Matching Items (481)
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Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java

Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
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As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily

As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them

Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find

Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
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In the past decade, a significant shift has emerged around immigration policy, as advocates and policymakers have made various efforts to pass state and local policies related to immigrant integration or restrictions. This thesis offers original insights into current dynamics in immigration federalism through interviews with lawmakers and community activists

In the past decade, a significant shift has emerged around immigration policy, as advocates and policymakers have made various efforts to pass state and local policies related to immigrant integration or restrictions. This thesis offers original insights into current dynamics in immigration federalism through interviews with lawmakers and community activists in Arizona, a leading state when it comes to restricting the lives of undocumented immigrants. Advancing a new framework that connects the lived experience of officials and activists to partisanship, policy, key events, demographics, and racializing events, this thesis bridges isolated bodies of scholarship on immigration and seeks to demonstrate how every person (not just immigrant) are part of America’s current challenges to become a more inclusive nation of immigrants.

ContributorsNeville, Christopher Francis (Author) / Colbern, Allan (Thesis director) / Martinez-Orosco, Rafael (Committee member) / School of Social and Behavioral Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Something Like Human explores corporate social responsibility through a triple lens, providing a content analysis using previous literature and history as the standards for evaluation. Section I reviews the history of corporate social responsibility and how it is currently understood and employed today. Section II turns its focus to a

Something Like Human explores corporate social responsibility through a triple lens, providing a content analysis using previous literature and history as the standards for evaluation. Section I reviews the history of corporate social responsibility and how it is currently understood and employed today. Section II turns its focus to a specific socially conscious corporation, Lush Cosmetics, examining its practices considering the concepts provided in Section I and performing a close analysis of its promotional materials. Section III consists of a mock marketing campaign designed for Lush in light of their social commitments. By the end of this thesis, the goal for the reader is to ask: Can major corporations be something like human?

ContributorsDalgleish, Alayna Rose (Author) / Gruber, Diane (Thesis director) / Thornton, Leslie-Jean (Committee member) / School of Social and Behavioral Sciences (Contributor, Contributor) / Walter Cronkite School of Journalism and Mass Comm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Veterans are approximately 30% more likely than non-veterans to suffer from severe hearing impairment. Tinnitus, or ringing in the ears, which is increasingly common among military service men and women, has been linked to significant cognitive and psychological impairment and can be worsened by the same sounds that trigger post-traumatic

Veterans are approximately 30% more likely than non-veterans to suffer from severe hearing impairment. Tinnitus, or ringing in the ears, which is increasingly common among military service men and women, has been linked to significant cognitive and psychological impairment and can be worsened by the same sounds that trigger post-traumatic stress disorder (PTSD). In fact, tinnitus and PTSD often present as comorbidities, and recent studies suggest these two disorders may share a common neurological pathway. Additional studies are required to better understand the connection between hearing loss and impaired cognitive function such as that observed in with PTSD. Here, we use the fruit fly, Drosophila melanogaster, to explore the relationship between hearing loss and cognitive function. Negative geotaxis climbing assays and courtship behavior analysis were used to examine neurobehavioral changes induced by prolonged, intense auditory stimulation. Preliminary results suggest that exposure to loud noise for an extended period of time significantly affected Drosophila behavior, with males being more sensitive than females. Based on our results, there appears to be a potential connection between noise exposure and behavior, further suggesting that Drosophila could be an effective model to study the link between hearing loss and PTSD.

ContributorsMichael, Allison Faye (Author) / Hackney-Price, Jennifer (Thesis director) / Sellner, Erin (Committee member) / School of Social and Behavioral Sciences (Contributor) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Socioeconomic differences have driven society and laid the foundation for the types of opportunities and resources one is eligible to receive. Higher socioeconomic status provides individuals the chance of obtaining an overall better education, occupation, and income. We see this with particular clarity when we examine healthcare. The World Health

