## 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.

Language
• English
Resource Type
• Masters Thesis
Date Range
2015 2019

## Recent Submissions

In trading, volume is a measure of how much stock has been exchanged in a given period of time. Since every stock is distinctive and has an alternate measure of shares, volume can be contrasted with historical volume inside a stock to spot changes. It is likewise used to affirm value patterns, breakouts, and spot potential reversals. In my thesis, I hypothesize that the concept of trading volume can be extrapolated to social media (Twitter). The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its …

Contributors
Awasthi, Piyush, Davulcu, Hasan, Tong, Hanghang, et al.
Created Date
2015

The increasing usage of smart-phones and mobile devices in work environment and IT industry has brought about unique set of challenges and opportunities. ARM architecture in particular has evolved to a point where it supports implementations across wide spectrum of performance points and ARM based tablets and smart-phones are in demand. The enhancements to basic ARM RISC architecture allow ARM to have high performance, small code size, low power consumption and small silicon area. Users want their devices to perform many tasks such as read email, play games, and run other online applications and organizations no longer desire to provision …

Contributors
Chowdhary, Ankur, Huang, Dijiang, Tong, Hanghang, et al.
Created Date
2015

Micro-blogging platforms like Twitter have become some of the most popular sites for people to share and express their views and opinions about public events like debates, sports events or other news articles. These social updates by people complement the written news articles or transcripts of events in giving the popular public opinion about these events. So it would be useful to annotate the transcript with tweets. The technical challenge is to align the tweets with the correct segment of the transcript. ET-LDA by Hu et al [9] addresses this issue by modeling the whole process with an LDA-based graphical …

Contributors
Acharya, Anirudh, Kambhampati, Subbarao, Davulcu, Hasan, et al.
Created Date
2015

This thesis studies recommendation systems and considers joint sampling and learning. Sampling in recommendation systems is to obtain users' ratings on specific items chosen by the recommendation platform, and learning is to infer the unknown ratings of users to items given the existing data. In this thesis, the problem is formulated as an adaptive matrix completion problem in which sampling is to reveal the unknown entries of a $U\times M$ matrix where $U$ is the number of users, $M$ is the number of items, and each entry of the $U\times M$ matrix represents the rating of a user to an …

Contributors
Zhu, Lingfang, Xue, Guoliang, He, Jingrui, et al.
Created Date
2015

The amount of time series data generated is increasing due to the integration of sensor technologies with everyday applications, such as gesture recognition, energy optimization, health care, video surveillance. The use of multiple sensors simultaneously for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis. Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic …

Contributors
Garg, Yash, Candan, Kasim Selcuk, Chowell-Punete, Gerardo, et al.
Created Date
2015

Many neurological disorders, especially those that result in dementia, impact speech and language production. A number of studies have shown that there exist subtle changes in linguistic complexity in these individuals that precede disease onset. However, these studies are conducted on controlled speech samples from a specific task. This thesis explores the possibility of using natural language processing in order to detect declining linguistic complexity from more natural discourse. We use existing data from public figures suspected (or at risk) of suffering from cognitive-linguistic decline, downloaded from the Internet, to detect changes in linguistic complexity. In particular, we focus on …

Contributors
Wang, Shuai, Berisha, Visar, LaCross, Amy, et al.
Created Date
2016

Measuring node centrality is a critical common denominator behind many important graph mining tasks. While the existing literature offers a wealth of different node centrality measures, it remains a daunting task on how to intervene the node centrality in a desired way. In this thesis, we study the problem of minimizing the centrality of one or more target nodes by edge operation. The heart of the proposed method is an accurate and efficient algorithm to estimate the impact of edge deletion on the spectrum of the underlying network, based on the observation that the edge deletion is essentially a local, …

Contributors
Peng, Ruiyue, Tong, Hanghang, He, Jingrui, et al.
Created Date
2016

High-level inference tasks in video applications such as recognition, video retrieval, and zero-shot classification have become an active research area in recent years. One fundamental requirement for such applications is to extract high-quality features that maintain high-level information in the videos. Many video feature extraction algorithms have been purposed, such as STIP, HOG3D, and Dense Trajectories. These algorithms are often referred to as “handcrafted” features as they were deliberately designed based on some reasonable considerations. However, these algorithms may fail when dealing with high-level tasks or complex scene videos. Due to the success of using deep convolution neural networks (CNNs) …

Contributors
Hu, Sheng-Hung, Li, Baoxin, Turaga, Pavan, et al.
Created Date
2016

Cyberbullying is a phenomenon which negatively affects individuals. Victims of the cyberbullying suffer from a range of mental issues, ranging from depression to low self-esteem. Due to the advent of the social media platforms, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include use of standards and guidelines, human moderators, use of blacklists based on profane words, and regular expressions to manually detect cyberbullying. However, these mechanisms fall short in social media and do not scale well. Users in social media use intentional evasive expressions like, obfuscation of abusive words, which necessitates the development of …

Contributors
Dani, Harsh, Liu, Huan, Tong, Hanghang, et al.
Created Date
2017

Due to vast resources brought by social media services, social data mining has received increasing attention in recent years. The availability of sheer amounts of user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information in social networks could provide another rich source in deriving implicit information for social data mining. However, the vast majority of existing studies overwhelmingly focus on positive links between users while negative links are also prevailing in real- world social networks such as distrust relations in Epinions and foe links in Slashdot. Though recent studies …

Contributors
Cheng, Kewei, Liu, Huan, Tong, Hanghang, et al.
Created Date
2017