Computer Sciences Commons^™

Implementing Tontinecoin, Prashant Pardeshi

Master's Projects

One of the alternatives to proof-of-work (PoW) consensus protocols is proof-of- stake (PoS) protocols, which address its energy and cost related issues. But they suffer from the nothing-at-stake problem; validators (PoS miners) are bound to lose nothing if they support multiple blockchain forks. Tendermint, a PoS protocol, handles this problem by forcing validators to bond their stake and then seizing a cheater’s stake when caught signing multiple competing blocks. The seized stake is then evenly distributed amongst the rest of validators. However, as the number of validators increases, the benefit in finding a cheater compared to the cost of monitoring …

Usage And Effects Of Video Games In Education, Kevin Sagara

ART 108: Introduction to Games Studies

In modern society, the act of playing games entices certain prejudices towards those who decide to participate in such activities. Those prejudices normally revolve around negative stereotypes where games are meant for non-social adults and are seen as something akin to addiction if gone too far. But how much gaming is needed to be considered as an addiction and what actual effects does it have on mental growth, social capabilities and academics? As a community revolving around technology as much as we are, understanding how video games play a role in our social space is an important aspect to growth. …

Understanding Impact Of Twitter Feed On Bitcoin Price And Trading Patterns, Ashrit Deebadi

Master's Projects

‘‘Cryptocurrency trading was one of the most exciting jobs of 2017’’. ‘‘Bit- coin’’,‘‘Blockchain’’, ‘‘Bitcoin Trading’’ were the most searched words in Google during 2017. High return on investment has attracted many people towards this crypto market. Existing research has shown that the trading price is completely based on speculation, and its trading volume is highly impacted by news media. This paper discusses the existing work to evaluate the sentiment and price of the cryptocurrency, the issues with the current trading models. It builds possible solutions to understand better the semantic orientation of text by comparing different machine learning techniques and …

Land Registry On Blockchain, Mugdha Patil

Master's Projects

The commercial real estate market is a significant part of the global economy, currently dominated by a small set of firms and organizations that lack transparency. The process of property transfers also requires third party intervention which is expensive. In many countries, the process of title transfers is problematic. We are still in the initial steps of digitization, due to the improvement required in terms of use of technology to represent assets in digital forms. Increase in liquidity of investments and purchases, proper management, documentation as well as ease of access is the future of real estate. Blockchain technologies have …

Benchmarking Mongodb Multi-Document Transactions In A Sharded Cluster, Tushar Panpaliya

Master's Projects

Relational databases like Oracle, MySQL, and Microsoft SQL Server offer trans- action processing as an integral part of their design. These databases have been a primary choice among developers for business-critical workloads that need the highest form of consistency. On the other hand, the distributed nature of NoSQL databases makes them suitable for scenarios needing scalability, faster data access, and flexible schema design. Recent developments in the NoSQL database community show that NoSQL databases have started to incorporate transactions in their drivers to let users work on business-critical scenarios without compromising the power of distributed NoSQL features [1].

MongoDB is …

How Do Video Games Affect The Brain?, Kenneth Lee

ART 108: Introduction to Games Studies

Video games are a wildly popular past time in not only America, but other countries as well. Its popularity has done nothing but increase in an exponential fashion, engulfing more and more members of society in its reach; however, its gaining traction in combination with behavioral observations and frequent violent events compelled many to question its effects on those who play them. Some attribute tragedies like recent school shootings to the vast array of First Person Shooter(FPS) games, blaming it for engraining in adolescents’ and adults’ brains a sense of increased aggression and violence. Others praise educational games for stimulating …

Recreating The Virtual Bullets That Pierces Through The Soul Of Gaming: Video Games And Its Cultural Perception On America’S Violent Homefrontier, Brandon Palomino

ART 108: Introduction to Games Studies

For the past fifty years, video games have been around as a form of entertainment we consume on a daily basis. Unlike the books and movies seen in pop culture today, video games take us on virtual journeys where people participate in unique gameplay objectives based on player involvement. Because the idea of player engagement was rare at the time, it was inevitable that video games would revolutionize the way we view entertainment onwards. With video games becoming increasingly popular throughout the years, the debate of whether they are truly violent in nature has rubbed many people like myself the …

Improved User News Feed Customization For An Open Source Search Engine, Timothy Chow

