Artificial Intelligence and Robotics Commons^™

An Education Theory Of Fault For Autonomous Systems, William D. Smart, Cindy M. Grimm, Woodrow Hartzog

Notre Dame Journal on Emerging Technologies

Automated systems like self-driving cars and “smart” thermostats are a challenge for fault-based legal regimes like negligence because they have the potential to behave in unpredictable ways. How can people who build and deploy complex automated systems be said to be at fault when they could not have reasonably anticipated the behavior (and thus risk) of their tools? Part of the problem is that the legal system has yet to settle on the language for identifying culpable behavior in the design and deployment for automated systems. In this article we offer an education theory of fault for autonomous systems—a new …

Technological Tethereds: Potential Impact Of Untrustworthy Artificial Intelligence In Criminal Justice Risk Assessment Instruments, Sonia M. Gipson Rankin

Faculty Scholarship

Issues of racial inequality and violence are front and center in today’s society, as are issues surrounding artificial intelligence (AI). This Article, written by a law professor who is also a computer scientist, takes a deep dive into understanding how and why hacked and rogue AI creates unlawful and unfair outcomes, particularly for persons of color.

Black Americans are disproportionally featured in criminal justice, and their stories are obfuscated. The seemingly endless back-to-back murders of George Floyd, Breonna Taylor, and Ahmaud Arbery, and heartbreakingly countless others have finally shaken the United States from its slumbering journey towards intentional criminal justice …

Learning And Simulation Algorithms For Constraint Physical Systems, Shuqi Yang

Dartmouth College Master’s Theses

This thesis explores two computational approaches to learn and simulate complex physical systems exhibiting constraint characteristics. The target applications encompass both solids and fluids. On the solid side, we proposed a new family of data-driven simulators to predict the behaviors of an unknown physical system by learning its underpinning constraints. We devised a neural projection operator facilitated by an embedded recursive neural network to interactively enforce the learned underpinning constraints and to predict its various physical behaviors. Our method can automatically uncover a broad range of constraints from observation point data, such as length, angle, bending, collision, boundary effects, and …

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

USF Tampa Graduate Theses and Dissertations

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

Ai Use In Claims Processing And Utilization Review, Robert Rosenthal Dds

The Journal of the Michigan Dental Association

This paper investigates the use of artificial intelligence (AI) in claims processing and utilization review in the dental industry. This article aims to explore the potential benefits of AI in this area, such as increased efficiency, accuracy, and fraud detection. The paper begins by providing an overview of the current state of claims processing and utilization review in the dental industry. It then discusses the potential applications of AI in this area, such as automated claims adjudication, predictive analytics, and image recognition. The paper then presents a case study of P&R Dental Strategies, LLC, a leading business intelligence solutions provider …

The Emergence Of Artificial Intelligence In Dental Care Delivery, Robert A. Faiella D.M.D., M.M.Sc., M.B.A., Shaju Puthussery M.S.

The Journal of the Michigan Dental Association

This comprehensive review explores the transformative role of Artificial Intelligence (AI) in the evolution of dental care delivery. As oral health specialists, dentists continually seek to enhance their ability to prevent, diagnose, and manage oral diseases while maintaining and improving patient oral health. The integration of AI offers unprecedented opportunities to revolutionize dental practice and patient care.

AI is rapidly advancing in healthcare, including dental care, with a projected global healthcare AI market value of $45.2 billion by 2026. This technology can potentially revolutionize prevention, diagnosis, treatment planning, and treatment outcomes.

Aspects of AI in dentistry include:

· Diagnostic Accuracy …

Feature Extraction And Design In Deep Learning Models, Daniel Perez

Computational Modeling & Simulation Engineering Theses & Dissertations

The selection and computation of meaningful features is critical for developing good deep learning methods. This dissertation demonstrates how focusing on this process can significantly improve the results of learning-based approaches. Specifically, this dissertation presents a series of different studies in which feature extraction and design was a significant factor for obtaining effective results. The first two studies are a content-based image retrieval system (CBIR) and a seagrass quantification study in which deep learning models were used to extract meaningful high-level features that significantly increased the performance of the approaches. Secondly, a method for change detection is proposed where the …

