Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: Sustainability
Description
We solve the problem of activity verification in the context of sustainability. Activity verification is the process of proving the user assertions pertaining to a certain activity performed by the user. Our motivation lies in incentivizing the user for engaging in sustainable activities like taking public transport or recycling. Such incentivization schemes require the system to verify the claim made by the user. The system verifies these claims by analyzing the supporting evidence captured by the user while performing the activity. The proliferation of portable smart-phones in the past few years has provided us with a ubiquitous and relatively cheap platform, having multiple sensors like accelerometer, gyroscope, microphone etc. to capture this evidence data in-situ. In this research, we investigate the supervised and semi-supervised learning techniques for activity verification. Both these techniques make use the data set constructed using the evidence submitted by the user. Supervised learning makes use of annotated evidence data to build a function to predict the class labels of the unlabeled data points. The evidence data captured can be either unimodal or multimodal in nature. We use the accelerometer data as evidence for transportation mode verification and image data as evidence for recycling verification. After training the system, we achieve maximum accuracy of 94% when classifying the transport mode and 81% when detecting recycle activity. In the case of recycle verification, we could improve the classification accuracy by asking the user for more evidence. We present some techniques to ask the user for the next best piece of evidence that maximizes the probability of classification. Using these techniques for detecting recycle activity, the accuracy increases to 93%. The major disadvantage of using supervised models is that it requires extensive annotated training data, which expensive to collect. Due to the limited training data, we look at the graph based inductive semi-supervised learning methods to propagate the labels among the unlabeled samples. In the semi-supervised approach, we represent each instance in the data set as a node in the graph. Since it is a complete graph, edges interconnect these nodes, with each edge having some weight representing the similarity between the points. We propagate the labels in this graph, based on the proximity of the data points to the labeled nodes. We estimate the performance of these algorithms by measuring how close the probability distribution of the data after label propagation is to the probability distribution of the ground truth data. Since labeling has a cost associated with it, in this thesis we propose two algorithms that help us in selecting minimum number of labeled points to propagate the labels accurately. Our proposed algorithm achieves a maximum of 73% increase in performance when compared to the baseline algorithm.
ContributorsDesai, Vaishnav (Author) / Sundaram, Hari (Thesis advisor) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Anglophone music festivals in the U.S. can be traced back to singing schools of the 1700s, which eventually blossomed into regular, outdoor musical performances, growing in popularity between 1840 and 1875. The first annual music festival in the United States was founded in Massachusetts in 1858. Modern single-destination music festivals grew in popularity in the United Kingdom and the United States during the late twentieth century. Although the Woodstock Music and Arts Fair of 1969 was not executed perfectly, it was an iconic event with a lasting cultural impact. Modern music festivals are modeled on the rural open-air festivals of the 1970s. In the past sixty years, the music industry has had to reconcile with the environmental impact of single-destination music festivals. Capitalistic ventures are inherently at odds with the environment—even music streaming has a significant carbon footprint. Corporate entities have been known to make insincere efforts to address their environmental impact, a tactic known as “greenwashing.” Music festivals hosting thousands of attendees generate a large amount of human waste on top of the already significant carbon emissions associated with travel, transport of equipment, and production. Event organizers must take significant measures to appeal to modern-day environmentally-conscious audiences. Burning Man and Bonnaroo are two events that once stood out among other large, corporate festivals for being developed by independent organizers. The two festivals are hosted on two strikingly different environments for which the organizers have made unique sustainability considerations. Burning Man celebrates radical individualism and self-reliance in a dry Nevada lakebed desert. On the other hand, Bonnaroo, hosted on the humid, rolling grassland of Tennessee is branded as an environmentally responsible event. The organizers of both festivals have promoted sustainability in their respective efforts to mitigate the environmental byproducts of their events, producing varying results. Sustainable festival practices have been utilized at Bonnaroo since its inception, whereas many of the longstanding traditions of Burning Man are antithetical to sustainability. This case study explores the rise of these two festivals, the environmentally conscious values held by both, and how they have changed over time.
ContributorsSheller, Nikhita (Author) / Norton, Kay (Thesis advisor) / Navarro, Fernanda (Committee member) / Little, Bliss (Committee member) / Arizona State University (Publisher)
Created2022