Digital Commons Network^™

Interactive Example-Based Explanations To Improve Health Professionals’ Onboarding With Ai For Human-Ai Collaborative Decision Making, Min Hun Lee, Renee Bao Xuan Ng, Silvana Xinyi Choo, Shamala Thilarajah

Research Collection School Of Computing and Information Systems

A growing research explores the usage of AI explanations on user’s decision phases for human-AI collaborative decision-making. However, previous studies found the issues of overreliance on ‘wrong’ AI outputs. In this paper, we propose interactive example-based explanations to improve health professionals’ onboarding with AI for their better reliance on AI during AI-assisted decision-making. We implemented an AI-based decision support system that utilizes a neural network to assess the quality of post-stroke survivors’ exercises and interactive example-based explanations that systematically surface the nearest neighborhoods of a test/task sample from the training set of the AI model to assist users’ onboarding with …

Self-Adaptive Fine-Grained Multi-Modal Data Augmentation For Semi-Supervised Muti-Modal Coreference Resolution, Li Zheng, Boyu Chen, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Donghong Ji

Research Collection School Of Computing and Information Systems

Coreference resolution, an essential task in natural language processing, is particularly challenging in multi-modal scenarios where data comes in various forms and modalities. Despite advancements, limitations due to scarce labeled data and underleveraged unlabeled data persist. We address these issues with a self-adaptive fine-grained multi-modal data augmentation framework for semi-supervised MCR, focusing on enriching training data from labeled datasets and tapping into the untapped potential of unlabeled data. Regarding the former issue, we first leverage text coreference resolution datasets and diffusion models,to perform fine-grained text-to-image generation with aligned text entities and image bounding boxes. We then introduce a self-adaptive selection …

An Empirical Study To Evaluate Aigc Detectors On Code Content, Jian Wang, Shangqing Liu, Xiaofei Xie, Yi Li

Research Collection School Of Computing and Information Systems

Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with Large Language Models (LLMs), like ChatGPT, emerging as a leading AIGC model that produces high-quality responses across various applications, including software development and maintenance. Despite its potential, the misuse of LLMs, especially in security and safetycritical domains, such as academic integrity and answering questions on Stack Overflow, poses significant concerns. Numerous AIGC detectors have been developed and evaluated on natural language data. However, their performance on code-related content generated by LLMs remains unexplored. To fill this gap, in this paper, we present an empirical study evaluating …

Leveraging Imitation Learning In Agricultural Robotics: A Comprehensive Survey And Comparative Analysis, Siavash Mahmoudi, Amirreza Davar, Pouya Sohrabipour, Ramesh Bahadur Bist, Yang Tao, Dongyi Wang

Biological and Agricultural Engineering Faculty Publications and Presentations

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control processes. This paper presents an exhaustive insight focusing on the implementation of imitation learning techniques in agricultural robotics. The survey rigorously examines varied research endeavors utilizing imitation learning to address pivotal agricultural challenges. Methodologically, this survey comprehensively investigates multifaceted aspects of imitation learning applications in agricultural robotics. The survey encompasses the identification of agricultural tasks that can potentially be addressed through imitation learning, detailed analysis of specific models …

Antitrust After The Coming Wave, Daniel A. Crane

Articles

A coming wave of general-purpose technologies, including artificial intelligence ("AI"), robotics, quantum computing, synthetic biology, energy expansion, and nanotechnology, is likely to fundamentally reshape the economy and erode the assumptions on which the antitrust order is predicated. First, AI-driven systems will vastly improve firms' ability to detect (and even program) consumer preferences without the benefit of price signals, which will undermine the traditional information-producing benefit of competitive markets. Similarly, these systems will be able to determine comparative producer efficiency without relying on competitive signals. Second, AI systems will invert the salient characteristics of human managers, whose intentions are opaque but …

An Efficient Fourier Caching Algorithm For Walk On Spheres, Zihong Zhou

Dartmouth College Master’s Theses

Walk on Spheres (WoS) is a grid-free Monte Carlo method for solving elliptic partial differential equations (PDEs).
Rather than discretizing the domain, WoS leverages the mean-value principle to obtain Monte Carlo estimates by recursively averaging the solution over the largest contained sphere, terminating upon reaching the boundary.
Unfortunately, WoS requires many independent estimates to achieve noise-free results.

