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- Title
- THE 1980'S AND TODAY;AN ANALYSIS OF WOMEN'S SUBJECTIVE WELL-BEING.
- Creator
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Coleman, Michelle, Gay, David, University of Central Florida
- Abstract / Description
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The purpose of this study is to augment the existing literature concerning the relationship between marital status, gender, social networks, and cohort effect on dimensions of subjective well-being for women. Multiple dimensions of subjective well-being are examined. Multiple regression and logistic regression are employed to examine the effects of marital status, social networks, and cohort effects on the dependent variables that tap the dimensions of subjective well-being. The analysis...
Show moreThe purpose of this study is to augment the existing literature concerning the relationship between marital status, gender, social networks, and cohort effect on dimensions of subjective well-being for women. Multiple dimensions of subjective well-being are examined. Multiple regression and logistic regression are employed to examine the effects of marital status, social networks, and cohort effects on the dependent variables that tap the dimensions of subjective well-being. The analysis controls for age, race, education, income, religious attendance and region of residence. The findings report some inconsistency in regards to the current literature. Social networks and support are found to be the most constant independent predictor of subjective well-being. While the effects of being divorced and separated, as well as cohort membership, are not as consistent, the findings are notable and should be addressed in future research addressing subjective well-being.
Show less - Date Issued
- 2006
- Identifier
- CFE0001230, ucf:46895
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001230
- Title
- Reconceptualizing Responsiveness for Network Governance: Insights from Cross-Sector Efforts to Assist the Displaced Population From Puerto Rico in Central Florida.
- Creator
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Prysmakova, Safiya, Bryer, Thomas, Hu, Qian, Sadiq, Abdul-Akeem, Piccolo, Ronald F, Meek, Jack, University of Central Florida
- Abstract / Description
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This study further expands current knowledge on responsiveness in the public administration field and examines factors that contribute to more responsive public service delivery networks. This research reconceptualized the concept of responsiveness under the lens of New Public Governance as a legitimate democratic public value and answered the following research questions: What constructs constitute to the concept of public service responsiveness? How can public service responsiveness be...
Show moreThis study further expands current knowledge on responsiveness in the public administration field and examines factors that contribute to more responsive public service delivery networks. This research reconceptualized the concept of responsiveness under the lens of New Public Governance as a legitimate democratic public value and answered the following research questions: What constructs constitute to the concept of public service responsiveness? How can public service responsiveness be measured at the network level? Does the complexity of public service provision affect perceived public service network responsiveness? How do collaborative processes across network partners, community support, and resource munificence affect the responsiveness of public delivery networks? The study utilized a multi-method case study approach. The case of the study is focused on the cross-sector efforts in response to the crisis, caused by the massive displacement of the Puerto Rican population to Central Florida after Hurricane Maria. The data was collected using surveys administrated to the displaced population, and interviews conducted with the managers of service delivery organizations. Using quantitative methods, this study developed a valid and reliable model for measuring perceived public service network responsiveness, which is built on the constructs that include the sufficiency of service provision, dignity, clarity of communication and public engagement. The findings suggested that the displaced population that sought a higher number of low complexity services had a more negative perception of public service network responsiveness. The study suggested that negative perception in low complexity service provision can be caused by the low capability of the public service system and low level of public input, and can be characterized as (")consumeristic(") approach. The qualitative findings showed that collaborative processes can affect the responsiveness of public service networks. Increased community support proved to be a positive factor for public service network responsiveness, while a lack of flexible funding is a negative factor for public service network responsiveness.
Show less - Date Issued
- 2019
- Identifier
- CFE0007852, ucf:52762
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007852
- Title
- Leaning Robust Sequence Features via Dynamic Temporal Pattern Discovery.
- Creator
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Hu, Hao, Wang, Liqiang, Zhang, Shaojie, Liu, Fei, Qi, GuoJun, Zhou, Qun, University of Central Florida
- Abstract / Description
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As a major type of data, time series possess invaluable latent knowledge for describing the real world and human society. In order to improve the ability of intelligent systems for understanding the world and people, it is critical to design sophisticated machine learning algorithms for extracting robust time series features from such latent knowledge. Motivated by the successful applications of deep learning in computer vision, more and more machine learning researchers put their attentions...
Show moreAs a major type of data, time series possess invaluable latent knowledge for describing the real world and human society. In order to improve the ability of intelligent systems for understanding the world and people, it is critical to design sophisticated machine learning algorithms for extracting robust time series features from such latent knowledge. Motivated by the successful applications of deep learning in computer vision, more and more machine learning researchers put their attentions on the topic of applying deep learning techniques to time series data. However, directly employing current deep models in most time series domains could be problematic. A major reason is that temporal pattern types that current deep models are aiming at are very limited, which cannot meet the requirement of modeling different underlying patterns of data coming from various sources. In this study we address this problem by designing different network structures explicitly based on specific domain knowledge such that we can extract features via most salient temporal patterns. More specifically, we mainly focus on two types of temporal patterns: order patterns and frequency patterns. For order patterns, which are usually related to brain and human activities, we design a hashing-based neural network layer to globally encode the ordinal pattern information into the resultant features. It is further generalized into a specially designed Recurrent Neural Networks (RNN) cell which can learn order patterns in an online fashion. On the other hand, we believe audio-related data such as music and speech can benefit from modeling frequency patterns. Thus, we do so by developing two types of RNN cells. The first type tries to directly learn the long-term dependencies on frequency domain rather than time domain. The second one aims to dynamically filter out the ``noise" frequencies based on temporal contexts. By proposing various deep models based on different domain knowledge and evaluating them on extensive time series tasks, we hope this work can provide inspirations for others and increase the community's interests on the problem of applying deep learning techniques to more time series tasks.
Show less - Date Issued
- 2019
- Identifier
- CFE0007470, ucf:52679
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007470
- Title
- Training Neural Networks Through the Integration of Evolution and Gradient Descent.
