ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Business Administration
- All Subjects: deep learning
Description
This action research study is the culmination of several action cycles investigating cognitive information processing and learning strategies based on students approach to learning theory and assessing students' meta-cognitive learning, motivation, and reflective development suggestive of deep learning. The study introduces a reading assignment as an integrative teaching method with the purpose of challenging students' assumptions and requiring them to think from multiple perspectives thus influencing deep learning. The hypothesis is that students who are required to critically reflect on their own perceptions will develop the deep learning skills needed in the 21st century. Pre and post surveys were used to assess for changes in students' preferred approach to learning and reflective practice styles. Qualitative data was collected in the form of student stories and student literature circle transcripts to further describe student perceptions of the experience. Results indicate stories that include examples of critical reflection may influence students to use more transformational types of reflective learning actions. Approximately fifty percent of the students in the course increased their preference for deep learning by the end of the course. Further research is needed to determine the effect of narratives on student preferences for deep learning.
ContributorsBradshaw, Vicki (Author) / Carlson, David L. (Thesis advisor) / Jordan, Michelle (Committee member) / Gagnon, Marie (Committee member) / Arizona State University (Publisher)
Created2012
Description
Discriminative learning when training and test data belong to different distributions is a challenging and complex task. Often times we have very few or no labeled data from the test or target distribution, but we may have plenty of labeled data from one or multiple related sources with different distributions. Due to its capability of migrating knowledge from related domains, transfer learning has shown to be effective for cross-domain learning problems. In this dissertation, I carry out research along this direction with a particular focus on designing efficient and effective algorithms for BioImaging and Bilingual applications. Specifically, I propose deep transfer learning algorithms which combine transfer learning and deep learning to improve image annotation performance. Firstly, I propose to generate the deep features for the Drosophila embryo images via pretrained deep models and build linear classifiers on top of the deep features. Secondly, I propose to fine-tune the pretrained model with a small amount of labeled images. The time complexity and performance of deep transfer learning methodologies are investigated. Promising results have demonstrated the knowledge transfer ability of proposed deep transfer algorithms. Moreover, I propose a novel Robust Principal Component Analysis (RPCA) approach to process the noisy images in advance. In addition, I also present a two-stage re-weighting framework for general domain adaptation problems. The distribution of source domain is mapped towards the target domain in the first stage, and an adaptive learning model is proposed in the second stage to incorporate label information from the target domain if it is available. Then the proposed model is applied to tackle cross lingual spam detection problem at LinkedIn’s website. Our experimental results on real data demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsSun, Qian (Author) / Ye, Jieping (Committee member) / Xue, Guoliang (Committee member) / Liu, Huan (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2015
Description
Computer Vision as a eld has gone through signicant changes in the last decade.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
ContributorsVenkatesan, Ragav (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
Description
This dissertation offers three essays that investigate consumers’ health-related food choices and behaviors from three different, yet complementary, angles. The first essay uses an eye-tracking experiment to examine consumers’ visual attention to the Nutrition Facts Panels for healthy and unhealthy products. In this essay, I focus on how involvement and familiarity affect consumers’ attention toward the Nutrition Facts panel and how these two psychological factors interact with new label format changes in attracting consumers’ attention. In the second essay, I demonstrate using individual-level scanner data that nutritional attributes interact with marketing mix elements to affect consumers’ nutrition intake profiles and their intra-category substitution patterns. My findings suggest that marketing-mix sensitivities are correlated with consumers’ preferences for nutrient attributes in ways that depend on the “healthiness” of the nutrient. For instance, featuring promotes is positively correlated with “healthy” nutritional characteristics such as high-protein, low-fat, or low-carbohydrates, whereas promotion and display are positively correlated with preferences for “unhealthy” characteristics such as high-fat, or high-carbohydrates. I use model simulations to show that some marketing-mix elements are able to induce consumers to purchase items with higher maximum-content levels than others. The fourth chapter shows that dieters are not all the same. I develop and validate a new scale that measures lay theories about abstinence vs. moderation. My findings from a series of experiments indicate that dieters’ recovery from recalled vs. actual indulgences depend on whether they favor abstinence or moderation. However, compensatory coping strategies provide paths for people with both lay theories to recover after an indulgence, in their own ways. The three essays provide insights into individual differences that determine approaches of purchase behaviors, and consumption patterns, and life style that people choose, and these insights have potential policy implications to aid in designing the food-related interventions and policies to improve the healthiness of consumers’ consumption profiles and more general food well-being.
