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Title: An examination of readiness for self-directed learning and selected personnel variables at a large Midwestern electronics development and manufacturing corporation.
Creator: Durr, Richard E., Florida Atlantic University, Burrichter, Arthur W., Guglielmino, Lucy M.
Abstract/Description: Rapidly changing technology has dramatically affected the needs of the workforce. As a result, the need is great to implement training and education methods that are maximally effective for the adult learner and can be delivered in a timely and cost-effective manner. As a means toward helping achieve this goal, the concept of self-directed learning has been proposed. Effective implementation of self-directed learning methods has the potential to assist workers in adapting to the demands of...
Show moreRapidly changing technology has dramatically affected the needs of the workforce. As a result, the need is great to implement training and education methods that are maximally effective for the adult learner and can be delivered in a timely and cost-effective manner. As a means toward helping achieve this goal, the concept of self-directed learning has been proposed. Effective implementation of self-directed learning methods has the potential to assist workers in adapting to the demands of the information age. This study investigated and analyzed the relationship between scores on the Guglielmino Self-Directed Learning Readiness Scale (SDLRS) and multiple variables of employees at a large Midwestern company. Fourteen hypotheses were tested, using 27 statistical tests. Conclusions were drawn comparing and supplementing the findings of two earlier studies using similar variables. The SDLRS was administered to 607 employees in nine different occupation categories. The mean score for all respondents was 234, which is above the adult norm. A significant positive relationship was found between the mean SLDRS scores and performance ratings, creativity and problem solving required in the job, degree of change on the job, and education levels. These findings were congruent with those of Guglielmino and Guglielmino's (1981) study of an American utility company and Roberts' (1986) study of the Hong Kong Telephone Company. Other findings were also compared. The Guglielmino and Roberts studies found that a small sample of low performers with high SDLRS scores were in jobs that required low levels of creativity, problem-solving skills, and adaptation to change. In the present study, however, no such groups could be isolated. SDLRS scores of males were significantly higher than those of females and managers scored significantly higher than non-managers in the present study. No relationship was found between SDLRS scores and the following variables: age, years of service with the company, and degree of routine on the job. A significant difference in scores was found due to occupation classification. Sales managers and salespeople scored significantly higher than all other categories and manufacturing/factory, and clerical/administrative employees scored significantly lower.
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Date Issued: 1992
PURL: http://purl.flvc.org/fcla/dt/12312
Subject Headings: Adult learning, Experiential learning
Format: Document (PDF)

Title: Dyadic computer programming instruction for middle school students: friendship promotes learning.
Creator: Hartl, Amy C., DeLay, Dawn, Denner, Jill, Werner, Linda, Laursen, Brett, Richmond, Ashley D., Dirghangi, Shrija R., Hiatt, Cody, Dickson, Daniel J., Bortman, Gilly, Shawcross, Lauren, Graduate College
Date Issued: 2013-04-12
PURL: http://purl.flvc.org/fcla/dt/3361309
Subject Headings: Friendship, Learning
Format: Document (PDF)