- Creator
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Morse, Gregory, Stanley, Kenneth, Wu, Annie, Shah, Mubarak, Wiegand, Rudolf, University of Central Florida
- Abstract / Description
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Neural networks have achieved widespread adoption due to both their applicability to a wide range of problems and their success relative to other machine learning algorithms. The training of neural networks is achieved through any of several paradigms, most prominently gradient-based approaches (including deep learning), but also through up-and-coming approaches like neuroevolution. However, while both of these neural network training paradigms have seen major improvements over the past...
Show moreNeural networks have achieved widespread adoption due to both their applicability to a wide range of problems and their success relative to other machine learning algorithms. The training of neural networks is achieved through any of several paradigms, most prominently gradient-based approaches (including deep learning), but also through up-and-coming approaches like neuroevolution. However, while both of these neural network training paradigms have seen major improvements over the past decade, little work has been invested in developing algorithms that incorporate the advances from both deep learning and neuroevolution. This dissertation introduces two new algorithms that are steps towards the integration of gradient descent and neuroevolution for training neural networks. The first is (1) the Limited Evaluation Evolutionary Algorithm (LEEA), which implements a novel form of evolution where individuals are partially evaluated, allowing rapid learning and enabling the evolutionary algorithm to behave more like gradient descent. This conception provides a critical stepping stone to future algorithms that more tightly couple evolutionary and gradient descent components. The second major algorithm (2) is Divergent Discriminative Feature Accumulation (DDFA), which combines a neuroevolution phase, where features are collected in an unsupervised manner, with a gradient descent phase for fine tuning of the neural network weights. The neuroevolution phase of DDFA utilizes an indirect encoding and novelty search, which are sophisticated neuroevolution components rarely incorporated into gradient descent-based systems. Further contributions of this work that build on DDFA include (3) an empirical analysis to identify an effective distance function for novelty search in high dimensions and (4) the extension of DDFA for the purpose of discovering convolutional features. The results of these DDFA experiments together show that DDFA discovers features that are effective as a starting point for gradient descent, with significant improvement over gradient descent alone. Additionally, the method of collecting features in an unsupervised manner allows DDFA to be applied to domains with abundant unlabeled data and relatively sparse labeled data. This ability is highlighted in the STL-10 domain, where DDFA is shown to make effective use of unlabeled data.
Show less - Date Issued
- 2019
- Identifier
- CFE0007840, ucf:52819
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007840
- Title
- How do after-school staff use social networks to support at-risk youth? A social capital analysis.
- Creator
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Philp, Katherine, Gill, Michele, Biraimah, Karen, Bai, Haiyan, Hewitt, Randall, University of Central Florida
- Abstract / Description
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Little is known about the social capital of adults in after-school settings or the ways in which they use social contacts to support youth success, particularly for at-risk youth. Their effectiveness as brokers for learning opportunities may depend on aspects of their social capital: both the quantity and quality of their social networks as well as their attitudes and beliefs related to seeking help from social contacts. This mixed-methods study surveyed 50 after-school program staff serving...
Show moreLittle is known about the social capital of adults in after-school settings or the ways in which they use social contacts to support youth success, particularly for at-risk youth. Their effectiveness as brokers for learning opportunities may depend on aspects of their social capital: both the quantity and quality of their social networks as well as their attitudes and beliefs related to seeking help from social contacts. This mixed-methods study surveyed 50 after-school program staff serving teens in high-poverty neighborhoods to examine the characteristics of adult social capital and to explore attitudes towards mobilizing social resources to support youth. Surveys measured social network size (total contacts), network social status (average prestige of known occupations), and network orientations, as well as social resource mobilization (brokering). The results of an initial logistic regression found that only total known contacts was a significant predictor of resource mobilization. Six participants were identified for follow-up interviews. Exposing youth to novel experiences emerged as a critical theme related to youth interest development and adult brokering action. Interviews also indicated that structural elements of youth programs might influence the need for staff to draw on personal connections, suggesting possible targets for intervention. This study provides novel insight into the characteristics of the social networks held by adults working in after-school programs, as well as into the attitudes and beliefs held by these individuals towards brokering learning opportunities for youth.
Show less - Date Issued
- 2019
- Identifier
- CFE0007707, ucf:52419
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007707
- Title
- detecting anomalies from big data system logs.
- Creator
-
Lu, Siyang, Wang, Liqiang, Zhang, Shaojie, Zhang, Wei, Wu, Dazhong, University of Central Florida
- Abstract / Description
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Nowadays, big data systems (e.g., Hadoop and Spark) are being widely adopted by many domains for offering effective data solutions, such as manufacturing, healthcare, education, and media. A common problem about big data systems is called anomaly, e.g., a status deviated from normal execution, which decreases the performance of computation or kills running programs. It is becoming a necessity to detect anomalies and analyze their causes. An effective and economical approach is to analyze...
Show moreNowadays, big data systems (e.g., Hadoop and Spark) are being widely adopted by many domains for offering effective data solutions, such as manufacturing, healthcare, education, and media. A common problem about big data systems is called anomaly, e.g., a status deviated from normal execution, which decreases the performance of computation or kills running programs. It is becoming a necessity to detect anomalies and analyze their causes. An effective and economical approach is to analyze system logs. Big data systems produce numerous unstructured logs that contain buried valuable information. However manually detecting anomalies from system logs is a tedious and daunting task.This dissertation proposes four approaches that can accurately and automatically analyze anomalies from big data system logs without extra monitoring overhead. Moreover, to detect abnormal tasks in Spark logs and analyze root causes, we design a utility to conduct fault injection and collect logs from multiple compute nodes. (1) Our first method is a statistical-based approach that can locate those abnormal tasks and calculate the weights of factors for analyzing the root causes. In the experiment, four potential root causes are considered, i.e., CPU, memory, network, and disk I/O. The experimental results show that the proposed approach is accurate in detecting abnormal tasks as well as finding the root causes. (2) To give a more reasonable probability result and avoid ad-hoc factor weights calculating, we propose a neural network approach to analyze root causes of abnormal tasks. We leverage General Regression Neural Network (GRNN) to identify root causes for abnormal tasks. The likelihood of reported root causes is presented to users according to the weighted factors by GRNN. (3) To further improve anomaly detection by avoiding feature extraction, we propose a novel approach by leveraging Convolutional Neural Networks (CNN). Our proposed model can automatically learn event relationships in system logs and detect anomaly with high accuracy. Our deep neural network consists of logkey2vec embeddings, three 1D convolutional layers, a dropout layer, and max pooling. According to our experiment, our CNN-based approach has better accuracy compared to other approaches using Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) on detecting anomaly in Hadoop DistributedFile System (HDFS) logs. (4) To analyze system logs more accurately, we extend our CNN-based approach with two attention schemes to detect anomalies in system logs. The proposed two attention schemes focus on different features from CNN's output. We evaluate our approaches with several benchmarks, and the attention-based CNN model shows the best performance among all state-of-the-art methods.
