Commercial airports require routine recertification to ensure the safety of planes and passengers. To support this process, software has been developed to integrate Digital Elevation Models (DEMs) from the United States Geological Survey (USGS) into two flight navigational aid multipath modeling programs: the Ohio University Glideslope Model (OUGS), which predicts glide slope system performance in non-uniform terrain, and the Ohio University NAVAID Performance Prediction Model (OUNPPM), which simulates localizer, glide slope, and VHF omnidirectional ranging (VOR) system behavior. This integration aims to improve model accuracy for enhanced flight safety. The new software collects DEM data from the USGS's National map in the form of Geo-Tagged Image File Format (GeoTIFF) files, which store elevation data as raster grids, where each pixel represents reflects the elevation at a specific geographic point. A webscraper downloads and saves these TIFF files, which are accessed via an Application Programming Interface (API). The API constructs a 2D array of elevation data for the selected area with data from saved DEMs. This array is shrunk using bi-linear interpolation to reduce its size while preserving the data's integrity. By reducing the resolution from 300px to 150px, the size of the dataset drops from 1.28 MB to 327 KB while keeping accuracy relatively unchanged. Processing time is reduced from 5.83s to 1.45s. which allows for fast and efficient data input with little loss of detail in terrain. Interpolation can be applied to create smaller datasets, reducing size while maintaining relative accuracy.

This paper examines whether AI can generate game content that provides an experience indistinguishable from human-created content. Specifically, we evaluate beatmaps for the rhythm game Dance Dance Revolution produced by an LSTMbased neural network model. Through a human trial study of 13 participants, we compare player ratings for AI-generated vs. human-curated beatmaps over dimensions of fun, annoyingness, naturalness, and length. In addition, players attempt to identify the origin of each beatmap as either human or AI-generated. Safe and Sound, the song with the best rated AI-generated beatmap, had ratings that rivaled human-curated beatmaps, however, the other AI beatmaps showed there is still a gap. For origin identification, AI maps were only correctly identified $63 \%$ and Human maps were misidentified as AI $36 \%$ of the time. These results indicate that while the AI exhibits promising similarities to human performance, key differences still remain. Refinement of the generative model such as adding more advanced architecture like the transformer or further increasing the size of training data is needed to fully match human beatmap creation. This work represents an initial “Turing Test” for procedural content generation in games, and as baseline human results, laying groundwork and inspiration for rigorous human-centered evaluation of AI creativity and experience generation.

Accurate cloud base height estimation holds paramount significance in aviation, weather applications, or solar irradiance nowcasting. Although effective, existing solutions, such as ceilometers, are cost-prohibitive and lack portability, limiting their utility for smaller airports, vertiports, and ad hoc operations. This paper introduces an innovative, cost-effective solution leveraging stereo camera setups combined with deep learning. Synthetic data is generated to test the model uti-lizing a virtual environment. Stereo-matching techniques obtained disparity maps from the captured image pairs. Building upon this data, we trained a model to transform these images into precise cloud height predictions. Our approach marries the affordability of stereo cameras with the computational prowess of deep learning, offering a promising low-cost alternative to traditional cloud height estimation instruments. Rather than 3D reconstruction methods, we focused on training a CNN model, using disparity maps of the captured images. Our CNN-based regression model predicts the cloud base height from the 500 m to 3500 m range, With the 77 test samples, we got an RMSE of 57.78 meters.

In 2022, the IEEE Computer Society provided funding for a hardware-security based Capture the Flag competition. This paper reports my experiences orchestrating the CTF and draws comparisons to two software-security based CTFs at the same institution in adjacent semesters. Much of the literature in this area is focused on selecting problems to enhance the skills of the attendees or generate outcomes that persist. In contrast, this paper is intended to provide tips, warnings, and other general advice for first time, or niche CTF organizers.

In this paper, we recount efforts in developing cybersecurity workshops for K-12 teachers, intended to learn skills to better educate the cybersecurity workers of tomorrow. In 2021, we provided two one-day virtual workshops and in 2022 we provided one two-day in-person workshop to high school teachers to increase cybersecurity awareness in three areas: general cybersecurity issues, software security, and hardware security. Both the online and in-person workshops employed Google classroom and Jupyter Notebooks, and high school teachers were provided with Raspberry Pi Zeros to use as part of the workshops. This paper describes the design and implementation of the workshops and also provides evidence demonstrating the effectiveness of the workshops, as well as commentary to provide guidance for future efforts.

