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 …

Evaluating The Basement Design Of Low-Rise Building With Two-Stage Analysis Using Bim Integration: Hangar Study Case, Given Tohho, Jessica Sjah, Ayomi Dita Rarasati, Bambang Trigunarsyah

Smart City

Building Information Modelling (BIM) has revolutionized the way the construction industry designs, constructs, and manages buildings. Certainly, the utilization of BIM can optimize the usage of materials in a construction project, considering the high level of concrete consumption globally and its significant environmental impact. The implementation of BIM is intended to calculate the volume of concrete and steel material usage in the design process of low-rise buildings with basements, exemplified in this case by a 5-story laboratory hangar with a 1-story basement. The building design is carried out through a two-stage analysis, which involves separating the upper portion from 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 …

Shader-Based Real-Time Image Tracking For Mobile Augmented Reality, Andrew Wang Chen

Computer Science Senior Theses

Image target tracking is a technique widely used in a variety of augmented reality (AR) applications to trigger AR interaction and accurately locate virtual objects relative to physical space. This project is a Unity image tracking pipeline based on the ORB feature detection and description technique that seeks to be robust enough to track images despite partial occlusion, uneven lighting, and image target depth. This pipeline employs compute shader code to conduct image tracking computations on the GPU to track images in real-time for mobile AR apps.

Multi-Agent Youtube Content Discovery Bot, Ishmam Ahmed Solaiman

Theses and Dissertations

YouTube Content Discovery Bot (YTCDB) is a cutting-edge multi-agent system designed to revolutionize the video discovery process. Traditionally, researchers have faced the arduous task of manually sorting through YouTube videos to find relevant content. YTCDB leverages an analytics-driven approach to autonomously discover videos given a seed video. Each task or process within YTCDB, such as comment scraping, gathering statistics, and collecting channel data, can be efficiently handled by one or multiple agents working in tandem. This distributed approach allows for seamless coordination and delegation of tasks, ensuring optimal performance and scalability. Insights gathered from video barcoding and content analysis of …

An Alternative Approach To Data Carving Portable Document Format (Pdf) Files, Kevin Hughes, Michael Black

Journal of Cybersecurity Education, Research and Practice

Traditional data carving relies on the successful identification of headers and trailers, unique hexadecimal signatures which are exclusive to specific file types. This can present a challenge for digital forensics examiners when pitted against modern anti-forensics techniques. The interest of this study is file signature obfuscation, a technique which alters headers and trailers. This research will focus on the development of a new, proof-of-concept algorithm that analyzes content in segments based on unique elements found within the body of a file. The file type being targeted is the Portable Document Format (PDF) and this research is built upon previously successful …

Ranking Cloud Service Providers Using Swara-Marcos In Type-2 Neutrosophic Number Set Environment, Mai Mohamed, Shaimaa Ayman, Rui Yong, Jun Ye

Neutrosophic Systems with Applications

Cloud computing is a model for allowing suitable, on-demand network access to a shared store of resources such as servers, networks, storage, apps, and services, modified according to specific needs or requirements. The main goal of cloud technology development is to increase the use of resources that work together to achieve reliability at the lowest cost. Cloud service providers (CSPs) have gained popularity in recent years due to their accessibility and availability, as well as the growing quantity of cloud service providers (CSPs) that appear. Choosing (CSPs) has grown to be a challenging decision for many companies. The paper aims …

Ranking Cloud Service Providers Using Swara-Marcos In Type-2 Neutrosophic Number Set Environment, Mai Mohamed, Shaimaa Ayman, Rui Yong, Jun Ye

Neutrosophic Systems with Applications

Cloud computing is a model for allowing suitable, on-demand network access to a shared store of resources such as servers, networks, storage, apps, and services, modified according to specific needs or requirements. The main goal of cloud technology development is to increase the use of resources that work together to achieve reliability at the lowest cost. Cloud service providers (CSPs) have gained popularity in recent years due to their accessibility and availability, as well as the growing quantity of cloud service providers (CSPs) that appear. Choosing (CSPs) has grown to be a challenging decision for many companies. The paper aims …

Decentralized Optimization Over Slowly Time-Varying Graphs: Algorithms And Lower Bounds, Dmitry Metelev, Aleksandr Beznosikov, Alexander Rogozin, Alexander Gasnikov, Anton Proskurnikov

