Matching Items (24)

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Backend Construction of a Web Service

Description

A growing number of stylists \u2014 cosmetologists \u2014 are finding it harder to afford the basic necessities such as rent. However, the ever-increasing presence of smartphones and the increasing need

A growing number of stylists \u2014 cosmetologists \u2014 are finding it harder to afford the basic necessities such as rent. However, the ever-increasing presence of smartphones and the increasing need for on-demand services like Uber and Uber Eats creates a unique opportunity for stylists \u2014 Clippr. Clippr is an application that aims to connect individual stylists directly to their customers. The application gives stylists a platform to create and display their own prices, services, and portfolio. Customers get the benefit of finding a stylist that suits them and booking instantly. This project outlines the backend for the Clippr application. It goes over the framework, REST API, and various functionalities of the application. Additionally, the project also covers the work that is still needed to successfully launch the application.

Contributors

Agent

Created

Date Created
  • 2017-05

Immersion of Choice

Description

The topic of my creative project centers on the question of "How can the audience's choices influence dancers' improvisation?" This dance work seeks to redefine the relationship between audience and

The topic of my creative project centers on the question of "How can the audience's choices influence dancers' improvisation?" This dance work seeks to redefine the relationship between audience and performers through integration of audience, technology, and movement in real-time. This topic was derived from the fields of Computer Science and Dance. To answer my main question, I need to explore how I can interconnect the theory of Computer Science/fundamentals of a web application and the elements of dance improvisation. This topic interests me because it focuses on combining two studies that do not seem related. However, I find that when I am coding a web application, I can insert blocks of code. This relates to dance improvisation where I have a movement vocabulary, and I can insert different moves based on the context. The idea of gathering data from an audience in real time also interests me. I find that data is most useful when a story can be deduced from that data. To figure out how I can use dance to create and tell a story about the data that is collected, I find that to be intriguing as well. The main goals of my Creative Project are to learn the skills needed to develop a web application using the knowledge and theory that I am acquiring through Computer Science as well as learning about the skills needed to produce a performance piece. My object for the overall project is to create an audience-interactive experience that presents choices for dancers and creates a connection between two completely different studies: Computer Science and Dance. My project will consist of having the audience enter their answers to preset questions via an online voting application. The stage background screen will be utilized to show the question results in percentages in the form of a chart. The dancers will then serve as a live interpretation of these results. This Creative Project will serve as a gateway between the work that has been cultivated in my studies and the real world. The methods involve exploring movement qualities in improvisation, communicating with my cast about what worked best for the transitions between each section of the piece, and testing for the web applications. I learned the importance of having structure within improvisational movement for the purpose of choreography. The significance of structure is that it provides direction, clarity, and a sense of unification for the dancers. I also learned the basics of the programming language, Python, in order to develop the two real-time web applications. The significance of learning Python is that I will be able to add this to my skillset of programming languages as well as build upon my knowledge of Computer Science and develop more real-world applications in the future.

Contributors

Agent

Created

Date Created
  • 2018-05

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A Novel Historical Safety Metric for Evaluating Road Networks

Description

37,461 automobile accident fatalities occured in the United States in 2016 ("Quick Facts 2016", 2017). Improving the safety of roads has traditionally been approached by governmental agencies including the National

37,461 automobile accident fatalities occured in the United States in 2016 ("Quick Facts 2016", 2017). Improving the safety of roads has traditionally been approached by governmental agencies including the National Highway Traffic Safety Administration and State Departments of Transporation. In past literature, automobile crash data is analyzed using time-series prediction technicques to identify road segments and/or intersections likely to experience future crashes (Lord & Mannering, 2010). After dangerous zones have been identified road modifications can be implemented improving public safety. This project introduces a historical safety metric for evaluating the relative danger of roads in a road network. The historical safety metric can be used to update routing choices of individual drivers improving public safety by avoiding historically more dangerous routes. The metric is constructed using crash frequency, severity, location and traffic information. An analysis of publically-available crash and traffic data in Allgeheny County, Pennsylvania is used to generate the historical safety metric for a specific road network. Methods for evaluating routes based on the presented historical safety metric are included using the Mann Whitney U Test to evaluate the significance of routing decisions. The evaluation method presented requires routes have at least 20 crashes to be compared with significance testing. The safety of the road network is visualized using a heatmap to present distribution of the metric throughout Allgeheny County.

Contributors

Agent

Created

Date Created
  • 2017-12

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Exploring the Influence of Visualized Data: Inclusion and Collaboration Between University Members

Description

Visualizations are an integral component for communicating and evaluating modern networks. As data becomes more complex, info-graphics require a balance between visual noise and effective storytelling that is often restricted

Visualizations are an integral component for communicating and evaluating modern networks. As data becomes more complex, info-graphics require a balance between visual noise and effective storytelling that is often restricted by layouts unsuitable for scalability. The challenge then rests upon researchers to effectively structure their information in a way that allows for flexible, transparent illustration. We propose network graphing as an operative alternative for demonstrating community behavior over traditional charts which are unable to look past numeric data. In this paper, we explore methods for manipulating, processing, cleaning, and aggregating data in Python; a programming language tailored for handling structured data, which can then be formatted for analysis and modeling of social network tendencies in Gephi. We implement this data by applying an algorithm known as the Fruchterman-Reingold force-directed layout to datasets of Arizona State University’s research and collaboration network. The result is a visualization that analyzes the university’s infrastructure by providing insight about community behaviors between colleges. Furthermore, we highlight how the flexibility of this visualization provides a foundation for specific use cases by demonstrating centrality measures to find important liaisons that connect distant communities.

