Artificial Intelligence and Robotics Commons^™

Virtual Conferencing Fatigue: Look‑Alike Avatar And Facial Attractiveness, Yuxin Liu, Keng Siau, Xueqing Wang, Yang Yang

Research Collection School Of Computing and Information Systems

The rapid evolution of avatar-related technologies provides extensive opportunities for diverse avatar applications in various areas. This study aims to investigate the innovative use of avatars to mitigate virtual conferencing fatigue, which refers to the physical and mental exhaustion from the inappropriate use of virtual conferencing applications. Grounded in Self-Awareness Theory, the research compares the impact of using real faces and user-look-alike avatars on virtual conferencing fatigue, delving into its underlying factors. In addition, the study examines the role of facial attractiveness enhancement on virtual conferencing fatigue. Laboratory experiments with a 2-by-2 between-subject design are employed to test hypotheses. The …

Modeling And Regulating A Ride-Sourcing Market Integrated With Vehicle Rental Services, Dong Mo, Hai Wang, Zeen Cai, W. Y. Szeto, Xiqun (Michael) Chen

Research Collection School Of Computing and Information Systems

With the popularity of on-demand ride services worldwide, ride-sourcing platforms must maintain an adequate fleet size and cope with growing travel demand. Recently, platforms have attempted to provide vehicle rental services to drivers who do not own cars, then recruited them to provide on demand ride services. This helps lower the entry barrier for drivers and offers another profitable business for platforms. From the government's perspective, however, it is challenging to coordinately regulate a ride-sourcing business and vehicle rental business. This paper proposes a bi-level optimization model to investigate how the government regulates the ride-sourcing market integrated with vehicle rental …

Reinforcement Learning Based Online Request Scheduling Framework For Workload-Adaptive Edge Deep Learning Inference, Xinrui Tan, Hongjia Li, Xiaofei Xie, Lu Guo, Nirwan Ansari, Xueqing Huang, Liming Wang, Zhen Xu, Yang Liu

Research Collection School Of Computing and Information Systems

The recent advances of deep learning in various mobile and Internet-of-Things applications, coupled with the emergence of edge computing, have led to a strong trend of performing deep learning inference on the edge servers located physically close to the end devices. This trend presents the challenge of how to meet the quality-of-service requirements of inference tasks at the resource-constrained network edge, especially under variable or even bursty inference workloads. Solutions to this challenge have not yet been reported in the related literature. In the present paper, we tackle this challenge by means of workload-adaptive inference request scheduling: in different workload …

Mvgamba : Unify 3d Content Generation As State Space Sequence Modeling, Xuanyu Yi, Zike Wu, Qiuhong Shen, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, Shuicheng Yan, Xinchao Wang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Recent 3D large reconstruction models (LRMs) can generate high-quality 3D content in sub-seconds by integrating multi-view diffusion models with scalable multi-view reconstructors. Current works further leverage 3D Gaussian Splatting as 3D representation for improved visual quality and rendering efficiency. However, we observe that existing Gaussian reconstruction models often suffer from multi-view inconsistency and blurred textures. We attribute this to the compromise of multi-view information propagation in favor of adopting powerful yet computationally intensive architectures (e.g., Transformers). To address this issue, we introduce MVGamba, a general and lightweight Gaussian reconstruction model featuring a multi-view Gaussian reconstructor based on the RNN-like State …

Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly for generating vocal melodies and simulating singing voices. While NLP techniques have been effective in analyzing vocal performance data to assess quality and style, the automatic transcription of vocal performances into sheet music remains a significant challenge. Manual transcription tools often fall short due to the intricate dynamics of vocal expression. This study tackles the automation of vocal performance transcription into sheet music using innovative techniques, including large language models (LLMs). We propose a method to translate vocal audio input into display-ready sheet music effectively. …

Lova3 : Learning To Visual Question Answering, Asking And Assessment, Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Bai Shou, Mike Zheng Shou

Research Collection School Of Computing and Information Systems

Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can more effectively utilize data, leading to better comprehension and learning outcomes. Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. Inspired by the human learning mechanism, we introduce LOVA3 , an innovative framework named “Learning tO Visual question Answering, Asking and Assessment,” designed to equip MLLMs with these additional capabilities. Our approach involves the creation of two supplementary training tasks GenQA and EvalQA, aiming …

Unified Generative And Discriminative Training For Multi-Modal Large Language Models, Wei Chow, Juncheng Li, Kaihang Pan, Qifan Yu, Hao Fei, Zhiqi Ge, Shuai Yang, Siliang Teng, Hanwang Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

In recent times, Vision-Language Models (VLMs) have been trained under two predominant paradigms. Generative training has enabled Multimodal Large Language Models (MLLMs) to tackle various complex tasks, yet issues such as hallucinations and weak object discrimination persist. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation. This paper addresses these challenges by proposing a unified approach that integrates the strengths of both paradigms. Considering interleaved image-text sequences as the general format of input samples, we introduce a structure-induced training strategy that imposes semantic relationships between input …

Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …

Real-Time Network Simulations For Ml/Dl Ddos Detection Using Docker, Luis D. Garcia

Master's Theses

As the integration of artificial intelligence (AI) within cybersecurity continues to

grow, machine learning (ML) and deep learning (DL) models are increasingly used to

detect cyber attacks. However, these models are rarely evaluated in real-time attack

scenarios to see how subtle changes from the real networking environment can affect

their predictions. To address this issue, we propose a scalable, platform-independent

Docker testbed specifically designed for simulating real-time Distributed Denial of

Service (DDoS) attack scenarios that allows researchers to deploy and evaluate their

pre-trained, ML and DL detection models. Our framework is simple to configure

and can run across Intel and …

Quantum Visual Feature Encoding Revisited, Xuan-Bac Nguyen, Hoang-Quan Nguyen, Hugh Churchill, Samee U. Khan, Khoa Luu

Computer Science and Computer Engineering Faculty Publications and Presentations

Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited. This paper, therefore, revisits the quantum visual encoding strategies, the initial step in quantum machine learning. Investigating the root cause, we uncover that the existing quantum encoding design fails to ensure information preservation of the visual features after the encoding process, thus complicating the learning process of the quantum machine learning models. In particular, the problem, termed the “Quantum Information Gap” (QIG), leads to an information gap between classical and corresponding quantum features. We provide theoretical proof and practical examples with visualization …

Computational Representation, Analysis And Verification Of Requirements In Engineering Design And Systems Engineering, Chandan Kumar Sahu

All Dissertations

Systems are developed to satisfy a set of requirements derived from stakeholders’ needs, defining the problem space for which the system is created as a feasible solution. The system design process begins with eliciting these requirements and concludes with validating whether the created system meets them. Requirements engineering (RE) encompasses elicitation, representation, analysis, documentation, verification, and validation. However, challenges in RE, such as imprecision in natural language (NL), proprietary restrictions, and a lack of standardized quality metrics, hinder the creation of well-formed and comprehensive requirements. These challenges complicate formalization and analysis of requirements.

This dissertation addresses these challenges by proposing …

Artificial Intelligence And Machine Learning In Cancer Pain: A Systematic Review, Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Kareem A Wahid, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used …

Enhancing Low-Resource Language Performance In Multilingual Large Language Models, Mingqi Li

All Dissertations

The large language models play an important role in many natural language tasks. However, training these models requires large amounts of data, which is not available for many languages. A noticeable performance gap exists between English and other languages, with low-resource languages showcasing this gap prominently. Therefore, it becomes imperative to improve large language models for low-resource languages. To address these challenges, we developed knowledge distillation and strategic prompt-learning, and attention alignment methods to improve the representation capabilities of large language models for low-resource language, and then enhanced their performance in downstream tasks.

In our first study, we developed a …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) Integrated Hyperspectral Imaging Understanding For Woody Breast In Poultry Processing, Chaitanya Kumar Reddy Pallerla

Graduate Theses and Dissertations

The development and implementation of a Wide & Deep (WD) learning model tailored for classification and regression tasks utilizing spectral data provides a robust solution to evaluate woody breast (WB) conditions in poultry fillets. This process begins with thorough data preprocessing, which includes loading spectral and classification datasets, imputing missing values with medians, and splitting the data into training and testing sets to ensure rigorous model evaluation. The WD model architecture integrates wide linear models and deep neural networks to harness the strengths of both approaches. The wide component excels at memorizing sparse feature interactions, while the deep component captures …

Decoding Neural Networks: An Information-Theoretic Guide To Interpretability, Error Analysis And Efficiency, Mackenzie J. Meni

Theses and Dissertations

This dissertation addresses critical challenges in neural network design by leveraging entropy-based techniques to improve model efficiency, interpretability, and bias reduction. Focusing on the unique demands of computer vision applications, particularly object detection and classification for real-time systems, this work introduces a series of innovative methods centered on information theory. At the core of these methods is the Probabilistic Explanations of Entropic Knowledge (PEEK) framework, a tool developed to analyze and visualize entropy distributions across feature maps. PEEK offers insights into information flow within neural networks, making it possible to pinpoint layers that contribute meaningfully to decision-making or identify those …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) For Spatially Heterogenous Property Awared Chicken Woody Breast Classification And Hardness Regression, Chaitanya Pallerla, Yihong Feng, Casey M. Owens, Ramesh Bahadur Bist, Siavash Mahmoudi, Pouya Sohrabipour, Amirreza Davar, Dongyi Wang

