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- All Subjects: Machine Learning
Description
Stress is one of the critical factors in daily lives, as it has a profound impact onperformance at work and decision-making processes. With the development of IoT
technology, smart wearables can handle diverse operations, including networking and
recording biometric signals. Also, it has become easier for individual users to selfdetect stress with recorded data since these wearables as well as their accompanying
smartphones now have data processing capability. Edge computing on such devices
enables real-time feedback and in turn preemptive identification of reactions to stress.
This can provide an opportunity to prevent more severe consequences that might
result if stress is unaddressed. From a system perspective, leveraging edge computing
allows saving energy such as network bandwidth and latency since it processes data in
proximity to the data source. It can also strengthen privacy by implementing stress
prediction at local devices without transferring personal information to the public
cloud.
This thesis presents a framework for real-time stress prediction using Fitbit and
machine learning with the support from cloud computing. Fitbit is a wearable tracker
that records biometric measurements using optical sensors on the wrist. It also provides developers with platforms to design custom applications. I developed an application for the Fitbit and the user’s accompanying mobile device to collect heart rate
fluctuations and corresponding stress levels entered by users. I also established the
dataset collected from police cadets during their academy training program. Machine
learning classifiers for stress prediction are built using classic models and TensorFlow
in the cloud. Lastly, the classifiers are optimized using model compression techniques
for deploying them on the smartphones and analyzed how efficiently stress prediction
can be performed on the edge.
ContributorsSim, Sang-Hun (Author) / Zhao, Ming (Thesis advisor) / Roberts, Nicole (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2022
Description
Demand for processing machine learning workloads has grown incredibly over the past few years. Kubernetes, an open-source container orchestrator, has been widely used by public and private cloud providers for building scalable systems for meeting this demand. The data used to train machine learning workloads can be sensitive in nature, and organizations may prefer to be responsible for their data security and governance by housing it on on-premises systems. Hybrid cloud gives organizations the flexibility to use both on-premises and cloud infrastructure together, leveraging the advantages of both. While there is a long list of benefits, Kubernetes has limitations by design that limit a user’s abilities in a hybrid cloud environment. The Kubernetes control plane does not allow for the management of worker nodes across cloud providers. This boundary puts new responsibilities on the end-user when deploying a hybrid cloud workload. The end-user must create their clusters and specify which cluster the workload will be scheduled to ahead of time. The Kubernetes scheduler will not take the capacity of another cluster into account. To address these limitations, this thesis presents a new hybrid cloud Kubernetes scheduler that can create new clusters on-demand and burst machine learning workloads to a public cloud when on-premises resources are insufficient. Workloads begin scheduling on an on-premises Kubernetes cluster. When the on-premises cluster’s capacity is exhausted, a new Kubernetes cluster is created on-demand in a public cloud provider, and machine learning tasks waiting in the Kubernetes scheduling queue are dynamically migrated to the public cloud provider’s Kubernetes cluster. The public Kubernetes cluster is dynamically sized and auto scaled based on the pending tasks’ demand. When migrating tasks, the data dependencies among tasks are considered, and a region is dynamically chosen to reduce migration time and cost. The scheduler is experimentally evaluated with real-world machine learning workloads, including predicting if a subscriber will stay with a subscription service, predicting the discount needed to retain a subscription customer, predicting if a credit card transaction is fraudulent, and simulated real-world job arrival behavior in a real hybrid cloud environment. Results show that the scheduler can substantially reduce the workload execution time by dynamically migrating tasks from on-premises to public cloud and minimizing the cost by dynamically sizing and scaling the public cluster.
