Success of Cultural Products and Fundamental Social Motives

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Description

Which evolutionarily important social motives are cultural products about? Songs from the 2020 Billboard Hot 100 year-end chart were rated in terms of their relevance to the fundamental social motives. These songs were thought to be about seeking a romantic

Which evolutionarily important social motives are cultural products about? Songs from the 2020 Billboard Hot 100 year-end chart were rated in terms of their relevance to the fundamental social motives. These songs were thought to be about seeking a romantic partner, followed by maintaining romantic relationships, breakups, and acquiring or maintaining status. Songs were thought to be least about avoiding infectious diseases and caring for children. Relative success of a song was found to be largely unassociated with which motive it reflects but significantly related to simplicity of the lyrics and prestige associated with the artist.

Date Created
2022
Agent

AI-assisted Programming Question Generation: Constructing Semantic Networks of Programming Knowledge by Local Knowledge Graph and Abstract Syntax Tree

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Description

Persistent self-assessment is the key to proficiency in computer programming. The process involves distributed practice of code tracing and writing skills which encompasses a large amount of training that is tailored for the student's learning condition. It requires the instructor

Persistent self-assessment is the key to proficiency in computer programming. The process involves distributed practice of code tracing and writing skills which encompasses a large amount of training that is tailored for the student's learning condition. It requires the instructor to efficiently manage the learning resource and diligently generate related programming questions for the student. However, programming question generation (PQG) is not an easy job. The instructor has to organize heterogeneous types of resources, i.e., conceptual programming concepts and procedural programming rules. S/he also has to carefully align the learning goals with the design of questions in regard to the topic relevance and complexity. Although numerous educational technologies like learning management systems (LMS) have been adopted across levels of programming learning, PQG is still largely based on the demanding creation task performed by the instructor without advanced technological support. To fill this gap, I propose a knowledge-based PQG model that aims to help the instructor generate new programming questions and expand existing assessment items. The PQG model is designed to transform conceptual and procedural programming knowledge from textbooks into a semantic network model by the Local Knowledge Graph (LKG) and the Abstract Syntax Tree (AST). For a given question, the model can generate a set of new questions by the associated LKG/AST semantic structures. I used the model to compare instructor-made questions from 9 undergraduate programming courses and textbook questions, which showed that the instructor-made questions had much simpler complexity than the textbook ones. The analysis also revealed the difference in topic distributions between the two question sets. A classification analysis further showed that the complexity of questions was correlated with student performance. To evaluate the performance of PQG, a group of experienced instructors from introductory programming courses was recruited. The result showed that the machine-generated questions were semantically similar to the instructor-generated questions. The questions also received significantly positive feedback regarding the topic relevance and extensibility. Overall, this work demonstrates a feasible PQG model that sheds light on AI-assisted PQG for the future development of intelligent authoring tools for programming learning.

Date Created
2022
Agent

The Rhizosphere: Subterranean Listening

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Description

For about a decade, I have thought of composing as a form of sonic gardening. The processes are very similar in that I cultivate ecosystems of interrelated parts, whether in sound or in the soil. My interests in creating sonic

For about a decade, I have thought of composing as a form of sonic gardening. The processes are very similar in that I cultivate ecosystems of interrelated parts, whether in sound or in the soil. My interests in creating sonic ecosystems and in learning more about environmental issues motivated me to research soil health and the rhizosphere, the microbiome around a plant's root system. For my dissertation project I have composed a piece titled The Rhizosphere inspired by the processes and behaviors found in the rhizosphere for percussion sextet of about 8 minutes in duration. This piece was commissioned by the Arizona Contemporary Music Ensemble, with a performance date of April 21, 2022. In this document, I discuss issues relating to soil and sustainability, provide a survey of relevant sound art, and describe processes and features of the rhizosphere. I share how I mapped different aspects of the rhizosphere to various sonic parameters and processes in my composition. I then consider The Rhizosphere as it relates to other pieces in my portfolio, specifically works inspired by nature or environmental issues. During my doctoral studies I have been inspired by and sought to depict plants (Dandelion) and desert (Desertification and Desert Rain God), among others.

