Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work.

Achieving precise real-time localization and ensuring robustness are critical challenges in multi-user mobile AR applications. Leveraging collaborative information to augment tracking accuracy on lightweight devices and fortify overall system robustness emerges as a crucial necessity. In this paper, we propose a robust centralized collaborative rnulti-agent VI-SLAM system for mobile AR interaction and server-side efficient consistent mapping. The system deploys a lightweight VIO frontend on mobile devices for real-time tracking, and a backend running on a remote server to update multiple submaps. When overlapping areas between submaps across agents are detected, the system performs submap fusion to establish a globally consistent map. Additionally, we propose a map registration and fusion strategy based on covisibility areas for online registration and fusion in multi-agent scenarios. To improve the tracking accuracy of the frontend on agent, we introduce a strategy for updating the global map to the local map at a moderate frequency between the camera-rate pose estimation of the frontend VIO and the low-frequency global map optimization, using a tightly coupled strategy to achieve consistency of the multi-agent frontend poses estimation in the global map. The effectiveness of the proposed method is further confirmed by executing backend mapping on the server and deploying VIO frontends on multiple mobile devices for AR demostration. Additionally, we discuss the scalability of the proposed system by analyzing network traffic, synchronization frequency, and other factors at both the agent and server ends.

Due to complexities in geologic structure, heterogeneity, and insufficient borehole information, shale formation faces challenges in accurately estimating the elastic properties of rock which triggers severe technical challenges in safe drilling and completion. These geomechanical properties could be computed from acoustic logs, however, accurate estimation is critical due to log deficit and a higher recovery expense of inadequate datasets. To fill the gap, this study focuses on predicting the sonic properties of rock using deep neural network (Bi-directional long short-time memory, Bi-LSTM) and random forest (RF) algorithms to estimate and evaluate the geomechanical properties of the potential unconventional formation, Permian Basin, situated in West Texas. A total of three wells were examined using both single-well and cross-well prediction algorithms. Log-derived single-well prediction models include a 75:25 ratio for training and testing the data whereas the cross-well includes two wells for training and the remaining well was used for testing. The selected well input logs include compressional wave slowness, resistivity, gamma-ray, porosity, and bulk density to predict shear wave slowness. The results using RF and Bi-LSTM show a promising prediction of geomechanical properties for Permian Basin wells. RF algorithm performed superior for both single and grouped well prediction. The single-well prediction method using the RF algorithm provided the highest accuracy of 99.90% whereas Bi-LSTM gave 93.60%. The best accuracy for a grouped well prediction was achieved employing Bi-LSTM and RF models, i.e., 96.01% and 93.80%. The average prediction including RF and Bi-LSTM algorithms demonstrated that accuracy for single well and cross well prediction is 96% and 94% respectively with an error below 7%. These outcomes show the astonishing capability of artificial intelligence (AI) models trained to create a realistic prediction to unlock unconventional potential when datasets are inadequate. Given adequate training data, operators could leverage these efficient tools by utilizing them to examine fracture interpretations with reduced cost and time when datasets are incomplete and thus increase the hydrocarbon recovery potential.

Virtual reality (VR) makes learning more interesting for students and could help them remember what they have learned better than traditional methods. However, a student could get distracted in a VR environment because of stress, wandering thoughts, unwanted noise, outside sounds, etc. Distractions could be classified as either external (due to the environment) or internal (due to internal thoughts). To identify external distractions, previous researchers have used eye-gaze data. Eye-gaze data cannot, however, detect internal distractions because a user may be looking at the educational material in VR while also thinking about something else. We explored the usage of electroencephalogram (EEG) data to detect internal distractions. We designed an educational VR environment and trained three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and k-nearest-neighbors (kNN), to detect internal distractions of students. For data labeling, we considered two window lengths (20 and 30 seconds) starting at 5 seconds after the distraction task started. We did cross-subject and cross-session tests, and our results show that kNN provides a better accuracy (64%) compared to RF and SVM. We also found that the shorter window length of 20 seconds provided a slightly better accuracy then the 30 second window. Our results are not far from such random guessing. Therefore, our contribution lies more in the fostering of ideas for future work that must employ more advanced and sophisticated techniques.

Virtual Reality (VR) provides a more engaging learning experience to students and could improve knowledge retention compared to traditional learning methods. However, a student could get distracted in the VR environment due to stress, mind-wandering, unwanted noise, external sounds, etc. Distractions could be classified as either external (due to environment) or internal (due to internal thoughts). Past researchers have used eye-gaze data to detect external distractions. However, eye-gaze data can not measure internal distractions since a user could look at the educational content and may be thinking about something else. We explored the usage of electroencephalogram (EEG) data to detect internal distraction. We designed an educational VR environment and trained three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA), to detect internal distractions of students. Our preliminary study results show that RF provides a better accuracy (98%) compared to SVM and LDA.