Title: EFFECTS OF SELF-PACED POSTINFORMATIVE FEEDBACK INTERVALS AND TASK COMPLEXITY ON CONCEPT IDENTIFICATION (INTERTRIAL, COMPUTER).
Creator: PALACIO, FRANCES LABRIOLA., Florida Atlantic University
Abstract/Description: The purpose of this study was to determine the effects of the self-paced postinformative feedback interval (PIFI) and task complexity on concept identification. Eight independent groups of learners served in a factorial design which combined four PIFI durations (self-paced - 15 sec) and two levels of task complexity (2 and 4 irrelevant stimulus dimensions). Instructions and tasks were presented to 64 subjects via microcomputers. The criterion performance of 16 consecutively correct stimulus...
Show moreThe purpose of this study was to determine the effects of the self-paced postinformative feedback interval (PIFI) and task complexity on concept identification. Eight independent groups of learners served in a factorial design which combined four PIFI durations (self-paced - 15 sec) and two levels of task complexity (2 and 4 irrelevant stimulus dimensions). Instructions and tasks were presented to 64 subjects via microcomputers. The criterion performance of 16 consecutively correct stimulus identifications as well as total number of trials and total time to criterion were recorded by the computer program. Factorial analyses of variance, a priori tests, Newman-Keuls' pairwise comparisons, and one-way analyses of variance were used to statistically determine significant differences among the means of the groups studied. Results of all statistical analyses were considered significant at the .05 level. Results indicated that there was no significant difference in terms of total trials or total time to criterion between the self-paced PIFI condition and the combined data for the three fixed PIFI conditions. For the low complexity task, the 8-second PIFI condition required significantly fewer trials to criterion than self-paced PIFI. For the higher complexity task, the self-paced PIFI condition required significantly less total time to criterion than 15-second fixed PIFI. Results also indicated that self-paced PIFI durations for initial trials were significantly longer than those of final trials for performances at both levels of task complexity. Findings were interpreted as demonstrating a need for absorption time during PIFIs which may not be adequately provided in a totally self-paced environment. Thus, a reduction in the efficiency inherent in the self-paced mode, stemming from the gradual decrease in PIFI durations as problem solution is approached, is experience.
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Date Issued: 1986
PURL: http://purl.flvc.org/fcla/dt/11884
Subject Headings: Learning, Psychology of, Concept learning
Format: Document (PDF)

Title: An Examination of the Role of Learning in the Work of Community Leaders.
Creator: Phares, Leatrice Turlis, Guglielmino, Lucy M., Florida Atlantic University
Abstract/Description: This study was designed to examine self-directed learning readiness of volunteer community leaders and to determine if and how they used self-directed learning in their community leadership roles. The sample included volunteer community leaders in South Florida serving as board members in community leadership organizations and Rotarians serving in leadership roles in the Rotary District 6990. The results support the conclusion that community leaders are self-directed learners. They make...
Show moreThis study was designed to examine self-directed learning readiness of volunteer community leaders and to determine if and how they used self-directed learning in their community leadership roles. The sample included volunteer community leaders in South Florida serving as board members in community leadership organizations and Rotarians serving in leadership roles in the Rotary District 6990. The results support the conclusion that community leaders are self-directed learners. They make extensive use of learning projects in their community leadership roles, use a variety of learning methods, and have a need for ongoing learning. The study suggests that designers of training for community leaders might find it valuable to reevaluate and update traditional training programs, utilize or support the identified key methods of learning and recognize that training needs to be applicable, cutting edge, and go beyond local boundaries.
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Date Issued: 2006
PURL: http://purl.flvc.org/fau/fd/FA00000687
Subject Headings: Experiential learning, Adult learning, Self-culture, Organizational learning
Format: Document (PDF)

Title: The Effect of the Conditioned Emotional Response (CER) on the Subsequent Acquisition of a Temporal Discrimination.
Creator: McNeely, Joseph J., Otten, Cynthia S., Florida Atlantic University
Abstract/Description: Twenty 100 day old male rats were trained to behavioral criterion in a CER paradigm. Two shock levels (.1 and .2 ma) were employed to establish these criteria. Half of the Ss reached medium suppression (suppression ratios between .39 and .11) of a bar pressing response; half achieved high suppression (suppression ratios less than .10). The animals were subsequently exposed to 15 daily sessions of FI training utilizing a head, panel pressing response for food. Five of the medium suppression...
Show moreTwenty 100 day old male rats were trained to behavioral criterion in a CER paradigm. Two shock levels (.1 and .2 ma) were employed to establish these criteria. Half of the Ss reached medium suppression (suppression ratios between .39 and .11) of a bar pressing response; half achieved high suppression (suppression ratios less than .10). The animals were subsequently exposed to 15 daily sessions of FI training utilizing a head, panel pressing response for food. Five of the medium suppression group and five of the high suppression group were exposed to the conditioned suppression CS (a light) during the FI acquisition periods . The remaining rats underwent FI training in the absence of the CS. An Index of Curvature was employed to measure each FI period record and to indicate the degree of acquisition of FI scalloping. Analysis of variance for the four groups revealed only the progression over days to be a significant source of variation. Analysis of linear trend indicated a strong linearity in the variance over 15 days for all groups, but revealed no clear differences between the groups. Some tendencies indicate a slight superiority in acquisition by the medium suppression group which was exposed to the CS during FI training. The high suppression group which was exposed to the light was noticeably inferior in FI discrimination. These results possibly demonstrate an "arousal- interference" mechanism for the CER, but the data do not support the conclusion that the conditioned suppression signal (CS) has a differential effect on subsequent acquisition of an unrelated temporal discrimination. A history of shook treatment, or of CER training, may be responsible, however, for the overall poor acquisition of FI scalloping that was demonstrated by all four groups in this study.
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Date Issued: 1969
PURL: http://purl.flvc.org/fau/fd/FA00000799
Subject Headings: Emotional conditioning, Discrimination learning, Learning, Psychology of
Format: Document (PDF)

