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You searched for +publisher:"University of Arkansas" +contributor:("Xintao Wu"). Showing records 1 – 11 of 11 total matches.

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University of Arkansas

1. Wu, Yongkai. Achieving Causal Fairness in Machine Learning.

Degree: PhD, 2020, University of Arkansas

  Fairness is a social norm and a legal requirement in today's society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974)… (more)

Subjects/Keywords: Algorithmic Bias; Causal Inference; Fairness; Machine Learning; Artificial Intelligence and Robotics; Theory and Algorithms

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APA (6th Edition):

Wu, Y. (2020). Achieving Causal Fairness in Machine Learning. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/3632

Chicago Manual of Style (16th Edition):

Wu, Yongkai. “Achieving Causal Fairness in Machine Learning.” 2020. Doctoral Dissertation, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/3632.

MLA Handbook (7th Edition):

Wu, Yongkai. “Achieving Causal Fairness in Machine Learning.” 2020. Web. 30 Oct 2020.

Vancouver:

Wu Y. Achieving Causal Fairness in Machine Learning. [Internet] [Doctoral dissertation]. University of Arkansas; 2020. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/3632.

Council of Science Editors:

Wu Y. Achieving Causal Fairness in Machine Learning. [Doctoral Dissertation]. University of Arkansas; 2020. Available from: https://scholarworks.uark.edu/etd/3632


University of Arkansas

2. Zheng, Panpan. Dynamic Fraud Detection via Sequential Modeling.

Degree: PhD, 2020, University of Arkansas

  The impacts of information revolution are omnipresent from life to work. The web services have signicantly changed our living styles in daily life, such… (more)

Subjects/Keywords: Dirichlet process; Fraud Detection; Machine Learning; Mixture Model; Sequential Model; Survival Analysis; Databases and Information Systems; Information Security

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APA (6th Edition):

Zheng, P. (2020). Dynamic Fraud Detection via Sequential Modeling. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/3633

Chicago Manual of Style (16th Edition):

Zheng, Panpan. “Dynamic Fraud Detection via Sequential Modeling.” 2020. Doctoral Dissertation, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/3633.

MLA Handbook (7th Edition):

Zheng, Panpan. “Dynamic Fraud Detection via Sequential Modeling.” 2020. Web. 30 Oct 2020.

Vancouver:

Zheng P. Dynamic Fraud Detection via Sequential Modeling. [Internet] [Doctoral dissertation]. University of Arkansas; 2020. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/3633.

Council of Science Editors:

Zheng P. Dynamic Fraud Detection via Sequential Modeling. [Doctoral Dissertation]. University of Arkansas; 2020. Available from: https://scholarworks.uark.edu/etd/3633


University of Arkansas

3. Li, Ang. Privacy-Preserving Photo Taking and Accessing for Mobile Phones.

Degree: PhD, 2018, University of Arkansas

  Today, we are living in environments that are full of cameras embedded in devices such as smart phones and wearables. These mobile devices and… (more)

Subjects/Keywords: Mobile Phone; Photo; Privacy; Information Security

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APA (6th Edition):

Li, A. (2018). Privacy-Preserving Photo Taking and Accessing for Mobile Phones. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2915

Chicago Manual of Style (16th Edition):

Li, Ang. “Privacy-Preserving Photo Taking and Accessing for Mobile Phones.” 2018. Doctoral Dissertation, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/2915.

MLA Handbook (7th Edition):

Li, Ang. “Privacy-Preserving Photo Taking and Accessing for Mobile Phones.” 2018. Web. 30 Oct 2020.

Vancouver:

Li A. Privacy-Preserving Photo Taking and Accessing for Mobile Phones. [Internet] [Doctoral dissertation]. University of Arkansas; 2018. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/2915.

Council of Science Editors:

Li A. Privacy-Preserving Photo Taking and Accessing for Mobile Phones. [Doctoral Dissertation]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2915


University of Arkansas

4. Hammer, Jon C. Improving the Efficacy of Context-Aware Applications.

Degree: PhD, 2018, University of Arkansas

  In this dissertation, we explore methods for enhancing the context-awareness capabilities of modern computers, including mobile devices, tablets, wearables, and traditional computers. Advancements include… (more)

Subjects/Keywords: Computer Vision; Context-aware Computing; Depth-based Positioning; Logical Sensors; Machine Learning; Graphics and Human Computer Interfaces; OS and Networks

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APA (6th Edition):

Hammer, J. C. (2018). Improving the Efficacy of Context-Aware Applications. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2703

Chicago Manual of Style (16th Edition):

Hammer, Jon C. “Improving the Efficacy of Context-Aware Applications.” 2018. Doctoral Dissertation, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/2703.