Master's Projects

Yioop is an open source search engine project hosted on the site of the same name.It offers several features outside of searching, with one such feature being a news feed. The current news feed system aggregates articles from a curated list of news sites determined by the owner. However in its current state, the feed list is limited in size, constrained by the hardware that the aggregator is run on. The goal of my project was to overcome this limit by improving the current storage method used. The solution was derived by making use of IndexArchiveBundles and IndexShards, both of …

Developing A Mongodb Monitoring System Using Nosql Databases For Monitored Data Management, Anjitha Karattu Thodi

Master's Projects

MongoDB is a NoSQL database, specifically used to efficiently store and access a large quantity of unstructured data over a distributed cluster of nodes. As the number of nodes in the cluster increases, it becomes difficult to manually monitor different components of the database. This poses an interesting problem of monitoring the MongoDB database to view the state of the system at any point. Although a few proprietary monitoring tools exist to monitor MongoDB clusters, they are not freely available for use in academia. Therefore, the focus of this project is to create a monitoring system that is completely built …

Educational Games: A Basic Understanding And Effective Uses, Kaitlin Kirkman

ART 108: Introduction to Games Studies

Since the invention of video games, many people would perceive these digital games as just entertainment. Increasingly there has been a shift into taking video games and turning them into educational programs to help people learn. In this paper, I discuss the different forms of educational games which are serious games, it’s subcategory serious educational games, and educational simulations. I compare and contrast the differences as well as provide examples. I also discuss effective ways to use educational games so that they can be more understood as an effective method for teaching. The concepts I discuss about effective educational games …

How Do We Deal With Cheaters?, Colin Kyle

ART 108: Introduction to Games Studies

Ever since computer games have existed, the people who have played them have found ways to modify the game in order to change the way it plays or break the rules in the hopes of appearing to be better at the game. The concept of changing took on a whole new idea when online multiplayer games were created, now people not only cheated to beat the game, they also were able to cheat in order to compete better against other real people. Cheating in multiplayer online games has become very commonplace and most competitive games now have some sort of …

Need For Adoption Of Koha Integrated Library Management Software In Nigerian Academic Libraries, Isaac Echezonam Anyira

Library Philosophy and Practice (e-journal)

The main objective of this paper is to justify the need for Nigerian libraries which have not automated their functions to adopt of Koha ILMS as their automation software. The paper therefore examined the need for automation, the reasons for the choice of Koha ILMS and potential benefits accruable to the libraries, and functions that Koha can facilitate their perform in their libraries. The paper identified the need for automation to include need to handle information explosion, for effective management of library operations, to improve operation speed, resource sharing etc; the choice of Koha was informed by its features and …

Securing The Emerging Technologies Of Autonomous And Connected Vehicles, Shahab Tayeb, Matin Pirouz

Mineta Transportation Institute

The Internet of Vehicles (IoV) aims to establish a network of autonomous and connected vehicles that communicate with one another through facilitation led by road-side units (RSUs) and a central trust authority (TA). Messages must be efficiently and securely disseminated to conserve resources and preserve network security. Currently, research in this area lacks consensus about security schemes and methods of disseminating messages. Furthermore, a current deficiency of information regarding resource optimization prevents further efficient development of this network. This paper takes an interdisciplinary approach to these issues by merging both cybersecurity and data science to optimize and secure the network. …

Graph Classification With Kernels, Embeddings And Convolutional Neural Networks, Monica Golahalli Seenappa, Katerina Potika, Petros Potikas

Faculty Publications, Computer Science

In the graph classification problem, given is a family of graphs and a group of different categories, and we aim to classify all the graphs (of the family) into the given categories. Earlier approaches, such as graph kernels and graph embedding techniques have focused on extracting certain features by processing the entire graph. However, real world graphs are complex and noisy and these traditional approaches are computationally intensive. With the introduction of the deep learning framework, there have been numerous attempts to create more efficient classification approaches. We modify a kernel graph convolutional neural network approach, that extracts subgraphs (patches) …

Black Box Analysis Of Android Malware Detectors, Guruswamy Nellaivadivelu, Fabio Di Troia, Mark Stamp

Faculty Publications, Computer Science

If a malware detector relies heavily on a feature that is obfuscated in a given malware sample, then the detector will likely fail to correctly classify the malware. In this research, we obfuscate selected features of known Android malware samples and determine whether these obfuscated samples can still be reliably detected. Using this approach, we discover which features are most significant for various sets of Android malware detectors, in effect, performing a black box analysis of these detectors. We find that there is a surprisingly high degree of variability among the key features used by popular malware detectors.