Title: MAKING A DIFFERENCE IN THE WORLD THROUGH SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS: A PHENOMENOLOGICAL STUDY OF THE LIVED EXPERIENCES OF GRADUATE STUDENTS ENGAGING IN STEMBASED ACADEMIC SERVICE-LEARNING.
Creator: Hackman, Aaron Kyle, Bloom, Jennifer, Florida Atlantic University, Department of Educational Leadership and Research Methodology, College of Education
Abstract/Description: This phenomenological study sought to understand the lived experiences of graduate students engaged in STEM-related Academic Service-Learning (AS-L). For the purposes of this study, Academic Service-Learning is a form of experiential learning whereby students complete a service project as a component of a specific course. This study looked at these student AS-L project experiences at the graduate level as a component of STEM-based courses. While the impact of Academic Service-Learning on the...
Show moreThis phenomenological study sought to understand the lived experiences of graduate students engaged in STEM-related Academic Service-Learning (AS-L). For the purposes of this study, Academic Service-Learning is a form of experiential learning whereby students complete a service project as a component of a specific course. This study looked at these student AS-L project experiences at the graduate level as a component of STEM-based courses. While the impact of Academic Service-Learning on the undergraduate experience is well documented, there is no research to date on the graduate experience, much less on graduate students in STEM fields. By understanding the lived experiences of graduate students in STEM-based Academic Service-Learning, this study attempts to fill that gap. The research questions that guided my study were: (1) What types of project-based experiences are graduate students performing in their Academic-Service-Learning designated courses? (2) What are the lived experiences of graduate students who are conducting AS-L projects in the community as a component of a STEM-based AS-L course as perceived by students, faculty, and community partners. (3) What are the lived experiences of the community partners who are hosting the students for their AS-L projects.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014234
Subject Headings: Experiential learning, Graduate students, STEM, Service learning
Format: Document (PDF)

Title: Knots in the woods: an assessment of the effects of location on self-directed experiential learning.
Creator: Coyle, Jasmine, Owen, Dianne, Florida Atlantic University, Charles E. Schmidt College of Science, Center for Environmental Studies
Abstract/Description: My research measured completion and retention of procedural learning tasks, and declarative and procedural components of engagement in indoor and outdoor settings. Instructor-assisted Self-Directed Learning and Non-instructor-assisted Self-Directed Learning were implemented in the context of an Experiential Learning approach. Experimental covariates included student-specific variables such as background and experience, and environment-specific variables such as temperature, and humidity. AIC...
Show moreMy research measured completion and retention of procedural learning tasks, and declarative and procedural components of engagement in indoor and outdoor settings. Instructor-assisted Self-Directed Learning and Non-instructor-assisted Self-Directed Learning were implemented in the context of an Experiential Learning approach. Experimental covariates included student-specific variables such as background and experience, and environment-specific variables such as temperature, and humidity. AIC model averaging was used to identify the best-fitting mixed GLM models. Neither location, nor pedagogic method, proved to be a significant predictor of the probability that a student would complete the most complex of the procedural learning tasks, and the percent of students completing this task was not significantly higher in outdoor groups than in indoor groups. Neither location nor pedagogic method was a significant predictor of retention of procedural knowledge or engagement with learning materials. The level of voluntary collaboration was higher in outdoor groups than in indoor groups.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004095, http://purl.flvc.org/fau/fd/FA00004095
Subject Headings: Active learning, Education, Higher -- Philosophy, Experiential learning, Group learning in education, Inquiry based learning
Format: Document (PDF)

