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- All Subjects: Computer Science
Description
Machine learning models convert raw data in the form of video, images, audio,
text, etc. into feature representations that are convenient for computational process-
ing. Deep neural networks have proven to be very efficient feature extractors for a
variety of machine learning tasks. Generative models based on deep neural networks
introduce constraints on the feature space to learn transferable and disentangled rep-
resentations. Transferable feature representations help in training machine learning
models that are robust across different distributions of data. For example, with the
application of transferable features in domain adaptation, models trained on a source
distribution can be applied to a data from a target distribution even though the dis-
tributions may be different. In style transfer and image-to-image translation, disen-
tangled representations allow for the separation of style and content when translating
images.
This thesis examines learning transferable data representations in novel deep gen-
erative models. The Semi-Supervised Adversarial Translator (SAT) utilizes adversar-
ial methods and cross-domain weight sharing in a neural network to extract trans-
ferable representations. These transferable interpretations can then be decoded into
the original image or a similar image in another domain. The Explicit Disentangling
Network (EDN) utilizes generative methods to disentangle images into their core at-
tributes and then segments sets of related attributes. The EDN can separate these
attributes by controlling the ow of information using a novel combination of losses
and network architecture. This separation of attributes allows precise modi_cations
to speci_c components of the data representation, boosting the performance of ma-
chine learning tasks. The effectiveness of these models is evaluated across domain
adaptation, style transfer, and image-to-image translation tasks.
text, etc. into feature representations that are convenient for computational process-
ing. Deep neural networks have proven to be very efficient feature extractors for a
variety of machine learning tasks. Generative models based on deep neural networks
introduce constraints on the feature space to learn transferable and disentangled rep-
resentations. Transferable feature representations help in training machine learning
models that are robust across different distributions of data. For example, with the
application of transferable features in domain adaptation, models trained on a source
distribution can be applied to a data from a target distribution even though the dis-
tributions may be different. In style transfer and image-to-image translation, disen-
tangled representations allow for the separation of style and content when translating
images.
This thesis examines learning transferable data representations in novel deep gen-
erative models. The Semi-Supervised Adversarial Translator (SAT) utilizes adversar-
ial methods and cross-domain weight sharing in a neural network to extract trans-
ferable representations. These transferable interpretations can then be decoded into
the original image or a similar image in another domain. The Explicit Disentangling
Network (EDN) utilizes generative methods to disentangle images into their core at-
tributes and then segments sets of related attributes. The EDN can separate these
attributes by controlling the ow of information using a novel combination of losses
and network architecture. This separation of attributes allows precise modi_cations
to speci_c components of the data representation, boosting the performance of ma-
chine learning tasks. The effectiveness of these models is evaluated across domain
adaptation, style transfer, and image-to-image translation tasks.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2018
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
This paper presents a system to deliver automated, noninvasive, and effective fine motor rehabilitation through a rhythm-based game using a Leap Motion Controller. The system is a rhythm game where hand gestures are used as input and must match the rhythm and gestures shown on screen, thus allowing a physical therapist to represent an exercise session involving the user's hand and finger joints as a series of patterns. Fine motor rehabilitation plays an important role in the recovery and improvement of the effects of stroke, Parkinson's disease, multiple sclerosis, and more. Individuals with these conditions possess a wide range of impairment in terms of fine motor movement. The serious game developed takes this into account and is designed to work with individuals with different levels of impairment. In a pilot study, under partnership with South West Advanced Neurological Rehabilitation (SWAN Rehab) in Phoenix, Arizona, we compared the performance of individuals with fine motor impairment to individuals without this impairment to determine whether a human-centered approach and adapting to an user's range of motion can allow an individual with fine motor impairment to perform at a similar level as a non-impaired user.
ContributorsShah, Vatsal Nimishkumar (Author) / McDaniel, Troy (Thesis director) / Tadayon, Ramin (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper presents an overview of The Dyadic Interaction Assistant for Individuals with Visual Impairments with a focus on the software component. The system is designed to communicate facial information (facial Action Units, facial expressions, and facial features) to an individual with visual impairments in a dyadic interaction between two people sitting across from each other. Comprised of (1) a webcam, (2) software, and (3) a haptic device, the system can also be described as a series of input, processing, and output stages, respectively. The processing stage of the system builds on the open source FaceTracker software and the application Computer Expression Recognition Toolbox (CERT). While these two sources provide the facial data, the program developed through the IDE Qt Creator and several AppleScripts are used to adapt the information to a Graphical User Interface (GUI) and output the data to a comma-separated values (CSV) file. It is the first software to convey all 3 types of facial information at once in real-time. Future work includes testing and evaluating the quality of the software with human subjects (both sighted and blind/low vision), integrating the haptic device to complete the system, and evaluating the entire system with human subjects (sighted and blind/low vision).
ContributorsBrzezinski, Chelsea Victoria (Author) / Balasubramanian, Vineeth (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
The Oasis app is a self-appraisal tool for potential or current problem gamblers to take control of their habits by providing periodic check-in notifications during a gambling session and allowing users to see their progress over time. Oasis is backed by substantial background research surrounding addiction intervention methods, especially in the field of self-appraisal messaging, and applies this messaging in a familiar mobile notification form that can effectively change user’s behavior. User feedback was collected and used to improve the app, and the results show a promising tool that could help those who need it in the future.
