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- All Subjects: Computer Science
- All Subjects: Nutrition
- Creators: Arizona State University
- Creators: Yang, Yezhou
Description
This dissertation is focused on building scalable Attribute Based Security Systems (ABSS), including efficient and privacy-preserving attribute based encryption schemes and applications to group communications and cloud computing. First of all, a Constant Ciphertext Policy Attribute Based Encryption (CCP-ABE) is proposed. Existing Attribute Based Encryption (ABE) schemes usually incur large, linearly increasing ciphertext. The proposed CCP-ABE dramatically reduces the ciphertext to small, constant size. This is the first existing ABE scheme that achieves constant ciphertext size. Also, the proposed CCP-ABE scheme is fully collusion-resistant such that users can not combine their attributes to elevate their decryption capacity. Next step, efficient ABE schemes are applied to construct optimal group communication schemes and broadcast encryption schemes. An attribute based Optimal Group Key (OGK) management scheme that attains communication-storage optimality without collusion vulnerability is presented. Then, a novel broadcast encryption model: Attribute Based Broadcast Encryption (ABBE) is introduced, which exploits the many-to-many nature of attributes to dramatically reduce the storage complexity from linear to logarithm and enable expressive attribute based access policies. The privacy issues are also considered and addressed in ABSS. Firstly, a hidden policy based ABE schemes is proposed to protect receivers' privacy by hiding the access policy. Secondly,a new concept: Gradual Identity Exposure (GIE) is introduced to address the restrictions of hidden policy based ABE schemes. GIE's approach is to reveal the receivers' information gradually by allowing ciphertext recipients to decrypt the message using their possessed attributes one-by-one. If the receiver does not possess one attribute in this procedure, the rest of attributes are still hidden. Compared to hidden-policy based solutions, GIE provides significant performance improvement in terms of reducing both computation and communication overhead. Last but not least, ABSS are incorporated into the mobile cloud computing scenarios. In the proposed secure mobile cloud data management framework, the light weight mobile devices can securely outsource expensive ABE operations and data storage to untrusted cloud service providers. The reported scheme includes two components: (1) a Cloud-Assisted Attribute-Based Encryption/Decryption (CA-ABE) scheme and (2) An Attribute-Based Data Storage (ABDS) scheme that achieves information theoretical optimality.
ContributorsZhou, Zhibin (Author) / Huang, Dijiang (Thesis advisor) / Yau, Sik-Sang (Committee member) / Ahn, Gail-Joon (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2011
Description
This study examined the effect of consuming pinto, black, and dark red kidney beans with white rice in comparison to a white rice only control meal on the glycemic response of adults with type 2 diabetes (T2D). These bean and rice combinations are part of many traditional diets. Seventeen subjects with T2D treated by diet and/or metformin were randomly assigned to 4 treatments: white rice (control), pinto beans/rice, black beans/rice, and dark red kidney beans/rice. All treatments were portioned by weight and matched for available carbohydrate content of ∼ 50 grams. Capillary whole blood samples were collected at baseline and at 30, 60, 90, 120, 150 and 180 minutes posttreatment and assessed for glucose concentration using the YSI Stat Plus Analyzer. Net change glucose responses were significantly lower for the pinto, black, and dark red kidney bean and rice meals than control at 90, 120 and 150 minutes posttreatment (P < 0.05). Incremental area under the curve (iAUC) values were also significantly reduced for the bean/rice meals containing pinto (P < 0.01) and black beans (P < 0.05) in contrast to the rice control. Results suggest that the combination of whole beans and rice may be beneficial to those with T2D to assist with blood glucose management.