Title: RELATIONAL TRAINING ON A DIMENSION AND ITS EFFECT ON TRANSPOSITION BEHAVIOR ON AN ORTHOGONAL DIMENSION.
Creator: POMEROY, MICHAEL LEE., Florida Atlantic University
Abstract/Description: An attempt was made to investigate the abstract concept of relation. It was hypothesized that the learning of the relational concept independent of particular stimuli or dimensions is possible even in nonverbal animals. One group of rats was trained on a discrimination that could only be solved with relational learning; a second group of rats was trained on a discrimination that could be solved only with absolute learning. Both groups were then trained on a discrimination that could be...
Show moreAn attempt was made to investigate the abstract concept of relation. It was hypothesized that the learning of the relational concept independent of particular stimuli or dimensions is possible even in nonverbal animals. One group of rats was trained on a discrimination that could only be solved with relational learning; a second group of rats was trained on a discrimination that could be solved only with absolute learning. Both groups were then trained on a discrimination that could be learned in either a relational or absolute manner, and transposition testing was done to determine the method utilized. Results indicated animals with original relational learning solved the second discrimination relationally; animal s with original specific learning solved the second discrimination in an absolute manner.
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Date Issued: 1974
PURL: http://purl.flvc.org/fcla/dt/13671
Subject Headings: Relationism, Discrimination learning
Format: Document (PDF)

Title: TRANSPOSITION: A FURTHER TEST OF ABSOLUTE VERSUS RELATIONAL PREDICTIONS.
Creator: BIZAILLON, PAUL DUNN., Florida Atlantic University, Adamson, Robert E.
Abstract/Description: An attempt was made to investigate the intradimensional transfer of a simultaneously presented, double brightness discrimination in male hooded rats. It was hypothesized that in a paradigm designed to emphasize interstimulus cues over individual stimulus cues, subjects would exhibit transposition in testing, even when this involved approaching a previously negative stimulus, and avoiding a previously positive stimulus, under extinction conditions. Results of two tests (with a partial...
Show moreAn attempt was made to investigate the intradimensional transfer of a simultaneously presented, double brightness discrimination in male hooded rats. It was hypothesized that in a paradigm designed to emphasize interstimulus cues over individual stimulus cues, subjects would exhibit transposition in testing, even when this involved approaching a previously negative stimulus, and avoiding a previously positive stimulus, under extinction conditions. Results of two tests (with a partial reinforcement retraining session between them) indicated significant transposition on all measures except trial of first choice on Test I for the group trained to approach the brighter stimulus in any pair presented. Results were interpreted as being in support of relational theory which stresses the importance of dimensional salience in the establishment of relational responding.
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Date Issued: 1975
PURL: http://purl.flvc.org/fcla/dt/13749
Subject Headings: Brightness perception, Discrimination learning
Format: Document (PDF)

