Digital Commons Network^™

Fuzzy-Ahp Based Decision Support System For The Selection Of Optimal Maintenance Strategy For Meter Gauge Railway Infrastructure: A Review, Hamisi J. Maulid

Tanzania Journal of Engineering and Technology (TJET)

There are many uncertainties and complexities associated with maintaining Meter Gauge Railway (MGR) infrastructure, which calls for a methodical approach to decision-making. The development and application of a fuzzy-AHP-based decision support system (DSS) to select the optimal maintenance strategy for the MGR are presented in this study. The review covers research from 2013 to 2023 and focusses on the use of Multi-Criteria Decision Making (MCDM) and Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) techniques in railway infrastructure maintenance. To manage the inherent uncertainties and subjective judgements involved in maintenance decision-making, the Fuzzy-AHP methodology combines fuzzy logic with the Analytic Hierarchy Process (AHP). …

The Future Of Foreign Language Learning In The Age Of Artificial Intelligence: A Critical Analysis Of Trends, Challenges, And Opportunities, Ahmad Aljanadbah, Rashid Hamad Al Marri, Hamad Mubarak Almarri

All Works

The integration of Artificial Intelligence (AI) in foreign language education is transforming how languages are taught and learned. This study investigates the current trends, advantages, and challenges of AI applications in language acquisition. Employing a qualitative content analysis method, the research analyzes scholarly publications and practical innovations in AI-based tools, such as adaptive learning systems, intelligent chatbots, and automated writing feedback. The findings highlight AI's potential to enhance personalized instruction, boost learner engagement, and expand access to authentic language materials. Nonetheless, the study also reveals notable limitations, including concerns about data privacy, diminished human interaction, and ethical risks related to …

An Exploratory Analysis Of Automated Deception Detection For Mental Health Applications, Sayde Leya King

USF Tampa Graduate Theses and Dissertations

Deception in mental health settings can undermine therapeutic relationships, compromise treatment efficacy, and impact patient outcomes. Yet, research shows that mental health clinicians often perform no better than chance at detecting deceptive behavior in therapy. Automated deception detection, leveraging artificial intelligence (AI) and multimodal behavioral cues—such as eye gaze, body gestures, and facial expressions—offers a promising alternative. However, most existing research focuses on high-stakes legal contexts, limiting its applicability to mental health settings.

This dissertation addresses this gap by pursuing three key research objectives using a mixed-methods approach. First, we investigate mental health clinicians’ perspectives on AI-assisted deception detection through …

Exploring The Potential Of Large Language Models (Llms) To Simulate Social Group Dynamics: A Case Study Using The Board Game "Secret Hitler", Kaj Hansteen Izora, Christof Teuscher

Northeast Journal of Complex Systems (NEJCS)

This study explores the capacity of large language model-powered agents to simulate human-like behavior in multi-agent social systems. Using Secret Hitler — a hidden-role board game centered on trust, deception, and strategic communication — we evaluate how LLM agents navigate dynamic group interactions. Our findings show that agents exhibit human-like behaviors, including strategic temporal adaptation, contextual reasoning, and complex social cognition such as theory of mind and implicit coordination. Notably, 85% of agent decisions factored in at least two other players’ mental states, highlighting their capacity for multi-agent mental state inference. However, they struggled with key aspects of human gameplay, …

Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler

SMU Data Science Review

Large Language Models (LLMs) are transforming conversational AI, yet their dependence on prompt-supplied context exposes them to context-switch attacks that covertly steer dialogue toward sensitive or malicious ends. A 70 one-sided conversation transcript evaluation set was constructed spanning various fraudulent scenarios. Each transcript embeds adversarial patterns drawn while preserving natural conversational flow. We introduce a hybrid defense that pairs a BERT-based semantic-drift detector (cosine-similarity threshold = 0.70) with a curated keyword and hack-phrase scanner to counter these threats. In aggregate, the system delivered 100 % recall, intercepting every simulated phishing or data-harvesting attempt. The keyword layer achieved perfect precision, generating …

Topic Shift Detection And Triggering In Natural Dialogue Systems: A Lightweight Approach, Rohith Perumandla

College of Computing and Digital Media Dissertations

This research address a key challenge in dialogue system: enabling the proactive, human-like shifting using lightweight approaching using MobileBERT (~25M) model was proposed and fine-tuned for topic shift detection, augmented with liguistic featuers for for topic trigger detection. Despite its smaller size (~25M parameters), the MobileBERT-based system achieved competitive results (F1 = 74.16%,) compared to the much larger XLNet model (~110M parameters, F1 = 79.95%), while offering greater efficiency. The topic trigger module, combining MobileBERT with linguistic features, further demonstrated effective performance (F1 = 71.61%).

