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Title: Feature selection techniques and applications in bioinformatics.
Creator: Dittman, David, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Possibly the largest problem when working in bioinformatics is the large amount of data to sift through to find useful information. This thesis shows that the use of feature selection (a method of removing irrelevant and redundant information from the dataset) is a useful and even necessary technique to use in these large datasets. This thesis also presents a new method in comparing classes to each other through the use of their features. It also provides a thorough analysis of the use of...
Show morePossibly the largest problem when working in bioinformatics is the large amount of data to sift through to find useful information. This thesis shows that the use of feature selection (a method of removing irrelevant and redundant information from the dataset) is a useful and even necessary technique to use in these large datasets. This thesis also presents a new method in comparing classes to each other through the use of their features. It also provides a thorough analysis of the use of various feature selection techniques and classifier in different scenarios from bioinformatics. Overall, this thesis shows the importance of the use of feature selection in bioinformatics.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3175016
Subject Headings: Bioinformatifcs, Data mining, Technological innovations, Computational biology, Combinatorial group theory, Filters (Mathematics), Ranking and selection (Statistics)
Format: Document (PDF)

Title: TOWARDS DEPLOYABLE QUANTUM-SAFE CRYPTOSYSTEMS.
Creator: Koziel, Brian, Azarderakhsh, Reza, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: It is well known that in the near future, a large-scale quantum computer will be unveiled, one that could be used to break the cryptography that underlies our digital infrastructure. Quantum computers operate on quantum mechanics, enabling exponential speedups to certain computational problems, including hard problems at the cornerstone of our deployed cryptographic algorithms. With a vulnerability in this security foundation, our online identities, banking information, and precious data is...
Show moreIt is well known that in the near future, a large-scale quantum computer will be unveiled, one that could be used to break the cryptography that underlies our digital infrastructure. Quantum computers operate on quantum mechanics, enabling exponential speedups to certain computational problems, including hard problems at the cornerstone of our deployed cryptographic algorithms. With a vulnerability in this security foundation, our online identities, banking information, and precious data is now vulnerable. To address this, we must prepare for a transition to post-quantum cryptography, or cryptosystems that are protected from attacks by both classical and quantum computers. This is a dissertation proposal targeting cryptographic engineering that is necessary to deploy isogeny-based cryptosystems, one known family of problems that are thought to be difficult to break, even for quantum computers. Isogeny-based cryptography utilizes mappings between elliptic curves to achieve public-key encryption, digital signatures, and other cryptographic objectives necessary to support our digital infrastructure's security. This proposal focuses on three aspects of isogeny-based cryptography: 1) cryptographic engineering of isogeny-based cryptosystems; 2) developing and optimizing security-enabling isogeny applications; and 3) improving the security from known and emerging implementation attacks. By improving each of these aspects, we are providing confidence in the deployability of isogeny-based cryptography and helping to prepare for a post-quantum transition.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013998
Subject Headings: Cryptography, Quantum computers
Format: Document (PDF)

Title: EFFICIENT AND SECURE IMPLEMENTATION OF CLASSIC AND POST-QUANTUM PUBLIC-KEY CRYPTOGRAPHY.
Creator: Bisheh, Niasar Mojtaba, Azarderakhsh, Reza, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: To address the increased interest in crypto hardware accelerators due to performance and efficiency concerns, implementing hardware architectures of different public-key cryptosystems has drawn growing attention. Pure hardware methodology enhances architecture’s performance over a hardware/software co-design scheme at the cost of a more extended design cycle, reducing the flexibility, and demands customized data paths for different protocol-level operations. However, using pure hardware...
Show moreTo address the increased interest in crypto hardware accelerators due to performance and efficiency concerns, implementing hardware architectures of different public-key cryptosystems has drawn growing attention. Pure hardware methodology enhances architecture’s performance over a hardware/software co-design scheme at the cost of a more extended design cycle, reducing the flexibility, and demands customized data paths for different protocol-level operations. However, using pure hardware architecture makes the design smaller, faster, and more efficient. This dissertation mainly focuses on designing crypto accelerators that can be used in embedded systems and Internet-of-Things (IoT) devices where performance and efficiency are critical as a hardware accelerator to offload computations from the microcontroller units (MCU). In particular, our objective is to create a system-on-chip (SoC) crypto-accelerator with an MCU that achieves high area-time efficiency. Our implementation can also be integrated as an off-chip solution; however, other criteria, such as performance, are often as important or more important than efficiency in the external crypto-chip design, which is beyond of this work. Not only does our architecture inherently provide protection against timing and simple power analysis (SPA) attacks, but also some advanced security mechanisms to avoid differential power analysis (DPA) attacks are included, which is missing in the literature. In a nutshell, the contributions are summarized as follows:
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013981
Subject Headings: Cryptography, Public key cryptography, Curves, Elliptic, Quantum computers
Format: Document (PDF)

