Digital Commons Network^™

Data Analysis Of Twitter’S Nasdaq100 Sentiments And Topics As Indicators For News Articles Retrieval: Fine-Tuning Roberta And Rag., Kagan Timur

ICT

This study investigates the combination of sentiment analysis using the VADER lexicon and semantic analysis through Latent Dirichlet Allocation (LDA) to identify real-life events, focusing on Twitter datasets. The research shows that while sentiment analysis alone may be insufficient, combining it with semantic analysis improves the process, particularly for identifying relevant news articles and understanding brand perception on social media. The study also fine-tunes the RoBERTa model for question-answering tasks, yielding significant improvements in the SQuAD evaluation metric. The exact match (EM) score rose dramatically from 2.06% to 62%, and the F1 score improved from 9.41% to 65%. A retrieval …

4. Developing Media And Information Literacy Through Dialogues About Ai, Rosa Thornley, Dory Rosenberg

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

Inviting students to dialogue about Artificial Intelligence (Al) can help to develop media and information literacy. Instead of establishing restrictive policies, we present a method for instructor-facilitated dialogues to teach students to analyze, evaluate, and interact with generative language models. This recursive inquiry process causes students to critically think about the spectrum of AI capabilities and limitations. Practicing this inquiry habit over time directs students towards intuitively questioning the Al which leads them to govern their own choices when they become rhetorically aware of emerging technologies and how it will affect their own research and writing. For instructors in higher …

1. Navigating The New Frontier Of Generative Ai In Peer Review And Academic Writing, Chris Mayer

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

This chapter provides an overview of the current landscape and implications of Generative Al (GenAl) in higher education, particularly focusing on its role in academic writing and peer review. The emergence of GenAl tools such as ChatGPT is a transformative development in education, with widespread adoption at unprecedented speed. Large Language Models such as ChatGPT offer great potential for enhancing education and academic writing but also raise serious ethical concerns and tensions including access and usability, the perpetuation of Standard Academic English, and issues of linguistic justice. There is also the need for new approaches and pedagogies for how to …

Zero-Shot Cross-Lingual Pos Tagging For Filipino, Jimson Paulo Layacan, Isaiah Edri W. Flores, Katrina Bernice M. Tan, Ma. Regina Justina Estuar, Jann Railey E. Montalan, Marlene M. De Leon

Department of Information Systems & Computer Science Faculty Publications

Supervised learning approaches in NLP, exemplified by POS tagging, rely heavily on the presence of large amounts of annotated data. However, acquiring such data often requires significant amount of resources and incurs high costs. In this work, we explore zero-shot cross-lingual transfer learning to address data scarcity issues in Filipino POS tagging, particularly focusing on optimizing source language selection. Our zero-shot approach demonstrates superior performance compared to previous studies, with top-performing fine-tuned PLMs achieving F1 scores as high as 79.10%. The analysis reveals moderate correlations between cross-lingual transfer performance and specific linguistic distances–featural, inventory, and syntactic–suggesting that source languages with …

Multi-Semantic-Stage Neural Networks For Robust And Interpretable Deep Learning, Christopher J. Menart

Browse all Theses and Dissertations

Deep neural networks have great representational power. However, most deep neural nets today optimize directly for performance on a single task defined only by labeled training data. This excludes potential sources of knowledge and ways of learning which could improve their performance, and address challenges, such as explainability, which are pressing to the field. We propose a framework for neural network architecture which generalizes it to a graph of many semantically-meaningful variables. We call it the Multi-Semantic-Stage Neural Network (MSSNN). An MSSNN models its domain as a web of conditional probabilities, i.e. a collection of inter-related tasks which can learn …

Natural Language Processing And Neurosymbolic Ai: The Role Of Neural Networks With Knowledge-Guided Symbolic Approaches, Emily Barnes, James Hutson

Faculty Scholarship

Neurosymbolic AI (NeSy AI) represents a groundbreaking approach in the realm of Natural Language Processing (NLP), merging the pattern recognition of neural networks with the structured reasoning of symbolic AI to address the complexities of human language. This study investigates the effectiveness of neurosymbolic AI in providing nuanced understanding and contextually relevant responses, driven by the need to overcome the limitations of existing models in handling complex linguistic tasks and abstract reasoning. Employing a hybrid methodology that combines multimodal contextual modeling with rule-governed inferences and memory activations, the research delves into specific applications like Named Entity Recognition (NER), where architectures …

Investigating Efficiency Of Free-For-All Models In A Matchmaking Context, Emil Gensby, Bryan S. Weber, Anders H. Christiansen

