Matching Items (253)
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- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
- Creators: Dean, W.P. Carey School of Business
Description
An ethical dilemma is not a matter of “right” versus “wrong,” but rather it is a situation of conflicting values. A common ethical dilemma is that of honesty versus loyalty—is it better to tell the truth, or remain loyal to the company? In the Japanese culture, truth is circumstantial and can vary with different situations. In a way, the Japanese idea of honesty reflects how highly they value loyalty. This overlap of values results in the lack of an ethical dilemma for the Japanese, which creates a new risk for fraud. Without this struggle, a Japanese employee does not have strong justification against committing fraud if it aligns with his values of honesty and loyalty.
This paper looks at the Japanese values relating to honesty and loyalty to show how much these ideas overlap. The lack of a conflict of values creates a risk for fraud, which will be shown through an analysis of the scandals of two Japanese companies, Toshiba and Olympus. These scandals shine light on the complexity of the ethical dilemma for the Japanese employees; since their sense of circumstantial honesty encourages them to lie if it maintains the harmony of the group, there is little stopping them from committing the fraud that their superiors asked them to commit.
In a global economy, understanding the ways that values impact business and decisions is important for both interacting with others and anticipating potential conflicts, including those that may result in or indicate potential red flags for fraud.
This paper looks at the Japanese values relating to honesty and loyalty to show how much these ideas overlap. The lack of a conflict of values creates a risk for fraud, which will be shown through an analysis of the scandals of two Japanese companies, Toshiba and Olympus. These scandals shine light on the complexity of the ethical dilemma for the Japanese employees; since their sense of circumstantial honesty encourages them to lie if it maintains the harmony of the group, there is little stopping them from committing the fraud that their superiors asked them to commit.
In a global economy, understanding the ways that values impact business and decisions is important for both interacting with others and anticipating potential conflicts, including those that may result in or indicate potential red flags for fraud.
ContributorsTabar, Kelly Ann (Author) / Samuelson, Melissa (Thesis director) / Goldman, Alan (Committee member) / WPC Graduate Programs (Contributor) / W.P. Carey School of Business (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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. 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.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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 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.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Although there are some key qualities that all good leaders employ, variations in effective leadership approaches are evident across different cultures. This project sought to compare and analyze the differences and similarities in leadership principles between Chinese and American business cultures, with emphasis on the divergence caused by the influences of history, culture and politics.
ContributorsLe Tourneur, Maxine Archondakis (Author) / McKinnon, David (Thesis director) / LePine, Marcie (Committee member) / Department of Supply Chain Management (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
A look at how the Vietnam War influenced immigrant and first-generation children's perception of culture. This thesis focuses on Vietnamese-American immigration as a whole, and on subjects on the American west coast. Interviews were conducted with eleven subjects to examine the most profound influences on culture and how native culture is passed on through the generations. Focuses include cultural identity, cultural inheritance, prominent native and adoptive cultural values, and culture as affected by adversity.
ContributorsTran, Yvana (Author) / Loebenberg, Abby (Thesis director) / Suk, Mina (Committee member) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Limited research has analyzed how culture might influence the utilization of social support. To address this deficiency, the present study investigated preferences for social support among East-Asian, Hispanic, and White participants. In this set of studies, a comprehensive social support taxonomy was constructed in order to better identify and conceptualize the various support subtypes found in the literature. Based on the taxonomy, a questionnaire measure for preferences of different types of social support was developed. Participants were asked to rate how helpful they would find each supportive action made by a friend or family member on a seven-point Likert scale. Based on the responses of 516 Amazon Mechanical Turk workers, a five-factor solution for an 18-item scale emerged from a factor analysis. The social support subscales supported by the factor analysis were emotional, tangible, self-referencing, reappraisal, and distraction. The questionnaire was used to assess similarities and differences among East-Asian, Hispanic, and White participants in terms of preferences for providing and receiving social support. Based on the results of 299 college-age students, an analysis of variance on individually standardized ("ipsatized") responses was conducted in order to eliminate the positioning effect of culture. A main effect of ethnicity (p=.05) and an interaction between ethnicity and sex (p=.02) were significant for the preference of tangible social support. A main effect of ethnicity (p=.04) and an interaction between ethnicity and sex (p=.05) were significant for the preference of reappraisal social support. Clinical implications of our research findings are discussed.
ContributorsCampagna, Allegra Xiu Hong (Author) / Shiota, Michelle N. (Thesis director) / Campos, Belinda (Committee member) / Yee, Claire (Committee member) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The purpose of this thesis is to explore if any correlation exists between the proposed components of happiness with overall self-perceived happiness. This thesis also explores how introversion and extraversion, gender, and working status affects the proposed components of happiness for college students and how their happiness influences engagement, motivation, preference of organizational culture, and the activities that they engage in. This research was gathered from secondary sources and a survey that was given to undergraduate students at Arizona State University. We found that well-being, gratitude, achievement, psychological empowerment, and affection contribute to both extraverts and introverts' happiness. In addition, we found that extraverts reported higher means than introverts in each factor; including happiness in general and what contributes to it. Contrary to popular belief, our research shows that autonomy either had no correlation or negatively correlates with happiness. In addition, we found that both extraverts and introverts participate in social and nonsocial activities rather than solely on their expected type of activity. Our research also shows that females reported higher means than males on gratitude, achievement, and autonomy. One significant implication of this study is that it can help individuals to better understand themselves and people they interact with.
ContributorsVasquez, Delia (Co-author) / Lopez, Miguel (Co-author) / LePine, Marcie (Thesis director) / Arce, Alma (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of Psychology (Contributor) / School of Accountancy (Contributor)
Created2014-12
Description
This research looks at a group of students from Tumaini Children's Home in Nyeri, Kenya. The purpose of this paper is to explore why this particular group of students is so academically successful. Quantitative research was taken from the average 2013 test scores of Tumaini students who took the Kenyan Certificate of Primary Education (KCPE) exam in comparison to the scores of students who are not residing in the orphanage. Qualitative research involves interviews from those students who live in Tumaini and interviews from adults who are closely connected to the orphanage. The purpose is to understand why the students are performing so well academically and what support they have created for themselves that allows them to do so.
ContributorsTooker, Amy Elizabeth (Author) / Puckett, Kathleen (Thesis director) / Cocchiarella, Martha (Committee member) / Barrett, The Honors College (Contributor) / Division of Teacher Preparation (Contributor)
Created2014-12