With the advent of Industrial 4.0 and the push toward Industry 5.0, the data generated by the industries have become surprisingly large. This abundance of data significantly boosts machine and deep learning models for Predictive Maintenance (PdM). The PdM plays a vital role in extending the lifespan of industrial equipment and machines while also helping to reduce the risk of unscheduled downtime. Given its multidisciplinary nature, the field of PdM has been approached from many different angles: this comprehensive survey aims at providing an up-to-date overview focused on all the learning-based industrial PdM strategies, discussing weaknesses and strengths. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing a systematic and complete review of the literature. In particular, firstly, we explore the main learning models used for PdM, mainly Convolutional Neural Networks (ConvNets), Autoencoders (AEs), Generative Adversarial Networks (GANs), and Transformers, also giving an overview of the newest models such as diffusion models and foundation models. Then, we discuss the main learning paradigms applied to PdM, i.e., supervised, unsupervised, ensemble, transfer, federated, and reinforcement learning. Furthermore, this work discusses the pipeline of the data-driven PdM and its benefits, practical applications, datasets, and benchmarks. In addition, the evaluation metrics for each PdM stage and the state-of-the-art hardware devices used are discussed. Finally, the challenges and future work are presented.

Smart manufacturing systems often use data to optimize production processes. Artificial intelligence algorithms can be used to tune the parameters of production recipes to achieve the desired results. Indeed, such algorithms will require to be trained by using data collected while executing manufacturing operations. However, the required sets of data are rarely available to most production companies. This work-in-progress paper introduces the Print+Mill dataset: a collection of data related to additive and subtractive manufac-turing operations. The data are collected during the execution of various production recipes, utilizing different materials and process parameters. For each recipe, the dataset includes data on the materials and parameters used, sensor readings from the machinery, such as power consumption and temperature, and information on the quality of the resulting product. The data are collected in a complex research facility; in the future, we plan to extend the dataset by considering other manufacturing operations, materials, and types of field data.

In this demo we present an application featuring the integration of Virtual Reality (VR) technologies with the demonstration laboratory (ICELab) built around Industry 4.0/5.0 concepts. In particular, we showcase a digital twin of the real laboratory that allows the user to explore its environment in VR and interact with the different machinery to obtain several data and information.

In this paper, we present an example of complete integration of eXtended Reality technologies within a demonstration laboratory showcasing Industry 4.0/5.0 compliant machinery in realistic scenarios of use. We describe the design choices and the implementation of the augmented and virtual reality applications developed and potentially usable to support different real-world tasks, featuring advanced gesture-based interaction modes. We also describe the optimized communication architecture used to synchronize data between the cyber-physical factory environment with all its components, its industrial digital twin, and the augmented and virtual replica of the factory. Example tasks supported with the tools in public demonstrations allow users wearing Microsoft HoloLens 2 or Meta Quest 2 headsets to monitor the status of the prototype production line and operate on it, locally or remotely. An example video showing the applications is available in the supplementary material.

Modeling user preferences in photographic images is often reduced to analyzing intermediate explicit representations (e.g. textual tags) as means of capturing the objective and subjective properties of image perception, trying to distill the essence of what gives pleasure. We propose an alternative approach that bypasses the necessity to build an explicit conceptual coding of image preferences, operating directly on the raw properties of the images, extracted with heterogeneous feature descriptors. This is achieved through the counting grid model, which fuses together content-based and aesthetics themes into a 2D map in an unsupervised way. We show that certain locations in this map correspond to perceptually intuitive image classes, even without relying on tags or other user-defined information. Moreover, we show that users' individual preferences can be represented as distributions over the map, allowing us to evaluate the affinity between different users' appreciations. We experiment on a large Flickr dataset, clustering users by affinity, and validating these clusters by checking users that belong to the same Flickr photo groups.

We propose a novel methodology for re-identification, based on Pictorial Structures (PS). Whenever face or other biometric information is missing, humans recognize an individual by selectively focusing on the body parts, looking for part-to-part correspondences. We want to take inspiration from this strategy in a re-identification context, using PS to achieve this objective. For single image re-identification, we adopt PS to localize the parts, extract and match their descriptors. When multiple images of a single individual are available, we propose a new algorithm to customize the fit of PS on that specific person, leading to what we call a Custom Pictorial Structure (CPS). CPS learns the appearance of an individual, improving the localization of its parts, thus obtaining more reliable visual characteristics for re-identification. It is based on the statistical learning of pixel attributes collected through spatio-temporal reasoning. The use of PS and CPS leads to state-of-the-art results on all the available public benchmarks, and opens a fresh new direction for research on re-identification.

This paper proposes a new approach to model-based clustering under prior knowl- edge. The proposed formulation can be interpreted from two different angles: as penalized logistic regression, where the class labels are only indirectly observed (via the probability density of each class); as finite mixture learning under a group- ing prior. To estimate the parameters of the proposed model, we derive a (gener- alized) EM algorithm with a closed-form E-step, in contrast with other recent approaches to semi-supervised probabilistic clustering which require Gibbs sam- pling or suboptimal shortcuts. We show that our approach is ideally suited for image segmentation: it avoids the combinatorial nature Markov random field pri- ors, and opens the door to more sophisticated spatial priors (e.g., wavelet-based) in a simple and computationally efficient way. Finally, we extend our formulation to work in unsupervised, semi-supervised, or discriminative modes.