ContributorsXie, Yi (Author) / Richards, Timothy (Thesis advisor) / Mandel, Naomi (Committee member) / Grebitus, Carola (Committee member) / Arizona State University (Publisher)
Created2018
Description
The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
Description
This article can be divided into six parts.
The first chapter analyzes the background, theatrical and particle reasons of this research. The author argues that the management of law firm needs a set of good system. The first one is operating the law firm in scale, and the other on is corporate management model, which shall be constructed in detail in the paper and will be put into practice by the law firm in which the author is worked.
The second chapter will introduce modern management theory, combining the situation of management in our law firm to analyze, raising some reasonable suggestions and instructions to promote our law firm to achieve the corporate management.
In the third chapter, the first chapter, starting with the review of the development process of foreign and our law firms, listing the organizational forms and the characteristics of our law firm, analyzing the situation and the drawbacks of the law firm management.
The fourth and fifth chapter introduce he background, the connotation of the corporate management model, listing the development and successful experience of some typical cases in respect of corporate management.
In the last chapter, the construction of corporate management model will be introduced in terms of organization form, human resource management and informationizing development.
The corporate management model is not mature in china. Though it is not easy to reform the existing model, but it should be believed that the development benefiting the legal industry will be achieved.
The first chapter analyzes the background, theatrical and particle reasons of this research. The author argues that the management of law firm needs a set of good system. The first one is operating the law firm in scale, and the other on is corporate management model, which shall be constructed in detail in the paper and will be put into practice by the law firm in which the author is worked.
The second chapter will introduce modern management theory, combining the situation of management in our law firm to analyze, raising some reasonable suggestions and instructions to promote our law firm to achieve the corporate management.
In the third chapter, the first chapter, starting with the review of the development process of foreign and our law firms, listing the organizational forms and the characteristics of our law firm, analyzing the situation and the drawbacks of the law firm management.
The fourth and fifth chapter introduce he background, the connotation of the corporate management model, listing the development and successful experience of some typical cases in respect of corporate management.
In the last chapter, the construction of corporate management model will be introduced in terms of organization form, human resource management and informationizing development.
The corporate management model is not mature in china. Though it is not easy to reform the existing model, but it should be believed that the development benefiting the legal industry will be achieved.
ContributorsZhu, Ping (Author) / Gu, Bin (Thesis advisor) / Chang, Chun (Thesis advisor) / Zhu, Ning (Committee member) / Arizona State University (Publisher)
Created2017
Description
Machine learning models convert raw data in the form of video, images, audio,
text, etc. into feature representations that are convenient for computational process-
ing. Deep neural networks have proven to be very efficient feature extractors for a
variety of machine learning tasks. Generative models based on deep neural networks
introduce constraints on the feature space to learn transferable and disentangled rep-
resentations. Transferable feature representations help in training machine learning
models that are robust across different distributions of data. For example, with the
application of transferable features in domain adaptation, models trained on a source
distribution can be applied to a data from a target distribution even though the dis-
tributions may be different. In style transfer and image-to-image translation, disen-
tangled representations allow for the separation of style and content when translating
images.
This thesis examines learning transferable data representations in novel deep gen-
erative models. The Semi-Supervised Adversarial Translator (SAT) utilizes adversar-
ial methods and cross-domain weight sharing in a neural network to extract trans-
ferable representations. These transferable interpretations can then be decoded into
the original image or a similar image in another domain. The Explicit Disentangling
Network (EDN) utilizes generative methods to disentangle images into their core at-
tributes and then segments sets of related attributes. The EDN can separate these
attributes by controlling the ow of information using a novel combination of losses
and network architecture. This separation of attributes allows precise modi_cations
to speci_c components of the data representation, boosting the performance of ma-
chine learning tasks. The effectiveness of these models is evaluated across domain
adaptation, style transfer, and image-to-image translation tasks.
text, etc. into feature representations that are convenient for computational process-
ing. Deep neural networks have proven to be very efficient feature extractors for a
variety of machine learning tasks. Generative models based on deep neural networks
introduce constraints on the feature space to learn transferable and disentangled rep-
resentations. Transferable feature representations help in training machine learning
models that are robust across different distributions of data. For example, with the
application of transferable features in domain adaptation, models trained on a source
distribution can be applied to a data from a target distribution even though the dis-
tributions may be different. In style transfer and image-to-image translation, disen-
tangled representations allow for the separation of style and content when translating
images.
