Matching Items (27)
Description
The Internet is transforming its look, in a short span of time we have come very far from black and white web forms with plain buttons to responsive, colorful and appealing user interface elements. With the sudden rise in demand of web applications, developers are making full use of the power of HTML5, JavaScript and CSS3 to cater to their users on various platforms. There was never a need of classifying the ways in which these languages can be interconnected to each other as the size of the front end code base was relatively small and did not involve critical business logic. This thesis focuses on listing and defining all dependencies between HTML5, JavaScript and CSS3 that will help developers better understand the interconnections within these languages. We also explore the present techniques available to a developer to make his code free of dependency related defects. We build a prototype tool, HJCDepend, based on our model, which aims at helping developers discover and remove defects early in the development cycle.
ContributorsVasugupta (Author) / Gary, Kevin (Thesis advisor) / Lindquist, Timothy (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2014
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
The moderating effects of five characteristics of peers--their effortful control, anger, sadness, aggression, and positive peer behavior--were investigated in two separate series of analyses of preschooler's social behavior: (a) the relation between children's own effortful control and social behavior, and (b) the relation between children's shyness and reticent behavior. Latent variable interactions were conducted in a structural equation framework. Peer context anger and effortful control, albeit with unexpected results, interacted with children's own characteristics to predict their behavior in both the EC and shy model series; these were the only significant interactions obtained for the EC model series. The relation between shyness and reticent behavior, however, showed the greatest impact of peer context and, conversely, the greatest susceptibility to environmental variations; significant interactions were obtained in all five models, despite the limited range of peer context sadness and aggression observed in this study.
ContributorsHuerta, Snježana (Author) / Eisenberg, Nancy (Thesis advisor) / Spinrad, Tracy (Committee member) / Pina, Armando (Committee member) / Geiser, Christian (Committee member) / Arizona State University (Publisher)
Created2012
Description
The Semantic Web contains large amounts of related information in the form of knowledge graphs such as DBpedia. These knowledge graphs are typically enormous and are not easily accessible for users as they need specialized knowledge in query languages (such as SPARQL) as well as deep familiarity of the ontologies used by these knowledge graphs. So, to make these knowledge graphs more accessible (even for non- experts) several question answering (QA) systems have been developed over the last decade. Due to the complexity of the task, several approaches have been undertaken that include techniques from natural language processing (NLP), information retrieval (IR), machine learning (ML) and the Semantic Web (SW). At a higher level, most question answering systems approach the question answering task as a conversion from the natural language question to its corresponding SPARQL query. These systems then utilize the query to retrieve the desired entities or literals. One approach to solve this problem, that is used by most systems today, is to apply deep syntactic and semantic analysis on the input question to derive the SPARQL query. This has resulted in the evolution of natural language processing pipelines that have common characteristics such as answer type detection, segmentation, phrase matching, part-of-speech-tagging, named entity recognition, named entity disambiguation, syntactic or dependency parsing, semantic role labeling, etc.
This has lead to NLP pipeline architectures that integrate components that solve a specific aspect of the problem and pass on the results to subsequent components for further processing eg: DBpedia Spotlight for named entity recognition, RelMatch for relational mapping, etc. A major drawback in this approach is error propagation that is a common problem in NLP. This can occur due to mistakes early on in the pipeline that can adversely affect successive steps further down the pipeline. Another approach is to use query templates either manually generated or extracted from existing benchmark datasets such as Question Answering over Linked Data (QALD) to generate the SPARQL queries that is basically a set of predefined queries with various slots that need to be filled. This approach potentially shifts the question answering problem into a classification task where the system needs to match the input question to the appropriate template (class label).
This thesis proposes a neural network approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination for the need of laborious feature engineering that can be cumbersome and error prone. The input question would be encoded into a vector representation. The model will be trained and evaluated on the LC-QuAD Dataset (Large-scale Complex Question Answering Dataset). The dataset was created explicitly for machine learning based QA approaches for learning complex SPARQL queries. The dataset consists of 5000 questions along with their corresponding SPARQL queries over the DBpedia dataset spanning 5042 entities and 615 predicates. These queries were annotated based on 38 unique templates that the model will attempt to classify. The resulting model will be evaluated against both the LC-QuAD dataset and the Question Answering Over Linked Data (QALD-7) dataset.
The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset.
After slot filling, the overall system achieves a macro F-score 0.419 on the LC- QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.
