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Full-Text Articles in Computer Sciences

Conflict Profiles And Team Outcomes In Cross-Disciplinary Teams: An Integrated Latent Profile Analysis And Natural Language Processing Approach, Francisco Cima, Pilar Pazos Jan 2024

Conflict Profiles And Team Outcomes In Cross-Disciplinary Teams: An Integrated Latent Profile Analysis And Natural Language Processing Approach, Francisco Cima, Pilar Pazos

Engineering Management & Systems Engineering Faculty Publications

Team conflict is a naturally emerging phenomenon resulting from individuals' interactions during project execution. Cross-disciplinary teams can experience higher levels of conflict than single-discipline teams because of the increased diversity of knowledge and perspectives. Research has shown that team conflict can emerge from different types of disagreements (cognitive and interpersonal), which have different implications for team functioning. Past empirical research has focused on the impact of both conflict types independent from each other while overlooking their combined effects. This work examines the conflict profiles resulting from the combined levels of interpersonal and cognitive disagreements and their association with team outcomes. …

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Uncertainty Quantification In Large Language Models Through Convex Hull Analysis, Ferhat Ozgur Catak, Murat Kuzlu Jan 2024

Uncertainty Quantification In Large Language Models Through Convex Hull Analysis, Ferhat Ozgur Catak, Murat Kuzlu

Engineering Technology Faculty Publications

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. …

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A Prototype Of A Conversational Virtual University Support Agent Powered By A Large Language Model That Addresses Inquiries About Policies In The Student Handbook, Joseph Benjamin R. Ilagan, Jose Ramon Ilagan Jan 2024

A Prototype Of A Conversational Virtual University Support Agent Powered By A Large Language Model That Addresses Inquiries About Policies In The Student Handbook, Joseph Benjamin R. Ilagan, Jose Ramon Ilagan

Quantitative Methods and Information Technology Faculty Publications

Universities gain a competitive advantage by deliberately improving overall service, student, faculty, and staff experience, leading to attractiveness, retention, and improved outcomes. Quality services are achieved partly by addressing employee satisfaction, specifically in the work environment. This paper presents a prototype study of a virtual university support agent, a system grounded in a Large Language Model (LLM) engineered to address inquiries from university students, faculty and staff related to the student handbook. The study investigates the integration of generative artificial intelligence and natural conversation properties inherent in LLMs to overcome customer service shortcomings identified in previous chatbot applications. The LLMs' …

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Exploratory Prompting Of Large Language Models To Act As Co-Pilots For Augmenting Business Process Work In Document Classification, Jose Ramon Ilagan, Joseph Benjamin R. Ilagan, Claire Louisse Basallo, Zachary Matthew Alabastro Jan 2024

Exploratory Prompting Of Large Language Models To Act As Co-Pilots For Augmenting Business Process Work In Document Classification, Jose Ramon Ilagan, Joseph Benjamin R. Ilagan, Claire Louisse Basallo, Zachary Matthew Alabastro

Quantitative Methods and Information Technology Faculty Publications

Businesses deal with different types of documents containing unstructured documents. The data in these documents must be converted into digital forms other automated systems could only process. One generic use case is document classification, which usually involves manual transformation due to human understanding needed in the process. These documents go beyond those generated through regular business transactions and operations and also include web-based content such as online news, blogs, e-mails, and various digital libraries. Recent developments in robotic process automation (RPA) and artificial intelligence (AI) aim to automate the otherwise expensive, time-consuming, and repetitive manual steps. Through more powerful natural …

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Towards Dynamic Context Detection From Voice Commands And Conversations With Smart Assistants In Smart Homes, Jeniya Sultana Jan 2024

Towards Dynamic Context Detection From Voice Commands And Conversations With Smart Assistants In Smart Homes, Jeniya Sultana

Graduate Theses/Dissertations

Voice-enabled interactions have become increasingly popular with the rise of voice assistants. Identifying contexts or meanings from voice commands and conversations with smart assistants can contribute to the autonomous control of smart home devices and appliances. To improve automation, there is a growing need for efficient context detection that eliminates the need to memorize voice commands. To address this need, I followed a two-step approach in my research. In the first step, I developed a unique context recognition model using a transformer, an attention mechanism, and a fully connected neural network. I trained this model on a conversational dataset of …

