Digital Commons Network^™

Using Large Pre-Trained Language Models To Track Emotions Of Cancer Patients On Twitter, Will Baker

Computer Science and Computer Engineering Undergraduate Honors Theses

Twitter is a microblogging website where any user can publicly release a message, called a tweet, expressing their feelings about current events or their own lives. This candid, unfiltered feedback is valuable in the spaces of healthcare and public health communications, where it may be difficult for cancer patients to divulge personal information to healthcare teams, and randomly selected patients may decline participation in surveys about their experiences. In this thesis, BERTweet, a state-of-the-art natural language processing (NLP) model, was used to predict sentiment and emotion labels for cancer-related tweets collected in 2019 and 2020. In longitudinal plots, trends in …

Why Chomsky Normal Form: A Pedagogical Note, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

To simplify the design of compilers, Noam Chomsky proposed to first transform a description of a programming language -- which is usually given in the form of a context-free grammar -- into a simplified "normal" form. A natural question is: why this specific normal form? In this paper, we provide an answer to this question.

Five Revolutionary Ideas In The 1950s-70s Science: 90th Birthday Of Revolt Pimenov, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

This year, Revolt Pimenov, a philosophical thinker whose main ideas were in geometry of space-time, would have turned 90. In this essay, we explain how in the 1950s-70s, when he was most productive, his were two of the five natural and important revolutionary scientific ideas -- along with fuzzy logic, constructive mathematics, and scalar-tensor theory of gravitation, ideas that, in our opinions, still have potential to change the world.

Using Sentiment And Emotion Analysis Of News Articles To Analyze The Effects Of Leader’S Statements On Covid-19 Spread, Poojitha Thota

Computer Science and Engineering Theses - Archive

Leaders generally include government officials, politicians, etc. Their statements can highly affect people’s decisions in many ways. Currently, in the pandemic situation, many statements were being passed every hour and day, which showed an impact on the spread of corona virus cases at certain location. So, this paper proposes a supervised model to analyze the variations of COVID-19 data based upon the leader’s statements passed at certain time and location. The proposed methodology consists of sentiment and emotion analysis for the leader’s statements to determine the true intentions of the leader. The leader’s statements are a collection of data obtained …

Detecting Bots Using A Hybrid Approach, Edmund Kofi Genfi

Theses, Dissertations and Culminating Projects

Artificial intelligence (AI) remains a crucial aspect for improving our modern lives but it also casts several social and ethical issues. One issue is of major concern, investigated in this research, is the amount of content users consume that is being generated by a form of AI known as bots (automated software programs). With the rise of social bots and the spread of fake news more research is required to understand how much content generated by bots is being consumed. This research investigates the amount of bot generated content relating to COVID-19. While research continues to uncover the extent to …

Dialogue State Tracking With Incremental Reasoning, Lizi Liao, Le Hong Long, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-theart methods in terms of joint …

Automatically Selecting Follow-Up Questions For Deficient Bug Reports, Mia Mohammad Imran, Agnieszka Ciborowska, Kostadin Damevski

Computer Science Faculty Research & Creative Works

The availability of quality information in bug reports that are created daily by software users is key to rapidly fixing software faults. Improving incomplete or deficient bug reports, which are numerous in many popular and actively developed open-source software projects, can make software maintenance more effective and improve software quality. In this paper, we propose a system that addresses the problem of bug report incompleteness by automatically posing follow-up questions, intended to elicit answers that add value and provide missing information to a bug report. Our system is based on selecting follow-up questions from a large corpus of already posted …

Characterising The Knowledge About Primitive Variables In Java Code Comments, Mahfouth Alghamdi, Shinpei Hayashi, Takashi Kobayashi, Christoph Treude

Research Collection School Of Computing and Information Systems

Primitive types are fundamental components available in any programming language, which serve as the building blocks of data manipulation. Understanding the role of these types in source code is essential to write software. Little work has been conducted on how often these variables are documented in code comments and what types of knowledge the comments provide about variables of primitive types. In this paper, we present an approach for detecting primitive variables and their description in comments using lexical matching and advanced matching. We evaluate our approaches by comparing the lexical and advanced matching performance in terms of recall, precision, …

Angrybert: Joint Learning Target And Emotion For Hate Speech Detection, Md Rabiul Awal, Rui Cao, Roy Ka-Wei Lee, Sandra Mitrović

