Matching Items (57)
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- All Subjects: artificial intelligence
- All Subjects: Social Media
- Creators: Computer Science and Engineering Program
- Creators: Department of Psychology
- Status: Published
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions. Pancake shaftless vibration motors are mounted on the back of a chair to provide vibrotactile stimulation in the context of a dyadic (one-on-one) interaction across a table. This work explores the design of spatiotemporal vibration patterns that can be used to convey the basic building blocks of facial movements according to the Facial Action Unit Coding System. A behavioral study was conducted to explore the factors that influence the naturalness of conveying affect using vibrotactile cues.
ContributorsBala, Shantanu (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Psychology (Contributor)
Created2014-05
Description
The objective of this research is to determine an approach for automating the learning of the initial lexicon used in translating natural language sentences to their formal knowledge representations based on lambda-calculus expressions. Using a universal knowledge representation and its associated parser, this research attempts to use word alignment techniques to align natural language sentences to the linearized parses of their associated knowledge representations in order to learn the meanings of individual words. The work includes proposing and analyzing an approach that can be used to learn some of the initial lexicon.
ContributorsBaldwin, Amy Lynn (Author) / Baral, Chitta (Thesis director) / Vo, Nguyen (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Although previous research has explored the relationship between social media use and well-being, many studies are contradictory of each other and conclude varying findings relating to social media use and outspokenness. This study explores the relationship between active and passive social media use, perceived social media expertise, and outspokenness using the potentially mediating variable of perceived social acceptance. 162 participants, recruited through Amazon Mechanical Turk (MTurk) and ASU’s SONA systems, completed a survey relating to their own use of social media, perceived social acceptance, and outspokenness. Contradictory to my first hypotheses, no significant correlations were found between social media use and social media expertise. However, correlation analyses revealed that active social media use is related to an increased amount of perceived social media expertise (r = 0.23, p < .004). Perceived social media expertise was significantly positively correlated with outspokenness (r = 0.19, p < 0.015); however, it was not correlated with perceived social acceptance. When examining these relationships separately by gender, a strong association was found for males between active social media use and outspokenness, whereas passive social media use and outspokenness were negatively correlated for females. The results of this study add to previous research in the field of social media and outspokenness and lend new ideas for future research on these topics, such as exploring the gender differences that are associated with these variables. Further research in the area is needed for a more complete understanding of how one’s social media use affects his/her outspokenness and how gender modifies these effects.
ContributorsRubino, Kelli Erika (Co-author) / Rubino, Kelli (Co-author) / Mickelson, Kristin (Thesis director) / Halavais, Alexander (Committee member) / Department of Psychology (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Since the advent of social media, researchers have studied how platforms like Facebook and Instagram can influence our relationships, and more specifically, how social networking sites can impact what happens when these relationships dissolve. Less is known about the newer platform Snapchat, which provides ephemeral updates as they occur to one's friend list, as well as self-destructing direct messages between individuals. The present study utilized survey responses from 84 college-aged individuals and eight semi-structured, in-depth interviews to study the relationship between using Snapchat to engage with or monitor one's ex-partner, the level of distress that results from these behaviors, and an individual's overall breakup distress level. A significant positive correlation was found between each of these variables, indicating that remaining connected with one's ex-partner on Snapchat may contribute to one's level of distress, or alternatively, that more distressed individuals are turning to Snapchat to monitor their ex-partner. Pairing this quantitative data with in-depth interviews allowed for more robust and generalizable findings. Qualitative details supported the statistical analysis to indicate that one's overall breakup distress level may be leading individuals to use Snapchat to monitor their ex-partner or exaggerate their own speed of recovery. Future research should analyze these same variables in a larger, more representative sample by following couples as their breakups occur in real-time to capture more comprehensive participant experiences.
ContributorsAter, Brittany Alexis (Author) / Parker, John (Thesis director) / Bodford, Jessica (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Department of Marketing (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In 2012, Chick-fil-A president Dan Cathy's "came out" about his anti-same sex marriage views, launching an enormous negative backlash across social media networks. To counteract this, former governor Mike Huckabee called on his Facebook fans to support the company on "Chick-fil-A Appreciation Day," both on Facebook and in person. The project examines both the backlash and Appreciation Day on social media networks. Posts on the Appreciation Day Facebook event page and similar posts on Twitter were first broken down in the framework of supportive and oppositional posts and then analyzed in further contexts. Comments on official Chick-fil-A Facebook statuses were then examined in a similar fashion. The research concludes that a strong support system both online and offline were necessary for Chick-fil-A to recover from its backlash. The controversy that ensued is ultimately a case study in the growing influence of Facebook as a tool for small-scale activism.
ContributorsKuiland, Zachary Rico (Author) / Cheong, Pauline (Thesis director) / Szeli, Eva (Committee member) / Lim, Merlyna (Committee member) / Barrett, The Honors College (Contributor) / School of Criminology and Criminal Justice (Contributor) / Department of Psychology (Contributor)
Created2013-05
Description
Social media has quickly become a dominant tool for businesses across all sectors due to its two-way communication capabilities. Previous research has suggested that companies, particularly the hospitality and travel industry, should be engaging in authentic dialogue with its audience members, be using vibrant imagery and be monitoring and promoting user-generated content and electronic-word-of-mouth. These elements were observed for six luxury hotels and resorts in the Southwestern United States over the course of a month on Facebook, Twitter and TripAdvisor. In addition, three two-part electronic-questionnaires were administered to three of the six luxury hotels and resorts to determine industry perspectives on these subjects and to serve as a comparison of social media tactics in this sector. There were social media differences and similarities based on the location and size of the hotel. Facebook was comprised of 42 percent advertising and used large amounts of imagery to promote the properties. There was very little user-generated content and word-of-mouth. Twitter was comprised of 31 percent dialogue and 22 percent user-generated content. Five of the six properties responded to reviews on TripAdvisor. Three crisis responses via social media were also observed. Later research may choose to include more analytic-based research and examine other social media platforms.
ContributorsWininger, Emily Renee (Author) / Wu, Xu (Thesis director) / Ostrom, Amy (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Social Transformation (Contributor)
Created2014-05
Description
Penetration testing is regarded as the gold-standard for understanding how well an organization can withstand sophisticated cyber-attacks. However, the recent prevalence of markets specializing in zero-day exploits on the darknet make exploits widely available to potential attackers. The cost associated with these sophisticated kits generally precludes penetration testers from simply obtaining such exploits – so an alternative approach is needed to understand what exploits an attacker will most likely purchase and how to defend against them. In this paper, we introduce a data-driven security game framework to model an attacker and provide policy recommendations to the defender. In addition to providing a formal framework and algorithms to develop strategies, we present experimental results from applying our framework, for various system configurations, on real-world exploit market data actively mined from the darknet.
ContributorsRobertson, John James (Author) / Shakarian, Paulo (Thesis director) / Doupe, Adam (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Classical planning is a field of Artificial Intelligence concerned with allowing autonomous agents to make reasonable decisions in complex environments. This work investigates
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
ContributorsNakhleh, Julia Blair (Author) / Srivastava, Siddharth (Thesis director) / Fainekos, Georgios (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.
ContributorsRawal, Samarth Chetan (Author) / Baral, Chitta (Thesis director) / Anwar, Saadat (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05