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Museums are agents of change with a strong identity value. The cultural and social role they are called to play reflects a relevant component of community well-being. In this context, the processes of cohesion and social integration of which museums are an integral part require the definition and development of inclusive and accessible approaches to the cultural fruition of every single stakeholder involved in the creation of cultural value, including visitors with disabilities. The objective pursued is placed in the context of obtaining insights made up of the major museum institutions. In this direction, the present study investigates through a qualitative approach the presence of best-in-class approaches to the definition and management of cultural itineraries accessible to people with physical, sensory, and cognitive disabilities. The case analysis reveals that the accessible approach to cultural participation is expressed in a systemic vision of the culture-based experience guided by top-down processes in which participatory and integrated project management is integrated. According to an analysis of the international cases of museum institutions that are experimenting with innovative and attractive strategies, some theoretical implications are expected to achieve a strengthening of literature on inclusive and accessible cultural participation and some practical implications for addressing museum management to strengthen the wellbeing of the community. [ABSTRACT FROM AUTHOR]

Dense optical flow estimation is complex and time consuming, with state-of-the-art methods relying either on large synthetic data sets or on pipelines requiring up to a few minutes per frame pair. In this paper, we address the problem of optical flow estimation in the automotive scenario in a self-supervised manner. We argue that optical flow can be cast as a geometrical warping between two successive video frames and devise a deep architecture to estimate such transformation in two stages. First, a dense pixel-level flow is computed with a projective bootstrap on rigid surfaces. We show how such global transformation can be approximated with a homography and extend spatial transformer layers so that they can be employed to compute the flow field implied by such transformation. Subsequently, we refine the prediction by feeding a second, deeper network that accounts for moving objects. A final reconstruction loss compares the warping of frame $X_{t}$ with the subsequent frame $X_{t+1}$ and guides both estimates. The model has the speed advantages of end-to-end deep architectures while achieving competitive performances, both outperforming recent unsupervised methods and showing good generalization capabilities on new automotive data sets. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce cognitive load by bridging the gap between the task-at-hand and relevant information by displaying information without disturbing the user's focus. AR is particularly useful in the manufacturing environment where a diverse set of tasks such as assembly and maintenance must be performed in the most cost-effective and efficient manner possible. While AR systems have seen immense research innovation in recent years, the current strategies utilised in AR for camera calibration, detection, tracking, camera position and orientation (pose) estimation, inverse rendering, procedure storage, virtual object creation, registration, and rendering are still mostly dominated by traditional non-AI approaches. This restricts their practicability to controlled environments with limited variations in the scene. Classical AR methods can be greatly improved through the incorporation of various AI strategies like deep learning, ontology, and expert systems for adapting to broader scene variations and user preferences. This research work provides a review of current AR strategies, critical appraisal for these strategies, and potential AI solutions for every component of the computational pipeline of AR systems. Given the review of current work in both fields, future research work directions are also outlined. [ABSTRACT FROM AUTHOR]

In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practise available malware features for IoT devices and lack real-time prediction behaviours. More research is thus required on malware detection to cope with real-time misclassification of the input IoT data. Motivated by this, in this article, we propose an adversarial self-supervised architecture for detecting malware in IoT networks, SETTI, considering samples of IoT network traffic thatmay not be labeled. In the SETTI architecture, we design three self-supervised attack techniques, namely, Self-MDS, GSelf-MDS, and ASelf-MDS. The Self-MDS method considers the IoT input data and the adversarial sample generation in real-time. The GSelf-MDS builds a generative adversarial network model to generate adversarial samples in the self-supervised structure. Finally, ASelf-MDS utilises three well-known perturbation sample techniques to develop adversarial malware and inject it over the self-supervised architecture. Also, we apply a defence method to mitigate these attacks, namely, adversarial self-supervised training, to protect the malware detection architecture against injecting the malicious samples. To validate the attack and defence algorithms, we conduct experiments on two recent IoT datasets: IoT23 and NBIoT. Comparison of the results shows that in the IoT23 dataset, the Self-MDS method has the most damaging consequences from the attacker's point of view by reducing the accuracy rate from 98% to 74%. In the NBIoT dataset, the ASelf-MDS method is the most devastating algorithm that can plunge the accuracy rate from 98% to 77%. [ABSTRACT FROM AUTHOR]