We propose an acceleration technique for WoS, inspired by irradiance caching methods, that computes the solution at a sparse set of locations, and extrapolates these cached values to local neighborhoods. A key insight is that WoS can be extended to compute not only …

Can Federated Learning Solve Ai’S Data Privacy Problem?: A Legal Analysis, Warren B. Chik, Florian Gamper

Research Collection Yong Pung How School Of Law

Federated learning (FL) is a method of training AI systems on different datasets without sharing data. The promise of FL is to enable AI systems to be trained on data, including personal data, while preserving data privacy and confidentiality, and thus, inter alia, facilitate compliance with data protection legislation. FL has generated a considerable interest amongst the computer science community, yet there is a dearth of legal analysis of FL. This is a problem because the question of whether FL facilitates compliance with data protection legislation is a legal question. This article will fill this lacuna by providing a comprehensive …

Trust And Robotics: A Multi-Staged Decision-Making Approach To Robots In Community, Wenxi Zhang, Willow Wong, Mark Findlay

Research Collection Yong Pung How School Of Law

With the desired outcome of social good within the wider robotics ecosystem, trust is identified as the central adhesive of the human–robot interaction (HRI) interface. However, building trust between humans and robots involves more than improving the machine’s technical reliability or trustworthiness in function. This paper presents a holistic, community-based approach to trust-building, where trust is understood as a multifaceted and multi-staged looped relation that depends heavily on context and human perceptions. Building on past literature that identifies dispositional and learned stages of trust, our proposed decision to trust model considers more extensively the human and situational factors influencing how …

Architecting Standards: Leveraging Ia To Drive Emerging Technology Standards​, Noreen Y. Whysel

Publications and Research

In this talk for the annual meeting of the W3C Information Architecture Community Group, I discuss how standards bodies can learn from the Information Architecture field to improve accessibility and usability. By adopting user-centered design principles and engaging in iterative feedback loops, standards organizations can create more relevant and practical standards that align with real-world applications. Emphasizing collaboration with industry experts and end-users will help ensure that standards evolve along with technological advancements.

In the meantime, key standards are examined, such as ISO 9241, which provides guidelines for usability and user-centered design, and the W3C's Web Content Accessibility Guidelines (WCAG), …

Exploring Saliency Bias In Manipulation Detection, Joshua Krinsky, Alan Bettis, Qiuyu Tang, Daniel Moreira, Aparna Bharati

Computer Science: Faculty Publications and Other Works

The social media-fuelled explosion of fake news and misinformation supported by tampered images has led to growth in the development of models and datasets for image manipulation detection. However, existing detection methods mostly treat media objects in isolation, without considering the impact of specific manipulations on viewer perception. Forensic datasets are usually analyzed based on the manipulation operations and corresponding pixel-based masks, but not on the semantics of the manipulation, i.e., type of scene, objects, and viewers’ attention to scene content. The semantics of the manipulation play an important role in spreading misinformation through manipulated images. In an attempt to …

Multi-Agent System-Based Framework For An Intelligent Management Of Competency Building, Fatma Outay, Nafaa Jabeur, Fahmi Bellalouna, Tasnim Al Hamzi

All Works

To measure the effectiveness of learning activities, intensive research works have focused on the process of competency building through the identification of learning stages as well as the setup of related key performance indictors to measure the attainment of specific learning objectives. To organize the learning activities as per the background and skills of each learner, individual learning styles have been identified and measured by several researchers. Despite their importance in personalizing the learning activities, these styles are difficult to implement for large groups of learners. They have also been rarely correlated with each specific learning stage. New approaches are, …

