associated with a preference for type of learning method, and to determine any difference in learning satisfaction between the two modalities. Methods: The study was conducted using a two-group pretest-posttest experimental design. Forty-one practicing volunteer nurses with no current critical care experience living in southwest Florida were randomly assigned to either the ECCO (n=19) or face-to-face (n=22) group. Those in the face-to-face group attended 20 hours of classroom instruction taught by an expert educator. Those in the ECCO group completed the lessons on line and had an optional 2 hour face-to-face discussion component. Pre-test measures included the Basic Knowledge Assessment Test (BKAT-7), modified ECCO Cardiovascular (CV) Examination, and Kolb Learning Style Inventory (LSI). Post-tests included the BKAT-7, modified CV Examination, and Affective Measures Survey. Results: The majority of subjects were female, married, and educated at the associate degree level. Their mean age was 39.5 + 12 years, and they averaged 9.9 + 11.7 years of nursing experience. The diverging learning style was assessed in 37% of subjects. Classroom instruction was preferred by 61% of participants. No statistical differences were noted between groups on any demographic variables or baseline knowledge. Learning outcomes were compared by repeated measures analysis of variance. Mean scores of subjects in both groups increased statistically on both the BKAT-7 and modified CV Examination (p=<.01); however, no significant differences (p> .05) were found between groups. Preference for online versus classroom instruction was not associated with learning style (X2 = 3.39, p = .34). Satisfaction with learning modality was significantly greater for those in the classroom group (t=4.25, p=.000). Discussion/Implications: This is the first study to evaluate the ECCO orientation program and contributes to the growing body of knowledge exploring e-learning versus traditional education. The results of this study provide evidence that the ECCO critical care education produces learning outcomes at least equivalent to traditional classroom instruction, regardless of the learning style of the student. As participant satisfaction was more favorable toward the classroom learning modality, consideration should be given to providing blended learning if using computer-based orientation programs. Replication of this study with a variety of instructors in varied geographic locations, expanded populations, larger samples, and different subject matter is recommended.
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Date Issued: 2008
Identifier: CFE0002192, ucf:47888
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002192

Title: Complementary Layered Learning.
Creator: Mondesire, Sean, Wu, Annie, Wiegand, Rudolf, Sukthankar, Gita, Proctor, Michael, University of Central Florida
Abstract / Description: Layered learning is a machine learning paradigm used to develop autonomous robotic-based agents by decomposing a complex task into simpler subtasks and learns each sequentially. Although the paradigm continues to have success in multiple domains, performance can be unexpectedly unsatisfactory. Using Boolean-logic problems and autonomous agent navigation, we show poor performance is due to the learner forgetting how to perform earlier learned subtasks too quickly (favoring plasticity) or...
Show moreLayered learning is a machine learning paradigm used to develop autonomous robotic-based agents by decomposing a complex task into simpler subtasks and learns each sequentially. Although the paradigm continues to have success in multiple domains, performance can be unexpectedly unsatisfactory. Using Boolean-logic problems and autonomous agent navigation, we show poor performance is due to the learner forgetting how to perform earlier learned subtasks too quickly (favoring plasticity) or having difficulty learning new things (favoring stability). We demonstrate that this imbalance can hinder learning so that task performance is no better than that of a sub-optimal learning technique, monolithic learning, which does not use decomposition. Through the resulting analyses, we have identified factors that can lead to imbalance and their negative effects, providing a deeper understanding of stability and plasticity in decomposition-based approaches, such as layered learning.To combat the negative effects of the imbalance, a complementary learning system is applied to layered learning. The new technique augments the original learning approach with dual storage region policies to preserve useful information from being removed from an agent's policy prematurely. Through multi-agent experiments, a 28% task performance increase is obtained with the proposed augmentations over the original technique.
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Date Issued: 2014
Identifier: CFE0005213, ucf:50626
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005213

Title: Relating First-person and Third-person Vision.
Creator: Ardeshir Behrostaghi, Shervin, Borji, Ali, Shah, Mubarak, Hu, Haiyan, Atia, George, University of Central Florida
Abstract / Description: Thanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones and glasses, we have access to a plethora of videos captured from the first person (egocentric) perspective. Capturing the world from the perspective of one's self, egocentric videos bear characteristics distinct from the more traditional third-person (exocentric) videos. In many computer vision tasks (e.g. identification, action recognition, face recognition, pose estimation, etc.),...
Show moreThanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones and glasses, we have access to a plethora of videos captured from the first person (egocentric) perspective. Capturing the world from the perspective of one's self, egocentric videos bear characteristics distinct from the more traditional third-person (exocentric) videos. In many computer vision tasks (e.g. identification, action recognition, face recognition, pose estimation, etc.), the human actors are the main focus. Hence, detecting, localizing, and recognizing the human actor is often incorporated as a vital component. In an egocentric video however, the person behind the camera is often the person of interest. This would change the nature of the task at hand, given that the camera holder is usually not visible in the content of his/her egocentric video. In other words, our knowledge about the visual appearance, pose, etc. on the egocentric camera holder is very limited, suggesting reliance on other cues in first person videos. First and third person videos have been separately studied in the past in the computer vision community. However, the relationship between first and third person vision has yet to be fully explored. Relating these two views systematically could potentially benefit many computer vision tasks and applications. This thesis studies this relationship in several aspects. We explore supervised and unsupervised approaches for relating these two views seeking different objectives such as identification, temporal alignment, and action classification. We believe that this exploration could lead to a better understanding the relationship of these two drastically different sources of information.
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Date Issued: 2018
Identifier: CFE0007151, ucf:52322
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007151