Date Created
2022
Agent

Time Sensitive Networking in Multimedia and Industrial Control Applications

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Description

Ethernet based technologies are emerging as the ubiquitous de facto form of communication due to their interoperability, capacity, cost, and reliability. Traditional Ethernet is designed with the goal of delivering best effort services. However, several real time and control applications

Ethernet based technologies are emerging as the ubiquitous de facto form of communication due to their interoperability, capacity, cost, and reliability. Traditional Ethernet is designed with the goal of delivering best effort services. However, several real time and control applications require more precise deterministic requirements and Ultra Low Latency (ULL), that Ethernet cannot be used for. Current Industrial Automation and Control Systems (IACS) applications use semi-proprietary technologies that provide deterministic communication behavior for sporadic and periodic traffic, but can lead to closed systems that do not interoperate effectively. The convergence between the informational and operational technologies in modern industrial control networks cannot be achieved using traditional Ethernet. Time Sensitive Networking (TSN) is a suite of IEEE standards designed by augmenting traditional Ethernet with real time deterministic properties ideal for Digital Signal Processing (DSP) applications. Similarly, Deterministic Networking (DetNet) is a Internet Engineering Task Force (IETF) standardization that enhances the network layer with the required deterministic properties needed for IACS applications. This dissertation provides an in-depth survey and literature review on both standards/research and 5G related material on ULL. Recognizing the limitations of several features of the standards, this dissertation provides an empirical evaluation of these approaches and presents novel enhancements to the shapers and schedulers involved in TSN. More specifically, this dissertation investigates Time Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), and Cyclic Queuing and Forwarding (CQF) schedulers. Moreover, the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv can be used to manage and control scheduled traffic streams with periodic properties along with best-effort traffic on the same network infrastructure. Both the centralized network/distributed user model (hybrid model) and the fully-distributed (decentralized) IEEE 802.1Qcc model are examined on a typical industrial control network with the goal of maximizing scheduled traffic streams. Finally, since industrial applications and cyber-physical systems require timely delivery, any channel or node faults can cause severe disruption to the operational continuity of the application. Therefore, the IEEE 802.1CB, Frame Replication and Elimination for Reliability (FRER), is examined and tested using machine learning models to predict faulty scenarios and issue remedies seamlessly.

Date Created
2022
Agent

Measurement, Detection, and Parameter Estimation of Single Photon Correlations

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Description

The continuous time-tagging of photon arrival times for high count rate sources isnecessary for applications such as optical communications, quantum key encryption,
and astronomical measurements. Detection of Hanbury-Brown and Twiss (HBT) single
photon correlations from thermal sources, such as stars,

The continuous time-tagging of photon arrival times for high count rate sources isnecessary for applications such as optical communications, quantum key encryption,
and astronomical measurements. Detection of Hanbury-Brown and Twiss (HBT) single
photon correlations from thermal sources, such as stars, requires a combination of high
dynamic range, long integration times, and low systematics in the photon detection
and time tagging system. The continuous nature of the measurements and the need
for highly accurate timing resolution requires a customized time-to-digital converter
(TDC). A custom built, two-channel, field programmable gate array (FPGA)-based
TDC capable of continuously time tagging single photons with sub clock cycle timing
resolution was characterized. Auto-correlation and cross-correlation measurements
were used to constrain spurious systematic effects in the pulse count data as a function
of system variables. These variables included, but were not limited to, incident
photon count rate, incoming signal attenuation, and measurements of fixed signals.
Additionally, a generalized likelihood ratio test using maximum likelihood estimators
(MLEs) was derived as a means to detect and estimate correlated photon signal
parameters. The derived GLRT was capable of detecting correlated photon signals in
a laboratory setting with a high degree of statistical confidence. A proof is presented
in which the MLE for the amplitude of the correlated photon signal is shown to be the
minimum variance unbiased estimator (MVUE). The fully characterized TDC was used
in preliminary measurements of astronomical sources using ground based telescopes.
Finally, preliminary theoretical groundwork is established for the deep space optical
communications system of the proposed Breakthrough Starshot project, in which
low-mass craft will travel to the Alpha Centauri system to collect scientific data from
Proxima B. This theoretical groundwork utilizes recent and upcoming space based
optical communication systems as starting points for the Starshot communication
system.

