Physical Sciences and Mathematics Commons^™

On M-Impact Regions And Standing Top-K Influence Problems, Bo Tang, Kyriakos Mouratidis, Mingji Han

Research Collection School Of Computing and Information Systems

In this paper, we study the ��-impact region problem (mIR). In a context where users look for available products with top-�� queries, mIR identifies the part of the product space that attracts the most user attention. Specifically, mIR determines the kind of attribute values that lead a (new or existing) product to the top-�� result for at least a fraction of the user population. mIR has several applications, ranging from effective marketing to product improvement. Importantly, it also leads to (exact and efficient) solutions for standing top-�� impact problems, which were previously solved heuristically only, or whose current solutions face …

Spatially-Invariant Style-Codes Controlled Makeup Transfer, Han Deng, Chu Han, Hongmin Cai, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Transferring makeup from the misaligned reference image is challenging. Previous methods overcome this barrier by computing pixel-wise correspondences between two images, which is inaccurate and computational-expensive. In this paper, we take a different perspective to break down the makeup transfer problem into a two-step extraction-assignment process. To this end, we propose a Style-based Controllable GAN model that consists of three components, each of which corresponds to target style-code encoding, face identity features extraction, and makeup fusion, respectively. In particular, a Part-specific Style Encoder encodes the component-wise makeup style of the reference image into a style-code in an intermediate latent space …

Boosting Video Representation Learning With Multi-Faceted Integration, Zhaofan Qiu, Yao Ting, Chong-Wah Ngo, Xiao-Ping Zhang, Dong Wu, Tao Mei

Research Collection School Of Computing and Information Systems

Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the …

Soarnet, Deep Learning Thermal Detection For Free Flight, Jake T. Tallman

Master's Theses

Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not …

Knowledge And Anxiety About Covid-19 In The State Of Qatar, And The Middle East And North Africa Region—A Cross Sectional Study, Sathyanarayanan Doraiswamy, Sohaila Cheema, Maisonneuve Patrick, Amit Abraham, Ingmar Weber, Jisun An, Albert B. Lowenfels, Ravinder Mamtani

Research Collection School Of Computing and Information Systems

While the coronavirus disease 2019 (COVID-19) pandemic wreaked havoc across the globe, we have witnessed substantial mis- and disinformation regarding various aspects of the disease. We conducted a cross-sectional study using a self-administered questionnaire for the general public (recruited via social media) and healthcare workers (recruited via email) from the State of Qatar, and the Middle East and North Africa region to understand the knowledge of and anxiety levels around COVID-19 (April–June 2020) during the early stage of the pandemic. The final dataset used for the analysis comprised of 1658 questionnaires (53.0% of 3129 received questionnaires; 1337 [80.6%] from the …

An Economic Analysis Of Rebates Conditional On Positive Reviews, Jianqing Chen, Zhiling Guo, Jian Huang

Research Collection School Of Computing and Information Systems

Strategic sellers on some online selling platforms have recently been using a conditional-rebate strategy to manipulate product reviews under which only purchasing consumers who post positive reviews online are eligible to redeem the rebate. A key concern for the conditional rebate is that it can easily induce fake reviews, which might be harmful to consumers and society. We develop a microbehavioral model capturing consumers’ review-sharing benefit, review-posting cost, and moral cost of lying to examine the seller’s optimal pricing and rebate decisions. We derive three equilibria: the no-rebate, organic-review equilibrium; the low-rebate, boosted-authentic-review equilibrium; and the high-rebate, partially-fake-review equilibrium. We …

Riding Through The Silver Tsunami: A Data Driven Approach To Improve Senior Citizens’ Engagement With Community Senior Activity Centres, Joshua Jie Feng Lam, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

In Singapore, 1 in 4 persons will be elderly by 2030 In preparation for the Silver Tsunami, the Singapore government and community care providers have collaborations to promote active, independent living amongst elders Current implementation of data driven population health is focused on well being indices using data collected from the general population There is no literature on the use of data analytics in assessing elder

How-To Present News On Social Media: A Causal Analysis Of Editing News Headlines For Boosting User Engagement, Kunwoo Park, Haewoon Kwak, Jisun An, Sanjay Chawla

Research Collection School Of Computing and Information Systems

To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, the research community does not own a sufficient understanding of what kinds of editing strategies effectively promote audience engagement. In this study, we aim to fill the gap by analyzing media outlets' current practices using a data-driven approach. We first build a parallel corpus of original news articles and their corresponding tweets that eight media outlets shared. Then, we explore how those …