Enhancing Resilience And Reducing Waste In Food Supply Chains: A Systematic Review And Future Directions Leveraging Emerging Technologies, Asmaa Seyam, May Ei Barachi, Cheng Zhang, Bo Du, Jun Shen, Sujith Samuel Mathew

All Works

The sustainability of food supply chains is gaining increasing attention, particularly after the COVID-19 pandemic. A food supply system that simultaneously prioritises resilience and minimises wastage is crucial. It is found that many studies have explored reducing food waste and increasing supply chain resilience as separate objectives, but research is scarce investigating both objectives in conjunction. This paper presents a comprehensive systematic review focusing on existing solutions to reducing food waste and enhancing resilience. It discusses future directions, particularly leveraging emerging technologies such as the Internet of Things, blockchain, artificial intelligence, and machine learning. The studies are categorised into three …

Exploring How Uncertain Labels From Non-Consensus Panels Affect Machine Learning, Amal Almansour

College of Computing and Digital Media Dissertations

A dataset becomes meaningful for analysis when it contains more representative features. Machine and deep learning models rely on annotated instances for training. The annotation process is usually done either by humans (experts or crowdsourcing) or by models. In many cases, the variability between humans (the inter-observer variability) in evaluation leads to uncertainty in the learning process. Due to the lack of reliable labels in large datasets, the inter-observer variability can be quantified with different methods to estimate the ground truth label (i.e., referenced standard label) for model learning.

In health care, with the rise of artificial intelligence in clinical …

Revolutionizing Public Safety And Criminal Justice Through Ai, Alan Saquella

Publications

Artificial Intelligence (AI) is rapidly transforming public safety, criminal justice and security by fundamentally changing how crimes are committed, investigated and prevented. As AI tools become increasingly sophisticated, law enforcement and corporate security professionals are utilizing these advancements to enhance their capabilities. However, integrating AI into these sectors also brings significant challenges, including ethical concerns, recruitment difficulties, and the surge in crime rates. This article examines the transformative impact of AI, the ongoing efforts to unify AI applications across public safety and security sectors, and expert advice on overcoming the associated challenges.

How State Universities Are Addressing The Shortage Of Cybersecurity Professionals In The United States, Gary Harris

Journal of Cybersecurity Education, Research and Practice

Cybersecurity threats have been a serious and growing problem for decades. In addition, a severe shortage of cybersecurity professionals has been proliferating for nearly as long. These problems exist in the United States and globally and are well documented in literature. This study examined what state universities are doing to help address the shortage of cybersecurity professionals since higher education institutions are a primary source to the workforce pipeline. It is suggested that the number of cybersecurity professionals entering the workforce is related to the number of available programs. Thus increasing the number of programs will increase the number of …

Educating With Ai, Grace Seo, David Wicks

AI and the Future of Work

This conference track explores the integration of AI within teaching and learning, with a focus on practical approaches that leverage AI technologies to optimize teaching practices and enhance students’ learning experience. The topics include the essential AI literacies in educational contexts, collaborative learning with AI, and the use of AI for enhanced learning assessments.

Time-Series Feature Selection For Solar Flare Forecasting, Yagnashree Velanki, Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

Computer Science Student Research

Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple …

Advancing Emotional Health Assessments: A Hybrid Deep Learning Approach Using Physiological Signals For Robust Emotion Recognition, Amna Waheed Awan, Imran Taj, Shehzad Khalid, Syed Muhammad Usman, Ali Shariq Imran, Muhammad Usman Akram

All Works

Emotional health significantly impacts physical and psychological well-being, with emotional imbalances and cognitive disorders leading to various health issues. Timely diagnosis of mental illnesses is crucial for preventing severe disorders and enhancing medical care quality. Physiological signals, such as Electrocardiograms (ECG) and Electroencephalograms (EEG), which reflect cardiac and neuronal activities, are reliable for emotion recognition as they are less susceptible to manipulation than physical signals. Galvanic Skin Response (GSR) is also closely linked to emotional states. Researchers have developed various methods for classifying signals to detect emotions. However, these signals are susceptible to noise and are inherently non-stationary, meaning they …

