Digital Commons Network^™

Demystifying The "Social Media Algorithm": The Legacy Of Surveillance Advertising And Platformization, Garrett Crites

Honors Projects

Recently, more individuals are becoming aware that they are being served content on social media platforms by automated means. Due to the lack of transparency, a colloquial understanding of the “social media algorithm” has emerged in popular discourse. To shed light on the real–world phenomena that these ideas surround, I look at the rise of surveillance advertising and the platformization of the internet in conjunction with the automated platform operations employed by large social media platforms like Facebook, YouTube, TikTok, and X. In doing so I provide a clearer idea of the colloquial “social media algorithm” to encourage the reader …

Machines Of The Absurd: Leveraging Generative Ai For Creativity, Humor, And Playfulness, Tyler Sanders

College of Computing and Digital Media Dissertations

Machines of The Absurd is a collection of four projects exploring how generative AI can be leveraged for creativity, humor and playfulness.

1. neverOS — A node-based visual playground for interacting with large language models.

2. Other Calc — An iOS app with a calculator interface, where players can “calculate” text instead of numbers.

3. What Must Burn — An experiment where players type in text that can be dragged into a campfire to produce contextually appropriate sound effects.

4. Jazz vs Waffles — A turn-based comedy game, where players battle anything they type in.

Together, these projects make the …

Experimental Methods In Predicting Market Drift And Other Portfolio Optimization Factors Using Graph Theory, Perry Harrison Zhang

Computer Science Senior Theses

No abstract provided.

Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco

Theses and Dissertations

Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …

The Characteristics Of Digital Transformation Leadership: Theorizing The Practitioner Voice, Pat Mccarthy, David Sammon, Ibrahim Alhassan

Department of Computer Science Publications

Digital Transformation (DT) is more than simply integrating a new digital technology into the organization. Despite a growing volume of research, however, there is little coverage of the characteristics of DT leadership. Using a grounded approach, where 16 practitioner voices are central to the theorizing output, we present 10 DT leadership characteristics. Each characteristic links what action a DT leader needs to take and how a DT leader enables that action. We also asked 30 DT leaders to evaluate the importance of each of the 10 DT leadership characteristics. Our approach strengthens the relevance for practitioners striving for the best …

Navigating The Ethical Terrain Of Ai In Higher Education: Strategies For Mitigating Bias And Promoting Fairness, Emily Barnes, James Hutson

Faculty Scholarship

Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their …

Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson

Faculty Scholarship

The AI8-Point Model, derived from extensive experience in technology, AI, and higher education administration, addresses the critical need for cost-effective, high-impact strategies tailored to higher education. Despite the transformative potential of AI in enhancing student engagement, optimizing processes, and improving educational outcomes, institutions often struggle with practical implementation. The AI8-Point Model fills this gap by offering strategies that balance cost and impact. Visualized as a circle divided into four quadrants, the model encompasses phases of student engagement and institutional interaction: pre-enrollment beyond institutional control, pre-enrollment within institutional control, post-enrollment within institutional control, and post-enrollment beyond institutional control. Each quadrant contains …

Enhancing Government Service Delivery: A Case Study Of Acqar Implementation And Lessons Learned From Chatgpt Integration In A Singapore Government Agency, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection Lee Kong Chian School Of Business

This paper presents the pilot implementation of AI Based Citizen Question-Answer Recommender (ACQAR) as an attempt to enhance citizen service delivery within a Singaporean government agency. Drawing insights from previous studies on the Empath library's use in Service Level Agreement (SLA) prediction and the implementation of the Citizen Question-Answer system (CQAS), we redesigned the pilot system, ACQAR. ACQAR integrates the outputs from Empath X SLA predictor and CQAS as essential inputs to the ChatGPT engine, creating contextually aware responses for customer service officers to use as responses to the citizens.Empath X SLA predictor anticipates the expected service response time based …

Virtual Field Environments Capstone Software Review, Ashton Sawyer

University Honors Theses

This is a review of the Virtual Field Environments computer science capstone project, sponsored by geology professor Rick Hugo. The tool aims to create and render VFEs, interactable 360° environments hosted on the web that are used as virtual field trips for K-12 students. This essay discusses the development process, including understanding requirements, tool and technology selection, problem-solving, and decision-making strategies. It also highlights the differences between the capstone and the other core computer science courses, and how those differences help to prepare students for the workforce. The project was completed over the course of twenty weeks by a team …