Title: KINOVA ROBOTIC ARM MANIPULATION WITH PYTHON PROGRAMMING.
Creator: Veit, Cameron, Zhong, Xiangnan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: As artificial intelligence (AI), such as reinforcement learning (RL), has continued to grow, the introduction of AI for use in robotic arms in order to have them autonomously complete tasks has become an increasingly popular topic. Robotic arms have recently had a drastic spike in innovation, with new robotic arms being developed for a variety of tasks both menial and complicated. One robotic arm recently developed for everyday use in close proximity to the user is the Kinova Gen 3 Lite, but...
Show moreAs artificial intelligence (AI), such as reinforcement learning (RL), has continued to grow, the introduction of AI for use in robotic arms in order to have them autonomously complete tasks has become an increasingly popular topic. Robotic arms have recently had a drastic spike in innovation, with new robotic arms being developed for a variety of tasks both menial and complicated. One robotic arm recently developed for everyday use in close proximity to the user is the Kinova Gen 3 Lite, but limited formal research has been conducted about controlling this robotic arm both with an AI and in general. Therefore, this thesis covers the implementation of Python programs in controlling the robotic arm physically as well as the use of a simulation to train an RL based AI compatible with the Kinova Gen 3 Lite. Additionally, the purpose of this research is to identify and solve the difficulties in the physical instance and the simulation as well as the impact of the learning parameters on the robotic arm AI. Similarly, the issues in connecting two Kinova Gen 3 Lites to one computer at once are also examined. This thesis goes into detail about the goal of the Python programs created to move the physical robotic arm as well as the overall setup and goal of the robotic arm simulation for the RL method. In particular, the Python programs for the physical robotic arm pick up the object and place it at a different location, identifying a method to prevent the gripper from crushing an object without a tactile sensor in the process. The thesis also covers the effect of various learning parameters on the accuracy and steps to goal curves of an RL method designed to make a Kinova Gen 3 Lite grab an object in a simulation. In particular, a neural network implementation of RL method with one of the learning parameters changed in comparison to the optimal learning parameters. The neural network is trained using Python Anaconda to control a Kinova Gen 3 Lite robotic arm model for a simulation made in the Unity compiler.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014022
Subject Headings: Robotics, Artificial intelligence, Reinforcement learning
Format: Document (PDF)

Title: OPTIMIZING ECC IMPLEMENTATIONS ON EMBEDDED DEVICES.
Creator: Owens, Daniel, Azarderakhsh, Reza, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: As the cryptographic community turns its focus toward post-quantum cryptography, the demand for classical cryptographic schemes such as Elliptic Curve Cryptography (ECC) remains high. ECC is mature, well studied, and used in a wide range of applications such as securing visits to web pages through a web browser, Bitcoin, and the Internet of Things (IoT). In this work we present an optimized implementation of the Edwards Curve Digital Signature Algorithm (EdDSA) operations Key Generation and...
Show moreAs the cryptographic community turns its focus toward post-quantum cryptography, the demand for classical cryptographic schemes such as Elliptic Curve Cryptography (ECC) remains high. ECC is mature, well studied, and used in a wide range of applications such as securing visits to web pages through a web browser, Bitcoin, and the Internet of Things (IoT). In this work we present an optimized implementation of the Edwards Curve Digital Signature Algorithm (EdDSA) operations Key Generation and Sign using the Ed25519 parameter on the ARM Cortex-M4, and we discuss the optimization of field and group arithmetic to produce high throughput cryptographic primitives. In addition, we discuss several techniques for optimizing scalar multiplication, and present timing and memory consumption for each, as well as comparisons to other works. Our fastest implementation performs an Ed25519 Key Generation operation in 250,785 cycles and signing in 435,426 cycles utilizing 6.1 kB of additional Read Only Memory (ROM) space.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014027
Subject Headings: Cryptography, Embedded Internet devices
Format: Document (PDF)

