Digital Commons Network^™

Enhancing Online Toxicity Detection On Gaming Networks: A Novel Embeddings-Based Valence Lexicon Approach, Heba Ismail, Ashraf Khalil, Ahmed Jasmy

All Works

Online toxicity and violent speech on gaming networks pose significant threats to societal well-being, particularly among adolescents, and are linked to severe consequences such as suicide. This highlights an urgent need for effective toxicity detection methods tailored to these platforms. Traditional rule-based approaches are inherently limited, and the performance of predictive models in detecting online toxicity is critically dependent on the quality and representativeness of their training data. However, the distinct linguistic characteristics of discourse on gaming networks present unique challenges in curating representative training samples using existing valence lexicons, often resulting in suboptimal detection accuracy. In this study, we …

Limitations In Speech Recognition For Young Adults With Down Syndrome, Franceli L. Cibrian, Yingying 'Yuki' Chen, Kayla Anderson, Cecilia Marie Abrahamsson, Vivian Genaro Motti

Engineering Faculty Articles and Research

Speech recognition has the potential to make technology more accessible to users. However, the accuracy of speech recognition remains limited for users with disabilities, including those with Down Syndrome, and the types and frequencies of recognition errors are poorly understood. This paper characterizes these problems, focusing on errors occurring when recognizing Down Syndrome speech. We analyze the transcripts from six speech recognition algorithms (Google, IBM, Otter.ai, Microsoft, AssemblyAI, OpenAI) using the audio content of 15 individuals with Down Syndrome (331 dialogues; 3428 words). Our analysis shows: (1) significant difference in speech recognition accuracy for people with Down Syndrome compared to …

Adapting Biological Mechanisms Into Machine Learning Structures And Gradient Propagation, Hao Yu

Theses and Dissertations

As neuromorphic computing gains traction, researchers look for learning schemes that are more suitable for brain-like structures. Although many algorithms have been discovered, such as surrogate gradient or Spike Timing Dependent Plasticity, those algorithms are either unsuitable for on-chip training or not biologically aligned. The goal of this thesis is to discover a learning algorithm which is biologically aligned and suitable for on-chip training. Through the discoveries of our two papers, we are able to introduce the concepts of synaptic clefts and neurotransmitters into a common spiking neuron network. We also designed an error propagation method aligning with biology. With …

Debugprograde: Improving Automated Assessment Of Coding Assignments With A Focus On Debugging, Amit Patel, Hardik Joshi

Journal of Informatics and Web Engineering

In education, the evaluation of programming assignments is challenging, especially to do with the debugging aspect. Self-grading technologies are unable to capture the level of understanding of students and context-bound responses. In light of these, we created DebugProGrade to take what we normally know about grading and improve it with semantic analysis and keyword extraction. DebugProGrade identified 1000 first-year BCA students who in Google Forms provided their answers to evaluate error detection and solution proposals for a basic C programming assignment. For the explanations’ specificity and for the context-level evaluation, the system employs the SBERT embedding, namely, the sentence-transformer bidirectional …

Zombie-Auras: Genai And Hybrid Text Production, Joshua Nieubuurt

English Faculty Publications

This paper explores the iterative evolutions of textual production and their impact on the “aura” of texts, as conceptualized by Walter Benjamin. The study identifies three key phases of textual production: the natural, the mechanized, and the digitized, each progressively displacing the “cult value” of texts. This cult value is lost through increased ease of creation, reproduction, dissemination, and dislocation of creators and audiences in time and space. The advent of Generative AI (GenAI) marks the latest evolution, transforming the “aura” into a memetic “zombie” form—familiar yet opaque, evoking both the sublime and fear. By examining Benjamin’s notion of “aura” …

Vaxbot-Hpv: A Gpt-Based Chatbot For Answering Hpv Vaccine-Related Questions, Yiming Li, Jianfu Li, Manqi Li, Evan Yu, Danniel Rhee, Muhammad Amith, Lu Tang, Lara S Savas, Licong Cui, Cui Tao

Faculty, Staff and Student Publications

OBJECTIVE: Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine.

