GR-397 Conceptualizing a TOC-Enhanced Chatbot: Pattern Recognition and Interaction

A chatbot is a software which is capable of communicating with human by using natural language processing. In our project, we plan to develop a Python-based chatbot that integrates theory of computation (TOC) concepts, including finite automata and regular expressions. The chatbot will interact with users, recognizing patterns and keywords in their inputs. We’ll begin by defining initial regular expressions for basic user interactions including greetings and inquiries.Future developments may enhance regular expressions and broaden the chatbot’s TOC-related capabilities, creating a versatile educational tool with practical TOC applications

Improve Trust and Adversarial Robustness of AI models in Remote Sensing

Remote sensing-based sensors are important in diagnosing remote objects for security and sensitive government installations, including the Department of Defense (DOD), Depart- ment of Homeland Security (DHS) and the Environmental Protection Agency (EPA). In the past, a human operator was required to apply their judgment to perform the mapping and monitoring of objects via remote sensing sensors, including multi-spectral images, which required careful and tedious prior selection. However, with deep learning, these features in the multi-spectral, multi-temporal, and multiple-modality remote sensing data sets are auto- matically learned. Despite their benefits, these deep learning models are often termed black box models due to their opacity in decision-making. This lack of transparency can hinder user trust and compromise the security of these models. Choosing a better explainable AI (XAI) approach is crucial as it provides insights into model decisions, thereby improv- ing the trust and security of deep learning models in critical applications. A well-chosen XAI method enhances the understanding of how models make decisions, identifying poten- tial biases and vulnerabilities that could be exploited in adversarial attacks. Explainability helps in diagnosing errors, ensuring compliance with regulatory standards, and facilitating human oversight. Furthermore, interpretable models can enhance collaboration between AI systems and human experts, leading to better decision-making processes. In high-stakes environments understanding how and why a model makes certain decisions can prevent critical errors and improve response strategies. Our research aims to improve the interpretability and reliability of the algorithms in remote sensing applications. We conducted a comparative analysis of two explainable AI models, Grad-CAM and Score-CAM, to interpret decisions in multi-spectral datasets such as UC-Merced and EuroSAT and established an automated approach to determine which explainable AI method performs better. This automated approach involves evaluating the explainable AI methods using specific metrics: ROAD, SIC, and infidelity. Our empiri- cal analysis demonstrated that, on average, Score-CAM outperformed Grad-CAM, as ev- idenced by these metrics. By systematically utilizing these evaluation metrics, we devel- oped a method for reliably identifying the superior explainable AI approach. This can help researchers with a clear choice for enhancing model interpretability and trustworthiness. In addition, we responded appropriately to one of the most important threats in AI ad- versarial examples, which are designed to alter benign data inputs in a way that deceives AI algorithms to compromise the security of remote sensing systems. To address these threats, we devised an adversarial robustness approach that allows for the correct predic- tion of the accurate AI model even when adversarial perturbations have been added. We incorporated explainable-AI guided features & data augmentation methods to build a ro- bust AI model against adversarial attacks. As for the results, we were able to show that our proposed approach was more resistant to adversarial attacks, especially Projected Gradient Descent (PGD), in the EuroSAT and AID datasets. This integrated effort advances not only the interpretability, but also the robustness of AI models in the context of remote sensing, opening the field up to improved safety and efficiency

On Fuzzy Extended Hexagonal b-Metric Spaces with Applications to Nonlinear Fractional Differential Equations

The focus of this research article is to investigate the notion of fuzzy extended hexagonal b-metric spaces as a technique of broadening the fuzzy rectangular b-metric spaces and extended fuzzy rectangular b-metric spaces as well as to derive the Banach fixed point theorem and several novel fixed point theorems with certain contraction mappings. The analog of hexagonal inequality in fuzzy extended hexagonal b-metric spaces is specified as follows utilizing the function b(c,d): mhc,d,t+s+u+v+w≥mhc,e,tb(c,d)∗mhe,f,sb(c,d)∗mhf,g,ub(c,d)∗mhg,k,vb(c,d)∗mhk,d,wb(c,d) for all t,s,u,v,w>0 and c≠e,e≠f,f≠g,g≠k,k≠d. Further to that, this research attempts to provide a feasible solution for the Caputo type nonlinear fractional differential equations through effective applications of our results obtained