Research Collection School Of Computing and Information Systems

Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community. However, most of the existing methods adopt a supervised approach that depended heavily on the annotated hate speech datasets, which are imbalanced and often lack training samples for hateful content. This paper addresses the research gaps by proposing a novel multitask learning-based model, AngryBERT, which jointly learns hate speech detection with sentiment classification and target identification as secondary relevant tasks. We conduct extensive experiments to augment three commonly-used hate speech detection datasets. Our experiment …

Factors That Affect E-Learning Platforms After The Spread Of Covid-19: Post Acceptance Study, Rana Saeed Al-Maroof, Khadija Alhumaid, Iman Akour, Said Salloum

All Works

The fear of vaccines has led to population rejection due to various reasons. Students have had their own inquiries towards the effectiveness of the vaccination, which leads to vaccination hesitancy. Vaccination hesitancy can affect students' perception, hence, acceptance of e-learning platforms. Therefore, this research attempts to explore the post-acceptance of e-learning platforms based on a conceptual model that has various variables. Each variable contributes differently to the post-acceptance of the e-learning platform. The research investigates the moderating role of vaccination fear on the post-acceptance of e-learning platforms among students. Thus, the study aims at exploring students' perceptions about their post-acceptance …

Plprepare: A Grammar Checker For Challenging Cases, Jacob Hoyos

Electronic Theses and Dissertations

This study investigates one of the Polish language’s most arbitrary cases: the genitive masculine inanimate singular. It collects and ranks several guidelines to help language learners discern its proper usage and also introduces a framework to provide detailed feedback regarding arbitrary cases. The study tests this framework by implementing and evaluating a hybrid grammar checker called PLPrepare. PLPrepare performs similarly to other grammar checkers and is able to detect genitive case usages and provide feedback based on a number of error classifications.

Retrieval-Augmented Generation For Code Summarization Via Hybrid Gnn, Shangqing Liu, Yu Chen, Xiaofei Xie, Jingkai Siow, Yang Liu

Research Collection School Of Computing and Information Systems

Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Most previous approaches either rely on retrieval-based (which can take advantage of similar examples seen from the retrieval database, but have low generalization performance) or generation-based methods (which have better generalization performance, but cannot take advantage of similar examples). This paper proposes a novel retrieval-augmented mechanism to combine the benefits of both worlds. Furthermore, to mitigate …

Mapping Renewal: How An Unexpected Interdisciplinary Collaboration Transformed A Digital Humanities Project, Elise Tanner, Geoffrey Joseph

Digital Initiatives Symposium

Funded by a National Endowment for Humanities (NEH) Humanities Collections and Reference Resources Foundations Grant, the UA Little Rock Center for Arkansas History and Culture’s “Mapping Renewal” pilot project focused on creating access to and providing spatial context to archival materials related to racial segregation and urban renewal in the city of Little Rock, Arkansas, from 1954-1989. An unplanned interdisciplinary collaboration with the UA Little Rock Arkansas Economic Development Institute (AEDI) has proven to be an invaluable partnership. One team member from each department will demonstrate the Mapping Renewal website and discuss how the collaborative process has changed and shaped …

Prediction Of Days-On-Market For Single-Family Homes In The Housing Market Of Savannah, Keagan Galbraith

Honors College Theses

The number of days that a home stays on the housing market (Days-On-Market—DOM) provides crucial information about the real estate market’s behavior that affects the buyer’s/seller’s decision (at the micro-level) and indicates the level of risk associated with real estate investments and identifies the housing bubbles (at the macro level). Housing data has a mixture of simple and complex attributes. A complex attribute in contrast with a simple attribute, has an array of values for a real estate property, which creates a major challenge in prediction of DOM. DOM is a binary attribute with values of “short” (£ six months) …

Oer Immersive Mulitmedia Materials Project: Vr As An Agent Of Change, Alexander Isin, Jade Basilius, Johana Barrero, Angeles Fernandez-Cifuentes

Showcase of Osprey Advancements in Research and Scholarship (SOARS)

OER-Immersive Multimedia Materials Project: VR as an Agent of Change Alexander Isin, Computer Science/ Spanish Major (Main researcher) Jade Basilius, Communication/ Spanish Major (Main researcher) Mentors: Johana Barrero, Ph. D., Ångeles Fernández Cifuentes, Ph. D. This project aims at the design and development of immersive instructional materials (with a focus on virtual reality) for the Spanish language, literature, and culture classroom. It specifically involves the design of an interactive 360-degree experience in Spanish about the Florida Keys Coral Reefs and their environmental, socioeconomic, and cultural impact on the community. My involvement in the project focused on the research of different …