Title: Adversarial Attacks On Vision Algorithms Using Deep Learning Features.
Creator: Michel, Andy, Jha, Sumit Kumar, Leavens, Gary, Valliyil Thankachan, Sharma, University of Central Florida
Abstract / Description: Computer vision algorithms, such as those implementing object detection, are known to be sus-ceptible to adversarial attacks. Small barely perceptible perturbations to the input can cause visionalgorithms to incorrectly classify inputs that they would have otherwise classified correctly. Anumber of approaches have been recently investigated to generate such adversarial examples fordeep neural networks. Many of these approaches either require grey-box access to the deep neuralnet being...
Show moreComputer vision algorithms, such as those implementing object detection, are known to be sus-ceptible to adversarial attacks. Small barely perceptible perturbations to the input can cause visionalgorithms to incorrectly classify inputs that they would have otherwise classified correctly. Anumber of approaches have been recently investigated to generate such adversarial examples fordeep neural networks. Many of these approaches either require grey-box access to the deep neuralnet being attacked or rely on adversarial transfer and grey-box access to a surrogate neural network.In this thesis, we present an approach to the synthesis of adversarial examples for computer vi-sion algorithms that only requires black-box access to the algorithm being attacked. Our attackapproach employs fuzzing with features derived from the layers of a convolutional neural networktrained on adversarial examples from an unrelated dataset. Based on our experimental results,we believe that our validation approach will enable designers of cyber-physical systems and otherhigh-assurance use-cases of vision algorithms to stress test their implementations.
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Date Issued: 2017
Identifier: CFE0006898, ucf:51714
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006898

Title: Online, Supervised and Unsupervised Action Localization in Videos.
Creator: Soomro, Khurram, Shah, Mubarak, Heinrich, Mark, Hu, Haiyan, Bagci, Ulas, Yun, Hae-Bum, University of Central Florida
Abstract / Description: Action recognition classifies a given video among a set of action labels, whereas action localization determines the location of an action in addition to its class. The overall aim of this dissertation is action localization. Many of the existing action localization approaches exhaustively search (spatially and temporally) for an action in a video. However, as the search space increases with high resolution and longer duration videos, it becomes impractical to use such sliding window...
Show moreAction recognition classifies a given video among a set of action labels, whereas action localization determines the location of an action in addition to its class. The overall aim of this dissertation is action localization. Many of the existing action localization approaches exhaustively search (spatially and temporally) for an action in a video. However, as the search space increases with high resolution and longer duration videos, it becomes impractical to use such sliding window techniques. The first part of this dissertation presents an efficient approach for localizing actions by learning contextual relations between different video regions in training. In testing, we use the context information to estimate the probability of each supervoxel belonging to the foreground action and use Conditional Random Field (CRF) to localize actions. In the above method and typical approaches to this problem, localization is performed in an offline manner where all the video frames are processed together. This prevents timely localization and prediction of actions/interactions - an important consideration for many tasks including surveillance and human-machine interaction. Therefore, in the second part of this dissertation we propose an online approach to the challenging problem of localization and prediction of actions/interactions in videos. In this approach, we use human poses and superpixels in each frame to train discriminative appearance models and perform online prediction of actions/interactions with Structural SVM. Above two approaches rely on human supervision in the form of assigning action class labels to videos and annotating actor bounding boxes in each frame of training videos. Therefore, in the third part of this dissertation we address the problem of unsupervised action localization. Given unlabeled videos without annotations, this approach aims at: 1) Discovering action classes using a discriminative clustering approach, and 2) Localizing actions using a variant of Knapsack problem.
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Date Issued: 2017
Identifier: CFE0006917, ucf:51685
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006917