J Mich Dent Assoc April 2021

The Journal of the Michigan Dental Association

In the April 2021 issue of the Journal of the Michigan Dental Association, we offer a comprehensive range of original feature content showcasing the latest developments in dental practice and knowledge, including:

1. AI in Dental Care Delivery: Explore the groundbreaking role of Artificial Intelligence (AI) and Machine Learning in dental care, revolutionizing efficiency, safety, care outcomes, and treatment planning consistency.
2. AI in Dental Claims Processing: Discover how AI is employed by third-party payers to streamline dental claims processing, resulting in cost containment and the proactive identification of potential fraud, waste, and abuse.
3. Evidence-Based Dentistry: As part of …

Cybersecurity Risk Assessment Using Graph Theoretical Anomaly Detection And Machine Learning, Goksel Kucukkaya

Engineering Management & Systems Engineering Theses & Dissertations

The cyber domain is a great business enabler providing many types of enterprises new opportunities such as scaling up services, obtaining customer insights, identifying end-user profiles, sharing data, and expanding to new communities. However, the cyber domain also comes with its own set of risks. Cybersecurity risk assessment helps enterprises explore these new opportunities and, at the same time, proportionately manage the risks by establishing cyber situational awareness and identifying potential consequences. Anomaly detection is a mechanism to enable situational awareness in the cyber domain. However, anomaly detection also requires one of the most extensive sets of data and features …

An Analysis Of The Interpretability Of Neural Networks Trained On Magnetic Resonance Imaging For Stroke Outcome Prediction, Esra Zihni, John D. Kelleher, Bryony Mcgarry

Conference papers

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology.

Efficient Retrieval Of Matrix Factorization-Based Top-K Recommendations: A Survey Of Recent Approaches, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Top-k recommendation seeks to deliver a personalized list of k items to each individual user. An established methodology in the literature based on matrix factorization (MF), which usually represents users and items as vectors in low-dimensional space, is an effective approach to recommender systems, thanks to its superior performance in terms of recommendation quality and scalability. A typical matrix factorization recommender system has two main phases: preference elicitation and recommendation retrieval. The former analyzes user-generated data to learn user preferences and item characteristics in the form of latent feature vectors, whereas the latter ranks the candidate items based on the …

Mixed Dish Recognition With Contextual Relation And Domain Alignment, Lixi Deng, Jingjing Chen, Chong-Wah Ngo, Qianru Sun, Sheng Tang, Yongdong Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Mixed dish is a food category that contains different dishes mixed in one plate, and is popular in Eastern and Southeast Asia. Recognizing the individual dishes in a mixed dish image is important for health related applications, e.g. to calculate the nutrition values of the dish. However, most existing methods that focus on single dish classification are not applicable to the recognition of mixed dish images. The main challenge of mixed dish recognition comes from three aspects: a wide range of dish types, the complex dish combination with severe overlap between different dishes and the large visual variances of same …

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

Research Collection School Of Computing and Information Systems

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

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

Research Collection School Of Computing and Information Systems

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

Variable Autoencoders For Biosensor Data Augmentation, Solomon Kim

Honors Theses

Over the past decade machine learning and artificial intelligence's resurgence spawned the desire to mimic human creative ability. Initially attempts to create images, music, and text flooded the community, though little has been learned regarding constrained, one-dimensional data generation. This paper demonstrates a variational autoencoder approach to this problem. By modeling biosensor current and concentration data we aim to augment the existing dataset. In training a multi-layer neural network based encoder and decoder we were able to generate realistic, original samples., These results demonstrate the ability to realistically augment datasets, improving training of machine learning models designed to predict concentration …

Using Machine Learning For Detection Of Covid-19, Justin Rickert

Honors Projects

Currently, the most widely used diagnostic tool for COVID-19 is the RT-PCR nasal swab test recommended by the CDC. However, some studies have shown that chest CT scans have the potential to be more accurate and are also capable of detecting the virus in its earlier stages. Unfortunately, CT results are not instantaneously available as it may be days before a radiologist can review the scan. This delay is one of the factors preventing the widespread use of CT scans for COVID detection. To address the delay, this project investigated Convolutional Neural Networks, an advanced form of machine learning used …

