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With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications

With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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Description

Humans use emotions to communicate social cues to our peers on a daily basis. Are we able to identify context from facial expressions and match them to specific scenarios? This experiment found that people can effectively distinguish negative and positive emotions from each other from a short description. However, further

Humans use emotions to communicate social cues to our peers on a daily basis. Are we able to identify context from facial expressions and match them to specific scenarios? This experiment found that people can effectively distinguish negative and positive emotions from each other from a short description. However, further research is needed to find out whether humans can learn to perceive emotions only from contextual explanations.

ContributorsCulbert, Bailie (Author) / Hartwell, Leland (Thesis director) / McAvoy, Mary (Committee member) / School of Life Sciences (Contributor) / School of Criminology and Criminal Justice (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Since the inception of what is now known as the Behavioral Analysis Unit (BAU) at the Federal Bureau of Investigation (FBI) in the 1970s, criminal profiling has become an increasingly prevalent entity in both forensic science and the popular imagination. The fundamental idea of which profiling is premised – behavior

Since the inception of what is now known as the Behavioral Analysis Unit (BAU) at the Federal Bureau of Investigation (FBI) in the 1970s, criminal profiling has become an increasingly prevalent entity in both forensic science and the popular imagination. The fundamental idea of which profiling is premised – behavior as a reflection of personality – has been the subject of a great deal of misunderstanding, with professionals and nonprofessionals alike questioning whether profiling represents an art or a science and what its function in forensic science should be. To provide a more thorough understanding of criminal profiling’s capabilities and its efficacy as a law enforcement tool, this thesis will examine the application of criminal profiling to investigations, various court rulings concerning profiling’s admissibility, and the role that popular media plays in the perception and function of the practice. It will also discuss how future research and regulatory advancements may strengthen criminal profiling’s scientific merit and legitimacy.

ContributorsGeraghty, Bridget Elizabeth (Author) / Kobojek, Kimberly (Thesis director) / Gruber, Diane (Committee member) / School of International Letters and Cultures (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

COVID-19 has shocked the bedrock of society, impacting both human life and the economy. Accompanying this shock has been the psychological distress inflicted onto the general population as a result of the emotion strain stemming from isolation/quarantine policies, being sick with COVID-19, dealing with COVID-19 losses, and post-COVID syndrome and

COVID-19 has shocked the bedrock of society, impacting both human life and the economy. Accompanying this shock has been the psychological distress inflicted onto the general population as a result of the emotion strain stemming from isolation/quarantine policies, being sick with COVID-19, dealing with COVID-19 losses, and post-COVID syndrome and its effect on quality of life. The psychological distress has been experienced by the general population, but compared to middle age (30-50) and older adults (>50 years of age), it has been young adults (18-30 years old) who have been more psychologically affected (Glowacz & Schmits, 2020). Psychological distress, specifically anxiety and depression, has been exacerbated by feelings of uncertainty, fear of illness, losing loved ones, and fear of post-COVID syndrome. Post-COVID syndrome, as with other post-viral syndromes such as post viral SARS involve lingering symptoms such as myalgic encephalomyelitis or Chronic Fatigue Syndrome (CFS), and loss of motivation (Underhill, 2015). In addition to these symptoms, patients suffering from post-COVID syndrome have also presented brain inflammation and damaged brain blood vessels (Meinhardt et al., 2021), Endotheliitis (Varga et al., 2020), CV abnormalities and changes in glucose metabolism (Williams et al., 2020). CV abnormalities and changes in glucose metabolism are connected to chronic illnesses like diabetes and heart disease respectively. These chronic illnesses are then associated with higher risk for depression as a result of the stress induced by the symptoms and their impact on quality of life (NIMH, 2021). Further monitoring, and research will be important to gauge ultimate physiological and psychological impact of COVID-19.

ContributorsPiedra Gonzalez, Michael (Author) / Vargas, Perla (Thesis director) / Oh, Hyunsung (Committee member) / College of Health Solutions (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

This paper explores the psychological experiences of domestic workers in three contemporary Latin American films: Roma (Mexico, 2018), Crímenes de familia (Argentina, 2020) and Que Horas Ela Volta? (Brazil, 2015). Specifically, the motherhood of these three protagonists is explored and analyzed using psychological research that pertains to motherhood, trauma, and

This paper explores the psychological experiences of domestic workers in three contemporary Latin American films: Roma (Mexico, 2018), Crímenes de familia (Argentina, 2020) and Que Horas Ela Volta? (Brazil, 2015). Specifically, the motherhood of these three protagonists is explored and analyzed using psychological research that pertains to motherhood, trauma, and the relationships between domestic workers and the families that employ them. This paper reveals that contemporary Latin American cinema portrays domestic workers as having negative experiences of motherhood as a direct result of their occupation and proposes for further protections, policy change, and psychological research to take place for domestic workers in Latin America and beyond.