Show less - Date Issued
- 2019
- Identifier
- CFE0007673, ucf:52499
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007673
- Title
- Effective Task Transfer Through Indirect Encoding.
- Creator
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Verbancsics, Phillip, Stanley, Kenneth, Sukthankar, Gita, Georgiopoulos, Michael, Garibay, Ivan, University of Central Florida
- Abstract / Description
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An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from...
Show moreAn important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To this end, (1) the bird's eye view (BEV) representation is introduced, which can represent different tasks on the same two-dimensional map. Because the BEV represents state information associated with positions instead of objects, it can be scaled to more objects without manipulation. In this way, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV, which is (2) demonstrated in this dissertation.Yet a challenge for such representation is that a raw two-dimensional map is high-dimensional and unstructured. This dissertation demonstrates how this problem is addressed naturally by the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach. HyperNEAT evolves an indirect encoding, which compresses the representation by exploiting its geometry. The dissertation then explores further exploiting the power of such encoding, beginning by (3) enhancing the configuration of the BEV with a focus on modularity. The need for further nonlinearity is then (4) investigated through the addition of hidden nodes. Furthermore, (5) the size of the BEV can be manipulated because it is indirectly encoded. Thus the resolution of the BEV, which is dictated by its size, is increased in precision and culminates in a HyperNEAT extension that is expressed at effectively infinite resolution. Additionally, scaling to higher resolutions through gradually increasing the size of the BEV is explored. Finally, (6) the ambitious problem of scaling from the Keepaway task to the Half-field Offense task is investigated with the BEV. Overall, this dissertation demonstrates that advanced representations in conjunction with indirect encoding can contribute to scaling learning techniques to more challenging tasks, such as the Half-field Offense RoboCup soccer domain.
Show less - Date Issued
- 2011
- Identifier
- CFE0004174, ucf:49071
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004174
- Title
- MEASURING MULTILEVEL CONSTRUCTS: THEORETICAL AND METHODOLOGICAL FEATURES OF TEAM BEHAVIORAL PROCESS UNDER COMPILATIONAL MODELS.
- Creator
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Murase, Toshio, Dechurch, Leslie, Salas, Eduardo, Bowers, Clint, Kapucu, Naim, University of Central Florida
- Abstract / Description
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Since at least the 1950s, researchers interested in studying the dynamics of small groups have struggled with how best to measure interaction processes. Although team process measurement issues are not particularly unique in terms of content, measuring multilevel phenomena presents an interesting problem because structural aspects are integral components of emergence. The elemental content of multilevel phenomena is wholly unique and distinguishable from the elemental content of composite...
Show moreSince at least the 1950s, researchers interested in studying the dynamics of small groups have struggled with how best to measure interaction processes. Although team process measurement issues are not particularly unique in terms of content, measuring multilevel phenomena presents an interesting problem because structural aspects are integral components of emergence. The elemental content of multilevel phenomena is wholly unique and distinguishable from the elemental content of composite units, and emerges as individual behaviors compile to higher levels of analyses. Analogous to chemical structures, behavioral phenomena manifest at higher levels in different structural patterns as members connect to one another through dynamic interactions. Subsequently, multilevel phenomena are more appropriately characterized in terms of pattern in addition to the traditionally measured intensity. The vast majority of teams research conceptualizes and operationalizes multilevel phenomena based on compositional (i.e., additive) models. This approach impedes the further advancement of the science of team effectiveness by capturing content and intensity, but not structure. This dissertation argues that compilational models better capture content, intensity, and structure, and therefore represent a preferred alternative for conceptualizing and operationalizing team processes. This dissertation details measurement issues associated with compositional models in teams research, and provides concepts helpful for reconceptualizing team processes as compilational forms.
Show less - Date Issued
- 2011
- Identifier
- CFE0004145, ucf:49048
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004145
- Title
- ALGEBRAIC ASPECTS OF (BIO) NANO-CHEMICAL REACTION NETWORKS AND BIFURCATIONS IN VARIOUS DYNAMICAL SYSTEMS.
- Creator
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Chen, Teng, Brennan, Joseph, University of Central Florida
- Abstract / Description
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The dynamics of (bio) chemical reaction networks have been studied by different methods. Among these methods, the chemical reaction network theory has been proven to successfully predicate important qualitative properties, such as the existence of the steady state and the asymptotic behavior of the steady state. However, a constructive approach to the steady state locus has not been presented. In this thesis, with the help of toric geometry, we propose a generic strategy towards this question...
Show moreThe dynamics of (bio) chemical reaction networks have been studied by different methods. Among these methods, the chemical reaction network theory has been proven to successfully predicate important qualitative properties, such as the existence of the steady state and the asymptotic behavior of the steady state. However, a constructive approach to the steady state locus has not been presented. In this thesis, with the help of toric geometry, we propose a generic strategy towards this question. This theory is applied to (bio)nano particle con gurations. We also investigate Hopf bifurcation surfaces of various dynamical systems.
Show less - Date Issued
- 2011
- Identifier
- CFE0003933, ucf:48689
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003933
- Title
- An Index to Measure Efficiency of Hospital Networks for Mass Casualty Disasters.
