Computer Sciences Commons^™

Learning For Clinical Outcome Prediction From Big Medical Data, Jiawen Yao

Computer Science and Engineering Dissertations - Archive

With the advance of recent technological innovations, nowadays scientists can easily capture and store tremendous amounts of different types of medical data such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), big pathological images and high dimensional cell profiling data. Developing deep learning and machine learning to analyze such large-scale medical data sets for patient health care is an interesting but challenging problem. Inspired by the trend, in this dissertation, we focus on solving real-world problems, like survival analysis on image-omics data and reducing uncertainty from undersampled MRI. Survival analysis is a crucial tool in the clinical study of cancer …

Understanding And Optimizing Parallel Performance In Multi-Tenant Cloud, Yong Zhao

Computer Science and Engineering Dissertations - Archive

As a critical component of resource management in multicore systems, fair schedulers in hypervisors and operating systems (OSes) must follow a simple invariant: guarantee that the computing resources such as CPU cycles are fairly allocated to each vCPU or thread. As simple as it may seem, we found this invariant is broken when parallel programs with blocking synchronization are colocated with CPU intensive programs in hypervisors such as Xen, KVM and OSes such as Linux CFS. On the other hand, schedulers in virtualized environment usually reside in two different layers: one is in the hypervisor which aims to schedule vCPU …

Ai Education Matters: Data Science And Machine Learning With Magic: The Gathering, Todd W. Neller

Computer Science Faculty Publications

In this column, we briefly describe a rich dataset with many opportunities for interesting data science and machine learning assignments and research projects, we take up a simple question, and we offer code illustrating use of the dataset in pursuit of answers to the question.

Computational Approaches For Finding Disease Related Genes And Rnas, Negin Fraidouni

Computer Science and Engineering Dissertations - Archive

Finding candidate genes that could cause specific diseases has been the subject of many studies. This is an important research task, however in the biological experimentation domain it can be very expensive and time consuming. So an alternative way is to find gene expression values from partial measurements and try to predict the rest. By using computational methods, we can statistically estimate these relationships faster and in a more efficient way, providing domain experts suggestions on what exploration of likely relationships they should be focusing. One common computational approach is to model the gene expression data as a matrix (where …

Krahasimi I Autentifikimit Me Shumë Faktorë Të Tre Platformave Cloud, Alban Gojani

Theses and Dissertations

Qëllimi i këtij punimi është krahasimi i autentifikimit me shumë faktorë (MFA) të tre platformave cloud më të përdorura: Microsot Azure, Amazon Web Services dhe Google Cloud Platform. MFA-ja është ndër metodat më të sigurta për autentifikim që përdoret në sisteme kompjuterike. Materiali i përdorur për këtë punim është marrë nga burime të ndryshme, gjegjësisht nga dokumentacionet e platformave cloud të lartëpërmendura, hulumtime shkencore me tema të ngjajshme, artikuj dhe publikime shkencore në internet dhe nga enciklopedia. Fillimisht është shtjelluar MFA-ja si mekanizëm në vete dhe historia e tij, pastaj janë përshkruar MFA-të e këtyre platformave dhe në fund është …

Aplikimi I Sistemeve Te Informacionit E-Health Care Në Kosovë, Ardit Imeri

Theses and Dissertations

Gjithkush flet për eHealth këto ditë, por pak njerëz kanë dalë me një përkufizim të qartë të këtij termi relativisht të ri. Pak në përdorim para vitit 1999, ky term tani duket se shërben si një fjalë e përgjithshme, që përdoret për të karakterizuar jo vetëm "mjekësinë e digjitalizuar", por gjithashtu pothuajse çdo gjë që lidhet me kompjuterët dhe mjekësinë. Termi eHealth është krijuar dhe pëdorur duke u bazuar në fjalët tjera “e-fjal” si përshembull “e-commerce, e-business, e-mail”, me të cilën fjalë do të mund ti referoheshim mundësive të reja që interneti po i hap në fushën e kujdesit mjeksor. …

Analiza E Sentimentit Të Opinionit Duke Përdorur Qasjen Naïve Bayes Dhe Entropinë Maksimale, Armir Bahtiri

Theses and Dissertations

Teknologjia e informacionit ka bërë të mundshme të kemi qasje në informata më lehtë se kurdo herë më parë. Më rritjen e numrit ta pajisje mobile, njerëzit kanë qasje në llogaritë e tyre në çdo kohë dhe thuajse çdo vend. Shfrytëzuesit e shërbimeve të internetit shprehin mendimet dhe pikëpamjet e tyre të ndryshme publikisht me shkrime të ndryshme. Pjesë e lartë e këtyre të dhënave është subjektive, që paraqet opinion të njerëzve rreth dukurive dhe proceseve të ndryshme.