Predicting Bus Travel Times In Washington, Dc Using Artificial Neural Networks (Anns), Stephen Arhin, Babin Manandhar, Hamdiat Baba Adam, Adam Gatiba

Mineta Transportation Institute

Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington …

Homophily Outlier Detection In Non-Iid Categorical Data, Guansong Pang, Longbing Cao, Ling Chen

Research Collection School Of Computing and Information Systems

Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier …

Time Period-Based Top-K Semantic Trajectory Pattern Query, Munkh-Erdene Yadamjav, Farhana Murtaza Choudhury, Zhifeng Bao, Baihua Zheng

Research Collection School Of Computing and Information Systems

The sequences of user check-ins form semantic trajectories that represent the movement of users through time, along with the types of POIs visited. Extracting patterns in semantic trajectories can be widely used in applications such as route planning and trip recommendation. Existing studies focus on the entire time duration of the data, which may miss some temporally significant patterns. In addition, they require thresholds to define the interestingness of the patterns. Motivated by the above, we study a new problem of finding top-k semantic trajectory patterns w.r.t. a given time period and categories by considering the spatial closeness of POIs. …

Spectral Tensor Train Parameterization Of Deep Learning Layers, A. Obukhov, M. Rakhuba, A. Liniger, Zhiwu Huang, S. Georgoulis, D. Dai, Van Gool L.

Research Collection School Of Computing and Information Systems

We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context. The low-rank property leads to parameter efficiency and permits taking computational shortcuts when computing mappings. Spectral properties are often subject to constraints in optimization problems, leading to better models and stability of optimization. We start by looking at the compact SVD parameterization of weight matrices and identifying redundancy sources in the parameterization. We further apply the Tensor Train (TT) decomposition to the compact SVD components, and propose a non-redundant differentiable parameterization of fixed TT-rank tensor manifolds, termed the Spectral Tensor Train Parameterization (STTP). We …

10-Minute Ebd: Artificial Intelligence In Orthodontics, Jayne Kessel Dds

The Journal of the Michigan Dental Association

This Ten-Minute Evidence-Based Dentistry Article provides an example of the implementation of the EBD search process with trusted search engines for the identification of the best literature through critical appraisal to answer a clinical question. "For patients receiving orthodontic care, is an AI-generated treatment plan as likely to achieve acceptable outcomes?" Orthodontic treatment planning is a complex and time-consuming process that requires a high degree of expertise. Artificial intelligence (AI) has the potential to assist orthodontists in this process by automating some of the tasks involved, such as cephalometric analysis, surgery decisions, extraction decisions, and anchorage decisions.

A recent systematic …

Powered By Ai, Christopher J. Smiley

The Journal of the Michigan Dental Association

Artificial Intelligence (AI) is revolutionizing dental practice through its ability to process vast amounts of data, enhance diagnosis, and improve patient care. However, AI introduces the challenge of bias and ethical considerations. Dentists and dental benefit providers are utilizing AI for early disease detection and efficient data management, but transparency and fairness in AI algorithms are vital. The Rome Call for AI Ethics emphasizes ethical, non-biased AI development. In the broader context, AI-driven marketing and predictive behavior raise concerns about privacy and ethical data use. The dental community must embrace AI's power while upholding ethical standards and transparency.

Learning To Fuse Asymmetric Feature Maps In Siamese Trackers, Wencheng Han, Xingping Dong, Fahad Shahbaz Khan, Ling Shao, Jianbing Shen

Computer Vision Faculty Publications

Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCorr) to obtain multi-channel correlation information from the two feature maps (target and search region). However, DW-XCorr has several limitations within Siamese-based tracking: it can easily be fooled by distractors, has fewer activated channels and provides weak discrimination of object boundaries. Further, DW-XCorr is a handcrafted parameter-free module and cannot fully benefit from offline learning on large-scale data. We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on …

