Physical Sciences and Mathematics Commons^™

On The Human Factors Impact Of Polyglot Programming On Programmer Productivity, Phillip Merlin Uesbeck

UNLV Theses, Dissertations, Professional Papers, and Capstones

Polyglot programming is a common practice in modern software development. This practice is often considered useful to create software by allowing developers to use whichever language they consider most well suited for the different parts of their software. Despite this ubiquity of polyglot programming there is no empirical research into how this practice affects software developers and their productivity. In this dissertation, after reviewing the state of the art in programming language and linguistic research pertaining to the topic, this matter is investigated by way of two empirical studies with 109 and 171 participants solving programming tasks. Based on the …

Processj: The Jvmcsp Code Generator, Oswaldo Benjamin Cisneros Merino

UNLV Theses, Dissertations, Professional Papers, and Capstones

We as a society have achieved greatness because we work together. There is power in numbers. However, when it comes to programming we have not been able to achieve the same level of symbiosis. This is because concurrent programming has been stigmatized as an advance and ab- stract subject allegedly harder than sequential programming. Additionally, traditional approaches to solving concurrent problems using sequential programming become unnecessarily difficult be- cause most of what newcomers are taught when it comes to concurrent programming (e.g., message passing and threads), while being technically correct, is completely irrelevant to the problems at hand. Rather than …

Coarse-Grained, Fine-Grained, And Lock-Free Concurrency Approaches For Self-Balancing B-Tree, Edward R. Jorgensen Ii

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation examines the concurrency approaches for a standard, unmodified B-Tree which is one of the more complex data structures. This includes the coarse grained, fine-grained locking, and the lock-free approaches. The basic industry standard coarse-grained approach is used as a base-line for comparison to the more advanced fine-grained and lock-free approaches. The fine-grained approach is explored and algorithms are presented for the fine-grained B-Tree insertion and deletion. The lock-free approach is addressed and an algorithm for a lock-free B- Tree insertion is provided. The issues associated with a lock-free deletion are discussed. Comparison trade-offs are presented and discussed. As …

Managing Iot Data On Hyperledger Blockchain, Akhil David

UNLV Theses, Dissertations, Professional Papers, and Capstones

Blockchain is a rapidly evolving technology known for its security, immutability and decentralized nature. At its heart, it’s used for storing various kinds of data like transactions. But it is not limited to just the transactions or the cryptocurrency. It can also be used to store many other things like assets, IoT data or even multimedia data like songs, pictures, and videos.

The number of IoT devices being connected to the internet is increasing day by day. In fact, Garter (Analyst Firm) predicts there will be 20.4 Billion IoT devices by the end of 2020 [IOTb]. With the increase in …

Static Malware Detection Using Deep Neural Networks On Portable Executables, Piyush Aniruddha Puranik

UNLV Theses, Dissertations, Professional Papers, and Capstones

There are two main components of malware analysis. One is static malware analysis and the other is dynamic malware analysis. Static malware analysis involves examining the basic structure of the malware executable without executing it, while dynamic malware analysis relies on examining malware behavior after executing it in a controlled environment. Static malware analysis is typically done by modern anti-malware software by using signature-based analysis or heuristic-based analysis.

This thesis proposes the use of deep neural networks to learn features from a malware’s portable executable (PE) to minimize the occurrences of false positives when recognizing new malware. We use the …

Improving Ocr Post Processing With Machine Learning Tools, Jorge Ramon Fonseca Cacho

UNLV Theses, Dissertations, Professional Papers, and Capstones

Optical Character Recognition (OCR) Post Processing involves data cleaning steps for documents that were digitized, such as a book or a newspaper article. One step in this process is the identification and correction of spelling and grammar errors generated due to the flaws in the OCR system. This work is a report on our efforts to enhance the post processing for large repositories of documents.

The main contributions of this work are:

• Development of tools and methodologies to build both OCR and ground truth text correspondence for training and testing of proposed techniques in our experiments. In particular, we …

Performance Comparison Of Message Queue Methods, Sanika Raje

UNLV Theses, Dissertations, Professional Papers, and Capstones

Message queues are queues of messages that facilitate communication between applications. A queue is a line of messages or events waiting to be handled in a sequential manner. A message queue is a queue of messages sent between applications. It includes a sequence of work objects that are waiting to be processed. For a distributed system to work, it needs to pass information between various machines. No single machine is responsible for the entire system, but all information is interrelated. Hence a major concern of distributed systems is this transfer of data. Which also proves to be one of the …