In this paper, we describe efforts to promote a robust cyber security workforce through a series of online workshops for K-12 teachers and grades 7-12 students. In 2021, we provided virtual workshops to high school teachers and students to increase cyber security awareness in three areas, (i) general cybersecurity issues, (ii) software security, and (iii) hardware security. The workshops employed Google classroom and Jupyter Notebooks, and high school teachers were provided with hardware (Raspberry Pi Zeros) to use as part of the workshops. We describe the design and implementation of the workshops and share evidence to demonstrate the effectiveness of the workshops and provide insights for future professional development for teachers. Our central question was: What impact do workshops have on teachers' preparation to effectively teach cybersecurity topics to their students and what do teachers report learning from their experiences in the workshop? We provide insights into workshops' effectiveness for computer science teachers and for STEM teachers who are not computer science teachers.

Image based imposters suffer from common errors called disocclusion artifacts where portions of the scene that should be occluded by real geometry are visible when using image based imposters. These artifacts are the result of parallax error created by camera motion where regions of a mesh that were not visible at the time of imposter generation have become visible. This document presents an analysis of a computationally inexpensive on-line technique [Mourning et al. 2014] to resolve these disocclusions by stretching existing imposter [Maciel and Shirley 1995] texture information over new geometry bridging the gap between imposters. [Mourning et al. 2014] only presented automatic metrics showing improved image quality compared to traditional techniques; in order to corroborate the findings in [Mourning et al. 2014], human trials were performed to determine if human subjects found a similar increase in image quality. Results show a statistically significant improvement in image quality over traditional imposters.

This paper presents a technique for interactively colliding with and deforming mesostructures at a per-texel level. It is compatible with a broad range of existing mesostructure rendering techniques including both safe and unsafe ray-height field intersection algorithms. This technique is able to replace traditional 3D geometrical deformations (vertex-based) with 2D image space operations (pixel-based) that are parallelized on a GPU without CPU-GPU data shuffling and integrates well with existing physics engines. Additionally, surface and material properties may be specified at a per-texel level enabling a mesostructure to possess varying attributes intrinsic to its surface and collision behavior. Furthermore, this approach may replace traditional decals with image-based operations that naturally accumulate deformations without inserting any new geometry. This technique provides a simple and efficient way to make almost every surface in a virtual world responsive to user actions and events. It requires no preprocessing time and storage requirements of one additional texture or less. The algorithm uses existing inverse displacement map algorithms as well as existing physics engines and can be easily incorporated into new or existing game pipelines.

This paper presents a technique for interactively deforming and colliding with mesostructures at a per-texel level. It is compatible with a broad range of existing mesostructure rendering techniques including both safe and unsafe ray-height field intersection algorithms. This technique integrates well with existing physics engines and is able to reduce traditional 3D geometrical deformations (vertex-based) to 2D image space operations (pixel-based) that are parallelized on a GPU without CPU-GPU data shuffling. Additionally, surface and material properties may be specified at a per-texel level enabling a mesostructure to possess varying attributes intrinsic to its surface and collision behavior; furthermore, this offers an image-based alternative to traditional decals. This technique provides a simple way to make almost every surface in a virtual world responsive to user actions and events. It requires no preprocessing time and storage requirements of one additional texture or less. The algorithm uses existing displacement map algorithms as well as existing physics engines and can be easily incorporated into new or existing game pipelines.

This paper describes a visualization system for the Joint Precision Aircraft Landing System (JPALS) that integrates remote sensor data with well-known databases of terrain imagery, elevation, and surveyed aviation locations. This visualization system allows teams of research engineers and managers to collaborate in real-time, during test flights, in a distributed fashion. Each user interacts with their own copy of the virtual world, updated constantly in real-time, to manipulate and investigate in a fashion that best accomplishes their own specific task. This allows the entire team to collaborate simultaneously, which mitigates the common latency associated with team members performing offline tasks, such as inspecting log files or verifying MatLab plots. Test flights utilizing our visualization system have shown that it enabled the research team to make decisions on the fly, such as changing the antenna type and placement, and also allowed post-flight analysis and prediction of desirable changes. It could, for instance, predict optimal antenna placement for a given flight path. This can save a project the large expense of performing additional test flights if analysis had been performed offline between flights. Since our system is GPS based, it can be used as an alternate truth system to validate traditional instrument approach and landing systems. The recording and playback features of our visualization can also be used as a teaching aid in flight training for both pilots and air traffic controllers.