Computer Sciences Commons^™

Annotation Of Multimedia Learning Materials For Semantic Search, Sheetal Rajgure

Dissertations

Multimedia is the main source for online learning materials, such as videos, slides and textbooks, and its size is growing with the popularity of online programs offered by Universities and Massive Open Online Courses (MOOCs). The increasing amount of multimedia learning resources available online makes it very challenging to browse through the materials or find where a specific concept of interest is covered. To enable semantic search on the lecture materials, their content must be annotated and indexed. Manual annotation of learning materials such as videos is tedious and cannot be envisioned for the growing quantity of online materials. One …

Programming Frameworks For Mobile Sensing, Hillol Debnath

Dissertations

The proliferation of smart mobile devices in people’s daily lives is making context-aware computing a reality. A plethora of sensors available in these devices can be utilized to understand users’ context better. Apps can provide more relevant data or services to the user based on improved understanding of user’s context. With the advent of cloud-assisted mobile platforms, apps can also perform collaborative computation over the sensing data collected from a group of users. However, there are still two main issues: (1) A lack of simple and effective personal sensing frameworks: existing frameworks do not provide support for real-time fusing of …

Evaluating Relevance And Reliability Of Twitter Data For Risk Communication, Xiaohui Liu

Dissertations

While Twitter has been touted to provide up-to-date information about hazard events, the relevance and reliability of tweets is yet to be tested. This research examined the relevance and reliability of risk information extracted from Twitter during the 2013 Colorado floods using five different approaches. The first approach examined the relationship between tweet volume and precipitation amount. The second approach explored the relationship between geo-tagged tweets and degree of damage. In the third approach, the spatiotemporal distribution of tweets was compared with flood extent. In the fourth approach, risk information from tweets were compared with survey responses obtained in a …

Detecting User Demographics In Twitter To Inform Health Trends In Social Media, Christopher R. Markson

Dissertations

The widespread and popular use of social media and social networking applications offer a promising opportunity for gaining knowledge and insights regarding population health conditions thanks to the diversity and abundance of online user-generated information (UGHI) relating to healthcare and well-being. However, users on social media and social networking sites often do not supply their complete demographic information, which greatly undermines the value of the aforementioned information for health 2.0 research, e.g., for discerning disparities across population groups in certain health conditions. To recover the missing user demographic information, existing methods observe a limited scope of user behaviors, such as …

Performance Optimization And Energy Efficiency Of Big-Data Computing Workflows, Tong Shu

Dissertations

Next-generation e-science is producing colossal amounts of data, now frequently termed as Big Data, on the order of terabyte at present and petabyte or even exabyte in the predictable future. These scientific applications typically feature data-intensive workflows comprised of moldable parallel computing jobs, such as MapReduce, with intricate inter-job dependencies. The granularity of task partitioning in each moldable job of such big data workflows has a significant impact on workflow completion time, energy consumption, and financial cost if executed in clouds, which remains largely unexplored. This dissertation conducts an in-depth investigation into the properties of moldable jobs and provides an …

Determining Feasibility Resilience: Set Based Design Iteration Evaluation Through Permutation Stability Analysis, James E. Ross

Dissertations

The goal of robust design is to select a design that will still perform satisfactorily even with unexpected variation in design parameters. A resilient design will accommodate unanticipated future system requirements. Through studying the variations of system parameters through the use of multi objective optimization, a designer hopes to locate a robustly resilient design, which performs current mission well even with varying system parameters and is able to be easily repurposed to new missions. This ability to withstand changes is critical because it is common for the product of a design to undergo changes throughout its life cycle. This subject …

Development And Evaluation Of Machine Learning Algorithms For Biomedical Applications, Turki Talal Turki

Dissertations

Gene network inference and drug response prediction are two important problems in computational biomedicine. The former helps scientists better understand the functional elements and regulatory circuits of cells. The latter helps a physician gain full understanding of the effective treatment on patients. Both problems have been widely studied, though current solutions are far from perfect. More research is needed to improve the accuracy of existing approaches.

This dissertation develops machine learning and data mining algorithms, and applies these algorithms to solve the two important biomedical problems. Specifically, to tackle the gene network inference problem, the dissertation proposes (i) new techniques …

Big Data Analytics In Computational Biology And Bioinformatics, Kevin Byron

Dissertations

Big data analytics in computational biology and bioinformatics refers to an array of operations including biological pattern discovery, classification, prediction, inference, clustering as well as data mining in the cloud, among others. This dissertation addresses big data analytics by investigating two important operations, namely pattern discovery and network inference.