Këto informata, të qasshme për ne, mund të merren dhe me një procesim të mëtejmë të analizohen për të kuptuar të dhëna …

Artificial Intelligence The Use Of Knowledge Graphs, Besar Mehmeti

Theses and Dissertations

In this digital age, an overwhelming amount of unstructured data found in the web is increasing at an unprecedented rate. In response, information extraction techniques have been developed to automatically extract information from unstructured text and populate knowledge bases. This thesis will introduce the reader to the idea of Knowledge Graphs, the history of how they were first experimented on and later developed, and in particular, this thesis is going to elaborate how Diffbot, Wikidata and IBM Watson technologies are modeled, stored and queried. By having this information at our fingertips, we believe that we can make smarter decisions, keep …

Roli I I Marketingut Digjital Në Agjensionet Turistike Në Kosovë, Fetije Braha

Theses and Dissertations

Marketingu digjital sot konsiderohet si një ndër format më të rëndësishme të promocionit,e cila me kërkimet më të reja paraqet një rëndësi strategjike për bizneset që kanë bërë përpjekjet e tyre drejt tregut elektronik. Ideja e marketingut digjital dhe cilësia e shërbimeve elektronike përbën sfidën për agjensionet turistike sot dhe në të ardhmen. Sipas publikimit të Eurostat-it , në vitin 2018, Kosova ka përqindjen e familjeve që kanë qasje në internet në shtëpi më të lartën në Rajon (93%), kjo përqindje është më e lartë se edhe në vetë vendet e BE-së (89%). Për një kohë të shkurtër, marketingu në …

Cloud Computing Adoption In Public Sector, Krenare Hoxha

Theses and Dissertations

Teknologjia e Cloud Computing është zhvillimi në kuadër të teknologjisë informative që kërkon një shqyrtim të kujdesshëm. Cloud Computing është projektuar për të qenë një teknologji efektive për kompanitë e organizatat të ndryshme përmes avantazheve që ajo ofron, siç janë: fleksibiliteti, shkallëzueshmëria, elasticiteti dhe reduktimi i shpenzimeve. Ajo ofron metodën “pay as you go” përmes së cilës paguhet vetëm për shërbimin që shfrytëzohet duke mos bërë shpenzime shtesë. Të dhënat transferohen, përpunohen dhe ruhen në infrastrukturat e shërbimeve Cloud për të cilat shërbime përgjegjës janë ofruesit Cloud.

Krahas benefiteve të Cloud Computing, organizatat e sektorit publik në fillim kanë pasur …

Publication And Evaluation Challenges In Games & Interactive Media, Elizabeth L. Lawley

Presentations and other scholarship

Faculty in the fields of games and interactive media face significant challenges in publishing and documenting their scholarly work for evaluation in the tenure and promotion process. These challenges include selecting appropriate publication venues and assigning authorship for works spanning multiple disciplines; archiving and accurately citing collaborative digital projects; and redefining “peer review,” impact, and dissemination in the context of creative digital works. In this paper I describe many of these challenges, and suggest several potential solutions.

Convex And Non-Convex Optimization Methods For Machine Learning, Fariba Zohrizadeh

Computer Science and Engineering Dissertations - Archive

This dissertation is concerned with modeling fundamental and challenging machine learning tasks as convex/non-convex optimization problems and designing a mechanism that could solve them in a cost and time-effective manner. Extensive theoretical and practical studies are carried out to give deeper insights into the robustness and effectiveness of the formulated problems. In what follows, we investigate some well-known tasks that frequently arise in machine learning applications. Image Segmentation: Image segmentation is a fundamental and challenging task in computer vision with diverse applications in various areas. One of the major challenges in image segmentation is to determine the optimal number of …

Developing And Securing Software For Small Space Systems, Brandon L. Shirley

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The space systems industry is moving towards smaller multi-vendor satellites, known as Small Space. This shift is driven by economic and technological factors that necessitate hardware and software components that are modular, reusable, and secure. This research addresses two problems associated with the development of modular, reusable, and secure space systems: developing software for space systems (the Development Problem) and securing space systems (the Security Problem). These two problems are interrelated and this research addresses them together.

