Digital Commons Network^™

Phoneme Recognition For Pronunciation Improvement, Matthew Heywood

Theses/Capstones/Creative Projects

This project aims to improve English pronunciation by investigating speech errors and developing a tool to provide precise feedback. The study focuses on creating a new pronunciation tool that offers localized feedback, identifies specific errors, and suggests corrective measures. By addressing the shortcomings of current methods, this research seeks to enhance pronunciation refinement.

Utilizing cutting-edge technology, the tool leverages speech-to-phoneme AI models and modified lazy string matching algorithms to compare the user's spoken input with the intended pronunciation. This allows for a detailed analysis of discrepancies, providing users actionable insights into their phonetic errors. The speech-to-phoneme AI models mark a …

Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction, Kaixiang Huang, Jingru Yang, Jin Wang, Shengfeng He, Zhan Wang, Haiyan He, Qifeng Zhang, Guodong Lu

Research Collection School Of Computing and Information Systems

This paper addresses the significant challenges in 3D Semantic Scene Graph (3DSSG) prediction, essential for understanding complex 3D environments. Traditional approaches, primarily using PointNet and Graph Convolutional Networks, struggle with effectively extracting multi-grained features from intricate 3D scenes, largely due to a focus on global scene processing and single-scale feature extraction. To overcome these limitations, we introduce Granular3D, a novel approach that shifts the focus towards multi-granularity analysis by predicting relation triplets from specific sub-scenes. One key is the Adaptive Instance Enveloping Method (AIEM), which establishes an approximate envelope structure around irregular instances, providing shape-adaptive local point cloud sampling, thereby …

Data Collector Selection Ranking-Based Method For Collaborative Multi-Tasks In Ubiquitous Environments, Belal Z. Hassan, Ahmed. A. A. Gad-Elrab, Mohamed S. Farag, S. E. Abu-Youssef

Al-Azhar Bulletin of Science

In Ubiquitous Computing and the Internet of Things, the sensing and control of objects involve numerous devices collecting and transmitting data. However, connecting these devices without fostering collaboration leads to suboptimal system performance. As the number of connected sensing devices in Internet of Things increases, efficient task accomplishment through collaboration becomes imperative. This paper proposes a Data Collector Selection Method for Collaborative Multi-Tasks to address this challenge, considering task preferences and uncertainty in data collectors' contributions. The proposed method incorporates three key aspects: (1) Using Fuzzy Analytical Hierarchy Process to determine optimal weights for task preferences; (2) Ranking data collectors …

Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco

Theses and Dissertations

Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …

The Efficacy Of Using Machine Learning Techniques For Identifying And Classifying “Fake News”, Muhammad Islam

Dissertations, Theses, and Capstone Projects

In today's digital world, detecting fake news has emerged as a critical challenge, one that has significant effects on democracy and public discourse at large both regionally and globally. This research studies how diversity of news sources in training datasets affects how well machine learning models can classify fake vs true news. I used the Linear Support Vector Classification (LinearSVC) to create and compare two classification models: one was trained on a dataset that only had real news from a singular source, Reuters (Dataset 1), and the other was trained on a dataset that contained real news from Reuters, The …

Leveraging Transformer Models For Genre Classification, Andreea C. Craus, Ben Berger, Yves Hughes, Hayley Horn

SMU Data Science Review

As the digital music landscape continues to expand, the need for effective methods to understand and contextualize the diverse genres of lyrical content becomes increasingly critical. This research focuses on the application of transformer models in the domain of music analysis, specifically in the task of lyric genre classification. By leveraging the advanced capabilities of transformer architectures, this project aims to capture intricate linguistic nuances within song lyrics, thereby enhancing the accuracy and efficiency of genre classification. The relevance of this project lies in its potential to contribute to the development of automated systems for music recommendation and genre-based playlist …

Academic Search And Discovery Tools In The Age Of Ai And Large Language Models: An Overview Of The Space, Aaron Tay

AI for Research Week

In the ever-evolving landscape of academic research, “AI tools” for literature search and synthesis are currently getting a lot of attention. These tools promise to ramp up productivity, enabling us to accomplish more in less time or absorb more knowledge without drowning in endless reading. With the sheer number of these systems increasing daily, it's natural to wonder: are they really worth our time and money? And if they are, how should we go about picking the right one from the multitude of options?

