Physical Sciences and Mathematics Commons^™

Human Centered Approaches And Taxonomies For Explainable Artificial Intelligence, Helen Sheridan, Emma Murphy, Dympna O'Sullivan

Conference papers

Recent interest within the research community related to explainable artificial intelligence (XAI) has led to a profuse amount of literature on the subject. Those who wish to tackle the domain from an HCI focus may be presented with overwhelming material, most of which does not pertain to human aspects of XAI. Taxonomies can serve to categorize a subject into topic areas and distill content into an overview of the field. This late breaking work intends to help those within the HCI community with a focus on XAI to understand relevant aspects of human centered XAI. We also present a taxonomy …

Bringing Our Full Selves Into Computing: Designing, Building, And Fostering Equitable Computing Education Communities, Francisco Enrique Vicente Castro, Earl W. Huff, Amber Solomon, Briana Christina Bettin

Michigan Tech Publications, Part 2

A key problem in achieving access to and sustained presence in computing and computing education (CEd) in the United States is environments that are all too often designed in ways that are harmful and inequitable toward systemically minoritized communities. While various scholars have explored ways to design equitable spaces within differing contexts, often, equity is framed merely as an issue of access. This dismisses the lived experiences of minoritized communities and can lead to shallow perspectives of equity that fail to address community concerns and further push these communities out of sustained participation and presence in computing and CEd. Drawing …

Concolic Testing For Scripting Languages, Zhe Li

Dissertations and Theses

Scripting languages, such as JavaScript and Lua, are becoming more and more popular. They are typically easy to learn and use, making them accessible to a wide range of developers, even those with limited programming experience. Lua, for instance, is a lightweight, efficient, and versatile scripting language. It is designed to be easy to integrate into other systems and is often used as an embedded scripting language in larger applications such as NMap, which is a network scanning tool.

As another example, web front-end development with JavaScript (JS) is a popular choice for developers due to its ability to add …

Experimental Design For Scientific Discovery, Quan Minh Nguyen

McKelvey School of Engineering Theses & Dissertations

Experimental design offers an elegant model of many problems where one navigates within a vast search space seeking data points with certain characteristics. A multitude of applications in science and engineering fall under this umbrella, with drug and materials discovery being prime examples. The experimental design approach maintains a probabilistic model of the search space, and uses Bayesian decision theory accounting for this model to guide the accumulation of observed data to maximize an experimentation objective of interest. This dissertation explores Bayesian optimization and active search, two realizations of the experimental design framework that model discovery tasks. While existing solutions …

Design And Implementation Of Truly Random Number Generation Using Memristors For In-Memory Computing, Nick Felker

Theses and Dissertations

This paper proposes a new security module based on non-volatile memory. The module uses a memristor-based true random number generator to generate random numbers which can be used for cryptography. The module is implemented in software using a modified RISC-V instruction set architecture. The paper evaluates the performance of the module using the RISC-V simulator Gem5. The results show that the module can generate random numbers at a rate of 63 microseconds per number, which is faster than the standard C library’s random number generator. The module can also be used to scramble strings of characters and generate hashes of …

Peatmoss: A Dataset And Initial Analysis Of Pre-Trained Models In Open-Source Software, Wenxin Jiang, Jerin Yasmin, Jason Jones, Nicholas Synovic, Jiashen Kuo, Nathaniel Bielanski, Yuan Tian, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse. This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with …

Decentralized Consensus And Governance For Collaborative Intelligence, Huiwen Liu

Dissertations and Theses Collection (Open Access)

The data economy today is becoming increasingly collaborative in nature. Take business intelligence, for example. To unleash the full potential of big data, it is essential to integrate multi-source data depicting entities from a multi-faceted and multi-modal perspective, which, not surprisingly, is not achievable by any company alone. In collaborative intelligence, there are two core issues, namely "trust" and "incentive". The core mechanisms to solve these two problems are consensus and tokenization separately.