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A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari Jan 2024

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

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A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu Jan 2024

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

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Review Classification Using Natural Language Processing And Deep Learning, Brian Nazareth Dec 2023

Review Classification Using Natural Language Processing And Deep Learning, Brian Nazareth

Electronic Theses, Projects, and Dissertations

Sentiment Analysis is an ongoing research in the field of Natural Language Processing (NLP). In this project, I will evaluate my testing against an Amazon Reviews Dataset, which contains more than 100 thousand reviews from customers. This project classifies the reviews using three methods – using a sentiment score by comparing the words of the reviews based on every positive and negative word that appears in the text with the Opinion Lexicon dataset, by considering the text’s variating sentiment polarity scores with a Python library called TextBlob, and with the help of neural network training. I have created a neural …

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Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources, Duncan Arnfield Dec 2023

Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources, Duncan Arnfield

Electronic Theses and Dissertations

This work examined the relative effectiveness of multilayer perceptron, random forest, and multinomial naïve Bayes classifiers, trained using bag of words and term frequency-inverse dense frequency transformations of documents in the Fake News Corpus and Fake and Real News Dataset. The goal of this work was to help meet the formidable challenges posed by proliferation of fake news to society, including the erosion of public trust, disruption of social harmony, and endangerment of lives. This training included the use of context-categorized fake news in an effort to enhance the tools’ effectiveness. It was found that term frequency-inverse dense frequency provided …

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Assessing The Effectiveness Of A Chatbot Workshop As Experiential Teaching And Learning Tool To Engage Undergraduate Students, Kyong Jin Shim, Thomas Menkhoff, Ying Qian Teo, Clement Shi Qi Ong Dec 2023

Assessing The Effectiveness Of A Chatbot Workshop As Experiential Teaching And Learning Tool To Engage Undergraduate Students, Kyong Jin Shim, Thomas Menkhoff, Ying Qian Teo, Clement Shi Qi Ong

Research Collection School Of Computing and Information Systems

In this paper, we empirically examine and assess the effectiveness of a chatbot workshop as experiential teaching and learning tool to engage undergraduate students enrolled in an elective course “Doing Business with A.I.” in the Lee Kong Chian School of Business (LKCSB) at Singapore Management University. The chatbot workshop provides non-STEM students with an opportunity to acquire basic skills to build a chatbot prototype using the ‘Dialogflow’ program. The workshop and the experiential learning activity are designed to impart conversation and user-centric design know how and know why to students. A key didactical aspect which informs the design and flow …

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Exploring The Impact Of Training Datasets On Turkish Stance Detection, Muhammed Sai̇d Zengi̇n, Berk Utku Yeni̇sey, Mücahi̇d Kutlu Nov 2023

Exploring The Impact Of Training Datasets On Turkish Stance Detection, Muhammed Sai̇d Zengi̇n, Berk Utku Yeni̇sey, Mücahi̇d Kutlu

Turkish Journal of Electrical Engineering and Computer Sciences

Stance detection has garnered considerable attention from researchers due to its broad range of applications, including fact-checking and social computing. While state-of-the-art stance detection models are usually based on supervised machine learning methods, their effectiveness is heavily reliant on the quality of training data. This problem is more prevalent in stance detection task because the stance of a text is intimately tied to the target under consideration. While numerous datasets exist for stance detection, determining their suitability for a specific target can be challenging. In this work, we focus on Turkish stance detection and explore the impact of training data …

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Complex Knowledge Base Question Answering: A Survey, Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Zhao Wayne Xin, Ji Rong Wen Nov 2023

Complex Knowledge Base Question Answering: A Survey, Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Zhao Wayne Xin, Ji Rong Wen

Research Collection School Of Computing and Information Systems

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performances on complex questions are still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances in KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and …

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Predictive Ai For The S&P 500 Index, Jacqueline Rose Perry Aug 2023

