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- All Subjects: Computer Science
- Genre: Academic theses
- Creators: Arizona State University
Description
In the last decade deep learning based models have revolutionized machine learning and computer vision applications. However, these models are data-hungry and training them is a time-consuming process. In addition, when deep neural networks are updated to augment their prediction space with new data, they run into the problem of catastrophic forgetting, where the model forgets previously learned knowledge as it overfits to the newly available data. Incremental learning algorithms enable deep neural networks to prevent catastrophic forgetting by retaining knowledge of previously observed data while also learning from newly available data.
This thesis presents three models for incremental learning; (i) Design of an algorithm for generative incremental learning using a pre-trained deep neural network classifier; (ii) Development of a hashing based clustering algorithm for efficient incremental learning; (iii) Design of a student-teacher coupled neural network to distill knowledge for incremental learning. The proposed algorithms were evaluated using popular vision datasets for classification tasks. The thesis concludes with a discussion about the feasibility of using these techniques to transfer information between networks and also for incremental learning applications.
This thesis presents three models for incremental learning; (i) Design of an algorithm for generative incremental learning using a pre-trained deep neural network classifier; (ii) Development of a hashing based clustering algorithm for efficient incremental learning; (iii) Design of a student-teacher coupled neural network to distill knowledge for incremental learning. The proposed algorithms were evaluated using popular vision datasets for classification tasks. The thesis concludes with a discussion about the feasibility of using these techniques to transfer information between networks and also for incremental learning applications.
ContributorsPatil, Rishabh (Author) / Venkateswara, Hemanth (Thesis advisor) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020
Description
Traumatic injuries are the leading cause of death in children under 18, with head trauma being the leading cause of death in children below 5. A large but unknown number of traumatic injuries are non-accidental, i.e. inflicted. The lack of sensitivity and specificity required to diagnose Abusive Head Trauma (AHT) from radiological studies results in putting the children at risk of re-injury and death. Modern Deep Learning techniques can be utilized to detect Abusive Head Trauma using Computer Tomography (CT) scans. Training models using these techniques are only a part of building AI-driven Computer-Aided Diagnostic systems. There are challenges in deploying the models to make them highly available and scalable.
The thesis models the domain of Abusive Head Trauma using Deep Learning techniques and builds an AI-driven System at scale using best Software Engineering Practices. It has been done in collaboration with Phoenix Children Hospital (PCH). The thesis breaks down AHT into sub-domains of Medical Knowledge, Data Collection, Data Pre-processing, Image Generation, Image Classification, Building APIs, Containers and Kubernetes. Data Collection and Pre-processing were done at PCH with the help of trauma researchers and radiologists. Experiments are run using Deep Learning models such as DCGAN (for Image Generation), Pretrained 2D and custom 3D CNN classifiers for the classification tasks. The trained models are exposed as APIs using the Flask web framework, contained using Docker and deployed on a Kubernetes cluster.
The results are analyzed based on the accuracy of the models, the feasibility of their implementation as APIs and load testing the Kubernetes cluster. They suggest the need for Data Annotation at the Slice level for CT scans and an increase in the Data Collection process. Load Testing reveals the auto-scalability feature of the cluster to serve a high number of requests.
The thesis models the domain of Abusive Head Trauma using Deep Learning techniques and builds an AI-driven System at scale using best Software Engineering Practices. It has been done in collaboration with Phoenix Children Hospital (PCH). The thesis breaks down AHT into sub-domains of Medical Knowledge, Data Collection, Data Pre-processing, Image Generation, Image Classification, Building APIs, Containers and Kubernetes. Data Collection and Pre-processing were done at PCH with the help of trauma researchers and radiologists. Experiments are run using Deep Learning models such as DCGAN (for Image Generation), Pretrained 2D and custom 3D CNN classifiers for the classification tasks. The trained models are exposed as APIs using the Flask web framework, contained using Docker and deployed on a Kubernetes cluster.
The results are analyzed based on the accuracy of the models, the feasibility of their implementation as APIs and load testing the Kubernetes cluster. They suggest the need for Data Annotation at the Slice level for CT scans and an increase in the Data Collection process. Load Testing reveals the auto-scalability feature of the cluster to serve a high number of requests.
ContributorsVikram, Aditya (Author) / Sanchez, Javier Gonzalez (Thesis advisor) / Gaffar, Ashraf (Thesis advisor) / Findler, Michael (Committee member) / Arizona State University (Publisher)
Created2020
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
Description
Availability of affordable image and video capturing devices as well as rapid development of social networking and content sharing websites has led to the creation of new type of content, Social Media. Any system serving the end user’s query search request should not only take the relevant images into consideration but they also need to be divergent for a well-rounded description of a query. As a result, the automated optimization of image retrieval results that are also divergent becomes exceedingly important.