Synthesis Of Zno: Zro2 Nanocomposites Using Green Method For Medical Applications, Mohammed J. Tuama, Maysoon F. Alias

Karbala International Journal of Modern Science

These days, nanocomposites are very popular, especially in medical applications. The spread of diseases in general, and those caused by microbes and cancerous diseases in particular, and the increased resistance of these diseases to antibiotics, have led to the need for the rapid, low-cost, and environmentally friendly production of nanocomposites. To create the chemical G-ZnO: ZrO2 and S-ZnO: ZrO2 (green technique), two different plant extracts were utilized: Z. officinal and S. aromaticum. The effective synthesis and acceptable properties features of the nanoparticles were confirmed using characterization techniques such as X-ray diffraction (XRD), Fourier transform infrared (FTIR) , diffuse reflectance spectroscopy …

Detecting Lgbtq+ Instances Of Cyberbullying, Arslan Bisharat, Manuel Madrigal, Mohammed Abuhamad, Deborah Hall, Yasin Silva

Computer Science: Faculty Publications and Other Works

Social media continues to have an impact on the trajectory of humanity. However, its introduction has also weaponized keyboards, allowing the abusive language normally reserved for in-person bullying to jump onto the screen, i.e., cyberbullying. Cyberbullying poses a significant threat to adolescents globally, affecting the mental health and well-being of many. A group that is particularly at risk is the LGBTQ+ community, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. Therefore, it is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members. …

Systematic Review On Isolation, Purification, Characterization, And Industrial Applications Of Thermophilic Microbial Α- Amylases, Rugaiyah A. Arfah, Sarlan Sarlan, Abdul Karim, Anita Anita, Ahyar Ahmad, Paulina Taba, Harningsih Karim, Siti Halimah Larekeng, Dorothea Agnes Rampisela, Rusdina Bte Ladju

Karbala International Journal of Modern Science

The α-amylase enzyme, sourced from diverse organisms, including plants, animals, and bacteria, plays a crucial role in multiple industries, notably food processing sectors like cakes, fruit juices, and starch syrup. Research identifies thermophilic organisms as prime sources of this enzyme thriving at temperatures ranging from 41°C to 122°C. The enzyme purification was carried out using liquid-liquid extraction, which involved the exchange of substances between two liquid phases that were immiscible or partially soluble. The optimal temperature for α-amylase was 45 to 90°C. The best pH for bacterial and fungal α-amylases ranged from 5.0 to 10.5 and 5.0 to 9.0. Based …

Investigating The Intersection Of Ai And Cybercrime: Risks, Trends, And Countermeasures, Sanaika Shetty, Kyung-Shick Choi, Insun Park

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Integrated Model Of Cybercrime Dynamics: A Comprehensive Framework For Understanding Offending And Victimization In The Digital Realm, Troy Smith Phd

International Journal of Cybersecurity Intelligence & Cybercrime

This article introduces the Integrated Model of Cybercrime Dynamics (IMCD), a novel theoretical framework for examining the complex interplay between individual characteristics, online behavior, environmental factors, and outcomes related to cybercrime offending and victimization. The model incorporates key concepts from existing theories, empirical evidence, and interdisciplinary perspectives to provide a comprehensive framework. In contrast to traditional criminological theories, the proposed model integrates concepts from multiple disciplines to offer a holistic framework that captures the complexity of cybercrime and specifically caters for the uniqueness of cyberspace. The article will provide a detailed overview of the conceptual model, its theoretical underpinnings drawing …

Some Studies On Mathematical Morphology In Remotely Sensed Data Analysis, Geetika Barman

