Physical Sciences and Mathematics Commons^™

Countermeasures Against Various Network Attacks Using Machine Learning Methods, Yi Li

USF Tampa Graduate Theses and Dissertations

With the rapid development of a computer network, our lives are already inseparable from it. Wireless Fidelity (Wi-Fi) is in use everywhere; more and more devices are connected to the Internet, and many companies and individuals tend to store their data and information online. Furthermore, it is now very convenient to communicate with each other through email and text messages. However, widespread networks also provide more attack surfaces for attackers. There are a variety of network attacks aimed at information theft. To better defend against those network attacks, one needs to have a broad knowledge of existing attacks. In this …

System Support Of Concurrent Database Query Processing On A Gpu, Hao Li

USF Tampa Graduate Theses and Dissertations

The unrivaled computing capabilities of modern GPUs meet the demand of processing massive amounts of data seen in many application domains. While traditional HPC systems support applications as standalone entities that occupy the entire GPU, we propose a GPU-based DBMS (G-DBMS) that can run multiple tasks concurrently. To that end, system-level management mechanisms like resource allocation and buffer manager are needed to build such a concurrent database query processing system and fully unleash the GPUs’ computing power. However, CUDA does not provide enough OS-level functionalities to support it. Thus our research is focusing on implementing the optimization of resource allocation …

Discrete Models And Algorithms For Analyzing Dna Rearrangements, Jasper Braun

USF Tampa Graduate Theses and Dissertations

In this work, language and tools are introduced, which model many-to-many mappings that comprise DNA rearrangements in nature. Existing theoretical models and data processing methods depend on the premise that DNA segments in the rearrangement precursor are in a clear one-to-one correspondence with their destinations in the recombined product. However, ambiguities in the rearrangement maps obtained from the ciliate species Oxytricha trifallax violate this assumption demonstrating a necessity for the adaptation of theory and practice.

In order to take into account the ambiguities in the rearrangement maps, generalizations of existing recombination models are proposed. Edges in an ordered graph model …

Multimodal Data Fusion And Attack Detection In Recommender Systems, Mehmet Aktukmak

USF Tampa Graduate Theses and Dissertations

The commercial platforms that use recommender systems can collect relevant information to produce useful recommendations to the platform users. However, these sources usually contain missing values, imbalanced and heterogeneous data, and noisy observations. Such characteristics render the process of exploiting the information nontrivial, as one should carefully address them during the data fusion process. In addition to the degenerative characteristics, some entries can be fake, i.e., they can be the outcomes of malicious intents to manipulate the system. These entries should be eliminated before incorporation to any recommendation task. Detecting such malicious attacks quickly and accurately and then mitigating them …

Unifying Security Policy Enforcement: Theory And Practice, Shamaria Engram

USF Tampa Graduate Theses and Dissertations

Security policies stipulate restrictions on the behaviors of systems to prevent themfrom behaving in harmful ways. One way to ensure that systems satisfy the constraints of a security policy is through the use of security enforcement mechanisms. To understand the fundamental limitations of such mechanisms, formal methods are employed to prove properties and reason about their behaviors. The particular formalism employed, however, typically depends on the time at which a mechanism operates.

Mechanisms operating before a program's execution are static mechanisms, and mechanisms operating during a program's execution are dynamic mechanisms. Static mechanisms are fundamentally limited in the types of …

Digital Identity: A Human-Centered Risk Awareness Study, Toufic N. Chebib

USF Tampa Graduate Theses and Dissertations

Cybersecurity threats and compromises have been at the epicenter of media attention; their risk and effect on people’s digital identity is something not to be taken lightly. Though cyber threats have affected a great number of people in all age groups, this study focuses on 55 to 75-year-olds, as this age group is close to retirement or already retired. Therefore, a notable compromise impacting their digital identity can have a major impact on their life.

