Business Commons^™

Financial Time Series Fusion, Completion, And Prediction With Deep Neural Networks, Dan Zhou

Dissertations

Time-series analysis is essential for a wide range of financial applications, including but not limited to bond valuation, firm earnings forecasts, firm fundamentals predictions, and firm characteristics imputations. Given its considerable value, the financial community has shown a strong interest in refining and advancing time-series analysis techniques. The study in this dissertation contributes to this field by employing advanced machine learning approaches, specifically graph neural networks, deep neural networks, and matrix/tensor methods. The primary objectives are twofold: first, to reveal complex correlations within financial time series to improve prediction accuracy, and second, to enhance the process of integrating and imputing …

Sensing With Integrity: Responsible Sensor Systems In An Era Of Ai, David Eisenberg

Dissertations

Deep and machine learning now offer immense benefits for consumer choice, decision-making, medicine, mental health and education, smart cities, and intelligent transportation and driver safety. However, as communication and Internet technology further advances, these benefits have the potential to be outweighed by compromises to privacy, personal freedom, consumer trust, and discrimination. While ethical consequences for personal freedom and equity rise from these technological advances, the issue may not be the technology itself but a lack of regulation and policy that allow abuses to occur. A first study examines how emerging sensor-based technologies, limited to only accelerometer and gyroscope data from …

Neural Operator For Accelerating Coronal Magnetic Field Computations In Bifrost Mhd Model, Yutao Du

Theses

The application of the Tensorized Fourier Neural Operator (TFNO) to significantly enhance the computational efficiency of coronal magnetic field calculations within the Bifrost Magnetohydrodynamics (MHD) model is introduced in this study. Leveraging simulated data from the European Sunrise Science Data Center, the TFNO? an extension of the Fourier Neural Operator (FNO) that incorporates tensor decomposition for improved handling of high-dimensional data?is employed to solve time-varying partial differential equations (PDEs) over a 3D domain. The performance of the TFNO is compared with traditional machine learning methods, including Vision Transformer and CNN-RNN (encoder-decoder) architectures, to demonstrate its accuracy, computational efficiency, and scalability. …

Twitter Sentiment Analysis: Applications In Healthcare And Finance, Jiali Wang

Dissertations

This research explores the influence of Twitter sentiment on healthcare and finance industries. It assesses how Twitter sentiment and culture measure influence COVID-19 statistics, and it investigates the impact of Twitter sentiment on S&P 1500 stock mispricing. Furthermore, it examines how tweet sentiment predicts major industry returns.

The first part examines how Hofstede’s Culture Dimensions (HCD) and Twitter economic uncertainty index (TEU) relate to COVID-19 infection rate and death rate. The results show certain aspects in HCD, such as power distance index (PDI) and masculinity (MAS) both are negatively and significantly associated with the infection rate, while indulgence (IVR) and …

Optimizing Incentives For Systems With Heterogeneous Agents, Chen Chen

Dissertations

This dissertation explores new models and applications based on the game theory of incentives. This exploration starts with controlling an invasive insect problem to address one of the most significant challenges facing our forests, the invasion of the Emerald ash borer (EAB), a non-native, wood-boring insect that threatens to kill most ash trees in North America, through designing two new cost-sharing programs between the landowners and local governments. Ash trees are one of North America’s most widely distributed tree genera and a vital part of the green infrastructure of cities, where they provide residents with numerous social, economic, and ecological …

Entrepreneurship And Heterogeneity Among Firms' Strategies: Three Essays, Xi Zhang

Dissertations

The first essay of this dissertation focuses on the entrepreneurship survival in the early stage, during which time an entrepreneur plays the game at the "edge of chaos" and improvises in real-time to learn the strategic playing field. It examines the social networks of entrepreneurs and the impact on new venture survival. Specifically, it explores how the entrepreneurs' social connections with other entrepreneurs and their types of employment differentially affect survival during the different stages of the entrepreneurial journey in the United States and India. Using the Global Entrepreneurship Monitor (GEM) dataset, this study documents not only how the social …

Representation Learning In Finance, Ajim Uddin

Dissertations

Finance studies often employ heterogeneous datasets from different sources with different structures and frequencies. Some data are noisy, sparse, and unbalanced with missing values; some are unstructured, containing text or networks. Traditional techniques often struggle to combine and effectively extract information from these datasets. This work explores representation learning as a proven machine learning technique in learning informative embedding from complex, noisy, and dynamic financial data. This dissertation proposes novel factorization algorithms and network modeling techniques to learn the local and global representation of data in two specific financial applications: analysts’ earnings forecasts and asset pricing.

