Matching Items (40)
Description
Survey indicates a rise of 81% in mobile data usage in the year 2013. A fair share of this total data demand can be attributed to video streaming. The encoding structure of videos, introduces nuances that can be utilized to ensure a fair and optimal means of streaming the video data. This dissertation proposes a novel user and packet scheduling algorithm that guarantees a fair allocation of resources. MS-SSIM index
is used to calculate the mean opinion score (DMOS) to evaluate the quality of the received video. Simulations indicate that the proposed algorithm outperforms existing algorithms in the literature.
is used to calculate the mean opinion score (DMOS) to evaluate the quality of the received video. Simulations indicate that the proposed algorithm outperforms existing algorithms in the literature.
ContributorsChoudhuri, Sabarna (Author) / Ying, Lei (Thesis advisor) / Bliss, Dan (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Juror impartiality is necessary for a fair and just legal system, but is true juror impartiality
realistic? The current study investigated the role of implicit and explicit social-cognitive biases in jurors’ conceptualizations of insanity, and the influence of those biases in juror verdict decisions. It was hypothesized that by analyzing the role of implicit and explicit biases in insanity defense cases, jurors’ attitudes towards those with mental illnesses and attitudes towards the insanity defense would influence jurors’ final verdict decisions. Two hundred and two participants completed an online survey which included a trial vignette incorporating an insanity defense (adapted from Maeder et al., 2016), the Insanity Defense Attitude Scale (Skeem, Louden, & Evans, 2004), Community Attitudes Towards the Mentally Ill Scale (Taylor & Dear, 1981), and an Implicit Association Test (Greenwald et al., 1998). While implicit associations concerning mental illness and dangerousness were significantly related to mock jurors’ verdicts, they no longer were when explicit insanity defense attitudes were added to a more complex model including all measured attitudes and biases. Insanity defense attitudes were significantly related to jurors’ verdicts over and above attitudes about the mentally ill and implicit biases concerning the mentally ill. The potentially biasing impact of jurors’ insanity defense attitudes and the impact of implicit associations about the mentally ill in legal judgments are discussed.
realistic? The current study investigated the role of implicit and explicit social-cognitive biases in jurors’ conceptualizations of insanity, and the influence of those biases in juror verdict decisions. It was hypothesized that by analyzing the role of implicit and explicit biases in insanity defense cases, jurors’ attitudes towards those with mental illnesses and attitudes towards the insanity defense would influence jurors’ final verdict decisions. Two hundred and two participants completed an online survey which included a trial vignette incorporating an insanity defense (adapted from Maeder et al., 2016), the Insanity Defense Attitude Scale (Skeem, Louden, & Evans, 2004), Community Attitudes Towards the Mentally Ill Scale (Taylor & Dear, 1981), and an Implicit Association Test (Greenwald et al., 1998). While implicit associations concerning mental illness and dangerousness were significantly related to mock jurors’ verdicts, they no longer were when explicit insanity defense attitudes were added to a more complex model including all measured attitudes and biases. Insanity defense attitudes were significantly related to jurors’ verdicts over and above attitudes about the mentally ill and implicit biases concerning the mentally ill. The potentially biasing impact of jurors’ insanity defense attitudes and the impact of implicit associations about the mentally ill in legal judgments are discussed.
ContributorsHamza, Cassandra (Author) / Neal, Tess M.S. (Thesis advisor) / Schweitzer, Nicholas (Committee member) / Hall, Deborah (Committee member) / Arizona State University (Publisher)
Created2018
Description
Cultivation theory states that consuming television cultivates a social reality in the real world which aligns with the reality present in television. When the television show CSI was released, researchers studied a form of cultivation stemming from the show titled the "CSI Effect." One of the components of the CSI Effect is the tendency of those who watch CSI to be more likely to overestimate the presence of forensic evidence present in a trial and place more trust in such evidence. In recent years, several true crime documentaries that examined controversial cases have been released. In a similar vein of research conducted on CSI, the current study examines true crime documentaries and their possible impacts on viewers’ judgments and beliefs about the criminal justice system. In the current study, participants were provided with a mock case and asked about their perceptions of the case along with their viewership habits. While overall true crime documentary viewership did not influence judgments of evidence manipulation or perceptions of police, findings point to viewership of the targeted documentaries being associated with feelings of mistrust towards the criminal justice system overall, while the lesser-viewed documentaries correlated with judgments of strength and responsibility of the defendant in the case. One possible explanation is that individual characteristics may serve as the driving factor in how individuals choose what to watch when the popularity of the show is not as well-known.