Machine Learning Faculty Publications

We consider a decentralized convex unconstrained optimization problem, where the cost function can be decomposed into a sum of strongly convex and smooth functions, associated with individual agents, interacting over a static or time-varying network. Our main concern is the convergence rate of first-order optimization algorithms as a function of the network’s graph, more specifically, of the condition numbers of gossip matrices. We are interested in the case when the network is time-varying but the rate of changes is restricted. We study two cases: randomly changing network satisfying Markov property and a network changing in a deterministic manner. For the …

How To Propagate Uncertainty Via Ai Algorithms, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Any data processing starts with measurement results. Measurement results are never absolutely accurate. Because of this measurement uncertainty, the results of processing measurement results are, in general, somewhat different from what we would have obtained if we knew the exact values of the measured quantities. To make a decision based on the result of data processing, we need to know how accurate is this result, i.e., we need to propagate the measurement uncertainty through the data processing algorithm. There are many techniques for uncertainty propagation. Usually, they involve applying the same data processing algorithm several times to appropriately modified data. …

For Discrete-Time Linear Dynamical Systems Under Interval Uncertainty, Predicting Two Moments Ahead Is Np-Hard, Luc Jaulin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In the first approximation, when changes are small, most real-world systems are described by linear dynamical equations. If we know the initial state of the system, and we know its dynamics, then we can, in principle, predict the system's state many moments ahead. In practice, however, we usually know both the initial state and the coefficients of the system's dynamics with some uncertainty. Frequently, we encounter interval uncertainty, when for each parameter, we only know its range, but we have no information about the probability of different values from this range. In such situations, we want to know the range …

How To Make Ai More Reliable, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the reasons why the results of the current AI methods (especially deep-learning-based methods) are not absolutely reliable is that, in contrast to more traditional data processing techniques which are based on solid mathematical and statistical foundations, modern AI techniques use a lot of semi-heuristic methods. These methods have been, in many cases, empirically successful, but the absence of solid justification makes us less certain that these methods will work in other cases as well. To make AI more reliable, it is therefore necessary to provide mathematical foundations for the current semi-heuristic techniques. In this paper, we show that …

What To Do If An Inflexible Tolerance Problem Has No Solutions: Probabilistic Justification Of Piegat's Semi-Heuristic Idea, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, it is desirable to select the control parameters x1, ..., xn in such a way that the resulting quantities y1, ..., ym of the system lie within desired ranges. In such situations, we usually know the general formulas describing the dependence of yi on xj, but the coefficients of these formulas are usually only known with interval uncertainty. In such a situation, we want to find the tuples for which all yi's are in the desired intervals for all possible tuples of coefficients. But what if no such parameters are possible? Since we cannot guarantee the …

Why Magenta Is Not A Real Color, And How It Is Related To Fuzzy Control And Quantum Computing, Victor L. Timchenko, Yuriy P. Kondratenko, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

It is well known that every color can be represented as a combination of three basic colors: red, green, and blue. In particular, we can get several colors by combining two of the basic colors. Interestingly, while a combination of two neighboring colors leads to a color that corresponds to a certain frequency, the combination of two non-neighboring colors -- red and blue -- leads to magenta, a color that does not correspond to any frequency. In this paper, we provide a simple explanation for this phenomenon, and we also show that a similar phenomenon happens in two other areas …

Why Fully Consistent Quantum Field Theories Require That The Space-Time Be At Least 10-Dimensional: A Commonsense Field-Based Explanation, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

It is known that quantum field theories that describe fields in our usual 4-dimensional space-times are not fully consistent: they predict meaningless infinite values for some physical quantities. There are some known tricks to avoid such infinities, but it is definitely desirable to have a fully consistent theory, a theory that would produce correct results without having to use additional tricks. It turns out that the only way to have such a theory is to consider space-times of higher dimensions, the smallest of which is 10. There are complex mathematical reasons for why 10 is the smallest such dimension. However, …

Towards A More Subtle (And Hopefully More Adequate) Fuzzy "And"-Operation: Normalization-Invariant Multi-Input Aggregation Operators, Yusuf Güven, Vladik Kreinovich

Departmental Technical Reports (CS)

