Computer Sciences Commons^™

Robust Noise-Based Attacks Against Audio Event Detection Systems, Rodrigo Augusto Silva Dos Santos

Computer Science and Engineering Dissertations - Archive

The massive advances on the field of deep neural networks in the 2000 and 2010 decades led to an overwhelming adoption of these algorithms on all sorts of domains and applications. Under this widespread adoption scenario, it is natural that these neural networks have also been employed on safety-related use cases, bringing substantial improvements to the performance of existing as well as novel systems. Examples of these safety-inclined applications include scene recognition, object detection and tracking, speech recognition, audio event detection and classification, just to cite a few ones. Unfortunately, these neural network algorithms have been shown to be vulnerable …

Designing Large-Scale Key-Value Systems On High-Speed Storage Devices, Xingsheng Zhao

Computer Science and Engineering Dissertations - Archive

With the evolution of new technologies, such as edge computing, full self-driving, virtual reality, and multi-media streaming, the volume of data is growing at an accelerated speed. The global data volume could achieve 175~zettabytes by 2025. With this huge amount of data, the focus of data management has been shifted from traditional SQL databases to NoSQL databases, which provide higher performance and better scalability. Key-value (KV) stores are a common type of NoSQL database and are becoming a major storage infrastructure in various application domains. With the development of high-speed storage devices, such as NVMe SSD, Open-channel SSD, and non-volatile …

Efficient Algorithms And Human-In-The-Loop Approaches For Attribute Design And Selection, Md Abdus Salam

Computer Science and Engineering Dissertations - Archive

Feature engineering and feature selection are two important aspects of data science pipeline. Due to the advancement of data collection techniques in recent years, huge amount of data is becoming available in different industries. Consequently, the importance of data science is increasing for business analytic purpose. Different tools and techniques are being developed to assist data scientists to complete their tasks efficiently. One of the main human involvements in the data science task is for feature engineering and selection. These pre-processing steps will prepare the data in the format desired to be fed into various machine learning algorithms to accomplish …

A Dark Web Pharma Framework For A More Efficient Investigation Of Dark Web Covid-19 Vaccine Products., Francisca Afua Opoku-Boateng

Masters Theses & Doctoral Dissertations

Globally, as the COVID-19 pandemic persists, it has not just imposed a significant impact on the general well-being of individuals, exposing them to unprecedented financial hardships and online information deception. However, it has also forced consumers, buyers, and suppliers to look toward a darkened economic world – the Dark Web world – a sinister complement to the internet, driven by financial gains, where illegal goods and services are advertised sold. As the Dark Web gains an increase in recognition by normal web users during this pandemic, how to perform cybercrime investigations on the Dark Web becomes challenging for manufacturers, investigators, …

An Application Of Machine Learning To Analysis Of Packed Mac Malware, Kimo Bumanglag

Masters Theses & Doctoral Dissertations

The macOS operating system is increasingly targeted by malware. Software written for macOS, both benign and malicious, is in the Mach-O executable format. Malware authors may frustrate analysts through obfuscation methods such as packing. The field of malware research on Windows is well-established but is less so on the macOS platform. Thus far, no research has been identified that studies how machine learning can be used to detected packed Mach-O malware. This research applies supervised machine learning techniques to the classification of packed Mach-O malware. This research will answer three research questions. First, whether machine learning can classify packed Mach-O …

Two Project On Information Systems Capabilities And Organizational Performance, Giridhar Reddy Bojja

Masters Theses & Doctoral Dissertations

Information systems (IS), as a multi-disciplinary research area, emphasizes the complementary relationship between people, organizations, and technology and has evolved dramatically over the years. IS and the underlying Information Technology (IT) application and research play a crucial role in transforming the business world and research within the management domain. Consistent with this evolution and transformation, I develop a two-project dissertation on Information systems capabilities and organizational outcomes.

