Computer Sciences Commons^™

Dtitd: An Intelligent Insider Threat Detection Framework Based On Digital Twin And Self-Attention Based Deep Learning Models, Zhi Qiang Wang, Abdulmotaleb El Saddik

Computer Vision Faculty Publications

Recent statistics and studies show that the loss generated by insider threats is much higher than that generated by external attacks. More and more organizations are investing in or purchasing insider threat detection systems to prevent insider risks. However, the accurate and timely detection of insider threats faces significant challenges. In this study, we proposed an intelligent insider threat detection framework based on Digital Twins and self-attentions based deep learning models. First, this paper introduces insider threats and the challenges in detecting them. Then this paper presents recent related works on solving insider threat detection problems and their limitations. Next, …

What You See Is Not What You Know: Studying Deception In Deepfake Video Manipulation, Cathryn Allen, Bryson R. Payne, Tamirat Abegaz, Chuck Robertson

Journal of Cybersecurity Education, Research and Practice

Research indicates that deceitful videos tend to spread rapidly online and influence people’s opinions and ideas. Because of this, video misinformation via deepfake video manipulation poses a significant online threat. This study aims to discover what factors can influence viewers’ capability to distinguish deepfake videos from genuine video footage. This work focuses on exploring deepfake videos’ potential use for deception and misinformation by exploring people’s ability to determine whether videos are deepfakes in a survey consisting of deepfake videos and original unedited videos. The participants viewed a set of four videos and were asked to judge whether the videos shown …

Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter

All Works

The growing sophistication of malware has resulted in diverse challenges, especially among security researchers who are expected to develop mechanisms to thwart these malicious attacks. While security researchers have turned to machine learning to combat this surge in malware attacks and enhance detection and prevention methods, they often encounter limitations when it comes to sourcing malware binaries. This limitation places the burden on malware researchers to create context-specific datasets and detection mechanisms, a time-consuming and intricate process that involves a series of experiments. The lack of accessible analysis reports and a centralized platform for sharing and verifying findings has resulted …

Synthesizing Sentience: Integrating Large Language Models And Autonomous Agents For Emulating Human Cognitive Complexity, Jay Ratican, James Hutson, Daniel Plate

Faculty Scholarship

The paper aims to present a novel methodology for emulating the intricacies of human cognitive complexity by ingeniously integrating large language models with autonomous agents. Grounded in the theoretical framework of the modular mind theory-originally espoused by Fodor and later refined by scholars such as Joanna Bryson—the study seeks to venture into the untapped potential of large language models and autonomous agents in mirroring human cognition. Recent advancements in artificial intelligence, exemplified by the inception of autonomous agents like Age in GPT, auto GPT, and baby AGI, underscore the transformative capacities of these technologies in diverse applications. Moreover, empirical studies …

Object Recognition With Deep Neural Networks In Low-End Systems, Lillian Davis

Mahurin Honors College Capstone Experience/Thesis Projects

Object recognition is an important area in computer vision. Object recognition has been advanced significantly by deep learning that unifies feature extraction and classification. In general, deep neural networks, such as Convolution Neural Networks (CNNs), are trained in high-performance systems. Aiming to extend the reach of deep learning to personal computing, I propose a study of deep learning-based object recognition in low-end systems, such as laptops. This research includes how differing layer configurations and hyperparameter values used in CNNs can either create or resolve the issue of overfitting and affect final accuracy levels of object recognition systems. The main contribution …

A Gentle Introduction To Chatgpt, Steven W. Holloway

Libraries

A guest lecture on the state of commercial generative transformer technology, mid-2023, to a general audience at Staunton Public Library.