Date Created
2022
Agent

Triply Periodic Minimal Surface Structure Porosity Effect on the Power Conversion Performance of a Thermogalvanic Brick

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Description

humans are currently facing issues with the high level of carbon emissions that will cause global warming and climate change, which worsens the earth’s
environment. Buildings generate nearly 40% of annual global CO2 emissions, of which
28% is from building

humans are currently facing issues with the high level of carbon emissions that will cause global warming and climate change, which worsens the earth’s
environment. Buildings generate nearly 40% of annual global CO2 emissions, of which
28% is from building operations, and 11% from materials and construction. These
emissions must be decreased to protect from further environmental harm. The good news
is there is a way that carbon emissions can be decreased. The use of thermogalvanic
bricks enables electricity generation by the temperature difference between the enclosure
above the ceiling (i.e., the attic in a single-family home) and the living space below. A
ceiling tile prototype was constructed that can make use of this temperature difference to
generate electricity using an electrochemical system called a thermogalvanic cell.
Furthermore, the application of triply periodic minimal surfaces (TPMS) can increase the
thermal resistance of the ceiling tile, which is important for practical applications. Here,
Schwarz P TPMS structures were 3D-printed from polyvinylidene fluoride (PVDF), and
inserted into the electrolyte solution between the electrodes. Graphite was used as
electrodes on the positive and negative sides of the tile, and Iron (II) and Iron (III)
perchlorate salts were used as electrolytes. The maximum generated power was measured
with different porosities of TPMS structure, and one experiment without a TPMS
structure. The results indicated that as the porosity of the TPMS structure increases, the
maximum power decreases. The experiment with no TPMS structure had the largest
maximum power.

Date Created
2022
Agent

Vehicle Re-identification Using a Multi-View Vehicle Dataset

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Description

There has been an explosion in the amount of data on the internet because of modern technology – especially image data – as a consequence of an exponential growth in the number of cameras existing in the world right now;

There has been an explosion in the amount of data on the internet because of modern technology – especially image data – as a consequence of an exponential growth in the number of cameras existing in the world right now; from more extensive surveillance camera systems to billions of people walking around with smartphones in their pockets that come with built-in cameras. With this sudden increase in the accessibility of cameras, most of the data that is getting captured through these devices is ending up on the internet. Researchers soon took leverage of this data by creating large-scale datasets. However, generating a dataset – let alone a large-scale one – requires a lot of man-hours. This work presents an algorithm that makes use of optical flow and feature matching, along with utilizing localization outputs from a Mask R-CNN, to generate large-scale vehicle datasets without much human supervision. Additionally, this work proposes a novel multi-view vehicle dataset (MVVdb) of 500 vehicles which is also generated using the aforementioned algorithm.There are various research problems in computer vision that can leverage a multi-view dataset, e.g., 3D pose estimation, and 3D object detection. On the other hand, a multi-view vehicle dataset can be used for a 2D image to 3D shape prediction, generation of 3D vehicle models, and even a more robust vehicle make and model recognition. In this work, a ResNet is trained on the multi-view vehicle dataset to perform vehicle re-identification, which is fundamentally similar to a vehicle make and recognition problem – also showcasing the usability of the MVVdb dataset.

Date Created
2022
Agent

Profiles of White Teachers Doing the Work: The Pedagogy of Shifting from Colorblindness to Addressing One’s Role in Structural Racism

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Description

This creative nonfiction dissertation study sought to describe the process by which three self-identified White teachers—who are engaged in structural initiatives in their schools, districts and/or communities for racial equity—transitioned from colorblindness to understanding white supremacy and structural racism. The

This creative nonfiction dissertation study sought to describe the process by which three self-identified White teachers—who are engaged in structural initiatives in their schools, districts and/or communities for racial equity—transitioned from colorblindness to understanding white supremacy and structural racism. The following overarching research question guided the study: What are three self-identified White teachers’ perceptions of their process of shifting from colorblindness to understanding their role in white supremacy and structural racism? The study also addressed the following sub-questions: (1) What are the pedagogical pivot places that occur in three self-identified White teachers’ processes of coming to understand white supremacy and structural racism? And (2) How do these pedagogical pivot places contribute to new ways of knowing in teachers’ shifting from colorblindness to understanding white supremacy and structural racism? This study contributes to the literature on White teachers’ process of shifting from colorblindness to racial consciousness and understanding white supremacy. It offers implications for shifting ideologies of White pre-service teachers and teachers in schools. However, efforts to dismantle structural racism need to extend beyond helping White teachers to understand their role in upholding white supremacy and fighting against structural racism to better meet the needs of their diverse students. Schools in the United States function to reproduce the hidden curriculua of whiteness and work. Because schools are amplifiers of the ideologies of the greater society, by disseminating the findings in the form of creative nonfiction, the study attempts to extend this work outside of schooling and into society to address the problem of structural racism in society.