Predictive Ai For The S&P 500 Index, Jacqueline Rose Perry

Computer Science Senior Theses

Artificial intelligence has powerful applications in virtually every field, and the financial world is no exception. Utilizing various elements of artificial intelligence, this research aims to predict the future value of the S&P 500 index using numerous models, and in doing so, identify relevant features. More specifically, models that include combinations of historical data, public sentiment, and technical indicators were employed to predict the stock price one day and three days forward. To account for public opinion, the sentiment of tweets and news headlines from the beginning of 2015 through the end of 2019 was calculated using FinBERT, a pre-trained …

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N-Shot Benchmarking Of Whisper On Diverse Arabic Speech Recognition, Bashar Talafha, Abdul Waheed, Muhammad Abdul-Mageed Aug 2023

N-Shot Benchmarking Of Whisper On Diverse Arabic Speech Recognition, Bashar Talafha, Abdul Waheed, Muhammad Abdul-Mageed

Natural Language Processing Faculty Publications

Whisper, the recently developed multilingual weakly supervised model, is reported to perform well on multiple speech recognition benchmarks in both monolingual and multilingual settings. However, it is not clear how Whisper would fare under diverse conditions even on languages it was evaluated on such as Arabic. In this work, we address this gap by comprehensively evaluating Whisper on several varieties of Arabic speech for the ASR task. Our evaluation covers most publicly available Arabic speech data and is performed under n-shot (zero-, few-, and full) finetuning. We also investigate the robustness of Whisper under completely novel conditions, such as in …

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Ocr Post-Processing Using Large Language Models, Mahdi Hajiali Aug 2023

Ocr Post-Processing Using Large Language Models, Mahdi Hajiali

UNLV Theses, Dissertations, Professional Papers, and Capstones

Optical Character Recognition (OCR) technology transforms textual visuals into an electronically readable, non-graphical format of the text. This allows the editing and other text manipulation of the content by language technology software such as machine translation, text comprehension, query-answering systems, and search engines. While Optical Character Recognition (OCR) systems continually progress towards greater precision, several complications persist when dealing with low-resolution source images or those with multicolored backgrounds. Consequently, the text derived from OCR necessitates additional refinement to optimize accuracy, beneficial for various subsequent applications. It is recognized that the character accuracy of OCR-generated text may influence certain natural language …

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Quantifying Implicit Bias In Judicial Legal Opinions: A Natural Language Processing Approach, Philip N. Surendran Jun 2023

Quantifying Implicit Bias In Judicial Legal Opinions: A Natural Language Processing Approach, Philip N. Surendran

Quantitative Social Science Undergraduate Senior Theses

Implicit bias and criminal justice are two concepts that have long been intertwined. There is no justice without neutrality, and yet how do we tell if the actors enforcing the system are actually impartial? In this thesis, I utilize recent advancements in machine learning to attempt to answer this question. Specifically, I use natural language processing to examine the text of opinions written by judges in appellate courts, and I leverage these findings to build quantifiable measures of implicit bias. In particular, I look at the over/under-representation of certain emotions, sentiments and linguistic styles as a proxy for disparate treatment …

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Automatic Scoring Of Speeded Interpersonal Assessment Center Exercises Via Machine Learning: Initial Psychometric Evidence And Practical Guidelines, Louis Hickman, Christoph N. Herde, Filip Lievens, Louis Tay Jun 2023

Automatic Scoring Of Speeded Interpersonal Assessment Center Exercises Via Machine Learning: Initial Psychometric Evidence And Practical Guidelines, Louis Hickman, Christoph N. Herde, Filip Lievens, Louis Tay

Research Collection Lee Kong Chian School Of Business

Assessment center (AC) exercises such as role-plays have established themselves as valuable approaches for obtaining insights into interpersonal behavior, but they are often considered the “Rolls Royce” of personnel assessment due to their high costs. The observation and rating process comprises a substantial part of these costs. In an exploratory case study, we capitalize on recent advances in natural language processing (NLP) by developing NLP-based machine learning (ML) models to investigate the possibility of automatically scoring AC exercises. First, we compared the convergent-related validity and contamination with word count of ML scores based on models that used different NLP methods …

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Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Böther, Daniel Martin Katz Jun 2023

Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Böther, Daniel Martin Katz

Research Collection Yong Pung How School Of Law

Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples—namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession—, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce textbased and graph-based methods to identify instances of law smells, confirming their utility in …