To solve the trust problem more effectively, we propose a systematic consensus evaluation framework to investigate whether existing consensus algorithms can do so. After a lot of …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Reinforcement Learning Based Proactive Entanglement Swapping For Quantum Networks, Tasdiqul Islam, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

Entanglement generation and swapping is a difficult process due to probabilistic nature of quantum mechanics. To overcome this issue, existing quantum routing algorithms try to create entanglement on multiple paths between source and destination. Although it is possible to save entangled qubits on unused links using quantum memories, the quantum routing algorithms discard them and try creating new entanglement in each time slot. In this work, we leverage the longevity of entanglement and introduce two enhancements to improve the performance of existing routing algorithms: (i) The generation and caching of entanglements across multiple time slots, and (ii) the proactively executing …

Effective Data Sharing In An Edge-Cloud Model: Security Challenges And Solutions, Arijit Karati, Sajal K. Das

Computer Science Faculty Research & Creative Works

The proposed protocol offers privacy-preserving authentication across several cloud platforms, flexible key management for consumer data protection, and effective user revocation. Performance evaluation demonstrates that the proposed framework supports low latency, safe unified remote access, and data privacy in the contemporary edge-enabled environment.

Introduction To Programming And Applied Analytics Using Python, Matt Brown

ATU Faculty OER Books and Materials

This open electronic textbook is a collection of lecture notes, assignments, and additional background material for a junior level analytics course targeted for business students, it is free to use and copy. The text assumes readers have not had prior programming or computing courses, but have had at least one analytics course. The textbook differs from other textbooks because it serves a dual purpose, to first introduce to students the Python programming language and secondly to introduce analytics programming in Python. It is not meant to be a comprehensive book on the Python language or data analytics, rather a semester’s …

Empowering Interprofessional Teams: Exploring Genai With The Health Sciences Library, Jess King, Teresa L. Hartman

Posters and Presentations: Leon S. McGoogan Health Sciences Library

The Leon S. McGoogan Health Sciences Library at the University of Nebraska Medical Center (UNMC) organized workshops to delve into Generative Artificial Intelligence (GenAI) applications in academic medical centers. These sessions, tailored for all skill levels, provided a safe forum for faculty and staff to engage with GenAI, increasing their digital literacy skills. Participants benefited from introductory sessions, hands-on activities, and reflective discussions, gaining practical insights into ethical GenAI use. These workshops form a vibrant GenAI community at UNMC, fostering collaboration and knowledge exchange among healthcare professionals and paving the way for continued technological integration in academic and clinical settings

Integrating Remote Sensing And Machine Learning To Determine Past, Current And Future Crop Water Use From The Nubian Sandstone Aquifer System, Moaz Ishag

Department of Biological Systems Engineering: Dissertations and Theses

The agriculture sector is a significant consumer of water, and sustainable water use begins with monitoring irrigated land. Delineating irrigated land supports decision-makers and promotes the sustainable use of this crucial resource. This study focuses on the Nubian Sandstone Aquifer System (NSAS), the largest aquifers in the world, which spans Egypt, Sudan, Libya, and Chad. The study aims to: 1) quantify the increase in irrigated hectares (both pivot and non-pivot) from 2000-2001 to 2023-2024; 2) identify major irrigated crop types and their water requirements; and 3) quantify groundwater crop water use from the NSAS using remote sensing via the Google …

Essays On Artificial Intelligence (Ai) In Management, Bowen Zhou

Dissertations and Theses Collection (Open Access)

This dissertation comprises three essays that investigate the transformative potential of Artificial Intelligence (AI) in business.

Chapter 1 investigates the fundamental issue of how integrating AI within R&D activities influences a firm’s market value. We developed an "AI Index" using patent data and textual analysis. Interestingly, empirical results indicate a negative correlation between AI integration and market value. However, this does not suggest that AI is an unviable avenue for exploration. Further analysis of the boundary conditions reveals that complementary assets are crucial for successful commercialisation, highlighting that while AI adoption is costly, these assets significantly enhance its market value. …

Cognitive Technologies, Tom Davenport

Asian Management Insights

AI and the revolution of work.

Professor Tom Davenport, the President’s Distinguished Professor of Information Technology and Management at Babson College, speaks about how companies can integrate generative Artificial Intelligence (GenAI) into their operations while ensuring workforce adaptation and skills development.