Title: Action Recognition, Temporal Localization and Detection in Trimmed and Untrimmed Video.
Creator: Hou, Rui, Shah, Mubarak, Mahalanobis, Abhijit, Hua, Kien, Sukthankar, Rahul, University of Central Florida
Abstract / Description: Automatic understanding of videos is one of the most active areas of computer vision research. It has applications in video surveillance, human computer interaction, video sports analysis, virtual and augmented reality, video retrieval etc. In this dissertation, we address four important tasks in video understanding, namely action recognition, temporal action localization, spatial-temporal action detection and video object/action segmentation. This dissertation makes contributions to above...
Show moreAutomatic understanding of videos is one of the most active areas of computer vision research. It has applications in video surveillance, human computer interaction, video sports analysis, virtual and augmented reality, video retrieval etc. In this dissertation, we address four important tasks in video understanding, namely action recognition, temporal action localization, spatial-temporal action detection and video object/action segmentation. This dissertation makes contributions to above tasks by proposing. First, for video action recognition, we propose a category level feature learning method. Our proposed method automatically identifies such pairs of categories using a criterion of mutual pairwise proximity in the (kernelized) feature space, and a category-level similarity matrix where each entry corresponds to the one-vs-one SVM margin for pairs of categories. Second, for temporal action localization, we propose to exploit the temporal structure of actions by modeling an action as a sequence of sub-actions and present a computationally efficient approach. Third, we propose 3D Tube Convolutional Neural Network (TCNN) based pipeline for action detection. The proposed architecture is a unified deep network that is able to recognize and localize action based on 3D convolution features. It generalizes the popular faster R-CNN framework from images to videos. Last, an end-to-end encoder-decoder based 3D convolutional neural network pipeline is proposed, which is able to segment out the foreground objects from the background. Moreover, the action label can be obtained as well by passing the foreground object into an action classifier. Extensive experiments on several video datasets demonstrate the superior performance of the proposed approach for video understanding compared to the state-of-the-art.
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Date Issued: 2019
Identifier: CFE0007655, ucf:52502
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007655

Title: An Engineering Analytics Based Framework for Computational Advertising Systems.
Creator: Chen, Mengmeng, Rabelo, Luis, Lee, Gene, Keathley, Heather, Rahal, Ahmad, University of Central Florida
Abstract / Description: Engineering analytics is a multifaceted landscape with a diversity of analytics tools which comes from emerging fields such as big data, machine learning, and traditional operations research. Industrial engineering is capable to optimize complex process and systems using engineering analytics elements and the traditional components such as total quality management. This dissertation has proven that industrial engineering using engineering analytics can optimize the emerging area of...
Show moreEngineering analytics is a multifaceted landscape with a diversity of analytics tools which comes from emerging fields such as big data, machine learning, and traditional operations research. Industrial engineering is capable to optimize complex process and systems using engineering analytics elements and the traditional components such as total quality management. This dissertation has proven that industrial engineering using engineering analytics can optimize the emerging area of Computational Advertising. The key was to know the different fields very well and do the right selection. However, people first need to understand and be experts in the flow of the complex application of Computational Advertising and based on the characteristics of each step map the right field of Engineering analytics and traditional Industrial Engineering. Then build the apparatus and apply it to the respective problem in question.This dissertation consists of four research papers addressing the development of a framework to tame the complexity of computational advertising and improve its usage efficiency from an advertiser's viewpoint. This new framework and its respective systems architecture combine the use of support vector machines, Recurrent Neural Networks, Deep Learning Neural Networks, traditional neural networks, Game Theory/Auction Theory with Generative adversarial networks, and Web Engineering to optimize the computational advertising bidding process and achieve a higher rate of return. The system is validated with an actual case study with commercial providers such as Google AdWords and an advertiser's budget of several million dollars.
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Date Issued: 2018
Identifier: CFE0007319, ucf:52118
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007319

