Matching Items (612)
Description
Customers in the modern world are accustomed to having immediate and simple access to an immense amount of information, and demand this immediacy in all businesses, especially in the restaurant industry. Now more than ever, restaurants are relying on third party delivery services such as UberEATS, Postmates, and GrubHub to satiate the appetite of their delivery market, and while this may seem like the natural progression, not all restaurant owners are comfortable moving in this direction. Pain points range from not wanting a third party to represent their business or the lack of supervision over the food in transit, and the time it takes to navigate the delivery landscape, to the fact that some food just doesn’t “travel” well. In addition to this, food delivery services can cause increased stress on a kitchen, and dig into the bottom line of an already slim restaurant margin. Simply put, customer reliance on these applications puts apprehensive restaurant owners at a competitive disadvantage.Our solution is simple—we want business owners to be able to take advantage of the huge market provided by third party delivery services, without the fear of compromising their brand. At DLVR Consulting, we listen to specific pain points of a customer and alleviate them through solutions developed by our in-house food, restaurant, and branding experts. Whether creating an entirely new “delivery” brand, menu curation, or payment processing service, we give the customer exactly what they need to feel comfortable using third-party delivery applications. In this plan, we will first take a deep dive into the problem and opportunity identified by both third-party research and first-hand interviews with successful restaurant owners and operators. After exploring the problem, we will propose our solution, who we will target with said solution, and what makes this solution unique and sellable. From here we will begin to explore the execution of our ideas, including our sales and marketing plans which will work in conjunction with our go-to-market strategy. We will explore key milestones and metrics we hope to meet in the coming year, as well as the team which will be taking DLVR from a plan to an implemented business. We will take a look at our three year financial forecast, and break this down further to monthly revenue, direct costs, and expenses. We will finish by taking a look at our required funding, and how we will attempt to gain said funding.
ContributorsClancy, Kevin (Co-author, Co-author) / Sebold, Brent (Thesis director) / Clancy, Keith (Committee member) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The most important task for a beginning computer science student, in order for them to succeed in their future studies, is to learn to be able to understand code. One of the greatest indicators of student success in beginning programming courses is the ability to read code and predict its output, as this shows that the student truly understands what each line of code is doing. Yet few tools available to students today focus on helping students to improve their ability to read code. The goal of the random Python program generator is to give students a tool to practice this important skill.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
ContributorsDiLorenzo, Kaitlyn (Author) / Meuth, Ryan (Thesis director) / Miller, Phillip (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Deep learning and AI have grabbed tremendous attention in the last decade. The substantial accuracy improvement by neural networks in common tasks such as image classification and speech recognition has made deep learning as a replacement for many conventional machine learning techniques. Training Deep Neural networks require a lot of data, and therefore vast of amounts of computing resources to process the data and train the model for the neural network. The most obvious solution to solving this problem is to speed up the time it takes to train Deep Neural networks.
AI and deep learning workloads are different from the conventional cloud and mobile workloads, with respect to: (1) Computational Intensity, (2) I/O characteristics, and (3) communication pattern. While there is a considerable amount of research activity on the theoretical aspects of AI and Deep Learning algorithms that run with greater efficiency, there are only a few studies on the infrastructural impact of Deep Learning workloads on computing and storage resources in distributed systems.
It is typical to utilize a heterogeneous mixture of CPU and GPU devices to perform training on a neural network. Google Brain has a developed a reinforcement model that can place training operations across a heterogeneous cluster. Though it has only been tested with local devices in a single cluster. This study will explore the method’s capabilities and attempt to apply this method on a cluster with nodes across a network.
AI and deep learning workloads are different from the conventional cloud and mobile workloads, with respect to: (1) Computational Intensity, (2) I/O characteristics, and (3) communication pattern. While there is a considerable amount of research activity on the theoretical aspects of AI and Deep Learning algorithms that run with greater efficiency, there are only a few studies on the infrastructural impact of Deep Learning workloads on computing and storage resources in distributed systems.
It is typical to utilize a heterogeneous mixture of CPU and GPU devices to perform training on a neural network. Google Brain has a developed a reinforcement model that can place training operations across a heterogeneous cluster. Though it has only been tested with local devices in a single cluster. This study will explore the method’s capabilities and attempt to apply this method on a cluster with nodes across a network.