Compiler-Provenance Identification In Obfuscated Binaries Using Vision Transformers, Wasif Khan, Saed Alrabaee, Mousa Al-Kfairy, Jie Tang, Kim Kwang Raymond Choo

All Works

Extracting compiler-provenance-related information (e.g., the source of a compiler, its version, its optimization settings, and compiler-related functions) is crucial for binary-analysis tasks such as function fingerprinting, detecting code clones, and determining authorship attribution. However, the presence of obfuscation techniques has complicated the efforts to automate such extraction. In this paper, we propose an efficient and resilient approach to provenance identification in obfuscated binaries using advanced pre-trained computer-vision models. To achieve this, we transform the program binaries into images and apply a two-layer approach for compiler and optimization prediction. Extensive results from experiments performed on a large-scale dataset show that the …

Stochastic Dominance: Cases Of Interval And P-Box Uncertainty, Kittawit Autchariyapanikul, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Traditional decision theory recommendation about making a decision assume that we know both the probabilities of different outcomes of each possible decision, and we know the utility function -- that describes the decision maker's preferences. Sometimes, we can make a recommendation even when we only have partial information about utility. Such cases are known as cases of stochastic dominance. In other cases, in addition to not knowing the utility function, we also only have partial information about the probabilities of different outcomes. For example, we may only known bounds on the outcomes (case of interval uncertainty) or bounds on the …

If Subsequent Results Are Too Easy To Obtain, The Proof Most Probably Has Errors: Explanation Of The Empirical Observation, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Many modern mathematical proofs are very complex, checking them is difficult; as a result, errors sneak into published proofs, even into proofs published in highly reputable journals. Sometimes, the errors are repairable, but sometimes, it turns out that the supposedly proven result is actually wrong. When the error is not noticed for some time, the published result is used to prove many other results -- and when the error is eventually found, all these new results are invalidated. This happened several times. Since it is not realistic to more thoroughly check all the proofs, and we want to minimize the …

For 2 X N Cases, Proportional Fitting Problem Reduces To A Single Equation, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, for each of two classifications, we know the probabilities that a randomly selected object belong to different categories. For example, we know what proportion of people are below 20 years old, what proportion is between 20 and 30, etc., and we also know what proportion of people earns less than 10K, between 10K and 20K, etc. In such situations, we are often interested in proportion of people who are classified by two classifications into two given categories. For example, we are interested in the proportion of people whose age is between 20 and 30 and whose …

Is Alaska Negative-Tax Arrangement Fair? Almost: Mathematical Analysis, Chon Van Le, Vladik Kreinovich

Departmental Technical Reports (CS)

In the State of Alaska there is no state income tax. Instead, there is a negative tex: every year every resident gets some money from the state. At present, every resident -- from the poorest to the richest -- gets the exact same amount of money: in 2024, it is expected to be around $1500. A natural question is: Is this fair? Maybe poor people should get more since their needs are greater? Maybe the rich people should get proportionally more, since fairness means equal added happiness for all, and for rich people, extra $1500 is barely noticeable? There have …

Why Angles Between Galactic Center Filaments And Galactic Plane Follow A Bimodal Distribution: A Symmetry-Based Explanation, Julio C. Urenda, Vladik Kreinovich

Departmental Technical Reports (CS)

Recent observations have shown that the angles between the Galaxy Center filaments and the Galactic plane follow a bimodal distribution: a large number of filaments are approximately orthogonal to the Galactic plane, a large number of filaments are approximately parallel to the Galactic plane, and much fewer filaments have other orientations. In this paper, we show this bimodal distribution can be explained by natural geometric symmetries.