Securing The Inbox: Advancing Phishing Email Detection With Fine-Tuned Bert, Fatima Rashed Al Saedi

Theses

In recent years, phishing attacks have persisted as a widespread threat in the contemporary digital environment, presenting substantial risks to individuals and organizations. Cybercriminals are devising increasingly sophisticated strategies to deceive users through malicious emails. In response to this challenge, this research focuses on developing a new tool for detecting phishing emails utilizing the BERT algorithm. The tool aims to enhance email security by accurately identifying deceptive emails and protecting users from potential cyber threats. The primary objective of this study is to investigate how leveraging the BERT algorithm can improve the detection of phishing emails compared to traditional methods. …

Smart Hpa: A Resource-Efficient Horizontal Pod Auto-Scaler For Microservice Architectures, Hussain Ahmad, Christoph Treude, Markus Wagner, Claudia Szabo

Research Collection School Of Computing and Information Systems

Microservice architectures have gained prominence in both academia and industry, offering enhanced agility, reusability, and scalability. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust the resources of microservices to accommodate fluctuating workloads. However, existing HPAs are not suitable for resourceconstrained environments, as they make scaling decisions based on the individual resource capacities of microservices, leading to service unavailability and performance degradation. Furthermore, HPA architectures exhibit several issues, including inefficient data processing and a lack of coordinated scaling operations. To address these concerns, we propose Smart HPA, a …

Enhancing Robustness Of Machine Learning Models Against Adversarial Attacks, Ronak Guliani

University Honors Theses

Machine learning models are integral for numerous applications, but they remain increasingly vulnerable to adversarial attacks. These attacks involve subtle manipulation of input data to deceive models, presenting a critical threat to their dependability and security. This thesis addresses the need for strengthening these models against such adversarial attacks. Prior research has primarily focused on identifying specific types of adversarial attacks on a limited range of ML algorithms. However, there is a gap in the evaluation of model resilience across algorithms and in the development of effective defense mechanisms. To bridge this gap, this work adopts a two-phase approach. First, …

The Cascading Effects Of Database Design, Liam Mccracken

University Honors Theses

This paper details how a relational database influenced the design of the rest of a software project. The software project in question is WonderTix, an open-source ticketing and donation platform for the use of Portland Playhouse under continuous development by teams of Portland State students as their computer science capstone project. The paper, a capstone review thesis, examines WonderTix as an instantiation of the Model-View-Controller design pattern, noting how the model, a relational database based on the SQL standard, influenced the design of the view and controller components. This influence is explored from three angles. First, when the implementation of …

Wipots: An Application Layer Protocol With Network Protocol Stack Enhancements For Wireless Power Transfer Networks, Muhammad Omer Farooq

Department of Computer Science Publications

Nowadays, a far-field wireless power transfer (WPT) system aims to deliver wireless power over a distance of a few meters. Communication among devices for the purpose of WPT in the far-field WPT system is unique as its purpose is to establish, maintain and monitor a WPT session among devices in the system. For proper functionality of a WPT system, a number of communication-, control- and management-related challenges need to be addressed. Hence, here an application layer protocol specifically designed for a WPT system is presented. The protocol provides essential control, management and communication functionalities to establish, maintain and monitor a …

Semantic Structuring Of Digital Documents: Knowledge Graph Generation And Evaluation, Erik E. Luu

Master's Theses

In the era of total digitization of documents, navigating vast and heterogeneous data landscapes presents significant challenges for effective information retrieval, both for humans and digital agents. Traditional methods of knowledge organization often struggle to keep pace with evolving user demands, resulting in suboptimal outcomes such as information overload and disorganized data. This thesis presents a case study on a pipeline that leverages principles from cognitive science, graph theory, and semantic computing to generate semantically organized knowledge graphs. By evaluating a combination of different models, methodologies, and algorithms, the pipeline aims to enhance the organization and retrieval of digital documents. …

Morp: Monocular Orientation Regression Pipeline, Jacob Gunderson

Master's Theses

Orientation estimation of objects plays a pivotal role in robotics, self-driving cars, and augmented reality. Beyond mere position, accurately determining the orientation of objects is essential for constructing precise models of the physical world. While 2D object detection has made significant strides, the field of orientation estimation still faces several challenges. Our research addresses these hurdles by proposing an efficient pipeline which facilitates rapid creation of labeled training data and enables direct regression of object orientation from a single image. We start by creating a digital twin of a physical object using an iPhone, followed by generating synthetic images using …