Exploring Character-Level Attacks On Neural Ranking Models, Surjyanee Halder

Master’s Dissertations

Neural ranking models (NRMs) have achieved state-of-the-art performance in information retrieval, yet they remain highly susceptible to subtle adversarial inputs such as character-level typos. This project explores the robustness of such systems by introducing a reinforcement learning (RL)-based query perturbation framework. RL agents—PPO, DQN, and A2C—were trained to minimally modify user queries (e.g., through character deletions or swaps) with the goal of significantly altering the resulting document rankings, as measured by Kendall’s Tau. Experiments were conducted on the TREC DL 2019 and 2020 benchmarks using two different neural rankers: Mini LM and a fine-tuned Character BERT model. The perturbation attacks …

Revitalization Of Endangered Languages With Ai, Ivory Yang

Dartmouth College Master’s Theses

The preservation and revitalization of endangered languages, particularly those with minimal digital presence, presents significant challenges for computational linguistics. This thesis addresses these challenges by proposing novel methods for language identification and data generation, focusing on underrepresented Indigenous languages, specifically Nüshu, Native American and Native Alaskan languages.

In the first study, a COLING 2025 paper, we present NüshuRescue, an AI-driven framework designed to facilitate the preservation of Nüshu, an endangered script used exclusively by Yao women in China. Using minimal seed data, we demonstrate how GPT-4-Turbo can generate new translations, expanding a publicly available Nüshu-Chinese corpus, achieving 48.69% accuracy in …

Sentiment Analysis Of The 2024 General Election Through Twitter Using Long-Short-Term Memory Algorithm, Angga Wahyu W, Haidar Hilmy Andana, Junta Zeniarja, Aris Febriyanto

Journal of Informatics and Web Engineering

This study analyses sentiment related to the 2024 Indonesian Presidential Election using the Long Short-Term Memory (LSTM) algorithm. A total of 2,400 tweets in the Indonesian language were gathered, with approximately 400 tweets sampled per week. In the data preparation, lexicon-based sentiment tagging, oversampling for class balance, and the creation and training of an LSTM model are all included in the study approach. The built model consists of embedding layers, Conv1D, and two LSTM layers. The LSTM model was selected due to its ability to capture long-range contextual dependencies in sequential text data like tweets, facilitated by its gate mechanisms …

Sentiment Analysis Of The Israel-Palestine Conflict On X: Insights From The Indonesian Perspective Using A Long Short-Term Memory Algorithm, Mohammad Taleb Noori, Muhammad Alif Rahman, Agus Purnomo, Aripin

Journal of Informatics and Web Engineering

The Israel-Palestine conflict which has persisted for decades drives mounting global interest that consequently influences public opinion worldwide. This article examines the sentiment analysis of X (Twitter) data pertaining to the conflict using the Long Short-Term Memory (LSTM) model. This study presents public reactions through an analysis of 1,700 tweets collected between May and July 2023 which encapsulate key recent developments. In this study, several steps were conducted, namely 1) crawling process to get raw data; 2) preprocessing: cleansing, case folding, tokenization, stop word removal, and stemming; 3) modelling and validation using the LSTM model; 4) model evaluation based on …

Classification Of Smartphone Product Reviews On E-Commerce Using The Recurrent Neural Network (Rnn) Method, Rajibul Anam, Fernanda Tata Pradhana, Imam Abu Yasin, Junta Zeniarja

Journal of Informatics and Web Engineering

Understanding how consumers behave in e-commerce is essential for businesses, especially in today’s digital world where people rely heavily on online shopping platforms. A key part of this understanding comes from sentiment analysis, which looks at customer reviews to find out what buyers really think and feel about products. However, analysing these reviews is not always straightforward. Many people use informal language, slang, or mixed languages, which makes it hard for computers to interpret their opinions accurately. On top of that, there is often an imbalance in the types of data available, particularly in developing countries, where some opinions might …