- Creator
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Bull Torres, Maria, Sepulveda, Jose, Sala-Diakanda, Serge, Geiger, Christopher, Kapucu, Naim, University of Central Florida
- Abstract / Description
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Disaster events have emphasized the importance of healthcare response activities due to the large number of victims. For instance, Hurricane Katrina in New Orleans, in 2005, and the terrorist attacks in New York City and Washington, D.C., on September 11, 2001, left thousands of wounded people. In those disasters, although hospitals had disaster plans established for more than a decade, their plans were not efficient enough to handle the chaos produced by the hurricane and terrorist attacks....
Show moreDisaster events have emphasized the importance of healthcare response activities due to the large number of victims. For instance, Hurricane Katrina in New Orleans, in 2005, and the terrorist attacks in New York City and Washington, D.C., on September 11, 2001, left thousands of wounded people. In those disasters, although hospitals had disaster plans established for more than a decade, their plans were not efficient enough to handle the chaos produced by the hurricane and terrorist attacks. Thus, the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) suggested collaborative planning among hospitals that provide services to a contiguous geographic area during mass casualty disasters. However, the JCAHO does not specify a methodology to determine which hospitals should be included into these cooperative plans. As a result, the problem of selecting the right hospitals to include in exercises and drills at the county level is a common topic in the current preparedness stages. This study proposes an efficiency index to determine the efficient response of cooperative-networks among hospitals before an occurrence of mass casualty disaster. The index built in this research combines operations research techniques, and the prediction of this index used statistical analysis. The consecutive application of three different techniques: network optimization, data envelopment analysis (DEA), and regression analysis allowed to obtain a regression equation to predict efficiency in predefined hospital networks for mass casualty disasters. In order to apply the proposed methodology for creating an efficiency index, we selected the Orlando area, and we defined three disaster sizes. Then, we designed networks considering two perspectives, hub-hospital and hub-disaster networks. In both optimization network models the objective function pursued to: reduce the travel distance and the emergency department (ED) waiting time in hospitals, increase the number of services offered by hospitals in the network, and offer specialized assistance to children. The hospital network optimization generated information for 75 hospital networks in Orlando. The DEA analyzed these 75 hospital networks, or decision making units (DMU's), to estimate their comparative efficiency. Two DEAs were performed in this study. As an output variable for each DMU, the DEA-1 considered the number of survivors allocated in less than a 40 miles range. As the input variables, the DEA-1 included: (i) The number of beds available in the network; (ii) The number of hospitals available in the network; and (iii) The number of services offered by hospitals in the network. This DEA-1 allowed the assignment of an efficiency value to each of the 75 hospital networks. As output variables for each DMU, the DEA-2 considered the number of survivors allocated in less than a 40 miles range and an index for ED waiting time in the network. The input variables included in DEA-2 are (i) The number of beds available in the network; (ii) The number of hospitals available in the network; and (iii) The number of services offered by hospitals in the network. These DEA allowed the assignment of an efficiency value to each of the 75 hospital networks. This efficiency index should allow emergency planners and hospital managers to assess which hospitals should be associated in a cooperative network in order to transfer survivors. Furthermore, JCAHO could use this index to evaluate the cooperating emergency hospitals' plans.However, DEA is a complex methodology that requires significant data gathering and handling. Thus, we studied whether a simpler regression analysis would substantially yield the same results. DEA-1 can be predicted using two regression analyses, which concluded that the average distances between hospitals and the disaster locations, and the size of the disaster explain the efficiency of the hospital network. DEA-2 can be predicted using three regressions, which included size of the disaster, number of hospitals, average distance, and average ED waiting time, as predictors of hospital network efficiency. The models generated for DEA-1 and DEA-2 had a mean absolute percent error (MAPE) around 10%. Thus, the indexes developed through the regression analysis make easier the estimation of the efficiency in predefined hospital networks, generating suitable predictors of the efficiency as determined by the DEA analysis. In conclusion, network optimization, DEA, and regressions analyses can be combined to create an index of efficiency to measure the performance of predefined-hospital networks in a mass casualty disaster, validating the hypothesis of this research.Although the methodology can be applied to any county or city, the regressions proposed for predicting the efficiency of hospital network estimated by DEA can be applied only if the city studied has the same characteristics of the Orlando area. These conditions include the following: (i) networks must have a rate of services lager than 0.76; (ii) the number of survivors must be less than 47% of the bed capacity EDs of the area studied; (iii) all hospitals in the network must have ED and they must be located in less than 48 miles range from the disaster sites, and (iv) EDs should not have more than 60 minutes of waiting time.The proposed methodology, in special the efficiency index, support the operational objectives of the 2012 ESF#8 for Florida State to handle risk and response capabilities conducting and participating in training and exercises to test and improve plans and procedures in the health response.
Show less - Date Issued
- 2012
- Identifier
- CFE0004524, ucf:49290
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004524
- Title
- The Relationship between Mentoring and Social Status at Work: A Social Network Status Study.
- Creator
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Flowers, Lakeesha, Jentsch, Kimberly, Fritzsche, Barbara, Wooten, William, Chepenik, Nancy, University of Central Florida
- Abstract / Description
-
Mentoring is an important means of developing talent. Typically, mentoring involves two individuals (-) a mentor, who provides career development and psychosocial support to a less experienced counterpart (the prot(&)#233;g(&)#233;). Because mentoring is related to several desired outcomes such as career advancement, and job satisfaction, it is important to understand which individual characteristics are important to obtaining or providing effective mentoring. It is also necessary to examine...