The dissertation starts by focusing on biological pattern discovery at a genomic scale. Research reveals that the secondary structure in non-coding RNA (ncRNA) is more conserved during evolution than its primary nucleotide sequence. Using a covariance model approach, the stems and loops of an ncRNA secondary structure are represented as a …

Viewability Prediction For Display Advertising, Chong Wang

Dissertations

As a massive industry, display advertising delivers advertisers’ marketing messages to attract customers through graphic banners on webpages. Display advertising is also the most essential revenue source of online publishers. Currently, advertisers are charged by user response or ad serving. However, recent studies show that users barely click or convert display ads. Moreover, about half of the ads are actually never seen by users. In this case, advertisers cannot enhance their brand awareness and increase return on investment. Publishers also lose much revenue. Therefore, the ad pricing standards are shifting to a new model: ad impressions are paid if they …

Investigation Of New Learning Methods For Visual Recognition, Qingfeng Liu

Dissertations

Visual recognition is one of the most difficult and prevailing problems in computer vision and pattern recognition due to the challenges in understanding the semantics and contents of digital images. Two major components of a visual recognition system are discriminatory feature representation and efficient and accurate pattern classification. This dissertation therefore focuses on developing new learning methods for visual recognition.

Based on the conventional sparse representation, which shows its robustness for visual recognition problems, a series of new methods is proposed. Specifically, first, a new locally linear K nearest neighbor method, or LLK method, is presented. The LLK method derives …

Statistical Learning Methods For Mining Marketing And Biological Data, Jie Zhang

Dissertations

Nowadays, the value of data has been broadly recognized and emphasized. More and more decisions are made based on data and analysis rather than solely on experience and intuition. With the fast development of networking, data storage, and data collection capacity, data have increased dramatically in industry, science and engineering domains, which brings both great opportunities and challenges. To take advantage of the data flood, new computational methods are in demand to process, analyze and understand these datasets.

This dissertation focuses on the development of statistical learning methods for online advertising and bioinformatics to model real world data with temporal …

Artificial Immune Systems: Applications, Multi-Class Classification, Optimizations, And Analysis, Brian Haroldo Schmidt

Dissertations

The focus of this research is the application of the Artificial Immune System (AIS) paradigm to a new research area along with the modifications necessary to adapt it to a new problem. In the past 10 years, there has been much research into the use of various Machine Learning (ML) algorithms in Network Flow Traffic Classification. AIS algorithms have thus far not been applied to this problem. Because AIS algorithms have been used extensively for Network Intrusion Detection applications, which is a similar area of research, the motivation to extend them to the network flow classification problem is clear.

This …

Protecting Sensitive Data In Clouds Using Active Data Bundles And Agent-Based Secure Multi-Party Computation, Akram Y. Sarhan

Dissertations

Protection of data in cloud computing including distributed environments is a critical concern for many enterprises. This study proposes a solution that protects sensitive data outsourced to a cloud throughout their entire life cycle—both in the cloud as well as outside of the cloud (e.g., during transmission to or from the cloud). The solution enhances the existing data protection approach known as Active Bundle scheme, which uses a Trusted Third Party (ABTTP).

The Active Data Bundle (ADB) was formerly called an Active Bundle (AB). It is a software construct that encapsulates data, metadata, and a virtual machine (VM). The metadata …

Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh

Dissertations

This thesis reviews the current state of photometric classification in Astronomy and identifies two main gaps: a dependence on handcrafted rules, and a lack of interpretability in the more successful classifiers. To address this, Deep Learning and Computer Vision were used to create a more interpretable model, using unsupervised training to reduce human bias.

The main contribution is the investigation into the impact of using unsupervised feature-extraction from multi-wavelength image data for the classification task. The feature-extraction is achieved by implementing an unsupervised Deep Belief Network to extract lower-dimensionality features from the multi-wavelength image data captured by the Sloan Digital …

Algorithms For Pre-Microrna Classification And A Gpu Program For Whole Genome Comparison, Ling Zhong

Dissertations

MicroRNAs (miRNAs) are non-coding RNAs with approximately 22 nucleotides that are derived from precursor molecules. These precursor molecules or pre-miRNAs often fold into stem-loop hairpin structures. However, a large number of sequences with pre-miRNA-like hairpin can be found in genomes. It is a challenge to distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (referred to as pseudo pre-miRNAs). The first part of this dissertation presents a new method, called MirID, for identifying and classifying microRNA precursors. MirID is comprised of three steps. Initially, a combinatorial feature mining algorithm is developed to identify suitable feature sets. Then, the …

