Matching Items (24)
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- All Subjects: Adolescence
- Creators: Department of Psychology
- Creators: Panchanathan, Sethuraman
- Resource Type: Text
Description
The thesis project merges interdisciplinary research to develop a self-directed creative intervention for immigrant youth, allowing them to make sense of their social and cultural identities. It takes research on self-awareness, multicultural identification, perceived belonging, and bibliotherapy to create a guided journal titled "Unearth," filled with art and writing prompts that are age-appropriate for adolescents and that serve as avenues for self-exploration. The project ultimately engages a focus group discussion to understand the usability and accessibility of the intervention.
ContributorsDizon, Arni Elyz (Co-author) / Nawrocki, Andie (Co-author) / Pina, Armando (Thesis director) / Benoit, Renee (Committee member) / Causadias, Jose (Committee member) / Department of Psychology (Contributor) / School of Social Transformation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively classify unseen instances occurring in a different, but related "target" domain. The algorithm is evaluated on real-world classification problems namely accelerometer based 3D gesture recognition, smart home activity recognition and text categorization. The performance on these datasets is analyzed and evaluated against popular boosting-based instance transfer techniques. In addition, supporting empirical studies, that investigate some of the less explored bottlenecks of boosting based instance transfer methods, are presented, to understand the suitability and effectiveness of this form of knowledge transfer.
ContributorsVenkatesan, Ashok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Li, Baoxin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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 fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning techniques today are not yet fully equipped to be trusted with this critical task. This work seeks to address this fundamental knowledge gap. Existing approaches that provide a measure of confidence on a prediction such as learning algorithms based on the Bayesian theory or the Probably Approximately Correct theory require strong assumptions or often produce results that are not practical or reliable. The recently developed Conformal Predictions (CP) framework - which is based on the principles of hypothesis testing, transductive inference and algorithmic randomness - provides a game-theoretic approach to the estimation of confidence with several desirable properties such as online calibration and generalizability to all classification and regression methods. This dissertation builds on the CP theory to compute reliable confidence measures that aid decision-making in real-world problems through: (i) Development of a methodology for learning a kernel function (or distance metric) for optimal and accurate conformal predictors; (ii) Validation of the calibration properties of the CP framework when applied to multi-classifier (or multi-regressor) fusion; and (iii) Development of a methodology to extend the CP framework to continuous learning, by using the framework for online active learning. These contributions are validated on four real-world problems from the domains of healthcare and assistive technologies: two classification-based applications (risk prediction in cardiac decision support and multimodal person recognition), and two regression-based applications (head pose estimation and saliency prediction in images). The results obtained show that: (i) multiple kernel learning can effectively increase efficiency in the CP framework; (ii) quantile p-value combination methods provide a viable solution for fusion in the CP framework; and (iii) eigendecomposition of p-value difference matrices can serve as effective measures for online active learning; demonstrating promise and potential in using these contributions in multimedia pattern recognition problems in real-world settings.
ContributorsNallure Balasubramanian, Vineeth (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Vovk, Vladimir (Committee member) / Arizona State University (Publisher)
Created2010
Description
Substance use during adolescence is a significant predictor of developing a later substance use disorder. An encouraging trend is that there have been recent declines in rates of adolescent substance use, including alcohol and marijuana. However, these two substances may be decreasing differently from one another as a result of age, period, and cohort effects. Therefore, the overall trend of decreased substance use in more recent generations of adolescents may be greater for one substance than the other. The current study tested declines in adolescent alcohol and marijuana use across two generations measured in 1988-1990 and 2006-2012. Methodological strengths include controls for demographic characteristics and for parental alcohol disorder (as a proxy for genetic risk). Moreover, we tested whether findings would replicate using two methods—first comparing all assessed members of one generational cohort with all assessed members of the other generational cohort, and then comparing only matched parent-child pairs. Testing this second matched sample removes some potential demographic and risk confounds that might occur across cohorts in typical epidemiological studies. Results demonstrated that the younger cohort of adolescents used both substances less than the older cohort, and this effect was stronger for alcohol than for marijuana. These results were replicated in both samples over and above demographic variables. The parent-child sample showed that children used less alcohol and marijuana than did their parent during the same age period, suggesting that these trends cannot simply be due to changes in the demographics of the adolescent population over time. Taken together with epidemiological studies, these findings suggest encouraging declines in adolescent substance use rates but also indicate less decline in marijuana use compared to alcohol use. This prompts further surveillance to determine if marijuana use rates may start increasing among adolescents in the future.