Title: EFFICIENT IMPLEMENTATION OF POST-QUANTUM CRYPTOGRAPHY.
Creator: Elkhatib, Rami, Azarderakhsh, Reza, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Cryptography relies on hard mathematical problems that current conventional computers cannot solve in a feasible amount of time. On the other hand, quantum computers, with their quantum mechanic construction, are presumed to be able to solve some of these problems in a reasonable amount of time. More specifically, the current hard problems that public key cryptography relies upon are expected to be easily broken during the quantum era, a time when large-scale quantum computers are available....
Show moreCryptography relies on hard mathematical problems that current conventional computers cannot solve in a feasible amount of time. On the other hand, quantum computers, with their quantum mechanic construction, are presumed to be able to solve some of these problems in a reasonable amount of time. More specifically, the current hard problems that public key cryptography relies upon are expected to be easily broken during the quantum era, a time when large-scale quantum computers are available. To address this problem ahead of time, researchers and institutions have proposed post-quantum cryptography (PQC), which is an area of research that focuses on quantum-resistant public key cryptography algorithms. One of the candidates in the NIST PQC standardization process is SIKE, an isogeny-based candidate. The main advantage of SIKE is that it provides the smallest key size out of all the NIST PQC candidates at the cost of performance. Therefore, the development of hardware accelerators for SIKE is very important to achieve high performance in time-constrained applications. In this thesis, we implement several accelerators for SIKE and its primitives using different design approaches, all of which are suitable for different applications. We deliver significant enhancements to SIKE’s most expensive component, the modular multiplier. We design SIKE using a hardware-based approach and a software-hardware codesign approach, the latter of which utilizes a RISC-V processor. We also design SIKE with multi-level security level support for applications that require support of multiple security levels with minimal area usage. We enclose our performance and area results, which provide a reference to evaluate our work with other implementations.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013986
Subject Headings: Cryptography, Quantum computers, Cryptography--Mathematics
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: INTELLIGENT OPERATION OF ROBOTIC ARMS BASED ON TURTLEBOT3 MOBILE ROBOTS.
Creator: Veit, Connor, Zhong, Xiangnan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: As technology progresses, tasks involving object manipulation that were once conducted by humans are now being accomplished through robots. Specifically, robots carry out these goals through the utilization of different forms of artificial intelligence, including deep learning via a convolutional neural network. One robot made to accomplish this purpose is the ROS controlled TurtleBot3 Waffle Pi with an OpenMANIPULATOR-X robotic arm. This type of TurtleBot3 was developed with the express...
Show moreAs technology progresses, tasks involving object manipulation that were once conducted by humans are now being accomplished through robots. Specifically, robots carry out these goals through the utilization of different forms of artificial intelligence, including deep learning via a convolutional neural network. One robot made to accomplish this purpose is the ROS controlled TurtleBot3 Waffle Pi with an OpenMANIPULATOR-X robotic arm. This type of TurtleBot3 was developed with the express purpose of education and research but may not be limited to those two usages. Based on the current design of this classification of TurtleBot3, it may have multiple applications outside the testing environment, granting it further uses in a variety of tasks. The TurtleBot3 is easy to setup to fulfill the purposes for which the TurtleBot3 Waffle Pi was designed, and the exploration into further uses would allow for the discovery of alternatives to some tasks that normally require more work. For that reason, this thesis was conducted to determine the various uses of the TurtleBot3 with a robotic arm and if this robot can be used outside of a testing environment for various real-world tasks.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014026
Subject Headings: Robotics, Artificial intelligence, Mobile robots
Format: Document (PDF)