METHODS: We constructed the knowledge base (KB) for VaxBot-HPV, which consists of 451 documents from biomedical literature and web sources on the HPV vaccine. We extracted 202 question-answer pairs from the KB and 39 questions generated by GPT-4 for training and …

Exploring & Exploiting High-Order Graph Structure For Sparse Knowledge Graph Completion, Tao He, Ming Liu, Yixin Cao, Zekun Wang, Zihao Zheng, Bing Qin

Research Collection School Of Computing and Information Systems

Sparse Knowledge Graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increase of graph sparsity. This problem is also exacerbated because of the widespread existence of sparse KGs in practical applications. To alleviate this challenge, we present a novel framework, LR-GCN, that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse KGC. The proposed approach comprises two main components: a GNN-based predictor and a reasoning path distiller. The reasoning path distiller explores high-order graph …

A Causality-Aware Paradigm For Evaluating Creativity Of Multimodal Large Language Models, Zhongzhan Huang, Shanshan Zhong, Pan Zhou, Shanghua Gao, Marink Zitnik, Liang Lin

Research Collection School Of Computing and Information Systems

Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and data-scarce nature of creativity, especially in multimodal scenarios. In this paper, we consider the comprehensive pipeline for evaluating the creativity of multimodal LLMs, with a focus on suitable evaluation platforms and methodologies. First, we find the Oogiri game—a creativity-driven task requiring humor, associative thinking, and the ability to produce unexpected responses to text, images, or both. This game aligns well with the input-output structure of modern …

Seven Hci Grand Challenges Revisited: Five-Year Progress, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Vincent G Duffy, Qin Gao, Waldemar Karwowski, Fiona Nah, Stavroula Ntoa, Pei-Luen Patrick Rau, Keng Siau, Jia Zhou

Research Collection School Of Computing and Information Systems

Motivated by the rapid technological advancements achieved in the last five years, and the pervasiveness of Artificial Intelligence, the paper investigates the evolving role of Human-Computer Interaction and revisits the seven grand challenges outlined in 2019: human-technology symbiosis, human-environment interactions, ethics, privacy and security, well-being, health and eudaimonia, accessibility and universal access, learning and creativity, and social organization and democracy. Through literature analysis, the paper reevaluates the status of each challenge and highlights emerging requirements. Key findings reveal the widespread impact of Artificial Intelligence across all domains and emphasize the need for improved AI transparency, alignment with human values, and …

Chatgpt Didn’T Write This: Evaluating The Impact Of Llms With A Case Study In Grading Cuny Language Immersion Program Student Essays, Benjamin Inbar

Dissertations, Theses, and Capstone Projects

This study evaluates the capabilities and limitations of large language models (LLMs), specifically OpenAI’s ChatGPT-4o, in grading essays from students in the City University of New York’s Language Immersion Program. The program serves English language learners with diverse linguistic and demographic backgrounds, offering intensive language instruction to prepare students for academic success in college. Using a dataset of 30 pre- and post-program essays scored by program instructors and ChatGPT-4o under three paradigms, this research explores the alignment between human and AI-generated scores across five rubric-based competency areas. Findings reveal that ChatGPT-4o aligns moderately with human grading, with the strongest agreement …

Ethnomathematics Projects For Projective Geometry In Distance Learning, Wen-Haw Chen, Ja'faruddin Arsyad Ide

Journal of Humanistic Mathematics

This article reports on an ethnomathematics project we developed and implemented in an online learning environment to connect with students’ cultural backgrounds and to highlight the links between their environment, culture, and mathematics. The project is based on Simple House Theory, a framework that involves the application of projective geometry in the design of traditional houses and building numbers. Our results show that this approach fostered active participation and engagement despite the standard constraints and challenges of distance learning.

