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Review Of Who Wrote This?: How Ai And The Lure Of Efficiency Threaten Human Writing, By Naomi S. Baron, Taylor J. Greene

Library Articles and Research

A review of Who Wrote This?: How AI and the Lure of Efficiency Threaten Human Writing, by Naomi S. Baron.

Content Moderation On Social Media: Social And Computational Standards And Implications, Mohit Singhal

Computer Science and Engineering Dissertations - Archive

Social media has become a powerful tool that reflects human communication's best and worst aspects. They allow individuals to freely express opinions, communicate with others, and learn about new stories. On the other hand, they have become fertile grounds for several forms of abuse, harassment, and the dissemination of misinformation. Social media platforms have established and employed content moderation to counteract the spread of abuse and misinformation.

Some critical challenges hinder the understanding of the social media content moderation ecosystem. This dissertation investigates various aspects of content moderation, including their coverage, fairness, and effectiveness. Firstly, it investigates how, in practice, …

Natural Language Generation From Large-Scale Open-Domain Knowledge Graphs, Xiao Shi

Computer Science and Engineering Dissertations - Archive

This dissertation delves into the realm of natural language generation (NLG) from expansive open-domain knowledge graphs, aiming to bridge the gap between existing methods primarily tested on limited datasets and the demands of real-world large-scale, diverse graph structures. Prior works in NLG often relied on small-scale or restricted datasets, neglecting the complexities of broader knowledge graphs. To address this, we introduce a new dataset called GraphNarrative, designed to encompass a wide range of graph structures and enhance the realism of NLG tasks.

The core contribution of this research lies in devising a novel approach to mitigating information hallucination, a common …

Claim Sensing: A Study Linking Factual Claims To Human Behaviors On Social Media, Zeyu Zhang

Computer Science and Engineering Dissertations - Archive

The ubiquity of social media has transformed it into a rich source for reflecting people's opinions, behaviors, and interactions. Users frequently encounter factual claims in news, stories, and political statements, which can be either true or false. These claims significantly shape people's minds and behaviors, influencing not only individual perspectives but also broader public discourse. This study explores individuals' behaviors and perceptions toward factual claims by leveraging the concept of "check-worthiness" to analyze the relationship between such claims and user behaviors across datasets containing tens of millions of social media posts, particularly tweets from the platform X (formerly Twitter). It …

Using Generative Large Language Models For Hierarchical Relationship Prediction In Medical Ontologies, Hao Liu, Shuxin Zhou, Zhehuan Chen, Yehoshua Perl, Jiayin Wang

School of Computing Faculty Scholarship and Creative Works

This study extends the exploration of ontology enrichment by evaluating the performance of various open-sourced Large Language Models (LLMs) on the task of predicting hierarchical relationships (IS-A) in medical ontologies including SNOMED CT Clinical Finding and Procedure hierarchies and the human Disease Ontology. With the previous finetuned BERT models for hierarchical relationship prediction as the baseline, we assessed eight open-source generative LLMs for the same task. We observed only three models, without finetuning, demonstrated comparable or superior performance compared to the baseline BERT -based models. The best performance model OpenChat achieved a macro average F1 score of 0.96 (0.95) on …

Mitigating Safety Issues In Pre-Trained Language Models: A Model-Centric Approach Leveraging Interpretation Methods, Weicheng Ma

Dartmouth College Ph.D Dissertations

Pre-trained language models (PLMs), like GPT-4, which powers ChatGPT, face various safety issues, including biased responses and a lack of alignment with users' backgrounds and expectations. These problems threaten their sociability and public application. Present strategies for addressing these safety concerns primarily involve data-driven approaches, requiring extensive human effort in data annotation and substantial training resources. Research indicates that the nature of these safety issues evolves over time, necessitating continual updates to data and model re-training—an approach that is both resource-intensive and time-consuming. This thesis introduces a novel, model-centric strategy for understanding and mitigating the safety issues of PLMs by …

Re–Operating The Bhumibol And Sirikit Dams Using Hybrid Neuro–Fuzzy Technique To Solve The Water Scarcity And Flooding Problems In The Chao Phraya River Basin, Khin Muyar Kyaw, Areeya Rittima, Yutthana Phankamolsil, Allan Sriratana Tabucanon, Wudhichart Sawangphol, Jidapa Kraisangka, Yutthana Talaluxmana, Varawoot Vudhivanich