ContributorsThompson, Sharon (Author) / Winham, Donna M (Thesis advisor) / Beezhold, Bonnie (Committee member) / Dixon, Kathleen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Like individual organisms, complex social groups are able to maintain predictable trajectories of growth, from initial colony foundation to mature reproductively capable units. They do so while simultaneously responding flexibly to variation in nutrient availability and intake. Leafcutter ant colonies function as tri-trophic systems, in which the ants harvest vegetation to grow a fungus that, in turn, serves as food for the colony. Fungal growth rates and colony worker production are interdependent, regulated by nutritional and behavioral feedbacks. Fungal growth and quality are directly affected by worker foraging decisions, while worker production is, in turn, dependent on the amount and condition of the fungus. In this dissertation, I first characterized the growth relationship between the workers and the fungus of the desert leafcutter ant Acromyrmex versicolor during early stages of colony development, from colony foundation by groups of queens through the beginnings of exponential growth. I found that this relationship undergoes a period of slow growth and instability when workers first emerge, and then becomes allometrically positive. I then evaluated how mass and element ratios of resources collected by the ants are translated into fungus and worker population growth, and refuse, finding that colony digestive efficiency is comparable to digestive efficiencies of other herbivorous insects and ruminants. To test how colonies behaviorally respond to perturbations of the fungus garden, I quantified activity levels and task performance of workers in colonies with either supplemented or diminished fungus gardens, and found that colonies adjusted activity and task allocation in response to the fungus garden size. Finally, to identify possible forms of nutrient limitation, I measured how colony performance was affected by changes in the relative amounts of carbohydrates, protein, and phosphorus available in the resources used to grow the fungus garden. From this experiment, I concluded that colony growth is primarily carbohydrate-limited.
ContributorsClark, Rebecca, 1981- (Author) / Fewell, Jennifer H (Thesis advisor) / Mueller, Ulrich (Committee member) / Liebig, Juergen (Committee member) / Elser, James (Committee member) / Harrison, Jon (Committee member) / Arizona State University (Publisher)
Created2011
Description
Internet sites that support user-generated content, so-called Web 2.0, have become part of the fabric of everyday life in technologically advanced nations. Users collectively spend billions of hours consuming and creating content on social networking sites, weblogs (blogs), and various other types of sites in the United States and around the world. Given the fundamentally emotional nature of humans and the amount of emotional content that appears in Web 2.0 content, it is important to understand how such websites can affect the emotions of users. This work attempts to determine whether emotion spreads through an online social network (OSN). To this end, a method is devised that employs a model based on a general threshold diffusion model as a classifier to predict the propagation of emotion between users and their friends in an OSN by way of mood-labeled blog entries. The model generalizes existing information diffusion models in that the state machine representation of a node is generalized from being binary to having n-states in order to support n class labels necessary to model emotional contagion. In the absence of ground truth, the prediction accuracy of the model is benchmarked with a baseline method that predicts the majority label of a user's emotion label distribution. The model significantly outperforms the baseline method in terms of prediction accuracy. The experimental results make a strong case for the existence of emotional contagion in OSNs in spite of possible alternative arguments such confounding influence and homophily, since these alternatives are likely to have negligible effect in a large dataset or simply do not apply to the domain of human emotions. A hybrid manual/automated method to map mood-labeled blog entries to a set of emotion labels is also presented, which enables the application of the model to a large set (approximately 900K) of blog entries from LiveJournal.
ContributorsCole, William David, M.S (Author) / Liu, Huan (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Candan, Kasim S (Committee member) / Arizona State University (Publisher)
Created2011
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 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
Description
This thesis addresses the problem of online schema updates where the goal is to be able to update relational database schemas without reducing the database system's availability. Unlike some other work in this area, this thesis presents an approach which is completely client-driven and does not require specialized database management systems (DBMS). Also, unlike other client-driven work, this approach provides support for a richer set of schema updates including vertical split (normalization), horizontal split, vertical and horizontal merge (union), difference and intersection. The update process automatically generates a runtime update client from a mapping between the old the new schemas. The solution has been validated by testing it on a relatively small database of around 300,000 records per table and less than 1 Gb, but with limited memory buffer size of 24 Mb. This thesis presents the study of the overhead of the update process as a function of the transaction rates and the batch size used to copy data from the old to the new schema. It shows that the overhead introduced is minimal for medium size applications and that the update can be achieved with no more than one minute of downtime.
