Datasets and Publications

A typical way to label datasets for Deep Neural Network (DNN) training and testing is through crowdsourcing. However, there is no assurance that crowd workers will adhere to the data labeling criteria, refrain from introducing personal bias, or from spamming random labels. In order to address this issue, we propose a graph-based technique to assess annotator trustworthiness and adjust their involvement in the labeling process. Our proposed method not only improves data labels accuracy, by considering the agreement between annotators and ranking them based on their labeling trustworthiness, but also aims to enhance DNN inference performance by providing more accurate training data labels. We examine the constraints of conventional multi-annotation label aggregation techniques and compare them to our approach. Lastly, we demonstrate that our proposed method remains robust to artificially injected noisy annotations, surpassing the performance of previous state-of the-art work. The effectiveness of the proposed method is validated on an intrinsically subjective task, namely text sentiment analysis. 

In many real world applications (natural disaster management, urban development, infrastructure inspection) Unmanned Aerial Vehicles (UAVs) perform flights on different times, for scene image acquisition. Visual Place Recognition (VPR) methods can match newly acquired images with older ones, when the new and/or the old ones are not georeferenced. Most VPR solutions are based on image retrieval, where a query image scene is visually compared with that of many related images in a database, and the most relevant ones are retrieved. Deep learning-based VPR performance relies a lot on image dataset acquisition conditions, e.g. structured/unstructured scene visualization, single/multi-view image acquisition, illumination variations, or on-road/aerial view. Most of VPR methods are trained and tested on on-road views. This paper addresses the issue of image retrieval performance when large image databases are employed. To this end, we perform a comparison of some state of the art VPR methods on UAV image datasets, where the amount of database images is scaled, examine how well they generalize, and expand on some dataset creation gaps for this task.

In the realm of Natural Disaster Management (NDM), timely communication with local authorities is paramount for an effective response. To achieve this, multi-agent systems play a pivotal role by proficiently identifying and categorizing various disasters. In the field of Distributed Deep Neural Network (D-DNN) inference, such approaches often require DNN nodes to transmit their results to the cloud for inference, or they necessitate the establishment of a fixed topology network to enable inference directly on the edge, a practice prone to security risks. In this work, we propose a decentralized inference strategy tailored for fire classification tasks. In this approach, individual DNN nodes communicate within a network and enhance their predictions by considering other DNN node inference outputs that contribute to improving their individual performance. The overall coordination of the system on a specific decision is achieved through a consensus protocol, which acts as a universally accepted inference rule adopted by all DNN nodes operating within the system. We present a comprehensive experimental analysis, of the forestfire classification task, focusing on enhancing both individual DNN node performance and the stability of the consensus protocol.

This contribution aims to recommend a fully-fledged privacy-assessment applicable to future uses of Autonomous Systems (AS) for Natural Disaster Management (NDM) purposes. It claims that certain implementations may interfere with the right to privacy and the protection of personal data and analyses challenges stemming from (non-) compliance with the General Data Protection Regulation (GDPR). Moreover, it subjects the use of autonomous systems to the European Court of Human Rights’ (ECtHR) Legality – Legitimacy – Necessity testing (LLN-check). On this basis, it proposes a targeted and ex ante privacy-assessment to address legal uncertainty, resulting from the GDPR’s tech-neutrality and case law’s ex post (after the harm) adjudication. The recommended scheme, ideally involving experts from various disciplines who would moreover be independent, could apply before the actual use of any AS and give a ‘proceed’, a ‘proceed with conditions’ or a ‘do not proceed’ decision.

For civil protection purposes, risk is the probability of a calamitous event occurring that may cause harmful effects on the population, residential and productive settlements and infrastructure, within a particular area, in a given period of time. The work was carried out with the aim of being able to establish the municipal fire danger and risk index (IR), which define, respectively, the degree of danger and fire risk calculated on a regional basis and referred to the individual municipal territory, exploiting the typical functions of GIS tools and the new steps forward made by the application of artificial intelligence; however, the horizon to be reached is to be able to transform the algorithms into automated processes that can be used in platforms capable of returning outputs to end users.

EDGEmergency is a platform designed for disaster management that can dynamically leverage the edge infrastructure potentially already present within the emergency perimeter. Edge devices, from IoT to smartphones, possess an increasingly significant computational capacity that can be exploited, by changing their behavior in real-time and creating a pervasive local environment, capable of adapting perfectly to the specific context of reference. EDGEmergency, in fact, allows the creation of a unified computation environment leveraging the Cloud-Edge-Client Continuum concept, through which a computation cluster with zero configurations is created on-the-fly. The platform thus allows the deployment of distributed microservices on existing edge devices, installed by default for other purposes, through a modular and incremental logic that has the role of adapting best to the needs of the individual emergency, through advanced tools for analysis and monitoring, using artificial intelligence.

Natural disasters are more and more often present in our daily life. Many are the cases where these events affect people and economies. In this context, there is the need for a technological intervention in support of first responders, with solutions capable of make decisions on the disaster areas. Indeed, considering these scenarios are time-sensitive, the intention is moving the computation units closer to those areas. In this paper, we propose a computing continuum architecture for offloading distributed intelligences over cloud, edge and deep edge layers. Exploiting the federated learning paradigm, enables mobile and stationary devices to independently train local models, contributing to the creation of the global common mode.

Natural disasters pose increasing threats to communities and economies worldwide, emphasizing the urgency for technological interventions to support first responders and decision-makers in affected areas. To address this need, we introduce a novel computing continuum architecture designed for efficient offloading of distributed intelligences across cloud, edge, and deep edge tiers. Our approach leverages an AI crosslayer framework, integrating service, network, and infrastructure management, to optimize decision-making processes in timesensitive disaster scenarios. By employing federated learning techniques, our architecture enables both mobile and stationary devices to autonomously train local models, contributing to the development of a comprehensive global common model. Through this collaborative approach, we aim to enhance the capabilities of disaster management systems, facilitating more effective responses to critical events.

Edge Computing, a rapidly evolving sector within information technology, redefines data processing and analysis by shifting it closer to the data source, away from centralized cloud servers. This paradigm promises substantial benefits for diverse applications. In the realm of Artificial Intelligence and Machine Learning, Federated Learning emerges as a pioneering technique that harnesses Edge Computing for statistical model training. Federated Learning presents numerous advantages over traditional centralized Machine Learning, including reduced latency, heightened privacy, and real-timedata processing. Nonetheless, it introduces concerns regarding energy consumption, particularly for battery-powered Edge devices designed for remote or harsh environments. This study provides a comprehensive assessment of power consumption within the context of Federated Learning operations. To achieve this, a Raspberry Pi 4 and an INA 219 current sensor are employed. Results show that, during communication operations, the power consumption of the target device increases from a minimum of 8% to a maximumof 32% with respect to its idle state. During the local training operations it increases respectively by up to 32% for a CNN model and by up to 40% for aRNN model.

The use of the Federated Learning paradigm could be disruptive in robotics, where data are naturally distributed among teams of agents and centralizing them would increase latency and break privacy. Unfortunately there are a lack of robot oriented framework for federated learning that use state ofthe art machine learning libraries. ROS2 (Robot Operating Systems) is a standard de-facto in robotics for building upteams of robots in a multi-node fully distributed manner. In this paper we presents the integration of ROS2 with PyTorch allowing an easy training of a global machine learning model starting from a set of local datasets. We present the architecture, the used methodology and finally we discuss the experimentation results over a well-known public dataset.