Ganmut: Learning Interpretable Conditional Space For A Gamut Of Emotions, S. D'Apolito, D.P. Paundel, Zhiwu Huang, A.R. Vergara, Gool L. Van

Research Collection School Of Computing and Information Systems

Humans can communicate emotions through a plethora of facial expressions, each with its own intensity, nuances and ambiguities. The generation of such variety by means of conditional GANs is limited to the expressions encoded in the used label system. These limitations are caused either due to burdensome labeling demand or the confounded label space. On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability. In this paper, we propose a novel GAN-based framework which learns an expressive and interpretable conditional space (usable as a label space) of emotions, instead of conditioning on …

Impact Of User Traversal On Performance Of Stem Learners In Immersive Virtual Environments, Eric W. Nersesian

Dissertations

The emerging technologies of augmented and virtual reality (AR/VR) may have vast implications to societal communication and representation of information. AR/VR computer interfaces are unique in that they may be placed spatially around the user in three-dimensional (3D) space; this affords new methods of both presentation and user interaction with the target information.

This may be especially impactful in the education of science, technology, engineering, and mathematics (STEM) professionals. Prior research has shown that simulations and visualizations improve the performance of STEM learners compared to live instruction and textbook reading. Yet, research into AR/VR as a learning environment for widespread …

Translating Natural Language Queries To Sparql, Shreya Satish Bhajikhaye

Master's Projects

The Semantic Web is an extensive knowledge base that contains facts in the form of RDF
triples. These facts are not easily accessible to the average user because to use them requires
an understanding of ontologies and a query language like SPARQL. Question answering systems
form a layer of abstraction on linked data to overcome these issues. These systems allow the
user to input a question in a natural language and receive the equivalent SPARQL query. The
user can then execute the query on the database to fetch the desired results. The standard
techniques involved in translating natural language questions …

Using Oracle To Solve Zookeeper On Two-Replica Problems, Ching-Chan Lee

Master's Projects

The project introduces an Oracle, a failure detector, in Apache ZooKeeper and makes it fault-tolerant in a two-node system. The project demonstrates the Oracle authorizes the primary process to maintain the liveness when the majority’s rule becomes an obstacle to continue Apache ZooKeeper service. In addition to the property of accuracy and completeness from Chandra et al.’s research, the project proposes the property of see to avoid losing transactions and the property of mutual exclusion to avoid split-brain issues. The hybrid properties render not only more sounder flexibility in the implementation but also stronger guarantees on safety. Thus, the Oracle …

Federated Learning In Gaze Recognition (Fligr), Arun Gopal Govindaswamy

College of Computing and Digital Media Dissertations

The efficiency and generalizability of a deep learning model is based on the amount and diversity of training data. Although huge amounts of data are being collected, these data are not stored in centralized servers for further data processing. It is often infeasible to collect and share data in centralized servers due to various medical data regulations. This need for diversely distributed data and infeasible storage solutions calls for Federated Learning (FL). FL is a clever way of utilizing privately stored data in model building without the need for data sharing. The idea is to train several different models locally …

Pitcher Effectiveness: A Step Forward For In Game Analytics And Pitcher Evaluation, Christopher Watkins, Vincent Berardi, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

With the introduction of Statcast in 2015, baseball analytics have become more precise. Statcast allows every play to be accurately tracked and the data it generates is easily accessible through Baseball Savant, which opens the opportunity for improved performance statistics to be developed. In this paper we propose a new tool, Pitcher Effectiveness, that uses Statcast data to evaluate starting pitchers dynamically, based on the results of in-game outcomes after each pitch. Pitcher Effectiveness successfully predicts instances where starting pitchers give up several runs, which we believe make it a new and important tool for the in-game and post-game evaluation …

Online Review Analysis From Two Perspectives: Customers And Business Owners, Eunjung Lee

Theses and Dissertations

As online reviews become increasingly prevalent, both online businesses and customers face big data challenges. Individuals are now relying on reviews derived from websites where the reliability of a source depends on the reviewers. Customers spend much time and effort looking for reviews that are useful for them. Accordingly, online review platforms aim to explore various approaches to select useful reviews and present them to customers. At the same time, for business owners, marketers, and e-commerce managers, it has become an essential strategy in recent years to collect as many online reviews as possible. If marketers and managers are able …

How Does Land Cover Classification In Google Earth Engine Compare With Traditional Methods Of Land Cover Classification? What Are The Tradeoffs?, Carlos Sebastian Reyes

