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- Title
- In Their Own Words: Older Adults' Perceptions of Effective and Ineffective Learning Experiences.
- Creator
- Duay, Deborah L., Bryan, Valerie, Florida Atlantic University
- Abstract/Description
-
The percentage of older adults in the American population is expected to increase from about 12% now to 22% by the year 2030 (Eisen, 2005). Educators can play an important role in managing the effec.,ts of this demographic shift by designing learning opportunities that increase older adults' motivation to participate and their ability to learn. Because older adults themselves can otTer important insights on what helps and hinders their learning, the purpose of this study was to explore the...
Show moreThe percentage of older adults in the American population is expected to increase from about 12% now to 22% by the year 2030 (Eisen, 2005). Educators can play an important role in managing the effec.,ts of this demographic shift by designing learning opportunities that increase older adults' motivation to participate and their ability to learn. Because older adults themselves can otTer important insights on what helps and hinders their learning, the purpose of this study was to explore the perceptions of adults over age 64 residing in a large metropolitan area in the southeastern United States on effective and ineffective learning experiences. Utilizing a qualitative design, the researcher interviewed 36 older adults involved in learning experiences at three distinct sites. Data were also collected through observations and document analysis. Five research questions were answered with the following four findings: 1) effective learning experiences are involving, 2) the instructor is a key component in the classroom, 3) familiar or relevant topics are interesting, and 4) the computer and the Internet are both loved and hated. The participants in this study value learning experiences that involve them in the classroom and keep them involved in the world. They enjoy asking questions, discussing ideas, and learning with friends and family members in environments free from the pressures of mandatory assignments and tests. They seek instructors who are knowledgeable about the subject, clear and understandable in their presentation, respectful of their experience, and effective at grabbing their attention through enthusiasm, humor, and relevant stories. When they discover effective instructors, they tend to take classes with them over and over again. However. when instructors' abilities are unknown, they look for learning experiences that will either expand their knowledge abcut something familiar or teach them something that will have some relevance in their lives. Finally, these seniors enjoy the convenience of accessing a wealth of information using computers and the Internet. Yet, they also experience considerable frustration in learning computer tasks and dealing with computer problems. Reommendations are provided for designing, marketing, and delivering quality learning experiences for senior adults.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00000663
- Subject Headings
- Continuing education, Learning, Psychology of, Adult learning, Experiential learning, Self-actualization (Psychology) in old age--United States
- Format
- Document (PDF)
- Title
- In-field practical application experiences.
- Creator
- Magoc, Matthew, Meltzer, Carol, Graduate College
- Date Issued
- 2011-04-08
- PURL
- http://purl.flvc.org/fcla/dt/3164621
- Subject Headings
- Experiential learning, Workplace literacy, Vocational guidance
- Format
- Document (PDF)
- Title
- CONNECTED MULTI-DOMAIN AUTONOMY AND ARTIFICIAL INTELLIGENCE: AUTONOMOUS LOCALIZATION, NETWORKING, AND DATA CONFORMITY EVALUATION.
- Creator
- Tountas, Konstantinos, Pados, Dimitris, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
The objective of this dissertation work is the development of a solid theoretical and algorithmic framework for three of the most important aspects of autonomous/artificialintelligence (AI) systems, namely data quality assurance, localization, and communications. In the era of AI and machine learning (ML), data reign supreme. During learning tasks, we need to ensure that the training data set is correct and complete. During operation, faulty data need to be discovered and dealt with to...
Show moreThe objective of this dissertation work is the development of a solid theoretical and algorithmic framework for three of the most important aspects of autonomous/artificialintelligence (AI) systems, namely data quality assurance, localization, and communications. In the era of AI and machine learning (ML), data reign supreme. During learning tasks, we need to ensure that the training data set is correct and complete. During operation, faulty data need to be discovered and dealt with to protect from -potentially catastrophic- system failures. With our research in data quality assurance, we develop new mathematical theory and algorithms for outlier-resistant decomposition of high-dimensional matrices (tensors) based on L1-norm principal-component analysis (PCA). L1-norm PCA has been proven to be resistant to irregular data-points and will drive critical real-world AI learning and autonomous systems operations in the future. At the same time, one of the most important tasks of autonomous systems is self-localization. In GPS-deprived environments, localization becomes a fundamental technical problem. State-of-the-art solutions frequently utilize power-hungry or expensive architectures, making them difficult to deploy. In this dissertation work, we develop and implement a robust, variable-precision localization technique for autonomous systems based on the direction-of-arrival (DoA) estimation theory, which is cost and power-efficient. Finally, communication between autonomous systems is paramount for mission success in many applications. In the era of 5G and beyond, smart spectrum utilization is key.. In this work, we develop physical (PHY) and medium-access-control (MAC) layer techniques that autonomously optimize spectrum usage and minimizes intra and internetwork interference.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013617
- Subject Headings
- Artificial intelligence, Machine learning, Tensor algebra
- Format
- Document (PDF)
- Title
- CONNECTING THE NOSE AND THE BRAIN: DEEP LEARNING FOR CHEMICAL GAS SENSING.
