Matching Items (16)
Filtering by
- All Subjects: Python
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Description
After freelancing on my own for the past year and a half, I have realized that one of the biggest obstacles to college entrepreneurs is a fear or apprehension to sales. As a computer science major trying to sell my services, I discovered very quickly that I had not been prepared for the difficulty of learning sales. Sales get a bad rap and very often is the last thing that young entrepreneurs want to try, but the reality is that sales is oxygen to a company and a required skill for an entrepreneur. Due to this, I compiled all of my knowledge into an e-book for young entrepreneurs starting out to learn how to open up a conversation with a prospect all the way to closing them on the phone. Instead of starting from scratch like I did, college entrepreneurs can learn the bare basics of selling their own services, even if they are terrified of sales and what it entails. In this e-book, there are tips that I have learned to deal with my anxiety about sales such as taking the pressure off of yourself and prioritizing listening more than pitching. Instead of trying to teach sales expecting people to be natural sales people, this e-book takes the approach of helping entrepreneurs that are terrified of sales and show them how they can cope with this fear and still close a client. In the future, I hope young entrepreneurs will have access to more resources that handle this fear and make it much easier for them to learn it by themselves. This e-book is the first step.
ContributorsMead, Kevin Tyler (Author) / Sebold, Brent (Thesis director) / Kruse, Gabriel (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The purpose of this project was to program a Raspberry Pi to be able to play music from both local storage on the Pi and from internet radio stations such as Pandora. The Pi also needs to be able to play various types of file formats, such as mp3 and FLAC. Finally, the project is also to be driven by a mobile app running on a smartphone or tablet. To achieve this, a client server design was employed where the Raspberry Pi acts as the server and the mobile app is the client. The server functionality was achieved using a Python script that listens on a socket and calls various executables that handle the different formats of music being played. The client functionality was achieved by programming an Android app in Java that sends encoded commands to the server, which the server decodes and begins playing the music that command dictates. The designs for both the client and server are easily extensible and allow for any future modifications to the project to be easily made.
ContributorsStorto, Michael Olson (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Agent Based modeling has been used in computer science to simulate complex phenomena. The introduction of Agent Based Models into the field of economics (Agent Based Computational Economics ACE) is not new, however work on making model environments simpler to design for individuals without a background in computer science or computer engineering is a constantly evolving topic. The issue is a trade off of how much is handled by the framework and how much control the modeler has, as well as what tools exist to allow the user to develop insights from the behavior of the model. The solutions looked at in this thesis are the construction of a simplified grammar for model construction, the design of an economic based library to assist in ACE modeling, and examples of how to construct interactive models.
ContributorsAnderson, Brandon David (Author) / Bazzi, Rida (Thesis director) / Kuminoff, Nicolai (Committee member) / Roberts, Nancy (Committee member) / Computer Science and Engineering Program (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The main objective of this thesis is to describe and analyze Clippr, an ASU startup founded by four students: Adam Lynch, Eric Gottfried, Ty Sivley, and Thomas Carpaneto. This paper will describe the formation of Clippr as a business, analyze the work and reasoning for dissolving the business, and suggest three pivots that could increase the chances of success for the future of Clippr. These three pivots are: mini salons, a concierge service, and an online resource. The idea for Clippr came from Sam, the team's friend's experience within the cosmetology industry. Sam graduated from cosmetology school in Phoenix and started his career as an assistant, which is the most common entry level position within the industry. Assistants do not get to work with clients and primarily do chores around the salon so he was not gaining any valuable experience. Eventually Sam found a position at a salon in Flagstaff. Unfortunately, he was not scheduled enough hours to pay his rent which forced him to travel back to Phoenix to cut his friend's and family's hair to make ends meet. Sam is not alone experiencing these issues within the industry, they are a common trend throughout the cosmetology field. It was found that there is a clear problem that affects every stylist: they struggle to reap the benefits of their self-employment. Most stylists become independent contractors where they are constrained by the salon's management. They are generally forced to work during the salon's hours of operations, promote specific products, adhere to a dress code, and forfeit their clients information. On the other hand, freelance workers outside of salons do enjoy greater freedoms within their work but with significant hurdles to overcome. They have a much harder time building a client base and face prohibitive start-up costs that make it harder to break into the industry.
ContributorsGottfried, Eric (Co-author) / Lynch, Adam (Co-author) / Sebold, Brent (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Machine learning has a near infinite number of applications, of which the potential has yet to have been fully harnessed and realized. This thesis will outline two departments that machine learning can be utilized in, and demonstrate the execution of one methodology in each department. The first department that will be described is self-play in video games, where a neural model will be researched and described that will teach a computer to complete a level of Super Mario World (1990) on its own. The neural model in question was inspired by the academic paper “Evolving Neural Networks through Augmenting Topologies”, which was written by Kenneth O. Stanley and Risto Miikkulainen of University of Texas at Austin. The model that will actually be described is from YouTuber SethBling of the California Institute of Technology. The second department that will be described is cybersecurity, where an algorithm is described from the academic paper “Process Based Volatile Memory Forensics for Ransomware Detection”, written by Asad Arfeen, Muhammad Asim Khan, Obad Zafar, and Usama Ahsan. This algorithm utilizes Python and the Volatility framework to detect malicious software in an infected system.
