Matching Items (20)
Description
The objective of this paper is to provide an educational diagnostic into the technology of blockchain and its application for the supply chain. Education on the topic is important to prevent misinformation on the capabilities of blockchain. Blockchain as a new technology can be confusing to grasp given the wide possibilities it can provide. This can convolute the topic by being too broad when defined. Instead, the focus will be maintained on explaining the technical details about how and why this technology works in improving the supply chain. The scope of explanation will not be limited to the solutions, but will also detail current problems. Both public and private blockchain networks will be explained and solutions they provide in supply chains. In addition, other non-blockchain systems will be described that provide important pieces in supply chain operations that blockchain cannot provide. Blockchain when applied to the supply chain provides improved consumer transparency, management of resources, logistics, trade finance, and liquidity.
ContributorsKrukar, Joel Michael (Author) / Oke, Adegoke (Thesis director) / Duarte, Brett (Committee member) / Hahn, Richard (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Cryptocurrencies are notorious for its volatility. But with its incredible rise in price, Bitcoin keep being on the top among the trending topics on social media. Although doubts continue to rise with price, Bloomberg even make critics on Bitcoin as ‘the biggest bubble in the history’, some investors still hold strong enthusiasm and confidence towards Bitcoin. As contradicting opinions increase, it is worthy to dive into discussions on social media and use a scientific method to evaluate public’s non-negligible role in crypto price fluctuation.
Sentiment analysis, which is a notably method in text mining, can be used to extract the sentiment from people’s opinion. It then provides us with valuable perception on a topic from the public’s attitude, which create more opportunities for deeper analysis and prediction.
The thesis aims to investigate public’s sentiment towards Bitcoin through analyzing 10 million Bitcoin related tweets and assigning sentiment points on tweets, then using sentiment fluctuation as a factor to predict future crypto fluctuation. Price prediction is achieved by using a machine learning model called Recurrent Neural Network which automatically learns the pattern and generate following results with memory. The analysis revels slight connection between sentiment and crypto currency and the Neural Network model showed a strong connection between sentiment score and future price prediction.
Sentiment analysis, which is a notably method in text mining, can be used to extract the sentiment from people’s opinion. It then provides us with valuable perception on a topic from the public’s attitude, which create more opportunities for deeper analysis and prediction.
The thesis aims to investigate public’s sentiment towards Bitcoin through analyzing 10 million Bitcoin related tweets and assigning sentiment points on tweets, then using sentiment fluctuation as a factor to predict future crypto fluctuation. Price prediction is achieved by using a machine learning model called Recurrent Neural Network which automatically learns the pattern and generate following results with memory. The analysis revels slight connection between sentiment and crypto currency and the Neural Network model showed a strong connection between sentiment score and future price prediction.
ContributorsZhu, Xiaoyu (Author) / Benjamin, Victor (Thesis director) / Qinglai, He (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Third-party mixers are used to heighten the anonymity of Bitcoin users. The mixing techniques implemented by these tools are often untraceable on the blockchain, making them appealing to money launderers. This research aims to analyze mixers currently available on the deep web. In addition, an in-depth case study is done on an open-source bitcoin mixer known as Penguin Mixer. A local version of Penguin Mixer was used to visualize mixer behavior under specific scenarios. This study could lead to the identification of vulnerabilities in mixing tools and detection of these tools on the blockchain.