- Creator
- Stark, Emily Nicole, Barenholtz, Elan, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
- Abstract/Description
-
The success of deep learning in applications including computer vision, natural language processing, and even the game of Go can only be a orded by powerful computational resources and vast data sets. Data sets coming from the medical application are often much smaller and harder to acquire. Here a novel data approach is explained and used to demonstrate how to use deep learning as a step in data discovery, classi cation, and ultimately support for further investigation. Data sets used to...
Show moreThe success of deep learning in applications including computer vision, natural language processing, and even the game of Go can only be a orded by powerful computational resources and vast data sets. Data sets coming from the medical application are often much smaller and harder to acquire. Here a novel data approach is explained and used to demonstrate how to use deep learning as a step in data discovery, classi cation, and ultimately support for further investigation. Data sets used to illustrate these successes come from common ion-separation techniques that allow for gas samples to be quantitatively analyzed. The success of this data approach allows for the deployment of deep learning to smaller data sets.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013416
- Subject Headings
- Deep Learning, Data sets, Gases--Analysis
- Format
- Document (PDF)
- Title
- Ensemble Learning Algorithms for the Analysis of Bioinformatics Data.
- Creator
- Fazelpour, Alireza, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Developments in advanced technologies, such as DNA microarrays, have generated tremendous amounts of data available to researchers in the field of bioinformatics. These state-of-the-art technologies present not only unprecedented opportunities to study biological phenomena of interest, but significant challenges in terms of processing the data. Furthermore, these datasets inherently exhibit a number of challenging characteristics, such as class imbalance, high dimensionality, small dataset...
Show moreDevelopments in advanced technologies, such as DNA microarrays, have generated tremendous amounts of data available to researchers in the field of bioinformatics. These state-of-the-art technologies present not only unprecedented opportunities to study biological phenomena of interest, but significant challenges in terms of processing the data. Furthermore, these datasets inherently exhibit a number of challenging characteristics, such as class imbalance, high dimensionality, small dataset size, noisy data, and complexity of data in terms of hard to distinguish decision boundaries between classes within the data. In recognition of the aforementioned challenges, this dissertation utilizes a variety of machine-learning and data-mining techniques, such as ensemble classification algorithms in conjunction with data sampling and feature selection techniques to alleviate these problems, while improving the classification results of models built on these datasets. However, in building classification models researchers and practitioners encounter the challenge that there is not a single classifier that performs relatively well in all cases. Thus, numerous classification approaches, such as ensemble learning methods, have been developed to address this problem successfully in a majority of circumstances. Ensemble learning is a promising technique that generates multiple classification models and then combines their decisions into a single final result. Ensemble learning often performs better than single-base classifiers in performing classification tasks. This dissertation conducts thorough empirical research by implementing a series of case studies to evaluate how ensemble learning techniques can be utilized to enhance overall classification performance, as well as improve the generalization ability of ensemble models. This dissertation investigates ensemble learning techniques of the boosting, bagging, and random forest algorithms, and proposes a number of modifications to the existing ensemble techniques in order to improve further the classification results. This dissertation examines the effectiveness of ensemble learning techniques on accounting for challenging characteristics of class imbalance and difficult-to-learn class decision boundaries. Next, it looks into ensemble methods that are relatively tolerant to class noise, and not only can account for the problem of class noise, but improves classification performance. This dissertation also examines the joint effects of data sampling along with ensemble techniques on whether sampling techniques can further improve classification performance of built ensemble models.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004588
- Subject Headings
- Bioinformatics., Data mining -- Technological innovations., Machine learning.
- Format
- Document (PDF)
- Title
- A Deep Learning Approach To Target Recognition In Side-Scan Sonar Imagery.
- Creator
- Einsidler, Dylan, Dhanak, Manhar R., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
-
Automatic target recognition capabilities in autonomous underwater vehicles has been a daunting task, largely due to the noisy nature of sonar imagery and due to the lack of publicly available sonar data. Machine learning techniques have made great strides in tackling this feat, although not much research has been done regarding deep learning techniques for side-scan sonar imagery. Here, a state-of-the-art deep learning object detection method is adapted for side-scan sonar imagery, with...