Title: DECENTRALIZED SYSTEMS FOR INFORMATION SHARING IN DYNAMIC ENVIRONMENT USING LOCALIZED CONSENSUS.
Creator: Zamir, Linir, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Achieving a consensus among a large number of nodes has always been a challenge for any decentralized system. Consensus algorithms are the building blocks for any decentralized network that is susceptible to malicious activities from authorized and unauthorized nodes. Proof-of-Work is one of the first modern approaches to achieve at least a 51% consensus, and ever since many new consensus algorithms have been introduced with different approaches of consensus achievement. These decentralized...
Show moreAchieving a consensus among a large number of nodes has always been a challenge for any decentralized system. Consensus algorithms are the building blocks for any decentralized network that is susceptible to malicious activities from authorized and unauthorized nodes. Proof-of-Work is one of the first modern approaches to achieve at least a 51% consensus, and ever since many new consensus algorithms have been introduced with different approaches of consensus achievement. These decentralized systems, also called blockchain systems, have been implemented in many applications such as supply chains, medical industry, and authentication. However, it is mostly used as a cryptocurrency foundation for token exchange. For these systems to operate properly, they are required to be robust, scalable, and secure. This dissertation provides a different approach of using consensus algorithms for allowing information sharing among nodes in a secured fashion while maintaining the security and immutability of the consensus algorithm. The consensus algorithm proposed in this dissertation utilizes a trust parameter to enforce cooperation, i.e., a trust value is assigned to each node and it is monitored to prevent malicious activities over time. This dissertation also proposes a new solution, named localized consensus, as a method that allows nodes in small groups to achieve consensus on information that is only relevant to that small group of nodes, thus reducing the bandwidth of the system. The proposed models can be practical solutions for immense and highly dynamic environments with validation through trust and reputation values. Application for such localized consensus can be communication among autonomous vehicles where traffic data is relevant to only a small group of vehicles and not the entirety of the system.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014028
Subject Headings: Blockchain, Consensus algorithms
Format: Document (PDF)

Title: REAL-TIME HIGHWAY TRAFFIC FLOW AND ACCIDENT SEVERITY PREDICTION IN VEHICULAR NETWORKS USING DISTRIBUTED MACHINE LEARNING AND BIG DATA ANALYSIS.
Creator: Alnami, Hani Mohammed, Mahgoub, Imadeldin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: In recent years, Florida State recorded thousands of abnormal traffic flows on highways that were caused by traffic incidents. Highway traffic congestion costed the US economy 101 billion dollars in 2020. Therefore, it is imperative to develop effective real-time traffic flow prediction schemes to mitigate the impact of traffic congestion. In this dissertation, we utilized real-life highway segment-based traffic and incident data obtained from Florida Department of Transportation (FDOT) for...
Show moreIn recent years, Florida State recorded thousands of abnormal traffic flows on highways that were caused by traffic incidents. Highway traffic congestion costed the US economy 101 billion dollars in 2020. Therefore, it is imperative to develop effective real-time traffic flow prediction schemes to mitigate the impact of traffic congestion. In this dissertation, we utilized real-life highway segment-based traffic and incident data obtained from Florida Department of Transportation (FDOT) for real-time incident prediction. We used eight years of FDOT real-life traffic and incident data for Florida I-95 highway to build prediction models for traffic accident severity. Accurate severity prediction is beneficial for responders since it allows the emergency center to dispatch the right number of vehicles without wasting additional resources.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014089
Subject Headings: Traffic flow, Traffic accidents, Machine learning, Big data, Traffic estimation
Format: Document (PDF)

Title: PATH PLANNING FOR THE HYBRID AERIAL UNDERWATER ROBOTIC SYSTEM.
Creator: Davis, Anthony C., Ouyang, Bing, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Marine food chains are highly stressed by aggressive fishing practices and environmental damage. Aquaculture has increasingly become a source of seafood which spares the deleterious impact to wild fisheries, but it requires continuous water quality data to successfully grow and harvest fish. Aerial drones have great potential to monitor large areas quickly and efficiently. The Hybrid Aerial Underwater Robotic System (HAUCS) is a swarm of unmanned aerial vehicles (UAVs) and underwater...
Show moreMarine food chains are highly stressed by aggressive fishing practices and environmental damage. Aquaculture has increasingly become a source of seafood which spares the deleterious impact to wild fisheries, but it requires continuous water quality data to successfully grow and harvest fish. Aerial drones have great potential to monitor large areas quickly and efficiently. The Hybrid Aerial Underwater Robotic System (HAUCS) is a swarm of unmanned aerial vehicles (UAVs) and underwater measurement devices designed to collect water quality data of aquaculture ponds. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem (VRP). A dataset is created to simulate the distribution of ponds on a farm and is used to assess the HAUCS Path Planning Algorithm (HPP). Its performance is compared with the Google Linear Optimization Package (GLOP) and a Graph Attention Model (GAM) for routing around the simulated farms. The three methods are then implemented on a team of waterproof drones and experimentally verified at Southern Illinois University’s (SIU) Aquaculture Research Center. GLOP and GAM are demonstrated to be efficient path planning methods for small farms, while HPP is likely to be more suited to large farms. HAUCS shows great value as a future direction for intelligent aquaculture, but issues with obstacle avoidance and robust waterproofing need to be addressed before commercialization. The future of aquaculture promises more integrated and sustainable operations by mimicking natural systems and leveraging deeper understandings of biology.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014108
Subject Headings: Drone aircraft, Drones, Aquaculture
Format: Document (PDF)