ContributorsNguyen, Andrew Hoang (Author) / Zhao, Ming (Thesis director) / Biookaghazadeh, Saman (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
If you’ve ever found yourself uttering the words “Honey, I forgot the—” or “how did I miss the—" when coming home from the grocery store, then you’re not alone. This everyday problem that we disregard as part of life may not seem like much, but it is the driving force behind my honors thesis.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.
ContributorsMathews, Nicolle (Author) / Meuth, Ryan (Thesis director) / Chen, Yinong (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
While there are many existing systems which take natural language descriptions and use them to generate images or text, few systems exist to generate 3d renderings or environments based on natural language. Most of those systems are very limited in scope and require precise, predefined language to work, or large well tagged datasets for their models. In this project I attempt to apply concepts in NLP and procedural generation to a system which can generate a rough scene estimation of a natural language description in a 3d environment from a free use database of models. The primary objective of this system, rather than a completely accurate representation, is to generate a useful or interesting result. The use of such a system comes in assisting designers who utilize 3d scenes or environments for their work.
ContributorsHann, Jacob R. (Author) / Kobayashi, Yoshihiro (Thesis director) / Srivastava, Siddharth (Committee member) / Computer Science and Engineering Program (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Social media is explosively popular in discussing socio-political issues. This work provides a preliminary study on how polarization occurs online. Chapter I begins by introducing limitations of the internet in maintaining a free flow of information. Not only do users seek out groups of like-minded individuals and insulate themselves from opposing views, social media platforms algorithmically curate content such that it will be in line with a user’s preconceived notions of the world. The work then defines polarization and carefully discusses its most prominent causes. It then shifts focus to analyze a closely-related issue regarding political discourse: outrage, which is both a noticeable effect of and further cause of polarization. It is clearly prevalent in traditional media, but for completion, I provide a case study to measure its incidence in social media. In Chapter II, I scrutinize the language used in the #MeToo movement on Twitter and draw conclusions about the issues Twitter users focus on and how they express their views. This chapter details the method I used, the challenges I faced in designing the exploratory study, and the results I found. I benchmark patterns I find in the Twitterverse against those I find in The Wall Street Journal. The analysis relies upon the metric of word similarity, based on proximity of and frequency of words used together, to make distinctions about what users are most commonly saying with respect to given topics, or keywords. Chapter III closes the essay with conclusions of socio-political polarization, discourse, and outrage in social media. Finally, the essay outlines potential channels for future work.
ContributorsJain, Niharika (Author) / Simhony, Avital (Thesis director) / Lewis, Paul (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / School of Politics and Global Studies (Contributor) / Department of Information Systems (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Pollution is an increasing problem around the world, and one of the main forms it takes is air pollution. Air pollution, from oxides and dioxides to particulate matter, continues to contribute to millions of deaths each year, which is more than the next three leading causes of environment-related death combined. Plus, the problem is only growing as industrial plants, factories, and transportation continues to rapidly increase across the globe. Those most affected include less developed countries and individuals with pre-existing respiratory conditions. Although many citizens know about this issue, it is often unclear what times and locations are worst in terms of pollutant concentration as it can vary on the time of day, local activity, and other variable factors. As a result, citizens lack the knowledge and resources to properly combat or avoid air pollution, as well as the data and evidence to support any sort of regulatory change. Many companies and organizations have tried to address this through Air Quality Indexes (AQIs) but are not focused enough to help the everyday citizen, and often fail to include many significant pollutants. Thus, we sought to address this issue in a cost-effective way through creating a network of IoT (Internet of Things) devices and deploying them in a select area of Tempe, Arizona. We utilized Arduino Microprocessors and Wireless Radio Frequency Transceivers to send and receive air pollution data in real time. Then, displayed this data in such a way that it could be released to the public via web or mobile app. Furthermore, the product is cheap enough to be reproduced and sold in bulk as well as scaled and customized to be compatible with dozens of different air quality sensors.