To help guide this study, the following research question was formulated, “What are the risk perceptions of individuals, between the ages of 55 and 75 …

The Efficiency And Accuracy Of Yolo For Neonate Face Detection In The Clinical Setting, Jacqueline Hausmann

USF Tampa Graduate Theses and Dissertations

There are many face detection classification models available for download and use in the modern technological world. Based in the field of deep neural networks, these off-the-shelf solutions are generally inadequate to solve real world challenges. This work presents how current approaches biased towards detecting adult human faces must be modified in order to better accommodate face detection of the neonate in a NICU setting.

YOLO is a powerful object detection algorithm. Due to optimizations such as Cross mini-batch Normalization, Modified Spatial Attention Modules, Modified Path Aggregation Networks, Self-Adversarial Training, Mosaic Data Augmentation, DropBox Regularization, Multi-Input Weighted Residual Connections and …

Control Of A Human Arm Robotic Unit Using Augmented Reality And Optimized Kinematics, Carlo Canezo

USF Tampa Graduate Theses and Dissertations

There are more than 350000 amputees in the US who suffer loss of functionality in their daily living activities, and roughly 100000 of them are upper arm amputees. Many of these amputees use prostheses to compensate part of their lost arm function, including power prostheses. Research on 6-7 degree of freedom powered prostheses is still relatively new, and most commercially available powered prostheses are typically limited to 1 to 3 degrees of freedom. Due to the myriad of possible options for various powered protheses from different manufacturers, each configuration is governed by a distinct control scheme typically specific to the …

Detecting Symptoms Of Chronic Obstructive Pulmonary Disease And Congestive Heart Failure Via Cough And Wheezing Sounds Using Smart-Phones And Machine Learning, Anthony Windmon

USF Tampa Graduate Theses and Dissertations

Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) are progressive disorders, and major health concerns among today’s aging population. COPD causes a large mucus buildup in the lungs, leading to chronic cough and difficulty to breathe. CHF causes fluid buildup in the lower lungs due to the failing heart, causing cough and difficulty to breath. People who are clinically diagnosed with COPD or CHF are expected to regularly monitor their symptoms and follow complex medical recommendations in an effort to prevent exacerbation. In this dissertation, we elaborate upon three different machine learning based techniques that we developed for …

Feature Selection Via Random Subsets Of Uncorrelated Features, Long Kim Dang

USF Tampa Graduate Theses and Dissertations

The role of feature selection is crucial in many applications. A few of these include computational biology, image classification and risk management. In biology, gene expression micro array data sets have been used extensively in many areas of research. These data sets typically suffer from an important problem: the ratio between the number of features over the number of examples is very high. This problem mainly affects prediction accuracy because it is best to collect more labeled examples than features. A correlation based random subspace ensemble feature selector (CCC_RSM) was proposed to handle this problem [5]. In this approach, first …

Establishing Topological Data Analysis: A Comparison Of Visualization Techniques, Tanmay J. Kotha

USF Tampa Graduate Theses and Dissertations

When visualizing data, we would like to convey both the data and the uncertainty associated with it. There are many incentives to do this, ranging from hurricane path projection to geographical surveys. Important decision making tasks rely upon humans perceiving a clear picture of the data and having confidence in their decisions. Topological Data Analysis has the potential to visualize the data as features or hierarchies in ways that are familiar to human intuition, and thus could help us convey the variation associated with uncertainty.

In this thesis, we evaluate four visualization techniques: color maps, isocontours, Reeb graphs, and persistence …

Understanding The Complex Ethical Landscape Of Artificial Intelligence Adoptions, Chrissann R. Ruehle

USF Tampa Graduate Theses and Dissertations

Although Artificial Intelligence (AI) has existed since the 1950’s, it has experienced a series of expansions and declines over the years. Currently, AI is on an upward trajectory and has prompted the fourth industrial revolution as many scientists have noted. Some firms have rapidly embraced this technology and experienced growth while others have been slow to adopt. Naturally, this expansion often has societal impacts. The aim of this study is to explore ethical considerations that arise during the adoption of this technology. This research addressed three questions: 1. How do market and regulatory forces reportedly shape Artificial Intelligence adoptions? 2. …

