This work introduces an integrated Last-Mile Delivery framework that combines AI-based optimization for shipment allocation and vehicle route planning with a Mixed Reality headset interface to assist warehouse operators in vehicle loading and facilitate real-time information sharing with fleet managers. A case study in the Apulia region demonstrates the feasibility of the proposal.

This work proposes the integration of Knowledge Representation and Reasoning techniques into Unmanned Aerial Vehicle (UAV) autopilot platforms to enable autonomous context management, improved operational efficiency, and real-time risk detection. Computational efficiency and explainability characterize the proposed approach, promoting trustworthiness of autonomous UAV units, as shown by two case study reports.

This paper proposes Tiny-ME Wasm, a WebAssembly port of the Tiny-ME Description Logics reasoning and matchmaking engine. The reasoner exposes standard and non-standard inference services through a JavaScript API on top of the Tiny-ME core compiled to WebAssembly. A case study highlights the usefulness of the proposed solution and a preliminary performance analysis demonstrates its usability on laptop and mobile devices.

This work proposes a Cloud-Edge AI microservice architecture based on Osmotic Computing principles. Notable features include: (i) containerized architecture enabling training and inference on the Edge, Cloud, or both, exploiting computational resources opportunistically to reach the best prediction accuracy; and (ii) microservice encapsulation of each architectural module, allowing a direct mapping with Commercial-Off-The-Shelf (COTS) components. Grounding on the proposed architecture: (i) a prototype has been realized with commodity hardware leveraging open-source software technologies; and (ii) it has been then used in a small-scale intelligent manufacturing case study, carrying out experiments. The obtained results validate the feasibility and key benefits of the approach.

This paper proposes a novel QoE adaptation approach based on dynamic ontology-based annotation of the streaming context and mobile matchmaking with DASH representation profiles in a Web Ontology Language (OWL) fragment, exploiting a WebAssembly port of the Tiny-ME embedded reasoning engine. The proposed framework enables adaptation based not only on network status, but also on client device capabilities, ambient conditions and multimedia content type. A case study validates the proposal, while early experiments support its sustainability.

This work demonstrates how embedding Knowledge Representation and Reasoning (KRR) capabilities into UAV autopilot platforms is beneficial in several key use cases. By annotating sensed information and internal parameters in terms of an ontology model, reasoning can be exploited for context management to improve UAV operational efficiency as well as confidence on detected events. Moreover, the monitoring of hazardous environments can be automated by performing inferences on annotations of sensor data, enabling real-time identification and handling of risks and critical scenarios.

The paper presents a blockchain platform extending Hyperledger Sawtooth with semantic Smart Contracts allowing annotated resource registration, discovery, explanation, and selection. The platform leverages non-standard inferences for semantic matchmaking, endowed with logic-based justification of results. A prototypical semantic-enhanced ridesharing marketplace has been built to validate the proposal.

This paper presents Cowl, a C library for processing Web Ontology Language (OWL) 2 ontologies, designed for devices with strict portability and efficiency constraints. Its architecture is described and main optimization strategies are outlined. Results of a preliminary experimental campaign validate its effectiveness by comparing it with other state-of-the-art OWL toolkits.

This paper proposes a Cloud-Edge AI microservice architecture, based on Osmotic Computing principles, which enables training and inference on the Edge, Cloud, or both, exploiting computational resources opportunistically to reach the best prediction accuracy. Microservice encapsulation of each architectural module allows a direct mapping with COTS components. A prototype has been realized with commodity hardware leveraging open-source software technologies, and it has then been used in a small-scale intelligent manufacturing case study, carrying out experiments that validate the feasibility and key benefits of the approach.

This paper presents Tiny-ME (the Tiny Matchmaking Engine), a matchmaking and reasoning engine for the Web Ontology Language (OWL), designed and implemented with a compact and portable C core. Main features are high resource efficiency and multiplatform support, spanning containerized microservices, desktops, mobile devices, and embedded boards. The OWLlink interface has been extended to enable non-standard reasoning services for matchmaking in Web, Cloud, and Edge computing. A prototype evaluation is proposed, including a case study on the Pixhawk Unmanned Aerial Vehicle (UAV) autopilot and performance highlights.

This paper introduces evOWLuator, a novel approach and a multiplatform framework devised to be both flexible and expandable. It features integration of traditional and mobile/embedded engines as well as ontology dataset management, reasoning test execution, and report generation. A case study consisting of an experimental setting for time, memory peak and energy footprint evaluation with eight reasoners and four different platforms allows showcasing usage and validating features and usability of the tool.

This paper presents Mini-ME Swift, the first Description Logics reasoner for iOS. It implements standard (Subsumption, Satisfiability, Classification, Consistency) and non-standard (Abduction, Contraction, Covering, Difference) inference tasks in an OWL 2 language fragment. Peculiarities and optimization are discussed and performance results are presented, comparing Mini-ME Swift with other state-of-the-art OWL reasoners.

Il contributo presenta la ricerca condotta nell’ambito dei sistemi avanzati di assistenza alla guida basati sul paradigma del Semantic Web of Things (SWoT). L’intelligenza artificiale è impiegata per: (i) annotare dati di funzionamento di un veicolo estratti dalle unità di controllo, mediante linguaggi di rappresentazione della conoscenza; (ii) elaborarli combinando ragionamento automatico e machine learning per la rilevazione del contesto e dei fattori di rischio; (iii) disseminare e fondere in maniera intelligente la conoscenza inferita all’interno di una rete veicolare.

The HowtUyoga project proposes an entrepreneurial training course aimed at transferring knowledge and expertise related to the creation and management of a plant for the reuse of organic waste resulting from coffee farming to produce mushroom self-cultivation kits. The goal is to establish virtuous dynamics of environmental, social and economic sustainability (2030 Agenda) while also involving trainers, entrepreneurs, financing institutions and other stakeholders.

The paper presents an early redesign of OWL API for iOS. A partial port has been developed, effective enough to support mobile reasoning engines in a moderately expressive fragment of OWL 2. Both architecture and mobile-oriented optimizations are discussed and preliminary performance results are sketched.