Balancing Darkness And Visibility: An Algorithmic Approach To Light Placement In Low-Light, Ray-Traced Scenes, Briana Kuo

Master's Theses

In recent years, digital media has seen incredible advancements in rendering visually stunning computer graphics scenes. Photo-realistic games, animated films, and more leave viewers blown away by the sheer beauty of their graphics. However, challenges arise when depicting dark scenes, often resulting in visual monotony and difficulty in comprehension due to insufficient detail within the scene. In order to enhance readability and visual interest of a scene, additional, artificial lights can be placed throughout a scene to enhance the aesthetic. These lights, however, must be strategically placed in order to retain an essence of darkness and maintain the delicate balance …

Does Generative Ai Facilitate Investor Trading? Evidence From Chatgpt Outages, Qiang Cheng, Pengkai Lin, Yue Zhao

Research Collection School Of Accountancy

In this paper, we use ChatGPT outages to investigate whether investors rely on generative artificial intelligence (GAI) to perform trading-related tasks and the associated impact on stock price informativeness. We first document a significant decline in stock trading volume during ChatGPT outages and find that the effect is stronger for firms with corporate news released immediately before or during the outages. We further document similar declines in the short-run price impact, return variance, and bid-ask spreads, consistent with a reduction in informed trading during the outage periods. Lastly, we use trading volume changes during outages to construct a firm-level measure …

The Efficacy Of Using Machine Learning Techniques For Identifying And Classifying “Fake News”, Muhammad Islam

Dissertations, Theses, and Capstone Projects

In today's digital world, detecting fake news has emerged as a critical challenge, one that has significant effects on democracy and public discourse at large both regionally and globally. This research studies how diversity of news sources in training datasets affects how well machine learning models can classify fake vs true news. I used the Linear Support Vector Classification (LinearSVC) to create and compare two classification models: one was trained on a dataset that only had real news from a singular source, Reuters (Dataset 1), and the other was trained on a dataset that contained real news from Reuters, The …

Combinatorial Creativity: Knowledge Graphs And Idea Generation In Crowdsourcing Innovation, Zhi Wei Vincent Mack

Dissertations and Theses Collection (Open Access)

This dissertation explores the dynamic interplay between combinatorial creativity and technology-driven innovation within various knowledge-intensive fields. It critically examines the role of combinatorial creativity in generating groundbreaking innovations by amalgamating existing ideas and technologies. This research incorporates a detailed examination of how knowledge, whether tacit or explicit, can be transformed into actionable data to foster innovation in crowdsourcing contexts. Chapter 2 provides an overview of the relevant literature on how Artificial Intelligence and Knowledge Management Systems can support combinatorial creativity. The study further delves into the transformative impact of knowledge management systems, particularly focusing on crowdsourcing platforms that leverage collective …

Community Discovery Over Attributed Graphs, Yudong Niu

Dissertations and Theses Collection (Open Access)

Community discovery, as a fundamental problem in graph mining, finds applications in various domains such as biological analysis, system optimization and fraud detection. Although many efforts have been made to address community discovery based on graph topology, few works have been devoted to community discovery over attributed graphs, where graphs are equipped with attribute information such as node and edge types. Thus, this thesis is devoted to designing innovative solutions that can utilize the attribute information together with graph topology for community discovery. In particular, we study novel problems with efficient algorithms for both homogeneous and heterogeneous attributed graphs and …

Assessing Job Vulnerability And Employment Growth In The Era Of Large Language Models (Llms), Prudence P. Brou

Dissertations, Theses, and Capstone Projects

This paper explores the impact of Large Language Models (LLMs) and artificial intelligence (AI) on white-collar occupations in the context of job vulnerability and employment growth. Utilizing the Kaggle dataset "Occupation Salary and Likelihood of Automation," the study employs a data-driven approach to analyze trends across states. Through interactive data visualization, the project aims to provide actionable insights for affected workers, businesses, and policymakers navigating the changing dynamics of the workforce amidst technological advancements.