Deep Gaussian Processes For Few-Shot Segmentation, Joakim Johnander, Johan Edstedt, Martin Danelljan, Michael Felsberg, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Few-shot segmentation is a challenging task, requiring the extraction of a generalizable representation from only a few annotated samples, in order to segment novel query images. A common approach is to model each class with a single prototype. While conceptually simple, these methods suffer when the target appearance distribution is multi-modal or not linearly separable in feature space. To tackle this issue, we propose a few-shot learner formulation based on Gaussian process (GP) regression. Through the expressivity of the GP, our approach is capable of modeling complex appearance distributions in the deep feature space. The GP provides a principled way …

On Generating Transferable Targeted Perturbations, Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Fatih Porikli

Computer Vision Faculty Publications

While the untargeted black-box transferability of adversarial perturbations has been extensively studied before, changing an unseen model's decisions to a specific 'targeted' class remains a challenging feat. In this paper, we propose a new generative approach for highly transferable targeted perturbations (TTP). We note that the existing methods are less suitable for this task due to their reliance on class-boundary information that changes from one model to another, thus reducing transferability. In contrast, our approach matches the perturbed image 'distribution' with that of the target class, leading to high targeted transferability rates. To this end, we propose a new objective …

Orthogonal Projection Loss, Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat, Salman Khan, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Deep neural networks have achieved remarkable performance on a range of classification tasks, with softmax cross-entropy (CE) loss emerging as the de-facto objective function. The CE loss encourages features of a class to have a higher projection score on the true class-vector compared to the negative classes. However, this is a relative constraint and does not explicitly force different class features to be well-separated. Motivated by the observation that ground-truth class representations in CE loss are orthogonal (one-hot encoded vectors), we develop a novel loss function termed 'Orthogonal Projection Loss' (OPL) which imposes orthogonality in the feature space. OPL augments …

Survey On Quantum Circuit Compilation For Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence To Heuristics, Janusz Kusyk, Samah Mohamed Saeed, Muharrem Umit Uyar

Publications and Research

Computationally expensive applications, including machine learning, chemical simulations, and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum architecture. Quantum circuit compilation (QCC) aims to generate feasible mappings such that a quantum circuit can be executed in a given hardware platform with acceptable confidence in outcomes. Physical constraints of a NISQ computer change frequently, requiring QCC process to be repeated often. When a circuit cannot directly be executed on a quantum hardware due to its …

Frequency Coordinated Control Strategy Of Microgrid Based On Fuzzy Prediction, Kunping Zhang, Hao Lin

Journal of System Simulation

Abstract: Aiming at the problem of frequency fluctuation of wind power generation connected to microgrid, a frequency coordinated control strategy based on model predictive control (MPC) is proposed. In this strategy, the wind turbine and plug-in hybrid electric vehicle (PHEV) are included in the frequency control system. The pitch angle of the fan and the charge and discharge of PHEV are controlled to adjust the grid frequency and supplement the frequency modulation resources of microgrid. WTG pitch angle control system and PHEV power control system are modeled, and their control principles are described. In order to prevent excessive use of …

Guaranteed Cost Preview And Repetitive Control For Uncertain Linear Discrete Time-Delay Systems, Yonghong Lan, Jinlin He

Journal of System Simulation

Abstract: For a class of uncertain linear discrete time-delay systems, a design method for guaranteed cost preview and repetitive controller is proposed . By introducing a repetitive controller in the forward channel to improve the tracking accuracy of the system, L-order difference operators are used to construct an augmented error system that contains preview information but does not include time delay, and the design problem of guaranteed cost preview and repetitive controller is converted into an output feedback adjustment problem. Using the Lyapunov stability theory and the linear matrix inequality method, the sufficient conditions for guaranteeing the asymptotic stability of …

Neural Network Optimized Sensorless Permanent Magnet Synchronous Motor Control System, Lixin Ma, Yongjie Zhu, Leyan Ji

Journal of System Simulation

Abstract: In order to solve the poor accuracy of the speed and rotor position of permanent magnet synchronous motor caused by sensor, a sensorless control system is proposed to calculate the speed and rotor position of PMSM with extended Kalman filtering algorithm. BP neural network algorithm is used to optimize the covariance matrix Q and R of EKF, which improves the accurate calculation values of rotational speed and rotor position. At the same time, the speed sliding mode controller combined with the current feed-forward decoupling unit are used to improve the stability of the whole control system. The simulation results …