Evolution Of Integration, Build, Test, And Release Engineering Into Devops And To Devsecops, Vishnu Pendyala

Faculty Research, Scholarly, and Creative Activity

Software engineering operations in large organizations are primarily comprised of integrating code from multiple branches, building, testing the build, and releasing it. Agile and related methodologies accelerated the software development activities. Realizing the importance of the development and operations teams working closely with each other, the set of practices that automated the engineering processes of software development evolved into DevOps, signifying the close collaboration of both development and operations teams. With the advent of cloud computing and the opening up of firewalls, the security aspects of software started moving into the applications leading to DevSecOps. This chapter traces the journey …

Continuous Deployment Transitions At Scale, Laurie Williams, Kent Beck, Jeffrey Creasey, Andrew Glover, James Holman, Jez Humble, David Mclaughlin, John Thomas Micco, Brendan Murphy, Jason A. Cox, Vishnu Pendyala, Steven Place, Zachary T. Pritchard, Chuck Rossi, Tony Savor, Michael Stumm, Chris Parnin

Faculty Research, Scholarly, and Creative Activity

Predictable, rapid, and data-driven feature rollout; lightning-fast; and automated fix deployment are some of the benefits most large software organizations worldwide are striving for. In the process, they are transitioning toward the use of continuous deployment practices. Continuous deployment enables companies to make hundreds or thousands of software changes to live computing infrastructure every day while maintaining service to millions of customers. Such ultra-fast changes create a new reality in software development. Over the past four years, the Continuous Deployment Summit, hosted at Facebook, Netflix, Google, and Twitter has been held. Representatives from companies like Cisco, Facebook, Google, IBM, Microsoft, …

Image-Based Malware Classification With Convolutional Neural Networks And Extreme Learning Machines, Mugdha Jain

Master's Projects

Research in the field of malware classification often relies on machine learning models that are trained on high level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this research, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or execution of code. Specifically, we visualize malware samples as images and employ image analysis techniques. In this context, we focus on two machine learning models, namely, Convolutional Neural Networks (CNN) and Extreme …

Hot Fusion Vs Cold Fusion For Malware Detection, Snehal Bichkar

Master's Projects

A fundamental problem in malware research consists of malware detection, that is, dis- tinguishing malware samples from benign samples. This problem becomes more challeng- ing when we consider multiple malware families. A typical approach to this multi-family detection problem is to train a machine learning model for each malware family and score each sample against all models. The resulting scores are then used for classification. We refer to this approach as “cold fusion,” since we combine previously-trained models—no retraining of these base models is required when additional malware families are considered. An alternative approach is to train a single model …

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg

Master's Projects

Inadequate drug experimental data and the use of unlicensed drugs may cause adverse drug reactions, especially in pediatric populations. Every year the U.S. Food and Drug Administration approves human prescription drugs for marketing. The labels associated with these drugs include information about clinical trials and drug response in pediatric population. In order for doctors to make an informed decision about the safety and effectiveness of these drugs for children, there is a need to analyze complex and often unstructured drug labels. In this work, first, an exploratory analysis of drug labels using a Natural Language Processing pipeline is performed. Second, …

Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten

Master's Projects

Wildfire damage assessments are important information for first responders, govern- ment agencies, and insurance companies to estimate the cost of damages and to help provide relief to those affected by a wildfire. With the help of Earth Observation satellite technology, determining the burn area extent of a fire can be done with traditional remote sensing methods like Normalized Burn Ratio. Using Very High Resolution satellites can help give even more accurate damage assessments but will come with some tradeoffs; these satellites can provide higher spatial and temporal resolution at the expense of better spectral resolution. As a wildfire burn area …

Toward Early Detection Of Pancreatic Cancer: An Evidence-Based Approach, Omid Sharagi

Master's Projects

This study observes how an evidential reasoning approach can be used as a diagnostic tool for early detection of pancreatic cancer. The evidential reasoning model combines the output of a linear Support Vector Classifier (SVC) with factors such as smoking history, health history, biopsy location, NGS technology used, and more to predict the likelihood of the disease. The SVC was trained using genomic data of pancreatic cancer patients derived from the National Cancer Institute (NIH) Genomic Data Commons (GDC). To test the evidential reasoning model, a variety of synthetic data was compiled to test the impact of combinations of different …