Perceptions And Barriers To Adopting Artificial Intelligence In K-12 Education: A Survey Of Educators In Fifty States, Karen Woodruff, James Hutson, Kathryn Arnone

Faculty Scholarship

Artificial Intelligence (AI) is making significant strides in the field of education, offering new opportunities for personalized learning and access to education for a more diverse population. Despite this potential, the adoption of AI in K-12 education is limited, and educators’ express hesitancy towards its integration due to perceived technological barriers and misconceptions. The purpose of this study is to examine the perceptions of K-12 educators in all 50 states of the USA towards AI, policies, training, and resources related to technology and AI, their comfort with technology, willingness to adopt new technologies for classroom instruction, and needs assessment for …

Artificial Intelligence In Cardiology: An Australian Perspective, Biyanka Jaltotage, Abdul R. Ihdayhid, Nick S. R. Lan, Faraz Pathan, Sanjay Patel, Clare Arnott, Gemma Figtree, Leonard Kritharides, Syed M. S. Islam, Clara K. Chow, James M. Rankin, Stephen J. Nicholls, Girish Dwivedi

Research outputs 2022 to 2026

Significant advances have been made in artificial intelligence technology in recent years. Many health care applications have been investigated to assist clinicians and the technology is close to being integrated into routine clinical practice. The high prevalence of cardiac disease in Australia places overwhelming demands on the existing health care system, challenging its capacity to provide quality patient care. Artificial intelligence has emerged as a promising solution. This discussion paper provides an Australian perspective on the current state of artificial intelligence in cardiology, including the benefits and challenges of implementation. This paper highlights some current artificial intelligence applications in cardiology, …

Ai-Supported Academic Advising: Exploring Chatgpt’S Current State And Future Potential Toward Student Empowerment, Daisuke Akiba, Michelle C. Fraboni

Publications and Research

Artificial intelligence (AI), once a phenomenon primarily in the world of science fiction, has evolved rapidly in recent years, steadily infiltrating into our daily lives. ChatGPT, a freely accessible AI-powered large language model designed to generate human-like text responses to users, has been utilized in several areas, such as the healthcare industry, to facilitate interactive dissemination of information and decision-making. Academic advising has been essential in promoting success among university students, particularly those from disadvantaged backgrounds. Unfortunately, however, student advising has been marred with problems, with the availability and accessibility of adequate advising being among the hurdles. The current study …

Framing And Intentionality In Artificial Intelligence Poetry, Alyson Elaine Marie Nichols

Student Theses and Dissertations

As natural language generation expands, increasing attention has been paid to neural networks and other machine learning techniques. This study offers a cheaper, simpler approach in the field of poetry generation. The Empath system uses a random-grammar model to constrain creativity with syntactical rules in order to produce novel and valuable poetry after the style of Emily Dickinson. It pays specific attention to using related words to create a sense of coherence and implements a framing system to prove that its creative choices are intentional. A survey of 118 students and teachers at Arkansas State University shows that Empath’s products …

Vision Language Navigation With Knowledge-Driven Environmental Dreamer, Fengda Zhu, Vincent C.S. Lee, Xiaojun Chang, Xiaodan Liang

Computer Vision Faculty Publications

Vision-language navigation (VLN) requires an agent to perceive visual observation in a house scene and navigate step-by-step following natural language instruction. Due to the high cost of data annotation and data collection, current VLN datasets provide limited instruction-trajectory data samples. Learning vision-language alignment for VLN from limited data is challenging since visual observation and language instruction are both complex and diverse. Previous works only generate augmented data based on original scenes while failing to generate data samples from unseen scenes, which limits the generalization ability of the navigation agent. In this paper, we introduce the Knowledge-driven Environmental Dreamer (KED), a …

Controllable Language Generation Using Deep Learning, Rohola Zandie

Electronic Theses and Dissertations

The advent of deep neural networks has sparked a revolution in Artificial Intelligence (AI), notably with the creation of Transformer models like GPT-X and ChatGPT. These models have surpassed previous methods in various Natural Language Processing (NLP) tasks. As the NLP field evolves, there is a need to further understand and question the capabilities of these models. Text generation, a crucial part of NLP, remains an area where our comprehension is limited while being critical in research.

This dissertation focuses on the challenging problem of controlling the general behaviors of language models such as sentiment, topical focus, and logical reasoning. …

The Development Of Artificial Intelligence-Based To Ols For Expert Peer Review Of Radiotherapy Treatment Plans, Mary Gronberg

Dissertations and Theses (Open Access)

Creating a patient-specific radiation treatment plan is a time-consuming and operator-dependent manual process. The treatment planner adjusts the planning parameters in a trial-and-error fashion in an effort to balance the competing clinical objectives of tumor coverage and normal tissue sparing. Often, a plan is selected because it meets basic organ at risk dose thresholds for severe toxicity; however, it is evident that a plan with a decreased risk of normal tissue complication probability could be achieved. This discrepancy between “acceptable” and “best possible” plan is magnified if either the physician or treatment planner lacks focal expertise in the disease site. …

Generative Pre-Trained Transformers (Gpt) And Space Health: A Potential Frontier In Astronaut Health During Exploration Missions, Ethan Waisberg, Joshua Ong, Mouayad Masalkhi, Nasif Zaman, Sharif Amit Kamran, Prithul Sarker, Andrew G Lee, Alireza Tavakkoli

Faculty, Staff and Student Publications

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.

