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- Genre: Academic theses
- Creators: Li, Baoxin
Description
As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the
burden on society. It will be essential to develop technology that can age with the
individual. One solution is to keep older adults in their homes longer through smart
home and smart living technology, allowing them to age in place. People have many
choices when choosing where to age in place, including their own homes, assisted
living facilities, nursing homes, or family members. No matter where people choose to
age, they may face isolation and financial hardships. It is crucial to keep finances in
mind when developing Smart Home technology.
Smart home technologies seek to allow individuals to stay inside their homes for
as long as possible, yet little work looks at how we can use technology in different
life stages. Robots are poised to impact society and ease burns at home and in the
workforce. Special attention has been given to social robots to ease isolation. As
social robots become accepted into society, researchers need to understand how these
robots should mimic natural conversation. My work attempts to answer this question
within social robotics by investigating how to make conversational robots natural and
reciprocal.
I investigated this through a 2x2 Wizard of Oz between-subjects user study. The
study lasted four months, testing four different levels of interactivity with the robot.
None of the levels were significantly different from the others, an unexpected result. I
then investigated the robot’s personality, the participant’s trust, and the participant’s
acceptance of the robot and how that influenced the study.
ContributorsMiller, Jordan (Author) / McDaniel, Troy (Thesis advisor) / Michael, Katina (Committee member) / Cooke, Nancy (Committee member) / Bryan, Chris (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Deep neural networks have been shown to be vulnerable to adversarial attacks. Typical attack strategies alter authentic data subtly so as to obtain adversarial samples that resemble the original but otherwise would cause a network's misbehavior such as a high misclassification rate. Various attack approaches have been reported, with some showing state-of-the-art performance in attacking certain networks. In the meanwhile, many defense mechanisms have been proposed in the literature, some of which are quite effective for guarding against typical attacks. Yet, most of these attacks fail when the targeted network modifies its architecture or uses another set of parameters and vice versa. Moreover, the emerging of more advanced deep neural networks, such as generative adversarial networks (GANs), has made the situation more complicated and the game between the attack and defense is continuing. This dissertation aims at exploring the venerability of the deep neural networks by investigating the mechanisms behind the success/failure of the existing attack and defense approaches. Therefore, several deep learning-based approaches have been proposed to study the problem from different perspectives. First, I developed an adversarial attack approach by exploring the unlearned region of a typical deep neural network which is often over-parameterized. Second, I proposed an end-to-end learning framework to analyze the images generated by different GAN models. Third, I developed a defense mechanism that can secure the deep neural network against adversarial attacks with a defense layer consisting of a set of orthogonal kernels. Substantial experiments are conducted to unveil the potential factors that contribute to attack/defense effectiveness. This dissertation also concludes with a discussion of possible future works of achieving a robust deep neural network.
ContributorsDing, Yuzhen (Author) / Li, Baoxin (Thesis advisor) / Davulcu, Hasan (Committee member) / Venkateswara, Hemanth Kumar Demakethepalli (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
Increased LV wall thickness is frequently encountered in transthoracicechocardiography (TTE). While accurate and early diagnosis is clinically important,
given the differences in available therapeutic options and prognosis, an extensive workup
is often required for establishing the diagnosis. I propose the first echo-based, automated
deep learning model with a fusion architecture to facilitate the evaluation and diagnosis
of increased left ventricular (LV) wall thickness. Patients with an established diagnosis
for increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac
amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 to
11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into
80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE
views were used to optimize a pre-trained InceptionResnetV2 model, each model output
was used to train a meta-learner under a fusion architecture. Model performance was
assessed by multiclass area under the receiver operating characteristic curve (AUROC).
A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191
HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual
view-dependent models, the apical 4 chamber model had the best performance (AUROC:
HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the
view-dependent models (AUROC: CA: 0.90, HCM: 0.93, and HTN/other: 0.92). I
successfully established an automatic end-to-end deep learning model framework that
accurately differentiates the major etiologies of increased LV wall thickness, including
HCM and CA from the background of HTN/other diagnoses.