An Integrated Transport Solution To Big Data Movement In High-Performance Networks, Daqing Yun

Dissertations

Extreme-scale e-Science applications in various domains such as earth science and high energy physics among multiple national institutions within the U.S. are generating colossal amounts of data, now frequently termed as “big data”. The big data must be stored, managed and moved to different geographical locations for distributed data processing and analysis. Such big data transfers require stable and high-speed network connections, which are not readily available in traditional shared IP networks such as the Internet. High-performance networking technologies and services featuring high bandwidth and advance reservation are being rapidly developed and deployed across the nation and around the globe …

Accelerating Data-Intensive Scientific Visualization And Computing Through Parallelization, Dongliang Chu

Dissertations

Many extreme-scale scientific applications generate colossal amounts of data that require an increasing number of processors for parallel processing. The research in this dissertation is focused on optimizing the performance of data-intensive parallel scientific visualization and computing.

In parallel scientific visualization, there exist three well-known parallel architectures, i.e., sort-first/middle/last. The research in this dissertation studies the composition stage of the sort-last architecture for scientific visualization and proposes a generalized method, namely, Grouping More and Pairing Less (GMPL), for order-independent image composition workflow scheduling in sort-last parallel rendering. The technical merits of GMPL are two-fold: i) it takes a prime factorization-based …

Context-Aware Collaborative Storage And Programming For Mobile Users, Mohammad A. Khan

Dissertations

Since people generate and access most digital content from mobile devices, novel innovative mobile apps and services are possible. Most people are interested in sharing this content with communities defined by friendship, similar interests, or geography in exchange for valuable services from these innovative apps. At the same time, they want to own and control their content. Collaborative mobile computing is an ideal choice for this situation. However, due to the distributed nature of this computing environment and the limited resources on mobile devices, maintaining content availability and storage fairness as well as providing efficient programming frameworks are challenging.

This …

Termination, Correctness And Relative Correctness, Nafi Diallo

Dissertations

Over the last decade, research in verification and formal methods has been the subject of increased interest with the need of more secure and dependable software. At the heart of software dependability is the concept of software fault, defined in the literature as the adjudged or hypothesized cause of an error. This definition, which lacks precision, presents at least two challenges with regard to using formal methods: (1) Adjudging and hypothesizing are highly subjective human endeavors; (2) The concept of error is itself insufficiently defined, since it depends on a detailed characterization of correct system states at each stage of …

A Data Science Approach To Pattern Discovery In Complex Structures With Applications In Bioinformatics, Lei Hua

Dissertations

Pattern discovery aims to find interesting, non-trivial, implicit, previously unknown and potentially useful patterns in data. This dissertation presents a data science approach for discovering patterns or motifs from complex structures, particularly complex RNA structures. RNA secondary and tertiary structure motifs are very important in biological molecules, which play multiple vital roles in cells. A lot of work has been done on RNA motif annotation. However, pattern discovery in RNA structure is less studied. In the first part of this dissertation, an ab initio algorithm, named DiscoverR, is introduced for pattern discovery in RNA secondary structures. This algorithm works by …

Schema-Aware Keyword Search On Linked Data, Ananya Dass

Dissertations

Keyword search is a popular technique for querying the ever growing repositories of RDF graph data on the Web. This is due to the fact that the users do not need to master complex query languages (e.g., SQL, SPARQL) and they do not need to know the underlying structure of the data on the Web to compose their queries. Keyword search is simple and flexible. However, it is at the same time ambiguous since a keyword query can be interpreted in different ways. This feature of keyword search poses at least two challenges: (a) identifying relevant results among a multitude …

Collaborative Development Of A Small Business Emergency Planning Model, Arthur Henry Hendela

Dissertations

Small businesses, which are defined by the US Small Business Administration as entities with less than 500 employees, suffer interruptions from diverse risks such as financial events, legal situations, or severe storms exemplified by Hurricane Sandy. Proper preparations can help lessen the length of the interruption and put employees and owners back to work. Large corporations generally have large budgets available for planning, business continuity, and disaster recovery. Small businesses must decide which risks are the most important and how best to mitigate those risks using minimal resources.