Artificial Intelligence Frameworks To Detect And Investigate The Pathophysiology Of Spaceflight Associated Neuro-Ocular Syndrome (Sans), Joshua Ong, Ethan Waisberg, Mouayad Masalkhi, Sharif Amit Kamran, Kemper Lowry, Prithul Sarker, Nasif Zaman, Phani Paladugu, Alireza Tavakkoli, Andrew G Lee

Student Papers, Posters & Projects

Spaceflight associated neuro-ocular syndrome (SANS) is a unique phenomenon that has been observed in astronauts who have undergone long-duration spaceflight (LDSF). The syndrome is characterized by distinct imaging and clinical findings including optic disc edema, hyperopic refractive shift, posterior globe flattening, and choroidal folds. SANS serves a large barrier to planetary spaceflight such as a mission to Mars and has been noted by the National Aeronautics and Space Administration (NASA) as a high risk based on its likelihood to occur and its severity to human health and mission performance. While it is a large barrier to future spaceflight, the underlying …

Accuracy Vs. Energy: An Assessment Of Bee Object Inference In Videos From On-Hive Video Loggers With Yolov3, Yolov4-Tiny, And Yolov7-Tiny, Vladimir A. Kulyukin, Aleksey V. Kulyukin

Computer Science Faculty and Staff Publications

A continuing trend in precision apiculture is to use computer vision methods to quantify characteristics of bee traffic in managed colonies at the hive's entrance. Since traffic at the hive's entrance is a contributing factor to the hive's productivity and health, we assessed the potential of three open-source convolutional network models, YOLOv3, YOLOv4-tiny, and YOLOv7-tiny, to quantify omnidirectional traffic in videos from on-hive video loggers on regular, unmodified one- and two-super Langstroth hives and compared their accuracies, energy efficacies, and operational energy footprints. We trained and tested the models with a 70/30 split on a dataset of 23,173 flying bees …

Enhancing Video-Based Learning Using Knowledge Tracing: Personalizing Students’ Learning Experience With Orbits, Shady Shehata, David Santandreu, Philip Purnell, Mark Thompson

Natural Language Processing Faculty Publications

As the world regains its footing following the COVID-19 pandemic, academia is striving to consolidate the gains made in students’ education experience. New technologies such as video-based learning have shown some early improvement in student learning and engagement. In this paper, we present ORBITS predictive engine at YOURIKA company, a video-based student support platform powered by knowledge tracing. In an exploratory case study of one master’s level Speech Processing course at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, half the students used the system while the other half did not. Student qualitative feedback was universally …

Face Readers: The Frontier Of Computer Vision And Math Learning, Beverly Woolf, Margrit Betke, Hao Yu, Sarah Adel Bargal, Ivan Arroyo, John J. Magee Iv, Danielle Allessio, William Rebelsky

Computer Science

The future of AI-assisted individualized learning includes computer vision to inform intelligent tutors and teachers about student affect, motivation and performance. Facial expression recognition is essential in recognizing subtle differences when students ask for hints or fail to solve problems. Facial features and classification labels enable intelligent tutors to predict students’ performance and recommend activities. Videos can capture students’ faces and model their effort and progress; machine learning classifiers can support intelligent tutors to provide interventions. One goal of this research is to support deep dives by teachers to identify students’ individual needs through facial expression and to provide immediate …

Singapore's Ai Applications In The Public Sector: Six Examples, Steven M. Miller

Research Collection School Of Computing and Information Systems

Steven M. Miller describes six instances in which Singapore has applied AI in the public sector, illustrating different ways of improving its engagement with the public by making government services more accessible, anywhere, anytime, and speeding its responses to public processes and feedback. He illustrates how its leaders made the city a living lab for AI use, and what they learned.