Show moreMentoring is an important means of developing talent. Typically, mentoring involves two individuals (-) a mentor, who provides career development and psychosocial support to a less experienced counterpart (the prot(&)#233;g(&)#233;). Because mentoring is related to several desired outcomes such as career advancement, and job satisfaction, it is important to understand which individual characteristics are important to obtaining or providing effective mentoring. It is also necessary to examine potential but unconfirmed outcomes of mentoring such as social network status. This study examined the relationships between several individual characteristics, namely social intelligence and emotional intelligence, and mentoring relationships. In addition, this study examined the relationships between mentoring and social network status. In this non-experimental study, there were several unique relationships among these constructs. The results indicate a person's social intelligence is indicative of their status as a mentor (or not a mentor) but is not related to status as a prot(&)#233;g(&)#233; (or not a prot(&)#233;g(&)#233;). In addition, a mentor's perception of the costs and benefits of mentoring were explained by the prot(&)#233;g(&)#233;'s social intelligence and emotional intelligence. A mentor's social intelligence also explained the quality of the mentoring given. Finally, a mentor's social network status was related to the prot(&)#233;g(&)#233;'s social network status but this relationship was not due to the mentoring received. This study provides one of the first examinations of the relationship between mentoring and social network status and provides areas for future research and practical considerations.
Show less - Date Issued
- 2012
- Identifier
- CFE0004308, ucf:49478
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004308
- Title
- Three essays on the marketing strategies of a durable goods manufacturer.
- Creator
-
Chau, Ngan, Desiraju, Ramarao, Krishnamoorthy, Anand, Joshi, Amit, Chintagunta, Pradeep, University of Central Florida
- Abstract / Description
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When purchasing durable goods, consumers not only pay for current but also future consumption; consequently, forward looking behavior is an important consideration in durable goods markets. For example, anticipating that prices will go down in the future, consumers may delay the purchase today; such behavior has a significant impact on the firm's marketing strategies. This dissertation investigates the impact of durability on two marketing strategies: new product introductions and supply...
Show moreWhen purchasing durable goods, consumers not only pay for current but also future consumption; consequently, forward looking behavior is an important consideration in durable goods markets. For example, anticipating that prices will go down in the future, consumers may delay the purchase today; such behavior has a significant impact on the firm's marketing strategies. This dissertation investigates the impact of durability on two marketing strategies: new product introductions and supply chain design. The first part of this dissertation (Chapter 3) examines a durable goods manufacturer's new product introduction strategy under different market environments where network effects and product compatibility are important. More specifically, this part explores the incentives of a firm to use either a replacement strategy or a skipping strategy---in the former, the firm commercializes the existing technology, while in the latter, it does not; in either case, an improved technology will be available in the future and the firm will introduce a new product at that time. Using a two-period analytical model with network effects, the analysis shows how the level of improvement in the new product, along with the type of compatibility between the products, interacts with network strength to determine the manufacturer's optimal strategy. Under gradual new product improvement, there is a strict preference for replacement. In contrast, under rapid new product improvement, that preference only holds in markets with relatively high levels of the network strength; at lower levels of the network strength, skipping is preferred; interestingly, for moderate values of the network strength, the level of product improvement affects the manufacturer's optimal choice differently under varying types of compatibility.The second part of this dissertation (Chapters 4 and 5) focuses on the supply chain design decisions of a durable goods manufacturer who is a sole supplier of an essential proprietary component for making the end product. Three different supply chain structures are considered. In the first, the manufacturer operates as a ``component supplier'' and sells the component to a downstream firm who then makes the end product. In the second structure, the manufacturer produces the end product using its component but does not make that component available to any other firms; here, the manufacturer operates as a ``sole entrant''. Finally, the manufacturer can operate as a ``dual distributor'' who not only makes the end product using its own component, but sells the component to a downstream firm who then competes against the manufacturer in the end product market.The extant literature on the optimal choice among the above supply chain structures has focused mainly on static settings in a framework of price competition. By contrast, researchers predominantly use quantity competition to examine durable goods markets in dynamic (i.e., multiple time period) settings. Moreover, the literature notes diversity in optimal firm behavior under the two types of (i.e., price and quantity) competition. Therefore, to transition from supply chain design in a static setting to a more dynamic one where consumers are forward-looking, this part utilizes Chapter 4 to analyze the manufacturer's choice using quantity competition in a static setting. This analysis (in Chapter 4) identifies precisely the shift in the manufacturer's choice of supply chain structure when moving from price competition to a quantity competition framework. With that analysis as a benchmark, the next chapter focuses on the manufacturer's choice in a dynamic setting. More specifically, Chapter 5 investigates the impact of durability on the optimality of the supply chain structures identified above. Using a two period setting, the analysis explores how the manufacturer's preference for different supply chain structures is modified. The findings reveal that, e.g., when durability is taken into account, the manufacturer's preference for the sole entrant role goes up, while the preference for the component supplier role goes down. Further, under certain conditions, the manufacturer may opt to be a dual distributor in the first period and then choose to become only a component supplier in the second period. The underlying rationale for such shifts in preference is directly linked to durability, which creates future competition and substantially reduces the manufacturer's profitability in the long run. Interestingly, this negative impact varies across different supply chain structures.Overall, this dissertation contributes to the current literature on durable goods and enhances our understanding of the impact of durability on the optimality of distinct marketing strategies, and provides insights that are valuable to both academics and managers.
Show less - Date Issued
- 2012
- Identifier
- CFE0004364, ucf:49428
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004364
- Title
- Load Estimation for Electric Power Distribution Networks.
- Creator
-
Eyisi, Chiebuka, Lotfifard, Saeed, Yuan, Jiann-Shiun, Wu, Xinzhang, University of Central Florida
- Abstract / Description
-
In electric power distribution systems, the major determinant in electricity supply strategy is the quantity of demand. Customers need to be accurately represented using updated nodal load information as a requirement for efficient control and operation of the distribution network. In Distribution Load Estimation (DLE), two major categories of data are utilized: historical data and direct real-time measured data. In this thesis, a comprehensive survey on the state-of-the-art methods for...
