Publications

The implementation of data protection regulations such as the GDPR and the California Consumer Privacy Act has sparked a growing interest in removing sensitive information from pre-trained models without requiring retraining from scratch, all while maintaining predictive performance on remaining data. Recent studies on machine unlearning for deep neural networks have resulted in different attempts that put constraints on the training procedure and which are limited to small-scale architectures and with poor adaptability to real-world requirements. In this paper, we develop an approach to delete information on a class from a pre-trained model, by injecting a trainable low-rank decomposition into the network parameters, and without requiring access to the original training set. Our approach greatly reduces the number of parameters to train as well as time and memory requirements. This allows a painless application to real-life settings where the entire training set is unavailable, and compliance with the requirement of time-bound deletion. We conduct experiments on various Vision Transformer architectures for class forgetting. Extensive empirical analyses demonstrate that our proposed method is efficient, safe to apply, and effective in removing learned information while maintaining accuracy.

This paper proposes a software architecture for monitoring and diagnostics of failures in industrial systems. The architecture aims to support the operator's decision-making process by enabling a real-time and intuitive understanding of system faults. The paper describes the methodology and implementation process applied to a real industrial case: a welding collaborative robotic application. However, the proposed software architecture can be easily extended to a broader number of industrial systems. The core of the idea is based on an ecosystem of Internet of Things (IoT) elements deployed in the automation systems that collect the system status and alarms to stream them to the cloud server. The industrial use case described in the paper is a collaborative robot-assisted welding solution for automated MIG/MAG welding produced by 'Indus-tria Tecnologica Italiana', an Italian SME company, with the brand name'MyWelder'. We investigated the system's impact on the operator's work and its effectiveness in supporting his/her decision-making process. Additionally, the validation process assessed the system's functionalities within this specific use case. The primary objective related to the use case is to establish a strategy that minimizes the production of defective parts, ultimately reducing waste.

Since their invention in 1949, barcodes have remained the preferred method for automatic data capture, playing a crucial role in supply chain management. To detect a barcode in an image, multiple algorithms have been proposed in the literature, with a significant increase of interest in the topic since the rise of deep learning. However, research in the field suffers from many limitations, including the scarcity of public datasets and code implementations, which hampers the reproducibility and reliability of published results. For this reason, we developed "BarBeR" (Barcode Benchmark Repository), a benchmark designed for testing and comparing barcode detection algorithms. This benchmark includes the code implementation of various detection algorithms for barcodes, along with a suite of useful metrics. It offers a range of test setups and can be expanded to include any localization algorithm. In addition, we provide a large, annotated dataset of 8748 barcode images, combining multiple public barcode datasets with standardized annotation formats for both detection and segmentation tasks. Finally, we share the results obtained from running the benchmark on our dataset, offering valuable insights into the performance of different algorithms.

In recent years, there has been a notable surge in the significance attributed to technologies facilitating secure and efficient cohabitation and collaboration between humans and machines, with a particular interest in robotic systems. A pivotal element in actualizing this novel and challenging collaborative paradigm involves different technical tasks, including the comprehension of 3D poses exhibited by both humans and robots through the utilization of non-intrusive systems, such as cameras. In this scenario, the availability of vision-based systems capable of detecting in real-time the robot's pose is needed as a first step towards a safe and effective interaction to, for instance, avoid collisions. Therefore, in this work, we propose a vision-based system, referred to as D-SPDH, able to estimate the 3D robot pose. The system is based on double-branch architecture and depth data as a single input; any additional information regarding the state of the internal encoders of the robot is not required. The working scenario is the Sim2Real, i.e., the system is trained only with synthetic data and then tested on real sequences, thus eliminating the time-consuming acquisition and annotation procedures of real data, common phases in deep learning algorithms. Moreover, we introduce SimBa++, a dataset featuring both synthetic and real sequences with new real-world double-arm movements, and that represents a challenging setting in which the proposed approach is tested. Experimental results show that our D-SPDH method achieves state-of-the-art and real-time performance, paving the way a possible future non-invasive systems to monitor human-robot interactions.

Few-shot learning (FSL) requires fine-tuning a pretrained model on a limited set of examples from novel classes. When applied to vision-and-language models, the dominant approach for FSL has been that of learning input prompts which can be concatenated to the input context of the model. Despite the considerable promise they hold, the effectiveness and expressive power of prompts are limited by the fact that they can only lie at the input of the architecture. In this article, we critically question the usage of learnable prompts, and instead leverage the concept of “implicit memory” to directly capture low- and high-level relationships within the attention mechanism at any layer of the architecture, thereby establishing an alternative to prompts in FSL. Our proposed approach, termed MemOp, exhibits superior performance across 11 widely recognized image classification datasets and a benchmark for contextual domain shift evaluation, effectively addressing the challenges associated with learnable prompts.

This article describes a methodology for the diagnosis of failures in multi-AGV (Automatic Guided Vehicles). Today, AGVs are establishing themselves in the most advanced automatic logistics solutions, providing performance and safety that cannot be achieved with handling solutions with manual forklifts. Furthermore, thanks to the application of Industry 4.0 digital technologies, very advanced tools are available to monitor the performance and diagnose faults of fleets of AGV. In particular, studies on fault diagnosis have mainly focused on (1) the diagnosis of internal components of the automatic truck and (2) the identification of failures in the functionality of the AGV in its interaction with the surrounding environment. This paper shows an approach to fault diagnosis in multi-AGVs system, considering the interaction between each single AGV and the environment, with the scope to help the user increase the system efficiency in an existing layout. The objective of the paper is to introduce and discuss a methodology to study the failure and the available recovery actions of the AGV navigation system. Moreover, the paper presents the real AGV data acquisition and processing architecture actually deployed on the factory shop floor, as well as the result from the experimental study in a real industrial environment. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Introduction. Length of Stay (LOS) and Prolonged Length of Stay (pLOS) are critical indicators of hospital efficiency. Reducing pLOS is crucial for patient safety, autonomy, and bed allocation. This study investigates different machine learning (ML) models to predict LOS and pLOS. Methods. We analyzed a dataset of patients discharged from a northern Italian hospital between 2022 and 2023 as a retrospective cohort study. We compared sixteen regression algorithms and twelve classification methods for predicting LOS as either a continuous or multi-class variable (1-3 days, 4-10 days, >10 days). We also evaluated pLOS prediction using the same models, having pLOS defined as any hospitalization with LOS longer than 8 days. We further analyzed all models using two versions of the same dataset: one containing only structured data (e.g. demographics and clinical information), whereas the second one also containing features extracted from free-text diagnosis. Results. Our results indicate that ensemble models achieved the highest prediction accuracy for both LOS and pLOS, outperforming traditional single-algorithm models, particularly when using both structured and unstructured data extracted from diagnoses. Discussion. The integration of ML, particularly ensemble models, can significantly improve LOS prediction and identify patients at increased risk of pLOS. This information can guide healthcare professionals and bed managers in making informed decisions to enhance patient care and optimize resource allocation.