Title: The effect of a modified self-paced postinformative feedback interval on concept formation and identification tasks.
Creator: Nilsen, June A., Florida Atlantic University, Kauffman, Dan
Abstract/Description: The purpose of this study was to determine if a modified self-paced postinformative feedback interval (mixed PIFI) is more efficient in concept formation and identification tasks than a pure self-paced or fixed PIFI. One hundred fourteen subjects randomly assigned to three different PIFI groups (mixed, self-paced, 6-second fixed) were presented with a two-category concept formation (CF) task followed by a four-category concept identification (CI) task. A computer program presented...
Show moreThe purpose of this study was to determine if a modified self-paced postinformative feedback interval (mixed PIFI) is more efficient in concept formation and identification tasks than a pure self-paced or fixed PIFI. One hundred fourteen subjects randomly assigned to three different PIFI groups (mixed, self-paced, 6-second fixed) were presented with a two-category concept formation (CF) task followed by a four-category concept identification (CI) task. A computer program presented instructions and task stimuli to subjects via microcomputer as well as regulated PIFIs and collected data. The composition and duration of the mixed PIFIs were determined after an analysis of the results of a pilot study. On the CF task, subjects received 5-second fixed PIFIs on the first 10 trials, followed by 5-second fixed PIFIs on positive instances of incorrect responses, 2-second fixed PIFIs on negative instances of incorrect responses and self-pacing on the rest of the trials. On the CI task the first 10 trails were set to 6-second fixed PIFIs and thereafter to 3-second fixed PIFIs on error trials and self-pacing on the rest of the trials. One-way analyses of variance were used to determine differences among the means of the groups studied with results considered significant at the.05 level. On the CF task, although time to criterion on the mixed and self-paced PIFIs were not significantly different, learners in the mixed PIFI group completed the task more quickly than those in the fixed PIFI group. There were no significant differences between groups on trials to criterion. On the CI task, although mixed and fixed PIFIs were not significantly different for trials to criterion, learners in the mixed PIFI group used fewer trails to solve the problems than those in the self-paced group. There were no significant differences between groups in time to criterion. Findings indicated that on the CI task, the mixed PIFI tested might be the compromise needed to reconcile the theoretical and practical dilemma of needing longer PIFIs for absorption and shorter PIFIs for subject satisfaction. Further research examining the composition of the mixed PIFI is needed.
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Date Issued: 1989
PURL: http://purl.flvc.org/fcla/dt/11938
Subject Headings: Concept learning, Feedback (Psychology)
Format: Document (PDF)

Title: DEEP LEARNING FOR CRIME PREDICTION.
Creator: Gacharich, Nicholas, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: In this research, we propose to use deep learning to predict crimes in small neighborhoods (regions) of a city, by using historical crime data collected from the past. The motivation of crime predictions is that if we can predict the number crimes that will occur in a certain week then the city officials and law enforcement can prepare resources and manpower more effectively. Due to inherent connections between geographic regions and crime activities, the crime numbers in different regions ...
Show moreIn this research, we propose to use deep learning to predict crimes in small neighborhoods (regions) of a city, by using historical crime data collected from the past. The motivation of crime predictions is that if we can predict the number crimes that will occur in a certain week then the city officials and law enforcement can prepare resources and manpower more effectively. Due to inherent connections between geographic regions and crime activities, the crime numbers in different regions (with respect to different time periods) are often correlated. Such correlation brings challenges and opportunities to employ deep learning to learn features from historical data for accurate prediction of the future crime numbers for each neighborhood. To leverage crime correlations between different regions, we convert crime data into a heat map, to show the intensity of crime numbers and the geographical distributions. After that, we design a deep learning framework to learn from such heat map for prediction. In our study, we look at the crime reported in twenty different neighbourhoods in Vancouver, Canada over a twenty week period and predict the total crime count that will occur in the future. We will look at the number of crimes per week that have occurred in the span of ten weeks and predict the crime count for the following weeks. The location of where the crimes occur is extracted from a database and plotted onto a heat map. The model we are using to predict the crime count consists of a CNN (Convolutional Neural Network) and a LSTM (Long-Short Term Memory) network attached to the CNN. The purpose of the CNN is to train the model spatially and understand where crimes occur in the images. The LSTM is used to train the model temporally and help us understand which week the crimes occur in time. By feeding the model heat map images of crime hot spots into the CNN and LSTM network, we will be able to predict the crime count and the most likely locations of the crimes for future weeks.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013723
Subject Headings: Deep learning, Crime forecasting
Format: Document (PDF)

Title: SCHEMATIC: AN EXPERIMENT IN MACHINE LEARNING USING CONCEPTUAL GRAPHS.
Creator: HALTERMAN, RICHARD L., Florida Atlantic University
Abstract/Description: Conceptual graphs form the basis of a powerful representation language for artificial intelligence research. SCHEMATIC is a system that uses a subset of conceptual graph theory in acquiring knowledge about a given domain. SCHEMATIC exhibits two types of learning. It will passively absorb information as imparted by the teacher, and it also has an active learning mode that, based on its current picture of the domain, aggressively queries the teacher for more information. The knowledge base,...
Show moreConceptual graphs form the basis of a powerful representation language for artificial intelligence research. SCHEMATIC is a system that uses a subset of conceptual graph theory in acquiring knowledge about a given domain. SCHEMATIC exhibits two types of learning. It will passively absorb information as imparted by the teacher, and it also has an active learning mode that, based on its current picture of the domain, aggressively queries the teacher for more information. The knowledge base, including the concept type hierarchy, the relation list, canonical forms, and the current domain, are dynamically maintained. Teacher interaction is handled exclusively with conceptual graphs. Action concepts are treated differently by SCHEMATIC, in that, once defined, they execute procedures that alter the domain.
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Date Issued: 1987
PURL: http://purl.flvc.org/fcla/dt/14421
Subject Headings: Machine learning, Artificial intelligence
Format: Document (PDF)