ContributorsBlunt, Thomas (Author) / Meuth, Ryan (Thesis director) / McDaniel, Troy (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
The impact of Artificial Intelligence (AI) has increased significantly in daily life. AI is taking big strides towards moving into areas of life that are critical such as
healthcare but, also into areas such as entertainment and leisure. Deep neural
networks have been pivotal in making all these advancements possible. But, a well-known problem with deep neural networks is the lack of explanations for the choices
it makes. To combat this, several methods have been tried in the field of research.
One example of this is assigning rankings to the individual features and how influential
they are in the decision-making process. In contrast a newer class of methods focuses
on Concept Activation Vectors (CAV) which focus on extracting higher-level concepts
from the trained model to capture more information as a mixture of several features
and not just one. The goal of this thesis is to employ concepts in a novel domain: to
explain how a deep learning model uses computer vision to classify music into different
genres. Due to the advances in the field of computer vision with deep learning for
classification tasks, it is rather a standard practice now to convert an audio clip into
corresponding spectrograms and use those spectrograms as image inputs to the deep
learning model. Thus, a pre-trained model can classify the spectrogram images
(representing songs) into musical genres. The proposed explanation system called
“Why Pop?” tries to answer certain questions about the classification process such as
what parts of the spectrogram influence the model the most, what concepts were
extracted and how are they different for different classes. These explanations aid the
user gain insights into the model’s learnings, biases, and the decision-making process.
ContributorsSharma, Shubham (Author) / Bryan, Chris (Thesis advisor) / McDaniel, Troy (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2022
Description
In motor learning, real-time multi-modal feedback is a critical element in guided training. Serious games have been introduced as a platform for at-home motor training due to their highly interactive and multi-modal nature. This dissertation explores the design of a multimodal environment for at-home training in which an autonomous system observes and guides the user in the place of a live trainer, providing real-time assessment, feedback and difficulty adaptation as the subject masters a motor skill. After an in-depth review of the latest solutions in this field, this dissertation proposes a person-centric approach to the design of this environment, in contrast to the standard techniques implemented in related work, to address many of the limitations of these approaches. The unique advantages and restrictions of this approach are presented in the form of a case study in which a system entitled the "Autonomous Training Assistant" consisting of both hardware and software for guided at-home motor learning is designed and adapted for a specific individual and trainer.
In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.
In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.
ContributorsTadayon, Ramin (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Amresh, Ashish (Committee member) / Glenberg, Arthur (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Facial Expressions Recognition using the Convolution Neural Network has been actively researched upon in the last decade due to its high number of applications in the human-computer interaction domain. As Convolution Neural Networks have the exceptional ability to learn, they outperform the methods using handcrafted features. Though the state-of-the-art models achieve high accuracy on the lab-controlled images, they still struggle for the wild expressions. Wild expressions are captured in a real-world setting and have natural expressions. Wild databases have many challenges such as occlusion, variations in lighting conditions and head poses. In this work, I address these challenges and propose a new model containing a Hybrid Convolutional Neural Network with a Fusion Layer. The Fusion Layer utilizes a combination of the knowledge obtained from two different domains for enhanced feature extraction from the in-the-wild images. I tested my network on two publicly available in-the-wild datasets namely RAF-DB and AffectNet. Next, I tested my trained model on CK+ dataset for the cross-database evaluation study. I prove that my model achieves comparable results with state-of-the-art methods. I argue that it can perform well on such datasets because it learns the features from two different domains rather than a single domain. Last, I present a real-time facial expression recognition system as a part of this work where the images are captured in real-time using laptop camera and passed to the model for obtaining a facial expression label for it. It indicates that the proposed model has low processing time and can produce output almost instantly.
ContributorsChhabra, Sachin (Author) / Li, Baoxin (Thesis advisor) / Venkateswara, Hemanth (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2019
Description
Fatigue in radiology is a readily studied area. Machine learning concepts appliedto the identification of fatigue are also readily available. However, the intersection
between the two areas is not a relative commonality. This study looks to explore the
intersection of fatigue in radiology and machine learning concepts by analyzing temporal
trends in multivariate time series data. A novel methodological approach using support
vector machines to observe temporal trends in time-based aggregations of time series data
is proposed. The data used in the study is captured in a real-world, unconstrained
radiology setting where gaze and facial metrics are captured from radiologists performing
live image reviews. The captured data is formatted into classes whose labels represent a
window of time during the radiologist’s review. Using the labeled classes, the decision
function and accuracy of trained, linear support vector machine models are evaluated to
produce a visualization of temporal trends and critical inflection points as well as the
contribution of individual features. Consequently, the study finds valid potential
justification in the methods suggested. The study offers a prospective use of maximummargin classification to demarcate the manipulation of an abstract phenomenon such as
fatigue on temporal data. Potential applications are envisioned that could improve the
workload distribution of the medical act.
ContributorsHayes, Matthew (Author) / McDaniel, Troy (Thesis advisor) / Coza, Aurel (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2022