Assessing The Credibility Of Cyber Adversaries, Jenny A. Wells, Dana S. Lafon, Margaret Gratian

International Journal of Cybersecurity Intelligence & Cybercrime

Online communications are ever increasing, and we are constantly faced with the challenge of whether online information is credible or not. Being able to assess the credibility of others was once the work solely of intelligence agencies. In the current times of disinformation and misinformation, understanding what we are reading and to who we are paying attention to is essential for us to make considered, informed, and accurate decisions, and it has become everyone’s business. This paper employs a literature review to examine the empirical evidence across online credibility, trust, deception, and fraud detection in an effort to consolidate this …

Why Decimal System? Why Communities With More Than 150 Folks Tend To Split? New Consequences Of The Seven Plus Minus Two Law, Leobardo Orea Amador, Vladik Kreinovich

Departmental Technical Reports (CS)

Why are we using the decimal system to describe numbers? Why all over the world, communities with more than 150 folks tend to split? In this paper, we show that both phenomena -- as well as some other phenomena -- can be explained if we take into account the seven plus minus two law, according to which a person can keep in immediate memory from 5 to 9 items.

Understanding God And His Redemptive Work In Computer Science, Mike Wallinga

Faculty Tenure Papers

Computer science as a discipline and computer scientists as a group tend to exhibit a technical, skills-focused disposition. Curriculum standards demand students acquire a long list of mathematical and programming abilities. Students both expect and are excited by the opportunity to learn the ins and outs of cutting-edge technology. Furthermore, programming a computer is an exercise in exactness and specificity. Is the semicolon in the right place? Are all of the parentheses matched? Does this Boolean logic or loop control statement contain an off-by-one error? Failing to state all of the required commands fully and exactly will result in a …

Fact-Checking Of Claims From The English Wikipedia Using Evidence In The Wild, Aalok Sathe

Honors Theses

Automated fact checking is a task in the domain of Natural Language Processing that deals with the verification of claims using evidence. Fact checking is becoming increasingly important as large amounts of human-generated information accumulate online. In the recent past, our society has witnessed large-scale spread of disinformation via the internet that has time and again led to noticeable disruptions in the fabric of society. Fact-checking would help mitigate the spread of disinformation by allowing large magnitudes of content to be automatically evaluated for disinformation. In this work, we construe and tackle multiple subtasks of fact checking using labeled data …

Integration And Analysis Of Gaze Behavior In Augmented Reality, George Villaume

Honors Capstones

No abstract provided.

Tour: Dynamic Topic And Sentiment Analysis Of User Reviews For Assisting App Release, Tianyi Yang, Cuiyun Gao, Jingya Zang, David Lo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

App reviews deliver user opinions and emerging issues (e.g., new bugs) about the app releases. Due to the dynamic nature of app reviews, topics and sentiment of the reviews would change along with app release versions. Although several studies have focused on summarizing user opinions by analyzing user sentiment towards app features, no practical tool is released. The large quantity of reviews and noise words also necessitates an automated tool for monitoring user reviews. In this paper, we introduce TOUR for dynamic TOpic and sentiment analysis of User Reviews. TOUR is able to (i) detect and summarize emerging app issues …

An Automated Framework For Connected Speech Evaluation Of Neurodegenerative Disease: A Case Study In Parkinson's Disease, Sai Bharadwaj Appakaya

USF Tampa Graduate Theses and Dissertations

Neurodegenerative diseases affect millions of people around the world. The progressive degeneration worsens the symptoms, heavily impacting the quality of life of the patients as well as the caregivers. Speech production is one of the physiological processes affected by neurodegenerative diseases like Alzheimer’s disease, amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD). Speech is the most basic form of communication, and the effect of neurodegeneration degrades speech production, thereby reducing social interaction and mental well-being. PD is the second most common neurodegenerative disease affecting speech production in 90% of the diagnosed individuals. Speech analysis methods for PD in clinical methods …

Spellchecking For Children In Web Search: A Natural Language Interface Case-Study, Casey Kennington, Jerry Alan Fails, Katherine Landau Wright, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the context of web search with children as the target audience. In particular, via a literature review we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude …

Enconter: Entity Constrained Progressive Sequence Generation Via Insertion-Based Transformer, Lee Hsun Hsieh, Yang Yin Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Pretrained using large amount of data, autoregressive language models are able to generate high quality sequences. However, these models do not perform well under hard lexical constraints as they lack fine control of content generation process. Progressive insertion-based transformers can overcome the above limitation and efficiently generate a sequence in parallel given some input tokens as constraint. These transformers however may fail to support hard lexical constraints as their generation process is more likely to terminate prematurely. The paper analyses such early termination problems and proposes the ENtity-CONstrained insertion TransformER (ENCONTER), a new insertion transformer that addresses the above pitfall …

Multi-Domain Dialogue State Tracking With Recursive Inference, Lizi Liao, Tongyao Zhu, Le Hong Long, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multi-domain dialogue state tracking (DST) is a critical component for monitoring user goals during the course of an interaction. Existing approaches have relied on dialogue history indiscriminately or updated on the most recent turns incrementally. However, in spite of modeling it based on fixed ontology or open vocabulary, the former setting violates the interactive and progressing nature of dialogue, while the later easily gets affected by the error accumulation conundrum. Here, we propose a Recursive Inference mechanism (ReInf) to resolve DST in multi-domain scenarios that call for more robust and accurate tracking capability. Specifically, our agent reversely reviews the dialogue …

Do Users Care About Ad's Performance Costs? Exploring The Effects Of The Performance Costs Of In-App Ads On User Experience, Cuiyun Gao, Jichuan Zeng, Federica Sarro, David Lo, Irwin King, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user experience and continuous profits. Previous studies mainly focus on addressing the hidden performance costs generated by ads, including consumption of memory, CPU, data traffic, and battery. However, there is no research on analyzing users’ perceptions of ads’ performance costs to our knowledge.Objective: To fill this gap and better understand the effects of performance costs of in-app ads on user experience, we conduct a study on analyzing user concerns about ads’ performance …

Escape From An Echo Chamber, Kuan-Chieh Lo, Shih-Chieh Dai, Aiping Xiong, Jing Jiang, Lun-Wei Ku

Research Collection School Of Computing and Information Systems

An echo chamber effect refers to the phenomena that online users revealed selective exposure and ideological segregation on political issues. Prior studies indicate the connection between the spread of misinformation and online echo chambers. In this paper, to help users escape from an echo chamber, we propose a novel news-analysis platform that provides a panoramic view of stances towards a particular event from different news media sources. Moreover, to help users better recognize the stances of news sources which published these news articles, we adopt a news stance classification model to categorize their stances into “agree”, “disagree”, “discuss”, or “unrelated” …

Deep Learning Based Models For Classification From Natural Language Processing To Computer Vision, Xianshan Qu

Theses and Dissertations

With the availability of large scale data sets, researchers in many different areas such as natural language processing, computer vision, recommender systems have started making use of deep learning models and have achieved great progress in recent years. In this dissertation, we study three important classification problems based on deep learning models.

First, with the fast growth of e-commerce, more people choose to purchase products online and browse reviews before making decisions. It is essential to build a model to identify helpful reviews automatically. Our work is inspired by the observation that a customer's expectation of a review can be …

Interrupting The Propaganda Supply Chain, Kyle Hamilton, Bojan Bozic, Luc Longo

Conference papers

In this early-stage research, a multidisciplinary approach is presented for the detection of propaganda in the media, and for modeling the spread of propaganda and disinformation using semantic web and graph theory. An ontology will be designed which has the theoretical underpinnings from multiple disciplines including the social sciences and epidemiology. An additional objective of this work is to automate triple extraction from unstructured text which surpasses the state-of-the-art performance.

Cross-Topic Rumor Detection Using Topic-Mixtures, Weijieying Ren, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu

Research Collection School Of Computing and Information Systems

There has been much interest in rumor detection using deep learning models in recent years. A well-known limitation of deep learning models is that they tend to learn superficial patterns, which restricts their generalization ability. We find that this is also true for cross-topic rumor detection. In this paper, we propose a method inspired by the “mixture of experts” paradigm. We assume that the prediction of the rumor class label given an instance is dependent on the topic distribution of the instance. After deriving a vector representation for each topic, given an instance, we derive a “topic mixture” vector for …