The Internet Of Things (Iot) In Healthcare: Taking Stock And Moving Forward, Abderahman Rejeb, Karim Rejeb, Horst Treiblmaier, Andrea Appolloni, Salem Alghamdi, Yaser Alhasawi, Mohammad Iranmanesh

Research outputs 2022 to 2026

Recent improvements in the Internet of Things (IoT) have allowed healthcare to evolve rapidly. This article summarizes previous studies on IoT applications in healthcare. A comprehensive review and a bibliometric analysis were performed to objectively summarize the growth of IoT research in healthcare. To begin, 2,990 journal articles were carefully selected for further investigation. These publications were analyzed based on various bibliometric metrics, including publication year, journals, authors, institutions, and countries. Keyword co-occurrence and co-citation networks were generated to unravel significant research hotspots. The findings show that IoT research has received considerable interest from the healthcare community. Based on the …

A Comparative Effectiveness Study On Opioid Use Disorder Prediction Using Artificial Intelligence And Existing Risk Models, Sajjad Fouladvand, Jeffery Talbert, Linda Phyliss Dwoskin, Heather M. Bush, Amy L. Meadows, Lars E. Peterson, Yash R. Mishra, Steven K. Roggenkamp, Fei Wang, Ramakanth Kavuluru, Jin Chen

Markey Cancer Center Faculty Publications

Opioid use disorder (OUD) is a leading cause of death in the United States placing a tremendous burden on patients, their families, and health care systems. Artificial intelligence (AI) can be harnessed with available healthcare data to produce automated OUD prediction tools. In this retrospective study, we developed AI based models for OUD prediction and showed that AI can predict OUD more effectively than existing clinical tools including the unweighted opioid risk tool (ORT). Data include 474,208 patients’ data over 10 years; 269,748 were females with an average age of 56.78 years. Cases are prescription opioid users with at least …

Balanced Blended Space: Foundational Human–Ai Dialogues In A Symmetry-Based Mediation Framework, David Smith

Publications and Research

This working paper documents the early development of the Balanced Blended Space (BBS) framework through a series of iterative interactions between a cognitive agent (human researcher) and a computational agent (AI system) conducted in 2023. The work is motivated by the need for a universal theoretical model capable of describing the integration of physical, virtual, and conceptual spaces, particularly in response to increasing fragmentation across contemporary communication systems.

BBS is proposed as a symmetry-based mediation framework in which relationships between domains—such as physical and virtual space, cognition and computation, and multiple sensory modalities—are treated as structurally equivalent and mappable. Central …

Safe Mdp Planning By Learning Temporal Patterns Of Undesirable Trajectories And Averting Negative Side Effects, Siow Meng Low, Akshat Kumar, Scott Sanner

Research Collection School Of Computing and Information Systems

In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects. In real world, often the state representation used may lack sufficient fidelity to specify such safety constraints. Operating based on an incomplete model can often produce unintended negative side effects (NSEs). To address these challenges, first, we associate safety signals with state-action trajectories (rather than just immediate state-action). This makes our safety model highly general. We also assume categorical safety labels are given for different trajectories, rather than a numerical cost function, which is harder to specify by the …

Singapore's Hospital To Home Program: Raising Patient Engagement Through Ai, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gian Lee Ang, Andy Wee An Ta, Steven M. Miller

Research Collection School Of Computing and Information Systems

Because of their complex care needs, many elderly patients are discharged from hospitals only to be readmitted for multiple stays within the following twelve months. John Abisheganaden and his fellow authors describe Singapore’s Hospital to Home program, a community care initiative fueled by artificial intelligence.