Project 1 examines the role of hospital operational effectiveness on the link between information systems capabilities and hospital performance. This project examines the cross-lagged effects on a sample of 217 hospitals measured over …

Code Review Practices For Refactoring Changes: An Empirical Study On Openstack, Mohamed Wiem Mkaouer, Eman Abdullah Alomar, Moatz Chouchen, Ali Ouni

Articles

Modern code review is a widely used technique employed in both industrial and open-source projects to improve software quality, share knowledge, and ensure adherence to coding standards and guidelines. During code review, developers may discuss refactoring activities before merging code changes in the code base. To date, code review has been extensively studied to explore its general challenges, best practices and outcomes, and socio-technical aspects. However, little is known about how refactoring is being reviewed and what developers care about when they review refactored code. Hence, in this work, we present a quantitative and qualitative study to understand what are …

Machine Learning Methods For Statistical Analysis And Representation Learning On Neuroimaging Data, Fan Yang

Computer Science and Engineering Dissertations - Archive

With the recent advance and widespread adoption of imaging technological innovations, clinical practitioner and scientists can easily acquire and store a large amount of various neuroimaging modalities, such as Diffusion Tensor Imaging (DTI), Magnetic Resonance Imaging (MRI), resting-state functional MRI (rs-fMRI) and Positron Emission Tomography (PET), etc. These novel imaging data sources cover a rich amount of factors that influence patients' cognitive health, offer an objective view of patients at unprecedented multi-resolution for the understanding of brain structure and function, and have the significant potential to improve healthcare by aiding better decision-making in diagnosing, monitoring and treating diseases. Machine Learning …

Using Antipatterns To Improve Database Code Fragments, And Utilizing Knowledge Graphs And Nlp Patterns To Extract Standardized Data Element Names, Bader Alshemaimri

Computer Science and Engineering Dissertations - Archive

Database code fragments exist in software systems by using SQL as the stan- dard language for relational databases. Traditionally, developers bind databases as backends to software systems for supporting user applications. However, these bind- ings are low-level code and implemented to persist user data, so Object Relational Mapping (ORM) frameworks take place to abstract database access details. These approaches are prone to problematic database code fragments that negatively im- pact the quality of software systems. In the first part of the dissertation, we survey problematic database code fragments in the literature and examine antipatterns that occur in low-level database access …

Unsupervised Domain Adaptation With Deep Neural Networks, Jinyu Yang

Computer Science and Engineering Dissertations - Archive

Deep neural networks (DNNs) demonstrate unprecedented achievements on various machine learning problems and applications. However, such impressive performance heavily relies on massive amounts of labeled data which requires considerable time and labor efforts to collect and annotate. To remedy this limitation, unsupervised domain adaptation (UDA) has attracted more and more attention in the past decade, owing to its capability in transferring the knowledge learned from a labeled source domain to an unlabeled target domain. UDA has proved its wide applicability in various vision tasks, for example, image classification and semantic segmentation. Despite its impressive success, the limitations of existing UDA …

Learning Causal Bounds Using Marginal Independence Information With Applications To Gene Expression Analysis, Borzou Alipourfard

Computer Science and Engineering Dissertations - Archive

ABSTRACT: Discovering causal relations is a fundamental goal of science. Randomized controlled experiments were often considered to be the only reliable method for tackling this task. However, in recent years, various causal discovery methods have been proposed that are capable of identifying causal relations from purely observational data. While these causal discovery methods provide a theoretical framework for bridging the gap from statistical relations to causal conclusions, causal discovery remains a challenging task in practice; this challenge arises because many of the assumptions made in obtaining these theoretical results are often not met in practice. This is especially true when …

Optimal Utility-Based Traffic Control For Datacenter Networks, Akshit Singhal

Computer Science and Engineering Dissertations - Archive

As datacenter applications with diverse service requirements proliferate, it becomes imperative to enable datacenter network flow rate allocation that satisfies minimum user-utility requirements, while allowing for user-utility-based fair resource allocation. Multiple Path Transmission Control Protocols (MPTCPs) allow flows to explore path diversity of datacenter networks and multihoming to improve throughput, reliability, and network resource utilization. The work in this dissertation aims to develop optimal utility-based datacenter traffic control protocols to meet diverse service requirements for datacenter applications. Specifically, this dissertation makes contributions on four highly related research topics. First, we put forward a HOListic traffic control framework for datacenter NETworks …

Implementing The Cms+ Sports Rankings Algorithm In A Javafx Environment, Luke Welch

Industrial Engineering Undergraduate Honors Theses

Every year, sports teams and athletes get cut from championship opportunities because of their rank. While this reality is easier to swallow if a team or athlete is distant from the cut, it is much harder when they are right on the edge. Many times, it leaves fans and athletes wondering, “Why wasn’t I ranked higher? What factors when into the ranking? Are the rankings based on opinion alone?” These are fair questions that deserve an answer. Many times, sports rankings are derived from opinion polls. Other times, they are derived from a combination of opinion polls and measured performance. …