Show moreIn electric power distribution systems, the major determinant in electricity supply strategy is the quantity of demand. Customers need to be accurately represented using updated nodal load information as a requirement for efficient control and operation of the distribution network. In Distribution Load Estimation (DLE), two major categories of data are utilized: historical data and direct real-time measured data. In this thesis, a comprehensive survey on the state-of-the-art methods for estimating loads in distribution networks is presented. Then, a novel method for representing historical data in the form of Representative Load Curves (RLCs) for use in real-time DLE is also described. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is used in this regard to determine RLCs. An RLC is a curve that represents the behavior of the load during a specified time span; typically daily, weekly or monthly based on historical data. Although RLCs provide insight about the variation of load, it is not accurate enough for estimating real-time load. This therefore, should be used along with real-time measurements to estimate the load more accurately. It is notable that more accurate RLCs lead to better real-time load estimation in distribution networks.This thesis addresses the need to obtain accurate RLCs to assist in the decision-making process pertaining to Radial Distribution Networks (RDNs).This thesis proposes a method based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) architecture to estimate the RLCs for Distribution Networks. The performance of the method is demonstrated and simulated, on a test 11kV Radial Distribution Network using the MATLAB software. The Mean Absolute Percent Error (MAPE) criterion is used to justify the accuracy of the RLCs.
Show less - Date Issued
- 2013
- Identifier
- CFE0004995, ucf:49555
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004995
- Title
- THE NEXT GENERATION BOTNET ATTACKS AND DEFENSES.
- Creator
-
Wang, Ping, Zou, Cliff, University of Central Florida
- Abstract / Description
-
A "botnet" is a network of compromised computers (bots) that are controlled by an attacker (botmasters). Botnets are one of the most serious threats to today's Internet; they are the root cause of many current Internet attacks, such as email spam, distributed denial of service (DDoS) attacks, click fraud, etc. There have been many researches on how to detect, monitor, and defend against botnets that have appeared and their attack techniques. However, it is equally important for us to...
Show moreA "botnet" is a network of compromised computers (bots) that are controlled by an attacker (botmasters). Botnets are one of the most serious threats to today's Internet; they are the root cause of many current Internet attacks, such as email spam, distributed denial of service (DDoS) attacks, click fraud, etc. There have been many researches on how to detect, monitor, and defend against botnets that have appeared and their attack techniques. However, it is equally important for us to investigate possible attack techniques that could be used by the next generation botnets, and develop effective defense techniques accordingly in order to be well prepared for future botnet attacks. In this dissertation, we focus on two areas of the next generation botnet attacks and defenses: the peer-to-peer (P2P) structured botnets and the possible honeypot detection techniques used by future botnets. Currently, most botnets have centralized command and control (C&C) architecture. However, P2P structured botnets have gradually emerged as a new advanced form of botnets. Without C&C servers, P2P botnets are more resilient to defense countermeasures than traditional centralized botnets. Therefore, we first systematically study P2P botnets along multiple dimensions: bot candidate selection, network construction and C&C mechanisms and communication protocols. As a further illustration of P2P botnets, we then present the design of an advanced hybrid P2P botnet, which could be developed by botmasters in the near future. Compared with current botnets, the proposed botnet is harder to be shut down, monitored, and hijacked. It provides robust network connectivity, individualized encryption and control traffic dispersion, limited botnet exposure by each bot, and easy monitoring and recovery by its botmaster. We suggest and analyze several possible defenses against this advanced botnet. Upon our understanding of P2P botnets, we turn our focus to P2P botnet countermeasures. We provide mathematical analysis of two P2P botnet mitigation approaches --- index poisoning defense and Sybil defense, and one monitoring technique - passive monitoring. We are able to give analytical results to evaluate their performance. And simulation-based experiments show that our analysis is accurate. Besides P2P botnets, we investigate honeypot-aware botnets as well. This is because honeypot techniques have been widely used in botnet defense systems, botmasters will have to find ways to detect honeypots in order to protect and secure their botnets. We point out a general honeypot-aware principle, that is security professionals deploying honeypots have liability constraint such that they cannot allow their honeypots to participate in real attacks that could cause damage to others, while attackers do not need to follow this constraint. Based on this principle, a hardware- and software- independent honeypot detection methodology is proposed. We present possible honeypot detection techniques that can be used in both centralized botnets and P2P botnets. Our experiments show that current standard honeypot and honeynet programs are vulnerable to the proposed honeypot detection techniques. In the meantime, we discuss some guidelines for defending against general honeypot-aware botnet attacks.
Show less - Date Issued
- 2010
- Identifier
- CFE0003443, ucf:48428
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003443
- Title
- VICTIMIZATION, RISKY BEHAVIORS, AND THE VIRTUAL WORLD.
- Creator
-
Morgan, Rachel, Jasinski, Jana, University of Central Florida
- Abstract / Description
-
Social networking sites, such as Facebook and MySpace, have become increasingly popular among teens and young adults because of the availability of the internet. Because these websites promote interpersonal connections and information sharing among individuals around the world, personal information to online "friends" may be shared carelessly. However, little is known about the correlation between engaging in online activities, sharing personal information online, and susceptibility to online...
Show moreSocial networking sites, such as Facebook and MySpace, have become increasingly popular among teens and young adults because of the availability of the internet. Because these websites promote interpersonal connections and information sharing among individuals around the world, personal information to online "friends" may be shared carelessly. However, little is known about the correlation between engaging in online activities, sharing personal information online, and susceptibility to online victimization and cyberbullying. This study analyzes data from the Parents & Teens 2006 Survey to examine the applicability of Routine Activities Theory as a theoretical framework for understanding cybervictimization and cyberbullying. Online teens and teens on social networking sites (SNS) were examined separately in this study to determine if social networking (SNS) teens were at an increased risk. The results indicated that participating in online activities and sharing personal information increased the risk for receiving a threatening email, instant message or text message. Teens whose parents did not have rules regulating their online activities and behaviors were also at an increased risk for receiving a threatening email, instant message or text message. The logistic regression models show that for social networking (SNS) teens, gender and age increase the odds of receiving a threat, compared to online teens.
Show less - Date Issued
- 2010
- Identifier
- CFE0003048, ucf:48348
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003048
- Title
- EXAMINING ENGINEERING & TECHNOLOGY STUDENTS ACCEPTANCE OF NETWORK VIRTUALIZATION TECHNOLOGY USING THE TECHNOLOGY ACCEPTANCE MODEL.