Publications and Research

We explore several popular (and unpopular) systems for matchmaking and ranking in free-for-all (FFA) environments. The commonplace existing methods involve the reinterpretation of established two-player ranking systems (ie. Elo/Glicko) and decomposing multiplayer games into a set of multiple one-vs-one pairings. This decomposition, while commonplace, is not part of the intended use-case of these two-player ranking systems. We are the first to formally explore this ad-hoc usage and reassuringly find evidence that it converges to correct values. Second, we identify a method that appears to dominate what appears to be the most common publicly used method. At the same time, this …

Context-Free Grammar Framework For Automatic Shooting Game Enemy Pattern Generation, Nitit Kaweeratanakit

Chulalongkorn University Theses and Dissertations (Chula ETD)

This research proposes a framework for generating enemy patterns for SHMUPs game. It is directly based on a grammar derived from the enemy behavior of existing commercial SHMUPs, and implemented using a new description language called "Enemy Pattern Description Language" (EPDL). EPDL contains all information required to construct the enemy, with no requirement of external data content. The language is human-readable and can be connected to any game engine of choice using an EPDL interpreter. The interpreter itself consists of lexer and recursive descent parser. The results shown in this research is implemented in. "rdnh", a private fork of Touhou …

Multi-Activity Student Knowledge And Behavior Modeling Via Transfer Learning, Siqian Zhao

Electronic Theses & Dissertations (2024 - present)

Online education systems have grown in popularity over the past few years, providing abundant opportunities for students to learn. As the number of students using these systems grows, it promotes the development of the Educational Data Mining (EDM) field, which leverages statistical, machine learning, and data mining technologies to explore large-scale educational data and develop methods to better understand student learning.

In this dissertation, we investigate two essential topics in EDM: Student Knowledge Tracing (KT) and Behavior Modeling (BM). KT aims to quantify and model student knowledge gained from learning activities, while BM focuses on tasks such as modeling student …

The Effectiveness Of Cybersecurity Training, Courtney Knight

Electronic Theses & Dissertations (2024 - present)

This study seeks to answer the question of what kinds of cybersecurity training are effective by reviewing the existing literature, analyzing for gaps, and bridging the connection with known learning techniques. The research is exploratory in nature, and is inductive – intending to develop a theory. Additionally, this study seeks to offer a taxonomy of cybersecurity training methods to be evaluated.

This study did not include security operation center (SOC), cyber incident response teams (CIRTs), or other types of advanced cybersecurity professional training. This research specifically focuses on training to improve the average employee and population’s tactical cybersecurity skills.

Several …

Evaluating Computational Reproducibility Of Jupyter Notebooks Using Machine Learning And Natural Language Processing, A S M Shahadat Hossain

Graduate Research Theses & Dissertations

In recent years, computational reproducibility, which refers to achieving consistent results upon rerunning an experiment, has become one of the major concerns of various research communities. Jupyter Notebook, as a web-based computational notebook application, offers useful features for running and publishing computational experiments through interactive environments. However, rerunning notebooks does not always reproduce the experimental results. This thesis aims to develop novel methods to evaluate reproducibility by comparing different types of outputs between original and rerun notebooks. It also explores the idea of using machine learning models to predict reproducibility of notebooks automatically without the need of rerunning them. Through …

Identifying The Origins Of Business’ Data Breaches Utilizing Covert Timing Channels, Gayle L. Frisbie

Master's Theses and Doctoral Dissertations

Cybersecurity events and data breaches are on the rise and are very costly to businesses. Businesses rely on connectivity and information systems to conduct business, yet those same information systems can be breached and the organization's data exposed. Today, there is a heavy reliance of organizations upon network connections to connect the entire organization in order to conduct business efficiently and from multiple locations. Covert timing channels are a cybersecurity attack method in which malicious actors embed privileged information into normal network traffic without authorization. Malicious actors, by carefully manipulating timing patterns in covert timing channels, can create a hidden …

Learning To Code With Github Copilot: A Resource For New Student Developers, Sarah Zelikovitz

Open Educational Resources

This resource provides a step-by-step guide for new student developers on using GitHub Copilot. It covers the process of signing up for GitHub's Educa􀀁on program, integra􀀁ng Copilot into two popular integrated development environments (IDEs), and using Copilot to generate, document, debug, and optimize code through prompt-based interactions. This guide empowers students to leverage AI-driven assistance in solving coding challenges. It also gives students an understanding of the limitations of AI, and how to use it safely and effectively.