Financial analysts’ earnings forecast …

Graph Enabled Cross-Domain Knowledge Transfer, Shibo Yao

Dissertations

The world has never been more connected, led by the information technology revolution in the past decades that has fundamentally changed the way people interact with each other using social networks. Consequently, enormous human activity data are collected from the business world and machine learning techniques are widely adopted to aid our decision processes. Despite of the success of machine learning in various application scenarios, there are still many questions that need to be well answered, such as optimizing machine learning outcomes when desired knowledge cannot be extracted from the available data. This naturally drives us to ponder if one …

Factors That Affect The Rate Of Preterm Birth: An Examination Of The Inter-Related Impacts Of Social Determinants, Behavior And Physical Health Status, Krystal Michelle Hunter

Dissertations

The work seeks to examine the factors that have significant relationships to the rate of preterm birth (PTB) along with its cost to society.

There are four papers within this work. This purpose of the first paper is to measure the impact that healthy behaviors have on the rate of PTB when modeled with other factors like household demographics, community deprivation, chronic disease and mental health. This work finds that positive health behaviors has a negative relationship with PTB. The interaction between body mass index (BMI) and social class and its relationship to PTB is examined in the second paper. …

Statistics-Based Anomaly Detection And Correction Method For Amazon Customer Reviews, Ishani Chatterjee

Dissertations

People nowadays use the Internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source of gathering information for data analytics, sentiment analysis, natural language processing, etc. The most critical challenge is interpreting this data and capturing the sentiment behind these expressions. Sentiment analysis is analyzing, processing, concluding, and inferencing subjective texts with the views. Companies use sentiment analysis to understand public opinions, perform market research, analyze brand reputation, recognize customer experiences, and study social media influence. According to the different needs for aspect granularity, …

Exploring, Understanding, Then Designing: Twitter Users’ Sharing Behavior For Minor Safety Incidents, Mashael Yousef Almoqbel

Dissertations

Social media has become an integral part of human lives. Social media users resort to these platforms for various reasons. Users of these platforms spend a lot of time creating, reading, and sharing content, therefore, providing a wealth of available information for everyone to use. The research community has taken advantage of this and produced many publications that allow us to better understand human behavior. An important subject that is sometimes discussed and shared on social media is public safety. In the past, Twitter users have used the platform to share incidents, share information about incidents, victims and perpetrators, and …

Multi-Stage Stochastic Optimization And Reinforcement Learning For Forestry Epidemic And Covid-19 Control Planning, Sabah Bushaj

Dissertations

This dissertation focuses on developing new modeling and solution approaches based on multi-stage stochastic programming and reinforcement learning for tackling biological invasions in forests and human populations. Emerald Ash Borer (EAB) is the nemesis of ash trees. This research introduces a multi-stage stochastic mixed-integer programming model to assist forest agencies in managing emerald ash borer insects throughout the U.S. and maximize the public benets of preserving healthy ash trees. This work is then extended to present the first risk-averse multi-stage stochastic mixed-integer program in the invasive species management literature to account for extreme events. Significant computational achievements are obtained using …

Participatory Learning: Measuring Learning And Educational Technology Acceptance, Erick Sanchez Suasnabar

Dissertations

Participatory Learning (PL) integrates several learning approaches, engaging students throughout the entire assignment process for both online and face-to-face courses. Beyond simply providing a solution, students also craft a problem (problem-based learning), grade each other (peer assessment and feedback), evaluate themselves (self-assessment), and can view others’ work (learning by example). This dissertation research explores the resulting learning effects. Contributions to both educational and Information Systems research include extending an early PL model and experiments that applied the PL approach to examinations, by validating and testing new constructs based on user activity and critical thinking. In addition, the study explores a …

Essays On Health Care Quality: Timeliness, Equity, And Efficiency, Abubakar-Sadiq Bouda Abdulai