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Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Bother, Daniel Martin Katz Jun 2023

Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Bother, Daniel Martin Katz

Research Collection Yong Pung How School Of Law

Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples—namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession—, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to identify instances of law smells, confirming their utility in …

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Multi-Head Attention Graph Convolutional Network Model: End-To-End Entity And Relation Joint Extraction Based On Multi-Head Attention Graph Convolutional Network, Zhihua Tao, Chunping Ouyang, Yongbin Liu, Tonglee Chung, Yixin Cao Jun 2023

Multi-Head Attention Graph Convolutional Network Model: End-To-End Entity And Relation Joint Extraction Based On Multi-Head Attention Graph Convolutional Network, Zhihua Tao, Chunping Ouyang, Yongbin Liu, Tonglee Chung, Yixin Cao

Research Collection School Of Computing and Information Systems

At present, the entity and relation joint extraction task has attracted more and more scholars' attention in the field of natural language processing (NLP). However, most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure information. The adjacency matrix constructed by the dependency tree can convey syntactic information. Dependency trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic description. At the same time, a large amount of irrelevant information will cause redundancy. This paper presents a novel end-to-end entity and relation joint extraction …

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Connecting Linguistic Expressions And Pain Relief Through Transformer Model Construction And Analysis, Sarah M. Chacko May 2023

Connecting Linguistic Expressions And Pain Relief Through Transformer Model Construction And Analysis, Sarah M. Chacko

Computer Science Senior Theses

Chronic pain is a widespread problem that significantly impacts quality of life. Overprescription and abuse of pain medication continues to be a major public health issue and can further burden patients due to a fragmented health care system. Previous research has suggested a possible psychological basis to pain and the potential for safer, non-pharmacological alternatives for pain relief. This project leverages language models to study chronic pain development and relief through psychological treatments, which will be assessed through responses to post-treatment interviews. A transformer-based natural language processing model is employed to identify connections between language expressions and pain on a …

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An Analysis Of Text-Based Machine Learning Models For Vulnerability Detection, Kollin Ryne Napier May 2023

An Analysis Of Text-Based Machine Learning Models For Vulnerability Detection, Kollin Ryne Napier

Theses and Dissertations

With an increase in complexity of software, developers rely more on reuse and dependencies in their source code via code snippets. As a result, it is becoming harder to identify and mitigate vulnerabilities. Although traditional analysis tools are still utilized, machine learning models are being adopted to expand efforts and combat such threats. Given the possibilities towards usage of such models, research in this area has introduced various approaches which vary in usability and prediction. In generalizing models to a more natural language approach, researchers have opted to train models on source code to identify existing and potential vulnerabilities. Exploratory …

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Enhancing Institutional Assessment And Reporting Through Conversational Technologies: Exploring The Potential Of Ai-Powered Tools And Natural Language Processing, James Hutson, Daniel Plate May 2023

Enhancing Institutional Assessment And Reporting Through Conversational Technologies: Exploring The Potential Of Ai-Powered Tools And Natural Language Processing, James Hutson, Daniel Plate

Faculty Scholarship

This study explores the potential of conversational technologies, AI-powered tools, and natural language processing (NLP) in enhancing institutional assessment and reporting processes in higher education. The traditional approach to assessment often involves labor-intensive manual analysis of extensive data and documents, which burdens institutions. To address these challenges, AI-powered tools, such as ChatGPT, LangChain, Poe, Claude, and others, along with NLP techniques, are investigated in relationship to their ability to improve institutional assessment practices and output. By leveraging these advanced technologies, assessment officers and institutional effectiveness, researchers can engage in dynamic conversations with data, transforming spreadsheets and documents from static artifacts …

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Open Source Intelligence For Cybersecurity Events Via Twitter Data, Dakota Dale May 2023

Open Source Intelligence For Cybersecurity Events Via Twitter Data, Dakota Dale

Graduate Theses and Dissertations

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain …

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Wearing Masks Implies Refuting Trump?: Towards Target-Specific User Stance Prediction Across Events In Covid-19 And Us Election 2020, Hong Zhang, Haewoon Kwak, Wei Gao, Jisun An May 2023