Open Access Theses & Dissertations

The project focuses on comparing land cover classification of traditional methods such as ArcGIS with newer ones such as Google Earth Engine (GEE) as well as discussing any potential tradeoffs. Two studies were performed in both platforms, the first involved analyzing land cover change in the Middle Rio Grande (MRG) region of southern New Mexico, far west Texas, and northern Chihuahua, Mexico. The MRG study focused on urban and agricultural change in the region using two different classification methods. The second study focused on creating a post-hurricane damage assessment (PDA) with the goal of developing an automated method of estimating …

A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami

Doctoral Dissertations and Master's Theses

Social engineering attacks (SE-attacks) in enterprises are hastily growing and are becoming increasingly sophisticated. Generally, SE-attacks involve the psychological manipulation of employees into revealing confidential and valuable company data to cybercriminals. The ramifications could bring devastating financial and irreparable reputation loss to the companies. Because SE-attacks involve a human element, preventing these attacks can be tricky and challenging and has become a topic of interest for many researchers and security experts. While methods exist for detecting SE-attacks, our literature review of existing methods identified many crucial factors such as the national cultural, organizational, and personality traits of employees that enable …

Two Essays On Leveraging Analytics To Improve Healthcare, Deepika Gopukumar

Theses and Dissertations

The healthcare cost has continued to increase over the past few years despite various policies, efforts, and initiatives taken by the government. It is still projected to grow over the next few years by the Centers for Medicare and Medicaid Services (CMS). Readmissions have been a major contributor to the increase in costs and have always been a contributing factor. To get a perspective, considering the fact that at least 9% of individuals who had COVID-19 were likely to get readmitted shortly, according to a study by the Centers for Disease Control and Prevention (CDC) COVID-19 response team, along with …

Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos

Electronic Theses and Dissertations

Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification …

Automatic Solution Summarization For Crash Bugs, Haoye Wang, Xin Xia, David Lo, John C. Grundy, Xinyu Wang

Research Collection School Of Computing and Information Systems

The causes of software crashes can be hidden anywhere in the source code and development environment. When encountering software crashes, recurring bugs that are discussed on Q&A sites could provide developers with solutions to their crashing problems. However, it is difficult for developers to accurately search for relevant content on search engines, and developers have to spend a lot of manual effort to find the right solution from the returned results. In this paper, we present CRASOLVER, an approach that takes into account both the structural information of crash traces and the knowledge of crash-causing bugs to automatically summarize solutions …

On The Root Of Trust Identification Problem, Ivan De Oliveira Nunes, Xuhua Ding, Gene Tsudik

Research Collection School Of Computing and Information Systems

Trusted Execution Environments (TEEs) are becoming ubiquitous and are currently used in many security applications: from personal IoT gadgets to banking and databases. Prominent examples of such architectures are Intel SGX, ARM TrustZone, and Trusted Platform Modules (TPMs). A typical TEE relies on a dynamic Root of Trust (RoT) to provide security services such as code/data confidentiality and integrity, isolated secure software execution, remote attestation, and sensor auditing. Despite their usefulness, there is currently no secure means to determine whether a given security service or task is being performed by the particular RoT within a specific physical device. We refer …

Unveiling The Mystery Of Api Evolution In Deep Learning Frameworks: A Case Study Of Tensorflow 2, Zejun Zhang, Yanming Yang, Xin Xia, David Lo, Xiaoxue Ren, John C. Grundy

Research Collection School Of Computing and Information Systems

API developers have been working hard to evolve APIs to provide more simple, powerful, and robust API libraries. Although API evolution has been studied for multiple domains, such as Web and Android development, API evolution for deep learning frameworks has not yet been studied. It is not very clear how and why APIs evolve in deep learning frameworks, and yet these are being more and more heavily used in industry. To fill this gap, we conduct a large-scale and in-depth study on the API evolution of Tensorflow 2, which is currently the most popular deep learning framework. We first extract …

Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Paruchuri, Akshat Kumar

Research Collection School Of Computing and Information Systems

In modular reinforcement learning (MRL), a complex decision making problem is decomposed into multiple simpler subproblems each solved by a separate module. Often, these subproblems have conflicting goals, and incomparable reward scales. A composable decision making architecture requires that even the modules authored separately with possibly misaligned reward scales can be combined coherently. An arbitrator should consider different module's action preferences to learn effective global action selection. We present a novel framework called GRACIAS that assigns fine-grained importance to the different modules based on their relevance in a given state, and enables composable decision making based on modern deep RL …

