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- All Subjects: semi-supervised learning
- All Subjects: Website
- Creators: McDaniel, Troy
Description
Most collegiate organizations aim to unite students with common interests and engage them in a like-minded community of peers. A significant sub-group of these organizations are classified under sororities and fraternities and commonly known as Greek Life. Member involvement is a crucial element for Greek Life as participation in philanthropic events, chapter meetings, rituals, recruitment events, etc. often reflects the state of the organization. The purpose of this project is to create a web application that allows members of an Arizona State University sorority to view their involvement activity as outlined by the chapter. Maintaining the balance between academics, sleep, a social life, and extra-curricular activities/organizations can be difficult for college students. With the use of this website, members can view their attendances, absences, and study/volunteer hours to know their progress towards the involvement requirements set by the chapter. This knowledge makes it easier to plan schedules and alleviate some stress associated with the time-management of sorority events, assignments/homework, and studying. It is also designed for the sorority leadership to analyze and track the participation of the membership. Members can submit their participation in events, making the need for manual counting and calculations disappear. The website administrator(s) can view and approve data from any and all members. The website was developed using HTML, CSS, and JavaScript in conjunction with Firebase for the back-end database. Human-Computer Interaction (HCI) tools and techniques were used throughout the development process to aide in prototyping, visual design, and evaluation. The front-end appearance of the website was designed to mimic the data manipulation used in the current involvement tracking system while presenting it in a more personalized and aesthetically pleasing manner.
ContributorsKolker, Madysen (Author) / McDaniel, Troy (Thesis director) / Tadayon, Arash (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
This paper introduces MisophoniAPP, a new website for managing misophonia. It will briefly discuss the nature of this chronic syndrome, which is the experience of reacting strongly to certain everyday sounds, or “triggers”. Various forms of Cognitive Behavioral Therapy and the Neural Repatterning Technique are currently used to treat misophonia, but they are not guaranteed to work for every patient. Few apps exist to help patients with their therapy, so this paper describes the design and creation of a new website that combines exposure therapy,
relaxation, and gamification to help patients alleviate their misophonic reflexes.
relaxation, and gamification to help patients alleviate their misophonic reflexes.
ContributorsNoziglia, Rachel Elisabeth (Author) / McDaniel, Troy (Thesis director) / Anderson, Derrick (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to overcome these challenges by facilitating transfer learning between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated unprecedented potential for utilizing unlabeled data to train classification models in defiance of discouragingly meager sets of annotated data.
In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.
In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.
ContributorsDudley, Andrew, M.S (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2019
Description
Humans have a great ability to recognize objects in different environments irrespective of their variations. However, the same does not apply to machine learning models which are unable to generalize to images of objects from different domains. The generalization of these models to new data is constrained by the domain gap. Many factors such as image background, image resolution, color, camera perspective and variations in the objects are responsible for the domain gap between the training data (source domain) and testing data (target domain). Domain adaptation algorithms aim to overcome the domain gap between the source and target domains and learn robust models that can perform well across both the domains.
This thesis provides solutions for the standard problem of unsupervised domain adaptation (UDA) and the more generic problem of generalized domain adaptation (GDA). The contributions of this thesis are as follows. (1) Certain and Consistent Domain Adaptation model for closed-set unsupervised domain adaptation by aligning the features of the source and target domain using deep neural networks. (2) A multi-adversarial deep learning model for generalized domain adaptation. (3) A gating model that detects out-of-distribution samples for generalized domain adaptation.
The models were tested across multiple computer vision datasets for domain adaptation.
The dissertation concludes with a discussion on the proposed approaches and future directions for research in closed set and generalized domain adaptation.
This thesis provides solutions for the standard problem of unsupervised domain adaptation (UDA) and the more generic problem of generalized domain adaptation (GDA). The contributions of this thesis are as follows. (1) Certain and Consistent Domain Adaptation model for closed-set unsupervised domain adaptation by aligning the features of the source and target domain using deep neural networks. (2) A multi-adversarial deep learning model for generalized domain adaptation. (3) A gating model that detects out-of-distribution samples for generalized domain adaptation.
The models were tested across multiple computer vision datasets for domain adaptation.
The dissertation concludes with a discussion on the proposed approaches and future directions for research in closed set and generalized domain adaptation.
ContributorsNagabandi, Bhadrinath (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020