This has lead to NLP pipeline architectures that integrate components that solve a specific aspect of the problem and pass on the results to subsequent components for further processing eg: DBpedia Spotlight for named entity recognition, RelMatch for relational mapping, etc. A major drawback in this approach is error propagation that is a common problem in NLP. This can occur due to mistakes early on in the pipeline that can adversely affect successive steps further down the pipeline. Another approach is to use query templates either manually generated or extracted from existing benchmark datasets such as Question Answering over Linked Data (QALD) to generate the SPARQL queries that is basically a set of predefined queries with various slots that need to be filled. This approach potentially shifts the question answering problem into a classification task where the system needs to match the input question to the appropriate template (class label).
This thesis proposes a neural network approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination for the need of laborious feature engineering that can be cumbersome and error prone. The input question would be encoded into a vector representation. The model will be trained and evaluated on the LC-QuAD Dataset (Large-scale Complex Question Answering Dataset). The dataset was created explicitly for machine learning based QA approaches for learning complex SPARQL queries. The dataset consists of 5000 questions along with their corresponding SPARQL queries over the DBpedia dataset spanning 5042 entities and 615 predicates. These queries were annotated based on 38 unique templates that the model will attempt to classify. The resulting model will be evaluated against both the LC-QuAD dataset and the Question Answering Over Linked Data (QALD-7) dataset.
The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset.
After slot filling, the overall system achieves a macro F-score 0.419 on the LC- QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.
ContributorsAthreya, Ram G (Author) / Bansal, Srividya (Thesis advisor) / Usbeck, Ricardo (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2018
Description
This study examined whether changes in intervention related gains from the REACH for Personal and Academic Success program, an indicated anxiety prevention school-based protocol, vary as a function of participant youth's exposure to overprotective parenting. This study also examined if ethnicity/race (Caucasian vs. Hispanic/Latino) interacts with overprotective parenting to predict program response. A total of 98 children (M age = 9.70, SD = .07; 77.60% girls; 60.20% Hispanic/Latino) received 1 of 2 protocols (REACH or academic support) and responses were measured at post-treatment and 1-year follow-up. Findings showed that child self-regulation skills improved in the school program (REACH) for children of parents with low levels of overprotection, and child self-regulation skills improved in the control program (academic support) for children of parents with high levels of overprotection. These findings were significant in the Hispanic/Latino subsample, but not in Caucasian youth.
ContributorsBromich, Bobbi Lynn (Author) / Pina, Armando (Thesis director) / Presson, Clark (Committee member) / Hahs, Adam (Committee member) / Department of Psychology (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
High concentrations of carbon monoxide and particulate matter can cause respiratory disease, illness, and death in high doses. Air pollution is a concern in many urban areas of emerging markets that rely on outdated technologies for transportation and electricity generation; rural air quality is also a concern when noting the high prevalence of products of incomplete combustion resulting from open fires for cooking and heating. Monitoring air quality is an essential step to identifying these and other factors that affect air quality, and thereafter informing engineering and policy decisions to improve the quality of air. This study seeks to measure changes in air quality across spatial and temporal domains, with a specific focus on microclimates within an urban area. A prototype, low-cost air quality monitoring device has been developed to measure the concentrations of particulate matter, ozone, and carbon monoxide multiple times per minute. The device communicates data wirelessly via cell towers, and can run off-grid using a solar PV-battery system. The device can be replicated and deployed across urban regions for high-fidelity emissions monitoring to explore the effect of anthropogenic and environmental factors on intra-hour air quality. Hardware and software used in the device is described, and the wireless data communication protocols and capabilities are discussed.
ContributorsReilly, Kyle (Co-author) / Birner, Michael (Co-author) / Johnson, Nathan (Thesis director) / Gary, Kevin (Committee member) / Barrett, The Honors College (Contributor)
Created2015-05
Description
As our relationship with technology continues to encourage people to spend more time engaged online, traditional means of journalism must adapt in order to communicate with audiences. While many news organizations default to social media outlets, the goal of this project is to allow users a more direct experience with reporters, photographers and editors. It will allow The State Press, the official, student-run news organization covering ASU, to create content within Slack, an internal messaging platform commonly used in newsrooms. Secondly, it will provide a means for viewers to conveniently ingest their news as it unfolds, with updates, media, and analysis appearing in front of them without having to refresh the page.