ContributorsTyagi, Preetika (Author) / Bazzi, Rida (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Mexican Americans have an increased risk for type 2 diabetes and premature cardiovascular disease (CVD). The association of hyperglycemia with traditional CVD risk factors in this population has been established, but there is limited data regarding other non-traditional CVD risk factors. Thus, this cross-sectional study was conducted to evaluate CVD risk among Mexican Americans by measuring concentrations of lipids, high-sensitivity C-reactive protein (hsCRP), and cholesterol in low-density-lipoprotein (LDL) and high-density-lipoprotein (HDL) subfractions. Eighty overweight/obese Mexican-American adults participating in the Maricopa Insulin Resistance Initiative were randomly selected from each of the following four groups (n = 20 per group): nomolipidemic
ormoglycemic controls (NC), dyslipidemic
ormoglycemic (DN), dyslipidemic/prediabetic (DPD) and dyslipidemic/diabetic (DD). Total cholesterol (TC) was 30% higher among DD than in NC participants (p<0.0001). The DPD group had 27% and 12% higher LDL-C concentrations than the NC and DN groups, respectively. Similarly, LDL-C was 29% and 13% higher in DD than in NC and DN participants (p=0.013). An increasing trend was observed in %10-year CVD risk with increasing degree of hyperglycemia (p<0.0001). The NC group had less cholesterol in sdLDL particles than dyslipidemic groups, regardless of glycemic status (p<0.0001). When hyperglycemia was part of the phenotype (DPD and DD), there was a greater proportion of total and HDL-C in sHDL particles in dyslipidemic individuals than in NC (p=0.023; p<0.0001; respectively). Percent 10-year CVD risk was positively correlated with triglyceride (TG) (r=0.384, p<0.0001), TC (r=0.340, p<0.05), cholesterol in sdLDL(r=0.247; p<0.05), and TC to HDL-C ratio (r=0.404, p<0.0001), and negatively correlated with HDL-C in intermediate and large HDL(r=-0.38, p=0.001; r=0.34, p=0.002, respectively). The TC/HDL-C was positively correlated with cholesterol in sdLDL particles (r=0.698, p<0.0001) and HDL-C in sHDL particles (r=0.602, p<0.0001), and negatively correlated with cholesterol in small (r=-0.35, p=0.002), intermediate (r=-0.91, p<0.0001) and large (r=-0.84, p<0.0001) HDL particles, and HDL-C in the large HDL particles (r=-0.562, p<0.0001). No significant association was found between %10-year CVD risk and hsCRP. Collectively, these results corroborate that dyslipidemic Mexican-American adults have higher CVD risk than normolipidemic individuals. Hyperglycemia may further affect CVD risk by modulating cholesterol in LDL and HDL subfractions.
ormoglycemic controls (NC), dyslipidemic
ormoglycemic (DN), dyslipidemic/prediabetic (DPD) and dyslipidemic/diabetic (DD). Total cholesterol (TC) was 30% higher among DD than in NC participants (p<0.0001). The DPD group had 27% and 12% higher LDL-C concentrations than the NC and DN groups, respectively. Similarly, LDL-C was 29% and 13% higher in DD than in NC and DN participants (p=0.013). An increasing trend was observed in %10-year CVD risk with increasing degree of hyperglycemia (p<0.0001). The NC group had less cholesterol in sdLDL particles than dyslipidemic groups, regardless of glycemic status (p<0.0001). When hyperglycemia was part of the phenotype (DPD and DD), there was a greater proportion of total and HDL-C in sHDL particles in dyslipidemic individuals than in NC (p=0.023; p<0.0001; respectively). Percent 10-year CVD risk was positively correlated with triglyceride (TG) (r=0.384, p<0.0001), TC (r=0.340, p<0.05), cholesterol in sdLDL(r=0.247; p<0.05), and TC to HDL-C ratio (r=0.404, p<0.0001), and negatively correlated with HDL-C in intermediate and large HDL(r=-0.38, p=0.001; r=0.34, p=0.002, respectively). The TC/HDL-C was positively correlated with cholesterol in sdLDL particles (r=0.698, p<0.0001) and HDL-C in sHDL particles (r=0.602, p<0.0001), and negatively correlated with cholesterol in small (r=-0.35, p=0.002), intermediate (r=-0.91, p<0.0001) and large (r=-0.84, p<0.0001) HDL particles, and HDL-C in the large HDL particles (r=-0.562, p<0.0001). No significant association was found between %10-year CVD risk and hsCRP. Collectively, these results corroborate that dyslipidemic Mexican-American adults have higher CVD risk than normolipidemic individuals. Hyperglycemia may further affect CVD risk by modulating cholesterol in LDL and HDL subfractions.