ContributorsWatters, Shannon Marie (Author) / Chassin, Laurie (Thesis director) / Presson, Clark (Committee member) / Department of Psychology (Contributor) / School of Social Transformation (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
It is interesting to reflect that the American legal system has not seriously applied any significant technological advances in many decades. It is fascinating that the same processes used to draft a will or estate plan are virtually the same as they were in the 1960’s. This seems to be a problem that should be concerning in this modern age. We would be hard pressed to observe doctors in the U.S. currently performing medical procedures as they would have in 1960 considering the technological advancements that have taken place in society since then. Many of the processes in the legal system are extremely static and even archaic. It seems to be an opportune time to revolutionize the whole system as advancements continue; but, this revolution must take into account both the positive and negative repercussions that are possible moving forward.
ContributorsWilladson, Conor Calista Carolena (Author) / Koretz, Lora (Thesis director) / Forst, Bradley (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Family influences are known predictors of adolescent health and well-being trajectories, yet little research has investigated how adolescents’ orientation to family may be associated with their physiological stress responses. Influenced by the strength-based approach to culture, this study evaluated 418 Hispanic adolescents' familism values and perceived life stress in family, school, and peer domains to investigate prospective associations with hypothalamic-pituitary-adrenal axis stress responses to the Group Public Speaking Task for Adolescents (GPST-A). Prior growth-mixture modeling on this sample revealed a five-class solution of cortisol responding to the GPST-A that was used here as the dependent variable: one class showed a more pronounced pattern of reactivity, potentially indicative of hyper-responsivity to the stress task; two classes showed evidence of a low to moderate cortisol response, potentially indicative of an adaptive physiological response to the challenge; and two classes showed patterns of non-responsivity, potentially indicative of hypo-responsivity. Results demonstrate that the role of familism is nuanced in the context of stressors, potentially offering both promotive and risk-amplifying effects for the physiological stress response system. This study offered several novel findings in the relation between cultural factors, salient stressors of adolescence, and HPA activity.
ContributorsSmola, Xochitl Arlene (Author) / Gonzales, Nancy (Thesis director) / Presson, Clark (Committee member) / Doane, Leah (Committee member) / Department of Psychology (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The literature has consistently established levels of adolescent maladjustment well above national norms for both socioeconomic (SES) extremes (Lyman & Luthar 2014). Yet literature on positive adolescent adjustment, and its protective or even corrective factors is lacking (Eisenberg, Zhou, & Coller, 2001). This study examined the effects of gender and SES on parent attachment in relation to reports of prosocial behavior. Eleventh grade adolescents (N = 397) were recruited from two public high schools for academically-gifted students who were either high or low-level SES (i.e. the extremes). The students provided passive consent and answered questions on their demographics, perceived relationship with their parents, and tendency to behave in a prosocial manner. Multivariate analyses of variance and follow up analyses of variance were run by gender and SES to determine main effects for gender and SES on parent attachment and prosocial behavior. Regressions following preliminary correlations analyzed whether parental attachment predicted higher levels of adolescent prosocial behavior. Results demonstrated that females communicated with their mothers significantly more and reported higher levels of prosocial behavior than their male counterparts. Findings with regard to SES revealed that high SES adolescents reported increased parent attachment, whereas low SES adolescents reported higher levels of community\u2014based prosocial behaviors. Finally, certain dimensions of parent attachment predicted increases and decreases only in specific prosocial behaviors. Because prosocial behaviors change throughout adolescence, future ventures should consider a longitudinal analysis to obtain a more comprehensive picture of adolescent positive adjustment.
ContributorsAli, Hira (Author) / Luthar, Suniya (Thesis director) / Infurna, Frank (Committee member) / Davis, Mary (Committee member) / Department of Psychology (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12