Title: MEASUREMENT, ANALYSIS, CLASSIFICATION AND DETECTION OF GUNSHOT AND GUNSHOT-LIKE SOUNDS.
Creator: Baliram, Rajesh Singh, Zhuang, Hanqi, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The recent uptick in senseless shootings in otherwise quiet and relatively safe environments is powerful evidence of the need, now more than ever, to reduce these occurrences. Artificial intelligence (AI) can play a significant role in deterring individuals from attempting these acts of violence. The installation of audio sensors can assist in the proper surveillance of surroundings linked to public safety, which is the first step toward AI-driven surveillance. With the increasing popularity...
Show moreThe recent uptick in senseless shootings in otherwise quiet and relatively safe environments is powerful evidence of the need, now more than ever, to reduce these occurrences. Artificial intelligence (AI) can play a significant role in deterring individuals from attempting these acts of violence. The installation of audio sensors can assist in the proper surveillance of surroundings linked to public safety, which is the first step toward AI-driven surveillance. With the increasing popularity of machine learning (ML) processes, systems are being developed and optimized to assist personnel in highly dangerous situations. In addition to saving innocent lives, supporting the capture of the responsible criminals is part of the AI algorithm that can be hosted in acoustic gunshot detection systems (AGDSs). Although there has been some speculation that these AGDSs produce a higher false positive rate (FPR) than reported in their specifications, optimizing the dataset used for the model’s training and testing will enhance its performance. This dissertation proposes a new gunshot-like sound database that can be incorporated into a dataset for improved training and testing of a ML gunshot detection model. Reduction of the sample bias (that is, a bias in ML caused by an incomplete database) is achievable. The Mel frequency cepstral coefficient (MFCC) feature extraction process was utilized in this research. The uniform manifold and projection (UMAP) algorithm revealed that the MFCCs of this newly created database were the closest sounds to a gunshot sound, as compared to other gunshot-like sounds reported in literature. The UMAP algorithm reinforced the outcome derived from the calculation of the distances of the centroids of various gunshot-like sounds in MFCCs’ clusters. Further research was conducted into the feature reduction aspect of the gunshot detection ML model. Reducing a feature set to a minimum, while also maintaining a high accuracy rate, is a key parameter of a highly efficient model. Therefore, it is necessary for field deployed ML applications to be computationally light weight and highly efficient. Building on the discoveries of this research can lead to the development of highly efficient gunshot detection models.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014110
Subject Headings: Firearms, Sound, Detectors, Machine learning
Format: Document (PDF)

Title: EMBEDDING LEARNING FOR COMPLEX DYNAMIC INFORMATION NETWORKS.
Creator: Wu, Man, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: With the rapid development of networking platforms and data intensive applications, networks (or graphs) are becoming convenient and fundamental tools to model the complex inter-dependence among big scale data. As a result, networks (or graphs) are being widely used in many applications, including citation networks [40], social media networks [71], and so on. However, the high complexity (containing many important information) as well as the dynamic nature of the network makes the graph...
Show moreWith the rapid development of networking platforms and data intensive applications, networks (or graphs) are becoming convenient and fundamental tools to model the complex inter-dependence among big scale data. As a result, networks (or graphs) are being widely used in many applications, including citation networks [40], social media networks [71], and so on. However, the high complexity (containing many important information) as well as the dynamic nature of the network makes the graph learning task more difficult. To have better graph representations (capture both node content and graph structure), many research efforts have been made to develop reliable and efficient algorithms. Therefore, the good graph representation learning is the key factor in performing well on downstream tasks. The dissertation mainly focuses on the graph representation learning, which aims to embed both structure and node content information of graphs into a compact and low dimensional space for a new representation learning. More specifically, in order to achieve an efficient and robust graph representation, the following four problems will be studied from different perspectives: 1) We study the problem of positive unlabeled graph learning for network node classification, and present a new deep learning model as a solution; 2) We formulate a new open-world learning problem for graph data, and propose an uncertain node representation learning approach and sampling strategy to solve the problem; 3) For cross-domain graph learning, we present a novel unsupervised graph domain adaptation problem, and propose an effective graph convolutional network algorithm to solve it; 4) We consider a dynamic graph as a network with changing nodes and edges in temporal order and propose a temporal adaptive aggregation network (TAAN) for dynamic graph learning. Finally, the proposed models are verified and evaluated on various real-world datasets.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014066
Subject Headings: Neural networks (Computer science), Machine learning, Graphs, Embeddings (Mathematics)
Format: Document (PDF)