Inceptionnext: When Inception Meets Convnext, Weihao Yu, Pan Zhou, Shuicheng Yan, Xinchao Wang

Research Collection School Of Computing and Information Systems

Inspired by the long-range modeling ability of ViTs, large-kernel convolutions are widely studied and adopted recently to enlarge the receptive field and improve model performance, like the remarkable work ConvNeXt which employs 7×7 depthwise convolution. Although such depthwise operator only consumes a few FLOPs, it largely harms the model efficiency on powerful computing devices due to the high memory access costs. For example, ConvNeXtT has similar FLOPs with ResNet-50 but only achieves ∼ 60% throughputs when trained on A100 GPUs with full precision. Although reducing the kernel size of ConvNeXt can improve speed, it results in significant performance degradation, which …

Gts: Gpu-Based Tree Index For Fast Similarity Search, Yifan Zhu, Ruiyao Ma, Baihua Zheng, Xiangyu Ke, Lu Chen, Yunjun Gao

Research Collection School Of Computing and Information Systems

Similarity search, the task of identifying objects most similar to a given query object under a specific metric, has gathered significant attention due to its practical applications. However, the absence of coordinate information to accelerate similarity search and the high computational cost of measuring object similarity hinder the efficiency of existing CPU-based methods. Additionally, these methods struggle to meet the demand for high throughput data management. To address these challenges, we propose GTS, a GPU-based tree index designed for the parallel processing of similarity search in general metric spaces, where only the distance metric for measuring object similarity is known. …

Securing Tomorrow: Synergizing Change Management And Cybersecurity In The Digital Era, Sharon L. Burton

Publications

In the rapidly evolving business environment of 2024, organizational change management (OCM) leaders face unprecedented challenges driven by technological advancements, digital transformation, the integration of remote work, and a heightened focus on sustainability. This study examines the efficacy of traditional OCM models in addressing these modern complexities. Through a qualitative methodology employing an extensive literature review, the research identifies vital issues such as resistance to change, digital transformation imperatives, the shift to remote and hybrid work models, and the imperative for sustainable and ethical business practices. The study posits that while classical OCM frameworks offer foundational insights, there is a …

Synthesis Of Dyes Sulfamidazole: Characterization, Evaluation, Molecular Docking And Global Descriptors By Density Functional Theory (Dft)., Athra G. Sager, Jawad Kadhim Abaies, Zeena R. Katoof

Karbala International Journal of Modern Science

In the present work, novel azo compounds of sulfamidazole were created via the reaction of diazonium salt of sulfamidazole with several aromatic molecules including (resorcinol, 2-nitro phenol, 3-nitro phenol, and 4-nitro phenol)) (Z1–Z4). The new compounds (Z1-Z4) were identified using FTIR, ¹HNMR techniques, in addition to melting point measurements. The biological activity of compounds (Z1-Z4) was studied against four kinds of bacteria including E. coli, Klebsiella pneumonia, Salmonella, and Staphylococcus aureus. The findings showed that all compounds (Z1-Z4) were active against the examined bacteria. Theoretical studies of the antibacterial ability of the prepared compound against DNA gyrase enzyme …

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 …

A Machine Learning-Assisted Steering And Scheduling Framework For Big-Data Scientific Workflows On Heterogeneous Computing Platforms, Yijie Zhang

Dissertations

In next-generation scientific applications, the exponential growth of big data necessitates advanced techniques for efficient data storage, processing, and analysis. This has led to the construction of intricate computing workflows, managed and orchestrated by powerful engines in big data systems as exemplified by Hadoop. As scientific applications increasingly shift towards simulation-centric approaches, traditional methodologies face new challenges in accommodating the complexity of extreme-scale numerical modeling with numerous tunable parameters. To address these challenges, this dissertation propose to develop a machine learning-assisted framework that enables autonomous computational steering of scientific simulations and optimized execution of big-data workflows on heterogeneous platforms. This …

Charting A Path To The Quintuple Aim: Harnessing Ai To Address Social Determinants Of Health, Yash Shah, Zachary Goldberg, Erika Harness, David Nash

College of Population Health Faculty Papers

The Quintuple Aim seeks to improve healthcare by addressing social determinants of health (SDOHs), which are responsible for 70-80% of medical outcomes. SDOH-related concerns have traditionally been addressed through referrals to social workers and community-based organizations (CBOs), but these pathways have had limited success in connecting patients with resources. Given that health inequity is expected to cost the United States nearly USD 300 billion by 2050, new artificial intelligence (AI) technology may aid providers in addressing SDOH. In this commentary, we present our experience with using ChatGPT to obtain SDOH management recommendations for archetypal patients in Philadelphia, PA. ChatGPT identified …

Nonlinear Classifiers For Wet-Neuromorphic Computing Using Gene Regulatory Neural Network, Adrian Ratwatte, Samitha Somathilaka, Sasitharan Balasubramaniam, Assaf A. Gilad

School of Computing: Faculty Publications

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the …