- Creator
-
Yousif, Wael K. Yousif, Boote, David, University of Central Florida
- Abstract / Description
-
This causal and correlational study was designed to extend the Technology Acceptance Model (TAM) and to test its applicability to Valencia Community College (VCC) Engineering and Technology students as the target user group when investigating the factors influencing their decision to adopt and to utilize VMware as the target technology. In addition to the primary three indigenous factors: perceived ease of use, perceived usefulness, and intention toward utilization, the model was also...
Show moreThis causal and correlational study was designed to extend the Technology Acceptance Model (TAM) and to test its applicability to Valencia Community College (VCC) Engineering and Technology students as the target user group when investigating the factors influencing their decision to adopt and to utilize VMware as the target technology. In addition to the primary three indigenous factors: perceived ease of use, perceived usefulness, and intention toward utilization, the model was also extended with enjoyment, external control, and computer self-efficacy as antecedents to perceived ease of use. In an attempt to further increase the explanatory power of the model, the Task-Technology Fit constructs (TTF) were included as antecedents to perceived usefulness. The model was also expanded with subjective norms and voluntariness to assess the degree to which social influences affect students decision for adoption and utilization. This study was conducted during the fall term of 2009, using 11 instruments: (1) VMware Tools Functions Instrument; (2) Computer Networking Tasks Characteristics Instrument; (3) Perceived Usefulness Instrument; (4) Voluntariness Instrument; (5) Subjective Norms Instrument; (6) Perceived Enjoyment Instrument; (7) Computer Self-Efficacy Instrument; (8) Perception of External Control Instrument; (9) Perceived Ease of Use Instrument; (10) Intention Instrument; and (11) a Utilization Instrument. The 11 instruments collectively contained 58 items. Additionally, a demographics instrument of six items was included to investigate the influence of age, prior experience with the technology, prior experience in computer networking, academic enrollment status, and employment status on student intentions and behavior with regard to VMware as a network virtualization technology. Data were analyzed using path analysis, regressions, and univariate analysis of variance in SPSS and AMOS for Windows. The results suggest that perceived ease of use was found to be the strongest determinant of student intention. The analysis also suggested that external control, measuring the facilitating conditions (knowledge, resources, etc) necessary for adoption was the highest predictor of perceived ease of use. Consistent with previous studies, perceived ease of use was found to be the strongest predictor of perceived usefulness followed by subjective norms as students continued to use the technology. Even though the integration of the task-technology fit construct was not helpful in explaining the variance in student perceived usefulness of the target technology, it was statistically significant in predicting student perception of ease of use. The study concluded with recommendations to investigate other factors (such as service quality and ease of implementation) that might contribute to explaining the variance in perceived ease of use as the primary driving force in influencing student decision for adoption. A recommendation was also made to modify the task-technology fit construct instruments to improve the articulation and the specificity of the task. The need for further examination of the influence of the instructor on student decision for adoption of a target technology was also emphasized.
Show less - Date Issued
- 2010
- Identifier
- CFE0003071, ucf:48313
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003071
- Title
- THE IMPACT OF SOCIAL CAPITAL ON YOUTH SUBSTANCE USE.
- Creator
-
Unlu, Ali, Wan, Thomas, University of Central Florida
- Abstract / Description
-
Substance use, such as alcohol, cigarette, and marijuana, is a threat to the health and well-being of the youth, their families, and society as well. Government supports and implements several programs to protect youth from substance use. The aim of this study is to evaluate the impact of social capital on youth behavior and to suggest evidence-based policy interventions. Social capital refers to individual embeddedness in web of social relations and their behaviors guided by social structure...
Show moreSubstance use, such as alcohol, cigarette, and marijuana, is a threat to the health and well-being of the youth, their families, and society as well. Government supports and implements several programs to protect youth from substance use. The aim of this study is to evaluate the impact of social capital on youth behavior and to suggest evidence-based policy interventions. Social capital refers to individual embeddedness in web of social relations and their behaviors guided by social structure. Therefore, adolescents' social interactions with their peers, parents, and community were investigated. The substance use was measured by the usage of cigarettes, alcohol, marijuana, and inhalants in the past year. The type of activities adolescents participate in, the time and type of intra-familial interactions between parents and adolescents, and the type of peer groups adolescents interact with were employed as indicators of social capital. In other words, this study focuses on the relationship between youth substance use and the impact of parents, peers, and youth activities. Moreover, the study examined not only the correlation between social capital and substance use, but also the variation in substance use among youth by age, gender, ethnicity, income level, and mobility. The data, National Survey on Drug Use and Health (2005, 2006, and 2007), was collected by the United States Department of Health and Human Service, Substance Abuse and Mental Health Services Administration Office of Applied Studies. The sample size for each year was around 17.000. Structural Equation Modeling (SEM) was used to test the hypothesized. The results of the statistical analysis supported the research hypothesis.Findings show that there is a relationship between youth substance use and social capital. All three dimensions of social capital (peer impact, family attachments, and youth activities) were found to be statistically significant. While peer influence is positively correlated with substance use, family attachment and youth activities have a negative relationship with substance use. The impact of social capital however varies by age, gender, ethnicity, mobility, and income level. The study also contributes to the social capital literature by integrating different perspectives in social capital and substance use literature. Moreover, it successfully demonstrates how social capital can be utilized as a policy and intervention tool.
Show less - Date Issued
- 2009
- Identifier
- CFE0002700, ucf:48237
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002700
- Title
- Worldwide Infrastructure for Neuroevolution: A Modular Library to Turn Any Evolutionary Domain into an Online Interactive Platform.
- Creator
-
Szerlip, Paul, Stanley, Kenneth, Laviola II, Joseph, Wu, Annie, Kim, Joo, University of Central Florida
- Abstract / Description
-
Across many scientific disciplines, there has emerged an open opportunity to utilize the scale and reach of the Internet to collect scientific contributions from scientists and non-scientists alike. This process, called citizen science, has already shown great promise in the fields of biology and astronomy. Within the fields of artificial life (ALife) and evolutionary computation (EC) experiments in collaborative interactive evolution (CIE) have demonstrated the ability to collect thousands...