The main focus of this thesis is to use visual description of a landmark by choosing the most diverse pictures that best describe all the details of the queried location from community-contributed datasets. For this, an end-to-end framework has been built, to retrieve relevant results that are also diverse. Different retrieval re-ranking and diversification strategies are evaluated to find a balance between relevance and diversification. Clustering techniques are employed to improve divergence. A unique fusion approach has been adopted to overcome the dilemma of selecting an appropriate clustering technique and the corresponding parameters, given a set of data to be investigated. Extensive experiments have been conducted on the Flickr Div150Cred dataset that has 30 different landmark locations. The results obtained are promising when evaluated on metrics for relevance and diversification.
The main focus of this thesis is to use visual description of a landmark by choosing the most diverse pictures that best describe all the details of the queried location from community-contributed datasets. For this, an end-to-end framework has been built, to retrieve relevant results that are also diverse. Different retrieval re-ranking and diversification strategies are evaluated to find a balance between relevance and diversification. Clustering techniques are employed to improve divergence. A unique fusion approach has been adopted to overcome the dilemma of selecting an appropriate clustering technique and the corresponding parameters, given a set of data to be investigated. Extensive experiments have been conducted on the Flickr Div150Cred dataset that has 30 different landmark locations. The results obtained are promising when evaluated on metrics for relevance and diversification.
ContributorsKalakota, Vaibhav Reddy (Author) / Bansal, Ajay (Thesis advisor) / Bansal, Srividya (Committee member) / Findler, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
Societal infrastructure is built with vision at the forefront of daily life. For those with
severe visual impairments, this creates countless barriers to the participation and
enjoyment of life’s opportunities. Technological progress has been both a blessing and
a curse in this regard. Digital text together with screen readers and refreshable Braille
displays have made whole libraries readily accessible and rideshare tech has made
independent mobility more attainable. Simultaneously, screen-based interactions and
experiences have only grown in pervasiveness and importance, precluding many of
those with visual impairments.
Sensory Substituion, the process of substituting an unavailable modality with
another one, has shown promise as an alternative to accomodation, but in recent
years meaningful strides in Sensory Substitution for vision have declined in frequency.
Given recent advances in Computer Vision, this stagnation is especially disconcerting.
Designing Sensory Substitution Devices (SSDs) for vision for use in interactive settings
that leverage modern Computer Vision techniques presents a variety of challenges
including perceptual bandwidth, human-computer-interaction, and person-centered
machine learning considerations. To surmount these barriers an approach called Per-
sonal Foveated Haptic Gaze (PFHG), is introduced. PFHG consists of two primary
components: a human visual system inspired interaction paradigm that is intuitive
and flexible enough to generalize to a variety of applications called Foveated Haptic
Gaze (FHG), and a person-centered learning component to address the expressivity
limitations of most SSDs. This component is called One-Shot Object Detection by
Data Augmentation (1SODDA), a one-shot object detection approach that allows a
user to specify the objects they are interested in locating visually and with minimal
effort realizing an object detection model that does so effectively.
The Personal Foveated Haptic Gaze framework was realized in a virtual and real-
world application: playing a 3D, interactive, first person video game (DOOM) and
finding user-specified real-world objects. User study results found Foveated Haptic
Gaze to be an effective and intuitive interface for interacting with dynamic visual
world using solely haptics. Additionally, 1SODDA achieves competitive performance
among few-shot object detection methods and high-framerate many-shot object de-
tectors. The combination of which paves the way for modern Sensory Substitution
Devices for vision.
severe visual impairments, this creates countless barriers to the participation and
enjoyment of life’s opportunities. Technological progress has been both a blessing and
a curse in this regard. Digital text together with screen readers and refreshable Braille
displays have made whole libraries readily accessible and rideshare tech has made
independent mobility more attainable. Simultaneously, screen-based interactions and
experiences have only grown in pervasiveness and importance, precluding many of
those with visual impairments.
Sensory Substituion, the process of substituting an unavailable modality with
another one, has shown promise as an alternative to accomodation, but in recent
years meaningful strides in Sensory Substitution for vision have declined in frequency.
Given recent advances in Computer Vision, this stagnation is especially disconcerting.
Designing Sensory Substitution Devices (SSDs) for vision for use in interactive settings
that leverage modern Computer Vision techniques presents a variety of challenges
including perceptual bandwidth, human-computer-interaction, and person-centered
machine learning considerations. To surmount these barriers an approach called Per-
sonal Foveated Haptic Gaze (PFHG), is introduced. PFHG consists of two primary
components: a human visual system inspired interaction paradigm that is intuitive
and flexible enough to generalize to a variety of applications called Foveated Haptic
Gaze (FHG), and a person-centered learning component to address the expressivity
limitations of most SSDs. This component is called One-Shot Object Detection by
Data Augmentation (1SODDA), a one-shot object detection approach that allows a
user to specify the objects they are interested in locating visually and with minimal
effort realizing an object detection model that does so effectively.