ContributorsDoughty, Kathryn A (Author) / Schweitzer, Nicholas J. (Thesis advisor) / Neal, Tess (Committee member) / Salerno, Jessica (Committee member) / Arizona State University (Publisher)
Created2018
Description
People often rely on experts' opinions and knowledge to inform their own decisions. This can be problematic, as expertise does not necessarily protect one from bias, and increased experience does not always increase an experts' accuracy (Cassidy & Buede, 2009; Goldberg, 1968; Molins et al., 2008). The nature of task characteristics of expert domains is associated with experts' performance (Shanteau 1992). The purpose of this thesis is to examine how people perceive experts in different disciplines, and to explore the factors that affect perceptions of expert objectivity. Perceptions of objectivity in 26 expert domains were examined. As hypothesized, higher ratings of clear and immediate feedback available to experts were associated with higher ratings of objectivity. However, other indicators of higher domain validity were not recognized by laypeople, such as higher levels of training and education. Contrary to our hypotheses, higher levels of familiarity with experts in a given domain and more experiences of disagreement with experts in a given domain were not associated with perceptions of objectivity. These results suggest that laypeople can correctly identify some indicators of the validity of different expert domains, but they cannot identify others. These perceptions affect how objectivity is perceived.
ContributorsVelez, Rebecca Ellen (Author) / Neal, Tess (Thesis director) / Salerno, Jessica (Committee member) / School of Social and Behavioral Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Prior expectations can bias evaluative judgments of sensory information. We show that information about a performer's status can bias the evaluation of musical stimuli, reflected by differential activity of the ventromedial prefrontal cortex (vmPFC). Moreover, we demonstrate that decreased susceptibility to this confirmation bias is (a) accompanied by the recruitment of and (b) correlated with the white-matter structure of the executive control network, particularly related to the dorsolateral prefrontal cortex (dlPFC). By using long-duration musical stimuli, we were able to track the initial biasing, subsequent perception, and ultimate evaluation of the stimuli, examining the full evolution of these biases over time. Our findings confirm the persistence of confirmation bias effects even when ample opportunity exists to gather information about true stimulus quality, and underline the importance of executive control in reducing bias.
ContributorsAydogan, Goekhan (Co-author, Committee member) / Flaig, Nicole (Co-author) / Larg, Edward W. (Co-author) / Margulis, Elizabeth Hellmuth (Co-author) / McClure, Samuel (Co-author, Thesis director) / Nagishetty Ravi, Srekar Krishna (Co-author) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Human perceptual dimensions of sound are not necessarily simple representations of the actual physical dimensions that make up sensory input. In particular, research on the perception of interactions between acoustic frequency and intensity has shown that people exhibit a bias to expect the perception of pitch and loudness to change together. Researchers have proposed that this perceptual bias occurs because sound sources tend to follow a natural regularity of a correlation between changes in intensity and frequency of sound. They postulate that the auditory system has adapted to expect this naturally occurring relationship to facilitate auditory scene analysis, the tracking and parsing sources of sound as listeners analyze their auditory environments. However, this correlation has only been tested with human speech and musical sounds. The current study explores if animal sounds also exhibit the same natural correlation between intensity and frequency and tests if people exhibit a perceptual bias to assume this correlation when listening to animal calls. Our principal hypotheses are that animal sounds will tend to exhibit a positive correlation between intensity and frequency and that, when hearing such sounds change in intensity, listeners will perceive them to also change in frequency and vice versa. Our tests with 21 animal calls and 8 control stimuli along with our experiment with participants responding to these stimuli supported these hypotheses. This research provides a further example of coupling of perceptual biases with natural regularities in the auditory domain, and provides a framework for understanding perceptual biases as functional adaptations that help perceivers more accurately anticipate and utilize reliable natural patterns to enhance scene analyses in real world environments.
