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 article 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 81 16,386 × 8,192 pixels panoramic images and several interactive features including maps, thumbnails, and menus. The software engine of the tracking model “Vice Versa” VR 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 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.

This paper introduces a new method for determining the shape similarity of complex three-dimensional (3D) mesh structures based on extracting a vector of important vertices, ordered according to a matrix of their most important geometrical and topological features. The correlation of ordered matrix vectors is combined with perceptual definition of salient regions in order to aid detection, distinguishing, measurement and restoration of real degradation and digitization errors. The case study is the digital 3D structure of the Camino Degli Angeli, in the Urbino’s Ducal Palace, acquired by the structure from motion (SfM) technique. In order to obtain an accurate, featured representation of the matching shape, the strong mesh processing computations are performed over the mesh surface while preserving real shape and geometric structure. In addition to perceptually based feature ranking, the new theoretical approach for ranking the evaluation criteria by employing neural networks (NNs) has been proposed to reduce the probability of deleting shape points, subject to optimization. Numerical analysis and simulations in combination with the developed virtual reality (VR) application serve as an assurance to restoration specialists providing visual and feature-based comparison of damaged parts with correct similar examples. The procedure also distinguishes mesh irregularities resulting from the photogrammetry process.

We examine the effects of imperfect phase estimation of a reference signal on the bit error rate and mutual information over a communication channel influenced by fading and thermal noise. The Two-Wave Diffuse-Power (TWDP) model is utilized for statistical characterization of propagation environment where there are two dominant line-of-sight components together with diffuse ones. We derive novel analytical expression of the Fourier series for probability density function arising from the composite received signal phase. Further, the expression for the bit error rate is presented and numerically evaluated. We develop efficient analytical, numerical and simulation methods for estimating the value of the error floor and identifying the range of acceptable signal-to-noise ratio (SNR) values in cases when the floor is present during the detection of multilevel phase-shift keying (PSK) signals. In addition, we use Monte Carlo simulations in order to evaluate the mutual information for modulation orders two, four and eight, and identify its dependence on receiver hardware imperfections under the given channel conditions. Our results expose direct correspondence between bit error rate and mutual information value on one side, and the parameters of TWDP channel, SNR and phase noise standard deviation on the other side. The results illustrate that the error floor values are strongly influenced by the phase noise when signals propagate over a TWDP channel. In addition, the phase noise considerably affects the mutual information.

We present a dual-locking method for securing trained neural networks, combining key-driven index permutation with PIN-based watermarking via Sparse Quantization Index Modulation (QIM). To incorporate cryptographic randomness into the locking mechanism, we independently apply a uniform random permutation to every row of the adaptively selected index vectors. The second stage embeds a robust, blind binary watermark into the bias coefficients, modulating its quantized values, and binding the network to a user-defined Personal Identification Number (PIN). Without the correct key, the network retains its architecture, but becomes functionally impaired due to disrupted internal representations. Unlocking is achieved through inverse permutation, fully restoring model accuracy. Simultaneously, the embedded watermark remains imperceptible, concealed within the host bias structure without affecting functionality. Upon extraction, it verifies the key association and confirms model authorship. To enhance both robustness and recoverability, we apply an adaptive key selection strategy that redistributes highmagnitude weights to low-sensitivity positions and vice versa. This maximizes degradation when locked, while ensuring full recovery when unlocked. Experiments on MNIST, CIFAR-10/100, and ImageNet-1K using fully connected networks, ResNet Convo-lutional Neural Networks (CNNs), and transformer architectures demonstrate that our method sharply reduces accuracy when locked to below 10& and even 0.5& for CNNs, while fully restoring it with the correct key. The embedded watermark does not degrade performance and reliably authenticates ownership. Embedding analysis across transformers and CNNs reveals the method’s diagnostic potential, where characteristic shifts in embedding distributions may indicate undertraining or suboptimal design. Overall, this approach offers strong protection and traceability for neural network models.

Purpose: This article discusses the role of the digital curator in cultural heritage, highlighting trends in training programs and best practices of museums in the digital age. It emphasizes the potential of geolocation in a Web Geographic Information System (Web-GIS) environment to enhance the accessibility and preservation of digitized museums. The analysis, conducted as part of the “DCbox – Digital Curator Mapping and Design” project, emphasizes the need for professionals specializing in digital cultural heritage and redefines the digital curator as a mediator between culture, technology and global audiences. Design/methodology/approach: The study explores the evolution of the curator’s role since 2000, examining opportunities and challenges of digital humanities and analyzing EU-funded projects and emerging trends. It focuses on the incorporation of digital technologies by museums through geolocated analysis, proposing a customized educational program for training professional digital curators. The conclusions and recommendations of the report are based on a thorough analysis of national situations and case studies from neighboring countries. Findings: The analysis of training programs and museum best practices led to the proposal of a customized educational program aimed at training professional digital curators. This program considers various multidisciplinary areas, blending specific topics and subjects to achieve a unique set of functional skills, encompassing both theoretical and practical aspects. Originality/value: The innovative aspect of this paper is the approach to geolocating digitized museums through a Web-based GIS, providing detailed informational profiles of various offerings, including augmented, virtual, mixed reality and virtual tours.