Show moreAutomatic target recognition capabilities in autonomous underwater vehicles has been a daunting task, largely due to the noisy nature of sonar imagery and due to the lack of publicly available sonar data. Machine learning techniques have made great strides in tackling this feat, although not much research has been done regarding deep learning techniques for side-scan sonar imagery. Here, a state-of-the-art deep learning object detection method is adapted for side-scan sonar imagery, with results supporting a simple yet robust method to detect objects/anomalies along the seabed. A systematic procedure was employed in transfer learning a pre-trained convolutional neural network in order to learn the pixel-intensity based features of seafloor anomalies in sonar images. Using this process, newly trained convolutional neural network models were produced using relatively small training datasets and tested to show reasonably accurate anomaly detection and classification with little to no false alarms.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013025
- Subject Headings
- Deep learning, Sidescan sonar, Underwater vision
- Format
- Document (PDF)
- Title
- A Comparison of Perceptions of Specific Learning Disabilities Teachers with Exceptional Student Education Lead Teachers Toward Goal Achievement.
- Creator
- Brater, Patricia Barrack, Urich, Ted R., Florida Atlantic University
- Abstract/Description
-
The purpose of this study was to assist in the program evaluation process by comparing perceptions of Specific Learning Disabilities teachers with Exceptional Education lead teachers toward indicators of goal achievement. The procedures employed in the study involved a review of the literature, and the design, field testing, and utilization of the Goal Achievement Instrument. Data was obtained from the 111 teacher questionnaires and the 111 questionnaires completed by lead teachers who rated...
Show moreThe purpose of this study was to assist in the program evaluation process by comparing perceptions of Specific Learning Disabilities teachers with Exceptional Education lead teachers toward indicators of goal achievement. The procedures employed in the study involved a review of the literature, and the design, field testing, and utilization of the Goal Achievement Instrument. Data was obtained from the 111 teacher questionnaires and the 111 questionnaires completed by lead teachers who rated individual teacher performance of goal indicators. Analysis of variance was utilized to determine whether there were differences in ratings between the groups. A follow-up study was completed to determine goal achievement indicators which might have been overlooked in the goal achievement indicator development process. 1. There were significant differences in responses between resource Specific Learning Disabilities teachers and lead teachers, indicating that data from neither group should be used in isolation to determine levels of goal achievement. 2. There were no significant differences between self-contained, elementary, and secondary Specific Learning Disabilities teachers when each group's ratings were compared to lead teacher ratings. This indicated that either teachers or lead teachers could be used to determine levels of goal achievement for these groups of teachers. 3. Teachers in all groups were achieving goals at a satisfactory level, as perceived by teachers and by lead teachers. 4. Several additional items were developed and recommended for inclusion to the Goal Achievement Instrument before use in the formal program evaluation process. In-service programs for teachers, guidance committee activities, strong financial support to the classes, and the positive attitude of teachers may have been important factors in leading to the high performance levels achieved by Specific Learning Disabilities teachers in Brevard County, Florida.
Show less - Date Issued
- 1983
- PURL
- http://purl.flvc.org/fau/fd/FA00000652
- Subject Headings
- Special education teachers, Learning disabilities, Exceptional children
- Format
- Document (PDF)
- Title
- STREAMLINING CLINICAL DETECTION OF ALZHEIMER’S DISEASE USING ELECTRONIC HEALTH RECORDS AND MACHINE LEARNING TECHNIQUES.
- Creator
- Kleiman, Michael J., Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
- Abstract/Description
-
Alzheimer’s disease is typically detected using a combination of cognitive-behavioral assessment exams and interviews of both the patient and a family member or caregiver, both administered and interpreted by a trained physician. This procedure, while standard in medical practice, can be time consuming and expensive for both the patient and the diagnostician especially because proper training is required to interpret the collected information and determine an appropriate diagnosis. The use of...
Show moreAlzheimer’s disease is typically detected using a combination of cognitive-behavioral assessment exams and interviews of both the patient and a family member or caregiver, both administered and interpreted by a trained physician. This procedure, while standard in medical practice, can be time consuming and expensive for both the patient and the diagnostician especially because proper training is required to interpret the collected information and determine an appropriate diagnosis. The use of machine learning techniques to augment diagnostic procedures has been previously examined in limited capacity but to date no research examines real-world medical applications of predictive analytics for health records and cognitive exam scores. This dissertation seeks to examine the efficacy of detecting cognitive impairment due to Alzheimer’s disease using machine learning, including multi-modal neural network architectures, with a real-world clinical dataset used to determine the accuracy and applicability of the generated models. An in-depth analysis of each type of data (e.g. cognitive exams, questionnaires, demographics) as well as the cognitive domains examined (e.g. memory, attention, language) is performed to identify the most useful targets, with cognitive exams and questionnaires being found to be the most useful features and short-term memory, attention, and language found to be the most important cognitive domains. In an effort to reduce medical costs and streamline procedures, optimally predictive and efficient groups of features were identified and selected, with the best performing and economical group containing only three questions and one cognitive exam component, producing an accuracy of 85%. The most effective diagnostic scoring procedure was examined, with simple threshold counting based on medical documentation being identified as the most useful. Overall predictive analysis found that Alzheimer’s disease can be detected most accurately using a bimodal multi-input neural network model using separated cognitive domains and questionnaires, with a detection accuracy of 88% using the real-world testing set, and that the technique of analyzing domains separately serves to significantly improve model efficacy compared to models that combine them.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013326
- Subject Headings
- Alzheimer's disease, Electronic Health Records, Machine learning
- Format
- Document (PDF)
- Title
- SEAWALL DETECTION IN FLORIDA COASTAL AREA FROM HIGH RESOLUTION IMAGERY USING MACHINE LEARNING AND OBIA.