ContributorsSamuels, Alana D (Author) / Tompkins, Cynthia (Thesis director) / Bezerra, Ligia (Committee member) / School of International Letters and Cultures (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Developed a business product with a team of CS students.

ContributorsPerri, Cole Thomas (Co-author) / Hernandez, Maximilliano (Co-author) / Schneider, Kaitlin (Co-author) / Call, Andy (Thesis director) / Hunt, Neil (Committee member) / School of Accountancy (Contributor) / Watts College of Public Service & Community Solut (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Curiosity has been linked with many benefits, including increased overall well-being (Lydon-Staley et al., 2020) and greater academic achievement (Gottfried et al., 2016). The value that children place on learning new things and exploring novel ideas is unrivaled by older individuals. However, little research has been conducted to examine

Curiosity has been linked with many benefits, including increased overall well-being (Lydon-Staley et al., 2020) and greater academic achievement (Gottfried et al., 2016). The value that children place on learning new things and exploring novel ideas is unrivaled by older individuals. However, little research has been conducted to examine how parents may be able to help foster their children’s curiosity in a way that teaches them how to effectively search for and synthesize information. This paper aims to determine how parents’ language during a storybook task is related to their children’s strategy to collect rewards during a search game. Preliminary results suggest that parents may be able to encourage more effective search by asking more close-ended questions. These findings provide insight into how parents and guardians may be able to encourage their children to become better adept at searching for information by taking in clues about their environment and modifying their behavior to maximize their efforts.

ContributorsScirpo, Kalie Rose (Author) / Lucca, Kelsey (Thesis director) / Kupfer, Anne (Committee member) / Department of Psychology (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
Query Expansion is a functionality of search engines that suggest a set of related queries for a user issued keyword query. In case of exploratory or ambiguous keyword queries, the main goal of the user would be to identify and select a specific category of query results among different categorical

Query Expansion is a functionality of search engines that suggest a set of related queries for a user issued keyword query. In case of exploratory or ambiguous keyword queries, the main goal of the user would be to identify and select a specific category of query results among different categorical options, in order to narrow down the search and reach the desired result. Typical corpus-driven keyword query expansion approaches return popular words in the results as expanded queries. These empirical methods fail to cover all semantics of categories present in the query results. More importantly these methods do not consider the semantic relationship between the keywords featured in an expanded query. Contrary to a normal keyword search setting, these factors are non-trivial in an exploratory and ambiguous query setting where the user's precise discernment of different categories present in the query results is more important for making subsequent search decisions. In this thesis, I propose a new framework for keyword query expansion: generating a set of queries that correspond to the categorization of original query results, which is referred as Categorizing query expansion. Two approaches of algorithms are proposed, one that performs clustering as pre-processing step and then generates categorizing expanded queries based on the clusters. The other category of algorithms handle the case of generating quality expanded queries in the presence of imperfect clusters.
ContributorsNatarajan, Sivaramakrishnan (Author) / Chen, Yi (Thesis advisor) / Candan, Selcuk (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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Description
A statement appearing in social media provides a very significant challenge for determining the provenance of the statement. Provenance describes the origin, custody, and ownership of something. Most statements appearing in social media are not published with corresponding provenance data. However, the same characteristics that make the social media environment

A statement appearing in social media provides a very significant challenge for determining the provenance of the statement. Provenance describes the origin, custody, and ownership of something. Most statements appearing in social media are not published with corresponding provenance data. However, the same characteristics that make the social media environment challenging, including the massive amounts of data available, large numbers of users, and a highly dynamic environment, provide unique and untapped opportunities for solving the provenance problem for social media. Current approaches for tracking provenance data do not scale for online social media and consequently there is a gap in provenance methodologies and technologies providing exciting research opportunities. The guiding vision is the use of social media information itself to realize a useful amount of provenance data for information in social media. This departs from traditional approaches for data provenance which rely on a central store of provenance information. The contemporary online social media environment is an enormous and constantly updated "central store" that can be mined for provenance information that is not readily made available to the average social media user. This research introduces an approach and builds a foundation aimed at realizing a provenance data capability for social media users that is not accessible today.
ContributorsBarbier, Geoffrey P (Author) / Liu, Huan (Thesis advisor) / Bell, Herbert (Committee member) / Li, Baoxin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of

Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011