The Role of Generative Artificial Intelligence (GAI) in Education: A Detailed Review for Enhanced Learning Experiences

The understanding of how users interact with the virtual cultural heritage could provide digital curators valuable insights into user behaviors, and also improve the overall user experience through the ability to observe and record interactions of virtual visitors. This paper introduces the new User Behavior (UB) tracking algorithm that we developed investigating a salience of the Virtual Reality (VR) panoramic regions. The algorithm extracts the importance of Region of Interest (ROI) determining patterns of the visitors’ virtual movement and interest in combination with statistics of captured browser activity. The input of our algorithm is the virtual online interactive platform (Virtual Museum of the Civic Art Gallery of Ascoli Piceno in Italy) with eighty-one 16386x8192 pixels panoramic images and several interactive features including maps, thumbnails, and menus. The software engine of the tracking model “Vice Versa” VR (3VR) operates on inverse functions of all descriptive functions (descriptors), which are assigned particularly to each interactive feature such as viewing multimedia content and observing the panoramic environment. The tracking experiment was performed online and the web virtual museum key study collected behavior information from 171 visitors around the world. Collected data, multimedia and textual content, and the coordinates of the ROIs are then subjected to standard statistics operations in order to define common patterns of UBs. Thus, we have discovered that the ROIs are mostly mapped onto the artworks and it is possible to obtain patterns about the main interests of users. The developed tool offers a guideline for the panoramic tours design and the potential benefits for museums are to understand the public, verify the effectiveness of choices, and re-shape a cultural offer based on visitors’ needs. Exploiting this kind of user experience, our algorithm ensures relevant feedback during virtual visits, and thus paves the way for further development of the recommender system.

3VR: Vice Versa Virtual Reality Algorithm to Track and Map User Experience

Recently, WiFi-based sensing is gaining immense attention in safety monitoring domain for people behavior detection and recognition. The underlying principle of WiFi sensing is that WiFi signal can capture signal changes caused in surroundings and extract the unique signal patterns corresponding to specific behaviors. The capacity allows for the detection,recognition and estimation of behavior attributions. In this paper, we leverage Channel State Information(CSI) to detect individuals entering and exiting doors for counting and analyzing gait behaviors to identify individuals. First, we proposed a sensing-indicator parameter to detect people’s presence and leverage the difference of two antennas to determine whether an individual is entering or exiting. Additionally, we utilize Doppler Frequency Shift(DFS) to estimate the presence of abreast people. Subsequently, we calibrate DFS to estimate stride frequency for gait velocity and stride length. To demonstrate the efficacy of our proposed method, we have designed several experimental schemes. Experiment results show that the detection accuracy of moving people reaches more than 95% in strong sensing zone and 65%-70% accuracy in weak sensing zone. The average accuracy of people counting is 90% and people identification can obtains good performance with less than six volunteers.

Non-intrusive People Counting and Identification Simultaneously with Commodity WiFi Devices

BeamCount: Indoor Crowd Counting Using Wi-Fi Beamforming Feedback Information

Non-permissible mobile device use places Bangladeshi museums at risk for theft and resource devastation. Implementing deep learning, especially the YOLO can assist in detecting and preventing unauthorized mobile device use, improving Bangladeshi museum security. A deep learning model called Non-Permissible Mobile Detection in Museums (NMDM) can be used to identify faces, phones, and camera regions in real time. The proposed (NMDM) model analyzes n frames from real-time monitoring video, where museums deploy CCTV for security. This NMDM model, which is based on YOLOv8, predicts object detection (such as the identification of mobile, camera, and face) while also using ensemble approaches to decide whether to detect non-permissible mobile devices. To train that model here we apply our NMDM dataset by capturing photos of the phone in different angles and different conditions, after augmentation, the overall dataset size is 10494. In terms of testing results the NMDM model displays remarkable precision (97.671%) and accuracy (95.180%). With multi-processing, it also significantly speeds up both CPU and GPU systems.

Non-permissible Mobile Detection to Enhance Security in Bangladeshi Museum: A Multiprocess YOLO-Based Approach

Multi-area visitor counting plays a critical role in museum management, which can help administrative staff better study visitor flows and hotspots, so that can ensure the quality and safety of visits. Internet of Things (IoT) techniques facilitate efficient recording and understanding of visitors’ spatial and temporal distribution in museums, and traditional visitor tracking applications use surveillance cameras or wireless connections with portable smart devices. However, these methods either involve privacy concerns or face the obstacle of getting natural behavioral data of all visitors. This paper explores an IoT monitoring methodology in the field of museum studies, proposing a commodity WiFi-based head counting framework that does not need the visitor to connect with any device. Our system analyzes the Channel State Information (CSI) amplitude fluctuations at the fixed receiver caused when visitors cross the line-of-sight (LoS) link. It enables multi-area visitor counting by achieving In-and-Out traffic detection at different sites with a convolutional neural network (CNN) algorithm. The method also allows for a rough classification of visitor types based on body size, and an extra transfer module is presented to reduce training time for increasing sensing scenarios. Over 2300 samples at 5 different sites were collected to test the usability. Experiment 1 implemented in 3 environments/deployments demonstrated that the proposed approach can be potentially implemented in variable sites of museums. It achieved high up to 95% and 99% accuracies for identifying the number and direction of people crossing, respectively. Experiment 2 sampled adults, children, and adult-child groups at a science museum and achieved approximately 89% classification accuracy of visitor types. Experiment 3 collected data for all cases in which up to 3 targets entered and exited simultaneously, and reached a recognition accuracy of around 88% for 9 different cases. The potential and limitations for the practical application of wireless contactless sensing to cultural spaces are discussed.

Towards Multi-area Contactless Museum Visitor Counting with Commodity WiFi

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Towards Multi-area Contactless Museum Visitor Counting with Commodity WiFi