Contributors

Agent

Created

Date Created
  • 2020-05

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Software for Agent-Based Computational Economics

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

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.

Contributors

Agent

Created

Date Created
  • 2016-05

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Project Stallion

Description

Creation of a database and Python API to clean, organize, and streamline data collection from an updated Qualtrics survey used to capture applicant information for the Fleischer Scholars Program run by the W. P. Carey UG Admissions Office.

Contributors

Agent

Created

Date Created
  • 2021-05

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Predicting the Binding Interactions of TNFR2 and PD-L1 on LT-ɑ, TRAF2, and CD80

Description

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.

Contributors

Agent

Created

Date Created
  • 2021-05

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Building an optimized NBA Team Team Composition: A Sports Analytical Approach

Description

Sports analytics is a growing field that attempts to showcase interesting aspects of a sport with the use of modern technology and machine learning techniques. This thesis will demonstrate how

Sports analytics is a growing field that attempts to showcase interesting aspects of a sport with the use of modern technology and machine learning techniques. This thesis will demonstrate how the NBA has progressed in the past decade by comparing the performance have five teams (SAS, OKC, PHO, MIN, and SAC). It will also provide key insight on what an NBA team should focus on to build an optimized NBA team composition, which will better their performance in the league, which will improve their chances of making into the playoffs. These teams were chosen after conducting extensive analysis on all NBA teams. These five teams were chosen because of the variability in performance (two successful and three less successful teams). Two successful teams, SAS and OKC, and three less successful teams, PHO, MIN, and SAC, were chosen to exemplify the different approaches of teams in the NBA and to distinguish what an NBA team should consider build an optimized team composition to better their performance in the league stage.

Contributors

Agent

Created

Date Created
  • 2021-05

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An Extendable Python IoT Server and Task Handler

Description

The Internet of Things (IoT) is term used to refer to the billions of Internet connected, embedded devices that communicate with one another with the purpose of sharing data or

The Internet of Things (IoT) is term used to refer to the billions of Internet connected, embedded devices that communicate with one another with the purpose of sharing data or performing actions. One of the core usages of the proverbial network is the ability for its devices and services to interact with one another to automate daily tasks and routines. For example, IoT devices are often used to automate tasks within the household, such as turning the lights on/off or starting the coffee pot. However, designing a modular system to create and schedule these routines is a difficult task.

Current IoT integration utilities attempt to help simplify this task, but most fail to satisfy one of the requirements many users want in such a system ‒ simplified integration with third party devices. This project seeks to solve this issue through the creation of an easily extendable, modular integrating utility. It is open-source and does not require the use of a cloud-based server, with users hosting the server themselves. With a server and data controller implemented in pure Python and a library for embedded ESP8266 microcontroller-powered devices, the solution seeks to satisfy both casual users as well as those interested in developing their own integrations.

Contributors

Agent

Created

Date Created
  • 2018-05

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ConstrictR and ConstrictPy: R Package and Python Tool for Microbiome Analysis

Description

I, Christopher Negrich, am the sole author of this paper, but the tools described were designed in collaboration with Andrew Hoetker. ConstrictR (constrictor) and ConstrictPy are an R package and

I, Christopher Negrich, am the sole author of this paper, but the tools described were designed in collaboration with Andrew Hoetker. ConstrictR (constrictor) and ConstrictPy are an R package and python tool designed together. ConstrictPy implements the functions and methods defined in ConstrictR and applies data handling, data parsing, input/output (I/O), and a user interface to increase usability. ConstrictR implements a variety of common data analysis methods used for statistical and subnetwork analysis. The majority of these methods are inspired by Lionel Guidi's 2016 paper, Plankton networks driving carbon export in the oligotrophic ocean. Additional methods were added to expand functionality, usability, and applicability to different areas of data science. Both ConstrictR and ConstrictPy are currently publicly available and usable, however, they are both ongoing projects. ConstrictR is available at github.com/cnegrich and ConstrictPy is available at github.com/ahoetker. Currently, ConstrictR has implemented functions for descriptive statistics, correlation, covariance, rank, sparsity, and weighted correlation network analysis with clustering, centrality, profiling, error handling, and data parsing methods to be released soon. ConstrictPy has fully implemented and integrated the features in ConstrictR as well as created functions for I/O and conversion between pandas and R data frames with a full feature user interface to be released soon. Both ConstrictR and ConstrictPy are designed to work with minimal dependencies and maximum available information on the algorithms implemented. As a result, ConstrictR is only dependent on base R (v3.4.4) functions with no libraries imported. ConstrictPy is dependent upon only pandas, Rpy2, and ConstrictR. This was done to increase longevity and independence of these tools. Additionally, all mathematical information is documented alongside the code, increasing the available information on how these tools function. Although neither tool is in its final version, this paper documents the code, mathematics, and instructions for use, in addition to plans for future work, for of the current versions of ConstrictR (v0.0.1) and ConstrictPy (v0.0.1).

Contributors

Agent

Created

Date Created
  • 2018-05