The Personal Foveated Haptic Gaze framework was realized in a virtual and real-
world application: playing a 3D, interactive, first person video game (DOOM) and
finding user-specified real-world objects. User study results found Foveated Haptic
Gaze to be an effective and intuitive interface for interacting with dynamic visual
world using solely haptics. Additionally, 1SODDA achieves competitive performance
among few-shot object detection methods and high-framerate many-shot object de-
tectors. The combination of which paves the way for modern Sensory Substitution
Devices for vision.
ContributorsFakhri, Bijan (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy L (Committee member) / Venkateswara, Hemanth (Committee member) / Amor, Heni (Committee member) / Arizona State University (Publisher)
Created2020
Description
Individuals with voice disorders experience challenges communicating daily. These challenges lead to a significant decrease in the quality of life for individuals with dysphonia. While voice amplification systems are often employed as a voice-assistive technology, individuals with voice disorders generally still experience difficulties being understood while using voice amplification systems. With the goal of developing systems that help improve the quality of life of individuals with dysphonia, this work outlines the landscape of voice-assistive technology, the inaccessibility of state-of-the-art voice-based technology and the need for the development of intelligibility improving voice-assistive technologies designed both with and for individuals with voice disorders. With the rise of voice-based technologies in society, in order for everyone to participate in the use of voice-based technologies individuals with voice disorders must be included in both the data that is used to train these systems and the design process. An important and necessary step towards the development of better voice assistive technology as well as more inclusive voice-based systems is the creation of a large, publicly available dataset of dysphonic speech. To this end, a web-based platform to crowdsource voice disorder speech was developed to create such a dataset. This dataset will be released so that it is freely and publicly available to stimulate research in the field of voice-assistive technologies. Future work includes building a robust intelligibility estimation model, as well as employing that model to measure, and therefore enhance, the intelligibility of a given utterance. The hope is that this model will lead to the development of voice-assistive technology using state-of-the-art machine learning models to help individuals with voice disorders be better understood.
ContributorsMoore, Meredith Kay (Author) / Panchanathan, Sethuraman (Thesis advisor) / Berisha, Visar (Committee member) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2020
Description
Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home assistants. It is hard to adopt them for computer-controlled systems due to the numerous complexities involved with their implementation in varying fields. The main challenge is the grounding of natural language base terms into the underlying system's primitives. The existing systems that do use natural language interfaces are specific to one problem domain only.
In this thesis, a domain-agnostic framework that creates natural language interfaces for computer-controlled systems has been developed by making the mapping between the language constructs and the system primitives customizable. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation purposes and machine learning models for language processing tasks. It has been evaluated within a simulation environment consisting of objects and a robot. This environment has been deployed as a web application, providing anonymous user testing for evaluation, and generating training data for machine learning components. Performance evaluation has been done on metrics such as time taken for a task or the number of instructions given by the user to the robot to accomplish a task. Additionally, the framework has been used to create a natural language interface for a database system to demonstrate its domain independence.
In this thesis, a domain-agnostic framework that creates natural language interfaces for computer-controlled systems has been developed by making the mapping between the language constructs and the system primitives customizable. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation purposes and machine learning models for language processing tasks. It has been evaluated within a simulation environment consisting of objects and a robot. This environment has been deployed as a web application, providing anonymous user testing for evaluation, and generating training data for machine learning components. Performance evaluation has been done on metrics such as time taken for a task or the number of instructions given by the user to the robot to accomplish a task. Additionally, the framework has been used to create a natural language interface for a database system to demonstrate its domain independence.
ContributorsTiwari, Sarthak (Author) / Bansal, Ajay (Thesis advisor) / Mehlhase, Alexandra (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2020
Description
In the presence of big data analysis, large volume of data needs to be systematically indexed to support analytical tasks, such as feature engineering, pattern recognition, data mining, and query processing. The volume, variety, and velocity of these data necessitate sophisticated systems to help researchers understand, analyze, and dis- cover insights from heterogeneous, multidimensional data sources. Many analytical frameworks have been proposed in the literature in recent years, but challenges to accuracy, speed, and effectiveness remain hence a systematic approach to perform data signature computation and query processing in multi-dimensional space is in people’s interest. In particular, real-time and near real-time queries pose significant challenges when working with large data sets.
To address these challenges, I develop an innovative robust multi-variate fea- ture extraction algorithm over multi-dimensional temporal datasets, which is able to help understand and analyze various real-world applications. Furthermore, to an- swer queries over these features, I develop a novel resource-aware indexing framework to approximately solve top-k queries by leveraging onion-layer indexing in conjunc- tion with locality sensitive hashing. The proposed indexing scheme allows people to answer top-k queries by only accessing a bounded amount of data, which optimizes big data small for queries.