Human detection in the wild is a research topic of paramount importance in computer vision, and it is the starting step for designing intelligent systems oriented to human interaction thatwork in complete autonomy. To achieve this goal, computer vision and machine learning should aim at superhuman capabilities. In this work, we address the problem of fine-grained human analysis under occlusions and perspective constraints. More specifically, we discuss some issues and some possible solutions to effectively detect people using pose estimation methods and to detect humans under occlusions both in the two-dimensional (2D) image plane and in the 3D space exploiting single monocular cameras. Dealing with occlusion can be done at the joint level or pixel level: We discuss two different solutions, the former based on a supervised neural network architecture for detecting occluded joints and the latter based on a semi-supervised specialized GAN that exploits both appearance and human shape attributes to determine the missing parts of the visible shape. To deal with perspective constraints, we further discuss a neural approach based on a double architecture that learns to create an optimal neural representation, which is useful to reconstruct the 3D position of human keypoints starting with simple RGB images. All these approaches have a critical point in common: the need for large annotated datasets. To have large, fair, consistent, transparent, and ethical datasets, we propose the adoption of synthetic datasets as, for example, JTA and MOTSynth. In this article, we discuss the pros and cons of using synthetic datasets while tackling several human-centered AI issues with respect to European GDPR rules for privacy.We further explore and discuss an application in the field of risk assessment by space occupancy estimation during the COVID-19 pandemic called Inter-Homines. [ABSTRACT FROM AUTHOR]

Intelligent cities are a business issue, both in academic literature and in the industrial world. The ability to provide users with value-added services through low-cost, low-cost intelligent objects is very interesting in many fields. Art and culture have always taken a critical part in human life. The museums are now an entertainment tool such as cinemas or theaters, museums and art galleries generally provide a brochure card or human guides. An intelligent IOT based museum is based on a portable device that acts as a museum guide. This portable video device captures user movements and has the ability to process image and only send images to the processing center to increase system performance and position information. In addition, the system interacts with the server to store multimedia content produced by the user. Finally, everyone can easily access the profile of the arts and history through intelligent devices that use mobile applications. [ABSTRACT FROM AUTHOR]

The Internet of Things, whose main goal is to automatically predict users’ desires, can find very interesting opportunities in the art and culture field, as the tourism is one of the main driving engines of the modern society. Currently, the innovation process in this field is growing at a slower pace, so the cultural heritage is a prerogative of a restricted category of users. To address this issue, a significant technological improvement is necessary in the culture-dedicated locations, which do not usually allow the installation of hardware infrastructures. In this paper, we design and validate a no-invasive indoor location-aware architecture able to enhance the user experience in a museum. The system relies on the user’s smartphone and a wearable device (with image recognition and localization capabilities) to automatically deliver personalized cultural contents related to the observed artworks. The proposal was validated in the MUST museum in Lecce (Italy). [ABSTRACT FROM AUTHOR]

Augmented user experiences in the cultural heritage domain are in increasing demand by the new digital native tourists of 21st century. In this paper, we propose a novel solution that aims at assisting the visitor during an outdoor tour of a cultural site using the unique first person perspective of wearable cameras. In particular, the approach exploits computer vision techniques to retrieve the details by proposing a robust descriptor based on the covariance of local features. Using a lightweight wearable board, the solution can localize the user with respect to the 3D point cloud of the historical landmark and provide him with information about the details at which he is currently looking. Experimental results validate the method both in terms of accuracy and computational effort. Furthermore, user evaluation based on real-world experiments shows that the proposal is deemed effective in enriching a cultural experience. [ABSTRACT FROM AUTHOR]