Deep Learning Predictive Modeling With Data Challenges (Small, Big, Or Imbalanced), Renhao Liu

USF Tampa Graduate Theses and Dissertations

In the real world, data used to build machine learning models always has different sizes and characteristics. These size and characteristic features, including small datasets, big datasets, imbalanced datasets, often lead to different challenges when training machine learning models. Models trained on a small number of observations tend to overfit the training data and produce inaccurate results. When it comes to big data, efficiently learning from "huge" size data in a short time becomes important. With an imbalanced dataset, learning is usually biased towards the majority class in the data and appropriate measurements are needed to check model performance.

As …

Beyond The Hype: Challenges Of Neural Networks As Applied To Social Networks, Anthony Hernandez

USF Tampa Graduate Theses and Dissertations

Recent advances in neural network-based machine learning algorithms promise a rev-olution in prediction tasks across a variety of domains. Of these, forecasting user activity insocial media is particularly relevant for problems such as modeling and predicting informa-tion diffusion and designing intervention techniques to mitigate disinformation campaigns.Another potential task is anonymizing social network datasets to facilitate their distributionand promote research. Given the success of deep generative models, it may be possible touse them for anonymization. Social media seems an ideal context for applying neural net-work techniques, as they provide large data sets and challenging prediction objectives. Yet,our experiments find a number …

Efficient Viewshed Computation Algorithms On Gpus And Cpus, Faisal F. Qarah

USF Tampa Graduate Theses and Dissertations

Nowadays with the advance in managing and collecting large data, GIS is one of the applications that suffer from lack of efficient data management methods. GIS data often come in form of maps with different types of data such as temperature, topology, and population.

This dissertation focuses on exact-viewsheds computation for large terrains, and due to the poor performance of current exact-viewshed algorithms that may need several hours to process a midsize map, we found the need for new algorithms that are capable of efficiently computing viewshed for large size maps. This work presents a highly-efficient exact-viewshed computation algorithm based …

Machine Learning For The Internet Of Things: Applications, Implementation, And Security, Vishalini Laguduva Ramnath

USF Tampa Graduate Theses and Dissertations

Artificial intelligence and ubiquitous sensor systems have seen tremendous advances in recent times, resulting in groundbreaking impact across domains such as healthcare, entertainment, and transportation through a collective ecosystem called the Internet of Things. The advent of 5G and improved wireless networks will further accelerate the research and development of tools in deep learning, sensor systems, and computing platforms by providing improved network latency and bandwidth. While tremendous progress has been made in the Internet of Things, current work has largely focused on building robust applications that leverage the data collected through ubiquitous sensor nodes to provide actionable rules and …

Relational Joins On Gpus For In-Memory Database Query Processing, Ran Rui

USF Tampa Graduate Theses and Dissertations

Relational join processing is one of the core functionalities in database management systems. Implementing join algorithms on parallel platforms, especially modern GPUs, has gain a lot of momentum in the past decade. This dissertation addresses the following issues on GPU join algorithms. First, we present empirical evaluations of a state-of-the-art work on GPU-based join processing. Since 2008, the compute capabilities of GPUs have increased following a pace faster than that of the multi-core CPUs. We run a comprehensive set of experiments to study how join operations can benefit from such rapid expansion of GPU capabilities. We also present improved GPU …

Active Deep Learning Method To Automate Unbiased Stereology Cell Counting, Saeed Alahmari

USF Tampa Graduate Theses and Dissertations

Cell quantification in histopathology images plays a significant role in understanding and diagnosing diseases such as cancer and Alzheimers. The gold-standard for quantifying cells in tissue sections is the unbiased stereology approach. Unfortunately, in unbiased stereology current practices rely on a well-trained human to manually count hundreds of cells in microscopy images. However, this human-based manual approach is time-consuming, labor-intensive, subject to human errors, recognition bias, fatigue, variable training, poor reproducibility, and inter-observer error. Thus, the lack of high-throughput technology for automating unbiased stereology analyses remains a major obstacle to further progress in a wide range of neuroscience and cancer …