Wearing Masks Implies Refuting Trump?: Towards Target-Specific User Stance Prediction Across Events In Covid-19 And Us Election 2020, Hong Zhang, Haewoon Kwak, Wei Gao, Jisun An

Research Collection School Of Computing and Information Systems

People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people’s stance, which we consider as their online behavior in …

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Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund Apr 2023

Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund

Modeling, Simulation and Visualization Student Capstone Conference

The sudden arrival of many migrants can present new challenges for host communities and create negative attitudes that reflect that tension. In the case of Colombia, with the influx of over 2.5 million Venezuelan migrants, such tensions arose. Our research objective is to investigate how those sentiments arise in social media. We focused on monitoring derogatory terms for Venezuelans, specifically veneco and veneca. Using a dataset of 5.7 million tweets from Colombian users between 2015 and 2021, we determined the proportion of tweets containing those terms. We observed a high prevalence of xenophobic and defamatory language correlated with the …

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Towards Nlp-Based Conceptual Modeling Frameworks, David Shuttleworth, Jose Padilla Apr 2023

Towards Nlp-Based Conceptual Modeling Frameworks, David Shuttleworth, Jose Padilla

Modeling, Simulation and Visualization Student Capstone Conference

This paper presents preliminary research using Natural Language Processing (NLP) to support the development of conceptual modeling frameworks. NLP-based frameworks are intended to lower the barrier of entry for non-modelers to develop models and to facilitate communication across disciplines considering simulations in research efforts. NLP drives conceptual modeling in two ways. Firstly, it attempts to automate the generation of conceptual models and simulation specifications, derived from non-modelers’ narratives, while standardizing the conceptual modeling process and outcome. Secondly, as the process is automated, it is simpler to replicate and be followed by modelers and non-modelers. This allows for using a common …

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Socially Aware Natural Language Processing With Commonsense Reasoning And Fairness In Intelligent Systems, Sirwe Saeedi Apr 2023

Socially Aware Natural Language Processing With Commonsense Reasoning And Fairness In Intelligent Systems, Sirwe Saeedi

Dissertations

Although Artificial Intelligence (AI) promises to deliver ever more user-friendly consumer applications, recent mishaps involving fake information and biased treatment serve as vivid reminders of the pitfalls of AI. AI can harbor latent biases and flaws that can cause harm in diverse and unexpected ways. It is crucial to understand the reasons for, mechanisms behind, and circumstances under which AI can fail. For instance, a lack of commonsense reasoning can lead to biased or unfair decisions made by Machine Learning (ML) systems. For example, if an ML system is trained on data that is biased or unrepresentative of the real …

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Beyond News Values On Twitter: Predicting Factors That Drive User Engagement In News, Zhiyan Zhong Apr 2023

Beyond News Values On Twitter: Predicting Factors That Drive User Engagement In News, Zhiyan Zhong

Dartmouth College Master’s Theses

When deciding on what news stories to cover, traditional journalism determines news values by following several elements of newsworthiness, such as impact, timeliness, and prominence. However, these guidelines do not always seem to correspond with the success of content on social media. As people are increasingly turning to social media for news, our research aims to understand and predict factors that drive user engagement for news on social media. In this study, we analyze news content published on Twitter, and examine a diverse set of characteristics like metrics retrieved from the Twitter API and semantics by natural language processing, including …

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Conversations With Chatgpt About C Programming: An Ongoing Study, James C. Davis, Yung-Hsiang Lu, George K. Thiruvathukal Mar 2023

Conversations With Chatgpt About C Programming: An Ongoing Study, James C. Davis, Yung-Hsiang Lu, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

AI (Artificial Intelligence) Generative Models have attracted great attention in recent years. Generative models can be used to create new articles, visual arts, music composition, even computer programs from English specifications. Among all generative models, ChatGPT is becoming one of the most well-known since its public announcement in November 2022. GPT means {\it Generative Pre-trained Transformer}. ChatGPT is an online program that can interact with human users in text formats and is able to answer questions in many topics, including computer programming. Many computer programmers, including students and professionals, are considering the use of ChatGPT as an aid. The quality …

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