Applied Environmental Research

The decision support system to reservoir re–operation using Artificial Intelligence has been broadly studied and proven in term of the operational performances for both single and multiple reservoir system, this study applied Adaptive Neuro Fuzzy Inference System (ANFIS) technique for reservoir re–operation in Chao Phraya River Basin aiming to reduce water scarcity and flooding problems in the central region of Thailand. ANFIS is an integrated approach in which neural networks are utilized to enhance the fuzzy inference system and create fuzzy “IF–Then” reservoir operational guidelines with proper membership functions for reservoir re–operation. In this study, ANFIS operating rules were trained …

A New-Type Deep Learning Model Based On Shapley Regulation For Containerized Freight Index Prediction, Yen-Chang Shih, Ming-Shue Lin, Taih-Cherng Lirn, Jih-Gau Juang

Journal of Marine Science and Technology–Taiwan

In this study, we have crafted an innovative methodology that represents a groundbreaking synthesis of deep learning techniques with cooperative game theory. In this study, we use the accuracy of data prediction by different LSTM models as a measurement index and assign different LSTM models corresponding weights through the Shapley value calculation method to construct a more accurate predictive analysis model. We use this improved Shapley regulation model to calibrate a long short-term memory (LSTM) neural network by using historical freight data to predict the China Container Freight Index (CCFI), the leading export container freight index commonly used in China. …

Identifying Accessibility Improvement Opportunities For Global Environmental Communication Websites, Alicia Mason, Josh Compton, Sakshi Bhati

Faculty Submissions

Digital environmental information presented with low web accessibility reduces parti-cipation in environmental advocacy and information seeking behaviors for many people with disabilities, creating a consequential digital divide. We use WEB AIM computer automated analysis of web accessibility to assess the general accessibility of online environmental communication messages. Results show 95.3% of the popular websites in our sample had indicators of potentially critical accessibility errors. Our findings highlight the importance of addressing digital environmental information accessibility shortcomings so that educational, informational and persuasive environ-mental messages can increase environmental literacy of vulnerable populations and help to bridge the digital divide.

แบบจำลองการจัดลำดับความสำคัญของความเสี่ยงของยูสเซอร์สตอรีสำหรับการจัดลำดับความสำคัญโดยใช้ตรรกะคลุมเครือ, เปรม สุนทรภาส

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในกระบวนการพัฒนาซอฟต์แวร์แบบแอไจล์ ยูสเซอร์สตอรีเป็นองค์ประกอบสำคัญที่สะท้อนความต้องการของผู้ใช้ แต่หากยูสเซอร์สตอรีมีคุณภาพไม่เพียงพอ อาจก่อให้เกิดความเสี่ยงที่ส่งผลกระทบต่อความสำเร็จของโครงการอย่างมีนัยสำคัญ ดังนั้นงานวิจัยนี้จึงนำเสนอแบบจำลองการจัดลำดับความสำคัญของความเสี่ยงของยูสเซอร์สตอรีโดยใช้ตรรกะคลุมเครือ เพื่อช่วยให้สามารถจัดการกับความไม่แน่นอนในการตัดสินใจได้อย่างมีประสิทธิภาพ โดยเริ่มจากการนิยามความเสี่ยงของยูสเซอร์สตอรีจำนวน 12 ประเภท พร้อมทั้งกำหนดเกณฑ์การประเมินใน 2 ด้าน ได้แก่ ด้านความพยายามในการบริหารจัดการ และด้านข้อจำกัดในการบริหารจัดการ จากนั้นจึงประยุกต์ใช้ตรรกะคลุมเครือร่วมกับตัวแปรภาษาและฟังก์ชันความเป็นสมาชิกในการคำนวณค่าคะแนนความเสี่ยง และจัดลำดับความสำคัญผ่านแบบจำลองที่พัฒนาขึ้น ผลการประเมินการใช้งานแบบจำลองกับผู้มีประสบการณ์ในโครงการแอไจล์พบว่า แบบจำลองสามารถช่วยในการจำแนกความเสี่ยงได้อย่างเหมาะสมและมีความสอดคล้องกับความคิดเห็นของผู้ประเมิน ซึ่งผลการประเมินสะท้อนถึงความสมเหตุสมผลของแบบจำลองและความสามารถในการประยุกต์ใช้จริง ดังนั้นงานวิจัยนี้จึงเป็นประโยชน์ต่อทีมพัฒนาในการประเมินและจัดลำดับความเสี่ยงของยูสเซอร์สตอรีได้อย่างเป็นระบบ และเพิ่มประสิทธิภาพในการบริหารความเสี่ยงในโครงการพัฒนาซอฟต์แวร์แบบแอไจล์ได้ดียิ่งขึ้น

