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Description
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
Description
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
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis describes a synthetic task environment, CyberCog, created for the purposes of 1) understanding and measuring individual and team situation awareness in the context of a cyber security defense task and 2) providing a context for evaluating algorithms, visualizations, and other interventions that are intended to improve cyber situation awareness. CyberCog provides an interactive environment for conducting human-in-loop experiments in which the participants of the experiment perform the tasks of a cyber security defense analyst in response to a cyber-attack scenario. CyberCog generates the necessary performance measures and interaction logs needed for measuring individual and team cyber situation awareness. Moreover, the CyberCog environment provides good experimental control for conducting effective situation awareness studies while retaining realism in the scenario and in the tasks performed.
ContributorsRajivan, Prashanth (Author) / Femiani, John (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Lindquist, Timothy (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
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
Description
This dissertation examines the conditions that foster or hinder success of university-based community design centers (CDCs) in the United States. Little is known about the normative underpinnings of CDCs, how successful these centers have been, which factors have contributed to or impeded their success, and how they have responded to the changes in social, political, professional and economic contexts. Adopting Giddens' theory of structuration as a research framework, this study examined CDCs via a mixed-methods sequential research design: a cross-sectional survey of CDCs on current definitions of success and metrics in use; and in-depth interviews to document the centers' histories of change or stasis, and how these changes influenced their successes. The findings of the first phase were utilized to develop a comprehensive success model for current CDCs that comprise measures related to organizational impacts, activities, and capacities. In the multiple case study analysis, four major rationales were identified: universities for public service, pragmatist learning theories, civic professionalism, and social change. These four rationales were evident in all of the studied cases at varying degrees. Using the concept of permeability, the study also exemplified how the processes of CDCs had transformative impacts in institutional, societal, and personal contexts. Multidisciplinarity has also emerged as a theme for the current organizational transformations of CDCs. The main argument that emerged from these findings is that it is not possible to identify a singular model or best practice for CDCs. The strengths and unique potentials of CDCs depend on the alternative rationales, involved agencies, and their social, political and spatial contexts. However, capitalizing on the distinctive attributes of the institutional context (i.e. the university), I consider some possibilities for university-based CDCs with an interdisciplinary structure, pushing the professional, curricular, and institutional boundaries, and striving for systemic change and social justice. In addition to contributing to the theoretical knowledge base, the findings provide useful information to various CDCs across the country, particularly today as they struggle with financial constraints while the community needs they provide are increasingly in demand. Since CDCs have a long history of community service and engagement, the findings can inform other university-community partnerships.
ContributorsTural, Elif (Author) / Ahrentzen, Sherry (Thesis advisor) / Meunier, John (Committee member) / Yabes, Ruth (Committee member) / Arizona State University (Publisher)
Created2011
Description
Customers today, are active participants in service experiences. They are more informed about product choices, their preferences and tend to actively influence customer and firm related outcomes. However, differences across customers become a significant challenge for firms trying to ensure that all customers have a `delightful' consumption experience. This dissertation studies customers as active participants in service experiences and considers three dimensions of customer participation -- in-role performance; extra-role performance-citizenship and elective behavior; and information sharing -- as its focal dependent variables. This study is grounded in services marketing, customer co-production and motivation literatures. The theoretical model proposes that customer behaviors are goal-directed and different consumers will have different reactions to the service quality because they have different assessments of progress towards their goals and (consequently) different levels of participation during the service experience. Customer role clarity and participation behavior will also influence the service experience and firm outcomes. A multi-step process was adopted to test the conceptual model, beginning with qualitative and quantitative pretests; followed by 2 studies (one cross-sectional and other longitudinal in nature). Results prove that customer participation behaviors are influenced by service quality directly and through the mediated path of progress towards goals. Assessment of progress towards goals directly influences customer participation behaviors cross-sectionally. Service quality from one service interaction influences customer in-role performance and information sharing in subsequent service interactions. Information sharing influences service quality in subsequent service interactions. Role-clarity influences in-role and extra-role performance cross-sectionally and influences these behaviors longitudinally only in the early stages of the customer-firm relationship. Due to multi-collinearity, the moderating effect of customer goals on assessment of progress towards goals could not be tested. The study findings contribute to the understanding of customer participation behaviors in service interactions for both academics and managers. It contributes to the literature by examining consumption during the service interaction; considering customers as active participants; explaining differences in customer participation; integrating a forward-looking component (assessment of progress towards goals) and a retrospective component (perceptions of service quality) to explain customer participation behaviors over time; defining and building measures for customer participation behavior.
ContributorsSaxena, Shruti (Author) / Mokwa, Michael (Thesis advisor) / Bitner, Mary Jo (Committee member) / Bolton, Ruth N (Committee member) / Olsen, Grant D (Committee member) / Arizona State University (Publisher)
Created2010
Description
In recent years, the length of time people use and keep belongings has decreased. With the acceptance of short-lived furniture and inexpensive replacements, the American mentality has shifted to thinking that discarding furniture is normal, often in the guise of recycling. Americans are addicted to landfills. The high cost of landfill real estate and other considerable ecological impacts created by the manufacturing of furniture should persuade people to give their belongings a longer life, but in reality, furniture is often prematurely discarded. This grounded theory study takes a multi-method approach to analyze why some types of furniture are kept longer and to theorize about new ways to design and sell furniture that lasts well past its warranty. Case studies bring new insight into designer intention, manufacturer intent, the world of auction-worthy collectables and heirlooms, why there is a booming second-hand furniture market and the growing importance of informed interior designers and architects who specify or help clients choose interior furnishings. An environmental life cycle assessment compares how the length of furniture life affects environmental impacts. A product's life could continue for generations if properly maintained. Designers and manufacturers hoping to promote longevity can apply the conclusions of this report in bringing new pieces to the market that have a much longer life span. This study finds areas of opportunity that promote user attachment, anticipate future repurposing, and provide services. This thinking envisions a paradigm for furniture that can re-invent itself over multiple generations of users, and ultimately lead to a new wave of desirable heirloom furniture.
ContributorsIngham, Sarah (Author) / White, Philip (Thesis advisor) / Wolf, Peter (Committee member) / Underhill, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
This research examines the effects of using similar vs. dissimilar models in health messages on message compliance. I find that level of self-awareness moderates the effect of model similarity on message compliance. Across three studies, I demonstrate that when self-awareness is high, a health message that contains a similar model leads to higher compliance than the same message containing a dissimilar model. On the other hand, when self-awareness is low, a health message that contains a similar model leads to lower message compliance than the same message containing a dissimilar model. Additionally, I demonstrate that the increased compliance observed when self-awareness is high and a similar model is used is associated with self-enhancing behavior and increased engagement with the ad, while the decreased compliance observed when self-awareness is low and a similar model is used is associated with disregarding the ad.
ContributorsLoveland, Katherine (Author) / Mandel, Naomi (Thesis advisor) / Miller, Elizabeth G. (Committee member) / Morales, Andrea C. (Committee member) / Smeesters, Dirk (Committee member) / Arizona State University (Publisher)
Created2011
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 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