Sentiment Analysis And Topic Modelling On Twitter Related To Mobile Legends: Bang Bang Game Using Lexicon-Based, Lda, And Svm, Hikmal Muhammad, Fauzi Adi Rafrastara, Kristoforus Adrian Setiadi, Arley Japardi

Journal of Informatics and Web Engineering

Mobile Legends: Bang Bang (MLBB) has become a significant phenomenon within the global e-sports landscape, attracting millions of active players and fans. This study presents a comprehensive sentiment analysis and topic modelling of MLBB-related discussions on Platform Twitter, combining a lexicon-based approach, Latent Dirichlet Allocation (LDA), and Support Vector Machine (SVM) classification within a unified analytical pipeline. A dataset of 4,313 tweets was analysed, revealing that 70.8% expressed neutral sentiment, suggesting that much of the community's communication is informational rather than emotionally charged. Positive sentiments were associated with game content updates and rewards, while negative sentiments focused on technical and …

Enhancing Multi-Step Stock Price Forecasting With Social Media Sentiment And Engagement Metrics, Damilare Olaniyan

Electronic Theses and Dissertations

This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities for extracting market-relevant signals from unstructured text. The study focuses on four major technology firms and combines sentiment features derived from Twitter with historical stock prices in a hybrid machine learning framework. Engagement-weighted sentiment, linguistic complexity, and polarity intensity were extracted using natural language processing techniques and incorporated into classification and regression models. Results show that including …

A New Model For Educational Program Assessments Using Automated Collective Concept Maps, Andrew R. Paullin

Master's Theses (2009 -)

This paper presents: Epistemological principles; the origins and theoretical foundations of concept maps; the current state of related works; the contributions, methods, results, and future road map of this research; and A New Model for Educational Program Assessments. An understanding that knowledge results from an evolution of cognitive structures enables a scientific approach to enhance the efficiency of learning. Building on prior works, this research provides a novel algorithm and tool to automatically create Collective Concept Maps from Individual Concept Maps by utilizing a dictionary, thesaurus, Natural Language Processing, Machine Learning / Artificial Intelligence, and domain expertise. Abstraction Filters enable …

Virtual Assistant For Pest Management, Viswanada Chakravarthy Karri

Master’s Dissertations

Effective pest identification and management are essential for ensuring agricultural productivity, especially in regions with limited expert access. This work proposes a virtual assistant based on a Retrieval-Augmented Generation (RAG) [1] framework to support pest management tasks. The system utilizes a multimodal dataset consisting of pest images and annotated textual interactions, adapted from the AgriLLaVA corpus [2]. The assistant combines retrieval mechanisms with generative language models to generate contextually grounded responses. It is designed for deployment on local hardware with limited computational resources, integrating open-source models for both retrieval and generation. Preliminary results suggest that this approach can provide accurate, …

Neat Vs. Scruffy: How Early Ai Researchers Classified Epistemic Cultures Of Knowledge Representation, Lindsay Poirier

Statistical and Data Sciences: Faculty Publications

Accounts of the history of symbolic artificial intelligence (AI) often categorize researchers and their programs through binary oppositions: logicist versus procedural, or alternatively, neat versus scruffy. These terms were often referenced throughout the 1970s and 1980s by AI researchers at institutions in the United States and Europe to document epistemic and esthetic differences in approaches to knowledge representation. In this article, I historicize and situate these binary oppositions, showing how researchers leveraged them in discourse to demarcate the beliefs and commitments underpinning certain AI approaches. I argue that the labels helped knowledge representation scholars identify, trouble, and grapple with the …

A Framework For Scalable And Controlled Hallucination Data Collection, Lin Ting Liang

Computer Science Senior Theses

This thesis addresses a key bottleneck in hallucination research: the scarcity and limitations of hallucination benchmark datasets. Existing datasets typically focus on a single type of hallucination and are expensive to produce due to the need for manual prompt creation and annotation. To overcome these challenges, we propose a novel mixture-of-experts (MoE) adversarial framework that actively induces hallucinations. Our framework employs three large language model (LLM) agents that iteratively and adversarially revise prompts to provoke hallucinated responses from a target question-answering model. It automates the generation of both intrinsic hallucinations (logical inconsistencies) and extrinsic hallucinations (inclusion of unverifiable external information). …

K-Mshc: Unmasking Minimally Sufficient Head Circuits In Large Language Models With Experiments On Syntactic Classification Tasks, Pratim Chowdhary, Peter Chin, Deepernab Chakrabarty

