This full research paper describes a comprehensive study on student learning within the metaverse. A trend has emerged in response to recent shifts in learning habits, which results in the need for adaptable remote learning solutions. Instead of using typical virtual conference tools, class communication can be boosted by utilizing the metaverse - a trendy platform transient from the traditional virtual world, letting users interact with their environment via a Virtual Reality (VR) device. Unlike its predecessors, the metaverse transcends the confines of the traditional virtual world, offering users immersive interactions facilitated by Virtual Reality (VR) devices. However, a notable gap exists in research regarding user interactions via VR devices. This study attempts to address this gap by exploring how the non-coding features inherent in existing metaverse platforms can be harnessed to enhance students' learning experiences. Through analysis and experiment, this research provides valuable insights and recommendations to aid future instructors in effectively integrating metaverse-based activities into their teaching methods. Our overarching objective in the study is to explore how the metaverse can amplify student learning performance and foster deeper engagement. Initially, a complete taxonomy of classroom activities is developed to evaluate the suitability of multiple existing metaverse platforms for educational purposes. Following this evaluation, Spatial. IO appears as the platform for hosting the course components for our investigation into student learning. Programming features are intentionally forbidden when creating learning materials to simulate the experience of an average user in developing classroom activities. Consequently, a collection of class components is fashioned in documents (e.g., posters) and video forms, measuring the effectiveness and potential drawbacks of utilizing the metaverse for educational purposes. Comparative analysis between learning experiences in the metaverse and traditional browser-based settings reveals fascinating insights. Students exhibited promoted focus within the metaverse environment. Document reading within the Metaverse via VR proves more effective than conventional browser-based platforms. Nevertheless, video-based learning experiences in the metaverse are found to be less effective than their browser counterparts. These findings can serve as invaluable references for future instructors who seek to leverage the metaverse as a tool for education.

Abstract Multilevel cache architectures are widely used in modern heterogeneous systems for performance improvement. However, satisfying the performance and security requirements at the same time is a challenge for such systems. A simple and efficient timing attack on the shared portions of multilevel hierarchical caches and its corresponding countermeasure is proposed here. The proposed attack prolongs the execution time of the victim threads by inducing intentional race conditions in shared memory spaces. Then, a thread‐mapping algorithm to detect such race conditions between a group of threads and resolve them as a countermeasure against the attack is proposed. The proposed countermeasure dynamically monitors races on cache blocks and distributes existing and new threads on processing cores to minimize cache contention. Upon detection of a high contention rate that might be either due to an attack or a natural race condition, two mechanisms, namely cache access‐rate reduction and thread migration, will be used by the countermeasure algorithm to resolve the race situation. Evaluations on SPECCPU 2006 benchmark suite show that the proposed algorithm not only protects the system against the introduced attack but also boosts the overall system performance by an average of 46.35% and 55.92% for the worst and average cases, respectively.

Robots frequently face complex tasks that require more than one action, where sequential decision-making (sdm) capabilities become necessary. The key contribution of this work is a robot sdm framework, called lcorpp, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.

Detecting traffic signal lights (e.g., red) is an important subject of intersection safety since many accidents are the result of road users' non-conforming behavior to traffic signals. This work shows that traffic signal phases can be inferred through traffic cameras in order to detect temporal violations of road users. The idea is to understand the traffic phase by learning the moving features of road users. Moving features are extracted and labeled according to two traffic signal lights (i.e., green, red) and different learning methods such as K Nearest Neighborhood (KNN), Naive Bayes (NB), Neural Network (NN), Deep Neural Network (DNN) and Support Vector Machine (SVM) are applied on training data. The experimental results of two different intersection videos shows that an accuracy of higher than 90% can be achieved by DNN and SVM when the feature size is appropriately selected.

Intersections are known for their integral and complex nature due to a variety of the participants' behaviors and interactions. This paper presents a review of recent studies on the behavior at intersections and the safety analysis for three types of participants at intersections: vehicles, drivers, and pedestrians. This paper emphasizes on techniques which are strong candidates for automation with visual sensing technology. A new behavior and safety classification is presented based on key features used for intersection design, planning, and safety. In addition, performance metrics are introduced to evaluate different studies, and insights are provided regarding the state of the art, inputs, algorithms, challenges, and shortcomings.

This work presents a tracking system which delivers count and behavior analysis of pedestrians by leveraging existing traffic camera infrastructure. The proposed system is able to detect either stationary or moving pedestrians through contextual fusion of appearance and motion cues. Pedestrian recognition performance is improved through cooperating tracking algorithms. Greedy bipartite graph matching is used to initialize newly detected pedestrians and optical flow is then utilized to handle tracking through partial occlusions. Experimental results including system evaluation and behavior analyses of pedestrians show the efficiency of the system to count and assess pedestrians' waiting time and crossing speeds. Additionally, heat-maps which indicate the waiting and moving locations of pedestrians at the intersection are provided to better understand usage patterns.

This paper presents a tracking method for vision-based queue analysis at junctions including queue length and waiting time estimation of vehicles. The tracking method works based on improving the optical flow method to track small and low quality vehicles with overlong waiting time. The improvement process is performed to keep track of overlong stopped vehicles with high level of robustness against occlusion by crossing pedestrians. The results of experiments are presented by estimating queue length, waiting time distribution and number of waiting vehicles for the highly cluttered video of a Las Vegas junction. The accuracy of the system is evaluated by comparing the queue analysis results with the ground truth.

This article presents a review of recent literature of intersection behavior analysis for three types of intersection participants; vehicles, drivers, and pedestrians. In this survey, behavior analysis of each participant group is discussed based on key features and elements used for intersection design, planning and safety analysis. Different methods used for data collection, behavior recognition and analysis are reviewed for each group and a discussion is provided on the state of the art along with challenges and future research directions in the field.

This work presents a framework to analyze traffic intersections by counting vehicles and pedestrians and assessing their behavior and safety. The major reason of developing this framework is to facilitate manual analyses from the video recordings by only providing detection files, typical paths, distance and conflict points. After tracking and recognizing paths, pedestrian and vehicle trajectories are extracted and their counting, behavior and safety information are estimated. Experimental results include estimated speed profile, turning movement count, waiting time, Time to Intersection (TTI) and Time To Collision (TTC) for two highly cluttered videos of Las Vegas intersections. The accuracy of 90%, 99% and 90% were obtained for vehicles waiting time, turning movement count and pedestrians crossing count. The semi-automatic system is a comprehensive solution for video based behavior, safety and counting analyses at intersections with high accuracy.