Artificial Intelligence Tool For The Study Of Covid-19 Microdroplet Spread Across The Human Diameter And Airborne Space, Hesham H. Alsaadi, Monther Aldwairi, Faten Yasin, Sandra C.P. Cachinho, Abdullah Hussein

All Works

The 2019 novel coronavirus (SARS-CoV-2 / COVID-19), with a point of origin in Wuhan, China, has spread rapidly all over the world. It turned into a raging pandemic wrecking havoc on health care facilities, world economy and affecting everyone’s life to date. With every new variant, rate of transmission, spread of infections and the number of cases continues to rise at an international level and scale. There are limited reliable researches that study microdroplets spread and transmissions from human sneeze or cough in the airborne space. In this paper, we propose an intelligent technique to visualize, detect, measure the distance …

Harnessing Artificial Intelligence For Early And Evolution Of Alzheimer’S Disease Detections And Enhancing Senior Mental Health Through Innovative Art-Singing Therapies: A Multidisciplinary Approach, Jocelyne Kiss, Geoffreyjen Edwards, Rachel Bouserhal, Elaine Champagne, Thierry Belleguic, Valéry Psyché, Charles Batcho, Carol Hudon, Sylsvie Ratté, Ingrid Verdruyckt, Marie-Hélène Parizeau, Aaron Liu-Rosenbaum, James Hudson, Marie-Louise Bourbeau, Marie Lemieux, Annik Charbonneau

Faculty Scholarship

The well-documented therapeutic potential of group singing for patients living with Alzheimer’s disease (PLAD) has been hindered by COVID-19 restrictions, exacerbating loneliness and cognitive decline among seniors in residential and long-term care centers (CHSLDs). Addressing this challenge, the multidisciplinary study aims to develop a patient-oriented virtual reality (XR) interaction system facilitating group singing for mental health support during confinement and enhancing the understanding of the links between Alzheimer’s disease, social interaction, and singing. The researchers also propose to establish an early AD detection system using voice, facial, and non-invasive biometric measurements and validate the efficacy of selected intervention practices. The …

Stability-Based Generalization Analysis For Mixtures Of Pointwise And Pairwise Learning, Jiahuan Wang, Jun Chen, Hong Chen, Bin Gu, Weifu Li, Xin Tang

Machine Learning Faculty Publications

Recently, some mixture algorithms of pointwise and pairwise learning (PPL) have been formulated by employing the hybrid error metric of “pointwise loss + pairwise loss” and have shown empirical effectiveness on feature selection, ranking and recommendation tasks. However, to the best of our knowledge, the learning theory foundation of PPL has not been touched in the existing works. In this paper, we try to fill this theoretical gap by investigating the generalization properties of PPL. After extending the definitions of algorithmic stability to the PPL setting, we establish the high-probability generalization bounds for uniformly stable PPL algorithms. Moreover, explicit convergence …

Aisha: A Custom Ai Library Chatbot Using The Chatgpt Api, Yrjo Lappalainen, Nikesh Narayanan

All Works

This article focuses on the development of a custom chatbot for Zayed University Library (United Arab Emirates) using Python and the ChatGPT API. The chatbot, named Aisha, was designed to provide quick and efficient reference and support services to students and faculty outside the library's regular operating hours. The article also discusses the benefits of chatbots in academic libraries, and reviews the early literature on ChatGPT's applicability in this field. The article describes the development process, perceived capabilities and limitations of the bot, and plans for further development. This project represents the first fully reported attempt to explore the potential …

Ai Art: Artists’ Best Friend Or Mortal Enemy?, Ethan Gabrys

Tredway Library Prize for First-Year Research

This paper analyzes the impacts and implications of generative AI software on art and examines the ethics of using such tools. Through the argument that careless use of these tools presents a danger to the art world as they risk devaluing human expression, Gabrys states that “as what it means to be human changes with each generation, new artists express sentiment through their art. Art has the ability to tell us about the human experience.” He concludes that the use of AI tools takes the skill and sentiment of human artists out of the equation, begging the question: if the …

Human-Ai Collaboration For Smart Education: Reframing Applied Learning To Support Metacognition, James Hutson, Daniel Plate

Faculty Scholarship

This chapter investigates the profound influence of intelligent virtual assistants (IVAs) on the educational domain, specifically in the realm of individualized learning and the instruction of writing abilities and content creation. IVAs, incorporating generative AI technologies such as ChatGPT and Stable Diffusion, hold the potential to bring about a paradigm shift in educational programs, emphasizing the enhancement of advanced metacognitive capacities rather than the fundamentals of communication. The subsequent recommendations stress the need to cultivate enduring proficiencies and ascertain tailored learning approaches for each learner, which will be indispensable for success in the evolving job market. In this context, prompt …