Title: Human Action Localization and Recognition in Unconstrained Videos.
Creator: Boyraz, Hakan, Tappen, Marshall, Foroosh, Hassan, Lin, Mingjie, Zhang, Shaojie, Sukthankar, Rahul, University of Central Florida
Abstract / Description: As imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing...
Show moreAs imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing discriminative sub-regions of images and videos when performing recognition tasks. In this thesis, we address the action detection and recognition problems. Action detection in video is a particularly difficult problem because actions must not only be recognized correctly, but must also be localized in the 3D spatio-temporal volume. We introduce a technique that transforms the 3D localization problem into a series of 2D detection tasks. This is accomplished by dividing the video into overlapping segments, then representing each segment with a 2D video projection. The advantage of the 2D projection is that it makes it convenient to apply the best techniques from object detection to the action detection problem. We also introduce a novel, straightforward method for searching the 2D projections to localize actions, termed Two-Point Subwindow Search (TPSS). Finally, we show how to connect the local detections in time using a chaining algorithm to identify the entire extent of the action. Our experiments show that video projection outperforms the latest results on action detection in a direct comparison.Second, we present a probabilistic model learning to identify discriminative regions in videos from weakly-supervised data where each video clip is only assigned a label describing what action is present in the frame or clip. While our first system requires every action to be manually outlined in every frame of the video, this second system only requires that the video be given a single high-level tag. From this data, the system is able to identify discriminative regions that correspond well to the regions containing the actual actions. Our experiments on both the MSR Action Dataset II and UCF Sports Dataset show that the localizations produced by this weakly supervised system are comparable in quality to localizations produced by systems that require each frame to be manually annotated. This system is able to detect actions in both 1) non-temporally segmented action videos and 2) recognition tasks where a single label is assigned to the clip. We also demonstrate the action recognition performance of our method on two complex datasets, i.e. HMDB and UCF101. Third, we extend our weakly-supervised framework by replacing the recognition stage with a two-stage neural network and apply dropout for preventing overfitting of the parameters on the training data. Dropout technique has been recently introduced to prevent overfitting of the parameters in deep neural networks and it has been applied successfully to object recognition problem. To our knowledge, this is the first system using dropout for action recognition problem. We demonstrate that using dropout improves the action recognition accuracies on HMDB and UCF101 datasets.
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Date Issued: 2013
Identifier: CFE0004977, ucf:49562
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004977

Title: Approximate In-memory computing on RERAMs.
Creator: Khokhar, Salman Anwar, Heinrich, Mark, Leavens, Gary, Yuksel, Murat, Bagci, Ulas, Rahman, Talat, University of Central Florida
Abstract / Description: Computing systems have seen tremendous growth over the past few decades in their capabilities, efficiency, and deployment use cases. This growth has been driven by progress in lithography techniques, improvement in synthesis tools, architectures and power management. However, there is a growing disparity between computing power and the demands on modern computing systems. The standard Von-Neuman architecture has separate data storage and data processing locations. Therefore, it suffers from a...
Show moreComputing systems have seen tremendous growth over the past few decades in their capabilities, efficiency, and deployment use cases. This growth has been driven by progress in lithography techniques, improvement in synthesis tools, architectures and power management. However, there is a growing disparity between computing power and the demands on modern computing systems. The standard Von-Neuman architecture has separate data storage and data processing locations. Therefore, it suffers from a memory-processor communication bottleneck, which is commonly referredto as the 'memory wall'. The relatively slower progress in memory technology compared with processing units has continued to exacerbate the memory wall problem. As feature sizes in the CMOSlogic family reduce further, quantum tunneling effects are becoming more prominent. Simultaneously, chip transistor density is already so high that all transistors cannot be powered up at the same time without violating temperature constraints, a phenomenon characterized as dark-silicon. Coupled with this, there is also an increase in leakage currents with smaller feature sizes, resultingin a breakdown of 'Dennard's' scaling. All these challenges cannot be met without fundamental changes in current computing paradigms. One viable solution is in-memory computing, wherecomputing and storage are performed alongside each other. A number of emerging memory fabrics such as ReRAMS, STT-RAMs, and PCM RAMs are capable of performing logic in-memory.ReRAMs possess high storage density, have extremely low power consumption and a low cost of fabrication. These advantages are due to the simple nature of its basic constituting elements whichallow nano-scale fabrication. We use flow-based computing on ReRAM crossbars for computing that exploits natural sneak paths in those crossbars.Another concurrent development in computing is the maturation of domains that are error resilient while being highly data and power intensive. These include machine learning, pattern recognition,computer vision, image processing, and networking, etc. This shift in the nature of computing workloads has given weight to the idea of (")approximate computing("), in which device efficiency is improved by sacrificing tolerable amounts of accuracy in computation. We present a mathematically rigorous foundation for the synthesis of approximate logic and its mapping to ReRAM crossbars using search based and graphical methods.
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Date Issued: 2019
Identifier: CFE0007827, ucf:52817
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007827

Title: Learning Algorithms for Fat Quantification and Tumor Characterization.
Creator: Hussein, Sarfaraz, Bagci, Ulas, Shah, Mubarak, Heinrich, Mark, Pensky, Marianna, University of Central Florida
Abstract / Description: Obesity is one of the most prevalent health conditions. About 30% of the world's and over 70% of the United States' adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the...
Show moreObesity is one of the most prevalent health conditions. About 30% of the world's and over 70% of the United States' adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice.
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Date Issued: 2018
Identifier: CFE0007196, ucf:52288
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007196