Identifying Noisy Labels In The Ground Truth Of Eating Episodes Self-Reported By Button Press On A Wrist-Worn Device, Tianyi Zhang

All Theses

This thesis considers the problem of identifying noisy labels in the ground truth of eating episodes (meals, snacks) as self-reported by participants collecting data in the wild. Participants wore a smartwatch-like device that tracked their wrist motion all day. They were instructed to press a button on the device at the start and end of each eating episode. The device and instructions were designed to be as simple to use as possible, but post-review of the ground truth provided by participants revealed a strong likelihood that a significant portion of the button presses may contain errors. For example, an error …

Cell Tracking At Low Frame Rate Using Deep Learning And Bayesian Integration, Xiang Zhang

All Dissertations

Tracking cells over time is a fundamental task in live-cell imaging, and often requires costly manual analysis if images are not acquired with high enough frame rate. Acquiring high frame rate images, however, can limit the number of conditions explored and cells analyzed, and contribute to photobleaching, which makes fluorophores dimmer and phototoxicity, which affects cell health and renders the resulting data unusable.

Assuming a relatively high frame rate in image acquisition, state-of-the-art cell tracking approaches rely on either spatial proximity or morphological similarity to link cells in consecutive frames. The problem is that, at low frame rate, both approaches …

Indoor Localization Using Solar Cells, Hamada Rizk, Dong Ma, Mahbub Hassan, Moustafa Youssef

Research Collection School Of Computing and Information Systems

The development of the Internet of Things (IoT) opens the doors for innovative solutions in indoor positioning systems. Recently, light-based positioning has attracted much attention due to the dense and pervasive nature of light sources (e.g., Light-emitting Diode lighting) in indoor environments. Nevertheless, most existing solutions necessitate carrying a high-end phone at hand in a specific orientation to detect the light intensity with the phone's light sensing capability (i.e., light sensor or camera). This limits the ease of deployment of these solutions and leads to drainage of the phone battery. We propose PVDeepLoc, a device-free light-based indoor localization system that …

Understanding Crowdsourcing Requesters’ Wage Setting Behaviors, Kotaro Hara, Yudai Tanaka

Research Collection School Of Computing and Information Systems

Requesters on crowdsourcing platforms like Amazon Mechanical Turk (AMT) compensate workers inadequately. One potential reason for the underpayment is that the AMT’s requester interface provides limited information about estimated wages, preventing requesters from knowing if they are offering a fair piece-rate reward. To assess if presenting wage information affects requesters’ reward setting behaviors, we conducted a controlled study with 63 participants. We had three levels for a between-subjects factor in a mixed design study, where we provided participants with: no wage information, wage point estimate, and wage distribution. Each participant had three stages of adjusting the reward and controlling the …

Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Software developers often use social media (such as Twitter) to shareprogramming knowledge such as new tools, sample code snippets,and tips on programming. One of the topics they talk about is thesoftware library. The tweets may contain useful information abouta library. A good understanding of this information, e.g., on thedeveloper’s views regarding a library can be beneficial to weigh thepros and cons of using the library as well as the general sentimentstowards the library. However, it is not trivial to recognize whethera word actually refers to a library or other meanings. For example,a tweet mentioning the word “pandas" may refer to …

Simple Or Complex? Together For A More Accurate Just-In-Time Defect Predictor, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may not reflect useful …

Tatl: Task Agnostic Transfer Learning For Skin Attributes Detection, Duy M.H. Nguyen, Thu T. Nguyen, Huong Vu, Hong Quang Pham, Manh-Duy Nguyen, Binh T. Nguyen, Daniel Sonntag

Research Collection School Of Computing and Information Systems

Existing skin attributes detection methods usually initialize with a pre-trained Imagenet network and then fine-tune on a medical target task. However, we argue that such approaches are suboptimal because medical datasets are largely different from ImageNet and often contain limited training samples. In this work, we propose Task Agnostic Transfer Learning (TATL), a novel framework motivated by dermatologists’ behaviors in the skincare context. TATL learns an attribute-agnostic segmenter that detects lesion skin regions and then transfers this knowledge to a set of attribute-specific classifiers to detect each particular attribute. Since TATL’s attribute-agnostic segmenter only detects skin attribute regions, it enjoys …

Efficient Encrypted Data Search With Expressive Queries And Flexible Update, Jianting Ning, Jiageng Chen, Kaitai Liang, Joseph K. Liu, Chunhua Su, Qianhong Wu