ContributorsKieley, James (Author) / Zhao, Ming (Thesis advisor) / Huang, Dijiang (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2021
Description
As threats emerge and change, the life of a police officer continues to intensify. To better support police training curriculums and police cadets through this critical career juncture, this thesis proposes a state-of-the-art framework for stress detection using real-world data and deep neural networks. As an integral step of a larger study, this thesis investigates data processing techniques to handle the ambiguity of data collected in naturalistic contexts and leverages data structuring approaches to train deep neural networks. The analysis used data collected from 37 police training cadetsin five different training cohorts at the Phoenix Police Regional Training Academy. The data was collected at different intervals during the cadets’ rigorous six-month training course. In total, data were collected over 11 months from all the cohorts combined. All cadets were equipped with a Fitbit wearable device with a custom-built application to collect biometric data, including heart rate and self-reported stress levels. Throughout the data collection period, the cadets were asked to wear the Fitbit device and respond to stress level prompts to capture real-time responses. To manage this naturalistic data, this thesis leveraged heart rate filtering algorithms, including Hampel, Median, Savitzky-Golay, and Wiener, to remove potentially noisy data. After data processing and noise removal, the heart rate data and corresponding stress level labels are processed into two different dataset sizes. The data is then fed into a Deep ECGNet (created by Prajod et al.), a simple Feed Forward network (created by Sim et al.), and a Multilayer Perceptron (MLP) network for binary classification. Experimental results show that the Feed Forward network achieves the highest accuracy (90.66%) for data from a single cohort, while the MLP model performs best on data across cohorts, achieving an 85.92% accuracy. These findings suggest that stress detection is feasible on a variate set of real-world data using deepneural networks.
ContributorsParanjpe, Tara Anand (Author) / Zhao, Ming (Thesis advisor) / Roberts, Nicole (Thesis advisor) / Duran, Nicholas (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2023
Description
Quantum computers provide a promising future, where computationally difficult
problems can be executed exponentially faster than the current classical computers we have in use today. While there is tremendous research and development in the creation of quantum computers, there is a fundamental challenge that exists in the quantum world. Due to the fragility of the quantum world, error correction methods have originated since 1995 to tackle the giant problem. Since the birth of the idea that these powerful computers can crunch and process numbers beyond the limit of the current computers, there exist several mathematical error correcting codes that could potentially give the required stability in the fragile and fault tolerant quantum world. While there has been a multitude of possible solutions, there is no one single error correcting code that is the key to solving the problem. Almost every solution presented has shared with it a limiting factor or an issue that prevents it from becoming the breakthrough that is desperately needed.
This paper gives an introductory knowledge of what is the quantum world and why there is a need for error correcting topologies. Finally, it introduces one recent topology that could be added to the list of possible solutions to this central problem. Rather than focusing on the mathematical frameworks, the paper introduces the main concepts so that most readers even outside the major field of computer science can understand what the main problem is and how this topology attempts to solve it.
problems can be executed exponentially faster than the current classical computers we have in use today. While there is tremendous research and development in the creation of quantum computers, there is a fundamental challenge that exists in the quantum world. Due to the fragility of the quantum world, error correction methods have originated since 1995 to tackle the giant problem. Since the birth of the idea that these powerful computers can crunch and process numbers beyond the limit of the current computers, there exist several mathematical error correcting codes that could potentially give the required stability in the fragile and fault tolerant quantum world. While there has been a multitude of possible solutions, there is no one single error correcting code that is the key to solving the problem. Almost every solution presented has shared with it a limiting factor or an issue that prevents it from becoming the breakthrough that is desperately needed.
This paper gives an introductory knowledge of what is the quantum world and why there is a need for error correcting topologies. Finally, it introduces one recent topology that could be added to the list of possible solutions to this central problem. Rather than focusing on the mathematical frameworks, the paper introduces the main concepts so that most readers even outside the major field of computer science can understand what the main problem is and how this topology attempts to solve it.
ContributorsAhmed, Umer (Author) / Colbourn, Charles (Thesis director) / Zhao, Ming (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
As threats emerge, change, and grow, the life of a police officer continues to intensify. To help support police training curriculums and police cadets through this critical career juncture, this study proposes a state of the art approach to stress prediction and intervention through wearable devices and machine learning models. As an integral first step of a larger study, the goal of this research is to provide relevant information to machine learning models to formulate a correlation between stress and police officers’ physiological responses on and off on the job. Fitbit devices were leveraged for data collection and were complemented with a custom built Fitbit application, called StressManager, and study dashboard, termed StressWatch. This analysis uses data collected from 15 training cadets at the Phoenix Police Regional Training Academy over a 13 week span. Close collaboration with these participants was essential; the quality of data collection relied on consistent “syncing” and troubleshooting of the Fitbit devices. After the data were collected and cleaned, features related to steps, calories, movement, location, and heart rate were extracted from the Fitbit API and other supplemental resources and passed through to empirically chosen machine learning models. From the results of these models, we formulate that events of increased intensity combined with physiological spikes contribute to the overall stress perception of a police training cadet
ContributorsParanjpe, Tara (Author) / Zhao, Ming (Thesis director) / Roberts, Nicole (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05