Title: An Exploratory Comparison of a Traditional and an Adaptive Instructional Approach for College Algebra.
Creator: Kasha, Ryan, Kincaid, John, Wiegand, Rudolf, Hartshorne, Richard, Morris, Cliff, University of Central Florida
Abstract / Description: This research effort compared student learning gains and attitudinal changes through the implementation of two varying instructional approaches on the topic of functions in College Algebra. Attitudinal changes were measured based on the Attitude Towards Mathematics Inventory (ATMI). The ATMI also provided four sub-scales scores for self-confidence, value of learning, enjoyment, and motivation. Furthermore, this research explored and compared relationships between students' level of mastery...
Show moreThis research effort compared student learning gains and attitudinal changes through the implementation of two varying instructional approaches on the topic of functions in College Algebra. Attitudinal changes were measured based on the Attitude Towards Mathematics Inventory (ATMI). The ATMI also provided four sub-scales scores for self-confidence, value of learning, enjoyment, and motivation. Furthermore, this research explored and compared relationships between students' level of mastery and their actual level of learning. This study implemented a quasi-experimental research design using a sample that consisted of 56 College Algebra students in a public, state college in Florida. The sample was enrolled in one of two College Algebra sections, in which one section followed a self-adaptive instructional approach using ALEKS (Assessment and Learning in Knowledge Space) and the other section followed a traditional approach using MyMathLab. Learning gains in each class were measured as the difference between the pre-test and post-test scores on the topic of functions in College Algebra. Attitude changes in each class were measured as the difference between the holistic scores on the ATMI, as well as each of the four sub-scale scores, which was administered once in the beginning of the semester and again after the unit of functions, approximately eight weeks into the course. Utilizing an independent t-test, results indicated that there was not a significant difference in actual learning gains for the compared instructional approaches. Additionally, independent t-test results indicated that there was not a statistical difference for attitude change holistically and on each of the four sub-scales for the compared instructional approaches. However, correlational analyses revealed a strong relationship between students' level of mastery learning and their actual learning level for each class with the self-adaptive instructional approach having a stronger correlation than the non-adaptive section, as measured by an r-to-z Fisher transformation test. The results of this study indicate that the self-adaptive instructional approach using ALEKS could more accurately report students' true level of learning compared to a non-adaptive instructional approach. Overall, this study found the compared instructional approaches to be equivalent in terms of learning and effect on students' attitude. While not statistically different, the results of this study have implications for math educators, instructional designers, and software developers. For example, a non-adaptive instructional approach can be equivalent to a self-adaptive instructional approach in terms of learning with appropriate planning and design. Future recommendations include further case studies of self-adaptive technology in developmental and college mathematics in other modalities such as hybrid or on-line courses. Also, this study should be replicated on a larger scale with other self-adaptive math software in addition to focusing on other student populations, such as K - 12. There is much potential for intelligent tutoring to supplement different instructional approaches, but should not be viewed as a replacement for teacher-to-student interactions.
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Date Issued: 2015
Identifier: CFE0005963, ucf:50821
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005963

Title: TOWARDS A SELF-CALIBRATING VIDEO CAMERA NETWORK FOR CONTENT ANALYSIS AND FORENSICS.
Creator: Junejo, Imran, Foroosh, Hassan, University of Central Florida
Abstract / Description: Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space....
Show moreDue to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how the network as a whole can be calibrated, such that each camera as a unit in the network is aware of its orientation with respect to all the other cameras in the network. Different types of cameras might be present in a multiple camera network and novel techniques are presented for efficient calibration of these cameras. Specifically: (i) For a stationary camera, we derive new constraints on the Image of the Absolute Conic (IAC). These new constraints are shown to be intrinsic to IAC; (ii) For a scene where object shadows are cast on a ground plane, we track the shadows on the ground plane cast by at least two unknown stationary points, and utilize the tracked shadow positions to compute the horizon line and hence compute the camera intrinsic and extrinsic parameters; (iii) A novel solution to a scenario where a camera is observing pedestrians is presented. The uniqueness of formulation lies in recognizing two harmonic homologies present in the geometry obtained by observing pedestrians; (iv) For a freely moving camera, a novel practical method is proposed for its self-calibration which even allows it to change its internal parameters by zooming; and (v) due to the increased application of the pan-tilt-zoom (PTZ) cameras, a technique is presented that uses only two images to estimate five camera parameters. For an automatically configurable multi-camera network, having non-overlapping field of view and possibly containing moving cameras, a practical framework is proposed that determines the geometry of such a dynamic camera network. It is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the geometry of a dynamic network. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic as well as on real data. Applications to path modeling, GPS coordinate estimation, and configuring mixed-reality environment are explored.
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Date Issued: 2007
Identifier: CFE0001743, ucf:47296
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001743