Poultry Science Faculty Publications and Presentations

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In …

Towards Comprehensive And Interpretable Video Understanding, Khoa Vo

Graduate Theses and Dissertations

Video understanding is a critical domain in computer vision, focusing on analysis of sequential visual data to extract meaningful spatiotemporal information for tasks such as action recognition, video captioning, video retrieval, and temporal action localization, etc. Despite significant advancements with spatio-temporal convolutional neural networks and attention-based video models, current methods face limitations, including inadequate representation of main actors, lack of fine-grained modeling of relevant objects, and limited interpretability.
This thesis addresses these challenges by proposing novel approaches that enhance video understanding through modeling interactions among entities (actors and objects) and between entities and the environment, while improving interpretability in the …

Automating Maritime Risk Data Collection And Identification Leveraging Large Language Models, Donghao Huang, Xiuju Fu, Xiaofeng Yin, Haibo Pen, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Maritime risk research is crucial yet challenging for improving safety, efficiency, and sustainability in maritime operations. This paper presents an innovative method for automating the collection and identification of risk data related to global maritime risks from news sources, addressing the limitations of traditional manual methods. To evaluate the proposed method, different learning-based models, including conventional machine learning approaches and advanced Large Language Models (LLMs) such as GPT-4 and LLaMA-3.1, are comprehensively studied for comparison. In addition, not only do we use popular evaluation metrics to assess the proposed method, but we also introduce a new evaluation metric, called the …

Enhancing Password Security And Memorability Using Machine Learning And Linguistic Patterns, Jared Wise

LSU New Orleans Theses and Dissertations

In the digital age, text-based passwords remain a primary method for securing online accounts. Yet, users frequently face a dilemma between creating passwords that are easy to remember and sufficiently secure against cyberattacks. This research introduces an approach to password generation that bridges this gap by utilizing linguistic patterns, particularly song lyrics, to develop highly secure and naturally memorable passwords. Using large lyric datasets gained from web scrapes from popular song lyric websites (AZ Lyrics, Genius), features are extracted from a corpus of over 5 million lyrics using sentence structure and natural language processing in a novel way. In using …

Habit Coach: Customising Rag-Based Chatbots To Support Behavior Change, Arian Fooroogh Mand Arabi, Cansu Koyuturk, Michael O'Mahony, Raffaella Calati, Dimitri Ognibene

Conference papers

This paper presents the iterative development of Habit Coach, a GPT-based chatbot designed to support users in habit change through personalized interaction. Employing a user-centered design approach, we developed the chatbot using a Retrieval-Augmented Generation (RAG) system, which enables behavior personalization without retraining the underlying language model (GPT-4). The system leverages document retrieval and specialized prompts to tailor interactions, drawing from Cognitive Behavioral Therapy (CBT) and narrative therapy techniques. A key challenge in the development process was the difficulty of translating declarative knowledge into effective interaction behaviors. In the initial phase, the chatbot was provided with declarative knowledge about CBT …

A Systematic Review Of The Effects Of Ai-Assisted Moderation On Individuals And Groups, Zehui Yu, Lukas Otto, Dennis Assenmacher, Claudia Wagner

Human-Machine Communication

This review paper provides a conceptualization of AI-assisted content moderation with various degrees of autonomy and summarizes experimental evidence for how different levels of automation in content moderation and related losses of autonomy affect individuals and groups. Our results show that current research predominantly focuses on individuallevel effects, necessitating a shift toward understanding the impact on groups. The study highlights gaps in exploring different levels of AI-assisted moderation interventions and misalignments of different conceptualizations that make comparing research results difficult. The discussion underscores the prevailing emphasis on harmful content removal and advocates for investigating more constructive moderation techniques, emphasizing the …

Designing Customized Loss Functions For Training Deep Neural Networks, Ali Pourramezan Fard

Electronic Theses and Dissertations

This dissertation explores the critical role of loss functions in enhancing the predictive performance of deep machine learning models. Loss functions are an integral element of all the ongoing advances we witness daily in this domain. I design custom loss functions and their impacts on various machine learning tasks, particularly in computer vision.

In the first stage of my research, I aim to improve the prediction performance of deep learning models by providing them with more precise feedback associated with task requirements. This led me to create the concept of assistive loss functions. My first proposed loss function, inspired by …

An Overview Of Generative Ai Initiatives At Minnesota State University, Mankato (So Far), Evan Rusch, Nat Gustafson-Sundell

Library Services Publications

At Minnesota State University, Mankato, we’ve undertaken several experiments and initiatives focused on Generative Artificial Intelligence. We provided several examples at the Generative AI in Libraries (GAIL) conference. For this presentation, we provided a revised and expanded overview of our initiatives for the Northern Ohio Technical Services Librarians (NOTSL) Fall General Meeting. We explained license-related restrictions on uses of AI. We discussed the limitations of the retrieval-augmented generation tools currently available in the library. We summarized how we’ve tested ChatBots to support licensing and we showed how we’ve tried to use AI to improve data visualization for collections outreach. We …

