Computer Sciences Commons^™

Machine Learning Approach For Predicting Cancer Using Gene Expression, Aashi Maharjan

UNLV Theses, Dissertations, Professional Papers, and Capstones

Cancer has become one of the major factors responsible for global deaths, due to late diagnoses and lack of proper treatment. It involves the abnormal and uncontrolled growth of cells inside the body, which might spread from one place to different parts. Ribonucleic acid (RNA) sequencing can detect the changes occurring inside cells and helps to analyze the transcriptome of gene expression patterns inside RNA. Machine learning techniques can assist in the prediction of cancer at an early stage, if data is available. The objective of this thesis is to build models and classify different types of cancer. For this …

Studies On Kernels Of Simple Polygons, Jason Mark

UNLV Theses, Dissertations, Professional Papers, and Capstones

The kernel of a simple polygon is the set of points in its interior from which all points inside the polygon are visible. We formally establish that for a given convex polygon Q we can always construct a larger simple polygon with many reflex vertices such that Q is the kernel of P. We present algorithms for decomposing a strongly monotone polygon into star-polygons. This decomposition is applied for developing an efficient algorithm for placing a small number of vertical towers to cover the entire given 1.5D terrain. We also present an experimental investigation of the proposed algorithm. The implementation …

Towards Multi-Modal Data Classification, Henry Ng

UNLV Theses, Dissertations, Professional Papers, and Capstones

A feature fusion multi-modal neural network (MMN) is a network that combines different modalities at the feature level to perform a specific task. In this paper, we study the problem of training the fusion procedure for MMN. A recent study has found that training a multi-modal network that incorporates late fusion produces a network that has not learned the proper parameters for feature extraction. These late fusion models perform very well during training but fall short to its single modality counterpart when testing. We hypothesize that jointly trained MMN have weight space that is too large for effective training. To …

Detection Of Snps Associated With Bone Loss Rate By Using Machine Learning Approaches, Avinash Yaganapu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Osteoporosis is one of the most common diseases seen in postmenopausal women, it decreases the bone density and quality, and later causes bone loss. Generally, bone loss occurs when bone losses its content and become porous: a sponge like substance. In most Genome Wide Association Studies (GWAS), researchers perform experiments with genomic data that contains some millions of numbers of single nucleotide polymorphisms (SNPs) and checks their association with the trait or disease. In this thesis, we performed two separate analyses with 2207 (of bone loss and bone gain) and 645 (of bone loss) instances separately. For predicting the SNPs …

Obtaining Real-World Benchmark Programs From Open-Source Repositories Through Abstract-Semantics Preserving Transformations, Maria Anne Rachel Paquin

Boise State University Theses and Dissertations

Benchmark programs are an integral part of program analysis research. Researchers use benchmark programs to evaluate existing techniques and test the feasibility of new approaches. The larger and more realistic the set of benchmarks, the more confident a researcher can be about the correctness and reproducibility of their results. However, obtaining an adequate set of benchmark programs has been a long-standing challenge in the program analysis community.

In this thesis, we present the APT tool, a framework we designed and implemented to automate the generation of realistic benchmark programs suitable for program analysis evaluations. Our tool targets intra-procedural analyses that …

Polyhedral+Dataflow Graphs, Eddie C. Davis

Boise State University Theses and Dissertations

This research presents an intermediate compiler representation that is designed for optimization, and emphasizes the temporary storage requirements and execution schedule of a given computation to guide optimization decisions. The representation is expressed as a dataflow graph that describes computational statements and data mappings within the polyhedral compilation model. The targeted applications include both the regular and irregular scientific domains.

The intermediate representation can be integrated into existing compiler infrastructures. A specification language implemented as a domain specific language in C++ describes the graph components and the transformations that can be applied. The visual representation allows users to reason about …

Privacy-Preserving Protocol For Atomic Swap Between Blockchains, Kiran Gurung

Boise State University Theses and Dissertations

Atomic swap facilitates fair exchange of cryptocurrencies without the need for a trusted authority. It is regarded as one of the prominent technologies for the cryptocurrency ecosystem, helping to realize the idea of a decentralized blockchain introduced by Bitcoin. However, due to the heterogeneity of the cryptocurrency systems, developing efficient and privacy-preserving atomic swap protocols has proven challenging. In this thesis, we propose a generic framework for atomic swap, called PolySwap, that enables fair ex-change of assets between two heterogeneous sets of blockchains. Our construction 1) does not require a trusted third party, 2) preserves the anonymity of the swap …

Do You Feel Me?: Learning Language From Humans With Robot Emotional Displays, David Mcneill

Boise State University Theses and Dissertations

In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. The second requirement poses a problem: If a robot is capable of asking a human teacher well-formed questions, it will lead the teacher to provide responses that are too advanced for a robot, which requires simple inputs and feedback to build word-level comprehension.