How Do Software Developers Use Github Actions To Automate Their Workflows?, Timothy Kinsman, Mairieli Wessel, Marco Gerosa, Christoph Treude

Research Collection School Of Computing and Information Systems

Automated tools are frequently used in social coding repositories to perform repetitive activities that are part of the distributed software development process. Recently, GitHub introduced GitHub Actions, a feature providing automated work-flows for repository maintainers. Although several Actions have been built and used by practitioners, relatively little has been done to evaluate them. Understanding and anticipating the effects of adopting such kind of technology is important for planning and management. Our research is the first to investigate how developers use Actions and how several activity indicators change after their adoption. Our results indicate that, although only a small subset of …

Tensor Low-Rank Representation For Data Recovery And Clustering, Pan Zhou, Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Multi-way or tensor data analysis has attracted increasing attention recently, with many important applications in practice. This article develops a tensor low-rank representation (TLRR) method, which is the first approach that can exactly recover the clean data of intrinsic low-rank structure and accurately cluster them as well, with provable performance guarantees. In particular, for tensor data with arbitrary sparse corruptions, TLRR can exactly recover the clean data under mild conditions; meanwhile TLRR can exactly verify their true origin tensor subspaces and hence cluster them accurately. TLRR objective function can be optimized via efficient convex programing with convergence guarantees. Besides, we …

Scope: Building And Testing An Integrated Manual-Automated Event Extraction Tool For Online Text-Based Media Sources, Matthew Crittenden

Undergraduate Honors Theses

Building on insights from two years of manually extracting events information from online news media, an interactive information extraction environment (IIEE) was developed. SCOPE, the Scientific Collection of Open-source Policy Evidence, is a Python Django-based tool divided across specialized modules for extracting structured events data from unstructured text. These modules are grouped into a flexible framework which enables the user to tailor the tool to meet their needs. Following principles of user-oriented learning for information extraction (IE), SCOPE offers an alternative approach to developing AI-assisted IE systems. In this piece, we detail the ongoing development of the SCOPE tool, present …

Data-Driven Recommendation Of Academic Options Based On Personality Traits, Aashish Ghimire

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The choice of academic major and, subsequently, an academic institution has a massive effect on a person’s career. It not only determines their career path but their earning potential, professional happiness, etc. [1] About 40% of people who are admitted to a college do not graduate within six years. Yet, very limited resources are available for students to help make those decisions, and each guidance counselor is responsible for roughly 400 to 900 students across the United States. A tool to help these decisions would benefit students, parents, and guidance counselors.

Various research studies have shown that personality traits affect …

Achieving Differential Privacy And Fairness In Machine Learning, Depeng Xu

Graduate Theses and Dissertations

Machine learning algorithms are used to make decisions in various applications, such as recruiting, lending and policing. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on matters like privacy and fairness. Currently, many studies only focus on protecting individual privacy or ensuring fairness of algorithms separately without taking consideration of their connection. However, there are new challenges arising in privacy preserving and fairness-aware machine learning. On one hand, there is fairness within the private model, i.e., how to meet both privacy and fairness requirements simultaneously in …

Network-Based Detection And Prevention System Against Dns-Based Attacks, Yasir Faraj Mohammed

Graduate Theses and Dissertations

Individuals and organizations rely on the Internet as an essential environment for personal or business transactions. However, individuals and organizations have been primary targets for attacks that steal sensitive data. Adversaries can use different approaches to hide their activities inside the compromised network and communicate covertly between the malicious servers and the victims. The domain name system (DNS) protocol is one of these approaches that adversaries use to transfer stolen data outside the organization's network using various forms of DNS tunneling attacks. The main reason for targeting the DNS protocol is because DNS is available in almost every network, ignored, …

Characteristic Reassignment For Hardware Trojan Detection, Noah Waller

Graduate Theses and Dissertations

With the current business model and increasing complexity of hardware designs, third-party Intellectual Properties (IPs) are prevalently incorporated into first-party designs. However, the use of third-party IPs increases security concerns related to hardware Trojans inserted by attackers. A core threat posed by Hardware Trojans is the difficulty in detecting such malicious insertions/alternations in order to prevent the damage. This thesis work provides major improvements on a soft IP analysis methodology and tool known as the Structural Checking tool, which analyzes Register-Transfer Level (RTL) soft IPs for determining their functionalities and screening for hardware Trojans. This is done by breaking down …