In this talk, I will share my views on how the space has developed over two …

Context Aware Music Recommendation And Playlist Generation, Elias Mann

SMU Journal of Undergraduate Research

There are many reasons people listen to music, and the type of music is largely determined by what the listener may be doing while they listen. For example, one may listen to one type of music while commuting, another while exercising, and yet another while relaxing. Without access to the physiological state of the user, current music recommendation methods rely on collaborative filtering - recommending music based on what other similar users listen to - and content based filtering - recommending songs based on their similarities to songs the user already prefers. With the rise in popularity of smart devices …

Text-To-Sql: A Methodical Review Of Challenges And Models, Ali Buğra Kanburoğlu, Faik Boray Tek

Turkish Journal of Electrical Engineering and Computer Sciences

This survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English …

“Use” As A Conscious Thought: Towards A Theory Of “Use” In Autonomous Things, Gohar Khan, A Karim Feroz

All Works

The way users perceive and use information systems artefacts has been mainly studied from the notion of behavioral beliefs, deliberate cognitive efforts, and physical actions performed by human actors to produce certain outcomes. The next generation of information systems, however, can sense, respond, and adapt to environments without necessitating similar cognitive efforts, physical contact, or explicit instructions to operate. Therefore, by leveraging theories of consciousness and technology use, this research aims to advance an alternative understanding of the "use" associated with the next generation of IS artefacts that do not require deliberate cognitive efforts, physical manipulation, or explicit instructions to …

A Nlp Approach To Automating The Generation Of Surveys For Market Research, Anav Chug

Honors College Theses

Market Research is vital but includes activities that are often laborious and time consuming. Survey questionnaires are one possible output of the process and market researchers spend a lot of time manually developing questions for focus groups. The proposed research aims to develop a software prototype that utilizes Natural Language Processing (NLP) to automate the process of generating survey questions for market research. The software uses a pre-trained Open AI language model to generate multiple choice survey questions based on a given product prompt, send it to a targeted email list, and also provides a real-time analysis of the responses …

Behavioral Intention For Ai Usage In Higher Education, Isaac A. Odai, Elliot Wiley

Student Research Symposium

This study sought to further understand the cognitive factors that influence undergraduate students' behavioral intention to use generative AI. Generative AI's presence in academic spaces opens the door for ethical and pedagogical questions. This study surveyed 51 undergraduate communication students to measure their attitudes, subjective norms, self efficacy and their behavioral intention to use GenAI for school work. The results of this study showed behavioral intent had a positive relationship with attitudes and subjective norms. The implications of these findings show that personal beliefs and the perceived beliefs of others are correlated to undergraduate students’ intent to use GenAI for …

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

Artificial Intelligence's Ability To Detect Online Predators, Olatilewa Osifeso

Electronic Theses, Projects, and Dissertations

Online child predators pose a danger to children who use the Internet. Children fall victim to online predators at an alarming rate, based on the data from the National Center of Missing and Exploited Children. When making online profiles and joining websites, you only need a name, an email and a password without identity verification. Studies have shown that online predators use a variety of methods and tools to manipulate and exploit children, such as blackmail, coercion, flattery, and deception. These issues have created an opportunity for skilled online predators to have fewer obstacles when it comes to contacting and …

Academic Literature Review In Age Of Ai And Large Language Models​, Aaron Tay

Research Collection Library

Explore the evolving landscape of academic research with a focus on open data and AI advancements, particularly in natural language processing. Join us for a practical presentation on leveraging emerging tools for literature review. Discover platforms like Connected Papers, ResearchRabbit, and Litmaps, offering paper exploration and recommendations based on initial 'seed papers.' Dive into AI-enhanced search engines like Elicit, Scispace, Semantic Scholar, and Scite.ai, powered by Large Language Models such as BERT and GPT. Learn about the latest developments, strengths, and weaknesses of these tools, and how they reshape literature review methods, from tool selection to query input techniques.

The Future Of Brain Tumor Diagnosis: Cnn And Transfer Learning Innovations, Shengyuan Wang

Mathematics, Statistics, and Computer Science Honors Projects

For the purpose of improving patient survival rates and facilitating efficient treatment planning, brain tumors need to be identified early and accurately classified. This research investigates the application of transfer learning and Convolutional Neural Networks (CNN) to create an automated, high-precision brain tumor segmentation and classification framework. Utilizing large-scale datasets, which comprise MRI images from open-accessible archives, the model exhibits the effectiveness of the method in various kinds of tumors and imaging scenarios. Our approach utilizes transfer learning techniques along with CNN architectures strengths to tackle the intrinsic difficulties of brain tumor diagnosis, namely significant tumor appearance variability and difficult …