- Creator
- Paudel, Sanjaya, Su, Hongbo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
- Abstract/Description
-
In this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image...
Show moreIn this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image analysis (OBIA) method were applied for image classification. However, Pixel based classification was discarded for having less accuracy in output. Three techniques within object-based classification-machine learning technique, knowledge-based technique and machine learning followed by knowledge-based technique were used to compare the most efficient method of classification. While performing the machine learning technique, three algorithms: Random Forest, support vector machine and decision tree were applied to test the best algorithm. Of all the approaches used, the combination of machine learning and a knowledge-based method was able to map the sea wall effectively.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013802
- Subject Headings
- Image analysis, Coasts--Florida, Machine learning
- Format
- Document (PDF)
- Title
- PERSONAL AND SCHOOL RELATED FACTORS PREDICTING RESILIENCE IN STUDENTS WITH LEARNING DISABILITIES.
- Creator
- Carson, Maureen M., Dukes, Charles, Florida Atlantic University, College of Education, Department of Exceptional Student Education
- Abstract/Description
-
This study was conducted to investigate factors that contribute to resilience in students with learning disabilities (LD). The risk-resilience framework provided the theoretical base for selecting school and personal factors that might predict resilience. School and personal data were requested from large, culturally and linguistically diverse samples of individuals diagnosed with LD. A 12 variable model and three cluster models (combined variables) were developed. Discriminant analysis and...
Show moreThis study was conducted to investigate factors that contribute to resilience in students with learning disabilities (LD). The risk-resilience framework provided the theoretical base for selecting school and personal factors that might predict resilience. School and personal data were requested from large, culturally and linguistically diverse samples of individuals diagnosed with LD. A 12 variable model and three cluster models (combined variables) were developed. Discriminant analysis and tests of significance of hit rates were conducted to assess the accuracy of the full model (all 12 variables) to the prediction of resilience, and full versus restricted model testing was done to assess individual variable and cluster (combinations of some variables) contributions to the model. Additionally, analyses of environmental, intrapersonal, and interpersonal cluster models were investigated to determine their relative contribution to the prediction of resilience in relation to the others. Results of the full model analysis and subsequent tests of significance of hit rate indicated modest cross validated classification accuracy for the total group, resilient group, and non-resilient group. However, the model was not significantly better than chance, overall, at predicting resilience and non-resilience in students with LD. Results of the analysis of individual predictor variables’ and clusters’ contributions to the model’s classification accuracy indicated that no individual variable within the full model, nor cluster of interrelated variables contributed significant incremental improvement in classification accuracy above and beyond that which is available from all other variables contained in the full model. The independent analysis of interrelated personal and school related factors clustered as environmental, interpersonal, and intrapersonal clusters revealed that, as unique and separate models, classification accuracy of cross-validated group cases were less than optimal for each cluster. The results further demonstrate that resilience is affected by both internal and external factors. Although the results also demonstrate that factors work together, a great deal is still to be learned regarding factors affecting resilience as well as their interplay in clusters of factors that affect resilience.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013291
- Subject Headings
- Learning disabilities, Resilience (Personality trait), Students
- Format
- Document (PDF)
- Title
- IMPACT OF FLIPPED CLASSROOM MODEL ON STUDENT LEARNING OUTCOMES FOR UNIVERSITY FITNESS/WELLNESS LEARNERS.
- Creator
- Breitkreuz, Helen Denise, Lieberman, Mary G., Florida Atlantic University, Department of Curriculum, Culture, and Educational Inquiry, College of Education
- Abstract/Description
-
The purpose of this quasi experimental, two group pretest posttest quantitative design study was to explore the influence of content delivery method for a lifetime fitness or wellness course on the impact of student learning outcomes. Also, student satisfaction of the course and instructor were examined. Specifically, two teaching methods of instruction were examined: the flipped classroom model (FCM) and the traditional lecture model (TLM). Cheng, Ritzhaupt, and Antonenko’s (2019) “Effects...