ContributorsLi, James Shuyue (Author) / Patel, Bhavik (Thesis advisor) / Li, Baoxin (Thesis advisor) / Banerjee, Imon (Committee member) / Arizona State University (Publisher)
Created2022
Description
Federated Learning (FL) is envisaged to be a promising solution for collaboratively training a machine learning model while keeping the training data decentralized and private. Instead of sharing raw data to the central entity, the participating client devices share focused updates for aggregation to ensure global convergence of the model. Owing to the shortcomings of manually handcrafted neural network architectures, the research community is striving to develop Neural Architecture Search (NAS) approaches to automatically search for optimal networks that fit the clients’ data. Despite the inaccessibility of clients’ data in an FL setting, the federated NAS literature has recently witnessed great progress to apply these NAS techniques to an FL setting. However, one of the key bottlenecks of Federated Learning is the cost of communication between clients and the server, and the state-of-the-art federated NAS techniques search for networks with millions of parameters that require several rounds of communication to find the optimal weight parameters. Also, deploying a network having millions of parameters on edge devices (which are the typical participants in an FL process) is infeasible due to its computational limitations and increased latency. Thus, this work proposes Weight-Agnostic Federated Neural Architecture Search (WFNAS), a novel evolutionary framework to search for well-performing and minimally connected weight-agnostic network architectures in an FL setting. As the connectivity of the networks themselves is the solution, there is no need for weight training and hyperparameter tuning, reducing the communication overhead significantly. The experiments indicate a gain of nearly 40% for orthogonal (vertical FL) data distributions compared to local training. This work is the first federated NAS technique in the literature for vertical FL. Although the experiments are performed in a resource-constrained environment, the aim of this thesis is to show a new direction of research to the FL community.
ContributorsThakkar, Om (Author) / Bazzi, Rida (Thesis advisor) / Li, Baoxin (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2021
Description
How to teach a machine to understand natural language? This question is a long-standing challenge in Artificial Intelligence. Several tasks are designed to measure the progress of this challenge. Question Answering is one such task that evaluates a machine's ability to understand natural language, where it reads a passage of text or an image and answers comprehension questions. In recent years, the development of transformer-based language models and large-scale human-annotated datasets has led to remarkable progress in the field of question answering. However, several disadvantages of fully supervised question answering systems have been observed. Such as generalizing to unseen out-of-distribution domains, linguistic style differences in questions, and adversarial samples. This thesis proposes implicitly supervised question answering systems trained using knowledge acquisition from external knowledge sources and new learning methods that provide inductive biases to learn question answering. In particular, the following research projects are discussed: (1) Knowledge Acquisition methods: these include semantic and abductive information retrieval for seeking missing knowledge, a method to represent unstructured text corpora as a knowledge graph, and constructing a knowledge base for implicit commonsense reasoning. (2) Learning methods: these include Knowledge Triplet Learning, a method over knowledge graphs; Test-Time Learning, a method to generalize to an unseen out-of-distribution context; WeaQA, a method to learn visual question answering using image captions without strong supervision; WeaSel, weakly supervised method for relative spatial reasoning; and a new paradigm for unsupervised natural language inference. These methods potentially provide a new research direction to overcome the pitfalls of direct supervision.