Many reasonable conditions have been formulated for a fuzzy "and"-operation: idempotency, commutativity, associativity, etc. It is known that the only "and"-operation that satisfies all these conditions is minimum, but minimum is not the most adequate description of expert's "and", and it often does not lead to the best control or the best decision. Many other more adequate "and"-operations (t-norms) have been proposed and effectively used, but they do not satisfy the natural idempotency condition. In this paper, we show that a small relaxation of the usual description of "and"-operations leads to the possibility of non-minimum idempotent operations. We also show …

Why Empirical Membership Functions Are Well-Approximated By Piecewise Quadratic Functions: Theoretical Explanation For Empirical Formulas Of Novak's Fuzzy Natural Logic, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Empirical analysis shows that membership functions describing expert opinions have a shape that is well described by a smooth combination of two quadratic segments. In this paper, we provide a theoretical explanation for this empirical phenomenon.

Why Is Grade Distribution Often Bimodal? Why Individualized Teaching Adds Two Sigmas To The Average Grade? And How Are These Facts Related?, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

To make education more effective, to better use emerging technologies in education, we need to better understand the education process, to gain insights on this process. How can we check whether a new idea is indeed a useful insight? A natural criterion is that the new idea should explain some previously-difficult-to-explain empirical phenomenon. Since one of the main advantages of emerging educational technologies -- such as AI -- is the possibility of individualized education, a natural phenomenon to explain is the fact -- discovered by Benjamin Bloom -- that individualization adds two sigmas to the average grade. In this paper, …

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 …

Perceptions And Aspirations Of Undergraduate Computer Science Students Towards Generative Ai: A Qualitative Inquiry, James Hutson, Theresa Jeevanjee

Faculty Scholarship

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, …

Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly …

Confronting Algorithms: Conscience Catching In The Criminal Trial And Beyond, Sherman J. Clark

University of Michigan Journal of Law Reform

Using the question of how to treat algorithmic evidence under the Confrontation Clause as an entry point, I argue that the use of AI in ethically salient situations presents a risk. It may cause us to avoid confronting our own responsibility. This matters because facing up to what we do, including what we delegate, can help us grow and thrive. Bearing responsibility can help us nurture vital capacities, including forms of empathy, honesty, and dignity. In the language of ethics, these are eudaimonist virtues—traits and capacities that can help us live well and fully. We should thus find ways of …

Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary …

Navigating The Complexities Of Ai: The Critical Role Of Interpretability And Explainability In Ensuring Transparency And Trust, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability and explainability of deep neural networks (DNNs) are paramount in artificial intelligence (AI), especially when applied to high-stakes fields such as healthcare, finance, and autonomous driving. The need for this study arises from the growing integration of AI into critical areas where transparency, trust, and ethical decision-making are essential. This paper explores the impact of architectural design choices on DNN interpretability, focusing on how different architectural elements like layer types, network depth, connectivity patterns, and attention mechanisms affect model transparency. Methodologically, the study employs a comprehensive review of case studies and experimental results to analyze the balance between …

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 …

Evaluating Methods For Assessing Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence (AI) and machine learning (ML), particularly as these models are increasingly deployed in high-stakes applications such as healthcare, finance, and autonomous systems. In the context of these technologies, interpretability refers to the extent to which a human can understand the cause of a decision made by a model. This article evaluates various methods for assessing the interpretability of DNNs, recognizing the significant challenges posed by their complex and opaque nature. The review encompasses both quantitative metrics and qualitative evaluations, aiming to identify effective strategies that enhance …

Design And Implementation Of A Vision-Based Deep-Learning Protocol For Kinematic Feature Extraction With Application To Stroke Rehabilitation, Juan Diego Luna Inga

Master's Theses

Stroke is a leading cause of long-term disability, affecting thousands of individuals annually and significantly impairing their mobility, independence, and quality of life. Traditional methods for assessing motor impairments are often costly and invasive, creating substantial barriers to effective rehabilitation. This thesis explores the use of DeepLabCut (DLC), a deep-learning-based pose estimation tool, to extract clinically meaningful kinematic features from video data of stroke survivors with upper-extremity (UE) impairments.

To conduct this investigation, a specialized protocol was developed to tailor DLC for analyzing movements characteristic of UE impairments in stroke survivors. This protocol was validated through comparative analysis using peak …

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 …