In a live interactive study, we tested the hypothesis that emotional displays …

Content Based Image Retrieval (Cbir) For Brand Logos, Enjal Parajuli

Boise State University Theses and Dissertations

This thesis explores the problem of automatically detecting the presence of logos in general images. Brand logos carry the goodwill of a company and are considered to be of high value in the corporate world, and thus automatically determining whether or not a logo is present in an image can be of interest for companies that wish to protect their brand. The problem of automated logo detection is inherently complex, but is further complicated through intentional obfuscation of logo images, for example by color shifting or other slight image modifications that leave the logo intact and easily recognizable by a …

Improving Scientist Productivity, Architecture Portability, And Performance In Parflow, Michael Burke

Boise State University Theses and Dissertations

Legacy scientific applications represent significant investments by universities, engineers, and researchers and contain valuable implementations of key scientific computations. Over time hardware architectures have changed. Adapting existing code to new architectures is time consuming, expensive, and increases code complexity. The increase in complexity negatively affects the scientific impact of the applications. There is an immediate need to reduce complexity. We propose using abstractions to manage and reduce code complexity, improving scientific impact of applications.

This thesis presents a set of abstractions targeting boundary conditions in iterative solvers. Many scientific applications represent physical phenomena as a set of partial differential equations …

On Privacy-Aware Escience Workflows, Khalid Belhajjame, Noura Faci, Zakaria Maamar, Vanilson Burégio, Edvan Soares, Mahmoud Barhamgi

All Works

© 2020, Springer-Verlag GmbH Austria, part of Springer Nature. Computing-intensive experiments in modern sciences have become increasingly data-driven illustrating perfectly the Big-Data era. These experiments are usually specified and enacted in the form of workflows that would need to manage (i.e., read, write, store, and retrieve) highly-sensitive data like persons’ medical records. We assume for this work that the operations that constitute a workflow are 1-to-1 operations, in the sense that for each input data record they produce a single data record. While there is an active research body on how to protect sensitive data by, for instance, anonymizing datasets, …

An Alternative Approach To Malware Research, Christopher James May

Honors Program Theses

Current antivirus programs have design flaws that allow malware to bypass detection. Despite this, malicious parties are usually the ones to find and exploit these flaws before they can be fixed. Therefore, a more proactive approach to malware research should become the new standard. To that end, a new programming language will be designed and created that sheds light on a couple of design flaws in current antivirus models. Fundamentally, antivirus programs have trouble detecting interpreted languages. In addition, it is suspected that antivirus programs are unable to detect an unknown programming language that is injected into another file thus …

Dynamics In Recommendations Of Updates For Free Open-Source Software, Shakre Elmane

Theses and Dissertations

In Free and Open-Source Software (FOSS) world, newer is not always better. Automatically updating to the latest version of FOSS applications involves real risks. The newer version could be missing features that are essential to some users, but are dropped by the developers. Another possible scenario, with even more serious consequences, is a project taken over by malicious developers who target users’ sensitive data, or try to control their systems. In this work we identify a set of security risks associated with changes of reviewers in automatic Free and Open-Source software (FOSS) updates. Automatic updates can be a prime target …

Blockchain Interoperability With Cross-Chain Stablecoin Payments, Ravi Balvantrai Pandhi

Theses and Dissertations

A Peer-to-peer payment system was one of the first proposed applications of blockchain technology. Cryptocurrencies have instead been used as a speculative investment. Stablecoins worth approximately 7 billion USD circulate the cryptocurrency market today, presenting a good case for their use in cryptocurrency payments [2]. Stablecoins have also been issued on a variety of blockchains that operate balkanized from each other. Cross-chain transfer between stablecoins is currently enabled by centralized middlemen. The actions of putting stablecoin transfer in the hands of intermediaries is a path towards centralization of control opposite to the decentralization ideology of blockchains. The inter blockchain transfer …

A Bio-Inspired Classification System For Cyber-Physical-Human Identity Resolution, Mary Catherine (Kay) Michel