ContributorsPakki, Jaswant (Author) / Doupe, Adam (Thesis director) / Shoshitaishvili, Yan (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using rule induction methods. The dataset is cleaned by removing entries with missing data. The new column is created to measure price difference to create a more accurate analysis on the change in price. Eight relevant variables are selected using cross validation: the total number of bitcoins, the total size of the blockchains, the hash rate, mining difficulty, revenue from mining, transaction fees, the cost of transactions and the estimated transaction volume. The in-sample data is modeled using a simple tree fit, first with one variable and then with eight. Using all eight variables, the in-sample model and data have a correlation of 0.6822657. The in-sample model is improved by first applying bootstrap aggregation (also known as bagging) to fit 400 decision trees to the in-sample data using one variable. Then the random forests technique is applied to the data using all eight variables. This results in a correlation between the model and data of 9.9443413. The random forests technique is then applied to an Ethereum dataset, resulting in a correlation of 9.6904798. Finally, an out-of-sample model is created for Bitcoin and Ethereum using random forests, with a benchmark correlation of 0.03 for financial data. The correlation between the training model and the testing data for Bitcoin was 0.06957639, while for Ethereum the correlation was -0.171125. In conclusion, it is confirmed that cryptocurrencies can have accurate in-sample models by applying the random forests method to a dataset. However, out-of-sample modeling is more difficult, but in some cases better than typical forms of financial data. It should also be noted that cryptocurrency data has similar properties to other related financial datasets, realizing future potential for system modeling for cryptocurrency within the financial world.
ContributorsBrowning, Jacob Christian (Author) / Meuth, Ryan (Thesis director) / Jones, Donald (Committee member) / McCulloch, Robert (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Ethereum smart contracts are susceptible not only to those vulnerabilities common to all software development domains, but also to those arising from the peculiar execution model of the Ethereum Virtual Machine. One of these vulnerabilities, a susceptibility to re-entrancy attacks, has been at the center of several high-profile contract exploits. Currently, there exist many tools to detect these vulnerabilties, as well as languages which preempt the creation of contracts exhibiting these issues, but no mechanism to address them in an automated fashion. One possible approach to filling this gap is direct patching of source files. The process of applying these patches to contracts written in Solidity, the primary Ethereum contract language, is discussed. Toward this end, a survey of deployed contracts is conducted, focusing on prevalence of language features and compiler versions. A heuristic approach to mitigating a particular class of re-entrancy vulnerability is developed, implemented as the SolPatch tool, and examined with respect to its limitations. As a proof of concept and illustrative example, a simplified version of the contract featured in a high-profile exploit is patched in this manner.
ContributorsLehman, Maxfield Chance Christian (Author) / Bazzi, Rida (Thesis director) / Doupe, Adam (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
This thesis will look into the cryptocurrency Basic Attention Token (BAT) and the browser they partnered with, Brave, to innovate the way that digital marketing dollars are spent. They believe that their browser and cryptocurrency will be able to remove inefficiencies the digital marketing world is full of, and also giving some power to users with their data. The vision of this team is to create a marketing agreement with users that makes it so the marketing space they offer is a verified real individual without compromising this person's user data to large firms and keeps all information stored locally. We will cover the international landscape, BAT and Brave from a branding perspective, and get into some research into interest in adoption.
ContributorsPenny, Troy Lyall (Author) / Kristofferson, Kirk (Thesis director) / Goegan, Brian (Committee member) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Unmanned aerial vehicles have received increased attention in the last decade due to their versatility, as well as the availability of inexpensive sensors (e.g. GPS, IMU) for their navigation and control. Multirotor vehicles, specifically quadrotors, have formed a fast growing field in robotics, with the range of applications spanning from surveil- lance and reconnaissance to agriculture and large area mapping. Although in most applications single quadrotors are used, there is an increasing interest in architectures controlling multiple quadrotors executing a collaborative task. This thesis introduces a new concept of control involving more than one quadrotors, according to which two quadrotors can be physically coupled in mid-flight. This concept equips the quadro- tors with new capabilities, e.g. increased payload or pursuit and capturing of other quadrotors. A comprehensive simulation of the approach is built to simulate coupled quadrotors. The dynamics and modeling of the coupled system is presented together with a discussion regarding the coupling mechanism, impact modeling and additional considerations that have been investigated. Simulation results are presented for cases of static coupling as well as enemy quadrotor pursuit and capture, together with an analysis of control methodology and gain tuning. Practical implementations are introduced as results show the feasibility of this design.
