Computer Sciences Commons^™

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.

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 …

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 …

Performance Evaluation Of Ipv6 And The Role Of Ipsec In Encrypting Data, Yasser Alhoaimel

Theses and Dissertations

Objectives This research concentrates on IPv6 security and its technical aspects. The performance of IPv6 has carefully evaluated, tested, and analyzed. Beyond comparing IPv4 and IPv6 in terms of security, this thesis does not focus on IPv4; preferably, the priority is to concentrate on the security basis of IPv6. The comparison was based on previous researches that can be found in the references section. Furthermore, the role of IPsec in encrypting data is another main objective of this research and is specifically explained. This research demonstrates how IPsec is used in encrypting data so that IPv6 extension headers will be …

First Hop Redundancy Protocols (Fhrp), Raghad Faisal Alshehri

Theses and Dissertations

When a network is designed, the most important thing that is always kept in mind is the factor of availability. However, a lot of researchers are still working on managing and implementing more dependable networks are resilient to severe failovers and can cope up under immense traffic loads. With that being said, a lot of questions still need to be answered according to the financial and implementation point of view. Correspondingly, the current study aims at answering all these questions and providing with the best optimal solutions to deploy dependable networks primary aimed towards hardening and enforcing various techniques and …

Incentivizing Cyber Security Investment In The Power Sector Using An Extended Cyber Insurance Framework, Jack P. Rosson, Mason J. Rice, Juan Lopez Jr., R. David Fass

Faculty Publications

Collaboration between the DHS Cybersecurity and Infrastructure Security Agency (CISA) and public-sector partners has revealed that a dearth of cyber-incident data combined with the unpredictability of cyber attacks have contributed to a shortfall in first-party cyber insurance protection in the critical infrastructure community. This research explores the foundations of insurance theory and adopts behavioral manipulation methods to incentivize cyber-security investment. We validate the model by applying power industry performance data from 2013-2015 to assess risk facing the industry. Results show that the model can successfully discriminate between individual power companies as well as geographic regions on the basis of risk …

B-Quest: Experience Your Gift, Mowhile Geek, Ailem Garcia, Ronald Torres, Juan Martin, Johan Quiroz, Robert Flores, Maria F. Torres

ICT

Birthdays are special events for everybody and for most of people it is an important date in their life, it is the day where they feel honoured and appreciated by their friends, family, acquaintances…

The main reason for creating an application dedicated to make from the Birthday event a fun game, is generated after bearing in mind how the tradition of giving presents to honour a birthday person is something important in many people's lives.

Another aspect considered during the conception of this application is how fast the world is moving, and how sometimes it could be beneficial to have …

Ecological Expenses Tracker – Ecoext, Asmer Bracho, Carina Lins, Eduardo Firino, Gabriel Oliveira, Miguelantonio Guerra

ICT

When talking about consuming and making purchases, it is always expected that there will also be a transaction in this act of trading and in this transaction a paper receipt is included, where the user gets a description of what they have spent and how much was spent.

The current dissertation demonstrates an application solution to reduce the cost of paper receipts that have been used in a daily basis stage and to track the value of expenses and incomes of a user who is consuming and spending their money when purchasing goods.

Additionally, we implement the Ecological Expense Tracker …

Supervised Machine Learning Models For Fake News Detection, Gofaas Group, Andrea Lopez, Adelo Vieira, Zafar Ahsan, Farooq Saqib, Shirley Marinho

ICT

Fake news or the distribution of disinformation has become one of the most challenging issues in society. News and information are churned out across online websites and platforms in real-time, with little or no way for the viewing public to determine what is real or manufactured. But an awareness of what we are consuming online is becoming apparent and efforts are underway to explore how we separate fake content from genuine and truthful information.

The most challenging part of fake news is determining how to spot it. In technology, there are ways to help us do this. Supervised machine learning …

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 …

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 …

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 …

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.

Analyzing And Estimating Cyberattack Trends By Performing Data Mining On A Cybersecurity Data Set, Chan Young Koh

Honors Program Theses and Projects

More than five billion personal information has been compromised over the past eight years through data breaches from notable companies, and the damage related to cybercrime is expected to reach six trillion USD annually by the year of 2021. Interestingly, recent cyberattacks were aimed specifically at credit agencies and companies that hold credit information of their customers and employees. The question is: “Why is it difficult to protect against or evade cyberattacks even for these prestigious companies?”. The purpose of this research is to bring the notion of notorious, rapidly-multiplying cyberthreats. Hence, the research focuses on analyzing cyberattack techniques and …