Research Collection School Of Computing and Information Systems

Outsourcing encrypted data to cloud servers that has become a prevalent trend among Internet users to date. There is a long list of advantages on data outsourcing, such as the reduction cost of local data management. How to securely operate encrypted data (remotely), however, is the top-rank concern over data owner. Liang et al. proposed a novel encrypted cloud-based data share and search system without loss of privacy. The system allows users to flexibly search and share encrypted data as well as updating keyword field. However, the search complexity of the system is of extreme inefficiency, O(nd), where d is …

Auxetic Interval Determination And Experimental Validation For A Three-Dimensional Periodic Framework, Ciprian S. Borcea, Freek G.J. Broeren, Just L. Herder, Ileana Streinu, Volkert Van Der Wijk

Computer Science: Faculty Publications

Auxetic behavior refers to lateral widening upon stretching or, in reverse, lateral shrinking upon compression. When an initially auxetic structure is actuated by compression or extension, it will not necessarily remain auxetic for larger deformations. In this paper, we investigate the auxetic range in the deformation of a periodic framework with one degree of freedom. We use geometric criteria to identify the interval where the deformation is auxetic and validate these theoretical findings with compression experiments on sample structures with (Formula presented.) unit cells.

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw

Electrical & Computer Engineering Theses & Dissertations

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …

Tiktok As A Digital Activism Space: Social Justice Under Algorithmic Control, Brittany Haslem

Institute for the Humanities Theses

TikTok, a video sharing application, has become the center of viral internet culture. The app has risen in popularity so quickly that scholarly literature investigating its vast societal impact is still nascent. TikTok is not only used to discuss popular culture topics and create trends, but also being utilized as a tool for social justice activism in the United States in the wake of a tumultuous year with major events such as the coronavirus pandemic, a resurgence of the Black Lives Matter movement, and the 2020 presidential election. TikTok activism is not without critiques, ranging from concerns of foreign government …

Data-Driven Framework For Understanding & Modeling Ride-Sourcing Transportation Systems, Bishoy Kelleny

Civil & Environmental Engineering Theses & Dissertations

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for …

Natural Attack For Pre-Trained Models Of Code, Zhou Yang, Jieke Shi, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Pre-trained models of code have achieved success in many important software engineering tasks. However, these powerful models are vulnerable to adversarial attacks that slightly perturb model inputs to make a victim model produce wrong outputs. Current works mainly attack models of code with examples that preserve operational program semantics but ignore a fundamental requirement for adversarial example generation: perturbations should be natural to human judges, which we refer to as naturalness requirement. In this paper, we propose ALERT (Naturalness Aware Attack), a black-box attack that adversarially transforms inputs to make victim models produce wrong outputs. Different from prior works, this …

Covid Synergy: A Machine Learning Approach Uncovering Potential Treatment Combinations For Sars-Cov-2, Jason Eden Sanchez

Open Access Theses & Dissertations

For more than two years, the COVID-19 pandemic has upended the lives of billions of individualsworldwide leading to disruptions in healthcare, the economy and society at large. As the pandemic enters its third year, the human impact cannot be overstated and the need to develop effective pharmaceuticals remains. Though there currently exits FDA-approved medications for COVID-19, the emergence of novel variants, such as Omicron, highlights the importance of discovering new therapies which will continue to be effective regardless of the pandemicâ??s progression. Because discovering new medications is a costly and timeintensive endeavor, my approach entails drug repurposing to test medications …

Interval Observer-Based Supervision Of Nonlinear Networked Control Systems, Afef Najjar, Thach Ngoc Dinh, Messaoud Amairi, Tarek Raissi

Turkish Journal of Electrical Engineering and Computer Sciences

Networked control system (NCS) is a multidisciplinary area that attracts increasing attention today. In this paper, we deal with remote supervision of a nonlinear networked control systems class subject to network imperfections. Different from many existing researches that consider only the problem of small and/or constant communication delays, we focus on large and time-varying network delays problem in both measurement and control channels. The proposed method is a set-membership estimation-based predictor approach computing a guaranteed set of admissible state values when the uncertainties (i.e. measurement noises and system disturbances) are considered unknown but bounded with a priori known bounds. The …

An Efficient End-To-End Deep Neural Network For Interstitial Lung Disease Recognition And Classification, Masum Shah Junayed, Afsana Ahsan Jeny, Md Baharul Islam, Ikhtiar Ahmed, Afm Shahen Shah

Turkish Journal of Electrical Engineering and Computer Sciences

The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with …