Why Seismicity In Ireland Is Low: A Possible Geometric Explanation, Julio C. Urenda, Aaron Velasco, Vladik Kreinovich

Departmental Technical Reports (CS)

For each geographic location, its seismicity level is usually determined by how close this location is to the boundaries of tectonic plates. However, there is one notable exception: while Ireland and Britain are at approximately the same distance from such boundaries, the seismicity level in Ireland is much lower than in Britain. A recent paper provided a partial explanation for this phenomenon: namely, it turns out that the lithosphere under Ireland is unusually thick, and this can potentially lead to lower seismicity. However, the current explanation of the relation between the lithosphere thickness and seismicity level strongly depends on the …

Green Finance Growth Prediction Model Based On Time-Series Conditional Generative Adversarial Networks, Aya Salama Abdelhady, Nadia Dahmani, Lobna M. Abouel-Magd, Ashraf Darwish, Aboul Ella Hassanien

All Works

Climate change mitigation necessitates increased investment in green sectors. This study proposes a methodology to predict green finance growth across various countries, aiming to encourage such investments. Our approach leverages time-series Conditional Generative Adversarial Networks (CT-GANs) for data augmentation and Nonlinear Autoregressive Neural Networks (NARNNs) for prediction. The green finance growth predicting model was applied to datasets collected from forty countries across five continents. The Augmented Dickey-Fuller (ADF) test confirmed the non-stationary nature of the data, supporting the use of Nonlinear Autoregressive Neural Networks (NARNNs). CT-GANs were then employed to augment the data for improved prediction accuracy. Results demonstrate the …

Contextualizing Interpersonal Data Sharing In Smart Homes, Weijia He, Nathan Reitinger, Atheer Almogbil, Yi-Shyuan Chiang, Timothy J. Pierson, David Kotz

Dartmouth Scholarship

A key feature of smart home devices is monitoring the environment and recording data. These devices provide security via motion-detection video alerts, cost-savings via thermostat usage history, and peace of mind via functions like auto-locking doors or water leak detectors. At the same time, the sharing of this information in interpersonal relationships---though necessary---is currently accomplished on an all-or-nothing basis. This can easily lead to oversharing in a multi-user environment. Although prior work has studied people's perceptions of information sharing with vendors or ISPs, the sharing of household data among users who interact personally is less well understood. Interpersonal situations make …

Who Wrote The Scientific News? Improving The Discernibility Of Llms To Human-Written Scientific News, Dominik Soós

Computer Science Theses & Dissertations

Large Language Models (LLMs) have rapidly advanced the field of Natural Language Processing and become powerful tools for generating and evaluating scientific text. Although LLMs have demonstrated promising as evaluators for certain text generation tasks, there is still a gap until they are used as reliable text evaluators for general purposes. In this thesis project, I attempted to fill this gap by examining the discernibility of LLMs from human-written and LLM-generated scientific news. This research demonstrated that although it was relatively straightforward for humans to discern scientific news written by humans from scientific news generated by GPT-3.5 using basic prompts, …

Adopt: An Environmentally-Friendly System For Alerting Drivers To Occluded Pedestrians Traffic, Abrar Abdulrahman Alali

Computer Science Theses & Dissertations

The emergence of sensing technologies and vehicular communications has brought significant opportunities for enhancing pedestrian safety on city streets. However, existing solutions rely on costly technologies such as computer vision and trajectory prediction to detect crossing pedestrians, while they have limits in detecting pedestrians who are occluded by parked cars. Despite the presence of collaborative perception by surrounding vehicles and infrastructure, there is a notable absence of incorporating existing parked cars themselves due to their insufficiency in detecting pedestrians and communicating with other cars while they are turned off. Furthermore, accommodating pedestrians on streets has been linked to an additional …

Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that …

Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two key components: ScratchNet and PatchNet. In detail, ScratchNet employs the parallel Multi-scale Partial Convolution Module to effectively repair scratches, learning from multi-scale local receptive fields. In contrast, the patch-misses necessitate the network to emphasize global information. To this end, we incorporate a transformer-based encoder and decoder …

Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with …

Performance Analysis Of Llama 2 Among Other Llms, Donghao Huang, Zhenda Hu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Llama 2, an open-source large language model developed by Meta, offers a versatile and high-performance solution for natural language processing, boasting a broad scale, competitive dialogue capabilities, and open accessibility for research and development, thus driving innovation in AI applications. Despite these advancements, there remains a limited understanding of the underlying principles and performance of Llama 2 compared with other LLMs. To address this gap, this paper presents a comprehensive evaluation of Llama 2, focusing on its application in in-context learning — an AI design pattern that harnesses pre-trained LLMs for processing confidential and sensitive data. Through a rigorous comparative …