Computer Science Senior Theses

Understanding which neural components drive specific capabilities in mid-sized language models ($\leq$10B parameters) remains a key challenge. We introduce the $(\bm{K}, \epsilon)$-Minimum Sufficient Head Circuit ($K$-MSHC), a methodology to identify minimal sets of attention heads crucial for classification tasks as well as Search-K-MSHC, an efficient algorithm for discovering these circuits. Applying our Search-K-MSHC algorithm to Gemma-9B, we analyze three syntactic task families: grammar acceptability, arithmetic verification, and arithmetic word problems. Our findings reveal distinct task-specific head circuits, with grammar tasks predominantly utilizing early layers, word problems showing pronounced activity in both shallow and deep regions, and arithmetic verification demonstrating a …

Character Relationship Prediction In Movies: Toward Emotionally-Aware Automatic Audio Descriptions, Seung Hyun Hahm

Computer Science Senior Theses

Automatic audio description (AD) systems support visually impaired audiences by narrating visual content, but they often fail to capture the interpersonal dynamics that underpin narrative understanding. In this work, we introduce a novel framework for character relationship prediction as a means of enriching audio descriptions with socially grounded context. Our contributions are threefold: (1) we propose the Character Relationship Module (CRM), which extends identity-aware video captioning with directed sentiment inference between character pairs; (2) we develop a scalable weak supervision pipeline that uses large language models to generate 669,520 relationship annotations across 202 films; and (3) we construct a complementary …

Motionteller: Multi-Modal Integration Of Wearable Time-Series With Llms For Health And Behavioral Understanding, Aiwei Zhang, Arvind Pillai, Andrew Campbell, Nicholas C. Jacobson

Computer Science Senior Theses

As wearable sensing becomes increasingly pervasive, a key challenge remains: how can we generate natural language summaries from raw physiological signals such as actigraphy - minute-level movement data collected via accelerometers? In this work, we introduce MotionTeller, a generative framework that natively integrates minute-level wearable activity data with large language models (LLMs). MotionTeller combines a pretrained actigraphy encoder with a lightweight projection module that maps behavioral embeddings into the token space of a frozen decoder-only LLM, enabling free-text, autoregressive generation of daily behavioral summaries.

We construct a novel dataset of 54,383 ⟨actigraphy, text⟩ pairs derived from real-world NHANES recordings, and …

An Approach To Stylometry Using Causal Language Models, Harrison F. Stropkay

Computer Science Senior Theses

We present a novel stylometric approach using large language models. By training separate models on individual authors' works, we find that each model achieves lower cross-entropy loss when predicting text from its training author compared to other authors' texts. Moreover, for any given text, the model trained on its true author’s corpus yields the lowest loss. We suggest that, in this way, a model trained on one author's works embodies the unique writing style of that author. We demonstrate our approach on works by eight known authors. This approach also confirms that R. P. Thompson wrote the well-studied 15th book …

Multi-Modal Emotion Appraisals Using Large Language Models, Carlos Guerrero Alvarez

Computer Science Senior Theses

This thesis investigates the capabilities of large language models (LLMs), specifically GPT4 and GPT4o, in appraising human emotional responses within strategic social scenarios. Building on the work of Houlihan et. al[7], we evaluate LLMs using a dataset of human emotion ratings from game-theoretic situations, later extending the experimental paradigm to include both text and multimodal (image and profession) inputs. Our methodology introduces novel prompting techniques for the experiment at hand, and we compare the performance of these techniques with expert perspectives to assess how different prompts influence model predictions. Results show that LLMs can approximate human emotional appraisals, with the …

Ecological Footprint Analysis Of Chatgpt (Gpt-3), Isabella Boulais

Computer Science and Software Engineering

Climate change is an escalating crisis that demands immediate action from all sectors, including the rapidly advancing field of artificial intelligence (AI). While AI offers climate solutions, its own environmental impact raises concerns. Unfortunately limited research due to rapid development, system complexity, and lack of standardized methodologies hinders our understanding of AI’s environmental consequences. This project aims to conduct a comprehensive ecological footprint analysis of OpenAI’s GPT-3 model that is used to power ChatGPT, establishing guidelines for assessing AI systems’ environmental impact and proposing a framework for improvement. Going beyond tracking carbon emissions, this project will outline the broader lifecycle …

Forging The Future, Kenneth Benoit

Asian Management Insights

How AI is rewriting the rules of knowledge, expertise, and practice.