The Promise And Pitfalls Of Genai In Education, Helen Crompton

STEMPS Faculty Publications

Since ChatGPT was made available to the public, artificial intelligence (AI) has had accelerated growth in the field of education at an unprecedented rate. This study examined the misuses and limitations of ChatGPT and similar generative AI as well as the affordances of these tools for teaching and learning. Using PRISMA protocol, this thematic systematic review examined extant literature from November 2022 to June 2024. The findings reveal five misuses and limitations trends in the literature of cheating, inaccuracies, plagiarism, bias, and data privacy. Affordances highlighted six areas of content creation, personalization, engagement, student coaching and assistance, assessment, and task …

Facilitating Mathematics And Computer Science Connections: A Cross-Curricular Approach, Kimberly E. Beck, Jessica F. Shumway, Umar Shehzad, Jody Clarke-Midura, Mimi Recker

Publications

In the United States, school curricula are often created and taught with distinct boundaries between disciplines. This division between curricular areas may serve as a hindrance to students' long-term learning and their ability to generalize. In contrast, cross-curricular pedagogy provides a way for students to think beyond the classroom walls and make important connections across disciplines. The purpose of this paper is a theoretical reflection on our use of Expansive Framing in our design of lessons across learning environments within the school. We provide a narrative account of our early work in using this theoretical framework to co-plan and enact …

Computer-Aided Craniofacial Superimposition Validation Study: The Identification Of The Leaders And Participants Of The Polish-Lithuanian January Uprising (1863–1864), Rubén Martos, Rosario Guerra, Fernando Navarro, Michela Peruch, Kevin Neuwirth, Andrea Valsecchi, Rimantas Jankauskas, Oscar Ibáñez

Faculty, Staff and Student Publications

In 2017, a series of human remains corresponding to the executed leaders of the "January Uprising" of 1863-1864 were uncovered at the Upper Castle of Vilnius (Lithuania). During the archeological excavations, 14 inhumation pits with the human remains of 21 individuals were found at the site. The subsequent identification process was carried out, including the analysis and cross-comparison of post-mortem data obtained in situ and in the lab with ante-mortem data obtained from historical archives. In parallel, three anthropologists with diverse backgrounds in craniofacial identification and two students without previous experience attempted to identify 11 of these 21 individuals using …

Optimizing Uav-Assisted Data Collection In Iot Sensor Networks Using Dual Cluster Head Strategy, Keiwan Soltani, Federico Coro, Sajal K. Das

Computer Science Faculty Research & Creative Works

The proliferation of the Internet of Things (IoT) has significantly impacted the integration of digital and physical realms, with Wireless Sensor Networks (WSN s) playing a crucial role. However, these sensor nodes often face challenges related to battery constraints and deployment in inaccessible terrains. The advent of Unmanned Aerial Vehicles (UAVs) presents a transformative solution, particularly for data collection from remote IoT devices. This work explores the application of UAV s to improve data collection in dense IoT sensor networks. We propose a novel approach called optimizing UAV-assisted data collection in IoT sensor networks using Dual Cluster Head (UAVDCH) that …

A Hypergraph Approach To Deep Learning Based Routing In Software-Defined Vehicular Networks, Ankur Nahar, Nishit Bhardwaj, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-Based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-Based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements …

Warmonger Attack: A Novel Attack Vector In Serverless Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

We debut the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a result, a malicious user on this platform can purposefully misbehave and cause these egress IPs to be blocked by the content server, resulting in a platform-wide denial of service. To validate the effectiveness of the Warmonger attack, we conducted extensive experiments over …

Secure Location-Based Authenticated Key Establishment Scheme For Maritime Communication, Saurabh Agrawal, Anusha Vangala, Ashok Kumar Das, Neeraj Kumar, Sachin Shetty, Sajal K. Das

Computer Science Faculty Research & Creative Works

Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther …

Prediction Of Carbonation Capacity Of Scms Using Ensemble Learning Method, Kangyi Cai, Jian Liu, Edward Mwanza, Mahelet G. Fikru, Hongyan Ma, Donald C. Wunsch

Economics Faculty Research & Creative Works

The utilization of supplementary cementitious materials (SCMs) subjected to carbonation processing represents a viable strategy to mitigate anthropogenic CO₂ emissions associated with concrete production, potentially contributing to the achievement of carbon neutrality. However, existing studies have limitations in effectively predicting the varying carbonation capacities of different SCMs, a gap that this research aims to address. Recent research efforts focused on the carbonation of waste-material-sourced SCMs are reviewed, along with a comparative discussion on diverse carbonation methods. A detailed data set encapsulating the properties of SCMs, and carbonation configurations was compiled. At the same time, six ensemble learning models were …

Three-Dimensional Laser-Assisted Printing Of Structures From Nanoparticles, Heng Pan, Chinmoy Podder, Wan Shou, Xiaowei Yu

Patents

A method for manufacturing a component comprising bombarding nanoparticles in a dispersion with a laser to transform the ligand and cause the nanoparticles to drop out of the dispersion and deposit onto a substrate; and bombarding additional nanoparticles in the dispersion with the laser to transform the ligand and cause the nanoparticles to drop out of the dispersion and deposit onto the nanoparticles previously deposited out of the dispersion.