Title: A FRAMEWORK FOR NON-INTRUSIVE OCEAN CURRENT TURBINE ROTOR BLADE IMBALANCE FAULT DETECTION.
Creator: Freeman, Brittny, Tang, Yufei, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Ocean current turbines (OCT) convert the kinetic energy housed within the earth’s ocean currents into electricity. However, due to the harsh environmental conditions that these turbines operate in, their system performance naturally degrades over time. This degradation correlates to high operation and maintenance (O&M) costs, which necessitates the need for robust condition monitoring and fault detection (CMFD). Unfortunately, OCT operational data is not publicly available in large and/or...
Show moreOcean current turbines (OCT) convert the kinetic energy housed within the earth’s ocean currents into electricity. However, due to the harsh environmental conditions that these turbines operate in, their system performance naturally degrades over time. This degradation correlates to high operation and maintenance (O&M) costs, which necessitates the need for robust condition monitoring and fault detection (CMFD). Unfortunately, OCT operational data is not publicly available in large and/or diverse enough quantities to develop such frameworks. Therefore, from an industry-wide perspective, the technologies needed to harvest this energy source are still in their infancy.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014094
Subject Headings: Marine turbines, Marine turbines--Blades
Format: Document (PDF)

Title: ANALYSIS OF DRIVING BEHAVIORS AND RELEVANT DRIVING PREFERENCES REGARDING SELF-DRIVING CARS.
Creator: Tolbert, Steven William, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: This thesis explores the cross-cultural demands from self-driving cars in regards to their trust, safety, and driving styles. Through the use of international survey data we establish several AI trust and behavior metrics that can be used for understanding cross-cultural expectations from self-driving cars that can potentially address problems of trust between passengers and self-driving cars, social acceptability of self-driving cars, and development of customized autonomous driving...
Show moreThis thesis explores the cross-cultural demands from self-driving cars in regards to their trust, safety, and driving styles. Through the use of international survey data we establish several AI trust and behavior metrics that can be used for understanding cross-cultural expectations from self-driving cars that can potentially address problems of trust between passengers and self-driving cars, social acceptability of self-driving cars, and development of customized autonomous driving technologies. Further this thesis provides a serverless data-collection framework for future research in driving behaviors.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014115
Subject Headings: Automated vehicles, Automated vehicles--Social aspects, Artificial intelligence, Human-machine systems
Format: Document (PDF)

Title: NETWORK FEATURE ENGINEERING AND DATA SCIENCE ANALYTICS FOR CYBER THREAT INTELLIGENCE.
Creator: Wheelus, Charles, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: While it is evident that network services continue to play an ever-increasing role in our daily lives, it is less evident that our information infrastructure requires a concerted, well-conceived, and fastidiously executed strategy to remain viable. Government agencies, Non-Governmental Organizations (\NGOs"), and private organizations are all targets for malicious online activity. Security has deservedly become a serious focus for organizations that seek to assume a more proactive posture; in...
Show moreWhile it is evident that network services continue to play an ever-increasing role in our daily lives, it is less evident that our information infrastructure requires a concerted, well-conceived, and fastidiously executed strategy to remain viable. Government agencies, Non-Governmental Organizations (\NGOs"), and private organizations are all targets for malicious online activity. Security has deservedly become a serious focus for organizations that seek to assume a more proactive posture; in order to deal with the many facets of securing their infrastructure. At the same time, the discipline of data science has rapidly grown into a prominent role, as once purely theoretical machine learning algorithms have become practical for implementation. This is especially noteworthy, as principles that now fall neatly into the field of data science has been contemplated for quite some time, and as much as over two hundred years ago. Visionaries like Thomas Bayes [18], Andrey Andreyevich Markov [65], Frank Rosenblatt [88], and so many others made incredible contributions to the field long before the impact of Moore's law [92] would make such theoretical work commonplace for practical use; giving rise to what has come to be known as "Data Science".
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013620
Subject Headings: Cyber security, Computer security, Information infrastructure, Predictive analytics
Format: Document (PDF)