This thesis examines learning transferable data representations in novel deep gen-
erative models. The Semi-Supervised Adversarial Translator (SAT) utilizes adversar-
ial methods and cross-domain weight sharing in a neural network to extract trans-
ferable representations. These transferable interpretations can then be decoded into
the original image or a similar image in another domain. The Explicit Disentangling
Network (EDN) utilizes generative methods to disentangle images into their core at-
tributes and then segments sets of related attributes. The EDN can separate these
attributes by controlling the ow of information using a novel combination of losses
and network architecture. This separation of attributes allows precise modi_cations
to speci_c components of the data representation, boosting the performance of ma-
chine learning tasks. The effectiveness of these models is evaluated across domain
adaptation, style transfer, and image-to-image translation tasks.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2018
Description
Due to the booming young mothers and fathers in the new era as well as the changes in the concept of parenting and the favorable liberalization of China's second child policy, the maternal-baby nursing market continues to grow, and it has become a must for businesses nowadays. In 2020, the size of the maternal-baby nursing market will reach 3.6 trillion. (Data: Yibang Power China's Maternal and Child Industry White Paper 2017).
The rapid development of mobile Internet, highly transparent information, consumers grasp the sovereignty, along with the rise of the middle class, consumption increase encouraged personalized, customized needs. The boundaries between online and offline are becoming increasingly blurred. Consumers are more inclined to choose multi-category with service channel providers. If the retailers still rely on good market resources, and the difference between the sales and purchase of commodities, they will face a huge challenge of the decrease in passenger flow and a decline in performance.
The paper takes the relationship between maternal-baby nursing retailers and targets customers as the study object, based on customer service of maternal-baby nursing retailer data, empirical studies, we found that this particular group, mothers and babies, especially value safety, quality, public praise and community review. If the retail enterprise attaches importance to establishing relationships with customers and enhances the relational viscosity through mutual trust, emotional formation and spread of public praise, it will help to increase the traffic volume and increase the output value of single customers.
The maternal-baby nursing retailers form a strong relationship between enterprises and customers by establishing a strong relationship between products and customers, employees and customers, and customers to customers. Maternal-baby nursing retailers operate single-customer value deeply, build a heavy membership system and manage customer assets, thereby enhancing their brand and performance.
The research on the method of establishing the strong tie can be considered as an analysis of feasibility. The research results of this paper will help to improve the overall customer service experience and satisfaction of the mother and infant retail industry, enhance the development of the whole industry and draw significance lessons from other service industries.
The rapid development of mobile Internet, highly transparent information, consumers grasp the sovereignty, along with the rise of the middle class, consumption increase encouraged personalized, customized needs. The boundaries between online and offline are becoming increasingly blurred. Consumers are more inclined to choose multi-category with service channel providers. If the retailers still rely on good market resources, and the difference between the sales and purchase of commodities, they will face a huge challenge of the decrease in passenger flow and a decline in performance.
The paper takes the relationship between maternal-baby nursing retailers and targets customers as the study object, based on customer service of maternal-baby nursing retailer data, empirical studies, we found that this particular group, mothers and babies, especially value safety, quality, public praise and community review. If the retail enterprise attaches importance to establishing relationships with customers and enhances the relational viscosity through mutual trust, emotional formation and spread of public praise, it will help to increase the traffic volume and increase the output value of single customers.
The maternal-baby nursing retailers form a strong relationship between enterprises and customers by establishing a strong relationship between products and customers, employees and customers, and customers to customers. Maternal-baby nursing retailers operate single-customer value deeply, build a heavy membership system and manage customer assets, thereby enhancing their brand and performance.