ContributorsLarsson, Daniel (Author) / Artemiadis, Panagiotis (Thesis advisor) / Marvi, Hamidreza (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2016
Description
In recent years, cryptocurrency has become an increasingly popular new alternative investment
among retail traders. Additionally, this attention has grown beyond retail traders and now presents itself as a prominent conversation among media, institutions, and academia. The purpose of this project is to explore the unappreciated aspect of cryptocurrency use such that it is capable of functioning in the foreign exchange markets (FOREX or FX markets). The inherent idea behind cryptocurrency is that it is accessible worldwide, protected, and verifiable via blockchain, holding the same monetary value regardless of location and minimizing the cost of cross-border payments by eliminating financial intermediaries in the traditional FOREX currency markets. Moreover, the goal of cryptocurrency intends to operate at faster rates than current traditional finance intermediaries. The article incorporates frequently debated aspects of cryptocurrency to identify the advantages and limitations of both cryptocurrency and traditional monetary systems. Thus, this research reveals the necessary fundamentals needed in cryptocurrency for the evolution in traditional financial structures and for widespread adoption to occur.
ContributorsKrygier, Jakob (Author) / Van Orden, Joseph (Thesis director) / Hill, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Department of Economics (Contributor)
Created2023-05
Description
This thesis serves as an experimental investigation into the potential of machine learning through attempting to predict the future price of a cryptocurrency. Through the use of web scraping, short interval data was collected on both Bitcoin and Dogecoin. Dogecoin was the dataset that was eventually used in this thesis due to its relative stability compared to Bitcoin. At the time of the data collection, Bitcoin became a much more frequent topic in the media and had more significant fluctuations due to it. The data was processed into consistent three separate, consistent timesteps, and used to generate predictive models. The models were able to accurately predict test data given all the preceding test data but were unable to autoregressively predict future data given only the first set of test data points. Ultimately, this project helps illustrate the complexities of extended future price prediction when using simple models like linear regression.
ContributorsMurwin, Andrew (Author) / Bryan, Chris (Thesis director) / Ghayekhloo, Samira (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description基于人类加密学，第一例加密数字货币“比特币”的概念最初由中本聪在2008年11月1日提出，于2009年1月3日正式诞生。经过14年的历程，整个加密货币世界得到了快速的发展、加密生态得到了极大的繁荣、有利的推动了整个人类数字文明的衍进和发展。截至2021年5月，加密货币总市值达到2.4万亿美元的峰值，加密数字货币的数量已经近6000种。单只市值超过10亿美元的数字货币有77个，单只市值超过100万美元的数字货币有1600个。虽然对加密货币究竟应该定义为商品还是证券仍存在巨大争议，但毫无疑问的是加密货币已经成为瞩目且不可忽视的一类投资性资产。
本论文试图从金融资产分析框架和行为金融学角度出发，探究比特币和以太币这两只最具有代表性的数字货币的价格影响因素。本论文分别从宏观和微观两个维度探究两个维度下的因素对价格波动的影响。从宏观视角出发探究问题一：通缩发行机制、联储货币政策、以及中美监管政策对比特币及以太币价格的影响。从微观视角出发探究问题二：加密货币的应用、市场行为金融的视角看其比特币及以太币价格波动的影响。
本论文通过应用定性和定量相结合的研究分析方法，运用一系列时间序列回归模型、相关性分析、区间统计分析、经典行为金融学动能效应模型等工具对上述两个维度的问题进行深度研究和论证，发现各个角度中所涵盖的因素对以比特币和以太币为代表的数字货币价格的影响，同时涵盖了影响的方向和影响的层度，并构建多因素定价模型。
ContributorsLiu, Hongjie (Author) / Zhang, Zhongju (Thesis advisor) / Hu, Jie (Thesis advisor) / Zheng, Zhiqiang (Committee member) / Arizona State University (Publisher)
Created2023