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- Member of: Theses and Dissertations
Description
I believe the human mind is not an accurate reproducer of objects and events, but a tool that constructs their qualities. Philosophers Bowman Clarke, James John, and Amy Kind have argued for and against similar points, while Daniel Hoffman and Jay Dowling have debated cases from a psychological perspective. My understanding of their discourse surfaces in Cognize Normal-Like Pleez, a video installation designed to capture the enigmatic connection between perceivers and the things they perceive. The composition encapsulates this theme through a series of five videos that disseminate confusing imagery paired with mangled sounds. The miniatures operate in sequence on computer monitors set inside a haphazardously ornamented tower. Though the original sources for each video communicate clear, familiar subjects, the final product deliberately obscures them. Sometimes sounds and images flicker for only brief moments, perhaps too fast for the human mind to fully process. Though some information comes through, important data supplied by the surrounding context is absent.
I invite the audience to rationalize this complexing conglomerate and reflect on how their established biases inform their opinion of the work. Each person likely draws from his or her experiences, cultural conditioning, knowledge, and other personal factors in order to create an individual conceptualization of the installation. Their subjective conclusions reflect my belief concerning a neurological basis for the origin of qualities. One’s connection to Cognize’s images and sounds, to me, is not derived solely from characteristics inherent to it, but also endowed by one’s mind, which not only constructs the attributes one normally associates with the images and sounds (as opposed to the physics and biology that lead to their construction), but also seamlessly incorporates the aforementioned biases. I realize my ideas by focusing the topics of the videos and their setting around the transmission of information and its obfuscation. Just as one cannot see or hear past the perceptual barriers in Cognize, I believe one cannot escape his or her mind to “sense” qualities in an objective, disembodied manner, because the mind is necessary for perception.
I invite the audience to rationalize this complexing conglomerate and reflect on how their established biases inform their opinion of the work. Each person likely draws from his or her experiences, cultural conditioning, knowledge, and other personal factors in order to create an individual conceptualization of the installation. Their subjective conclusions reflect my belief concerning a neurological basis for the origin of qualities. One’s connection to Cognize’s images and sounds, to me, is not derived solely from characteristics inherent to it, but also endowed by one’s mind, which not only constructs the attributes one normally associates with the images and sounds (as opposed to the physics and biology that lead to their construction), but also seamlessly incorporates the aforementioned biases. I realize my ideas by focusing the topics of the videos and their setting around the transmission of information and its obfuscation. Just as one cannot see or hear past the perceptual barriers in Cognize, I believe one cannot escape his or her mind to “sense” qualities in an objective, disembodied manner, because the mind is necessary for perception.
ContributorsLempke, John Paul (Author) / Suzuki, Kotoka (Thesis advisor) / Knowles, Kristina (Committee member) / Stover, Chris (Committee member) / Arizona State University (Publisher)
Created2018
Description
Intelligence is a loosely defined term, but it is a quality that we try to measure in humans, animals, and recently machines. Progress in artificial intelligence is slow, but we have recently made breakthroughs by paying attention to biology and neuroscience. We have not fully explored what biology has to offer us in AI research, and this paper explores aspects of intelligent behavior in nature that machines still struggle with.
ContributorsLahtinen, David (Author) / Gaffar, Ashraf (Thesis director) / Sanchez, Javier Gonzalez (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Microlending aims at providing low-barrier loans to small to medium scaled family run businesses that are financially disincluded historically. These borrowers might be in third world countries where traditional financing is not accessible. Lenders can be individual investors or institutions making risky investments or willing to help people who cannot access traditional banks or do not have the credibility to get loans from traditional sources. Microlending involves a charitable cause as well where lenders are not really concerned about what and how they are paid.
This thesis aims at building a platform that will support both commercial microlending as well as charitable donation to support the real cause of microlending. The platform is expected to ensure privacy and transparency to the users in order to attract more users to use the system. Microlending involves monetary transactions, hence possible security threats to the system are discussed.
Blockchain is one of the technologies which has revolutionized financial transactions and microlending involves monetary transactions. Therefore, blockchain is viable option for microlending platform. Permissioned blockchain restricts the user admission to the platform and provides with identity management feature. This feature is required to ensure the security and privacy of various types of participants on the microlending platform.
This thesis aims at building a platform that will support both commercial microlending as well as charitable donation to support the real cause of microlending. The platform is expected to ensure privacy and transparency to the users in order to attract more users to use the system. Microlending involves monetary transactions, hence possible security threats to the system are discussed.
Blockchain is one of the technologies which has revolutionized financial transactions and microlending involves monetary transactions. Therefore, blockchain is viable option for microlending platform. Permissioned blockchain restricts the user admission to the platform and provides with identity management feature. This feature is required to ensure the security and privacy of various types of participants on the microlending platform.
ContributorsSiddharth, Sourabh (Author) / Boscovic, Dragan (Thesis advisor) / Basnal, Srividya (Thesis advisor) / Sanchez, Javier Gonzalez (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