Matching Items (757)

Filtering by

Clear all filters

131363-Thumbnail Image.png

Feature Extraction on Sentiment Attitude Values to Better Predict the Stock Market Using Twitter Sentiment

Description

Behavioral economics suggests that emotions can affect an individual’s decision making. Recent research on this idea’s application on large societies hints that there may exist some correlation or maybe even some causation relationship between public sentiment—at least what can be

Behavioral economics suggests that emotions can affect an individual’s decision making. Recent research on this idea’s application on large societies hints that there may exist some correlation or maybe even some causation relationship between public sentiment—at least what can be pulled from Twitter—and the movement of the stock market. One major result of consistent research on whether or not public sentiment can predict the movement of the stock market is that public sentiment, as a feature, is becoming more and more valid as a variable for stock-market-based machine learning models. While raw values typically serve as invaluable points of data, when training a model, many choose to “engineer” new features for their models—deriving rates of change or range values to improve model accuracy.
Since it doesn’t hurt to attempt to utilize feature extracted values to improve a model (if things don’t work out, one can always use their original features), the question may arise: how could the results of feature extraction on values such as sentiment affect a model’s ability to predict the movement of the stock market? This paper attempts to shine some light on to what the answer could be by deriving TextBlob sentiment values from Twitter data, and using Granger Causality Tests and logistic and linear regression to test if there exist a correlation or causation between the stock market and features extracted from public sentiment.

Contributors

Agent

Created

Date Created
2020-05

131379-Thumbnail Image.png

Role of Metabolism in Antibiotic Resistance

Description

Each year, more and more multi-drug resistant bacterial strains emerge, thus complicating treatment and increasing the average stay in the intensive care unit. As antibiotics are being rendered inefficient, there is a need to look into ways of weakening the

Each year, more and more multi-drug resistant bacterial strains emerge, thus complicating treatment and increasing the average stay in the intensive care unit. As antibiotics are being rendered inefficient, there is a need to look into ways of weakening the internal state of bacterial cells to make them more susceptible to antibiotics. For this, we first need to understand what methods bacteria employ to fight against antibiotics. In this work, we have reviewed how bacteria respond to antibiotics. There is a similarity in response to antibiotic exposure and starvation (stringent stress) which changes the metabolic state. We have delineated what metabolism changes take place and how they are associated with oxidative stress. For example, there is a common change in NADH concentration that is tied to both metabolism and oxidative stress. Finally, we have compared the findings in literature with our research on an antibiotic-resistant RNA polymerase mutant that alters the gene expression profile in the general areas of metabolism and oxidative stress. Based on this thesis, we have suggested a couple of strategies to make antibiotics more efficient; however, as antibiotic-mediated killing is very complex, researchers need to delve deeper to understand and manipulate the full cellular response.

Contributors

Agent

Created

Date Created
2020-05

131386-Thumbnail Image.png

Input-Elicitation Methods for Crowdsourced Human Computation

Description

Collecting accurate collective decisions via crowdsourcing
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task

Collecting accurate collective decisions via crowdsourcing
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.

Contributors

Agent

Created

Date Created
2020-05

130934-Thumbnail Image.png

Automatic Water Shutoff Web Server Infrastructure and Smart Home Integration

Description

This thesis covers the continued development of an automatic water shutoff product developed as a capstone project by students in the college of engineering. The continued development covers the process of setting up a publicly accessible web server along

This thesis covers the continued development of an automatic water shutoff product developed as a capstone project by students in the college of engineering. The continued development covers the process of setting up a publicly accessible web server along with required server components and creating an Alexa skill for smart home integration.

Contributors

Agent

Created

Date Created
2020-12

130936-Thumbnail Image.png

Hana: An Open-Domain Chatbot Application for Language Learning

Description

Learning a new language can be very challenging. One significant aspect of learning a language is learning how to have fluent verbal and written conversations with other people in that language. However, it can be difficult to find other people

Learning a new language can be very challenging. One significant aspect of learning a language is learning how to have fluent verbal and written conversations with other people in that language. However, it can be difficult to find other people available with whom to practice conversations. Additionally, total beginners may feel uncomfortable and self-conscious when speaking the language with others. In this paper, I present Hana, a chatbot application powered by deep learning for practicing open-domain verbal and written conversations in a variety of different languages. Hana uses a pre-trained medium-sized instance of Microsoft's DialoGPT in order to generate English responses to user input translated into English. Google Cloud Platform's Translation API is used to handle translation to and from the language selected by the user. The chatbot is presented in the form of a browser-based web application, allowing users to interact with the chatbot in both a verbal or text-based manner. Overall, the chatbot is capable of having interesting open-domain conversations with the user in languages supported by the Google Cloud Translation API, but response generation can be delayed by several seconds, and the conversations and their translations do not necessarily take into account linguistic and cultural nuances associated with a given language.