Dissertations

According to the National Academy of Medicine (NAM) (formerly called the Institute of Medicine), a quality health care system embodies six attributes: timeliness, equity, safety, efficiency, effectiveness, and patient-centeredness. Timeliness is to avoid unnecessary delays in care delivery for patients and caregivers; equity is to ensure that the quality of care that patients receive does not vary based on their personal characteristics; safety is to ensure that the care that is intended to help patients does not harm them; efficiency is to avoid waste and optimize resource allocation to improve care delivery; effective care is one that relies on sound …

Reserve Price Optimization In Display Advertising, Achir Kalra

Dissertations

Display advertising is the main type of online advertising, and it comes in the form of banner ads and rich media on publishers' websites. Publishers sell ad impressions, where an impression is one display of an ad in a web page. A common way to sell ad impressions is through real-time bidding (RTB). In 2019, advertisers in the United States spent nearly 60 billion U.S. dollars on programmatic digital display advertising. By 2022, expenditures are expected to increase to nearly 95 billion U.S. dollars. In general, the remaining impressions are sold directly by the publishers. The only way for publishers …

Constructive Solution Methodologies To The Capacitated Newsvendor Problem And Surrogate Extension, Pinyuan Shan

Dissertations

The newsvendor problem is a single-period stochastic model used to determine the order quantity of perishable product that maximizes/minimizes the profit/cost of the vendor under uncertain demand. The goal is to fmd an initial order quantity that can offset the impact of backlog or shortage caused by mismatch between the procurement amount and uncertain demand. If there are multiple products and substitution between them is feasible, overstocking and understocking can be further reduced and hence, the vendor's overall profit is improved compared to the standard problem. When there are one or more resource constraints, such as budget, volume or weight, …

High-Speed Rail Safety Analysis Based On Dual-Weighted Complex Network, Liu Lv

Dissertations

This study uses a complex network model to analyze the causes of accidents in high-speed railway operations. By identifying the key factors that led to high-speed railway accidents, hidden safety hazards were discovered. This will help improve the operational safety of the U.S. high-speed rail line under construction.

The analysis uses the regional high-speed railway network in Guangzhou, China as a case study, including the railway (including high-speed railway) accidents that occurred in the company's jurisdiction from 2013 to 2017. With comparative analysis between general railways and high-speed railways, the changes of high-speed railway safety factors are explored. Data analysis …

Online Fulfillment: F-Warehouse Order Consolidation And Bops Store Picking Problems, Wen Zhu

Dissertations

Fulfillment of online retail orders is a critical challenge for retailers since the legacy infrastructure and control methods are ill suited for online retail. The primary performance goal of online fulfillment is speed or fast fulfillment, requiring received orders to be shipped or ready for pickup within a few hours. Several novel numerical problems characterize fast fulfillment operations and this research solves two such problems. Order fulfillment warehouses (F-Warehouses) are a critical component of the physical internet behind online retail supply chains. Two key distinguishing features of an F-Warehouse are (i) Explosive Storage Policy – A unique item can be …

A Comprehensive And Absolute Corporate Sustainability Assessment And Enhanced Input Output Life Cycle Assessment, Joseph M. Wright

Dissertations

Stresses due to economic activity are threatening to exceed environmental and societal limits with the potential to jeopardize local communities and create global crises. This research proposes new methodologies and analytic techniques to comprehensively assess corporate sustainability and enhance the efficiency of estimating environmental and social impacts with Input Output Life Cycle Assessment (IOLCA).

Sustainability assessments and management require consideration of both social and environmental impacts as outflows of economic activity. There are a number of assessment tools available to gain insight into environmental and social impacts; but in most cases, these approaches lack essential components for a comprehensive and …

Global Optimization Algorithms For Image Registration And Clustering, Cuicui Zheng

Dissertations

Global optimization is a classical problem of finding the minimum or maximum value of an objective function. It has applications in many areas, such as biological image analysis, chemistry, mechanical engineering, financial analysis, deep learning and image processing. For practical applications, it is important to understand the efficiency of global optimization algorithms. This dissertation develops and analyzes some new global optimization algorithms and applies them to practical problems, mainly for image registration and data clustering.