6. More Is Less?: Using Generative Ai For Idea Generation And Diversification In Early Writing Processes, Franziska Tsufim, Lainie Pomerleau

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

As writing teachers, we are strong proponents of process writing. At the same time, we are aware that early process work, especially in a group setting, can be time consuming and anxiety-inducing. Students may also self-censor when sharing work with peers especially if they are not confident in their ideas. Drawing on the process of nominal, electronic brainstorming, we created two different prompts that allowed students to incorporate generative Al into their idea generation process. This first activity improves the efficiency of individual idea generation, while the second exercise helps increase student confidence in their ideas in collaborative brainstorming situations. …

Digital Resurrection Of Historical Figures: A Case Study On Mary Sibley Through Customized Chatgpt, James Hutson, Paul Huffman, Jeremiah Ratican

Faculty Scholarship

This study investigates the emerging realm of digital resurrection, focusing on Mary Sibley (1800–1878), the esteemed founder of Lindenwood University. The core objective was to demonstrate the capability of advanced artificial intelligence, specifically a customized version of ChatGPT, in revitalizing historical figures for educational and engagement purposes. By integrating comprehensive diaries from Sibley with Claude 2.0, the research utilized a substantial autobiographical dataset to develop a GPT beta version that replicates her distinct voice and tone. The incorporation of her official portrait and diaries into the GPT Builder was pivotal, creating an interactive platform that accurately reflects her perspectives on …

Analysis Of Speech Recognition Systems And Error Correction Approaches, Saki Imai

Honors Theses

Despite significant advances in automatic speech recognition (ASR) accuracy, challenges remain. Naturally occurring conversation often involves multiple overlapping speakers, of different ages, accents and genders, as well as noisy environments and suboptimal audio recording equipment, all of which reduce ASR accuracy. In this study, we evaluate the accuracy of state of the art open source ASR systems across diverse conversational speech datasets, examining the impact of audio and speaker characteristics on WER. We then explore the potential of ASR ensembling plus post-ASR correction methods to improve transcription accuracy. Our findings underscore the need for robust error correction techniques and of …

Improving Question Answering Retrieval System Through Multi-Result Ranking Model, Danupat Khamnuansin

Chulalongkorn University Theses and Dissertations (Chula ETD)

Recent trends in various industries involve integrating artificial intelligence (AI) systems to enhance operational efficiency. Among these advancements, AI-supported question-answering (QA) systems have gained significant attention. These systems typically employ a two-stage process, integrating QA system capabilities with Information Retrieval (IR) methods. The introduction of the Retrieval Question Answering (ReQA) has further refined this process, offering a more practical solution, aiming to improve real-world applicability. Considering the wide availability of diverse QA retrieval models, employing a combination of multiple systems presents as a viable solution. However, the approach to combining QA retrieval systems remains relatively limited. We propose a method …

Enhancing Multilingual Sentence Representation Learning For The Job Recruitment Domain, Napat Laosaengpha

Chulalongkorn University Theses and Dissertations (Chula ETD)

With the advancement in natural language processing (NLP), there has been significant development in multilingual pretraining sentence encoder. Typically, these pretraining models are trained on large-scale datasets that consist of general text data from various sources such as Wikipedia. However, the general proposed models aren't enough to understand contexts in such a domain-specific, especially in the job recruitment domain. It is due to its niche nature and the lack of readily available related information. To enhance the existing multilingual pretraining sentence encoder and mitigate the aforementioned problems, we first propose multi-task dual-encoder framework to improve the sentence encoder for general-purpose …

Short, Full, Best: Analysis Of Different Conference Papers, Miguel Williams

Graduate Research Theses & Dissertations

Research, publish, repeat. This is the basic cycle of anyone in academia. Individuals in academia conduct research, write up your research into a paper or journal article, submit to a conference or journal, and repeat the process. If you're skilled you may even obtain the coveted best paper award from the conference. In this research, I compare full papers to short papers and full papers to best papers. I start by fine-tuning three transformer models for classification capabilities. After I calculate lexical diversity and readability metrics of the papers, I use the features to train three traditional machine learning models. …

Prospective Teachers' Conceptions Of Area, Betsy Mcneal, Sayonita Ghosh Hajra, Michael Battista, Ayse Ozturk

Teaching & Learning Faculty Publications

This study explored 10 prospective teachers’ (PTs’) understanding of the area of a rectangular region using square and non-square rectangular area-units. In an hour-long interview, each PT was first asked to explain how they would find the area of a given rectangular region in terms of a non-square notecard. For several PTs, this task prompted discussion of a square unit defined by the edge of the notecard. In a second task, PTs were presented with a rectangular array of squares and were asked to explain to a fictional child why multiplying length times width does not count the top left …