Title: The self-reported use of metacognitive reading strategies of community college students.
Creator: Munro, Sophia., College of Education, Department of Curriculum, Culture, and Educational Inquiry
Abstract/Description: College requires students to read strategically in order to be academically successful (Caverly, Nicholson, & Radcliffe, 2004). Strategic readers utilize a variety of strategies, including metacognitive reading strategies (Mokhtari & Reichard, 2002; Pressley & Afflerbach, 1995). However, not all students use the same strategies when reading academic text. The purpose of this study was to explore whether students enrolled in a developmental reading course report using different metacognitive...
Show moreCollege requires students to read strategically in order to be academically successful (Caverly, Nicholson, & Radcliffe, 2004). Strategic readers utilize a variety of strategies, including metacognitive reading strategies (Mokhtari & Reichard, 2002; Pressley & Afflerbach, 1995). However, not all students use the same strategies when reading academic text. The purpose of this study was to explore whether students enrolled in a developmental reading course report using different metacognitive reading strategies than students who are enrolled in a college-level English course. The Metacognitive Awareness of Reading Strategies Inventory (Mokhatari & Reichard, 2002) was administered to 423 students at a community college in the southeastern United States. The results of the Tests of Between-Subjects Effects indicated that the main effect for group membership was not significant. The results of the Tests of Within-Subjects Effects indicated that problem solving was reportedly used relatively equally by the two groups, but global and support reading strategies were used less by the English group,with the interaction effect even stronger for support strategies. The implications of this study on teaching and further research were also explored.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3333057
Subject Headings: Reading, Psychology of, Cognitive learning, Inquiry-based learning
Format: Document (PDF)

Title: Parallel Distributed Deep Learning on Cluster Computers.
Creator: Kennedy, Robert Kwan Lee, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Deep Learning is an increasingly important subdomain of arti cial intelligence. Deep Learning architectures, arti cial neural networks characterized by having both a large breadth of neurons and a large depth of layers, bene ts from training on Big Data. The size and complexity of the model combined with the size of the training data makes the training procedure very computationally and temporally expensive. Accelerating the training procedure of Deep Learning using cluster computers faces...
Show moreDeep Learning is an increasingly important subdomain of arti cial intelligence. Deep Learning architectures, arti cial neural networks characterized by having both a large breadth of neurons and a large depth of layers, bene ts from training on Big Data. The size and complexity of the model combined with the size of the training data makes the training procedure very computationally and temporally expensive. Accelerating the training procedure of Deep Learning using cluster computers faces many challenges ranging from distributed optimizers to the large communication overhead speci c to a system with o the shelf networking components. In this thesis, we present a novel synchronous data parallel distributed Deep Learning implementation on HPCC Systems, a cluster computer system. We discuss research that has been conducted on the distribution and parallelization of Deep Learning, as well as the concerns relating to cluster environments. Additionally, we provide case studies that evaluate and validate our implementation.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013080
Subject Headings: Deep learning., Neural networks (Computer science)., Artificial intelligence., Machine learning.
Format: Document (PDF)