Title: A NOVEL FRAMEWORK FOR ANALYSIS OF LOWER LIMB MOVEMENTS: INTEGRATION OF AUGMENTED REALITY AND SENSOR-BASED SYSTEMS.
Creator: Davis, Edward P., Pandya, Abhijit, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: In this thesis, an augmented reality device was coupled with motion sensor units to function as a system of cooperative technologies for usage within exercise science and neurorehabilitation. Specifically, in a subfield of exercise science called biomechanics, the assessment and analysis of movements are critical to the evaluation and prescription of improvements for physical function in both daily and sport-specific activities. Furthermore, the systematic combination of these technologies...
Show moreIn this thesis, an augmented reality device was coupled with motion sensor units to function as a system of cooperative technologies for usage within exercise science and neurorehabilitation. Specifically, in a subfield of exercise science called biomechanics, the assessment and analysis of movements are critical to the evaluation and prescription of improvements for physical function in both daily and sport-specific activities. Furthermore, the systematic combination of these technologies provided potential end-users with a modality to perform exercise within, and correlated feedback based upon the end-user’s exercise performance. Data collection specific to biomechanics can provide both the end-user and their evaluators with critical feedback that can be used to modify movement efficiency, improve exercise capacity, and evaluate exercise performance. By coordinating both technologies and completing movement-based experiments, the systems were successfully integrated.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013952
Subject Headings: Augmented reality, Biomechanics, Sensors
Format: Document (PDF)

Title: A PROBABILISTIC CHECKING MODEL FOR EFFECTIVE EXPLAINABILITY BASED ON PERSONALITY TRAITS.
Creator: Alharbi, Mohammed N., Huang, Shihong, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: It is becoming increasingly important for an autonomous system to be able to explain its actions to humans in order to improve trust and enhance human-machine collaboration. However, providing the most appropriate kind of explanations – in terms of length, format, and presentation mode of explanations at the proper time – is critical to enhancing their effectiveness. Explanation entails costs, such as the time it takes to explain and for humans to comprehend and respond. Therefore, the actual...
Show moreIt is becoming increasingly important for an autonomous system to be able to explain its actions to humans in order to improve trust and enhance human-machine collaboration. However, providing the most appropriate kind of explanations – in terms of length, format, and presentation mode of explanations at the proper time – is critical to enhancing their effectiveness. Explanation entails costs, such as the time it takes to explain and for humans to comprehend and respond. Therefore, the actual improvement in human-system tasks from explanations (if any) is not always obvious, particularly given various forms of uncertainty in knowledge about humans. In this research, we propose an approach to address this issue. The key idea is to provide a structured framework that allows a system to model and reason about human personality traits as critical elements to guide proper explanation in human and system collaboration. In particular, we focus on the two concerns of modality and amount of explanation in order to optimize the explanation experience and improve overall system-human utility. Our models are based on probabilistic modeling and analysis (PRISM-games) to determine at run time what the most effective explanation under uncertainty is. To demonstrate our approach, we introduce a self-adaptative system called Grid – a virtual game – and the Stock Prediction Engine (SPE), which allows an automated system and a human to collaborate on the game and stock investments. Our evaluation of these exemplars, through simulation, demonstrates that a human subject’s performance and overall human-system utility is improved when considering the psychology of human personality traits in providing explanations.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013894
Subject Headings: Human-computer interaction, Probabilistic modelling, Human-machine systems, Affective Computing
Format: Document (PDF)

