The Role of Hypocrisy in Slavery: Harriet Martineau and the Social Contract The social contract, by very definition, exists only by agreement of all parties to relinquish certain freedoms in exchange for safety and prosperity. This logically renders slavery a paradoxical phenomenon that should not exist within a social contract. How then, does slavery thrive within these systems? Hypocrisy exercised by those in power sows the injustices necessary to grow and maintain slavery. Harriet Martineau unapologetically calls attention to hypocritical practices, most prevalently within her work examining the education of women, marriage, and her critique of slavery. In this paper presentation for the International Martineau Society Annual Conference, I discussed Harriet Martineau’s work as it relates to oppression and slavery within the social contract. Although Martineau participates in the hypocritical systems that she denounces, which is in itself, hypocrisy, she leverages this duplicity in order to advocate for change.

With the rapid increase of technological capabilities, particularly in processing power and speed, the usage of machine learning is becoming increasingly widespread, especially in fields where real-time assessment of complex data is extremely valuable. This surge in popularity of machine learning gives rise to an abundance of potential research and projects on further broadening applications of artificial intelligence. From these opportunities comes the purpose of this thesis. Our work seeks to meaningfully increase our understanding of current capabilities of machine learning and the problems they can solve. One extremely popular application of machine learning is in data prediction, as machines are capable of finding trends that humans often miss. Our effort to this end was to examine the CVE dataset and attempt to predict future entries with Random Forests. The second area of interest lies within the great promise being demonstrated by neural networks in the field of autonomous driving. We sought to understand the research being put out by the most prominent bodies within this field and to implement a model on one of the largest standing datasets, Berkeley DeepDrive 100k. This thesis describes our efforts to build, train, and optimize a Random Forest model on the CVE dataset and a convolutional neural network on the Berkeley DeepDrive 100k dataset. We document these efforts with the goal of growing our knowledge on (and usage of) machine learning in these topics.

The mental health of ASU students has been negatively affected by the pandemic. Our research looks to prove that COVID-19 has caused an increase in stress levels while uncovering other relationships to stress. We obtained our data by conducting a survey through Google Forms that was exclusively accessible to ASU students. Stress levels were measured with the use of the Perceived Stress Scale (PSS). We find that the stress of ASU students from before the pandemic to during rises from 15 to 22 points, a 50% increase (n = 228). We discovered that women are more stressed than men before and during the pandemic. We also discovered that there is no difference between stresses among different races. We notice that there is a parabolic relationship between enrollment time and stress levels with the peak occurring during semesters 2-6. We also conclude that students who attended more than 5 events during the pandemic had lower stress scores, and those who had their videos on for at least 3 events had lower stress scores. Furthermore, students who utilized campus resources to manage their stress had higher stress levels than those who did not.

The mental health of ASU students has been negatively affected by the pandemic. Our research looks to prove that COVID-19 has caused an increase in stress levels while uncovering other relationships to stress. We obtained our data by conducting a survey through Google Forms that was exclusively accessible to ASU students. Stress levels were measured with the use of the Perceived Stress Scale (PSS). We find that the stress of ASU students from before the pandemic to during rises from 15 to 22 points, a 50% increase (n = 228). We discovered that women are more stressed than men before and during the pandemic. We also discovered that there is no difference between stresses among different races. We notice that there is a parabolic relationship between enrollment time and stress levels with the peak occurring during semesters 2-6. We also conclude that students who attended more than 5 events during the pandemic had lower stress scores, and those who had their videos on for at least 3 events had lower stress scores. Furthermore, students who utilized campus resources to manage their stress had higher stress levels than those who did not.

The mental health of ASU students has been negatively affected by the pandemic. Our research looks to prove that COVID-19 has caused an increase in stress levels while uncovering other relationships to stress. We obtained our data by conducting a survey through Google Forms that was exclusively accessible to ASU students. Stress levels were measured with the use of the Perceived Stress Scale (PSS). We find that the stress of ASU students from before the pandemic to during rises from 15 to 22 points, a 50% increase (n = 228). We discovered that women are more stressed than men before and during the pandemic. We also discovered that there is no difference between stresses among different races. We notice that there is a parabolic relationship between enrollment time and stress levels with the peak occurring during semesters 2-6. We also conclude that students who attended more than 5 events during the pandemic had lower stress scores, and those who had their videos on for at least 3 events had lower stress scores. Furthermore, students who utilized campus resources to manage their stress had higher stress levels than those who did not.