MLA Handbook (7th Edition):

Hammer, Jon C. “Improving the Efficacy of Context-Aware Applications.” 2018. Web. 30 Oct 2020.

Vancouver:

Hammer JC. Improving the Efficacy of Context-Aware Applications. [Internet] [Doctoral dissertation]. University of Arkansas; 2018. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/2703.

Council of Science Editors:

Hammer JC. Improving the Efficacy of Context-Aware Applications. [Doctoral Dissertation]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2703


University of Arkansas

5. Katla, Srinidhi. DPWeka: Achieving Differential Privacy in WEKA.

Degree: MS, 2017, University of Arkansas

  Organizations belonging to the government, commercial, and non-profit industries collect and store large amounts of sensitive data, which include medical, financial, and personal information.… (more)

Subjects/Keywords: Pure sciences; Communication and the arts; Applied sciences; Data mining; Data privacy; Differential privacy; Databases and Information Systems; Information Security

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APA (6th Edition):

Katla, S. (2017). DPWeka: Achieving Differential Privacy in WEKA. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1934

Chicago Manual of Style (16th Edition):

Katla, Srinidhi. “DPWeka: Achieving Differential Privacy in WEKA.” 2017. Masters Thesis, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/1934.

MLA Handbook (7th Edition):

Katla, Srinidhi. “DPWeka: Achieving Differential Privacy in WEKA.” 2017. Web. 30 Oct 2020.

Vancouver:

Katla S. DPWeka: Achieving Differential Privacy in WEKA. [Internet] [Masters thesis]. University of Arkansas; 2017. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/1934.

Council of Science Editors:

Katla S. DPWeka: Achieving Differential Privacy in WEKA. [Masters Thesis]. University of Arkansas; 2017. Available from: https://scholarworks.uark.edu/etd/1934


University of Arkansas

6. Godfrey, Luke. Neural Decomposition of Time-Series Data for Effective Generalization.

Degree: MS, 2015, University of Arkansas

  We present a neural network technique for the analysis and extrapolation of time-series data called Neural Decomposition (ND). Units with a sinusoidal activation function… (more)

Subjects/Keywords: Applied sciences; Other Computer Sciences

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APA (6th Edition):

Godfrey, L. (2015). Neural Decomposition of Time-Series Data for Effective Generalization. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1360

Chicago Manual of Style (16th Edition):

Godfrey, Luke. “Neural Decomposition of Time-Series Data for Effective Generalization.” 2015. Masters Thesis, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/1360.

MLA Handbook (7th Edition):

Godfrey, Luke. “Neural Decomposition of Time-Series Data for Effective Generalization.” 2015. Web. 30 Oct 2020.

Vancouver:

Godfrey L. Neural Decomposition of Time-Series Data for Effective Generalization. [Internet] [Masters thesis]. University of Arkansas; 2015. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/1360.

Council of Science Editors:

Godfrey L. Neural Decomposition of Time-Series Data for Effective Generalization. [Masters Thesis]. University of Arkansas; 2015. Available from: https://scholarworks.uark.edu/etd/1360


University of Arkansas

7. Nugroho, Amin Rois Sinung. Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps.

Degree: MS, 2016, University of Arkansas

  Due to the popularity of smart phones and mobile apps, a potential privacy risk with the usage of mobile apps is that, from the… (more)

Subjects/Keywords: Applied sciences; CPU usage; Cloud; Machine learning; Mobile; Mobile app; Privacy; Information Security

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APA (6th Edition):

Nugroho, A. R. S. (2016). Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1599

Chicago Manual of Style (16th Edition):

Nugroho, Amin Rois Sinung. “Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps.” 2016. Masters Thesis, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/1599.

MLA Handbook (7th Edition):

Nugroho, Amin Rois Sinung. “Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps.” 2016. Web. 30 Oct 2020.

Vancouver:

Nugroho ARS. Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps. [Internet] [Masters thesis]. University of Arkansas; 2016. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/1599.