Title: MACHINE LEARNING ALGORITHMS FOR PREDICTING BOTNET ATTACKS IN IOT NETWORKS.
Creator: Leevy, Joffrey, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The proliferation of Internet of Things (IoT) devices in various networks is being matched by an increase in related cybersecurity risks. To help counter these risks, big datasets such as Bot-IoT were designed to train machine learning algorithms on network-based intrusion detection for IoT devices. From a binary classification perspective, there is a high-class imbalance in Bot-IoT between each of the attack categories and the normal category, and also between the combined attack categories...
Show moreThe proliferation of Internet of Things (IoT) devices in various networks is being matched by an increase in related cybersecurity risks. To help counter these risks, big datasets such as Bot-IoT were designed to train machine learning algorithms on network-based intrusion detection for IoT devices. From a binary classification perspective, there is a high-class imbalance in Bot-IoT between each of the attack categories and the normal category, and also between the combined attack categories and the normal category. Within the scope of predicting botnet attacks in IoT networks, this dissertation demonstrates the usefulness and efficiency of novel machine learning methods, such as an easy-to-classify method and a unique set of ensemble feature selection techniques. The focus of this work is on the full Bot-IoT dataset, as well as each of the four attack categories of Bot-IoT, namely, Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), Reconnaissance, and Information Theft. Since resources and services become inaccessible during DoS and DDoS attacks, this interruption is costly to an organization in terms of both time and money. Reconnaissance attacks often signify the first stage of a cyberattack and preventing them from occurring usually means the end of the intended cyberattack. Information Theft attacks not only erode consumer confidence but may also compromise intellectual property and national security. For the DoS experiment, the ensemble feature selection approach led to the best performance, while for the DDoS experiment, the full set of Bot-IoT features resulted in the best performance. Regarding the Reconnaissance experiment, the ensemble feature selection approach effected the best performance. In relation to the Information Theft experiment, the ensemble feature selection techniques did not affect performance, positively or negatively. However, the ensemble feature selection approach is recommended for this experiment because feature reduction eases computational burden and may provide clarity through improved data visualization. For the full Bot-IoT big dataset, an explainable machine learning approach was taken using the Decision Tree classifier. An easy-to-learn Decision Tree model for predicting attacks was obtained with only three features, which is a significant result for big data.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013933
Subject Headings: Machine learning, Internet of things--Security measures, Big data, Intrusion detection systems (Computer security)
Format: Document (PDF)

Title: DEVELOPMENT OF MICROFLUIDIC PLATFORMS FOR INFECTIOUS DISEASES DIAGNOSIS AND SPERM CELL SORTING.
Creator: Sharma, Sandhya, Asghar, Waseem, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: In recent years, point-of-care (POC) microfluidic platforms have transformed the healthcare landscape as they offer rapid, low-cost, and easy operational benefits. POC diagnostics play an important role in expediting the testing process in resource-constrained areas. These platforms have become a powerful tool as they offer comparable results with gold-standard methods. The gold standard methods require sophisticated lab locations and expensive equipment, to process the samples which is a...
Show moreIn recent years, point-of-care (POC) microfluidic platforms have transformed the healthcare landscape as they offer rapid, low-cost, and easy operational benefits. POC diagnostics play an important role in expediting the testing process in resource-constrained areas. These platforms have become a powerful tool as they offer comparable results with gold-standard methods. The gold standard methods require sophisticated lab locations and expensive equipment, to process the samples which is a significant challenge particularly for people living in low-income countries. To address these limitations, herein, in my dissertation, I have developed POC microfluidic platforms that can be operated outside the laboratory using lesser equipment statistically hence reducing the testing cost and time. The developed POC chips are used for infectious diseases diagnosis for viruses such as Zika, Hepatitis C Virus (HCV), and severe acute respiratory syndrome coronavirus 2 (SARSCoV-2). The entire virus detection process was executed inside a uniquely designed, inexpensive, disposable self-driven microfluidic chip with high sensitivity and specificity. In addition to this, I have also developed a microfluidic platform for functional sperm cell sorting from raw semen samples. The microfluidic chip offers a platform where the sperm cells experience different shear stress in different parts of the chip that facilitates isolation of competent sperm cells without impacting their integrity. Simultaneously, it also allows effortless collection of sorted sperm cells from the collection chamber which holds clinical significance. All things considered, the developed devices are inexpensive, disposable, easy-to-use, and rapid that provide results within one hour.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013906
Subject Headings: Microfluidics, Point-of-care testing, Communicable diseases—Diagnosis
Format: Document (PDF)

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