Title: Negotiation of meaning in interlanguage talk.
Creator: Tegge, Friederike A., Florida Atlantic University, DuBravac, Stayc, Department of Languages, Linguistics and Comparative Literature, Dorothy F. Schmidt College of Arts and Letters
Abstract/Description: This small-scale study investigated the extent to which negotiations of meaning during methodologically focused communicative partner-activities were concerned with a grammatical target structure, the dative case following spatial prepositions in German. In addition, the impact of the negotiation of the target structure on subsequent learner performance was investigated. The subjects, beginning-level students of German, participated in two two-way information-gap activities, preceded and...
Show moreThis small-scale study investigated the extent to which negotiations of meaning during methodologically focused communicative partner-activities were concerned with a grammatical target structure, the dative case following spatial prepositions in German. In addition, the impact of the negotiation of the target structure on subsequent learner performance was investigated. The subjects, beginning-level students of German, participated in two two-way information-gap activities, preceded and followed by the same grammaticality judgment test. The interaction was audiotaped and transcribed. The improvement in accuracy between the pretest and the posttest was calculated and correlated with the number of negotiation moves. The results indicate that the subjects negotiated meaning, including form, frequently. However, no significant change in the subjects' subsequent performance was observed.
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Date Issued: 2004
PURL: http://purl.flvc.org/fcla/dt/13114
Subject Headings: Interlanguage (Language learning), Language transfer (Language learning), Second language acquisition
Format: Document (PDF)

Title: OPTIMIZED DEEP LEARNING ARCHITECTURES AND TECHNIQUES FOR EDGE AI.
Creator: Zaniolo, Luiz, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The recent rise of artificial intelligence (AI) using deep learning networks allowed the development of automatic solutions for many tasks that, in the past, were seen as impossible to be performed by a machine. However, deep learning models are getting larger, need significant processing power to train, and powerful machines to use it. As deep learning applications become ubiquitous, another trend is taking place: the growing use of edge devices. This dissertation addresses selected...
Show moreThe recent rise of artificial intelligence (AI) using deep learning networks allowed the development of automatic solutions for many tasks that, in the past, were seen as impossible to be performed by a machine. However, deep learning models are getting larger, need significant processing power to train, and powerful machines to use it. As deep learning applications become ubiquitous, another trend is taking place: the growing use of edge devices. This dissertation addresses selected technical issues associated with edge AI, proposes novel solutions to them, and demonstrates the effectiveness of the proposed approaches. The technical contributions of this dissertation include: (i) architectural optimizations to deep neural networks, particularly the use of patterned stride in convolutional neural networks used for image classification; (ii) use of weight quantization to reduce model size without hurting its accuracy; (iii) systematic evaluation of the impact of image imperfections on skin lesion classifiers' performance in the context of teledermatology; and (iv) a new approach for code prediction using natural language processing techniques, targeted at edge devices.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013822
Subject Headings: Artificial intelligence, Deep learning (Machine learning), Neural networks (Computer science)
Format: Document (PDF)

Title: IMAGE QUALITY AND BEAUTY CLASSIFICATION USING DEEP LEARNING.
Creator: Golchubian, Arash, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding...
Show moreThe field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding artifacts, saturation, and lighting, as well as for its’ aesthetic appeal. The purpose of such a mechanism could be detecting and discarding noisy, blurry, dark, or over exposed images, as well as detecting images that would be considered beautiful by a majority of viewers. In this dissertation, the detection of various quality and aesthetic aspects of an image using CNNs is explored. This research produced two datasets that are manually labeled for quality issues such as blur, poor lighting, and digital noise, and for their aesthetic qualities, and Convolutional Neural Networks were designed and trained using these datasets. Lastly, two case studies were performed to show the real-world impact of this research to traffic sign detection and medical image diagnosis.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014029
Subject Headings: Deep learning (Machine learning), Computer vision, Aesthetics, Image Quality
Format: Document (PDF)