Council of Science Editors:

Nugroho ARS. Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps. [Masters Thesis]. University of Arkansas; 2016. Available from: https://scholarworks.uark.edu/etd/1599


University of Arkansas

8. Godfrey, Luke Benjamin. Parameterizing and Aggregating Activation Functions in Deep Neural Networks.

Degree: PhD, 2018, University of Arkansas

  The nonlinear activation functions applied by each neuron in a neural network are essential for making neural networks powerful representational models. If these are… (more)

Subjects/Keywords: Activation function; Deep learning; Forecasting; Machine learning; Neural network; Parametric function; Artificial Intelligence and Robotics

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APA (6th Edition):

Godfrey, L. B. (2018). Parameterizing and Aggregating Activation Functions in Deep Neural Networks. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2655

Chicago Manual of Style (16th Edition):

Godfrey, Luke Benjamin. “Parameterizing and Aggregating Activation Functions in Deep Neural Networks.” 2018. Doctoral Dissertation, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/2655.

MLA Handbook (7th Edition):

Godfrey, Luke Benjamin. “Parameterizing and Aggregating Activation Functions in Deep Neural Networks.” 2018. Web. 30 Oct 2020.

Vancouver:

Godfrey LB. Parameterizing and Aggregating Activation Functions in Deep Neural Networks. [Internet] [Doctoral dissertation]. University of Arkansas; 2018. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/2655.

Council of Science Editors:

Godfrey LB. Parameterizing and Aggregating Activation Functions in Deep Neural Networks. [Doctoral Dissertation]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2655


University of Arkansas

9. Komoni, Erzen. A Continuous Space Generative Model.

Degree: MS, 2018, University of Arkansas

  Generative models are a class of machine learning models capable of producing digital images with plausibly realistic properties. They are useful in such applications… (more)

Subjects/Keywords: Continuous Functions; Deep Learning; Generative Models; Machine Learning; Neural Networks; Graphics and Human Computer Interfaces

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APA (6th Edition):

Komoni, E. (2018). A Continuous Space Generative Model. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2668

Chicago Manual of Style (16th Edition):

Komoni, Erzen. “A Continuous Space Generative Model.” 2018. Masters Thesis, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/2668.

MLA Handbook (7th Edition):

Komoni, Erzen. “A Continuous Space Generative Model.” 2018. Web. 30 Oct 2020.

Vancouver:

Komoni E. A Continuous Space Generative Model. [Internet] [Masters thesis]. University of Arkansas; 2018. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/2668.

Council of Science Editors:

Komoni E. A Continuous Space Generative Model. [Masters Thesis]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2668


University of Arkansas

10. Holliday, James B. Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy.

Degree: MS, 2018, University of Arkansas

  We propose a simple and efficient modification to the Asynchronous Advantage Actor Critic (A3C) algorithm that improves training. In 2016 Google’s DeepMind set a… (more)

Subjects/Keywords: Deep Learning; Exploration Strategy; Machine Learning; Reinforcement Learning; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces

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APA (6th Edition):

Holliday, J. B. (2018). Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2689

Chicago Manual of Style (16th Edition):

Holliday, James B. “Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy.” 2018. Masters Thesis, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/2689.

MLA Handbook (7th Edition):

Holliday, James B. “Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy.” 2018. Web. 30 Oct 2020.

Vancouver:

Holliday JB. Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy. [Internet] [Masters thesis]. University of Arkansas; 2018. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/2689.

Council of Science Editors:

Holliday JB. Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy. [Masters Thesis]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2689


University of Arkansas

11. Pan, Qiuping. Bayesian Network Modeling and Inference of GWAS Catalog.

Degree: MS, 2018, University of Arkansas

  Genome-wide association studies (GWASs) have received an increasing attention to understand genotype-phenotype relationships. The Bayesian network has been proposed as a powerful tool for… (more)

Subjects/Keywords: Bayesian Network; GWAS; GWAS Catalog; STIP; Bioinformatics; Genomics; OS and Networks

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APA (6th Edition):

Pan, Q. (2018). Bayesian Network Modeling and Inference of GWAS Catalog. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2709

Chicago Manual of Style (16th Edition):

Pan, Qiuping. “Bayesian Network Modeling and Inference of GWAS Catalog.” 2018. Masters Thesis, University of Arkansas. Accessed October 30, 2020. https://scholarworks.uark.edu/etd/2709.

MLA Handbook (7th Edition):

Pan, Qiuping. “Bayesian Network Modeling and Inference of GWAS Catalog.” 2018. Web. 30 Oct 2020.

Vancouver:

Pan Q. Bayesian Network Modeling and Inference of GWAS Catalog. [Internet] [Masters thesis]. University of Arkansas; 2018. [cited 2020 Oct 30]. Available from: https://scholarworks.uark.edu/etd/2709.

Council of Science Editors:

Pan Q. Bayesian Network Modeling and Inference of GWAS Catalog. [Masters Thesis]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2709

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