ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Davulcu, Hasan
For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.
For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.
The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.
This thesis develops a unique type of textual features that generalize triplets extracted from text, by clustering them into high-level concepts. These concepts are utilized as features to detect frames in text. Compared to uni-gram and bi-gram based models, classification and clustering using generalized concepts yield better discriminating features and a higher classification accuracy with a 12% boost (i.e. from 74% to 83% F-measure) and 0.91 clustering purity for Frame/Non-Frame detection.
The automatic discovery of complex causal chains among interlinked events and their participating actors has not yet been thoroughly studied. Previous studies related to extracting causal relationships from text were based on laborious and incomplete hand-developed lists of explicit causal verbs, such as “causes" and “results in." Such approaches result in limited recall because standard causal verbs may not generalize well to accommodate surface variations in texts when different keywords and phrases are used to express similar causal effects. Therefore, I present a system that utilizes generalized concepts to extract causal relationships. The proposed algorithms overcome surface variations in written expressions of causal relationships and discover the domino effects between climate events and human security. This semi-supervised approach alleviates the need for labor intensive keyword list development and annotated datasets. Experimental evaluations by domain experts achieve an average precision of 82%. Qualitative assessments of causal chains show that results are consistent with the 2014 IPCC report illuminating causal mechanisms underlying the linkages between climatic stresses and social instability.
This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.
the central idea is that multiple tenant applications can be developed using compo
nents stored in the SaaS infrastructure. Recently, MTA has been extended where
a tenant application can have its own sub-tenants as the tenant application acts
like a SaaS infrastructure. In other words, MTA is extended to STA (Sub-Tenancy
Architecture ). In STA, each tenant application not only need to develop its own
functionalities, but also need to prepare an infrastructure to allow its sub-tenants to
develop customized applications. This dissertation formulates eight models for STA,
and proposes a Variant Point based customization model to help tenants and sub
tenants customize tenant and sub-tenant applications. In addition, this dissertation
introduces Crowd- sourcing to become the core of STA component development life
cycle. To discover fit tenant developers or components to help building and com
posing new components, dynamic and static ranking models are proposed. Further,
rank computation architecture is presented to deal with the case when the number of
tenants and components becomes huge. At last, an experiment is performed to prove
rank models and the rank computation architecture work as design.
In spite of existing efforts towards understanding and removing bots on social media, there are at least two drawbacks associated with the current bot detection algorithms: general-purpose bot detection methods are designed to be conservative and not label a user as a bot unless the algorithm is highly confident and they overlook the effect of users who are manipulated by bots and (unintentionally) spread their content. This study is trifold. First, I design a Machine Learning model that uses content and context of social media posts to detect actionable ones among them; it specifically focuses on tweets in which people ask for help after major disasters. Second, I focus on bots who can be a facilitator of malicious content spreading during disasters. I propose two methods for detecting bots on social media with a focus on the recall of the detection. Third, I study the characteristics of users who spread the content of malicious actors. These features have the potential to improve methods that detect malicious content such as fake news.