This research uses a series of surveys followed by mathematical modeling to …

Mediating Chance Encounters Through Opportunistic Social Matching, Julia M. Mayer

Dissertations

Chance encounters, the unintended meeting between people unfamiliar with each other, serve as an important social lubricant helping people to create new social ties, such as making new friends or finding an activity, study or collaboration partner. Unfortunately, social barriers often prevent chance encounters in environments where people do not know each other and people have to rely on serendipity to meet or be introduced to interesting people around them. Little is known about the underlying dynamics of chance encounters and how systems could utilize contextual data to mediate chance encounters. This dissertation addresses this gap in research literature by …

Cognitive Big Data Analytics And Persuasive Social Influence Diffusion, Eman Ahmed Ghanim Abukhousa

Dissertations

Current demands in local and global economies and the pursuit of competitiveness are calling for data-driven strategies. Data-driven solutions analyze trends, make predictions about future events, and prescribe what to do next in an actionable manner. However, cognitive and behavioral data are distinguished by their multiplicity and rapid changes to meet evolving and dynamic goals of individuals. This research work is concerned with the utility of analytical solutions to synthesize and influence cognitive and behavioral adoption. We propose a multidimensional data model to identify and extract cognitive indicators for analysis and persuasive interventions. The process starts by discovering behavioral features …

Semantics And Result Disambiguation For Keyword Search On Tree Data, Cem Aksoy

Dissertations

Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a …

Task-Based User Profiling For Query Refinement (Toque), Chao Xu

Dissertations

The information needs of search engine users vary in complexity. Some simple needs can be satisfied by using a single query, while complicated ones require a series of queries spanning a period of time. A search task, consisting of a sequence of search queries serving the same information need, can be treated as an atomic unit for modeling user’s search preferences and has been applied in improving the accuracy of search results. However, existing studies on user search tasks mainly focus on applying user’s interests in re-ranking search results. Only few studies have examined the effects of utilizing search tasks …

Predicting Intake Of Applications For First Registration In The Property Registration Authority, Orlaith Mernagh

Dissertations

The motivation for this dissertation is rooted in a real business need. The Property Registration Authority is the state organisation tasked with maintaining a register of land ownership on the island of Ireland. The PRA currently faces a series of challenges; a high level of staff retiring and the inherent loss of knowledge associated with this trend, a lack of recruitment in recent years and a large increase in lodgement of applications for first registration as a result of legislation. The organisation therefore requires a reliable system for predicting future intake. Prior to this project, there has also been a …

Design Of Oppnet Virtual Machine For Opportunistic Resource Utilization Networks: A Universal Standard For Application-Level Resource Sharing, Mai A. Alduailij

Dissertations

Opportunistic resource utilization networks or oppnets are a paradigm for specialized ad hoc networks. The Oppnet Virtual Machine (OVM) is a collection of primitives designed to create a middleware for application-level resource acquisition by oppnets and oppnet-enabled systems and devices (where the latter are defined as the computational entities able to communicate with other oppnets or oppnet-enabled entities). Acquisition of communication resources is the foundation for acquisition of other resources.

The OVM primitives can be downloaded by any computational entity—making entity oppnet-enabled. OVM ensures that oppnet-enabled entities can communicate and acquire resources in an opportunistic and ad hoc manner. This …

Novel Software Defined Radio Architecture With Graphics Processor Acceleration, Lalith Narasimhan

Dissertations

Wireless has become one of the most pervasive core technologies in the modern world. Demand for faster data rates, improved spectrum efficiency, higher system access capacity, seamless protocol integration, improved security and robustness under varying channel environments has led to the resurgence of programmable software defined radio (SDR) as an alternative to traditional ASIC based radios. Future SDR implementations will need support for multiple standards on platforms with multi-Gb/s connectivity, parallel processing and spectrum sensing capabilities. This dissertation implemented key technologies of importance in addressing these issues namely development of cost effective multi-mode reconfigurable SDR and providing a framework to …

Mining Biological Networks Towards Protein Complex Detection And Gene-Disease Association, Eileen Marie Hanna

Dissertations

Large amounts of biological data are continuously generated nowadays, thanks to the advancements of high-throughput experimental techniques. Mining valuable knowledge from such data still motivates the design of suitable computational methods, to complement the experimental work which is often bound by considerable time and cost requirements. Protein complexes or groups of interacting proteins, are key players in most cellular events. The identification of complexes not only allows to better understand normal biological processes but also to uncover Disease-triggering malfunctions. Ultimately, findings in this research branch can highly enhance the design of effective medical treatments. The aim of this research is …