Theses and Dissertations

The Internet has created a need for understanding complex technology and identities today. Cybercrime can take years to solve, and a systematic design may aid in more rapid resolution. Classification of identities involves the arrangement of shared qualities or characteristics known as features set expression, useful for identifying specific types of cybercriminals based on empirical evidence and logic. In order for classification of a cyber identity to be explainable and acceptable to researchers, a holistic systematic approach is beneficial to organize natural and synthetic features of living and non-living organisms into a standardized model. Proven scientific methods in the biology …

Blockchain For Educational Certificate Distribution, Layla Asiri

Theses and Dissertations

There are many authenticity problems associated with paper certificates and diplomas. Besides the increase in the forgery of paper certificates and diplomas, other quality problems regarding paper certificates and diplomas deserve attention too. This study seeks to evaluate how alternative technologies may mitigate concerns for Florida Institute of Technology students and faculty and determine whether they support the adoption of Blockchain technology. In particular, this research aims to investigate the challenges of using Blockchain technology for issuing and verifying academic records, certificates, and diplomas for Florida Institute of Technology students and faculty. Blockchain technology can be difficult for institutions to …

Defense In Depth Of Resource-Constrained Devices, Ira Ray Jenkins

Dartmouth College Ph.D Dissertations

The emergent next generation of computing, the so-called Internet of Things (IoT), presents significant challenges to security, privacy, and trust. The devices commonly used in IoT scenarios are often resource-constrained with reduced computational strength, limited power consumption, and stringent availability requirements. Additionally, at least in the consumer arena, time-to-market is often prioritized at the expense of quality assurance and security. An initial lack of standards has compounded the problems arising from this rapid development. However, the explosive growth in the number and types of IoT devices has now created a multitude of competing standards and technology silos resulting in a …

Bridging The Gap Between Intent And Outcome: Knowledge, Tools & Principles For Security-Minded Decision-Making, Vijay Harshed Kothari

Dartmouth College Ph.D Dissertations

Well-intentioned decisions---even ones intended to improve aggregate security--- may inadvertently jeopardize security objectives. Adopting a stringent password composition policy ostensibly yields high-entropy passwords; however, such policies often drive users to reuse or write down passwords. Replacing URLs in emails with "safe" URLs that navigate through a gatekeeper service that vets them before granting user access may reduce user exposure to malware; however, it may backfire by reducing the user's ability to parse the URL or by giving the user a false sense of security if user expectations misalign with the security checks delivered by the vetting process. A short timeout …

A Mathematical Approach To Gomoku, Oscar Garcia

Theses and Dissertations

This goal of this thesis is to design and implement a light weighted AI for playing Gomoku with high level intelligence. Our work is built upon an innovative algebraic monomial theory to help assess values for each possible move and estimate chances for the AI to win at each move. With the help of the monomial theory, we are able to convert winning configurations into monomials of variables that represent the underlying board positions. In the existing approaches to building an AI for playing Gomoku, one common challenge is about how to represent the present configuration of the game along …

Algorithmic Assembly Of Nanoscale Structures, Austin Luchsinger

Theses and Dissertations

The development of nanotechnology has become one of the most significant endeavors of our time. A natural objective of this field is discovering how to engineer nanoscale structures. Limitations of current top-down techniques inspire investigation into bottom-up approaches to reach this objective. A fundamental precondition for a bottom-up approach is the ability to control the behavior of nanoscale particles. Many abstract representations have been developed to model systems of particles and to research methods for controlling their behavior. This thesis develops theories on two such approaches for building complex structures: the self-assembly of simple particles, and the use of simple …

Energy-Efficient Data Transmission With Clustering And Compressive Sensing In Wireless Sensor Networks, Alagirisamy Mukil

Student Works (2020-2029)

One of the most important application of wireless sensor network is environmental monitoring. The application involves lifetime of sensor nodes for longer duration associating its energy module. Wireless sensor nodes deployed in sensing field aggregate enormous amount of sensed data and transfer them to the sink. The inherent limitation of energy carried within the battery of sensor nodes fetches extreme difficulty to acquire adequate network lifetime, becoming a bottleneck in forwarding data to sink. Hence the motivation is to reduce the amount of data transfer and attain energy efficiency. This is achieved by clustering and compressive sensing techniques. First objective …

Supervised Optimal Decision Machine Learning Approach To Class- And Method-Level Data Preprocessing Towards Effective Software Defect Prediction, Felix Ebubeogu Amarachukwu

Student Works (2020-2029)

