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You searched for +publisher:"Vanderbilt University" +contributor:("Doug Fisher"). Showing records 1 – 3 of 3 total matches.

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Vanderbilt University

1. Mack, Daniel Leif Campana. Anomaly Detection from Complex Temporal Sequences in Large Data.

Degree: PhD, Computer Science, 2013, Vanderbilt University

As systems become more complex and the amount of operational data collected from these systems increases proportionally, new challenges arise about how this data can be used to better understand system operations, and detect unsafe behavior. For large systems made up of a number of interacting subsystems, detecting anomalous behavior while avoiding false alarms becomes an important problem. Anomaly detection in such systems must navigate large amounts of data that include a large number of operational runs under a variety of operating conditions, sensors, and long sequences of time series data that cover different aspects of system operation. From a safety viewpoint, we wish to use this data to improve the effectiveness of existing fault detection schemes. Of equal importance, is the development of methods that can detect previously unknown and undetected anomalies from the vast amounts of available operational data. In this thesis, we have developed two approaches for anomaly detection in complex systems. The first approach uses supervised learning methods to improve the detection efficiency and accuracy of known anomalies in available diagnostic reasoners. The second approach uses unsupervised learning methods applied to the large amounts of data to identify previously undiscovered anomalies in system operations. Once anomalous instances are identified, we find the most discriminatory features, which then provide targeted information to help characterize the nature of the newly found anomalies for further study. The methodologies developed in this thesis have been successfully applied to two big data domains. In the first domain, aircraft flight operations data is used for targeted improvement of known anomalies to improve diagnostic accuracy of a vehicle reasoner. This data is also used for identifying previously undetected or unknown anomalies during the takeoff phase of aircraft flight, which are then evaluated in terms of their potential impact on aviation safety. In the second domain, data recorded from pitches thrown in Major League Baseball games is used with our exploratory approach to identify anomalous games for individual pitchers, and then characterize these games in terms of the specific pitch types that differed from the nominal set thrown by these pitchers. Advisors/Committee Members: Gabor Karsai (committee member), Gautam Biswas (chair), Xenofon Koutsoukos (committee member), Julie A. Adams (committee member), Doug Fisher (committee member).

Subjects/Keywords: baseball; aviation safety; complexity measures; anomaly detection

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APA (6th Edition):

Mack, D. L. C. (2013). Anomaly Detection from Complex Temporal Sequences in Large Data. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-04092013-182409/ ;

Chicago Manual of Style (16th Edition):

Mack, Daniel Leif Campana. “Anomaly Detection from Complex Temporal Sequences in Large Data.” 2013. Doctoral Dissertation, Vanderbilt University. Accessed January 22, 2020. http://etd.library.vanderbilt.edu/available/etd-04092013-182409/ ;.

MLA Handbook (7th Edition):

Mack, Daniel Leif Campana. “Anomaly Detection from Complex Temporal Sequences in Large Data.” 2013. Web. 22 Jan 2020.

Vancouver:

Mack DLC. Anomaly Detection from Complex Temporal Sequences in Large Data. [Internet] [Doctoral dissertation]. Vanderbilt University; 2013. [cited 2020 Jan 22]. Available from: http://etd.library.vanderbilt.edu/available/etd-04092013-182409/ ;.

Council of Science Editors:

Mack DLC. Anomaly Detection from Complex Temporal Sequences in Large Data. [Doctoral Dissertation]. Vanderbilt University; 2013. Available from: http://etd.library.vanderbilt.edu/available/etd-04092013-182409/ ;

2. Zhang, Haifeng. Algorithmic Marketing with Data-Driven Simulations.

Degree: PhD, Computer Science, 2017, Vanderbilt University

Marketing researchers and practitioners care about why and how products or services are adopted by consumers. The influential theory of innovation diffusion has been established for decades, but modeling and simulating the diffusion process remains notoriously challenging. Lately, agent-based models (ABMs) have dominated traditional aggregate diffusion models, due to the remarkable advantage to capture individual heterogeneity and social and spatial interactions. Our critical review of the empirically-grounded ABMs of innovation diffusion, however, reveals that few such ABMs are calibrated properly, validated rigorously, and developed explicitly for prediction. This clearly limits their use in supporting decision-making in practice. The thesis contributes a rigorous data-driven agent-based modeling (DDABM) approach that relies on state-of-the-art machine learning techniques to effectively calibrate and validate agent behavior models on massive and rich individual adoption data. The models are integrated into multi-agent simulations to precisely forecast roof-top solar adoption and efficiently explore subsidizing strategies in San Diego county, USA. Historically, ABMs were used to answer âwhat-ifâ questions and draw insights on the efficacy of different policies, however, few could provide executable and quantitative decisions. Mathematical optimization has been widely used to provide numerical solutions in many domains, but little effort has been made to couple it with ABMs. By solving marketing optimization problems in several important settings, such as, dynamic seeding of emerging technologies, route planning for door-to-door targeted marketing, and budget optimization in multi-channel marketing, the thesis also strongly demonstrates how efficient algorithms can aid the design of effective marketing policies facilitated by data-driven simulations, like ABMs, providing optimal or near-optimal actionable plans for marketers. The presented research characterized by computational modeling techniques and algorithmic methods could lead to our ultimate goal of intelligent machine-automated marketing. Advisors/Committee Members: Yevgeniy Vorobeychik (chair), Gautam Biswas (committee member), Doug Fisher (committee member), Bradley Malin (committee member), William Rand (committee member).