An Empirical Study On The Efficacy Of Llm-Powered Chatbots In Basic Information Retrieval Tasks, Naja Faysal

Electronic Theses, Projects, and Dissertations

The rise of conversational user interfaces (CUIs) powered by large language models (LLMs) is transforming human-computer interaction. This study evaluates the efficacy of LLM-powered chatbots, trained on website data, compared to browsing websites for finding information about organizations across diverse sectors. A within-subjects experiment with 165 participants was conducted, involving similar information retrieval (IR) tasks using both websites (GUIs) and chatbots (CUIs). The research questions are: (Q1) Which interface helps users find information faster: LLM chatbots or websites? (Q2) Which interface helps users find more accurate information: LLM chatbots or websites?. The findings are: (Q1) Participants found information significantly faster …

Humanity Amid Innovation: Exploring Our Relationship To Technology, Sarah Durkee

Senior Theses and Projects

This thesis examines the impacts of technology on fundamental aspects of human nature and experience. Drawing on the works from Kant, Turing, Arendt, Benjamin, and Freud, it explores how rapid technological change is redefining human reason, intelligence, and creativity in the digital age. The first chapter analyzes whether modern online communication platforms realize or undermine Kant's vision of an enlightened public sphere fostering free discourse and critique. It argues that prioritizing engagement over substantive debate, these digital realms corrode the depth of interaction essential for cultivating human reason. The second chapter explores the pursuit of artificial intelligence as a reproduction …

Code Syntax Understanding In Large Language Models, Cole Granger

Undergraduate Honors Theses

In recent years, tasks for automated software engineering have been achieved using Large Language Models trained on source code, such as Seq2Seq, LSTM, GPT, T5, BART and BERT. The inherent textual nature of source code allows it to be represented as a sequence of sub-words (or tokens), drawing parallels to prior work in NLP. Although these models have shown promising results according to established metrics (e.g., BLEU, CODEBLEU), there remains a deeper question about the extent of syntax knowledge they truly grasp when trained and fine-tuned for specific tasks.

To address this question, this thesis introduces a taxonomy of syntax …

Deep Learning In Indus Valley Script Digitization, Deva Munikanta Reddy Atturu

Theses and Dissertations

This research introduces ASR-net(Ancient Script Recognition), a groundbreaking system that automatically digitizes ancient Indus seals by converting them into coded text, similar to Optical Character Recognition for modern languages. ASR-net, with an 95% success rate in identifying individual symbols, aims to address the crucial need for automated techniques in deciphering the enigmatic Indus script. Initially Yolov3 is utilized to create the bounding boxes around each graphemes present in the Indus Valley Seal. In addition to that we created M-net(Mahadevan) model to encode the graphemes. Beyond digitization, the paper proposes a new research challenge called the Motif Identification Problem (MIP) related …

Space Transformation For Open Set Recognition, Atefeh Mahdavi

Theses and Dissertations

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned by the models during training. In OSR, only a limited number of known classes are available at the time of training the model and the possibility of unknown classes never seen at training time emerges in the test environment. In such a setting, the unknown classes and their risk should be considered in the algorithm. Such systems require not only to identify and discriminate instances that belong to the source domain (i.e., the seen known classes contained in the training dataset) but also to reject unknown …

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining In Large Language Models (Llms), Jay Ratican, James Hutson

Faculty Scholarship

The conventional methodology for sentiment analysis within large language models (LLMs) has predominantly drawn upon human emotional frameworks, incorporating physiological cues that are inherently absent in text-only communication. This research proposes a paradigm shift towards an emotionallyagnostic approach to sentiment analysis in LLMs, which concentrates on purely textual expressions of sentiment, circumventing the confounding effects of human physiological responses. The aim is to refine sentiment analysis algorithms to discern and generate emotionally congruent responses strictly from text-based cues. This study presents a comprehensive framework for an emotionally-agnostic sentiment analysis model that systematically excludes physiological indicators whilst maintaining the analytical depth …

On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao

Research Collection School Of Computing and Information Systems

Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods primarily emphasize structural dependencies and their temporal changes. However, these approaches often overlook detailed temporal aspects or struggle with long-term dependencies. Furthermore, many solutions overly complicate the process by emphasizing intricate module designs to capture dynamic evolutions. In this work, we harness the strength of the Transformer’s self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. Our approach offers a simple Transformer model, called SimpleDyG, tailored for dynamic graph modeling without complex modifications. We …