Autonomous Driving Trajectory Prediction, Carlos Funes

Undergraduate Research Symposium Lightning Talks

Autonomous driving is undoubtedly one of the world's most revolutionary technologies, opening the door to a more secure traffic environment. This innovation has led to vehicles being able to drive by themselves without the necessity of a person behind the wheel, as well as cruise control, lane-keeping assist, and automatic emergency braking. Unfortunately, there is still plenty of work before autonomous driving becomes more popular among drivers. While at UNLV as an undergraduate student/research assistant, one of my goals is to learn how these technologies work to bring ideas into the automotive industry by refining solutions to problems within these …

It's Not As Bad As You Think: Detecting Ai-Generated Voices, Yong Qin Xu

Undergraduate Research Symposium Lightning Talks

Advances in machine learning have opened up the world to a brand new frontier of fraudulent phone calls which the average person may not be in any way prepared for. From imitations of a loved one's voice to lifelike mimicry of human callers, telephone scams may become harder than ever to anticipate or prevent now that criminals have the help of AI on their side. This is why in my research paper, I aim to analyze and compare two existing methods of detecting the authenticity of human voice recordings in order to demonstrate and explain currently available technology that's capable …

Vision-Language Integration For Enhanced Locomotion Mode Prediction, Ehsan Ahmadi

LSU Master's Theses

Wearable exoskeletons offer significant potential in enhancing human mobility in industrial environments. However, their adaptability to dynamic, task-intensive settings presents challenges, especially in accurately predicting locomotion modes such as ladder climbing, stair navigation, low-space movement, and obstacle navigation. This research proposes a multimodal framework that integrates visual data and speech commands to improve locomotion mode prediction in unpredictable environments. Multimodal data was collected using smart glasses, capturing both the user’s perspective (field-of-view, FOV) and voice during locomotion tasks. State-of-the-art models—CLIP, ImageBind, and GPT-4o—process these visual and linguistic inputs to predict locomotion activities. The models were evaluated in zero-shot and fine-tuned …

Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation, Reuven Muller

Master's Theses

Large Language Models (LLMs) have significantly advanced the field of natural language processing but remain resource-intensive and impractical for many organizations. Specialist models offer a viable alternative, often developed through Knowledge Distillation (KD) techniques. However, traditional KD methods rely on predefined static datasets to elicit knowledge from the teacher model, failing to dynamically address the weaknesses of the student model during training. This research introduces two novel methods for adaptive knowledge elicitation: Feedback-Driven Question Generation and Agent-Based Targeted Question Generation. These methods iteratively expand the training dataset based on the student model’s performance, leveraging a teacher model to generate targeted …

Artificial Intelligence Foundation Model Risk Identification And Governance Model From Esg Perspective, Jincheng Shi, Guoyu Wang, Yingchun Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The application ecology of artificial intelligence foundation model is rapidly expanding. The environment, society, and governance are facing new challenges and opportunities. Exploring the construction of a governance framework for the development risks of foundation model has important theoretical value and practical significance for promoting the healthy and sustainable development of artificial intelligence. Based on the theories of ESG and artificial intelligence governance, this study analyzes the development benefits and typical risks of foundation model from the perspective of ESG and then constructs a risk governance framework and implementation strategies for artificial intelligence foundation models. This study shows that a …

Participatory Ethical Regulations: Risk Challenges Of Artificial Intelligence Era And Construction Of Governance Logic, Chenggang Zhang, Lu Pan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Participatory ethical norms emphasize the involvement of diverse stakeholders, aiming to construct a more comprehensive and balanced ethical governance framework. The rapid development of artificial intelligence (AI) technology is leading society through unprecedented transformations, significantly impacting ethical perspectives, social governance models, and the symbiotic relationship between humans and technology. The participatory ethical norms, characterized by multi-stakeholder participation, interactivity, and openness, represent a crucial pathway for addressing the challenges posed by the rapid development of AI technology. Constructing an AI governance framework based on participatory ethical norms provides solutions for the sustainable, fair, and transparent development of AI from multiple aspects …

Ethical Risks And Challenges Of Chatgpt Applications In Education, Jingbo Fan, Hui Liang

Bulletin of Chinese Academy of Sciences (Chinese Version)

ChatGPT is a typical application in the field of natural language processing, with the potential to empower and revolutionize education. It can serve not only as a digital tutor for students but also as a virtual assistant for teachers, driving the transformation of student learning methods and teaching paradigms. Additionally, ChatGPT shows a wide range of applications in the research field. However, while bringing opportunities for educational development, ChatGPT also poses ethical risks and challenges to educational equity. Firstly, ChatGPT may exacerbate the digital divide, leading to unequal educational opportunities. Secondly, it presents risks such as knowledge alienation, algorithmic black-box …