ContributorsCoury, Abrahm Philip (Co-author) / Gillespie, Cody (Co-author) / Ren, Fengbo (Thesis director) / Shrivastava, Aviral (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Through the personal experience of volunteering at ASU Project Humanities, an organization that provides resources such as clothing and toiletries to the homeless population in Downtown Phoenix, I noticed efficiently serving the needs of the homeless population is an important endeavor, but the current processes for Phoenix nonprofits to collect data are manual, ad-hoc, and inefficient. This leads to the research question: is it possible to improve this process of collecting statistics on client needs, tracking donations, and managing resources using technology? Background research includes an interview with ASU Project Humanities, articles by analysts, and related work including case studies of current technologies in the nonprofit community. Major findings include i) a lack of centralized communication in nonprofits collecting needs, tracking surplus donations, and sharing resources, ii) privacy assurance is important to homeless individuals, and iii) pre-existing databases and technological solutions have demonstrated that technology has the ability to make an impact in the nonprofit community. To improve the process, standardization, efficiency, and automation need to increase. As a result of my analysis, the thesis proposes a prototype solution which includes two parts: an inventory database and a web application with forms for user input and tables for the user to view. This solution addresses standardization by showing a consistent way of collecting data on need requests and surplus donations while guaranteeing privacy of homeless individuals. This centralized solution also increases efficiency by connecting different agencies that cater to these clients. Lastly, the solution demonstrates the ability for resources to be made available to each organization which can increase automation. In conclusion, this database and web application has the potential to improve nonprofit organizations’ networking capabilities, resource management, and resource distribution. The percentile of homeless individuals connected to these resources is expected to increase substantially with future live testing and large-scale implementation.
ContributorsKhurana, Baani Kaur (Author) / Bazzi, Rida (Thesis director) / Sankar, Lalitha (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
A common design of multi-agent robotic systems requires a centralized master node, which coordinates the actions of all the agents. The multi-agent system designed in this project enables coordination between the robots and reduces the dependence on a single node in the system. This design change reduces the complexity of the central node, and makes the system more adaptable to changes in its topology. The final goal of this project was to have a group of robots collaboratively claim positions in pre-defined formations, and navigate to the position using pose data transmitted by a localization server.
Planning coordination between robots in a multi-agent system requires each robot to know the position of the other robots. To address this, the localization server tracked visual fiducial markers attached to the robots and relayed their pose to every robot at a rate of 20Hz using the MQTT communication protocol. The robots used this data to inform a potential fields path planning algorithm and navigate to their target position.
This project was unable to address all of the challenges facing true distributed multi-agent coordination and needed to make concessions in order to meet deadlines. Further research would focus on shoring up these deficiencies and developing a more robust system.
Planning coordination between robots in a multi-agent system requires each robot to know the position of the other robots. To address this, the localization server tracked visual fiducial markers attached to the robots and relayed their pose to every robot at a rate of 20Hz using the MQTT communication protocol. The robots used this data to inform a potential fields path planning algorithm and navigate to their target position.
This project was unable to address all of the challenges facing true distributed multi-agent coordination and needed to make concessions in order to meet deadlines. Further research would focus on shoring up these deficiencies and developing a more robust system.
ContributorsThibeault, Quinn (Author) / Meuth, Ryan (Thesis director) / Chen, Yinong (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
There exist many very effective calendar platforms out there, from Google Calendar, to Microsoft’s Outlook, and various implementations by other service providers. While all those services serve their purpose, they may be missing in the capacity to be easily portable for some, or the capacity to offer to the user a ranking of their various events and tasks in order of priority. This is that, while some of these services do offer reliable support for portability on smaller devices, it could be even more beneficial to the user to constantly have an idea of which calendar entry they should prioritize at a given point in time, based on the necessities of each entry and regardless of which entry occurs first on a chronologic line. Many of these capacities are missing in the technology currently used at ASU for course management. This project attempts to address this issue by providing a Software Application that offers to store a user’s calendar events and present those events back to the user after arranging them by order of priority. The project makes use of technologies such as Fibrease, Angular and Android to make the service available through a web browser as well as an Android mobile client. We explore possible avenues of implementations to make the services of this platform accessible and usable through other existing platforms such as Blackboard or Canvas. We also consider ways to incorporate this software into the already existing workflow of other web platforms such as Google Calendar, Blackboard or Canvas, by allowing one platform to be aware of any item creation or update from the other platform, and thus removing the necessity of creating one calendar entry multiple times in different platforms.
ContributorsNdombe, Kelly (Author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05