Contributors

Agent

Created

Date Created
2020-12

128119-Thumbnail Image.png

Universal Framework for Edge Controllability of Complex Network

Description

Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously

Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduced class of processes for edge dynamics, the switchboard dynamics, and exploit- ing the exact controllability theory, we develop a universal framework in which the controllability of any node is exclusively determined by its local weighted structure. This framework enables us to identify a unique set of critical nodes for control, to derive analytic formulas and articulate efficient algorithms to determine the exact upper and lower controllability bounds, and to evaluate strongly structural controllability of any given network. Applying our framework to a large number of model and real-world networks, we find that the interaction strength plays a more significant role in edge controllability than the network structure does, due to a vast range between the bounds determined mainly by the interaction strength. Moreover, transcriptional regulatory networks and electronic circuits are much more strongly structurally controllable (SSC) than other types of real-world networks, directed networks are more SSC than undirected networks, and sparse networks are typically more SSC than dense networks.

Contributors

Agent

Created

Date Created
2017-06-26

129287-Thumbnail Image.png

Universal Formalism of Fano Resonance

Description

The phenomenon of Fano resonance is ubiquitous in a large variety of wave scattering systems, where the resonance profile is typically asymmetric. Whether the parameter characterizing the asymmetry should be complex or real is an issue of great experimental interest.

The phenomenon of Fano resonance is ubiquitous in a large variety of wave scattering systems, where the resonance profile is typically asymmetric. Whether the parameter characterizing the asymmetry should be complex or real is an issue of great experimental interest. Using coherent quantum transport as a paradigm and taking into account of the collective contribution from all available scattering channels, we derive a universal formula for the Fano-resonance profile. We show that our formula bridges naturally the traditional Fano formulas with complex and real asymmetry parameters, indicating that the two types of formulas are fundamentally equivalent (except for an offset). The connection also reveals a clear footprint for the conductance resonance during a dephasing process. Therefore, the emergence of complex asymmetric parameter when fitting with experimental data needs to be properly interpreted. Furthermore, we have provided a theory for the width of the resonance, which relates explicitly the width to the degree of localization of the close-by eigenstates and the corresponding coupling matrices or the self-energies caused by the leads. Our work not only resolves the issue about the nature of the asymmetry parameter, but also provides deeper physical insights into the origin of Fano resonance. Since the only assumption in our treatment is that the transport can be described by the Green’s function formalism, our results are also valid for broad disciplines including scattering problems of electromagnetic waves, acoustics, and seismology.

Contributors

Agent

Created

Date Created
2015-01-01

129524-Thumbnail Image.png

Universal Flux-Fluctuation Law in Small Systems

Description

The relation between flux and fluctuation is fundamental to complex physical systems that support and transport flows. A recently obtained law predicts monotonous enhancement of fluctuation as the average flux is increased, which in principle is valid but only for

The relation between flux and fluctuation is fundamental to complex physical systems that support and transport flows. A recently obtained law predicts monotonous enhancement of fluctuation as the average flux is increased, which in principle is valid but only for large systems. For realistic complex systems of small sizes, this law breaks down when both the average flux and fluctuation become large. Here we demonstrate the failure of this law in small systems using real data and model complex networked systems, derive analytically a modified flux-fluctuation law, and validate it through computations of a large number of complex networked systems. Our law is more general in that its predictions agree with numerics and it reduces naturally to the previous law in the limit of large system size, leading to new insights into the flow dynamics in small-size complex systems with significant implications for the statistical and scaling behaviors of small systems, a topic of great recent interest.

Contributors

Agent

Created

Date Created
2014-10-27

133330-Thumbnail Image.png

The Impact of Time Constraints on HackerRank Assessments

Description

Technical interviews have become the standard for assessing candidates for software development roles. The purpose of this study is to determine whether time constraints impact the performance of individuals on HackerRank coding assessments. During the surveys and HackerRank assessment, subjects

Technical interviews have become the standard for assessing candidates for software development roles. The purpose of this study is to determine whether time constraints impact the performance of individuals on HackerRank coding assessments. During the surveys and HackerRank assessment, subjects wore two physiological sensors: a galvanic skin response bracelet, Shimmer3+GSR that measures emotional intensity and an EEG headset, B-Alert X24 that measures cognitive workload, engagement, and distraction. Subjects were also monitored by external sensors, such as an eye tracker to measure visual attention and by a facial-based emotion recognition system through a webcam to measure their visual attention and emotions. Through these metrics, as well as a Big Five personality demographic survey and mental demand survey, the study examines the difference in performance between strictly timed assessments and timed assessments with time to revise.

Contributors

Created

Date Created
2018-05

133334-Thumbnail Image.png

Developing Inventory Control and Build Management Software for Spacecraft Engineering

Description

Engineering an object means engineering the process that creates the object. Today, software can make the task of tracking these processes robust and straightforward. When engineering requirements are strict and strenuous, software custom-built for such processes can prove essential. The

Engineering an object means engineering the process that creates the object. Today, software can make the task of tracking these processes robust and straightforward. When engineering requirements are strict and strenuous, software custom-built for such processes can prove essential. The work for this project was developing ICDB, an inventory control and build management system created for spacecraft engineers at ASU to record each step of their engineering processes. In-house development means ICDB is more precisely designed around its users' functionality and cost requirements than most off-the-shelf commercial offerings. By placing a complex relational database behind an intuitive web application, ICDB enables organizations and their users to create and store parts libraries, assembly designs, purchasing and location records for inventory items, and more.

Contributors

Agent

Created

Date Created
2018-05