Matching Items (22)
152796-Thumbnail Image.png
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

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
150046-Thumbnail Image.png
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

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
156614-Thumbnail Image.png
Description
Academia is not what it used to be. In today’s fast-paced world, requirements

are constantly changing, and adapting to these changes in an academic curriculum

can be challenging. Given a specific aspect of a domain, there can be various levels of

proficiency that can be achieved by the students. Considering the wide array

Academia is not what it used to be. In today’s fast-paced world, requirements

are constantly changing, and adapting to these changes in an academic curriculum

can be challenging. Given a specific aspect of a domain, there can be various levels of

proficiency that can be achieved by the students. Considering the wide array of needs,

diverse groups need customized course curriculum. The need for having an archetype

to design a course focusing on the outcomes paved the way for Outcome-based

Education (OBE). OBE focuses on the outcomes as opposed to the traditional way of

following a process [23]. According to D. Clark, the major reason for the creation of

Bloom’s taxonomy was not only to stimulate and inspire a higher quality of thinking

in academia – incorporating not just the basic fact-learning and application, but also

to evaluate and analyze on the facts and its applications [7]. Instructional Module

Development System (IMODS) is the culmination of both these models – Bloom’s

Taxonomy and OBE. It is an open-source web-based software that has been

developed on the principles of OBE and Bloom’s Taxonomy. It guides an instructor,

step-by-step, through an outcomes-based process as they define the learning

objectives, the content to be covered and develop an instruction and assessment plan.

The tool also provides the user with a repository of techniques based on the choices

made by them regarding the level of learning while defining the objectives. This helps

in maintaining alignment among all the components of the course design. The tool

also generates documentation to support the course design and provide feedback

when the course is lacking in certain aspects.

It is not just enough to come up with a model that theoretically facilitates

effective result-oriented course design. There should be facts, experiments and proof

that any model succeeds in achieving what it aims to achieve. And thus, there are two

research objectives of this thesis: (i) design a feature for course design feedback and

evaluate its effectiveness; (ii) evaluate the usefulness of a tool like IMODS on various

aspects – (a) the effectiveness of the tool in educating instructors on OBE; (b) the

effectiveness of the tool in providing appropriate and efficient pedagogy and

assessment techniques; (c) the effectiveness of the tool in building the learning

objectives; (d) effectiveness of the tool in document generation; (e) Usability of the

tool; (f) the effectiveness of OBE on course design and expected student outcomes.

The thesis presents a detailed algorithm for course design feedback, its pseudocode, a

description and proof of the correctness of the feature, methods used for evaluation

of the tool, experiments for evaluation and analysis of the obtained results.
ContributorsRaj, Vaishnavi (Author) / Bansal, Srividya (Thesis advisor) / Bansal, Ajay (Committee member) / Mehlhase, Alexandra (Committee member) / Arizona State University (Publisher)
Created2018
156879-Thumbnail Image.png
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

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.
ContributorsAthreya, Ram G (Author) / Bansal, Srividya (Thesis advisor) / Usbeck, Ricardo (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2018
136225-Thumbnail Image.png
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 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

Class instructors at Arizona State University monitor students’ attendance for classes in which attendance is either mandatory or encouraged. Class monitoring can be done using traditional systems such as sign sheets and roll calls. From my initial observations while attending a class which utilized a sign sheet for class attendance

Class instructors at Arizona State University monitor students’ attendance for classes in which attendance is either mandatory or encouraged. Class monitoring can be done using traditional systems such as sign sheets and roll calls. From my initial observations while attending a class which utilized a sign sheet for class attendance monitoring, I thought the process took long and was inefficient. As a result, I created an automated system that would replace the traditional systems and improve the class monitoring process. Thus, this study aims to determine whether the automated system reduced the time it takes to monitor class attendance, and whether it was efficient.

To examine the above question, the automated system was deployed to 2 classes at Arizona State University. Additionally, surveys were distributed to 2 instructors and 33 students and they were asked to respond to questions relating to class attendance and the monitoring systems which were being used alternatively with the newly-created automated system. Analysis of the responses demonstrated that use of an automated system reduced the time it takes students to mark their presence, and thus increase the time used for other class activities. The results also indicate that the design of the automated system affects the overall time it takes to monitor attendance. On this basis, it is recommended that instructors utilize an automated system to monitor class attendance. Further research is needed to study the time it takes instructors to set up different monitoring systems in order to ascertain that an automated system reduces the overall time it takes to monitor attendance compared to other traditionally used systems.

ContributorsCyubahiro, Tresor (Author) / Heinrichs, Robert (Thesis director) / Mehlhase, Alexandra (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
133874-Thumbnail Image.png
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

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

Compass portal features tools that help teachers, psychologists, behavioral specialists gain insights on students’ performance through activities they have completed.

ContributorsNallagula, Nithin Sagar (Co-author) / Shah, Neha (Co-author) / Gary, Kevin (Thesis director) / Mehlhase, Alexadnra (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

COMPASS portal features tools that help teachers, psychologists, behavioral Specialists gain insights on students’ performance through activities they have completed.

ContributorsShah, Neha Manish (Co-author) / Nallagula, Nithin Sagar (Co-author) / Gary, Kevin (Thesis director) / Mehlhase, Alexandra (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

This thesis proposes a new steering system for agricultural machinery with the aim of improving the automation capabilities of farming robots. Accurate and reliable autonomous machinery has the potential to provide significant benefits to the efficiency of farming operations, but the existing systems for performing one of the most essential

This thesis proposes a new steering system for agricultural machinery with the aim of improving the automation capabilities of farming robots. Accurate and reliable autonomous machinery has the potential to provide significant benefits to the efficiency of farming operations, but the existing systems for performing one of the most essential automation functions, autonomous steering to keep machinery on the proper course, each have drawbacks that impact their usability in various scenarios. In order to address these issues, a new lidar-based system was developed for automatic steering in a typical farm field. This approach uses a two-dimensional lidar unit to scan the ground in front of the robot to detect and steer based on farm tracks, a common feature in many farm fields. This system was implemented and evaluated, with results demonstrating that the system is capable of providing accurate steering corrections.

ContributorsBrauer, Jude (Author) / Mehlhase, Alexandra (Thesis director) / Heinrichs, Robert (Committee member) / Barrett, The Honors College (Contributor) / Software Engineering (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2023-05