The Development Problem encompasses challenges that space systems developers face as they try to address the constraints induced by reduced budgets, …

Cerebro: Context-Aware Adaptive Fuzzing For Effective Vulnerability Detection, Yuekang Li, Yinxing Xue, Hongxu Chen, Xiuheng Wu, Cen Zhang, Xiaofei Xie, Haijun Wang, Yang Liu

Research Collection School Of Computing and Information Systems

Existing greybox fuzzers mainly utilize program coverage as the goal to guide the fuzzing process. To maximize their outputs, coverage-based greybox fuzzers need to evaluate the quality of seeds properly, which involves making two decisions: 1) which is the most promising seed to fuzz next (seed prioritization), and 2) how many efforts should be made to the current seed (power scheduling). In this paper, we present our fuzzer, Cerebro, to address the above challenges. For the seed prioritization problem, we propose an online multi-objective based algorithm to balance various metrics such as code complexity, coverage, execution time, etc. To address …

Deep Anomaly Detection With Deviation Networks, Guansong Pang, Chunhua Shen, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection …

Creating Top Ranking Options In The Continuous Option And Preference Space, Bo Tang, Kyriakos Mouratidis, Man Lung Yiu, Zhenyu Chen

Research Collection School Of Computing and Information Systems

Top-k queries are extensively used to retrieve the k most relevantoptions (e.g., products, services, accommodation alternatives, etc)based on a weighted scoring function that captures user preferences. In this paper, we take the viewpoint of a business owner whoplans to introduce a new option to the market, with a certain type ofclientele in mind. Given a target region in the consumer spectrum,we determine what attribute values the new option should have,so that it ranks among the top-k for any user in that region. Ourmethodology can also be used to improve an existing option, at theminimum modification cost, so that it ranks …

Inspect: Iterated Local Search For Solving Path Conditions, Fuxiang Chen, Aldy Gunawan, David Lo, Sunghun Kim

Research Collection School Of Computing and Information Systems

Automated test case generation is attractive as it can reduce developer workload. To generate test cases, many Symbolic Execution approaches first produce Path Conditions (PCs), a set of constraints, and pass them to a Satisfiability Modulo Theories (SMT) solver. Despite numerous prior studies, automated test case generation by Symbolic Execution is still slow, partly due to SMT solvers’ high computationally complexity. We introduce InSPeCT, a Path Condition solver, that leverages elements of ILS (Iterated Local Search) and Tabu List. ILS is not computational intensive and focuses on generating solutions in search spaces while Tabu List prevents the use of previously …

Enhancing Multi-Hop Sensor Calibration With Uncertainty Estimates, Balz Maag, Zimu Zhou, Lothar Thiele

Research Collection School Of Computing and Information Systems

Low-cost sensors, installed on mobile vehicles, provide a cost-effective way for fine-grained urban air pollution monitoring. However, frequent calibration is crucial for lowcost sensors to consistently deliver accurate measurements. Multi-hop calibration is a common practice to calibrate mobile sensor deployments, but is prone to severe error accumulation over hops. Prior research mitigates error accumulation by designing special calibration models, which only apply to linear models. In this paper, we propose an orthogonal approach by selecting reliable measurements for calibration at each hop. We analyze the impact of different data-induced uncertainties on calibration errors and devise a scheme to estimate these …

Latent Error Prediction And Fault Localization For Microservice Applications By Learning From System Trace Logs, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Dewei Liu, Qilin Xiang, Chuan He

Research Collection School Of Computing and Information Systems

In the production environment, a large part of microservice failures are related to the complex and dynamic interactions and runtime environments, such as those related to multiple instances, environmental configurations, and asynchronous interactions of microservices. Due to the complexity and dynamism of these failures, it is often hard to reproduce and diagnose them in testing environments. It is desirable yet still challenging that these failures can be detected and the faults can be located at runtime of the production environment to allow developers to resolve them efficiently. To address this challenge, in this paper, we propose MEPFL, an approach of …

A Novel Adaptive Lbp-Based Descriptor For Color Image Retrieval, Mohammad Sotoodeh, Mohammad Reza Moosavi, Reza Boostani

Computer Science Student Research

In this paper, we present two approaches to extract discriminative features for color image retrieval. The proposed local texture descriptors, based on Radial Mean Local Binary Pattern (RMLBP), are called Color RMCLBP (CRMCLBP) and Prototype Data Model (PDM). RMLBP is a robust to noise descriptor which has been proposed to extract texture features of gray scale images for texture classification.

For the first descriptor, the Radial Mean Completed Local Binary Pattern is applied to channels of the color space, independently. Then, the final descriptor is achieved by concatenating the histogram of the CRMCLBP_S/M/C component of each channel. Moreover, to enhance …

An Optimized Encoding Algorithm For Systematic Polar Codes, Xiumin Wang, Zhihong Zhang, Jun Li, Yu Wang, Haiyan Cao, Zhengquan Li, Liang Shan

Publications and Research

Many different encoding algorithms for systematic polar codes (SPC) have been introduced since SPC was proposed in 2011. However, the number of the computing units of exclusive OR (XOR) has not been optimized yet. According to an iterative property of the generator matrix and particular lower triangular structure of the matrix, we propose an optimized encoding algorithm (OEA) of SPC that can reduce the number of XOR computing units compared with existing non-recursive algorithms. We also prove that this property of the generator matrix could extend to different code lengths and rates of the polar codes. Through the matrix segmentation …

Pose Based Human Activity Recognition, Wenbo Li

Legacy Theses & Dissertations (2009 - 2024)

Pose based human activity recognition is an important step towards video understanding. The last decade has witnessed the great progress in this field which is driven by multiple technical innovations, i.e., kinect, pose estimation techniques, deep learning, etc.