Title: MULTIMEDIA COMPUTER-BASED TRAINING AND LEARNING: THE ROLE OF REFERENTIAL CONNECTIONS IN SUPPORTING COGNITIVE LEARNING OUTCOMES.
Creator: Scielzo, Sandro, Jentsch, Florian, University of Central Florida
Abstract / Description: Multimedia theory has generated a number of principles and guidelines to support computer-based training (CBT) design. However, the cognitive processes responsible for learning, from which these principles and guidelines stem from, are only indirectly derived by focusing on cognitive learning outcome differences. Unfortunately, the effects that cognitive processes have on learning are based on the assumption that cognitive learning outcomes are indicative of certain cognitive processes. Such...
Show moreMultimedia theory has generated a number of principles and guidelines to support computer-based training (CBT) design. However, the cognitive processes responsible for learning, from which these principles and guidelines stem from, are only indirectly derived by focusing on cognitive learning outcome differences. Unfortunately, the effects that cognitive processes have on learning are based on the assumption that cognitive learning outcomes are indicative of certain cognitive processes. Such circular reasoning is what prompted this dissertation. Specifically, this dissertation looked at the notion of referential connections, which is a prevalent cognitive process that is thought to support knowledge acquisition in a multimedia CBT environment. Referential connections, and the related cognitive mechanisms supporting them, are responsible for creating associations between verbal and visual information; as a result, their impact on multimedia learning is theorized to be far reaching. Therefore, one of the main goals of this dissertation was to address the issue of indirectly assessing cognitive processes by directly measuring referential connections to (a) verify the presence of referential connections, and (b) to measure the extent to which referential connections affect cognitive learning outcomes. To achieve this goal, a complete review of the prevalent multimedia theories was brought fourth. The most important factors thought to be influencing referential connections were extracted and cataloged into variables that were manipulated, fixed, covaried, or randomized to empirically examine the link between referential connections and learning. Specifically, this dissertation manipulated referential connections by varying the temporal presentation of modalities and the color coding of instructional material. Manipulating the temporal presentation of modalities was achieved by either presenting modalities simultaneously or sequentially. Color coding manipulations capitalized on pre-attentive highlighting and pairing of elements (i.e., pairing text with corresponding visuals). As such, the computer-based training varied color coding on three levels: absence of color coding, color coding without pairing text and corresponding visual aids, and color coding that also paired text and corresponding visual aids. The modalities employed in the experiment were written text and static visual aids, and the computer-based training taught the principles of flight to naïve participants. Furthermore, verbal and spatial aptitudes were used as covariates, as they consistently showed to affect learning. Overall, the manipulations were hypothesized to differentially affect referential connections and cognitive learning outcomes, thereby altering cognitive learning outcomes. Specifically, training with simultaneously presented modalities was hypothesized to be superior, in terms of referential connections and learning performance, to a successive presentation, and color coding modalities with pairing of verbal and visual correspondents was hypothesized to be superior to other forms of color coding. Finally, it was also hypothesized that referential connections would positively correlate with cognitive learning outcomes and, indeed, mediate the effects of temporal contiguity and color coding on learning. A total of 96 were randomly assigned to one of the six experimental groups, and were trained on the principles of flight. The key construct of referential connections was successfully measured with three methods. Cognitive learning outcomes were captured by a traditional declarative test and by two integrative (i.e., knowledge application) tests. Results showed that the two multimedia manipulation impacted cognitive learning outcomes and did so through corresponding changes of related referential connections (i.e., through mediation). Specifically, as predicted, referential connections mediated the impact of both temporal contiguity and color coding on lower- and higher-level cognitive learning outcomes. Theoretical and practical implications of the results are discussed in relation to computer-based training design principles and guidelines. Specifically, theoretical implications focus on the contribution that referential connections have on multimedia learning theory, and practical implications are brought forth in terms of instructional design issues. Future research considerations are described as they relate to further exploring the role of referential connections within multimedia CBT paradigms.
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Date Issued: 2008
Identifier: CFE0002224, ucf:47899
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002224

