Matching Items (6)
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- All Subjects: Information Technology
- Creators: Davulcu, Hasan
- Creators: Burtch, Gordon
- Member of: ASU Electronic Theses and Dissertations
- Status: Published
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this dissertation, two interrelated problems of service-based systems (SBS) are addressed: protecting users' data confidentiality from service providers, and managing performance of multiple workflows in SBS. Current SBSs pose serious limitations to protecting users' data confidentiality. Since users' sensitive data is sent in unencrypted forms to remote machines owned and operated by third-party service providers, there are risks of unauthorized use of the users' sensitive data by service providers. Although there are many techniques for protecting users' data from outside attackers, currently there is no effective way to protect users' sensitive data from service providers. In this dissertation, an approach is presented to protecting the confidentiality of users' data from service providers, and ensuring that service providers cannot collect users' confidential data while the data is processed or stored in cloud computing systems. The approach has four major features: (1) separation of software service providers and infrastructure service providers, (2) hiding the information of the owners of data, (3) data obfuscation, and (4) software module decomposition and distributed execution. Since the approach to protecting users' data confidentiality includes software module decomposition and distributed execution, it is very important to effectively allocate the resource of servers in SBS to each of the software module to manage the overall performance of workflows in SBS. An approach is presented to resource allocation for SBS to adaptively allocating the system resources of servers to their software modules in runtime in order to satisfy the performance requirements of multiple workflows in SBS. Experimental results show that the dynamic resource allocation approach can substantially increase the throughput of a SBS and the optimal resource allocation can be found in polynomial time
ContributorsAn, Ho Geun (Author) / Yau, Sik-Sang (Thesis advisor) / Huang, Dijiang (Committee member) / Ahn, Gail-Joon (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cyber systems, including IoT (Internet of Things), are increasingly being used ubiquitously to vastly improve the efficiency and reduce the cost of critical application areas, such as finance, transportation, defense, and healthcare. Over the past two decades, computing efficiency and hardware cost have dramatically been improved. These improvements have made cyber systems omnipotent, and control many aspects of human lives. Emerging trends in successful cyber system breaches have shown increasing sophistication in attacks and that attackers are no longer limited by resources, including human and computing power. Most existing cyber defense systems for IoT systems have two major issues: (1) they do not incorporate human user behavior(s) and preferences in their approaches, and (2) they do not continuously learn from dynamic environment and effectively adapt to thwart sophisticated cyber-attacks. Consequently, the security solutions generated may not be usable or implementable by the user(s) thereby drastically reducing the effectiveness of these security solutions.
In order to address these major issues, a comprehensive approach to securing ubiquitous smart devices in IoT environment by incorporating probabilistic human user behavioral inputs is presented. The approach will include techniques to (1) protect the controller device(s) [smart phone or tablet] by continuously learning and authenticating the legitimate user based on the touch screen finger gestures in the background, without requiring users’ to provide their finger gesture inputs intentionally for training purposes, and (2) efficiently configure IoT devices through controller device(s), in conformance with the probabilistic human user behavior(s) and preferences, to effectively adapt IoT devices to the changing environment. The effectiveness of the approach will be demonstrated with experiments that are based on collected user behavioral data and simulations.
In order to address these major issues, a comprehensive approach to securing ubiquitous smart devices in IoT environment by incorporating probabilistic human user behavioral inputs is presented. The approach will include techniques to (1) protect the controller device(s) [smart phone or tablet] by continuously learning and authenticating the legitimate user based on the touch screen finger gestures in the background, without requiring users’ to provide their finger gesture inputs intentionally for training purposes, and (2) efficiently configure IoT devices through controller device(s), in conformance with the probabilistic human user behavior(s) and preferences, to effectively adapt IoT devices to the changing environment. The effectiveness of the approach will be demonstrated with experiments that are based on collected user behavioral data and simulations.