Next-Generation Self-Organizing Communications Networks: Synergistic Application Of Machine Learning And User-Centric Technologies, Chetana V. Murudkar

USF Tampa Graduate Theses and Dissertations

The telecommunications industry is going through a metamorphic journey where the 5G and 6G technologies will be deeply rooted in the society forever altering how people access and use information. In support of this transformation, this dissertation proposes a fundamental paradigm shift in the design, performance assessment, and optimization of wireless communications networks developing the next-generation self-organizing communications networks with the synergistic application of machine learning and user-centric technologies.

This dissertation gives an overview of the concept of self-organizing networks (SONs), provides insight into the “hot” technology of machine learning (ML), and offers an intuitive understanding of the user-centric (UC) …

Sentiment Analysis In Peer Review, Zachariah J. Beasley

USF Tampa Graduate Theses and Dissertations

Sentiment analysis, a widely popular subfield of natural language processing, has recently been used in the classroom to predict student attrition or to determine the mood of students, teacher strengths and weaknesses, or student perception of internship experience. These are all helpful indicators for the enhancement of students' academic experience but none improve the information gathered from or the reliability of peer review. This is particularly important in large courses with complex assignments (e.g., essays, software projects, and presentations) where scalable grading is requisite. In this dissertation, we apply sentiment analysis not on an assignment itself, but on the meaningful …

Action Recognition Using The Motion Taxonomy, Maxat Alibayev

USF Tampa Graduate Theses and Dissertations

In the last years, modern action recognition frameworks with deep architectures have achieved impressive results on the large-scale activity datasets. All state-of-the-art models share one common attribute: two-stream architectures. One deep model takes RGB frames, while the other model is fed with pre-computed optical flow vectors. The outputs of both models are combined to be used as a final probability distribution for the action classes. When comparing the results of individual models with the fused model, it is common to see that that latter method is more superior. Researchers explain that phenomena with the fact that optical flow vectors serve …

Automating The Classification Of Mosquito Specimens Using Image Processing Techniques, Mona Minakshi

USF Tampa Graduate Theses and Dissertations

According to WHO (World Health Organization) reports, among all animals, mosquitoes are responsible for the most deaths worldwide. Mosquito borne diseases continue to pose grave dangers to global health. In 2015 alone, 214 million cases of malaria were registered worldwide. According to Centers for Disease Control and Prevention (CDC) report published in 2016, 62,500 suspected case of Zika were reported to the Puerto Rico Department of Health (PRDH) out of which 29,345 cases were found positive. The year 2019 was recorded as the worst for dengue in South East Asia. There are close to 4,500 species of mosquitoes (spread across …

Privacy-Preserving And Functional Information Systems, Thang Hoang

USF Tampa Graduate Theses and Dissertations

Information systems generally involve storage and analytics of large-scale data, many of which may be highly sensitive (e.g., personal information, medical records). It is vital to ensure that these systems not only provide essential functionalities at large scale efficiently but also achieve a high level of security against cyber threats. However, there are significant research challenges in offering security and privacy for such information systems while preserving their original functionalities (e.g., search, analytics) effectively. Hence, there is a critical need for efficient cryptographic protocols that can address data privacy vs. utilization dilemma for real-life applications.