Image-Based Localization Of User-Interfaces, Riti Gupta

Master's Projects

Image localization corresponds to translating the text present in the images from one language to other language. The aim of the project is to develop a methodology to translate the text in image captions from English to Hindi by taking context of the images into account. A lot of work has been done in this field [22], but our aim was to explore if the accuracy can be further improved by consideration of the additional information imparted by the images apart from the text. We have explored Deep Learning using neural networks for this project. In particular, Recurrent Neural Networks …

A Hybrid Approach For Multi-Document Text Summarization, Rashmi Varma

Master's Projects

Text summarization has been a long studied topic in the field of natural language processing. There have been various approaches for both extractive text summarization as well as abstractive text summarization. Summarizing texts for a single document is a methodical task. But summarizing multiple documents poses as a greater challenge. This thesis explores the application of Latent Semantic Analysis, Text-Rank, Lex-Rank and Reduction algorithms for single document text summarization and compares it with the proposed approach of creating a hybrid system combining each of the above algorithms, individually, with Restricted Boltzmann Machines for multi-document text summarization and analyzing how all …

3d Shape Prediction On Convolutional Deep Belief Networks, Gregory Y. Enriquez

Master's Projects

The field of image recognition software has grown immensely in recent years with the emergence of new deep learning techniques. Deep belief networks inspired by Hinton [11] were one of the earliest methodologies of deep learning in the late 2000s. More recently, convolutional neural networks have been used in deep learning techniques, architecture, and software to identify patterns in imagery in order to make predictions such as classification, image segmentation, etc. Traditional two-dimensional, or 2D, images stored as picture files, typically contain red, green, and blue color data for each individual pixel in the picture. However, more recent commercial 2.5D …

Music Retrieval System Using Query-By-Humming, Parth Patel

Master's Projects

Music Information Retrieval (MIR) is a particular research area of great interest because there are various strategies to retrieve music. To retrieve music, it is important to find a similarity between the input query and the matching music. Several solutions have been proposed that are currently being used in the application domain(s) such as Query- by-Example (QBE) which takes a sample of an audio recording playing in the background and retrieves the result. However, there is no efficient approach to solve this problem in a Query-by-Humming (QBH) application. In a Query-by-Humming application, the aim is to retrieve music that is …

Blockchain In Libraries, Michael Meth

Faculty Research, Scholarly, and Creative Activity

This issue of Library Technology Reports (vol. 55, no. 8), “Blockchain in Libraries,” examines the application of blockchain in libraries. Blockchain technology has the ability to transform how libraries provide services and organize information. To date, most of these applications are still in the conceptual stage. However, sooner or later, development and implementation will follow. This report is intended to provide a primer on the technology and some thought starters. In chapter 2, the concept of blockchain is explained. Chapter 3 provides eight thought and conversation starters that look at how blockchain could be applied in libraries. Chapter 4 looks …

Global Research Trend On Cyber Security: A Scientometric Analysis, Somesh Rai, Kunwar Singh Dr, Akhilesh Kumar Varma

Library Philosophy and Practice (e-journal)

Scientometrics is a quantitative analysis of scholarly literature related to a particular subject or area (well defined by some limits, scope and coverage), which helps to understand different aspects about the scholarly literature’s growth in various dimensions of knowledge. Similarly, this study is a quantitative analysis of the Global research trends in cyber security. Some works related to scientometrics of ‘deception, counter-deception in cyberspace’ had been published in 2011, but we have focused on ‘cyber security’ as the topic of research. For analysis we have utilised the published data available in Scopus database, which is directly related to ‘cyber security’. …

Digital Addiction: A Conceptual Overview, Amarjit Kumar Singh, Pawan Kumar Singh

Library Philosophy and Practice (e-journal)

Abstract

Digital addiction referred to an impulse control disorder that involves the obsessive use of digital devices, digital technologies, and digital platforms, i.e. internet, video game, online platforms, mobile devices, digital gadgets, and social network platform. It is an emerging domain of Cyberpsychology (Singh, Amarjit Kumar and Pawan Kumar Singh; 2019), which explore a problematic usage of digital media, device, and platforms by being obsessive and excessive. This article analyses, reviewed the current research, and established a conceptual overview on the digital addiction. The research literature on digital addiction has proliferated. However, we tried to categories the digital addiction, according …