Survey Of Planar And Outerplanar Graphs In Fuzzy And Neutrosophic Graphs, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

As many readers may know, graph theory is a fundamental branch of mathematics that explores networks made up of nodes and edges, focusing on their paths, structures, and properties [196]. A planar graph is one that can be drawn on a plane without any edges intersecting, ensuring planarity. Outerplanar graphs, a subset of planar graphs, have all their vertices located on the boundary of the outer face in their planar embedding. In recent years, outerplanar graphs have been formally defined within the context of fuzzy graphs. To capture uncertain parameters and concepts, various graphs such as fuzzy, neutrosophic, Turiyam, and …

Using Ontological Methods To Compare Cybersecurity Maturity Model Certification 2.0 And Cobit 19, Aaron Marshall Ramey

CCAC Theses and Dissertations

Cybersecurity frameworks developed by a variety of organizations and implemented by a much larger collection of organizations differ in their focus and application. Whether designed by a private or government organization, the primary goal is to provide a framework to assess and reduce risk. The Department of Defense (DoD) has recently implemented the second version of the Cybersecurity Maturity Model Certification (CMMC 2.0). In some situations, compliance with CMMC 2.0 has already become mandatory for the Defense Industrial Base (DIB). Compliance will soon be required for all Large Businesses (LB) and Small Businesses (SB) within the DIB. While COBIT 19 …

Comparative Analysis Of Data Augmentation On Sentiment Analysis In Three Distinct Languages, Hyesu Lee

All Graduate Theses, Dissertations, and Other Capstone Projects

Machine learning in natural language processing analyzes datasets to make future predictions for various filed in the real world. By training machine algorithms on the datasets of text, the model can learn patterns and structure of the text in many different languages. Then the model enables to perform the text classification, sentiment analysis, and other tasks. A large and balanced dataset is required to develop an accurate machine learning model. However, the collection of a reliable, large, and equally distributed dataset is a challenging and requires significant resources and time. As a solution to this challenge, a data augmentation technique …

Constructed Language (Conlang) Audio Honing, Ronald B. Oakes

CCAC Theses and Dissertations

An important aspect of a constructed language (conlang) is how it sounds when spoken. This dissertation designs and implements a tool to allow the user to hear how their conlang sounds when spoken. This tool will generate spoken language based on sample text in the constructed language. Further, it will enable the user to manipulate the phonetics of the language and hear how these changes impact the language in its spoken form.

This tool also allows users to assess the preferability of the language’s phonetics using Net Auditory Distance under the framework of Beats-and-Bindings Phonology. It was shown that the …

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 …

Dementia Detection Using Transfer Learning From Recorded Speech, Gun Wirojburapa

Chulalongkorn University Theses and Dissertations (Chula ETD)

Dementia is an irreversible and progressive condition that affects cognitive impairment, and millions of people are affected. Early detection provides patients with premature treatment and improves their quality of life. Traditional methods for dementia detection, such as cognitive assessment and neurological analysis, are expensive and time-consuming. Therefore, this thesis aims to research alternative methods to detect dementia, such as speech analysis. This thesis performs dementia classification by using transfer learning and machine learning techniques. Samples in the dataset consist of English and Chinese speakers. This study found transfer learning and machine learning techniques able to classify the speakers between dementia …

Thai-English Supported Automatic Speech Recognition For Endoscopic Reporting, Arpanant Saeng-Xuto

Chulalongkorn University Theses and Dissertations (Chula ETD)

This thesis presents the automatic speech recognition (ASR) system for endoscopic reporting that supports Thai-English code-switching. During endoscopic procedures, gastroenterologists are required to use both hands to handle instruments, thereby complicating the real-time documentation of abnormal findings. While recent advances in speech recognition offer promising solutions, existing models face difficulties with Thai-English code-switching and tend to overfit when fine-tuned on limited datasets. To overcome these limitations, we propose an ASR model enhanced with the Mixture of Experts (MoE) technique to improve transcription accuracy. Furthermore, the Named Entity Recognition (NER) model extracts gastrointestinal (GI) terminology from the transcriptions and classifies its …

Enhancing Large Language Models For Thai Legal Chatbots, Supachoke Hanwiboonwat

Chulalongkorn University Theses and Dissertations (Chula ETD)