Permutation Flow Shop Via Simulated Annealing And Neh, Pooja Bhatt

UNLV Theses, Dissertations, Professional Papers, and Capstones

Permutation Flow Shop Scheduling refers to the process of allocating operations of jobs to machines such that an operation starts to process on machine j only after the processing completes in j-1machine. At a time a machine can process only one operation and similarly a job can have only one operation processed at a time. Finding a schedule that minimizes the overall completion times for Permutation Flow Shop problems is NP-Hard if the number of machines is greater than 2. Sowe concentrates on approaches with approximate solutions that are good enough for the problems. Heuristics is one way to find …

Machine Learning Approach For Prediction Of Bone Mineral Density And Fragility Fracture In Osteoporosis, Bibek Bhattarai

UNLV Theses, Dissertations, Professional Papers, and Capstones

Osteoporosis is a prevailing bone disease, which weakens the bone and is one of the

major factors of disability, especially in elderly persons. In this thesis, we developed

various machine learning models to predict fracture risk of osteoporosis. These mod-

els were built to base their predictions on genotype and phenotype data of patients.

We performed two dierent types of analysis: fracture risk prediction (a classica-

tion model) and bone mineral density (BMD) prediction (a regression model). For

fracture risk prediction we implemented four dierent algorithms: logistic regression,

random forest, gradient boosting, and multi-layer perceptron (MLP) based on dier-

ent …

Machine Learning Prediction Of Primary Tissue Origin Of Cancer From Gene Expression Read Counts, Lohitha Chintham Reddy

UNLV Theses, Dissertations, Professional Papers, and Capstones

Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells. Generally, manufacturing of proteins by cells is controlled by genes. Each gene must have the correct instructions for making its protein, so that it allows the protein to perform the correct function for the cell. When one or more genes in a cell mutate and create an abnormal protein, that is when cancer begins. An abnormal protein provides different information compared to a normal protein. This can cause cells to multiply uncontrollably and cause cancer.

RNA sequencing (RNA-seq) can be used to figure out …

Machine Learning Classification Of Primary Tissue Origin Of Cancer From Dna Methylation Markers, Sravani Gannavarapu Surya Naga

UNLV Theses, Dissertations, Professional Papers, and Capstones

Cancer is one of the leading causes of death globally and was responsible for approximately 9.6 million deaths in 2018. One of the main reason for deaths from cancer is late-stage presentation and inaccessible diagnosis and treatment. Cancer often spreads from the part of the body where it started (primary site) to a different part of the body (metastatic site). Identifying the primary site of cancer plays a key role as it directs the appropriate treatment. Cancer which spreads needs the same treatment as its origin. Having this knowledge can help doctors to decide the type of treatment.

All cancers …

Approximation Algorithms For Illuminating 1.5d Terrain, Jiwan Khatiwada

UNLV Theses, Dissertations, Professional Papers, and Capstones

We review important algorithmic results for the coverage of 1.5D terrain by point guards. Finding the minimum number of point guards for covering 1.5D terrain is known to be NP-hard. We propose two approximation algorithms for covering 1.5D terrain by a fewer number of point guards. The first algorithm (Greedy Ranking Algorithm) is based on ranking vertices in term of number of visible edges from them. The second algorithm (Greedy Forward Marching Algorithm) works in greedy manner by scanning the terrain from left to right. Both algorithms are implemented in Python 2.7 programming language.

Benchmarking Permutation Flow Shop Problem: Adaptive And Enumerative Approaches Implementations Via Novel Threading Techniques, Hari Prasad Sapkota

UNLV Theses, Dissertations, Professional Papers, and Capstones

A large number of real-world planning problems are combinatorial optimization problems which are easy to state and have a finite but usually very large number of feasible solutions. The minimum spanning tree problem and the shortest path problem are some which are solvable through polynomial algorithms. Even though there are other problems such as crew scheduling, vehicle routing, production planning, and hotel room operations which have no properties such as to solve the problem with polynomial algorithms. All these problems are NP-hard. The permutation flow shop problem is also NP-hard problem and they require high computation. These problems are solvable …

Rethinking Timestamping: Time Stamp Counter Design For Virtualized Environment, Alexander Tabatadze

UNLV Theses, Dissertations, Professional Papers, and Capstones

Almost every processor supports Time Stamp Counter (TSC), which is a hardware register that increments its value every clock cycle. Due to its high resolution and accessibility, TSC is now widely used for a variety tasks that need time measurements such as wall clock, code benchmarking, or metering hardware usage for account billing.