ContributorsWilkinson, Zachary David (Author) / McBeath, Michael (Thesis director) / Glenberg, Arthur (Committee member) / Rutowski, Ronald (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2014-05
Description
The Internet has made it possible to exchange information at a rapid rate. With this extraordinary ability, media companies and various other organizations have been able to communicate thoughts and information to an extremely large audience. As a result, news subscribers are overwhelmed with biased information, which makes it very easy to be misinformed. Unfortunately, there is currently no way to stay truly informed without spending countless hours searching the Internet for different viewpoints and ultimately using that information to formulate a sound understanding. This project (nicknamed "Newsie") solves this problem by providing news subscribers with many news sources to every topic, thereby saving them time and ultimately paving a way to a more informed society. Since one of the main goals of this project is to provide information to the largest number of people, Newsie is designed with availability in mind. Unsurprisingly, the most accessible method of communication is the Internet \u2014 more specifically, a website. Users will be able to access Newsie via a webpage, and easily view to most recent headlines with their corresponding articles from several sources. Another goal of the project is to classify different articles and sources based on their bias. After reading articles, users will be able to vote on their biases. This provides a crowdsourced method of determining bias.
ContributorsAlimov, Robert Joseph (Author) / Meuth, Ryan (Thesis director) / Franceschini, Enos (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
In the United States, the Latino population is projected to reach approximately 28.6% of the total U.S. population by 2060. Despite their growing presence, Latinos encounter health disparities and worse health outcomes in comparison to their non-Hispanic White (NHW) counterparts. Latinos/ Hispanics have a higher prevalence of diabetes, hypertension, obesity, and late stage cancer diagnosis. Various social determinants of health (SDoH) such as socioeconomic status, education, insurance enrollment, language proficiency, immigration status among other variables intersect to influence Latino health status. However, even when all those factors were held equal, disparities remained. The aforementioned list did not include race/ethnicity, though race/ethnicity is a critical SDoH that influences one's access to care and the quality of care they receive. As such, examining the role of race may be the key to reducing persistent health disparities in access to care, quality of care, and health outcomes.
ContributorsChen, Diana (Author) / Reddy, Swapna (Thesis director) / Cuya Gavilano, Lorena (Committee member) / School for the Science of Health Care Delivery (Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
ABSTRACT
Psychological assessments contain important diagnostic information and are central to therapeutic service delivery. Therapists' personal biases, invalid cognitive schemas, and emotional reactions can be expressed in the language of the assessments they compose, causing clients to be cast in an unfavorable light. Logically, the opinions of subsequent therapists may then be influenced by reading these assessments, resulting in negative attitudes toward clients, inaccurate diagnoses, adverse experiences for clients, and poor therapeutic outcomes. However, little current research exists that addresses this issue. This study analyzed the degree to which strength-based, deficit-based, and neutral language used in psychological assessments influenced the opinions of counselor trainees (N= 116). It was hypothesized that participants assigned to each type of assessment would describe the client using adjectives that closely conformed to the language used in the assessment they received. The hypothesis was confirmed (p = .000), indicating significant mean differences between all three groups. Limitations and implications of the study were identified and suggestions for further research were discussed.
Psychological assessments contain important diagnostic information and are central to therapeutic service delivery. Therapists' personal biases, invalid cognitive schemas, and emotional reactions can be expressed in the language of the assessments they compose, causing clients to be cast in an unfavorable light. Logically, the opinions of subsequent therapists may then be influenced by reading these assessments, resulting in negative attitudes toward clients, inaccurate diagnoses, adverse experiences for clients, and poor therapeutic outcomes. However, little current research exists that addresses this issue. This study analyzed the degree to which strength-based, deficit-based, and neutral language used in psychological assessments influenced the opinions of counselor trainees (N= 116). It was hypothesized that participants assigned to each type of assessment would describe the client using adjectives that closely conformed to the language used in the assessment they received. The hypothesis was confirmed (p = .000), indicating significant mean differences between all three groups. Limitations and implications of the study were identified and suggestions for further research were discussed.
ContributorsScott, Angela N (Author) / Kinnier, Richard (Thesis advisor) / Homer, Judith (Committee member) / Kurpius, Sharon (Committee member) / Arizona State University (Publisher)
Created2015
Description
Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago.
Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect.
The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them.
The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.
Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect.
The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them.
The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.
ContributorsMorstatter, Fred (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Maciejewski, Ross (Committee member) / Carley, Kathleen M. (Committee member) / Arizona State University (Publisher)
Created2017