Show moreThe purpose of this quasi experimental, two group pretest posttest quantitative design study was to explore the influence of content delivery method for a lifetime fitness or wellness course on the impact of student learning outcomes. Also, student satisfaction of the course and instructor were examined. Specifically, two teaching methods of instruction were examined: the flipped classroom model (FCM) and the traditional lecture model (TLM). Cheng, Ritzhaupt, and Antonenko’s (2019) “Effects of the Flipped Classroom Instructional Strategy on Students’ Learning Outcomes: A Meta-Analysis,” which looked at 55 publications between 2000 and 2016, found statistically significant results in favor of the flipped classroom instructional strategy on student learning outcomes. Therefore, it was hypothesized that the flipped classroom model would improve undergraduate students’ learning outcomes of understanding of health content knowledge, physical activity level, physical fitness, and course satisfaction for a college-level lifetime fitness or wellness course as opposed to the traditional lecture class normally taught. Pretest and posttest data were collected.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013447
- Subject Headings
- Flipped classes, Health education (Higher), Learning
- Format
- Document (PDF)
- Title
- Machine Learning Algorithms with Big Medicare Fraud Data.
- Creator
- Bauder, Richard Andrew, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Healthcare is an integral component in peoples lives, especially for the rising elderly population, and must be affordable. The United States Medicare program is vital in serving the needs of the elderly. The growing number of people enrolled in the Medicare program, along with the enormous volume of money involved, increases the appeal for, and risk of, fraudulent activities. For many real-world applications, including Medicare fraud, the interesting observations tend to be less frequent...
Show moreHealthcare is an integral component in peoples lives, especially for the rising elderly population, and must be affordable. The United States Medicare program is vital in serving the needs of the elderly. The growing number of people enrolled in the Medicare program, along with the enormous volume of money involved, increases the appeal for, and risk of, fraudulent activities. For many real-world applications, including Medicare fraud, the interesting observations tend to be less frequent than the normative observations. This difference between the normal observations and those observations of interest can create highly imbalanced datasets. The problem of class imbalance, to include the classification of rare cases indicating extreme class imbalance, is an important and well-studied area in machine learning. The effects of class imbalance with big data in the real-world Medicare fraud application domain, however, is limited. In particular, the impact of detecting fraud in Medicare claims is critical in lessening the financial and personal impacts of these transgressions. Fortunately, the healthcare domain is one such area where the successful detection of fraud can garner meaningful positive results. The application of machine learning techniques, plus methods to mitigate the adverse effects of class imbalance and rarity, can be used to detect fraud and lessen the impacts for all Medicare beneficiaries. This dissertation presents the application of machine learning approaches to detect Medicare provider claims fraud in the United States. We discuss novel techniques to process three big Medicare datasets and create a new, combined dataset, which includes mapping fraud labels associated with known excluded providers. We investigate the ability of machine learning techniques, unsupervised and supervised, to detect Medicare claims fraud and leverage data sampling methods to lessen the impact of class imbalance and increase fraud detection performance. Additionally, we extend the study of class imbalance to assess the impacts of rare cases in big data for Medicare fraud detection.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013108
- Subject Headings
- Medicare fraud, Big data, Machine learning, Algorithms
- Format
- Document (PDF)
- Title
- Effects of Non-reinforced Test Trials on Transposition.
- Creator
- Royal, Jackson W., Adamson, Robert E., Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
- Abstract/Description
-
Ten male albino rats were trained In a brightness discrimination problem where they were reQuired to choose a sttmulus value of 0.053 ft/cd over one of 0.012 ft/cd. Pairs were matched accordlng to the number of trials requtred to reach a criterion of 18/20 correct responses then randomly assigned to one of two grouos for testing In transposition. Both groups were tested on the orlgtnally positive stimulus and a brighter one: 1.25 ft/cd for Group 8-C and 5.38 ft/cd for Group 8-D. By testing...
Show moreTen male albino rats were trained In a brightness discrimination problem where they were reQuired to choose a sttmulus value of 0.053 ft/cd over one of 0.012 ft/cd. Pairs were matched accordlng to the number of trials requtred to reach a criterion of 18/20 correct responses then randomly assigned to one of two grouos for testing In transposition. Both groups were tested on the orlgtnally positive stimulus and a brighter one: 1.25 ft/cd for Group 8-C and 5.38 ft/cd for Group 8-D. By testing for transposttlon wtth non-reinforced trials, contrary to the usual method, a tendency toward converging measures of transposltton was achteved. Transposition for Group 8-D, In the situation most dissimilar to training, was greater than for 8-C. These results were discussed from relational or Gestalt, Spence model, and Adaptation Level positions and It was shown that the results are contrary to traditional Gestalt predictions. It was oredlcted that, according to underlying assumptions of the Spence model, with continued non-reinforced trials, per cent of transposition for both groups would decrease until a chance level of responding was reached. That this did not occur cannot be explained by the Spence model. Because the variability was too great with such a small N, these results did not reach the .05 level of probability.