Doctoral Theses

The application of Mathematical Morphology (MM) techniques has proven to be beneficial in the extraction of shapebased and texture-based features during remote sensing image analysis. The characteristics of these techniques, such as nonlinear adaptability and comprehensive lattice structure, make them useful for contextual spatial feature analysis. Despite the advancements, there are still persistent challenges, including the curse of dimensionality, maintaining spatial correlation, and the adaptability of morphological operators in higher dimensions. The focus of this thesis is to explore the potential of MM-based methods to analyse spatial features in addressing these challenges, specifically in the context of spatialcontextual feature analysis …

Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra

Physical Therapy Faculty Articles and Research

In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus …

The Use Of Social Media For Organisational Learning: A Systematic Review Of Literature, Harry Moongela, Marie Hattingh

African Conference on Information Systems and Technology

Social media (categorised as Web 2.0 platforms) has been reported to significantly impact and improve organisational learning (OL), but there seems to be a lack of systematic literature reviews (SLR) covering the use of social media to facilitate OL. Thus, this article conducted a SLR for both social media and OL by presenting the significant concepts, models and frameworks from literature. A total number of 48 articles were found for the analysis method and the findings reveal important concepts such as types of OL, levels of OL and frameworks incorporating both social media and OL. The results also show that …

Exploring Artificial Intelligence: A Collaborative Small Group Analysis And Application, Ellamarie Powell

AI Assignment Library

In this small group project, students will collaborate to explore the principles and applications of Artificial Intelligence (AI). Each group will research, analyze, and present on a specific AI topic, highlighting its real-world implications and ethical considerations. The project involves team members contributing to various roles, including research, technical analysis, and presentation. The final deliverable will be a video presentation integrating individual contributions, showcasing a comprehensive understanding of AI and its impact on society. This assignment fosters teamwork, critical thinking, and effective communication skills.

Coarse-Gridded Simulation Of The Nonlinear Schrödinger Equation With Machine Learning, Benjamin F. Akers, Kristina O. F. Williams

Faculty Publications

A numerical method for evolving the nonlinear Schrödinger equation on a coarse spatial grid is developed. This trains a neural network to generate the optimal stencil weights to discretize the second derivative of solutions to the nonlinear Schrödinger equation. The neural network is embedded in a symmetric matrix to control the scheme’s eigenvalues, ensuring stability. The machine-learned method can outperform both its parent finite difference method and a Fourier spectral method. The trained scheme has the same asymptotic operation cost as its parent finite difference method after training. Unlike traditional methods, the performance depends on how close the initial data …

Adaptive Worlds: Generative Ai In Game Design And Future Of Gaming, And Interactive Media, Jay Ratican, James Hutson

Faculty Scholarship

Generative AI is revolutionizing the field of game design, introducing unprecedented adaptability and personalization in gameplay. The latest advancements in AI-driven engines enable real-time content creation, offering dynamic, player-driven experiences that diverge from traditional pre-programmed narratives. This shift marks a transition toward "choose your own adventure" formats, with an unlimited number of variations in levels, enemies, collectibles, and weaponry, tailored to each player's decisions. Google's GameNGen, for example, showcases AI's capacity to recreate classic games like DOOM, learning and generating gameplay in real time. These innovations are not restricted to gaming alone; they extend to edutainment, television, and film, where …

Regulating Algorithmic Harms, Sylvia Lu

Law & Economics Working Papers

In recent years, the rapid expansion of artificial intelligence (AI) innovations has led to a rise in algorithmic harms—harms emerging from AI operations that pose significant threats to civil rights and democratic values in today’s technological landscape. A facial recognition system for improving criminal detection wrongly collected sensitive personal data and flagged racial minorities as shoplifters. A risk-prediction algorithm adopted to identify patients denied medical treatment to Black individuals with poor health conditions. A social media algorithm intended to boost social engagement exacerbated addictive behavior and mental illness in teenagers. These harms are becoming increasingly ubiquitous yet often manifest in …