ContributorsQuigley, James Alan (Author) / Gary, Kevin (Thesis director) / Squire, Susan (Committee member) / Software Engineering (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Frequent flyers are students who make repeated, unplanned visits to the school nurse, mostly presenting with somatic symptoms like headaches, stomachaches, and musculoskeletal pain. Somatic symptoms are characteristic of pediatric anxiety symptoms and disorders, but the relation between anxiety symptoms and frequent flyer status never has been systematically examined. This study employs data corresponding to 209 students in the 4th and 5th grade (Mage = 9.51, 43.5% girls, 50.9% 51.2% Caucasian, 23.9% Hispanic/Latino) to examine the relation between students' visits to the school nurse (frequent flyer status) and anxiety, including possible variations by children's socio-demographic characteristics, including sex and race/ethnicity. Findings showed statistically significant relations between anxiety and an increased number of nurse visits. A relation between anxiety and sex leading to increased nurse visits was not statistically significant. The statistical model testing race/ethnicity and anxiety in relation to increased nurse visits was found to be significant but driven solely by anxiety. Implications for this study include reframing how frequent flyers are viewed by teachers and addressing possible anxiety in these students.
ContributorsCarnesi, Elizabeth Dora (Author) / Pina, Armando (Thesis director) / Glenberg, Arthur (Committee member) / Stoll, Ryan (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Longitudinal data from European-American (EA) and Mexican-American (MA) families (n = 179 mothers, fathers, and youth; 41% MA) was used to test a bio-psycho-cultural model of the effect of non-responsive parenting on externalizing problems in young adult offspring through the effect on the stress response system. Parenting behavior (acceptance, rejection, harsh discipline) was assessed when children were in late childhood (12-13 years), cortisol samples were collected during late adolescence (18-19 years), and externalizing problems were measured in young adulthood (21-22 years). Latent profile analyses were used to examine patterns of parenting behavior in EA and MA families. A path analysis framework was used to examine how non-responsive parenting interacted with acceptance to predict adolescent stress response and subsequent externalizing problems in EA and MA young adults. Results showed different patterns of parenting behavior in EA versus MA families, with MA families demonstrating a profile of high acceptance and high non-responsiveness at higher rates than EA families. In MA families, youth adherence to the traditional cultural value of familismo related to more positive perceptions of parenting behavior. Across ethnic groups, parent rejection only predicted higher externalizing problems in young adults when acceptance was high. The effect of parent harsh discipline on offspring stress response differed by ethnicity. In MA families, harsh discipline predicted dysregulated stress response in youth when acceptance was low. In EA families, harsh discipline did not relate to youth stress response. Overall, results increase the understanding of normative and adaptive parenting behaviors in MA families. Findings inform the development of culturally-competent parenting-focused interventions that can better prevent dysregulated stress response and externalizing behavior problems in ethnically diverse youth.
ContributorsMahrer, Nicole Eva (Author) / Luecken, Linda (Thesis advisor) / Wolchik, Sharlene (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Pina, Armando (Committee member) / Arizona State University (Publisher)
Created2015
Description
In the last decade, the number of people who own a mobile phone or portable electronic communication device has grown exponentially. Recent advances in smartphone technology have enabled mobile devices to provide applications (“mHealth apps”) to support delivering interventions, tracking health treatments, or involving a healthcare team into the treatment process and symptom monitoring. Although the popularity of mHealth apps is increasing, few lessons have been shared regarding user experience design and evaluation for such innovations as they relate to clinical outcomes. Studies assessing usability for mobile apps primarily rely on survey instruments. Though surveys are effective in determining user perception of usability and positive attitudes towards an app, they do not directly assess app feature usage, and whether feature usage and related aspects of app design are indicative of whether intended tasks are completed by users. This is significant in the area of mHealth apps, as proper utilization of the app determines compliance to a clinical study protocol. Therefore it is important to understand how design directly impacts compliance, specifically what design factors are prevalent in non-compliant users. This research studies the impact of usability features on clinical protocol compliance by applying a mixed methods approach to usability assessment, combining traditional surveys, log analysis, and clickstream analysis to determine the connection of design to outcomes. This research is novel in its construction of the mixed methods approach and in its attempt to tie usability results to impacts on clinical protocol compliance. The validation is a case study approach, applying the methods to an mHealth app developed for early prevention of anxiety in middle school students. The results of three empirical studies are shared that support the construction of the mixed methods approach.
ContributorsPatwardhan, Mandar (Author) / Gary, Kevin A (Thesis advisor) / Pina, Armando (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2016