Socioeconomic differences have driven society and laid the foundation for the types of opportunities and resources one is eligible to receive. Higher socioeconomic status provides individuals the chance of obtaining an overall better education, occupation, and income. We see this with particular clarity when we examine healthcare. The World Health Organization has regarded healthcare as a fundamental human right, except socioeconomically disadvantaged individuals not only do not have equal access to healthcare, but they also often receive a lower quality of care. These socioeconomic differences are often paired with racial differences, resulting in one group, or set of groups, having social advantage over the others. Although this problem has been discussed throughout the past century, it has not been properly addressed materially and practically. Unequal access to quality healthcare is especially highlighted throughout the COVID-19 pandemic, where there has been evidence that minorities, in particular Black communities, have received inadequate care. Quality healthcare has become unaffordable and a luxury that only certain groups get the privilege of receiving. Not only that, but the ongoing inequalities in the healthcare system have gone so far that they have instilled hostility and mistrust towards the healthcare system.

ContributorsMartinez Castro, Karen (Author) / Sturgess, Jessica (Thesis director) / Sellner, Erin (Committee member) / School of Social and Behavioral Sciences (Contributor) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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In 2020, the world was swept by a global pandemic. It disrupted the lives of millions; many lost their jobs, students were forced to leave schools, and children were left with little to do while quarantined at their houses. Although the media outlets covered very little of how children were

In 2020, the world was swept by a global pandemic. It disrupted the lives of millions; many lost their jobs, students were forced to leave schools, and children were left with little to do while quarantined at their houses. Although the media outlets covered very little of how children were being affected by COVID-19, it was obvious that their group was not immune to the issues the world was facing. Being stuck at home with little to do took a mental and physical toll on many kids. That is when EVOLVE Academy became an idea; our team wanted to create a fully online platform for children to help them practice and evolve their athletics skills, or simply spend part of their day performing a physical and health activity. Our team designed a solution that would benefit children, as well as parents that were struggling to find engaging activities for their kids while out of school. We quickly encountered issues that made it difficult for us to reach our target audience and make them believe and trust our platform. However, we persisted and tried to solve and answer the questions and problems that came along the way. Sadly, the same pandemic that opened the widow for EVOLVE Academy to exist, is now the reason people are walking away from it. Children want real interaction. They want to connect with other kids through more than just a screen. Although the priority of parents remains the safety and security of their kids, parents are also searching and opting for more “human” interactions, leaving EVOLVE Academy with little room to grow and succeed.

ContributorsHernandez, Melany (Co-author) / Parmenter, Taylor (Co-author) / Byrne, Jared (Thesis director) / Kunowski, Jeffrey (Committee member) / Lee, Christopher (Committee member) / Thunderbird School of Global Management (Contributor, Contributor) / School of Social and Behavioral Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

Evidence of a widening political divide between the Democrat and Republican parties creates concern over the possible ramifications of this phenomenon. One method of assessing dissonance between political parties is through a medium that is shaped by personal beliefs: online dating. Previous research finds that political choice homophily, or a

Evidence of a widening political divide between the Democrat and Republican parties creates concern over the possible ramifications of this phenomenon. One method of assessing dissonance between political parties is through a medium that is shaped by personal beliefs: online dating. Previous research finds that political choice homophily, or a preference for those who are politically similar, is relevant in online dating settings. Researchers find that individuals are significantly more likely to be attracted to or pursue others who have similar political views to themselves. There is, however, a gap in knowledge regarding online dating that occurs separately from conventional online dating sites. Mobile dating apps, applications that are highly popular amongst young adults, have not been thoroughly explored with political choice homophily in mind. The current study aims to test the correlation between dating preference and political beliefs, using the mobile dating app Bumble as a template for a mock (fake) dating app scenario. In the dating app survey that was distributed to 132 ASU students, participants completed a simulated “matching” section where they matched with a list of 15 fabricated Bumble profiles. The fake Bumble profiles randomly contained politically charged or politically neutral statements in the basic info section. Political affiliation of the participant was measured using a sliding scale that quantifies the unidimensional conservative-liberal spectrum on a 0-100 numerical scale. Findings of the survey show that there was no significant difference between participant preference towards politically charged vs. politically neutral mock profiles.

ContributorsGilmore, Ethan James (Author) / Lewis, Stephen (Thesis director) / Halavais, Alexander (Committee member) / School of Social and Behavioral Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05