Title: COMPUTATION IN SELF-ATTENTION NETWORKS.
Creator: Morris, Paul, Barenholtz, Elan, Florida Atlantic University, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science
Abstract/Description: Neural network models with many tunable parameters can be trained to approximate functions that transform a source distribution, or dataset, into a target distribution of interest. In contrast to low-parameter models with simple governing equations, the dynamics of transformations learned in deep neural network models are abstract and the correspondence of dynamical structure to predictive function is opaque. Despite their “black box” nature, neural networks converge to functions that...
Show moreNeural network models with many tunable parameters can be trained to approximate functions that transform a source distribution, or dataset, into a target distribution of interest. In contrast to low-parameter models with simple governing equations, the dynamics of transformations learned in deep neural network models are abstract and the correspondence of dynamical structure to predictive function is opaque. Despite their “black box” nature, neural networks converge to functions that implement complex tasks in computer vision, Natural Language Processing (NLP), and the sciences when trained on large quantities of data. Where traditional machine learning approaches rely on clean datasets with appropriate features, sample densities, and label distributions to mitigate unwanted bias, modern Transformer neural networks with self-attention mechanisms use Self-Supervised Learning (SSL) to pretrain on large, unlabeled datasets scraped from the internet without concern for data quality. SSL tasks have been shown to learn functions that match or outperform their supervised learning counterparts in many fields, even without task-specific finetuning. The recent paradigm shift to pretraining large models with massive amounts of unlabeled data has given credibility to the hypothesis that SSL pretraining can produce functions that implement generally intelligent computations.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014061
Subject Headings: Neural networks (Computer science), Machine learning, Self-supervised learning
Format: Document (PDF)

Title: NETWORK INTRUSION DETECTION AND DEEP LEARNING MECHANISMS.
Creator: Chatterjee, Suvosree, Cardei, Ionut, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Cyber attack is a strong threat to the digital world. So, it’s very essential to keep the network safe. Network Intrusion Detection system is the system to address this problem. Network Intrusion Detection system functions like a firewall, and monitors incoming and outgoing traffic like ingress and egress filtering fire wall. Network Intrusion Detection System does anomaly and hybrid detection for detecting known and unknown attacks. My thesis discusses about the several network cyber attacks...
Show moreCyber attack is a strong threat to the digital world. So, it’s very essential to keep the network safe. Network Intrusion Detection system is the system to address this problem. Network Intrusion Detection system functions like a firewall, and monitors incoming and outgoing traffic like ingress and egress filtering fire wall. Network Intrusion Detection System does anomaly and hybrid detection for detecting known and unknown attacks. My thesis discusses about the several network cyber attacks we face nowadays and I created several Deep learning models to detect accurately, I used NSL-KDD dataset which is a popular dataset, that contains several network attacks. After experimenting with different deep learning models I found some disparities in the training accuracy and validation accuracy, which is a clear indication of overfitting. To reduce the overfitting I introduced regularization and dropout in the models and experimented with different hyperparameters.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014128
Subject Headings: Deep learning (Machine learning), Cyberterrorism, Intrusion detection systems (Computer security)
Format: Document (PDF)

Title: A UNIFIED SOFT SENSING FRAMEWORK FOR COMPLEX DYNAMICAL SYSTEMS.
Creator: Huang, Yu, Tang, Yufei, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: In the past few years, the development of complex dynamical networks or systems has stimulated great interest in the study of the principles and mechanisms underlying the Internet of things (IoT). IoT is envisioned as an intelligent network infrastructure with a vast number of ubiquitous smart devices present in diverse application domains and have already improved many aspects of daily life. Many overtly futuristic IoT applications acquire data gathered via distributed sensors that can be...
Show moreIn the past few years, the development of complex dynamical networks or systems has stimulated great interest in the study of the principles and mechanisms underlying the Internet of things (IoT). IoT is envisioned as an intelligent network infrastructure with a vast number of ubiquitous smart devices present in diverse application domains and have already improved many aspects of daily life. Many overtly futuristic IoT applications acquire data gathered via distributed sensors that can be uniquely identified, localized, and communicated with, i.e., the support of sensor networks. Soft-sensing models are in demand to support IoT applications to achieve the maximal exploitation of transforming the information of measurements into more useful knowledge, which plays essential roles in condition monitoring, quality prediction, smooth control, and many other essential aspects of complex dynamical systems. This in turn calls for innovative soft-sensing models that account for scalability, heterogeneity, adaptivity, and robustness to unpredictable uncertainties. The advent of big data, the advantages of ever-evolving deep learning (DL) techniques (where models use multiple layers to extract multi-levels of feature representations progressively), as well as ever-increasing processing power in hardware, has triggered a proliferation of research that applies DL to soft-sensing models. However, many critical questions need to be further investigated in the deep learning-based soft-sensing.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013993
Subject Headings: Dynamical systems, Dynamics, Sensor networks, Deep learning (Machine learning)
Format: Document (PDF)

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