Sparse Representation Learning For Temporal Networks, Maxwell Mcneil

Electronic Theses & Dissertations (2024 - present)

Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …

Towards A Concurrency Platform For Scalable Multi-Axial Real-Time Hybrid Simulation, Marion Sudvarg, Oren Bell, Tyler Martin, Benjamin Standaert, Tao Zhang, Sun Beom Kwon, Chris Gill, Arun Prakash

Computer Science Faculty Research & Creative Works

Multi-axial real-time hybrid simulation (maRTHS) uses multiple hydraulic actuators to apply loads and deform experimental substructures, enacting both translational and rotational motion. This allows for an increased level of realism in seismic testing. However, this also demands the implementation of multiple-input, multiple-output control strategies with complex nonlinear behaviors. To realize true real-time hybrid simulation at the necessary sub-millisecond timescales, computational platforms will need to support these complexities at scale, while still providing deadline assurance. This paper presents initial work towards supporting (and is influenced by the need for) envisioned larger-scale future experiments based on the current maRTHS benchmark: it discusses …

Minerrouter : Effective Message Routing Using Contact-Graphs And Location Prediction In Underground Mine, Abhay Goyal, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Location-based distributed communication in underground mines has been a hard problem to solve due to unreliable centralized architecture such as leaky feeder systems, high attenuation, and the unavailability of GPS signals. Delay Tolerant Networks (DTN) enable decentralized message routing using the store-carry-forward method that can help in creating situational awareness needed to handle emergency and disaster scenarios. The ability to predict where the DTN nodes (miner) might have been at/are headed to (with respect to the mine regions and pillars) at different times, combined with contact-based routing and intelligent handling of buffer, can be used for better delivery of messages. …

Infusing Machine Learning And Computational Linguistics Into Clinical Notes, Funke V. Alabi, Onyeka Omose, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

Entering free-form text notes into Electronic Health Records (EHR) systems takes a lot of time from clinicians. A large portion of this paper work is viewed as a burden, which cuts into the amount of time doctors spend with patients and increases the risk of burnout. We will see how machine learning and computational linguistics can be infused in the processing of taking clinical notes. We are presenting a new language modeling task that predicts the content of notes conditioned on historical data from a patient's medical record, such as patient demographics, lab results, medications, and previous notes, with the …

Towards Explainable Neural Network Fairness, Mengdi Zhang

Dissertations and Theses Collection (Open Access)

Neural networks are widely applied in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug, non-transparent and subject to fairness issues. Discrimination has been observed in various machine learning models, including Large Language Models (LLMs), which calls for systematic fairness evaluation (i.e., testing, verification or even certification) before their deployment in ethic-relevant domains. If a model is found to be discriminating, we must apply systematic measure to improve its fairness. In the literature, multiple categories of fairness improving methods have been discussed, including pre-processing, in-processing and post-processing.
In this dissertation, …

Robustsentembed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning, Javad Rafiei Asl, Prajwal Panzade, Eduardo Blanco, Daniel Takabi, Zhipeng Cai

School of Cybersecurity Faculty Publications

Pre-trained language models (PLMs) have consistently demonstrated outstanding performance across a diverse spectrum of natural language processing tasks. Nevertheless, despite their success with unseen data, current PLM-based representations often exhibit poor robustness in adversarial settings. In this paper, we introduce RobustSentEmbed, a self-supervised sentence embedding framework designed to improve both generalization and robustness in diverse text representation tasks and against a diverse set of adversarial attacks. Through the generation of high-risk adversarial perturbations and their utilization in a novel objective function, RobustSentEmbed adeptly learns high-quality and robust sentence embeddings. Our experiments confirm the superiority of RobustSentEmbed over state-of-the-art representations. Specifically, …

Shedding Light On Software Engineering-Specific Metaphors And Idioms, Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski

Computer Science Faculty Research & Creative Works

Use of figurative language, such as metaphors and idioms, is common in our daily-life communications, and it can also be found in Software Engineering (SE) channels, such as comments on GitHub. Automatically interpreting figurative language is a challenging task, even with modern Large Language Models (LLMs), as it often involves subtle nuances. This is particularly true in the SE domain, where figurative language is frequently used to convey technical concepts, often bearing developer affect (e.g., 'spaghetti code). Surprisingly, there is a lack of studies on how figurative language in SE communications impacts the performance of automatic tools that focus on …

Landmark-Based Localization Using Stereo Vision And Deep Learning In Gps-Denied Battlefield Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

Localization in a battlefield environment is increasingly challenging as GPS connectivity is often denied or unreliable, and physical deployment of anchor nodes across wireless networks for localization can be difficult in hostile battlefield terrain. This paper proposes a novel framework for the localization of moving objects in non-GPS battlefield environments using stereo vision and a deep learning model by recognizing naturally existing or artificial landmarks as anchors. The proposed method utilizes a custom-calibrated stereo vision camera for distance estimation and the YOLOv8s model, which is trained and fine-tuned with our real-world dataset for landmark anchor recognition. The depth images are …