The Education Of Digital Design For Children, Neha Kharidi

Honors Program Theses and Projects

Digital design is a set of rules stating how computer software and hardware are joined together and interact to make a computer work. This is an important area of study that should be made more accessible to children, particularly of the middle school age. Although middle schools do provide access to computer science software, access to the hardware aspect is nonexistent. In this project, I proposed to create a mock up for an android app that aims to teach children beginner concepts in digital design. It is my goal to one day create an app in order to bring these …

Urban Underground Infrastructure Monitoring Iot: The Path Loss Analysis, Abdul Salam, Syed Shah

Faculty Publications

The extra quantities of wastewater entering the pipes can cause backups that result in sanitary sewer overflows. Urban underground infrastructure monitoring is important for controlling the flow of extraneous water into the pipelines. By combining the wireless underground communications and sensor solutions, the urban underground IoT applications such as real time wastewater and storm water overflow monitoring can be developed. In this paper, the path loss analysis of wireless underground communications in urban underground IoT for wastewater monitoring has been presented. It has been shown that the communication range of up to 4 kilometers can be achieved from an underground …

A Communication Architecture For Crowd Management In Emergency And Disruptive Scenarios, Alfredo J. Perez, Sherali Zeadally

Computer Science Faculty Publications

Crowd management aims to develop support infrastructures that can effectively manage crowds at any time. In emergency and disruptive scenarios this concept can minimize the risk to human life and to the infrastructure. We propose the Communication Architecture for Crowd Management (CACROM), which can support crowd management under emergency and disruptive scenarios. We identify, describe, and discuss the various components of the proposed architecture, and we briefly discuss open challenges in the design of crowd management systems for emergency and disruptive scenarios.

Efficient, Effective, And Realistic Website Fingerprinting Mitigation, Weiqi Cui, Jiangmin Yu, Yanmin Gong, David Chan-Tin

Computer Science: Faculty Publications and Other Works

Website fingerprinting attacks have been shown to be able to predict the website visited even if the network connection is encrypted and anonymized. These attacks have achieved accuracies as high as 92%. Mitigations to these attacks are using cover/decoy network traffic to add noise, padding to ensure all the network packets are the same size, and introducing network delays to confuse an adversary. Although these mitigations have been shown to be effective, reducing the accuracy to 10%, the overhead is high. The latency overhead is above 100% and the bandwidth overhead is at least 30%. We introduce a new realistic …

Community Detection Via Neighborhood Overlap And Spanning Tree Computations, Ketki Kulkarni, Aris Pagourtzis, Katerina Potika, Petros Potikas, Dora Souliou

Faculty Publications, Computer Science

Most social networks of today are populated with several millions of active users, while the most popular of them accommodate way more than one billion. Analyzing such huge complex networks has become particularly demanding in computational terms. A task of paramount importance for understanding the structure of social networks as well as of many other real-world systems is to identify communities, that is, sets of nodes that are more densely connected to each other than to other nodes of the network. In this paper we propose two algorithms for community detection in networks, by employing the neighborhood overlap metric …

Yelp Improved : Aggregating Restaurant Reviews, Kunal Sonar

Creative Activity and Research Day - CARD

In the near future, online food delivery service companies would occupy a big market share in the food industry. This project aims to provide factual information from customer reviews as part of the numerous innovations in place to drive business and demands. Natural Language Processing is used to provide a comprehensive view of individual restaurants using technologies like NLTK, SpaCy, Gensim and Sklearn. Data of one million Las Vegas restaurant customer reviews is curated from the Yelp Dataset Challenge. Reviews are pre-processed, split into chunks of phrases and mapped to attributes like food, budget, service etc. These attributes are derived …

Deep Neural Network Architectures For Music Genre Classification, Kai Middlebrook, Shyam Sudhakaran, Kunal Sonar, David Guy Brizan

Creative Activity and Research Day - CARD

With the recent advancements in technology, many tasks in fields such as computer vision, natural language processing, and signal processing have been solved using deep learning architectures. In the audio domain, these architectures have been used to learn musical features of songs to predict: moods, genres, and instruments. In the case of genre classification, deep learning models were applied to popular datasets--which are explicitly chosen to represent their genres--and achieved state-of-the-art results. However, these results have not been reproduced on less refined datasets. To this end, we introduce an un-curated dataset which contains genre labels and 30-second audio previews for …

Review Of The Augmented Reality Systems For Shoulder Rehabilitation, Giuseppe Turini, Sara Condino, Rosanna Viglialoro, Marina Carbone, Marco Gesi, Vincenzo Ferrari