First, the dissertation presents a new global optimization algorithm which approximates the optimum using only function values. The basic idea is to use the points …

Changing The Focus: Worker-Centric Optimization In Human-In-The-Loop Computations, Mohammadreza Esfandiari

Dissertations

A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back …

An Automated Feedback System To Support Student Learning Of Conceptual Knowledge In Writing-To-Learn Activities, Ye Xiong

Dissertations

As a pedagogical strategy, Writing-to-Learn (WTL) intends to use writing to improve students’ understanding of course content. However, most of the existing feedback systems for writing are mainly focused on improving students’ writing skills rather than their conceptual development. In this dissertation, an automatic approach is proposed to generate timely, actionable, and individualized feedback based on comparing knowledge representations extracted from lecture slides and individual students’ writing assignments. The novelty of the proposed approach lies in the feedback generation: to help students assimilate new knowledge into their existing knowledge better, their current knowledge is modeled as a set of matching …

Mind Maps And Machine Learning: An Automation Framework For Qualitative Research In Entrepreneurship Education, Yasser Farha

Dissertations

Entrepreneurship Education researchers often measure entrepreneurial motivation of college students. It is important for stakeholders, such as policymakers and educators, to assert if entrepreneurship education can encourage students to become entrepreneurs, as well as to understand factors that influence entrepreneurial motivation. For that purpose, researchers have used different methods and instruments to measure students' entrepreneurial motivation. Most of these methods are quantitative, e.g., closed-ended surveys, whereas qualitative methods, e.g., open-ended surveys, are rarely used.

Mind maps are an attractive qualitative survey tool because they capture the individual's reflections, thoughts, and experiences. For Entrepreneurship Education, mind maps can be utilized to …

Peer-To-Peer Consumption In 3d Printing Design, Weizhi Chen

Dissertations

Three-dimensional printing or additive manufacturing is a new element in new product development that emphasizes on digitalization and innovation. However, due to its new emergence, existing research has rarely explored its mechanism and benefits especially in marketing, new product development and innovation. This research addresses the mechanism of 3D printing under collaborative consumption in the age of personal fabrication. The primary focus of this research lies at the intersection of marketing, 3D printing in collaborative consumption, and data science. Online peer-to-peer 3D printing sharing platform myminifactory.com is utilized as primary study context. In this research, two types of product design …

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Applied Deep Learning In Intelligent Transportation Systems And Embedding Exploration, Xiaoyuan Liang

Dissertations

Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization …

Assignment Of E-Commerce Orders To Fulfillment Warehouses, Ahmad Basem Zamka

Theses

For large e-commerce companies such as Amazon, when an order comes, this order might be available at more than one fulfillment centers. Therefore, the question of which fulfillment center this order should be fulfilled from would arise.

In a typical situation, customer demand is fulfilled from the closest fulfillment center. However, this approach does not always provide the optimal solution since there are so many factors that could be involved in making such a decision. These factors might include inventory balance, product correlations, and future demand.

Our decision model focuses on putting future orders in consideration while assigning orders to …

Virtual Smarts - Optimizing The Coalescing Of People For Collective Action Within Urban Communities, Stephen Thomas Ricken

Dissertations

Despite the importance of individuals coming together for social group-activities (e.g., pick-up volleyball), the process by which such groups coalesce is poorly understood, and as a consequence is poorly supported by technology. This is despite the emergence of Event-Based Social Network (EBSN) technologies that are specifically designed to assist group coalescing for social activities. Existing theories focus on group development in terms of norms and types, rather than the processes involved in initial group coalescence. This dissertation addresses this gap in the literature through four studies focusing on understanding the coalescing process for interest-based group activities within urban environments and …

Family Business: Innovation And Tradition In A Global Economy, Francesca Fornasari

Theses

Eighty-five percent of Italian companies are run as a family business. They are considered vital for Italian economy. The purpose of this thesis is to study how these companies challenge the global market to understand if the globalization can cause them disadvantages or benefits. The study explains what a family business is and who are the components that can be part of it. Then it focuses on the structure of the firms, how the families run their businesses and organize the tasks between the family members. This thesis considers the strategies of innovation adopted by the family to remain competitive …

Halal Certification For An Industrial Machine Intended To Come In Contact With Food, Luca Caffarelli

Theses

Halal Certification is a worldwide recognition that the products are permissible under Islamic law. These products are thus edible, drinkable or usable by Muslims. It can be extended to industrial machinery and tools. This Certification must be issued by a Notified Body under the supervision of an IMAM. Focus of the Certification are GMPs and Food Contact Materials and Articles. Four main phases to achieve Certification (the scheme is the same of ISO 14001, ISO 18001 and ISO 9001):

• Pre-Audit - The activity is to evaluate the documentation describing our internal system.
• Audit - The activity is to evaluate how …