Subjects/Keywords: Innovation Diffusion; Agent-based Modeling; Algorithm Design; Marketing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhang, H. (2017). Algorithmic Marketing with Data-Driven Simulations. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-06202017-101817/ ;

Chicago Manual of Style (16th Edition):

Zhang, Haifeng. “Algorithmic Marketing with Data-Driven Simulations.” 2017. Doctoral Dissertation, Vanderbilt University. Accessed January 22, 2020. http://etd.library.vanderbilt.edu/available/etd-06202017-101817/ ;.

MLA Handbook (7th Edition):

Zhang, Haifeng. “Algorithmic Marketing with Data-Driven Simulations.” 2017. Web. 22 Jan 2020.

Vancouver:

Zhang H. Algorithmic Marketing with Data-Driven Simulations. [Internet] [Doctoral dissertation]. Vanderbilt University; 2017. [cited 2020 Jan 22]. Available from: http://etd.library.vanderbilt.edu/available/etd-06202017-101817/ ;.

Council of Science Editors:

Zhang H. Algorithmic Marketing with Data-Driven Simulations. [Doctoral Dissertation]. Vanderbilt University; 2017. Available from: http://etd.library.vanderbilt.edu/available/etd-06202017-101817/ ;

3. Segedy, James René. Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments.

Degree: PhD, Computer Science, 2014, Vanderbilt University

Open-ended computer-based learning environments (OELEs) challenge learners to independently solve complex problems. These environments provide powerful opportunities for learners to develop and utilize strategies for self-regulated learning and problem-solving. However, novice learners often struggle in such open-ended environments, therefore, the extent of their effectiveness depends on the capabilities of their computer-based scaffolding agents: software agents embedded within the system that provide adaptive support to struggling learners. To be effective, these agents require systematic methods for effectively modeling and scaffolding (i.e., supporting) learners so that they can provide help that is targeted to addressing weaknesses in their problem-solving capabilities. The research presented in this dissertation has focused on expanding the repertoire of scaffolding agents in OELEs along two fronts. First, an approach to modeling learners called coherence graph analysis (CGA) has been developed. The CGA approach models learners in terms of: (i) the quality of their problem solutions; (ii) their skillfulness in solving open-ended problems; and (iii) the coherence between the actions they perform as part of their problem-solving tasks. Second, a three-stage approach to scaffolding students has been developed and evaluated through classroom studies. This scaffolding strategy actively helps students by: (i) offering to answer their questions; (ii) diagnosing their skill deficiencies; and (iii) requiring them to develop problem-solving skills through guided practice. These approaches were evaluated in a study with two instructional units in four 6th grade classrooms. The results demonstrated the utility of the CGA approach in predicting learnersâ performance and learning. Exploratory clustering analyses were employed to explore student behavior. The analyses identified a set of distinct and persistent behavioral profiles among the students. Despite significant changes in studentsâ behaviors, the set of behavioral profiles identified by the clustering analyses were similar for both instructional units. The analyses also revealed a productive strategy shift: of the 98 students who took part in the study, 60 of them exhibited improved problem-solving behaviors during the second instructional unit. Analyses also provided suggestive evidence for the value of the three-stage scaffolding strategy in helping students learn how to succeed at complex open-ended problem-solving tasks. Advisors/Committee Members: Dr. Gautam Biswas (chair), Dr. Julie Adams (committee member), Dr. Robert Bodenheimer (committee member), Dr. Doug Fisher (committee member), Dr. Doug Clark (committee member).

Subjects/Keywords: Scaffolding; Open-Ended Learning Environment; Learner Modeling; Adaptive Support; Learning Analytics

…collaboratively with several members of the Teachable Agents group at Vanderbilt University. The first… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Segedy, J. R. (2014). Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-06062014-120717/ ;

Chicago Manual of Style (16th Edition):

Segedy, James René. “Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments.” 2014. Doctoral Dissertation, Vanderbilt University. Accessed January 22, 2020. http://etd.library.vanderbilt.edu/available/etd-06062014-120717/ ;.

MLA Handbook (7th Edition):

Segedy, James René. “Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments.” 2014. Web. 22 Jan 2020.

Vancouver:

Segedy JR. Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments. [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2020 Jan 22]. Available from: http://etd.library.vanderbilt.edu/available/etd-06062014-120717/ ;.

Council of Science Editors:

Segedy JR. Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments. [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://etd.library.vanderbilt.edu/available/etd-06062014-120717/ ;

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