Wrinkles In Time : An Exploration Of Non-Uniform Temporal Resolution In Network Data, Daniel John Ditursi

Legacy Theses & Dissertations (2009 - 2024)

The continued proliferation of timestamped network data demands increasing sophistication in the analysis of that data. In particular, the literature amply demonstrates that the choice of temporal resolution has a profound impact on the solutions produced by many different methods in this domain -- answers differ when data is viewed second-by-second as opposed to week-by-week. Additionally, research also shows quite clearly that the rates at which network events happen are not constant -- some times are "faster" or "slower" than others, and these variations are not necessarily predictable. Given the above, it is clear that there must be problem settings …

Structural Bot Detection In Social Networks, Lale Madahali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Social network platforms are a major part of toady’s life. They are usually used for entertainment, news, advertisements, and branding for businesses and individuals alike. However, use of automated accounts, also known as bots, pollute this environment and avoid having a reliable clean online world. In this work, I address the problem of detecting bots in online social networks.

Blocks' Network: Redesign Architecture Based On Blockchain Technology, Moataz Hanif

Doctoral Dissertations and Master's Theses

The Internet is a global network that uses communication protocols. It is considered the most important system reached by humanity, which no one can abandon. However, this technology has become a weapon that threatens the privacy of users, especially in the client-server model, where data is stored and managed privately. Additionally, users have no power over their data that store in a private server, which means users’ data may interrupt by government or might be sold via service provider for-profit purposes. Furthermore, blockchain is a technology that we can rely on to solve issues related to client-server model if appropriately …

Krylov Subspace Spectral Methods With Non-Homogenous Boundary Conditions, Abbie Hendley

Master's Theses

For this thesis, Krylov Subspace Spectral (KSS) methods, developed by Dr. James Lambers, will be used to solve a one-dimensional, heat equation with non-homogenous boundary conditions. While current methods such as Finite Difference are able to carry out these computations efficiently, their accuracy and scalability can be improved. We will solve the heat equation in one-dimension with two cases to observe the behaviors of the errors using KSS methods. The first case will implement KSS methods with trigonometric initial conditions, then another case where the initial conditions are polynomial functions. We will also look at both the time-independent and time-dependent …

Analysis Of Social Unrest Events Using Spatio-Temporal Data Clustering And Agent-Based Modelling, Sudeep Basnet

School of Computing: Dissertations, Theses, and Student Research

Social unrest such as appeals, protests, conflicts, fights and mass violence can result from a wide ranging of diverse factors making the analysis of causal relationships challenging, with high complexity and uncertainty. Unrest events can result in significant changes in a society ranging from new policies and regulations to regime change. Widespread unrest often arises through a process of feedback and cascading of a collection of past events over time, in regions that are close to each other. Understanding the dynamics of these social events and extrapolating their future growth will enable analysts to detect or forecast major societal events. …

Distributed Edge Bundling For Large Graphs, Yves Tuyishime

School of Computing: Dissertations, Theses, and Student Research

Graphs or networks are widely used to depict the relationships between data entities in diverse scientific and engineering applications. A direct visualization (such as node-link diagram) of a graph with a large number of nodes and edges often incurs visual clutter. To address this issue, researchers have developed edge bundling algorithms that visually merge similar edges into curved bundles and can effectively reveal high-level edge patterns with reduced visual clutter. Although the existing edge bundling algorithms achieve appealing results, they are mostly designed for a single machine, and thereby the size of a graph they can handle is limited by …

Exploring Eye Tracking Data On Source Code Via Dual Space Analysis, Li Zhang

School of Computing: Dissertations, Theses, and Student Research

Eye tracking is a frequently used technique to collect data capturing users' strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a …

Impact Of Robotic Challenges On Fifth Grade Problem Solving, Julie Rankin

Department of Teaching, Learning, and Teacher Education: Theses and Other Student Research

This action research project was designed to investigate the impact of educational robotics in a fifth grade rural classroom. The integration of science, technology, engineering, and math in education (STEM) has sparked an increase of robotics in the classroom. The purpose of the study was to determine if problem-solving skills can be impacted through continuing involvement with challenges using various educational robotics and programming tools. The study sought to answer two research questions: (1) How does the introduction of robotics challenges in a fifth-grade classroom impact students’ problem solving skills? (2) How do robotics in the classroom impact student interest …