Show less - Date Issued
- 1968
- PURL
- http://purl.flvc.org/fau/fd/FA00012596
- Subject Headings
- Transfer of training, Discrimination learning, Extinction (Psychology)
- Format
- Document (PDF)
- Title
- CONTRAST EFFECTS IN THE ACQUISITION OF A BRIGHTNESS DISCRIMINATION.
- Creator
- MOORE, JOHN NICHOLAS, Florida Atlantic University
- Abstract/Description
-
An attempt was made to determine the effects of successive shifts in the quality of a reinforcing agent upon the acquisition of a brightness discrimination. Two levels of sucrose concentration (4 and 32%) were combined factorially during preadaptation and discrimination phases. Dependent measures included response rate and the number of errors made during discrimination acquisition. Results indicated non-significant negative contrast effects in errors and, in addition, negative contrast and...
Show moreAn attempt was made to determine the effects of successive shifts in the quality of a reinforcing agent upon the acquisition of a brightness discrimination. Two levels of sucrose concentration (4 and 32%) were combined factorially during preadaptation and discrimination phases. Dependent measures included response rate and the number of errors made during discrimination acquisition. Results indicated non-significant negative contrast effects in errors and, in addition, negative contrast and amount of reward effects in terms of response rate. It was hypothesized that the absence of amount of reward effects in errors and positive contrast in both dependent measures was a function of a partial between groups design and ceiling effects respectively.
Show less - Date Issued
- 1974
- PURL
- http://purl.flvc.org/fcla/dt/13669
- Subject Headings
- Discrimination learning, Brightness perception, Reward (Psychology)
- Format
- Document (PDF)
- Title
- THE RELATIVE EFFICIENCIES OF TWO PROCEDURES FOR THE EXTINCTION OF DISCRIMINATED AVOIDANCE CONDITIONING.
- Creator
- JACKSON, MASON CALVIN, JR., Florida Atlantic University, Otten, Cynthia S., Charles E. Schmidt College of Science, Department of Psychology
- Abstract/Description
-
Eighteen rats were used to study two procedures for the extinction of discriminated avoidance. One group (OE) was placed on extinction defined by presenting shocks as programmed but independently of the S's responses, while the other group (CE) was placed on classical extinction defined by the removal of all shocks. The two procedures were compared in terms of rate of decline and terminal level of extinction performance. In addition, the two groups were placed on a discrimination reversal...
Show moreEighteen rats were used to study two procedures for the extinction of discriminated avoidance. One group (OE) was placed on extinction defined by presenting shocks as programmed but independently of the S's responses, while the other group (CE) was placed on classical extinction defined by the removal of all shocks. The two procedures were compared in terms of rate of decline and terminal level of extinction performance. In addition, the two groups were placed on a discrimination reversal task in order to assess each procedure's effects on a new learning problem. The CE group reached a lower level of extinction performance in a fewer number of blocks than the OE Ss. Furthermore, the CE Ss were inferior to the OE Ss in terms of discrimination reversal performance as well. An interpretation of the results in terms of the removal and reinstatement of cues was offered although an alternative explanation relating to a change in the motivational states of the two groups during extinction was also presented. The interpretation in terms of the presence or absence of cues seemed to account for more of the present findings than the traditional one advocating changes in motivational levels resulting from the two divergent extinction operations.
Show less - Date Issued
- 1971
- PURL
- http://purl.flvc.org/fcla/dt/13433
- Subject Headings
- Extinction (Psychology), Discrimination learning, Avoidance (Psychology)
- Format
- Document (PDF)
- Title
- Cooperative self-organization in the perception of coherent motion.
- Creator
- Balz, Gunther William, Florida Atlantic University, Hock, Howard S.
- Abstract/Description
-
A row of dots is presented in a series of alternating frames; dots in each frame are located at the midpoints between dots of the preceding frame. Although the perceived frame-to-frame direction of motion could vary randomly, cooperativity is indicated by the emergence of two coherent motion patterns, one unidirectional, the other oscillatory. Small increases in the time between frames are sufficient for the bias, which maintains the previously established motion direction (unidirectional...