Identifying Cyberbullying Roles In Social Media, Manuel Sandoval, Mohammed Abuhamad, Patrick Furman, Mujtaba Nazari, Deborah Hall, Yasin N. Silva

Computer Science: Faculty Publications and Other Works

Social media has revolutionized communication, allowing people worldwide to connect and interact instantly. However, it has also led to increases in cyberbullying, which poses a significant threat to children and adolescents globally, affecting their mental health and well-being. It is critical to accurately detect the roles of individuals involved in cyberbullying incidents to effectively address the issue on a large scale. This study explores the use of machine learning models to detect the roles involved in cyberbullying interactions. After examining the AMiCA dataset and addressing class imbalance issues, we evaluate the performance of various models built with four underlying LLMs …

Incorporating Visual Information Into Natural Language Processing, Maxwell Mbabilla Aladago

Dartmouth College Ph.D Dissertations

Natural language describes entities in the world, some real and some abstract. It is also common practice to complement human learning of natural language with visual cues. This is evident in the heavily graphical nature of children’s literature which underscores the importance of visual cues in language acquisition. Similarly, the notion of “visual learners” is well recognized, reflecting the understanding that visual signals such as illustrations, gestures, and depictions effectively supplement language. In machine learning, two primary paradigms have emerged for training systems involving natural language. The first paradigm encompasses setups where pre-training and downstream tasks are exclusively in natural …

Classifying Items With The Rating Values 3 Using Text Reviews To Improve The Recommendation Accuracy In The Collaborative Filtering Approach, Ali Mohsin Ahmed Al-Sabaawi, Mohsin Hasan Hussein, Muyassar Dalli

Karbala International Journal of Modern Science

Collaborative filtering is a common aspect recently used in e-commerce to increase sales and overcome information overload. One significant limitation in collaborative filtering is data sparseness. Several studies have proposed alleviating this issue by utilizing extra information such as users’ reviews. However, the researchers have been concerned with using entire reviews irrespective of the users’ ratings. This requires extra processing time and might perplex the recommendation decision. In this study, after analyzing the users’ ratings and reviews, it was noted that when the rating values are 4 or 5, most of the reviews accompanying these ratings are positive. Otherwise, when …

Advanced Prediction Of Events And Temporal Expressions In Medical Text Using The Jena Api: Integrating Ontologies And Deep Learning, Hafida Tiaiba, Lyazid Sabri, Okba Kazar

Turkish Journal of Electrical Engineering and Computer Sciences

The automatic recognition of medical concepts and temporal expressions in narrative clinical text enhances the utility of electronic health records (EHRs) and supports clinical decision-making and research. However, challenges arise due to the complexity of medical language, ambiguity of terms, and variability in expression. To address these issues, the use of medical ontologies significantly improves data management in healthcare. A novel approach integrates various medical ontologies covering drugs, symptoms, diseases, anatomy, disease drivers, and food, and with convolutional neural networks (CNNs) -including Standard, Transposed, and Separable convolution models (CONSEPTR)- to extract both medical events (e.g., clinical departments, treatments, problems) and …

"It's Definitely New And Different...It's Really Engaging": Understanding The Power Of Storytelling Towards Secure Password Creation, Rizu Paudel, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

There is a dearth in existing literature to attain systematic understanding of leveraging digital storytelling in security designs. As we begin to address this gap, we focused on user authentication where the existing password composition policies and password meters often fail to help users around creating a strong and memorable password. To this end, we conducted a lab study with 19 participants, where we updated our initial designs in an iterative manner based on their feedback. We then conducted a between-subject online study with 104 participants over Amazon Mechanical Turk to evaluate our designs. We found that all of our …

A Dynamic Intuitionistic Fuzzy Group Decision Analysis For Sustainability Risk Assessment In Surface Mining Operation Projects, Mohammad Panahi Borujeni, Arash Behzadipour, Hossein Gitinavard

Journal of Sustainable Mining

With the increasing advancement of technology and science in the fields of engineering, different industries have different benefits that can help the global community in areas related to social, environmental, and economic requirements. The mining industry is one of the suitable sources of wealth production in different societies, which leads different societies to appropriate profitability. In this regard, one of the challenges facing managers and decision-makers is not related to financial challenges and risks. This research uses the dynamic intuitionistic fuzzy entropy method to compute the criteria weights, and the dynamic intuitionistic fuzzy maximizing similarity (DIF-SM) approach to compute the …

Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal Large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leaving richer spatial information of 3D images under-explored, and single-modality-based methods are limited by overlooking the critical clinical information contained in other modalities. To address this issue, this paper proposes Brain-Adapter, a novel approach that incorporates an extra bottleneck layer to learn new knowledge and instill it into the original pre-trained knowledge. The major idea is to …

Exploring The Trade-Offs: Unified Large Language Models Vs Local Fine-Tuned Models For Highly-Specific Radiology Nli Task, Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Zhengliang Liu, Lin Zhao, Yiwei Li, Haixing Dai, Chong Ma, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu

Computer Science Faculty Research & Creative Works

Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain specific. To this end, in …

Comparing Unidirectional, Bidirectional, And Word2vec Models For Discovering Vulnerabilities In Compiled Lifted Code, Gary Mccully, John Hastings, Shengjie Xu, Adam Fortier

Research & Publications

Ransomware and other forms of malware cause significant financial and operational damage to organizations by exploiting long-standing and often difficult-to-detect software vulnerabilities. To detect vulnerabilities such as buffer overflows in compiled code, this research investigates the application of unidirectional transformer-based embeddings, specifically GPT-2. Using a dataset of LLVM functions, we trained a GPT-2 model to generate embeddings, which were subsequently used to build LSTM neural networks to differentiate between vulnerable and non-vulnerable code. Our study reveals that embeddings from the GPT-2 model significantly outperform those from bidirectional models of BERT and RoBERTa, achieving an accuracy of 92.5\% and an F1-score …

Chalkboards To Chatbots: Helping Faculty Harness Ai For The Future Of Higher Education, Justin C. Grace

Regis University Student Publications (comprehensive collection)

Integrating artificial intelligence (AI) into nursing education presented significant opportunities yet posed challenges due to varied faculty readiness. This Doctor of Nursing Practice (DNP) quality improvement (QI) project evaluated an educational intervention aimed at enhancing nursing faculty's AI proficiency and confidence at Regis University’s Rueckert-Hartman College for Health Professions. Using a mixed-methods, pre- and post-intervention design, validated surveys assessed changes in faculty perceptions, knowledge, and skills related to AI. The intervention included a digital toolkit with nine instructional videos demonstrating practical AI applications using FreedAI’s large language model, ChatGPT, supported by voiceover narration and closed captioning. Data analysis involved descriptive …

Celebrating Faculty Scholarship 2025, Smith College Libraries

Celebrating Faculty Scholarship: Bibliographies

Scroll down to view the bibliography of selected scholarly and creative work produced by Smith College faculty and submitted by faculty for the 2025 celebration which took place in the Neilson Library Skyline Reading Room, Smith College on April 24, 2025.

Successfully Navigating The Disruption Ai Will Bring To Survey Research, David M. Rothschild, Trent D. Buskirk, Stephanie Eckman, D. Sunshine Hillygus, Frauke Kreuter, David Lazer

Information Technology & Decision Sciences Faculty Publications

Surveys are a core methodological tool in government, industry, and academia, providing essential data for theory development and evidence-based decision-making. As artificial intelligence continues its rapid advancement, it stands to fundamentally transform the entire survey lifecycle - from design and administration to analytics and reporting. Previous transitions to new technologies, such as telephone, internet, and non-probability surveys, led to divisions within the survey research community with real consequences for both the trajectory of research and trust in the industry. We believe the survey community should take proactive steps now to avoid similar challenges with AI integration. Specifically, our paper examines …

Neural Congruency Contrastive Learning Framework Validation Using Artificially Created Eeg Data For Dyslexia Research, Jacqueline M. Torres