ContributorsBallecer, Joshua (Author) / Yang, Yezhou (Thesis director) / Luo, Yiran (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
When creating computer vision applications, it is important to have a clear image of what is represented such that further processing has the best representation of the underlying data. A common factor that impacts image quality is blur, caused either by an intrinsic property of the camera lens or by introducing motion while the camera’s shutter is capturing an image. Possible solutions for reducing the impact of blur include cameras with faster shutter speeds or higher resolutions; however, both of these solutions require utilizing more expensive equipment, which is infeasible for instances where images are already captured. This thesis discusses an iterative solution for deblurring an image using an alternating minimization technique through regularization and PSF reconstruction. The alternating minimizer is then used to deblur a sample image of a pumpkin field to demonstrate its capabilities.
ContributorsSmith, Zachary (Author) / Espanol, Malena (Thesis director) / Ozcan, Burcin (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2023-05
Description
In this work, we explore the potential for realistic and accurate generation of hourly traffic volume with machine learning (ML), using the ground-truth data of Manhattan road segments collected by the New York State Department of Transportation (NYSDOT). Specifically, we address the following question– can we develop a ML algorithm that generalizes the existing NYSDOT data to all road segments in Manhattan?– by introducing a supervised learning task of multi-output regression, where ML algorithms use road segment attributes to predict hourly traffic volume. We consider four ML algorithms– K-Nearest Neighbors, Decision Tree, Random Forest, and Neural Network– and hyperparameter tune by evaluating the performances of each algorithm with 10-fold cross validation. Ultimately, we conclude that neural networks are the best-performing models and require the least amount of testing time. Lastly, we provide insight into the quantification of “trustworthiness” in a model, followed by brief discussions on interpreting model performance, suggesting potential project improvements, and identifying the biggest takeaways. Overall, we hope our work can serve as an effective baseline for realistic traffic volume generation, and open new directions in the processes of supervised dataset generation and ML algorithm design.
ContributorsOtstot, Kyle (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an anti-phishing solution, through a series of experiments testing different machine learning classifiers and URL features. With an end-goal implementation as a Chromium browser extension utilizing Python-based machine learning classifiers (those available via the scikit-learn library), my project uses a combination of Python, TypeScript, Node.js, as well as AWS Lambda and API Gateway to act as a solution capable of blocking phishing attacks from the web browser.
ContributorsYang, Branden (Author) / Osburn, Steven (Thesis director) / Malpe, Adwith (Committee member) / Ahn, Gail-Joon (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Next year, Arizona State University is launching a chatbot that will place their knowledge and services right into the palms of their students’ hands. Currently named Sunny, this virtual assistant will be able to answer questions regarding all aspects of college life, from orientation to housing, financial aid, schedules, intramurals, and more. Over the last semester, I have met with members of the Sunny development team to discuss their design and implementation plans. With their information plus a bit of outside research, I was able to combine several frameworks and technologies to build a prototype for Sunny. Prototypes allow developers to evaluate their designs early on, giving them ample time to make any necessary adjustments. I am confident that the Sunny development team will be able to learn from my basic implementation, from its triumphs and failures, to create the best possible chatbot for the students attending Arizona State University.
ContributorsGrossnickle, Brandon Michael (Co-author) / Grossnickle, Brandon (Co-author) / Balasooriya, Janaka (Thesis director) / Gray, Bobby (Committee member) / Longie, Joel (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
This thesis examines the applications of the Internet of Things and Artificial Intelligence within small-to-medium sized retail businesses. These technologies have become a common aspect of a modern business environment, yet there remains a level of unfamiliarity with these concepts for business owners to fully utilize these tools. The complexity behind IoT and AI has been simplified to provide benefits for a brick and mortar business store in regards to security, logistics, profit optimization, operations, and analytics. While these technologies can contribute to a business’s success, they potentially come with a high and unattainable financial cost. In order to investigate which aspects of businesses can benefit the most from these technologies, interviews with small-to-medium business owners were conducted and paired with an analysis of published research. These interviews provided specific pain points and issues that could potentially be solved by these technologies. The analysis conducted in this thesis gives a detailed summary of this research and provides a business model for two small businesses to optimize their Internet of Things and Artificial Intelligence to solve these pain points, while staying in their financial budget.
ContributorsAldrich, Lauren (Co-author) / Bricker, Danielle (Co-author) / Sebold, Brent (Thesis director) / Vermeer, Brandon (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05