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META-LEARNING AND ENSEMBLE METHODS FOR DEEP NEURAL NETWORKS
Title
META-LEARNING AND ENSEMBLE METHODS FOR DEEP NEURAL NETWORKS.
Creator
Liu, Feng, Dingding, Wang, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Deep Neural Networks have been widely applied in many different applications and achieve significant improvement over classical machine learning techniques. However, training a neural network usually requires large amount of data, which is not guaranteed in some applications such as medical image classification. To address this issue, people propose to implement meta learning and ensemble learning techniques to make deep learning trainers more powerful. This thesis focuses on using deep...
Show moreDeep Neural Networks have been widely applied in many different applications and achieve significant improvement over classical machine learning techniques. However, training a neural network usually requires large amount of data, which is not guaranteed in some applications such as medical image classification. To address this issue, people propose to implement meta learning and ensemble learning techniques to make deep learning trainers more powerful. This thesis focuses on using deep learning equipped with meta learning and ensemble learning to study specific problems. We first propose a new deep learning based method for suggestion mining. The major challenges of suggestion mining include cross domain issue and the issues caused by unstructured and highly imbalanced data structure. To overcome these challenges, we propose to apply Random Multi-model Deep Learning (RMDL) which combines three different deep learning architectures (DNNs, RNNs and CNNs) and automatically selects the optimal hyper parameter to improve the robustness and flexibility of the model. Our experimental results on the SemEval-2019 competition Task 9 data sets demonstrate that our proposed RMDL outperforms most of the existing suggestion mining methods.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013481
Subject Headings
Neural networks (Computer science), Deep learning, Neural Networks in Applications, Machine learning--Technique
Format
Document (PDF)
APPLICATION OF BLOCKCHAIN NETWORK FOR THE USE OF INFORMATION SHARING
Title
APPLICATION OF BLOCKCHAIN NETWORK FOR THE USE OF INFORMATION SHARING.
Creator
Zamir, Linir, Liu, Feng-Hao, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The Blockchain concept was originally developed to provide security in the Bitcoin cryptocurrency network, where trust is achieved through the provision of an agreed-upon and immutable record of transactions between parties. The use of a Blockchain as a secure, publicly distributed ledger is applicable to fields beyond finance, and is an emerging area of research across many other fields in the industry. This thesis considers the feasibility of using a Blockchain to facilitate secured...
Show moreThe Blockchain concept was originally developed to provide security in the Bitcoin cryptocurrency network, where trust is achieved through the provision of an agreed-upon and immutable record of transactions between parties. The use of a Blockchain as a secure, publicly distributed ledger is applicable to fields beyond finance, and is an emerging area of research across many other fields in the industry. This thesis considers the feasibility of using a Blockchain to facilitate secured information sharing between parties, where a lack of trust and absence of central control are common characteristics. Implementation of a Blockchain Information Sharing system will be designed on an existing Blockchain network with as a communicative party members sharing secured information. The benefits and risks associated with using a public Blockchain for information sharing will also be discussed.
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Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013351
Subject Headings
Blockchains (Databases), Blockchains (Databases)--Industrial applications, Data encryption (Computer science), Personal data protection, Bitcoin
Format
Document (PDF)
SELECTED APPLICATIONS OF MPC
Title
SELECTED APPLICATIONS OF MPC.
Creator
Ghaseminejad, Mohammad Raeini, Liu, Feng-Hao, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Secure multiparty computation (secure MPC) is a computational paradigm that enables a group of parties to evaluate a public function on their private data without revealing the data (i.e., by preserving the privacy of their data). This computational approach, sometimes also referred to as secure function evaluation (SFE) and privacy-preserving computation, has attracted significant attention in the last couple of decades. It has been studied in different application domains, including in...
Show moreSecure multiparty computation (secure MPC) is a computational paradigm that enables a group of parties to evaluate a public function on their private data without revealing the data (i.e., by preserving the privacy of their data). This computational approach, sometimes also referred to as secure function evaluation (SFE) and privacy-preserving computation, has attracted significant attention in the last couple of decades. It has been studied in different application domains, including in privacy-preserving data mining and machine learning, secure signal processing, secure genome analysis, sealed-bid auctions, etc. There are different approaches for realizing secure MPC. Some commonly used approaches include secret sharing schemes, Yao's garbled circuits, and homomorphic encryption techniques. The main focus of this dissertation is to further investigate secure multiparty computation as an appealing area of research and to study its applications in different domains. We specifically focus on secure multiparty computation based on secret sharing and fully homomorphic encryption (FHE) schemes. We review the important theoretical foundations of these approaches and provide some novel applications for each of them. For the fully homomorphic encryption (FHE) part, we mainly focus on FHE schemes based on the LWE problem [142] or RLWE problem [109]. Particularly, we provide a C++ implementation for the ring variant of a third generation FHE scheme called the approximate eigenvector method (a.k.a., the GSW scheme) [67]. We then propose some novel approaches for homomorphic evaluation of common functionalities based on the implemented (R)LWE [142] and [109] and RGSW [38,58] schemes. We specifically present some constructions for homomorphic computation of pseudorandom functions (PRFs). For secure computation based on secret sharing [150], we provide some novel protocols for secure trust evaluation (STE). Our proposed STE techniques [137] enable the parties in trust and reputation systems (TRS) to securely assess their trust values in each other while they keep their input trust values private. It is worth mentioning that trust and reputation are social mechanisms which can be considered as soft security measures that complement hard security measures (e.g., cryptographic and secure multiparty computation techniques) [138, 171].
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00014018
Subject Headings
Data encryption (Computer science), Computers, privacy and data protection, Computer security
Format
Document (PDF)
An experimental survey of the transition between two-state and downhill protein folding scenarios
Title
An experimental survey of the transition between two-state and downhill protein folding scenarios.
Creator
Liu, Feng, Du, Deguo, Fuller, Amelia A., Davoren, Jennifer E., Wipf, Peter, Kelly, Jeffery W., Gruebele, Martin
Date Issued
2008-02-19
PURL
http://purl.flvc.org/fau/flvc_fau_islandoraimporter_10.1073_pnas.0711908105_1647975037
Format
Document (PDF)
Evaluating β-turn mimics as β-sheet folding nucleators
Title
Evaluating β-turn mimics as β-sheet folding nucleators.
Creator
Fuller, Amelia A., Du, Deguo, Liu, Feng, Davoren, Jennifer E., Bhabha, Gira, Kroon, Gerard, Case, David A., Dyson, H. Jane, Powers, Evan T., Wipf, Peter, Gruebele, Martin, Kelly, Jeffery W.
Date Issued
2009-07-07
PURL
http://purl.flvc.org/fau/flvc_fau_islandoraimporter_10.1073_pnas.0813012106_1648043559
Format
Document (PDF)