To address these challenges, I develop an innovative robust multi-variate fea- ture extraction algorithm over multi-dimensional temporal datasets, which is able to help understand and analyze various real-world applications. Furthermore, to an- swer queries over these features, I develop a novel resource-aware indexing framework to approximately solve top-k queries by leveraging onion-layer indexing in conjunc- tion with locality sensitive hashing. The proposed indexing scheme allows people to answer top-k queries by only accessing a bounded amount of data, which optimizes big data small for queries.
ContributorsLiu, Sicong (Author) / Candan, Kasim Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2020
Description
Globalization is driving a rapid increase in motivation for learning new languages, with online and mobile language learning applications being an extremely popular method of doing so. Many language learning applications focus almost exclusively on aiding students in acquiring vocabulary, one of the most important elements in achieving fluency in a language. A well-balanced language curriculum must include both explicit vocabulary instruction and implicit vocabulary learning through interaction with authentic language materials. However, most language learning applications focus only on explicit instruction, providing little support for implicit learning. Students require support with implicit vocabulary learning because they need enough context to guess and acquire new words. Traditional techniques aim to teach students enough vocabulary to comprehend the text, thus enabling them to acquire new words. Despite the wide variety of support for vocabulary learning offered by learning applications today, few offer guidance on how to select an optimal vocabulary study set.
This thesis proposes a novel method of student modeling which uses pre-trained masked language models to model a student's reading comprehension abilities and detect words which are required for comprehension of a text. It explores the efficacy of using pre-trained masked language models to model human reading comprehension and presents a vocabulary study set generation pipeline using this method. This pipeline creates vocabulary study sets for explicit language learning that enable comprehension while still leaving some words to be acquired implicitly. Promising results show that masked language modeling can be used to model human comprehension and that the pipeline produces reasonably sized vocabulary study sets.
This thesis proposes a novel method of student modeling which uses pre-trained masked language models to model a student's reading comprehension abilities and detect words which are required for comprehension of a text. It explores the efficacy of using pre-trained masked language models to model human reading comprehension and presents a vocabulary study set generation pipeline using this method. This pipeline creates vocabulary study sets for explicit language learning that enable comprehension while still leaving some words to be acquired implicitly. Promising results show that masked language modeling can be used to model human comprehension and that the pipeline produces reasonably sized vocabulary study sets.
ContributorsEdgar, Vatricia Cathrine (Author) / Bansal, Ajay (Thesis advisor) / Acuna, Ruben (Committee member) / Mehlhase, Alexandra (Committee member) / Arizona State University (Publisher)
Created2020
Description
Currently, recommender systems are used extensively to find the right audience with the "right" content over various platforms. Recommendations generated by these systems aim to offer relevant items to users. Different approaches have been suggested to solve this problem mainly by using the rating history of the user or by identifying the preferences of similar users. Most of the existing recommendation systems are formulated in an identical fashion, where a model is trained to capture the underlying preferences of users over different kinds of items. Once it is deployed, the model suggests personalized recommendations precisely, and it is assumed that the preferences of users are perfectly reflected by the historical data. However, such user data might be limited in practice, and the characteristics of users may constantly evolve during their intensive interaction between recommendation systems.
Moreover, most of these recommender systems suffer from the cold-start problems where insufficient data for new users or products results in reduced overall recommendation output. In the current study, we have built a recommender system to recommend movies to users. Biclustering algorithm is used to cluster the users and movies simultaneously at the beginning to generate explainable recommendations, and these biclusters are used to form a gridworld where Q-Learning is used to learn the policy to traverse through the grid. The reward function uses the Jaccard Index, which is a measure of common users between two biclusters. Demographic details of new users are used to generate recommendations that solve the cold-start problem too.
Lastly, the implemented algorithm is examined with a real-world dataset against the widely used recommendation algorithm and the performance for the cold-start cases.
Moreover, most of these recommender systems suffer from the cold-start problems where insufficient data for new users or products results in reduced overall recommendation output. In the current study, we have built a recommender system to recommend movies to users. Biclustering algorithm is used to cluster the users and movies simultaneously at the beginning to generate explainable recommendations, and these biclusters are used to form a gridworld where Q-Learning is used to learn the policy to traverse through the grid. The reward function uses the Jaccard Index, which is a measure of common users between two biclusters. Demographic details of new users are used to generate recommendations that solve the cold-start problem too.
Lastly, the implemented algorithm is examined with a real-world dataset against the widely used recommendation algorithm and the performance for the cold-start cases.
ContributorsSargar, Rushikesh Bapu (Author) / Atkinson, Robert K (Thesis advisor) / Chen, Yinong (Thesis advisor) / Chavez-Echeagaray, Maria Elena (Committee member) / Arizona State University (Publisher)
Created2020