ContributorsBanerjee, Pratyay (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Committee member) / Blanco, Eduardo (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
The rapid growth of Internet-of-things (IoT) and artificial intelligence applications have called forth a new computing paradigm--edge computing. Edge computing applications, such as video surveillance, autonomous driving, and augmented reality, are highly computationally intensive and require real-time processing. Current edge systems are typically based on commodity general-purpose hardware such as Central Processing Units (CPUs) and Graphical Processing Units (GPUs) , which are mainly designed for large, non-time-sensitive jobs in the cloud and do not match the needs of the edge workloads. Also, these systems are usually power hungry and are not suitable for resource-constrained edge deployments. Such application-hardware mismatch calls forth a new computing backbone to support the high-bandwidth, low-latency, and energy-efficient requirements. Also, the new system should be able to support a variety of edge applications with different characteristics. This thesis addresses the above challenges by studying the use of Field Programmable Gate Array (FPGA) -based computing systems for accelerating the edge workloads, from three critical angles. First, it investigates the feasibility of FPGAs for edge computing, in comparison to conventional CPUs and GPUs. Second, it studies the acceleration of common algorithmic characteristics, identified as loop patterns, using FPGAs, and develops a benchmark tool for analyzing the performance of these patterns on different accelerators. Third, it designs a new edge computing platform using multiple clustered FPGAs to provide high-bandwidth and low-latency acceleration of convolutional neural networks (CNNs) widely used in edge applications. Finally, it studies the acceleration of the emerging neural networks, randomly-wired neural networks, on the multi-FPGA platform. The experimental results from this work show that the new generation of workloads requires rethinking the current edge-computing architecture. First, through the acceleration of common loops, it demonstrates that FPGAs can outperform GPUs in specific loops types up to 14 times. Second, it shows the linear scalability of multi-FPGA platforms in accelerating neural networks. Third, it demonstrates the superiority of the new scheduler to optimally place randomly-wired neural networks on multi-FPGA platforms with 81.1 times better throughput than the available scheduling mechanisms.
ContributorsBiookaghazadeh, Saman (Author) / Zhao, Ming (Thesis advisor) / Ren, Fengbo (Thesis advisor) / Li, Baoxin (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2021
Description
Applications over a gesture-based human-computer interface (HCI) require a new user login method with gestures because it does not have traditional input devices. For example, a user may be asked to verify the identity to unlock a device in a mobile or wearable platform, or sign in to a virtual site over a Virtual Reality (VR) or Augmented Reality (AR) headset, where no physical keyboard or touchscreen is available. This dissertation presents a unified user login framework and an identity input method using 3D In-Air-Handwriting (IAHW), where a user can log in to a virtual site by writing a passcode in the air very fast like a signature. The presented research contains multiple tasks that span motion signal modeling, user authentication, user identification, template protection, and a thorough evaluation in both security and usability. The results of this research show around 0.1% to 3% Equal Error Rate (EER) in user authentication in different conditions as well as 93% accuracy in user identification, on a dataset with over 100 users and two types of gesture input devices. Besides, current research in this area is severely limited by the availability of the gesture input device, datasets, and software tools. This study provides an infrastructure for IAHW research with an open-source library and open datasets of more than 100K IAHW hand movement signals. Additionally, the proposed user identity input method can be extended to a general word input method for both English and Chinese using limited training data. Hence, this dissertation can help the research community in both cybersecurity and HCI to explore IAHW as a new direction, and potentially pave the way to practical adoption of such technologies in the future.
ContributorsLu, Duo (Author) / Huang, Dijiang (Thesis advisor) / Li, Baoxin (Committee member) / Zhang, Junshan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Data mining, also known as big data analysis, has been identified as a critical and challenging process for a variety of applications in real-world problems. Numerous datasets are collected and generated every day to store the information. The rise in the number of data volumes and data modality has resulted in the increased demand for data mining methods and strategies of finding anomalies, patterns, and correlations within large data sets to predict outcomes. Effective machine learning methods are widely adapted to build the data mining pipeline for various purposes like business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The major challenges for effectively and efficiently mining big data include (1) data heterogeneity and (2) missing data. Heterogeneity is the natural characteristic of big data, as the data is typically collected from different sources with diverse formats. The missing value is the most common issue faced by the heterogeneous data analysis, which resulted from variety of factors including the data collecting processing, user initiatives, erroneous data entries, and so on. In response to these challenges, in this thesis, three main research directions with application scenarios have been investigated: (1) Mining and Formulating Heterogeneous Data, (2) missing value imputation strategy in various application scenarios in both offline and online manner, and (3) missing value imputation for multi-modality data. Multiple strategies with theoretical analysis are presented, and the evaluation of the effectiveness of the proposed algorithms compared with state-of-the-art methods is discussed.
Contributorsliu, Xu (Author) / He, Jingrui (Thesis advisor) / Xue, Guoliang (Thesis advisor) / Li, Baoxin (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021