Show moreAcross many scientific disciplines, there has emerged an open opportunity to utilize the scale and reach of the Internet to collect scientific contributions from scientists and non-scientists alike. This process, called citizen science, has already shown great promise in the fields of biology and astronomy. Within the fields of artificial life (ALife) and evolutionary computation (EC) experiments in collaborative interactive evolution (CIE) have demonstrated the ability to collect thousands of experimental contributions from hundreds of users across the glob. However, such collaborative evolutionary systems can take nearly a year to build with a small team of researchers. This dissertation introduces a new developer framework enabling researchers to easily build fully persistent online collaborative experiments around almost any evolutionary domain, thereby reducing the time to create such systems to weeks for a single researcher. To add collaborative functionality to any potential domain, this framework, called Worldwide Infrastructure for Neuroevolution (WIN), exploits an important unifying principle among all evolutionary algorithms: regardless of the overall methods and parameters of the evolutionary experiment, every individual created has an explicit parent-child relationship, wherein one individual is considered the direct descendant of another. This principle alone is enough to capture and preserve the relationships and results for a wide variety of evolutionary experiments, while allowing multiple human users to meaningfully contribute. The WIN framework is first validated through two experimental domains, image evolution and a new two-dimensional virtual creature domain, Indirectly Encoded SodaRace (IESoR), that is shown to produce a visually diverse variety of ambulatory creatures. Finally, an Android application built with WIN, #filters, allows users to interactively evolve custom image effects to apply to personalized photographs, thereby introducing the first CIE application available for any mobile device. Together, these collaborative experiments and new mobile application establish a comprehensive new platform for evolutionary computation that can change how researchers design and conduct citizen science online.
Show less - Date Issued
- 2015
- Identifier
- CFE0005889, ucf:50892
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005889
- Title
- Performance Evaluation of TCP Multihoming for IPV6 Anycast Networks and Proxy Placement.
- Creator
-
Alsharfa, Raya, Bassiouni, Mostafa, Guha, Ratan, Lin, Mingjie, University of Central Florida
- Abstract / Description
-
In this thesis, the impact of multihomed clients and multihomed proxy servers on the performance of modern networks is investigated. The network model used in our investigation integrates three main components: the new one-to-any Anycast communication paradigm that facilitates server replication, the next generation Internet Protocol Version 6 (IPv6) that offers larger address space for packet switched networks, and the emerging multihoming trend of connecting devices and smart phones to more...
Show moreIn this thesis, the impact of multihomed clients and multihomed proxy servers on the performance of modern networks is investigated. The network model used in our investigation integrates three main components: the new one-to-any Anycast communication paradigm that facilitates server replication, the next generation Internet Protocol Version 6 (IPv6) that offers larger address space for packet switched networks, and the emerging multihoming trend of connecting devices and smart phones to more than one Internet service provider thereby acquiring more than one IP address. The design of a previously proposed Proxy IP Anycast service is modified to integrate user device multihoming and Ipv6 routing. The impact of user device multihoming (single-homed, dual-homed, and triple-homed) on network performance is extensively analyzed using realistic network topologies and different traffic scenarios of client-server TCP flows. Network throughput, packet latency delay and packet loss rate are the three performance metrics used in our analysis. Performance comparisons between the Anycast Proxy service and the native IP Anycast protocol are presented. The number of Anycast proxy servers and their placement are studied. Five placement methods have been implemented and evaluated including random placement, highest traffic placement, highest number of active interface placements, K-DS placement and a new hybrid placement method. The work presented in this thesis provides new insight into the performance of some new emerging communication paradigms and how to improve their design. Although the work has been limited to investigating Anycast proxy servers, the results can be beneficial and applicable to other types of overlay proxy services such as multicast proxies.
Show less - Date Issued
- 2015
- Identifier
- CFE0005919, ucf:50825
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005919
- Title
- Confluence of Vision and Natural Language Processing for Cross-media Semantic Relations Extraction.
- Creator
-
Tariq, Amara, Foroosh, Hassan, Qi, GuoJun, Gonzalez, Avelino, Pensky, Marianna, University of Central Florida
- Abstract / Description
-
In this dissertation, we focus on extracting and understanding semantically meaningful relationshipsbetween data items of various modalities; especially relations between images and naturallanguage. We explore the ideas and techniques to integrate such cross-media semantic relationsfor machine understanding of large heterogeneous datasets, made available through the expansionof the World Wide Web. The datasets collected from social media websites, news media outletsand blogging platforms...
Show moreIn this dissertation, we focus on extracting and understanding semantically meaningful relationshipsbetween data items of various modalities; especially relations between images and naturallanguage. We explore the ideas and techniques to integrate such cross-media semantic relationsfor machine understanding of large heterogeneous datasets, made available through the expansionof the World Wide Web. The datasets collected from social media websites, news media outletsand blogging platforms usually contain multiple modalities of data. Intelligent systems are needed to automatically make sense out of these datasets and present them in such a way that humans can find the relevant pieces of information or get a summary of the available material. Such systems have to process multiple modalities of data such as images, text, linguistic features, and structured data in reference to each other. For example, image and video search and retrieval engines are required to understand the relations between visual and textual data so that they can provide relevant answers in the form of images and videos to the users' queries presented in the form of text.We emphasize the automatic extraction of semantic topics or concepts from the data available in any form such as images, free-flowing text or metadata. These semantic concepts/topics become the basis of semantic relations across heterogeneous data types, e.g., visual and textual data. A classic problem involving image-text relations is the automatic generation of textual descriptions of images. This problem is the main focus of our work. In many cases, large amount of text is associated with images. Deep exploration of linguistic features of such text is required to fully utilize the semantic information encoded in it. A news dataset involving images and news articles is an example of this scenario. We devise frameworks for automatic news image description generation based on the semantic relations of images, as well as semantic understanding of linguistic features of the news articles.
Show less - Date Issued
- 2016
- Identifier
- CFE0006507, ucf:51401
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006507