Title: Visionary Ophthalmics: Confluence of Computer Vision and Deep Learning for Ophthalmology.
Creator: Morley, Dustin, Foroosh, Hassan, Bagci, Ulas, Gong, Boqing, Mohapatra, Ram, University of Central Florida
Abstract / Description: Ophthalmology is a medical field ripe with opportunities for meaningful application of computer vision algorithms. The field utilizes data from multiple disparate imaging techniques, ranging from conventional cameras to tomography, comprising a diverse set of computer vision challenges. Computer vision has a rich history of techniques that can adequately meet many of these challenges. However, the field has undergone something of a revolution in recent times as deep learning techniques have...
Show moreOphthalmology is a medical field ripe with opportunities for meaningful application of computer vision algorithms. The field utilizes data from multiple disparate imaging techniques, ranging from conventional cameras to tomography, comprising a diverse set of computer vision challenges. Computer vision has a rich history of techniques that can adequately meet many of these challenges. However, the field has undergone something of a revolution in recent times as deep learning techniques have sprung into the forefront following advances in GPU hardware. This development raises important questions regarding how to best leverage insights from both modern deep learning approaches and more classical computer vision approaches for a given problem. In this dissertation, we tackle challenging computer vision problems in ophthalmology using methods all across this spectrum. Perhaps our most significant work is a highly successful iris registration algorithm for use in laser eye surgery. This algorithm relies on matching features extracted from the structure tensor and a Gabor wavelet (-) a classically driven approach that does not utilize modern machine learning. However, drawing on insight from the deep learning revolution, we demonstrate successful application of backpropagation to optimize the registration significantly faster than the alternative of relying on finite differences. Towards the other end of the spectrum, we also present a novel framework for improving RANSAC segmentation algorithms by utilizing a convolutional neural network (CNN) trained on a RANSAC-based loss function. Finally, we apply state-of-the-art deep learning methods to solve the problem of pathological fluid detection in optical coherence tomography images of the human retina, using a novel retina-specific data augmentation technique to greatly expand the data set. Altogether, our work demonstrates benefits of applying a holistic view of computer vision, which leverages deep learning and associated insights without neglecting techniques and insights from the previous era.
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Date Issued: 2018
Identifier: CFE0007058, ucf:52001
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007058

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

Title: A Study of Localization and Latency Reduction for Action Recognition.
Creator: Masood, Syed, Tappen, Marshall, Foroosh, Hassan, Stanley, Kenneth, Sukthankar, Rahul, University of Central Florida
Abstract / Description: The success of recognizing periodic actions in single-person-simple-background datasets, such as Weizmann and KTH, has created a need for more complex datasets to push the performance of action recognition systems. In this work, we create a new synthetic action dataset and use it to highlight weaknesses in current recognition systems. Experiments show that introducing background complexity to action video sequences causes a significant degradation in recognition performance. Moreover, this...
Show moreThe success of recognizing periodic actions in single-person-simple-background datasets, such as Weizmann and KTH, has created a need for more complex datasets to push the performance of action recognition systems. In this work, we create a new synthetic action dataset and use it to highlight weaknesses in current recognition systems. Experiments show that introducing background complexity to action video sequences causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning system parameters or by selecting better feature points. Instead, we show that the problem lies in the spatio-temporal cuboid volume extracted from the interest point locations. Having identified the problem, we show how improved results can be achieved by simple modifications to the cuboids.For the above method however, one requires near-perfect localization of the action within a video sequence. To achieve this objective, we present a two stage weakly supervised probabilistic model for simultaneous localization and recognition of actions in videos. Different from previous approaches, our method is novel in that it (1) eliminates the need for manual annotations for the training procedure and (2) does not require any human detection or tracking in the classification stage. The first stage of our framework is a probabilistic action localization model which extracts the most promising sub-windows in a video sequence where an action can take place. We use a non-linear classifier in the second stage of our framework for the final classification task. We show the effectiveness of our proposed model on two well known real-world datasets: UCF Sports and UCF11 datasets.Another application of the weakly supervised probablistic model proposed above is in the gaming environment. An important aspect in designing interactive, action-based interfaces is reliably recognizing actions with minimal latency. High latency causes the system's feedback to lag behind and thus significantly degrade the interactivity of the user experience. With slight modification to the weakly supervised probablistic model we proposed for action localization, we show how it can be used for reducing latency when recognizing actions in Human Computer Interaction (HCI) environments. This latency-aware learning formulation trains a logistic regression-based classifier that automatically determines distinctive canonical poses from the data and uses these to robustly recognize actions in the presence of ambiguous poses. We introduce a novel (publicly released) dataset for the purpose of our experiments. Comparisons of our method against both a Bag of Words and a Conditional Random Field (CRF) classifier show improved recognition performance for both pre-segmented and online classification tasks.
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Date Issued: 2012
Identifier: CFE0004575, ucf:49210
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004575

