Matching Items (8)

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State Press Live

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

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.

Contributors

Agent

Created

Date Created
  • 2018-05

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MEASURING AIR QUALITY USING WIRELESS SELF-POWERED DEVICES

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

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 Air pollution is a concern in many urban areas of emerging markets that rely on outdated technologies for transportation and electricity generation; rural air quality is also a concern when noting the high prevalence of products of incomplete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.

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Agent

Created

Date Created
  • 2015-05

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Dependency analysis in the HTML5, JavaScript and CSS3 Stack

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

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.

Contributors

Agent

Created

Date Created
  • 2014

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Template-Based Question Answering over Linked Data using Recursive Neural Networks

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

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.

Contributors

Agent

Created

Date Created
  • 2018

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Adaptive mHealth interventions for improving youth responsiveness and clinical outcomes

Description

Mobile health (mHealth) applications (apps) hold tremendous potential for addressing chronic health conditions. Smartphones are now the most popular form of computing, and the ubiquitous “always with us, always on”

Mobile health (mHealth) applications (apps) hold tremendous potential for addressing chronic health conditions. Smartphones are now the most popular form of computing, and the ubiquitous “always with us, always on” nature of mobile technology makes them amenable to interventions aimed and managing chronic disease. Several challenges exist, however, such as the difficulty in determining mHealth effects due to the rapidly changing nature of the technology and the challenges presented to existing methods of evaluation, and the ability to ensure end users consistently use the technology in order to achieve the desired effects. The latter challenge is in adherence, defined as the extent to which a patient conducts the activities defined in a clinical protocol (i.e. an intervention plan). Further, higher levels of adherence should lead to greater effects of the intervention (the greater fidelity to the protocol, the more benefit one should receive from the protocol). mHealth has limitations in these areas; the ability to have patients sustainably adhere to a protocol, and the ability to drive intervention effect sizes. My research considers personalized interventions, a new approach of study in the mHealth community, as a potential remedy to these limitations. Specifically, in the context of a pediatric preventative anxiety protocol, I introduce algorithms to drive greater levels of adherence and greater effect sizes by incorporating per-patient (personalized) information. These algorithms have been implemented within an existing mHealth app for middle school that has been successfully deployed in a school in the Phoenix Arizona metropolitan area. The number of users is small (n=3) so a case-by-case analysis of app usage is presented. In addition simulated user behaviors based on models of adherence and effects sizes over time are presented as a means to demonstrate the potential impact of personalized deployments on a larger scale.

Contributors

Agent

Created

Date Created
  • 2019

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CyberCog a synthetic task environment for measuring cyber situation awareness

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

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.

Contributors

Agent

Created

Date Created
  • 2011