Show moreA row of dots is presented in a series of alternating frames; dots in each frame are located at the midpoints between dots of the preceding frame. Although the perceived frame-to-frame direction of motion could vary randomly, cooperativity is indicated by the emergence of two coherent motion patterns, one unidirectional, the other oscillatory. Small increases in the time between frames are sufficient for the bias, which maintains the previously established motion direction (unidirectional motion), to be reversed, becoming a bias which inhibits that direction (oscillatory motion). Unidirectional motion, which predominates for small dot separations, and oscillatory motion, which predominates for large separations, are associated with short-range and long-range motion (Braddick, 1974) by manipulating the shape of the dots, their luminance, and the luminance of the inter-frame blank field. Pulsing/flicker emerges as a third perceptual state that competes with unidirectional motion for very small dot separations.
Show less - Date Issued
- 1991
- PURL
- http://purl.flvc.org/fcla/dt/14712
- Subject Headings
- Motion perception (Vision), Perceptual-motor learning
- Format
- Document (PDF)
- Title
- MACHINE LEARNING ALGORITHMS FOR THE DETECTION AND ANALYSIS OF WEB ATTACKS.
- Creator
- Zuech, Richard, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
The Internet has provided humanity with many great benefits, but it has also introduced new risks and dangers. E-commerce and other web portals have become large industries with big data. Criminals and other bad actors constantly seek to exploit these web properties through web attacks. Being able to properly detect these web attacks is a crucial component in the overall cybersecurity landscape. Machine learning is one tool that can assist in detecting web attacks. However, properly using...
Show moreThe Internet has provided humanity with many great benefits, but it has also introduced new risks and dangers. E-commerce and other web portals have become large industries with big data. Criminals and other bad actors constantly seek to exploit these web properties through web attacks. Being able to properly detect these web attacks is a crucial component in the overall cybersecurity landscape. Machine learning is one tool that can assist in detecting web attacks. However, properly using machine learning to detect web attacks does not come without its challenges. Classification algorithms can have difficulty with severe levels of class imbalance. Class imbalance occurs when one class label disproportionately outnumbers another class label. For example, in cybersecurity, it is common for the negative (normal) label to severely outnumber the positive (attack) label. Another difficulty encountered in machine learning is models can be complex, thus making it difficult for even subject matter experts to truly understand a model’s detection process. Moreover, it is important for practitioners to determine which input features to include or exclude in their models for optimal detection performance. This dissertation studies machine learning algorithms in detecting web attacks with big data. Severe class imbalance is a common problem in cybersecurity, and mainstream machine learning research does not sufficiently consider this with web attacks. Our research first investigates the problems associated with severe class imbalance and rarity. Rarity is an extreme form of class imbalance where the positive class suffers extremely low positive class count, thus making it difficult for the classifiers to discriminate. In reducing imbalance, we demonstrate random undersampling can effectively mitigate the class imbalance and rarity problems associated with web attacks. Furthermore, our research introduces a novel feature popularity technique which produces easier to understand models by only including the fewer, most popular features. Feature popularity granted us new insights into the web attack detection process, even though we had already intensely studied it. Even so, we proceed cautiously in selecting the best input features, as we determined that the “most important” Destination Port feature might be contaminated by lopsided traffic distributions.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013823
- Subject Headings
- Machine learning, Computer security, Algorithms, Cybersecurity
- Format
- Document (PDF)
- Title
- COLLECTION AND ANALYSIS OF SLOW DENIAL OF SERVICE ATTACKS USING MACHINE LEARNING ALGORITHMS.
- Creator
- Kemp, Clifford, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
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Application-layer based attacks are becoming a more desirable target in computer networks for hackers. From complex rootkits to Denial of Service (DoS) attacks, hackers look to compromise computer networks. Web and application servers can get shut down by various application-layer DoS attacks, which exhaust CPU or memory resources. The HTTP protocol has become a popular target to launch application-layer DoS attacks. These exploits consume less bandwidth than traditional DoS attacks....