Binary Document Filtering For Retrieval-Augmented Generation, Sreyan Saha

Master’s Dissertations

Retrieval-Augmented Generation (RAG) has become a popular technique to enhance Large Language Models (LLMs) with access to external information sources. However, the success of RAG systems critically depends on the relevance and quality of the retrieved documents. In particular, supplying irrelevant or noisy context can lead to degraded downstream generation quality. To address this, our project focuses on improving the document filtering stage in a RAG pipeline through binary relevance classification — deciding whether a retrieved document is suitable to include in the final context window based on its usefulness in directly answering the user query. We explore a wide …

Word Level Attack For Text Ranking, Tanmay Karmakar

Master’s Dissertations

Neural Ranking Models (NRMs) have become state-of-the-art in information retrieval, demonstrating remarkable effectiveness across various search and ranking tasks. However, their increasing deployment in real-world systems raises critical concerns about their robustness and susceptibility to adversarial attacks. This project investigates the fragility of modern NRMs by proposing and evaluating a document perturbation method based on targeted, single-word perturbation. Our approach strategically identifies an influential word depending on the query to be substituted or added in the document. We have done experiments on benchmark datasets to assess the impact of these minimal perturbations on ranking performance. Our findings reveal that even …

Ideal Query Expansion Using Reinforcement Learning, Madhuchchhanda Das

Master’s Dissertations

Information retrieval (IR) systems often struggle with short, ambiguous, or underspecified queries, leading to suboptimal document retrieval. Traditional query reformulation methods, such as those based on the Rocchio algorithm, rely on heuristic term selection and relevance feedback but typically apply fixed or manually tuned weights to expanded terms. This limits their adaptability and generalization across diverse query-document contexts. In this thesis, we propose a novel reinforcement learning (RL)-based framework to dynamically optimize term weighting in reformulated queries. We model the problem as a Markov Decision Process (MDP), where each state represents a query as a vector of term weights. An …

Cutting Through The Infodemic Efficiently: News Claims Surveillance And Llm-Based Lightweight Fact Verification, Xuan Zhang

Dissertations and Theses Collection (Open Access)

In the context of the current infodemic, the rapid spread of misinformation poses a severe threat to social stability and public health. Recently, the rise of deep learning technologies has offered the potential for accelerating the development of automated misinformation detection and verification. However, current technological capabilities and computational resources often prove inadequate for the exhaustive scrutiny required, rendering the enhancement of processing efficiency a critical imperative. Given the vast amount of data on the internet, current technology and computational power often fall short in timely and accurate scrutiny of each piece of information, making the improvement of processing efficiency …

Adapting Large Language Models For Parameter-Efficient Log Anomaly Detection, Ying Fu Lim, Jiawen Zhu, Guansong Pang

Research Collection School Of Computing and Information Systems

Log Anomaly Detection (LAD) seeks to identify atypical patterns in log data that are crucial to assessing the security and condition of systems. Although Large Language Models (LLMs) have shown tremendous success in various fields, the use of LLMs in enabling the detection of log anomalies is largely unexplored. This work aims to fill this gap. Due to the prohibitive costs involved in fully fine-tuning LLMs,we explore the use of parameter-efficient fine-tuning techniques (PEFTs) for adapting LLMs to LAD.To have an in-depth exploration of the potential of LLM-driven LAD, we present a comprehensive investigation of leveraging two of the most …

Optical Character Recognition For Early Handwriting Legibility Assessment, Franceli L. Cibrian, Kayla Anderson, Yingying 'Yuki' Chen, Lauren Min, Lizbeth Escobedo

Engineering Faculty Articles and Research

Monitoring children’s handwriting, such as avoiding writing assignments, displaying uneven letter formation, or showing slow writing speed, can help identify developmental and academic issues early. Poor handwriting affects up to 34% of children, leading to academic and self-esteem challenges. Handwriting assessments, typically conducted by teachers, are often delayed due to workload and could be subjective and inconsistent. This paper explores the potential of Optical Character Recognition (OCR) technology to augment and ease handwriting assessments. Based on an evaluation of 10 OCR algorithms using 33 handwriting samples assessed by two experts, the research indicates that Pen to Print and Google are …