Title: A Comparison of the Academic Achievement of English Learners and Non-English Learners in Digital and Non-Digital Learning Environments.
Creator: Vela, Enrique, Taylor, Rosemarye, Baldwin, Lee, Doherty, Walter, Nutta, Joyce, University of Central Florida
Abstract / Description: The purpose of this study was to identify the extent to which learning in a digital school environment impacts the reading and mathematics achievement of English learners (ELs) in elementary and secondary school settings. In addition, this study intended to determine the extent, if any, that learning in a digital school environment narrows the achievement gap in reading and mathematics between ELs and their non-EL counterparts in elementary and secondary schools. Based on data collected from...
Show moreThe purpose of this study was to identify the extent to which learning in a digital school environment impacts the reading and mathematics achievement of English learners (ELs) in elementary and secondary school settings. In addition, this study intended to determine the extent, if any, that learning in a digital school environment narrows the achievement gap in reading and mathematics between ELs and their non-EL counterparts in elementary and secondary schools. Based on data collected from the first year of a 1:1 digital pilot implementation in a large urban school district in Florida, the results of this study identified grade levels and school levels where the 2014 Florida Comprehensive Achievement Test (FCAT) 2.0 Reading and Mathematics Developmental Scale Scores (DSS) of ELs in digital school settings were significantly higher than in non-digital school settings. In addition, the study yielded some statistically significant differences in the learning gains in DSS of the 2014 FCAT 2.0 Reading and Mathematics of ELs and non-ELs in digital school settings. These findings may be used to inform the planning of technology integration, academic interventions, and teacher preparation that focuses on the academic improvement of ELs.
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Date Issued: 2016
Identifier: CFE0006410, ucf:51455
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006410

Title: Ubiquitous Computing in Public Education: The Effects of One-to-One Computer Initiatives on Student Achievement on Florida Standardized Assessments.
Creator: Lobeto, Fernando, Murray, Kenneth, Baldwin, Lee, Storey, Valerie A., Cintron Delgado, Rosa, University of Central Florida
Abstract / Description: The purpose of this study was to examine the effects of one-to-one computer initiatives on student achievement in reading and mathematics. This study compared the differences in FCAT 2.0 Reading and Mathematics scores between schools implementing one-to-one computer initiatives and schools implementing traditional modes of instruction. A second purpose of this study was to determine what effects one-to-one computer initiatives had on student FCAT 2.0 scores overall and by grade level, gender,...
Show moreThe purpose of this study was to examine the effects of one-to-one computer initiatives on student achievement in reading and mathematics. This study compared the differences in FCAT 2.0 Reading and Mathematics scores between schools implementing one-to-one computer initiatives and schools implementing traditional modes of instruction. A second purpose of this study was to determine what effects one-to-one computer initiatives had on student FCAT 2.0 scores overall and by grade level, gender, and socio-economic status. The study used an independent-samples t-test, a repeated measures ANOVA, and a factorial ANCOVA to answer four research questions in order to achieve the purpose stated above. An analysis of the results revealed that the first year of one-to-one initiatives had a slightly negative effect on elementary school students, a small but positive effect on middle school students, and no effect on high school students. Further, the study found that students did not score statistically significantly different after one year of one-to-one digital instruction than they had the previous year.
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Date Issued: 2016
Identifier: CFE0006349, ucf:51573
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006349

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

Title: 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

Machine Learning (6)	+ -
Computer Vision (5)	+ -
computer vision (5)	+ -
machine learning (5)	+ -
deep learning (4)	+ -

Deep Learning (3)	+ -
Action Recognition (2)	+ -
action recognition (2)	+ -
artificial intelligence (2)	+ -
computer-assisted instruction (2)	+ -
digital learning (2)	+ -
ALEKS (1)	+ -
Action Detection (1)	+ -
Action Localization (1)	+ -
Adversarial Attack (1)	+ -
Auction Theory (1)	+ -
CAI (1)	+ -
CALL Grammar Instruction (1)	+ -
Cancer (1)	+ -
Cancer Diagnosis (1)	+ -
Computational Advertising (1)	+ -
Computer-Assisted Language Learning (CALL) (1)	+ -
DFM (1)	+ -
ESOL (1)	+ -
Egocentric Vision (1)	+ -
Engineering Analytic (1)	+ -
English language learner (1)	+ -
Evolutionary Computation (1)	+ -
Forgetting (1)	+ -
GPS coordinate estimation (1)	+ -

University of Central Florida (22)	+ -
Shah, Mubarak (5)	+ -
Bagci, Ulas (4)	+ -
Foroosh, Hassan (4)	+ -
Heinrich, Mark (3)	+ -

Sukthankar, Rahul (3)	+ -
Wu, Annie (3)	+ -
Baldwin, Lee (2)	+ -
Hu, Haiyan (2)	+ -
Leavens, Gary (2)	+ -
Nutta, Joyce (2)	+ -
Stanley, Kenneth (2)	+ -
Sukthankar, Gita (2)	+ -
Tappen, Marshall (2)	+ -
Wiegand, Rudolf (2)	+ -
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Anzalone, Patricia (1)	+ -
Ardeshir Behrostaghi, Shervin (1)	+ -
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Chen, Mengmeng (1)	+ -
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Cintron Delgado, Rosa (1)	+ -
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Doherty, Walter (1)	+ -
Georgiopoulos, Michael (1)	+ -
Gong, Boqing (1)	+ -
Gonzalez, Avelino (1)	+ -
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