Show moreApplication-layer based attacks are becoming a more desirable target in computer networks for hackers. From complex rootkits to Denial of Service (DoS) attacks, hackers look to compromise computer networks. Web and application servers can get shut down by various application-layer DoS attacks, which exhaust CPU or memory resources. The HTTP protocol has become a popular target to launch application-layer DoS attacks. These exploits consume less bandwidth than traditional DoS attacks. Furthermore, this type of DoS attack is hard to detect because its network traffic resembles legitimate network requests. Being able to detect these DoS attacks effectively is a critical component of any robust cybersecurity system. Machine learning can help detect DoS attacks by identifying patterns in network traffic. With machine learning methods, predictive models can automatically detect network threats. This dissertation offers a novel framework for collecting several attack datasets on a live production network, where producing quality representative data is a requirement. Our approach builds datasets from collected Netflow and Full Packet Capture (FPC) data. We evaluate a wide range of machine learning classifiers which allows us to analyze slow DoS detection models more thoroughly. To identify attacks, we look at each dataset's unique traffic patterns and distinguishing properties. This research evaluates and investigates appropriate feature selection evaluators and search strategies. Features are assessed for their predictive value and degree of redundancy to build a subset of features. Feature subsets with high-class correlation but low intercorrelation are favored. Experimental results indicate Netflow and FPC features are discriminating enough to detect DoS attacks accurately. We conduct a comparative examination of performance metrics to determine the capability of several machine learning classifiers. Additionally, we improve upon our performance scores by investigating a variety of feature selection optimization strategies. Overall, this dissertation proposes a novel machine learning approach for detecting slow DoS attacks. Our machine learning results demonstrate that a single subset of features trained on Netflow data can effectively detect slow application-layer DoS attacks.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013848
- Subject Headings
- Machine learning, Algorithms, Denial of service attacks
- Format
- Document (PDF)
- Title
- A REVIEW AND ANALYSIS OF BOT-IOT SECURITY DATA FOR MACHINE LEARNING.
- Creator
- Peterson, Jared M., Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
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Machine learning is having an increased impact on the Cyber Security landscape. The ability for predictive models to accurately identify attack patterns in security data is set to overtake more traditional detection methods. Industry demand has led to an uptick in research in the application of machine learning for Cyber Security. To facilitate this research many datasets have been created and made public. This thesis provides an in-depth analysis of one of the newest datasets, Bot-IoT. The...
Show moreMachine learning is having an increased impact on the Cyber Security landscape. The ability for predictive models to accurately identify attack patterns in security data is set to overtake more traditional detection methods. Industry demand has led to an uptick in research in the application of machine learning for Cyber Security. To facilitate this research many datasets have been created and made public. This thesis provides an in-depth analysis of one of the newest datasets, Bot-IoT. The full dataset contains about 73 million instances (big data), 3 dependent features, and 43 independent features. The purpose of this thesis is to provide researchers with a foundational understanding of Bot-IoT, its development, its features, its composition, and its pitfalls. It will also summarize many of the published works that utilize Bot-IoT and will propose new areas of research based on the issues identified in the current research and in the dataset.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013838
- Subject Headings
- Machine learning, Cyber security, Big data
- Format
- Document (PDF)
- Title
- A MACHINE LEARNING APPROACH FOR OCEAN EVENT MODELING AND PREDICTION.
- Creator
- Muhamed, Ali Ali Abdullateef, Zhuang, Hanqi, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
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In the last decade, deep learning models have been successfully applied to a variety of applications and solved many tasks. The ultimate goal of this study is to produce deep learning models to improve the skills of forecasting ocean dynamic events in general and those of the Loop Current (LC) system in particular. A specific forecast target is to predict the geographic location of the (LC) extension and duration, LC eddy shedding events for a long lead time with high accuracy. Also, this...
Show moreIn the last decade, deep learning models have been successfully applied to a variety of applications and solved many tasks. The ultimate goal of this study is to produce deep learning models to improve the skills of forecasting ocean dynamic events in general and those of the Loop Current (LC) system in particular. A specific forecast target is to predict the geographic location of the (LC) extension and duration, LC eddy shedding events for a long lead time with high accuracy. Also, this study aims to improve the predictability of velocity fields (or more precisely, velocity volumes) of subsurface currents. In this dissertation, several deep learning based prediction models have been proposed. The core of these models is the Long-Short Term Memory (LSTM) network. This type of recurrent neural network is trained with Sea Surface Height (SSH) and LC velocity datasets. The hyperparameters of these models are tuned according to each model's characteristics and data complexity. Prior to training, SSH and velocity data are decomposed into their temporal and spatial counterparts.A model uses the Robust Principle Component Analysis is first proposed, which produces a six-week lead time in forecasting SSH evolution. Next, the Wavelet+EOF+LSTM (WELL) model is proposed to improve the forecasting capability of a prediction model. This model is tested on the prediction of two LC eddies, namely eddy Cameron and Darwin. It is shown that the WELL model can predict the separation of both eddies 10 and 14 weeks ahead respectively, which is two more weeks than the DAC model. Furthermore, the WELL model overcomes the problem due to the partitioning step involved in the DAC model. For subsurface currents forecasting, a layer partitioning method is proposed to predict the subsurface field of the LC system. A weighted average fusion is used to improve the consistency of the predicted layers of the 3D subsurface velocity field. The main challenge of forecasting of the LC and its eddies is the small number of events that have occurred over time, which is only once or twice a year, which makes the training task difficult. Forecasting the velocity of subsurface currents is equally challenging because of the limited insitu measurements.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013727
- Subject Headings
- Machine learning, Loop Current, Oceanography--Forecasting
- Format
- Document (PDF)
