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Managerial Factors in Effective Workplace Communication: Analyzing the effects of Tailoring Communication Styles and Verbalizing Expectations in the Workplace

Description

This project analyzes the large array of managerial leadership research in congruence with the wide field of workplace communication to attempt to determine the importance of refining communication channels between managers and employees as well as articulate the core competencies

This project analyzes the large array of managerial leadership research in congruence with the wide field of workplace communication to attempt to determine the importance of refining communication channels between managers and employees as well as articulate the core competencies a manager should exhibit when practicing exemplary communication in their respective work environment. The preliminary sections of this thesis will establish the currently existing foundations utilized and narrow the wide range of research available to applicable information regarding positive workplace communication, influencing factors in a feedback loop from the employee’s perspective, as well as leadership aspects and actions a manager can alter or initiate to improve their workplace’s environment through communicational refinement. This research is supplemented with a survey that was administered to Arizona State University student leaders who were involved in coordinating the Regional Business Conference on the Polytechnic campus. The survey data is designed to either confirm or contradict the findings of the literature. The objective of this project is to synthesize an overview of a manager’s responsibilities and recommend actions to tailor and improve workplace communication

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2020-05

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Prescription Information Extraction from Electronic Health Records using BiLSTM-CRF and Word Embeddings

Description

Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records

Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.

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2018-05

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ReL GoalD (Reinforcement Learning for Goal Dependencies)

Description

In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft.

In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.

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2018-05

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Pandora: A Play by Abbey Toye

Description

Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity? Hoping to create a better version of ourselves, The Engineer

Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity? Hoping to create a better version of ourselves, The Engineer must navigate the loss of her creation, and Pandora must navigate their new world. The original premiere run was March 27-28, 2018, original cast: Caitlin Andelora, Rikki Tremblay, and Michael Tristano Jr.

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2018-05

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Data Management Behind Machine Learning

Description

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.

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2018-05

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Tongues Becoming a Virtuous Woman: A Philosophical and Communicative Approach to Young Women's Speech

Description

Four hundred years after the word "virtuous," came to be associated with a woman's sexuality, today's female adolescent seemingly has everything. Yet, there is a psychological civil war raging in the psyche of the 21st century young American female because

Four hundred years after the word "virtuous," came to be associated with a woman's sexuality, today's female adolescent seemingly has everything. Yet, there is a psychological civil war raging in the psyche of the 21st century young American female because her mind is divided against itself due to the conflicting instructions of who and what she should be. She has so many choices; it is easy to become overwhelmed by them. Today's female youth is threatened. She communicates more and more, but her ability to express herself is inhibited because she is unsure of how to develop an authentic sense of self. It is a hermeneutic understanding of communication and what it means to be "virtuous" that can free young women to cultivate authentic self and continue to make decisions that support such a lifestyle. It is the aim of this thesis to reclaim the word "virtuous" for the benefit of today's young women. Deeper understanding of hermeneutics and communication allow us to view this word in a different light and read the entirety of Proverbs 31 as feminists. Young women have always faced challenges in adolescence, but a return to classical wisdom and philosophy will equip them to further advance themselves and their communities, rather than forcing them into a life of speaking tongue twisters. The virtuous young woman does not know what the future holds, but armed with the lessons of tradition and the fire of hope, she may speak a virtuous magic over the world with a tongue fit for the challenge.

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2018-05

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Communication Strategies for Effective Social Media Use in Local Governments

Description

An information influx and numerous modes of content delivery has resulted in local governments competing for the public's attention. A recent poll from the Public Technology Institute discovered that although 85% of Local Governments use social media to disseminate information

An information influx and numerous modes of content delivery has resulted in local governments competing for the public's attention. A recent poll from the Public Technology Institute discovered that although 85% of Local Governments use social media to disseminate information to their constituents, only 37% have an enterprise-wide social media strategy (PTI, 2017). Without a clear approach towards social media, Local Governments are failing to maximize their voices and often ineffective when reaching out to their constituents. Research has suggested, charisma is a successful tool for capturing an audience's attention and conveying a memorable message. Charisma can also be taught and executed not only through spoken rhetoric but in online social media platforms. Within this study, 18 local government employees participated in an educational workshop on the use of nine non-verbal "Charismatic Leadership Tactics". Participants completed a pre-workshop assignment which was later compared to a post-workshop assignment. Results showed, participants on average, increased their use of Charismatic Leadership Tactics by a mean of 61%. Researchers collected social media analytics one month prior and one month following the workshop from the City's social media accounts in which participants managed. Collectively, of the thirteen social media accounts, the overall total engagement was greater the month after the educational workshop compared to the month before the workshop. These results suggest charisma can be taught, charisma can be conveyed through micro-blogosphere platforms such as Twitter, and the use of Charismatic Leadership Tactics could be responsible for increasing follower engagement with social media content.

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2018-05

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Using Machine Learning to Predict the NBA

Description

Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and

Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and outcomes are easily measurable. Predicting the outcomes of sports events may also be easily profitable, predictions can be taken to a sportsbook and wagered on. A successful prediction model could easily turn a profit. The goal of this project was to build a model using machine learning to predict the outcomes of NBA games.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.

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Date Created
2019-05

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Twitch Streamer-Game Recommender System

Description

Abstract
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer

Abstract
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer data.

Introduction
As watching video games is becoming more popular, those interested are becoming interested in Twitch.tv, an online platform for guests to watch streamers play video games and interact with them. A streamer is an person who broadcasts them-self playing a video game or some other thing for an audience (the guests of the website.) The site allows the guest to first select the game/category to view and then displays currently active streamers for the guest to select and watch. Twitch records the games that a streamer plays along with the amount of time that a streamer spends streaming that game. This is how the score is generated for a streamer’s game. These three terms form the streamer-game-score (user-item-rating) tuples that we use to train out models.
The our problem’s solution is similar to the purpose of the Netflix prize; however, as opposed to suggesting a user a movie, the goal is to suggest a user a game. We built a model to predict the score that a streamer will have for a game. The score field in our data is fundamentally different from a movie rating in Netflix because the way a user influences a game’s score is by actively streaming it, not by giving it an score based off opinion. The dataset being used it the Twitch.tv dataset provided by Isaac Jones [1]. Also, the only data used in training the models is in the form of the streamer-game-score (user-item-rating) tuples. It will be known if these data points with limited information will be able to give an accurate prediction of a streamer’s score for a game. SVD and SVD++ are the baseis of the models being trained and tested. Scikit’s Surprise library in Python3 is used for the implementation of the models.

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2019-05

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The Capabilities and Obstacles of Integrating Machine Learning into a Supply Chain

Description

Only an Executive Summary of the project is included.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It

Only an Executive Summary of the project is included.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.

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2019-05