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This paper reports on the fourth year of a cybersecurity-focused research experience for undergraduates programs site in the summer of 2021. Due to the COVID-19 pandemic, the site operated in a hybrid mode during this summer, after operating entirely virtually during the summer of 2020. The paper compares the results of operating in a hybrid mode with two years of in-person operations and one year of virtual operations. It discusses the lessons learned when operating in the hybrid mode and makes recommendations for future hybrid REU implementations. 

Quantum noise is seen by many researchers as a problem to be resolved. Current solutions increase quantum computing system costs significantly by requiring numerous hardware qubits to represent a logical qubit to average the noise away. However, despite its deleterious effects on system performance and the increased costs it creates, it may have some potential uses. This paper evaluates those. Specifically, it considers how quantum noise could be used to support the fuzzing cybersecurity and testing technique and AI techniques such as certain swarm artificial intelligence algorithms. Fuzzing is used to identify vulnerabilities in software by generating massive amounts of input cases for a program. Quantum noise provides an effective built-in fuzzing capability that is centered around the actual answer to a computation. These same phenomena, of clustered and centered fuzz-noise around the answer of an operation, could be similarly useful to AI techniques that can make effective use of lots of point values for optimization. Effectively, by concurrently considering the ‘multiverse’ of possible results to an operation, created by compounding noise, more beneficial solutions that are proximal to the actual result of an operation can be identified via testing quantum noise points with an effectiveness algorithm. Both of these potential uses for quantum noise are considered herein. 

This paper discusses the benefits that can be enjoyed from the possible applications of quantum computing to the aerospace sector. It discusses these benefits for various tasks and applications such as artificial intelligence, key distribution and simulation. The challenge of determining what aerospace applications will be best allocated to quantum devices is also discussed. The method for best utilizing quantum computing for aerospace applications is also considered and the benefits of a grid-based implementation of computational resources are discussed. A collection of network-connected heterogenous devices could be used to support the effective use of computational resources. It could be used for the operations and development of simulations as well as machine learning and other software applications. This paper also presents a summary of potential use-cases, as well as a discussion of the work necessary to expedite the process of quantum computing development. 

Due to the recent COVID-19 outbreak, makeshift (MS) hospitals have become an important feature in healthcare systems worldwide. Healthcare personnel (HCP) need to be able to navigate quickly, effectively, and safely to help patients, while still maintaining their own well-being. In this study, a pathfinding algorithm to help HCP navigate through a hospital safely and effectively is developed and verified. Tests are run using a discretized 2D grid as a representation of an MS hospital plan, and total distance traveled and total exposure to disease are measured. The influence of the size of the 2D grid units, the shape of these units, and degrees of freedom in the potential movement of the HCP are investigated. The algorithms developed are designed to be used in MS hospitals where airborne illness is prevalent and could greatly reduce the risk of illness in HCP. In this study, it was found that the quantum-based algorithm would generate paths that accrued 50–66% less total disease quantum than the shortest path algorithm with also about a 33–50% increase in total distance traveled. It was also found that the mixed path algorithm-generated paths accrued 33–50% less quantum, but only increased total distance traveled by 10–20%. 

Intentionally deceptive online content seeks to manipulate individuals in their roles as voters, consumers, and participants in society at large. While this problem is pronounced, techniques to combat it may exist. To analyze the problem and potential solutions, we conducted three surveys relating to how news consumption decisions are made and the impact of labels on decision making. This article describes these three surveys and the data that were collected by them. 

This paper considers the use of a post metadata-based approach to identifying intentionally deceptive online content. It presents the use of an inherently explainable artificial intelligence technique, which utilizes machine learning to train an expert system, for this purpose. It considers the role of three factors (textual context, speaker background, and emotion) in fake news detection analysis and evaluates the efficacy of using key factors, but not the inherently subjective processing of post text itself, to identify deceptive online content. This paper presents initial work on a potential deceptive content detection tool and also, through the networks that it presents for this purpose, considers the interrelationships of factors that can be used to determine whether a post is deceptive content or not and their comparative importance. 

COVID-19 dramatically changed research experience for undergraduates (REU) programs during the summer of 2020. Given the uncertainty about the ability to conduct future REU programs onsite amidst a pandemic of unknown duration, it was decided to undertake the development of a model for online-based REUs and evaluate it using the evaluation tools that have been applied for some time to onsite programs. This paper presents the REU program at North Dakota State University during the summer of 2020 and its assessment. An overview of the program, as it ran in 2020, is provided. This is compared to the way the program operated in prior years as an onsite program. Specific changes that were made and their impact on the equivalency of the program to prior years are discussed. The approach used is presented as a model for potential use by other programs in the future. 

Fake news is prevalent in society. A variety of methods have been used in an attempt to mitigate the spread of misinformation and fake news ranging from using machine learning to detect fake news to paying fact checkers to manually fact check media to ensure its accuracy. In this paper, three studies were conducted at two universities with different regional demographic characteristics to gain a better understanding of respondents’ perception of online media labeling techniques. The first study deals with what fields should appear on a media label. The second study looks into what types of informative labels respondents would use. The third focuses on blocking type labels. Participants’ perceptions, preferences, and results are analyzed by their demographic characteristics. 

This paper presents the application of a modified implementation of the StegBlocks TCP method as part of the Distributed Electronic Warfare System. The existing system is not equipped with a secure information communication mechanism for transmission between assimilated hosts and from hosts back to the server. The method implemented utilizes network steganography to provide covert data transmission through the network using network packets. The proposed implementation is compared to another implementation of the same method on the aspects of the implementations’ usability, versatility, and applicability. Discussion on how the proposed implementation is more suitable than the alternate implementation is presented. Future proposed improvements to the implementation are also discussed.