Dynamic Storytelling Algorithms Using Contextual Aspects Of A Large Language Model, Alireza Pasha Nouri

Open Access Theses & Dissertations

Storytelling is a set of algorithms used to create narratives by connecting documents in a sequencethat accurately reflects the evolution of events and entities within a particular topic or theme. Early storytelling algorithms face challenges in encoding the progression and interconnections of information between consecutive texts, given that the conventional approaches rely primarily on connecting document pairs based on content overlap. They often neglect critical linguistic features, such as word contexts, semantics, the roles words play across different documents, and attention to the historical contexts of the underlying documents. Many existing storytelling models frequently produce story chains that, while connected …

Gpr-16 Attention Driven Framework For Detecting Mental Illness Causes From Social Media, Abm Adnan Azmee, Dinesh Chowdary Attota, Francis E Nweke

C-Day Computing Showcase

Mental health is a critical aspect of our overall well-being. Mental illness refers to conditions that impact an individual's psychological state, resulting in considerable distress, and limitations in functioning day-to-day tasks. Due to the progress of technology, social media has merged as the platform, for individuals to share their thoughts and emotions. The psychological state of individuals can be accessed with the help of data from these platforms. However, it is challenging for conventional machine learning models to analyze the diverse linguistic contexts of social media data. In this work, we propose a novel attention-driven deep framework to overcome these …

Ur-94 Emohydra: Multimodal Emotion Classification Using Heterogenous Modality Fusion, William A Stigall

C-Day Computing Showcase

Affective computing is a field of growing importance, as human society becomes more integrated with machines. Human feelings are both complex and multi-modal, expressed through various methods and nuances in behavior. In this work we introduce EmoHydra, a multi-modal model created through the fusion of three top-level models fine-tuned on text, vision, and speech respectively. Despite heterogenous heads performing well on the unseen data, as well as generalizing well to other benchmarks, logit concatenation proves to be ineffective at predicting Multimodal data, therefore we implement Multi-Head Attention as our fusion mechanism.

A System Of Communication Between Two Computers Using Novel Frequency Shift Keying Techniques, Jared Reyes

Honors Thesis

Frequency shift keying (FSK) is an old but powerful form of modulation that powered much of the early modems of the 1960’s, and the author felt inspired to make his own version of audio binary FSK modulation. He researched the general history and legacy of the Bell 103, a modem using FSK that defined telecommunication for the next few decades. Using research of the most common English characters of recent emails to determine which English characters should have the shortest bit length, a novel character encoding standard was created using variable bit rate. In addition, he has created a modulation …

Comprehensive Question And Answer Generation With Llama 2, Matous Hybl

MS in Computer Science Theses

Since the introduction of transformers, large language models have proven capable in many natural language processing fields. However, existing systems still face challenges in generating high-quality extractive questions. Base models and public chatbots fall short if the question source or quantity are critical. Our contribution is a question and answer generator for generating comprehensive, extractive questions and answers. This approach includes fine-tuning a LLaMA 2 base model for answer extraction (AE) and question generation (QG). We evaluate the resulting system using common automated metrics and a manual evaluation. We find that our system is comparable to the latest research and …

Immersive Japanese Language Learning Web Application Using Spaced Repetition, Active Recall, And An Artificial Intelligent Conversational Chat Agent Both In Voice And In Text, Marc Butler

MS in Computer Science Project Reports

In the last two decades various human language learning applications, spaced repetition software, online dictionaries, and artificial intelligent chat agents have been developed. However, there is no solution to cohesively combine these technologies into a comprehensive language learning application including skills such as speaking, typing, listening, and reading. Our contribution is to provide an immersive language learning web application to the end user which combines spaced repetition, a study technique used to review information at systematic intervals, and active recall, the process of purposely retrieving information from memory during a review session, with an artificial intelligent conversational chat agent both …

Lyraquist: Language Learning Via Music App, Vivian D'Souza, Siri Avula, Mahi Patel, Tanvi Singh, Ashley Bickham

Senior Theses

Lyraquist is a new language learning mobile app that encourages the practice of a foreign language through music. Language learners can connect their Spotify Premium account to Lyraquist to listen to music in their target languages and utilize tools such as translation and vocabulary lists to facilitate language practice. Through integration with Spotify, users can import and create Spotify playlists and search the service’s entire catalog. By combining daily listening habits with several tasks associated with language learning in one place, Lyraquist hopes to be a useful language learning tool.