Currently, developing a Thai legal question-answering system for the general public is highly challenging due to the complex, difficult-to-understand language and the extensive content of legal codes. This research proposes a Thai legal question-answering system designed for the public, aiming to establish best practices for developing effective legal QA systems. To improve performance, we created our own Thai legal QA dataset and incorporated data from various sources. We conducted comparative experiments to identify the most suitable language model for Thai legal contexts, and fine-tuned the models with diverse datasets for enhanced capabilities in legal QA and legal examinations. Additionally, we …

Emotion Recognition Through Advanced Signal Fusion And Kolmogorov-Arnold Networks, Aung Myo Thant

Chulalongkorn University Theses and Dissertations (Chula ETD)

Emotion recognition is an important aspect of affective computing that has many applications including human computer interaction and mental health care. In this thesis, Kolmogorov-Arnold Networks (KANs) are explored for the purpose of multimodal emotion recognition from EEG (Electroencephalogram) and eye movement signals of the SEED-V dataset. Both modalities were represented by Differential Entropy (DE) features and a two-branch KAN architecture was used which composed of seven KAN variants, namely, Efficient KAN, Jacobi KAN, Hermite KAN, Fourier KAN, Chebyshev KAN, RBF KAN, and Wavelet KAN (with different wavelet types including DOG, Morlet, Mexican hat, and Meyer). The strengths of two …

The Influence Of Echo Chamber On Thailand's 2023 Election, Isariyaporn Sukcharoenchaikul

Chulalongkorn University Theses and Dissertations (Chula ETD)

This research develops visual methods to explore the echo chamber effect, employing analytical approaches to investigate its dynamics through a case study on the 2023 General Election in Thailand. The study utilizes visualization techniques such as node-link diagrams, t-SNE projections, and heatmaps to analyze homophilic relationships, clustering tendencies, and polarization within online communities. To minimize inaccuracies and biases, network graphs are constructed based on contextual analysis of user-generated content, rather than relying on predefined relationship definitions (e.g., friendships, followers, retweets) or users' interpretations. The research uses the Echo Chamber Score (ECS) alongside visualizations to explore echo chambers, reveal significant variations …

Craftjai: Llm-Enhanced Generative Ai For Gamified Mood Journal, Kingrak Phairoh

Chulalongkorn University Theses and Dissertations (Chula ETD)

Mood journaling helps promote emotional self-awareness, but maintaining user motivation remains difficult. This study presents CRAFTJAI, a generative AI system that enhances journaling through emotionally aligned visual feedback. Instead of replacing diary entries, the system adds character-based imagery to support intrinsic motivation and symbolic emotional reflection. It consists of emotional feature extraction using a large language model, emotion-labeled dataset construction based on eight core moods, and LoRA fine-tuning of a diffusion-based text-to-image model to improve emotional fidelity and stylistic consistency. A web-based prototype was developed with Gradio for real-time evaluation. Drawing on gamification and self-determination theory, the system integrates emotional …

Integrating Generative Artificial Intelligence With Systems Architecting Diagram Creation: Advancement, Challenges, Opportunities And Future Perspectives, Cansu Yalim, Holly H. Handley

Engineering Management & Systems Engineering Faculty Publications

Generative AI (GenAI) serves as a powerful tool that can create a wide range of content, including but not limited to text, speech, images, code, videos, and 3D models. ChatGPT stands out as a particularly appealing Generative Pretrained Transformer (GPT) model that offers supplementary capabilities through GPTs and plugins. These extensions enable users to engage with the chatbot and improve its functionality, surpassing mere content generation. Our study delves into the potential of ChatGPT, specifically GPT-4, to expedite the creation of diagrams to support the system architecting process. To this end, we explored the use of ChatGPT's Diagrams Show Me …

A “War” Over Lobster And Whales: The Issue-Attention Cycle, Media Discourse, And Political Ecology Of Right Whale Science And Conservation In Six Us Newspapers, Marcus Reamer, Catherine Macdonald, Julia Wester, Russell Fielding, Meryl Shriver-Rice

Honors College Faculty Publications

News organizations and journalists are important and influential actors in environmental politics. Their reporting on social and environmental issues often follows issue-attention cycles (IACs) that emphasize drama and problematization to maintain public interest. This study examined nearly 13 years of news coverage to understand the media discourse about the Critically Endangered North Atlantic right whale (NARW) and its conservation. Content analysis and critical discourse analysis were used to analyze 356 texts published in six large US newspapers between January 1, 2010 and March 15, 2023. NARW conservation and management received increased public attention and an IAC began in 2017 after …