In this dissertation, we introduce …

Design Of Support Measures For Counting Frequent Patterns In Graphs, Jinghan Meng

USF Tampa Graduate Theses and Dissertations

In recent years, the popularity of graph datasets has grown rapidly. Frequent subgraph mining (FSM) from graphs becomes an important subject in computer science research. In this dissertation, we study single-graph as an effective model to represent information and its related graph mining techniques. In frequent pattern mining in a single-graph setting, there are two main problems: support measure and search scheme. We study the development of support measures, which are basically functions that map a pattern to its frequency count in a database. Our work is based on the hypergraph framework using the concept of occurrence/instance hypergraphs. We present …

Service Provisioning And Security Design In Software Defined Networks, Mohamed Rahouti

USF Tampa Graduate Theses and Dissertations

Information and Communications Technology (ICT) infrastructures and systems are being widely deployed to support a broad range of users and application scenarios. A key trend here is the emergence of many different "smart" technology paradigms along with an increasingly diverse array of networked sensors, e.g., for smart homes and buildings, intelligent transportation and autonomous systems, emergency response, remote health monitoring and telehealth, etc. As billions of these devices come online, ICT networks are being tasked with transferring increasing volumes of data to support intelligent real-time decision making and management. Indeed, many applications and services will have very stringent Quality of …

Keyless Anti-Jamming Communication Via Randomized Dsss, Ahmad Alagil

USF Tampa Graduate Theses and Dissertations

Nowadays, wireless networking is ubiquitous. In wireless communication systems, multiple nodes exchange data during the transmission time. Due to the natural use of the communication channel, it is crucial to protect the physical layer to make wireless channels between nodes more reliable. Jamming attacks consider one of the most significant threats on wireless communication. Spread spectrum techniques have been widely used to mitigate the effects of the jammer. Traditional anti-jamming approaches like Frequency Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) require a sender and a receiver to share a secret key prior to their communication. If this …

Artificial Intelligence Towards The Wireless Channel Modeling Communications In 5g, Saud Mobark Aldossari

USF Tampa Graduate Theses and Dissertations

Channel prediction is a mathematical predicting of the natural propagation of the signal that helps the receiver to approximate the affected signal, which plays an important role in highly mobile or dynamic channels. The standard wireless communication channel modeling can be facilitated by either deterministic or stochastic channel methodologies. The deterministic approach is based on the electromagnetic theories and every single object in that environment has to be known in that propagation space and an example of this method is ray tracing. While the stochastic modeling method is based on measurements that involve statistical distributions of the channel parameters and …

Models Of Secure Software Enforcement And Development, Hernan M. Palombo

USF Tampa Graduate Theses and Dissertations

Computer Security has been a pressing issue that affects our society in multiple ways. Although a plethora of security solutions have been proposed and implemented throughout the years, security continues to be a problem for at least two important reasons, (1) implementations of runtime enforcement mechanisms have not been modeled rigorously and thus may not be enforcing the policies that are expected to enforce, and (2) there are conflicting tensions in the software development process that hinder the implementation and maintenance of secure software. To investigate these issues, this dissertation is divided into two parts.

The first part of this …

Composition Of Atomic-Obligation Security Policies, Danielle Ferguson

USF Tampa Graduate Theses and Dissertations

There has been significant work to date on policy-specification languages that allow specification of arbitrary obligations, but there continues to exist open challenges in the composition of these arbitrary obligations, especially when obligations can be complex (i.e. consist more than one action). There are currently no solutions that allow complete and automatic resolution of conflicts between policies and other policies' obligations or that allow policies to react to the complex obligations of other policies. In particular, there is minimal work that considers the benefits and challenges of allowing complex obligations that operate in an atomic fashion, that is that execute …

Complexities Of Data, Tasks And Workflows In Health It Management, Gaurav Jetley

USF Tampa Graduate Theses and Dissertations

This dissertation focuses on three key aspects in health IT management: (1) Complexities in the collection of health data in electronic health record (EHR) systems and the use of EHR data in research, (2) Complexities of collaboration between physicians and AI for improving healthcare delivery, and (3) Complexities of workflows and collaborations between healthcare organization (HCO) staff during the delivery of care. The first dissertation essay (Chapter 1) examines the key data quality issues that arise in recorded health information in EHR systems, provides quality thresholds that the data needs to meet for mitigating errors and increasing reproducibility of downstream …