Computer Science Publications

Literature shows an increasing interest for the development of augmented reality (AR) applications in several fields, including rehabilitation. Current studies show the need for new rehabilitation tools for upper extremity, since traditional interventions are less effective than in other body regions. This review aims at: Studying to what extent AR applications are used in shoulder rehabilitation, examining wearable/non-wearable technologies employed, and investigating the evidence supporting AR effectiveness. Nine AR systems were identified and analyzed in terms of: Tracking methods, visualization technologies, integrated feedback, rehabilitation setting, and clinical evaluation. Our findings show that all these systems utilize vision-based registration, mainly with …

Securing Our Future Homes: Smart Home Security Issues And Solutions, Nicholas Romano

Senior Honors Theses

The Internet of Things, commonly known as IoT, is a new technology transforming businesses, individuals’ daily lives and the operation of entire countries. With more and more devices becoming equipped with IoT technology, smart homes are becoming increasingly popular. The components that make up a smart home are at risk for different types of attacks; therefore, security engineers are developing solutions to current problems and are predicting future types of attacks. This paper will analyze IoT smart home components, explain current security risks, and suggest possible solutions. According to “What is a Smart Home” (n.d.), a smart home is a …

Examining The Limits Of Predictability Of Human Mobility, Vaibhav Klukarni, Abhijit Mahalunkar, Benoit Garbinato, John D. Kelleher

Articles

We challenge the upper bound of human-mobility predictability that is widely used to corroborate the accuracy of mobility prediction models. We observe that extensions of recurrent-neural network architectures achieve significantly higher prediction accuracy, surpassing this upper bound. Given this discrepancy, the central objective of our work is to show that the methodology behind the estimation of the predictability upper bound is erroneous and identify the reasons behind this discrepancy. In order to explain this anomaly, we shed light on several underlying assumptions that have contributed to this bias. In particular, we highlight the consequences of the assumed Markovian nature of …

Building Consumer Trust In The Cloud: An Experimental Analysis Of The Cloud Trust Label Approach, Lisa Van Der Werff, Grace Fox, Ieva Masevic, Vincent C. Emeakaroha, John P. Morrison, Theo Lynn

Department of Computer Science Publications

The lack of transparency surrounding cloud service provision makes it difficult for consumers to make knowledge based purchasing decisions. As a result, consumer trust has become a major impediment to cloud computing adoption. Cloud Trust Labels represent a means of communicating relevant service and security information to potential customers on the cloud service provided, thereby facilitating informed decision making. This research investigates the potential of a Cloud Trust Label system to overcome the trust barrier. Specifically, it examines the impact of a Cloud Trust Label on consumer perceptions of a service and cloud service provider trustworthiness and trust in the …

2019 Petersheim Academic Exposition Schedule Of Events, Seton Hall University

Petersheim Academic Exposition

2019 Petersheim Academic Exposition

Real Time Facial Expression Recognition And Eye Gaze Estimation System, Suzan Anwar

Theses and Dissertations

The following dissertation proposes a gaze estimation and an emotion recognition system, as this system proposed is a face analysis package which not only contains face detection, but also eye tracking, emotion recognition, eye detection, as well as eye estimation. The system is comprised of a facial emotion recognition that can recognize the seven emotions including happiness, anger, sadness, neutral, surprise, disgust, and fear. This part of the system has an implemented Active Shape Model (ASM) tracker, as it is the emotion recognition part of the system, which through the webcam input can track 116 facial landmarks points to obtain …

A Tla+ Specification For A Set Associative Write Back Caching System, Sean Orme

Theses and Dissertations

This experiment attempted to push the limits of the TLA+ specification language and TLC model checker by specifying and checking a modern, set associative, write back caching system. This was done through the writing of a specification that focused on the operation of a single level, multi-CPU cache. While a fully associative and direct mapped version of the specification eventually model checked in a reasonable amount of time, the added complexity of a fully associative cache proved too much for an 80 processor high performance machine with almost 1TB of hard drive space. The use of a simulation mode allowed …

Using Neural Networks To Classify Pdes, Julia Balukonis, Sabrina Fuller, Haley Rosso

Mathematics & Computer Science Student Scholarship

Major: Mathematics
Minor: Computer Science and Film

Faculty Mentor: Dr. Lynette Boos, Mathematics and Computer Science

We designed two neural networks that can learn how to classify three different types of partial differential equations (PDEs). Our data consists of numerical solutions to three categories of PDEs: Burger’s, Diffusion, and Transport equations. Using TensorFlow and the Keras library, we performed two tasks – the first a binary classification of Burger’s and Diffusion equation data, and the second a multi-label classification incorporating the Transport Equations as well. Our binary classification network requires vector labels to perform efficiently. Furthermore, our tertiary classification network …