ContributorsNeupane, Srijana (Author) / Vega-Lopez, Sonia (Thesis advisor) / Shaibi, Gabriel Q (Committee member) / Johnston, Carol S (Committee member) / Arizona State University (Publisher)
Created2011
Description
Lots of previous studies have analyzed human tutoring at great depths and have shown expert human tutors to produce effect sizes, which is twice of that produced by an intelligent tutoring system (ITS). However, there has been no consensus on which factor makes them so effective. It is important to know this, so that same phenomena can be replicated in an ITS in order to achieve the same level of proficiency as expert human tutors. Also, to the best of my knowledge no one has looked at student reactions when they are working with a computer based tutor. The answers to both these questions are needed in order to build a highly effective computer-based tutor. My research focuses on the second question. In the first phase of my thesis, I analyzed the behavior of students when they were working with a step-based tutor Andes, using verbal-protocol analysis. The accomplishment of doing this was that I got to know of some ways in which students use a step-based tutor which can pave way for the creation of more effective computer-based tutors. I found from the first phase of the research that students often keep trying to fix errors by guessing repeatedly instead of asking for help by clicking the hint button. This phenomenon is known as hint refusal. Surprisingly, a large portion of the student's foundering was due to hint refusal. The hypothesis tested in the second phase of the research is that hint refusal can be significantly reduced and learning can be significantly increased if Andes uses more unsolicited hints and meta hints. An unsolicited hint is a hint that is given without the student asking for one. A meta-hint is like an unsolicited hint in that it is given without the student asking for it, but it just prompts the student to click on the hint button. Two versions of Andes were compared: the original version and a new version that gave more unsolicited and meta-hints. During a two-hour experiment, there were large, statistically reliable differences in several performance measures suggesting that the new policy was more effective.
ContributorsRanganathan, Rajagopalan (Author) / VanLehn, Kurt (Thesis advisor) / Atkinson, Robert (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2011
Description
Vitamin C is a micronutrient with many important physiological roles. It can function as a reducing agent, a free radical scavenger, and an enzyme cofactor. Much research has examined the potential of vitamin C supplements to enhance exercise capacity in trained athletes; however, little is known regarding the effects of vitamin C supplements on the promotion of leisure-time physical activity in the general population. This area deserves attention since 1/3 of Americans have below adequate vitamin C status, and since aversion to exercise, fatigue, and altered mood states are the earliest signs of poor vitamin C status. This study analyzed the effect of supplementing 500 mg twice daily of vitamin C on self-reported leisure-time activity levels and mood states in young men. Twenty-nine healthy, young men, aged 18-35 years, were stratified by age, BMI, smoking status, and plasma vitamin C concentrations and assigned to either a control (CON) or experimental group (VTC) for the 8-week randomized, double-blinded, parallel arm trial. Subjects were instructed to keep track of their leisure-time physical activity by filling out the validated Godin Leisure-Time Exercise Questionnaire weekly for the entire study. In addition, subjects took the self-administered Profile of Mood States (POMS) at baseline, week 4, and week 8 to observe mood states. Plasma vitamin C concentrations were analyzed at the initial screening, week 4, and week 8 of the study. Plasma vitamin C concentrations significantly differed by group at week 4 and week 8. Furthermore, vitamin C supplementation significantly increased self-reported mild, moderate, and strenuous activity levels during the 8-week trial. Overall, total physical activity scores increased nearly 50% in the VTC group as compared to 18% in the CON group (p=0.001). However, mood states were not significantly impacted by vitamin C supplementation during the trial. This study provides the first experimental evidence that supplementing 500 mg of vitamin C twice daily can be effective in increasing leisure-time physical activity in healthy young men. This study, however, was unable to link improvements in physical activity rates to improved mood states. Since sedentary behaviors have been implicated in the rise of obesity in the U.S., further research should be conducted to substantiate the finding that vitamin C supplementation increases physical activity.
ContributorsSchumacher, Sarah (Author) / Johnston, Carol (Thesis advisor) / Appel, Christy (Committee member) / Swan, Pamela (Committee member) / Arizona State University (Publisher)
Created2012
Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011