Software defect prediction provides actionable outputs to software teams while contributing to industrial success. Therefore, predicting the number of defects in a new version of software at both the class and method levels is an important goal of defect prediction studies to assist software teams in optimizing their test efforts towards improving software quality. However, despite remarkable achievements in defect prediction, the quality of the data applied in defect prediction studies has been a major concern, with related quality issues leading to numerous contradictory findings in machine learning research. In addition, a demonstrated approach for predicting the number of defects …

Using Continuous Sensor Data To Formalize A Model Of In-Home Activity Patterns, Beiyu Lin, Diane J. Cook, Maureen Schmitter-Edgecombe

Computer Science Faculty Publications

Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity. We propose a formal model of indoor routine behavior based on data from automatically-sensed and recognized activities. A mechanistic description of behavior patterns for identical activity is offered to both investigate behavioral norms with 99 smart homes and compare these norms between subgroups. We identify and model the patterns of human behaviors based on inter-arrival times, the time interval between two successive activities, for …

Machine Learning For Prediction Of Trabecular And Cortical Bone Mineral Density, Partha Chudal

UNLV Theses, Dissertations, Professional Papers, and Capstones

Osteoporosis becomes very common problem for people after a certain age, which results in fragility fractures without any previous symptoms. One of the primary predictors of osteoporosis is bone mineral density (BMD). BMD is the mineral content of bone, at the optimal levels, that makes the bone strong enough to bear the regular load and elastic enough to handle the irregular twisting load. Two of the major parts of the bone that help to acquire such property are trabecular and cortical bone. This thesis focuses on predicting the BMDs of trabecular and cortical bone for men. For this purpose we …

Semantic Understanding Of Smart Contracts: Executable Operational Semantics Of Solidity, Jiao Jiao, Shuanglong Kan, Shang Wei Lin, David Sanan, Yang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

Bitcoin has been a popular research topic recently. Ethereum (ETH), a second generation of cryptocurrency, extends Bitcoin's design by offering a Turing-complete programming language called Solidity to develop smart contracts. Smart contracts allow creditable execution of contracts on EVM (Ethereum Virtual Machine) without third parties. Developing correct and secure smart contracts is challenging due to the decentralized computation nature of the blockchain. Buggy smart contracts may lead to huge financial loss. Furthermore, smart contracts are very hard, if not impossible, to patch once they are deployed. Thus, there is a recent surge of interest in analyzing and verifying smart contracts. …

A 2020 Perspective On "Client Risk Informedness In Brokered Cloud Services: An Experimental Pricing Study", Di Shang, Robert J. Kauffman

Research Collection School Of Computing and Information Systems

Cloud computing and the cloud services market have advanced in the past ten years. Cloud services now include most information technology (IT) services from fundamental computing services to more cutting- edge artificial intelligence (AI) services. Accordingly, opportunities have emerged for research on the design of new market features to improve the cloud services market to benefit providers and users. Based on our observation of the recent development of cloud services, in this short research commentary, we share our agenda for future studies of this important sector of IT services.

Starhopper: A Touch Interface For Remote Object-Centric Drone Navigation, Jiannan Li, Ravin Balakrishnan, Tovi Grossman

Research Collection School Of Computing and Information Systems

Camera drones, a rapidly emerging technology, offer people the ability to remotely inspect an environment with a high degree of mobility and agility. However, manual remote piloting of a drone is prone to errors. In contrast, autopilot systems can require a significant degree of environmental knowledge and are not necessarily designed to support flexible visual inspections. Inspired by camera manipulation techniques in interactive graphics, we designed StarHopper, a novel touch screen interface for efficient object-centric camera drone navigation, in which a user directly specifies the navigation of a drone camera relative to a specified object of interest. The system relies …

Robust Graph Learning From Noisy Data, Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Learning graphs from data automatically have shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust principle component analysis (RPCA), where the quality of the graph plays a critical role. The proposed model is able to …

Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports. In this study, motivated by the …

Assistance For Target Selection In Mobile Augmented Reality, Vinod Asokan, Scott Bateman, Anthony Tang

Research Collection School Of Computing and Information Systems

Mobile augmented reality - where a mobile device is used to view and interact with virtual objects displayed in the real world - is becoming more common. Target selection is the main method of interaction in mobile AR, but is particularly difficult because targets in AR can have challenging characteristics such as being moving or occluded (by digital or real world objects). Because target selection is particularly difficult and error prone in mobile AR, we conduct a comparative study of target assistance techniques. We compared four different cursor-based selection techniques against the standard touch-to-select interaction, finding that a newly adapted …