However, if not carefully configured and interpreted, TSC-based time measurements can yield inaccurate readings. For instance, modern CPU may dynamically change its frequency or enter into low-power states. Also, time spent on scheduling events, system calls, page faults, etc. should be correctly accounted for. Even more complications …

Classification Of Vegetation In Aerial Imagery Via Neural Network, Gevand Balayan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis focuses on the task of trying to find a Neural Network that is best suited for identifying vegetation from aerial imagery. The goal is to find a way to quickly classify items in an image as highly likely to be vegetation(trees, grass, bushes and shrubs) and then interpolate that data and use it to mark sections of an image as vegetation. This has practical applications as well. The main motivation of this work came from the effort that our town takes in conserving water. By creating an AI that can easily recognize plants, we can better monitor the …

Storing Iot Data Securely In A Private Ethereum Blockchain, Vinay Kumar Calastry Ramesh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Internet of Things (IoT) is a set of technologies that enable network-connected devices to perform an action or share data among several connected devices or to a shared database. The actions can be anything from switching on an Air Conditioning device remotely to turning on the ignition of a car through a command issued from a remote location or asking Alexa or Google Assistant to search for weather conditions in an area. IoT has proved to be game-changing for many industries such as Supply Chain, Shipping and Transportation providing updates on the status of shipments in real time. This has …

Multi-Resolution Spatio-Temporal Change Analyses Of Hydro-Climatological Variables In Association With Large-Scale Oceanic-Atmospheric Climate Signals, Kazi Ali Tamaddun

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary objective of the work presented in this dissertation was to evaluate the change patterns, i.e., a gradual change known as the trend, and an abrupt change known as the shift, of multiple hydro-climatological variables, namely, streamflow, snow water equivalent (SWE), temperature, precipitation, and potential evapotranspiration (PET), in association with the large-scale oceanic-atmospheric climate signals. Moreover, both observed datasets and modeled simulations were used to evaluate such change patterns to assess the efficacy of the modeled datasets in emulating the observed trends and shifts under the influence of uncertainties and inconsistencies. A secondary objective of this study was to …

The Affective Perceptual Model: Enhancing Communication Quality For Persons With Pimd, Jadin Tredup

UNLV Theses, Dissertations, Professional Papers, and Capstones

Methods for prolonged compassionate care for persons with Profound Intellectual and Multiple Disabilities (PIMD) require a rotating cast of import people in the subjects life in order to facilitate interaction with the external environment. As subjects continue to age, dependency on these people increases with complexity of communications while the quality of communication decreases. It is theorized that a machine learning (ML) system could replicate the attuning process and replace these people to promote independence. This thesis extends this idea to develop a conceptual and formal model and system prototype.

The main contributions of this thesis are: (1) proposal of …

Analysis Of Bitcoin Cryptocurrency And Its Mining Techniques, Suman Ghimire

UNLV Theses, Dissertations, Professional Papers, and Capstones

Bitcoin is a peer-to-peer digital, decentralized cryptocurrency created by an individual under pseudonym Satoshi Nakamoto. In fact, it is the first digital, decentralized currency. Several developers and organizations have explored the importance of digital cryptocurrency and the concept of the blockchain. Bitcoin is assumed to be one of the secure and comfortable payment methods that can be used in the upcoming days. The backbone of Bitcoin mining is the concept of the blockchain, which is assumed to beone of the ingenious invention of this century. The blockchain is the collection of blocks that are linked together in such a way …

A Novel Feature Maps Covariance Minimization Approach For Advancing Convolutional Neural Network Performance, Bikram Basnet

UNLV Theses, Dissertations, Professional Papers, and Capstones

We present a method for boosting the performance of the Convolutional Neural Network (CNN) by reducing the covariance between the feature maps of the convolutional layers.

In a CNN, the units of a hidden layer are segmented into the feature/activation maps. The units within a feature map share the weight matrix (filter), or in simple terms look for the same feature. A feature map is the output of one filter applied to the previous layer. CNN search for features such as straight lines, and as these features are spotted, they get reported to the feature map. During the learning process, …

Batching Problems With Constraints, Shradha Kapoor

UNLV Theses, Dissertations, Professional Papers, and Capstones

There is an increasing demand for a phenomenon that can manifest benefits gained from grouping similar jobs together and then scheduling these groups efficiently. Batching is the decision of whether or not to put the jobs into same group based on certain criteria. Batching plays a major role in job scheduling in Information Technology, traffic controlling systems, and goods-flow management. A list batching problem refers to batching a list of jobs in the same order or priority as given in the problem.

In this thesis we consider a one-machine list batching problem under weighted average completion. Given sequence of jobs …

Generating Kernel Aware Polygons, Bibek Subedi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Problems dealing with the generation of random polygons has important applications for evaluating the performance of algorithms on polygonal domain. We review existing algorithms for generating random polygons. We present an algorithm for generating polygons admitting visibility properties. In particular, we propose an algorithm for generating polygons admitting large size kernels. We also present experimental results on generating such polygons.