The research on the method of establishing the strong tie can be considered as an analysis of feasibility. The research results of this paper will help to improve the overall customer service experience and satisfaction of the mother and infant retail industry, enhance the development of the whole industry and draw significance lessons from other service industries.
ContributorsWang, Jianguo (Author) / Gu, Bin (Thesis advisor) / Chen, Hong (Thesis advisor) / Cui, Haitao (Committee member) / Arizona State University (Publisher)
Created2018
Description
This study examines the 3 key questions of media budget allocation, to find our a better invest model. Including spending share of traditional media and digital media, program selection strategy and duration mix optimization to analyse the trend of sample A (a global cosmetic brand) . Based on every test media campaign, we do research of media performance and sales volumn, add youth consumer behavior result, to develop a media investment ROI model for this brand. Create the evaluation system according to past big data and find the learnings of different length TVC usage. Of course all relavant findings and implications will be summarized after every section.
ContributorsXu, Jin (Author) / Gu, Bin (Thesis advisor) / Chen, Xinlei (Thesis advisor) / Shao, Benjamin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Most advanced economies have evolved into service economies with the majority of their activity and jobs being in the service sector. The manufacturing sector is also going through a similar shift towards services. Manufacturers are increasingly complementing their products with new services in order to satisfy a broader array of customer needs and increase the value of their offerings. This shift has offered significant opportunities to the sector and the success of major firms such as IBM, Caterpillar, and Rolls-Royce in competing through services has been remarkable.
Despite the increased importance of services in the manufacturing sector, the academic literature is yet to investigate the many questions that arise under this new manufacturing paradigm. Perhaps for the same reason study of servitization is listed as a research priority in recent publications both in the field of service operations management and in the field services marketing. This dissertation covers three essays aimed at disentangling multiple aspects of the role of services in the manufacturing sector. The literature on the drivers and implications of transition towards services in manufacturing firms is limited. The three studies in this dissertation aim at shedding light on this issue.
Specifically, the first essay looks at the innovation benefits of service transactions with customers. This paper demonstrate the value of services in getting manufacturers closer to customers and allowing them glean useful information from their service interactions. The second essay investigates the antecedents of service strategy adoption. We suggest that the extant diversification theory does not fully explain servitization and this phenomenon represents a unique type of diversification, which is likely driven by different factors. Through econometric analysis of financial data over a 27-year period, this study explores characteristics of product, firm resources, competition, and industry that encourage adoption of service strategies in manufacturing sector. Finally, the third essay takes a deeper dive and focuses on dealerships, as service centers, in the automobile industry. It investigates the role of dealerships in the success of automakers and explores dealership traits that are critical for market success of an automobile brand.
Despite the increased importance of services in the manufacturing sector, the academic literature is yet to investigate the many questions that arise under this new manufacturing paradigm. Perhaps for the same reason study of servitization is listed as a research priority in recent publications both in the field of service operations management and in the field services marketing. This dissertation covers three essays aimed at disentangling multiple aspects of the role of services in the manufacturing sector. The literature on the drivers and implications of transition towards services in manufacturing firms is limited. The three studies in this dissertation aim at shedding light on this issue.
Specifically, the first essay looks at the innovation benefits of service transactions with customers. This paper demonstrate the value of services in getting manufacturers closer to customers and allowing them glean useful information from their service interactions. The second essay investigates the antecedents of service strategy adoption. We suggest that the extant diversification theory does not fully explain servitization and this phenomenon represents a unique type of diversification, which is likely driven by different factors. Through econometric analysis of financial data over a 27-year period, this study explores characteristics of product, firm resources, competition, and industry that encourage adoption of service strategies in manufacturing sector. Finally, the third essay takes a deeper dive and focuses on dealerships, as service centers, in the automobile industry. It investigates the role of dealerships in the success of automakers and explores dealership traits that are critical for market success of an automobile brand.
ContributorsGolara, Sina (Author) / Dooley, Kevin J (Thesis advisor) / Rogers, Dale (Committee member) / Kull, Thomas (Committee member) / Arizona State University (Publisher)
Created2018