Theses and Dissertations

Analysis of electroencephalogram (EEG) recordings in children with dyslexia has been commonly used to explore the neural mechanisms underlying reading disorders. Yet, challenges such as low signal-to-noise ratio (SNR), high inter-subject and inter-trial variability, and the inherently multivariate nature of EEG signals hinder the isolation of neural components elicited during reading diagnostic tests. To mitigate these challenges, the neural-congruency analysis framework was recently proposed, leveraging traditional machine learning optimization methods to incorporate domain knowledge about the congruency of neural responses across participants (i.e., consistent neural responses among proficient readers). However, the application of deep learning techniques, specifically contrastive learning, remains …

The Impact Of Llms Usage On Learning Outcomes For Software Development Students: A Focus On Prompt Engineering, Mohammed Owaidh Aljohani

CGU Theses & Dissertations

This study investigates the impact of large language model (LLM) usage, specifically ChatGPT, on student learning outcomes in programming education. The research adopts a mixed-methods approach, combining quantitative survey data from students and qualitative interviews with instructors. The study addresses three research questions: (1) the effect of LLM usage on undergraduate students' learning outcomes, (2) the influence of prompt engineering skills on this relationship, and (3) instructors' perceptions on these relationships. Quantitative data were collected from 159 students across two Saudi universities using a structured online survey with sections covering demographic information, LLM usage, self-reported programming understanding, and prompt engineering …

Towards Human Explainable Digital Forensics: Generating Human Interpretable Evidence For Semantic Understanding In Manipulated Images And Text, Yuwei Chen

Electronic Theses & Dissertations (2024 - present)

Detecting and characterizing manipulations in digital media continues to pose a significant challenge within the field of digital forensics. Despite notable advancements, the discipline often remains in a reactive stance against emerging threats. Current state-of-the-art methods, typically evaluated within academic settings, fails to mirror the complexities of real-world disinformation scenarios. These methods generally prioritize high performance based on quantitative metrics, yet they demonstrate a considerable dependency on training data and lack adaptability to new novel attack signatures. With the rapid evolution of attack methodologies, the dependency on highly accurate models that do not generalize or adapt well to unseen threats …

Textual Sediments: Genre And The Planetary Novel, William Bowden

Open Access Dissertations

This dissertation investigates how twenty-first-century prose fiction navigates the ecological and temporal dimensions of planetary crisis. In response to accelerating pressures - climate change, ecological collapse, and the intensification of capitalist modernity - literary studies has turned increasingly to the planetary as a conceptual frame. Yet this shift has often outpaced the development of adequate critical vocabularies and narrative methodologies. By drawing on ecocriticism, narrative theory, phenomenology, and related fields, Textual Sediments proposes a new framework for reading what it terms the planetary novel: a sedimentary genre characterized by temporal layering, formal hybridity, and a recursive engagement with scale. Through …

Hybrid Agentic System For Schema-Aware Nl2sql Generation, David Omondi Onyango

All Graduate Theses, Dissertations, and Other Capstone Projects

The natural language to SQL (NL2SQL) task enables non-expert users to interact with relational databases via natural language interfaces. However, NL2SQL frameworks often rely on Large Language Models (LLMs), raising concerns about computational overhead, data privacy, and deployment in resource-limited environments. To address these issues, we propose a hybrid schema-aware agentic system using Small Language Models (SLMs) as primary agents, with a selective LLM fallback mechanism. The LLM activates only when errors are detected in SLM-generated queries, reducing inference costs. Experiments on the BIRD benchmark dataset show our system achieves an execution accuracy of 53.91% and validation efficiency score of …

Enhancing The Accuracy Of Image Classification For Degenerative Brain Diseases With Cnn Ensemble Models Using Mel-Spectrograms, Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

Mathematics & Statistics Faculty Publications

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice …

Embedding With Large Language Models For Classification Of Hipaa Safeguard Compliance Rules, Md Abdur Rahman, Md Abdul Barek, Abm Kamrul Islam Riad, Md Mostafizur Rahman, Bajlur Rashid, Md Raihan Mia, Hossain Shahriar, Guillermo Francia, Fan Wu, Alfredo Cuzzocrea, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to …