On Fuzzy Extended Hexagonal <i>b</i>-Metric Spaces with Applications to Nonlinear Fractional Differential Equations

The focus of this research article is to investigate the notion of fuzzy extended hexagonal b-metric spaces as a technique of broadening the fuzzy rectangular b-metric spaces and extended fuzzy rectangular b-metric spaces as well as to derive the Banach fixed point theorem and several novel fixed point theorems with certain contraction mappings. The analog of hexagonal inequality in fuzzy extended hexagonal b-metric spaces is specified as follows utilizing the function b(c,d): mhc,d,t+s+u+v+w≥mhc,e,tb(c,d)∗mhe,f,sb(c,d)∗mhf,g,ub(c,d)∗mhg,k,vb(c,d)∗mhk,d,wb(c,d) for all t,s,u,v,w>0 and c≠e,e≠f,f≠g,g≠k,k≠d. Further to that, this research attempts to provide a feasible solution for the Caputo type nonlinear fractional differential equations through effective applications of our results obtained

New Fixed Point Theorem on Triple Controlled Metric Type Spaces with Applications to Volterra–Fredholm Integro-Dynamic Equations

The objective of the research article is two-fold. Firstly, we present a fixed point result in the context of triple controlled metric type spaces with a distinctive contractive condition involving the controlled functions. Secondly, we consider an initial value problem associated with a nonlinear Volterra–Fredholm integro-dynamic equation and examine the existence and uniqueness of solutions via fixed point theorem in the setting of complete triple controlled metric type spaces. Furthermore, the theorem is applied to illustrate the existence of a unique solution to an integro-dynamic equation

Ambulatory antibiotic prescription rates for acute respiratory infection rebound two years after the start of the COVID-19 pandemic.

BackgroundDuring the COVID-19 pandemic, acute respiratory infection (ARI) antibiotic prescribing in ambulatory care markedly decreased. It is unclear if antibiotic prescription rates will remain lowered.MethodsWe used trend analyses of antibiotics prescribed during and after the first wave of COVID-19 to determine whether ARI antibiotic prescribing rates in ambulatory care have remained suppressed compared to pre-COVID-19 levels. Retrospective data was used from patients with ARI or UTI diagnosis code(s) for their encounter from 298 primary care and 66 urgent care practices within four academic health systems in New York, Wisconsin, and Utah between January 2017 and June 2022. The primary measures included antibiotic prescriptions per 100 non-COVID ARI encounters, encounter volume, prescribing trends, and change from expected trend.ResultsAt baseline, during and after the first wave, the overall ARI antibiotic prescribing rates were 54.7, 38.5, and 54.7 prescriptions per 100 encounters, respectively. ARI antibiotic prescription rates saw a statistically significant decline after COVID-19 onset (step change -15.2, 95% CI: -19.6 to -4.8). During the first wave, encounter volume decreased 29.4% and, after the first wave, remained decreased by 188%. After the first wave, ARI antibiotic prescription rates were no longer significantly suppressed from baseline (step change 0.01, 95% CI: -6.3 to 6.2). There was no significant difference between UTI antibiotic prescription rates at baseline versus the end of the observation period.ConclusionsThe decline in ARI antibiotic prescribing observed after the onset of COVID-19 was temporary, not mirrored in UTI antibiotic prescribing, and does not represent a long-term change in clinician prescribing behaviors. During a period of heightened awareness of a viral cause of ARI, a substantial and clinically meaningful decrease in clinician antibiotic prescribing was observed. Future efforts in antibiotic stewardship may benefit from continued study of factors leading to this reduction and rebound in prescribing rates