ContributorsBuduru, Arun Balaji (Author) / Yau, Sik-Sang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Davulcu, Hasan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2016
Description
Continuous advancements in biomedical research have resulted in the production of vast amounts of scientific data and literature discussing them. The ultimate goal of computational biology is to translate these large amounts of data into actual knowledge of the complex biological processes and accurate life science models. The ability to rapidly and effectively survey the literature is necessary for the creation of large scale models of the relationships among biomedical entities as well as hypothesis generation to guide biomedical research. To reduce the effort and time spent in performing these activities, an intelligent search system is required. Even though many systems aid in navigating through this wide collection of documents, the vastness and depth of this information overload can be overwhelming. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also facilitate discovery of the unknown information implicitly conveyed in the texts. This thesis presents the different approaches used for large scale biomedical named entity recognition, and the challenges faced in each. It also proposes BioEve: an integrative framework to fuse a faceted search with information extraction to provide a search service that addresses the user's desire for "completeness" of the query results, not just the top-ranked ones. This information extraction system enables discovery of important semantic relationships between entities such as genes, diseases, drugs, and cell lines and events from biomedical text on MEDLINE, which is the largest publicly available database of the world's biomedical journal literature. It is an innovative search and discovery service that makes it easier to search
avigate and discover knowledge hidden in life sciences literature. To demonstrate the utility of this system, this thesis also details a prototype enterprise quality search and discovery service that helps researchers with a guided step-by-step query refinement, by suggesting concepts enriched in intermediate results, and thereby facilitating the "discover more as you search" paradigm.
avigate and discover knowledge hidden in life sciences literature. To demonstrate the utility of this system, this thesis also details a prototype enterprise quality search and discovery service that helps researchers with a guided step-by-step query refinement, by suggesting concepts enriched in intermediate results, and thereby facilitating the "discover more as you search" paradigm.
ContributorsKanwar, Pradeep (Author) / Davulcu, Hasan (Thesis advisor) / Dinu, Valentin (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2010
Description
Various activities move online in the era of the digital economy. Platform design and policy can heavily affect online user activities and result in many expected and unexpected consequences. In this dissertation, I conduct empirical studies on three types of online platforms to investigate the influence of their platform policy on their user engagement and associated outcomes. Specifically, in Study 1, I focus on goal-directed platforms and study how the introduction of the mobile channel affects users’ goal pursuit engagement and persistence. In Study 2, I focus on social media and online communities. I study the introduction of machine-powered platform regulation and its impacts on volunteer moderators’ engagement. In Study 3, I focus on online political discourse forums and examine the role of identity declaration in user participation and polarization in the subsequent political discourse. Overall, my results highlight how various platform policies shape user behavior. Implications on multi-channel adoption, human-machine collaborative platform governance, and online political polarization research are discussed.
ContributorsHe, Qinglai (Author) / Santanam, Raghu (Thesis advisor) / Hong, Yili (Thesis advisor) / Burtch, Gordon (Committee member) / Arizona State University (Publisher)
Created2021
Description
Biases in online platforms pose a threat to social inclusion. I examine the influence of social biases on online platforms. In my dissertation, I conduct empirical studies on online crowdfunding platforms (prosocial lending and educational crowdfunding) to investigate the influence of funders' or recipients' social backgrounds on the funding dynamics. In the first study, I examine the influence of a novel source of bias in online philanthropic lending, namely that associated with religious differences. I further propose a set of contextual moderators that characterize individuals’ offline (local) and online social contexts, which I argue combine to determine the influence of religion distance on lending activity. In the second study, I theoretically and empirically explore the role of value homophily in shifting lending priorities in online pro-social platforms. Considering the full spectrum of cultural influences, I develop the concept of “culturalist choice homophily,” where value-based similarities emerge based on the culturally-motivated behaviors and “historicist choice homophily,” where value-based similarities emerge based on similarities in historical-cultural barriers. Further, I introduce a novel content-context value congruence perspective for crisis fundraising, where the synergy between a borrowers’ request reasoning and the optimal crisis outcome determines the volume of lending received by crisis victims. I utilize the Arab Spring crisis in a Difference-in-Difference (DID) setting to test my hypotheses. Finally, in the third study, I add to the recent literature on the impact of the design of educational crowdfunding in alleviating inequality for public schools' fundraising. I particularly explore the effects of the platform intervention in terms of signaling students’ need to alleviate biases toward racially and economically disadvantaged students. Utilizing data from DonorsChoose.org, I first show that the online platform cannot automatically make up for all biases, especially toward classrooms with students with a higher level of poverty or racially marginalized communities. Further, I show that labeling projects as equity-focus can alleviate biases. However, the results are heterogeneous across different sources of identity. In particular, I discuss that equity-focus labeling has a greater impact on improving inequality toward hard-to-observe identities, e.g., economically disadvantaged students, than easy-to-observe identities such as racially underprivileged communities.
ContributorsSabzehzar, Amin (Author) / Raghu, T.S. (Thesis advisor) / Hong, Yili (Kevin) (Thesis advisor) / Burtch, Gordon (Committee member) / Arizona State University (Publisher)
Created2022