Date Created
2022
Agent

A Description of Arizona Schools FFVP Service Related to Nutrition Education

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Description

Background: The USDA Fresh Fruit and Vegetable Program (FFVP) provides accessibility, variety, and nutrition education to increase exposure to fresh F/V to school children. The aim of this study examines Arizona FFVP’s nutrition education frequency and delivery in relation to

Background: The USDA Fresh Fruit and Vegetable Program (FFVP) provides accessibility, variety, and nutrition education to increase exposure to fresh F/V to school children. The aim of this study examines Arizona FFVP’s nutrition education frequency and delivery in relation to the effects by the COVID-19 pandemic for SY 2019-2020, SY 2020-2021, and SY 2021 - 2022. Methods: A retrospective cross-sectional analysis of 57 Arizona school food managers (FSM) (and alike positions) (N=143; n=57; 18%(10) used Spanish; 82%(47) used English; FFVP site-level FSM = 81%(41); FFVP Non-FSM
= 19% (11); 88% (50) rural; 12%(7) urban) during SY2019 – 2020, SY2020 – 2021, and SY2021 - 2022. Participants were surveyed for their perspective of FFVP service, nutrition education, and partnerships. The statistical analysis used quantitative and qualitative content variables presented as percentages.
Results: Nutrition education were consistently delivered twice a week. FFVP service delivered most commonly 2 days/week (TF1 at 63% (26); TF2 at 59% (20),TF3 at 54% (19); TF 4 at 53% (19)). FFVP nutrition education was most frequently delivered 2days/week (TF1 at 55% (18); TF2 at 55% (18); TF3 at 54% (15); TF4 at 54% (20)). Teachers were most responsible for delivering nutrition education weekly in classrooms TF1 at 55%(21), TF2 at 44%(16), TF3 at 38% (15), TF4 at 45%. Most frequent method to select nutrition education was based on produce served that week (TF1 at 40% (23), TF2 at 36% (21), TF 3 at 39% (21),TF4 at 40% (24)) and day TF1 at 37% (21), TF2 at 36% (21), TF3 at 35% (19), TF4 42% (25).
Conclusion: FSM and alike positions are important to FFVP services. Arizona FFVP schools were able provide consistent nutrition education even through a nationwide school closure caused by COVID-19 pandemic.

Date Created
2022
Agent

Bayesian Methods for Tuning Hyperparameters of Loss Functions in Machine Learning

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Description

The introduction of parameterized loss functions for robustness in machine learning has led to questions as to how hyperparameter(s) of the loss functions can be tuned. This thesis explores how Bayesian methods can be leveraged to tune such hyperparameters. Specifically,

The introduction of parameterized loss functions for robustness in machine learning has led to questions as to how hyperparameter(s) of the loss functions can be tuned. This thesis explores how Bayesian methods can be leveraged to tune such hyperparameters. Specifically, a modified Gibbs sampling scheme is used to generate a distribution of loss parameters of tunable loss functions. The modified Gibbs sampler is a two-block sampler that alternates between sampling the loss parameter and optimizing the other model parameters. The sampling step is performed using slice sampling, while the optimization step is performed using gradient descent. This thesis explores the application of the modified Gibbs sampler to alpha-loss, a tunable loss function with a single parameter $\alpha \in (0,\infty]$, that is designed for the classification setting. Theoretically, it is shown that the Markov chain generated by a modified Gibbs sampling scheme is ergodic; that is, the chain has, and converges to, a unique stationary (posterior) distribution. Further, the modified Gibbs sampler is implemented in two experiments: a synthetic dataset and a canonical image dataset. The results show that the modified Gibbs sampler performs well under label noise, generating a distribution indicating preference for larger values of alpha, matching the outcomes of previous experiments.

Date Created
2022
Agent