AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems

A robust extended kalman filter with gaussian mixture input observations for stitching and reconstructing rolling stocks from a single camera video flow

Algorithms for aligning images and stitching them into seamless photo- mosaics are among the oldest and most widely used in computer vision. Image stitching algorithms create the high-resolution photo-mosaics used to produce today’s digital maps and satellite photos. They also come bundled with most digital cameras and can be used to create beautiful ultra wide-angle panoramas. The scope of this thesis is to write a robust algorithm that reconstruct the geometry of a large moving vehicle that passes in front of a fixed camera capturing frames at fixed frequency. The camera is arranged in a portal structure embedding among the camera, a laser and fixed illumination source. The target object whose geometry has to be estimated is a train traveling through the portal at a variable velocity. State of the Art stitching algorithms are not suitable for the case study due to the portal structure, i.e. the fixed illumination combined with the motion of the train makes the luminance of the pixels variable over the time and then makes that algorithm frail. Therefore, the purpose of this thesis is to combine some methods presents on the State of the art with other algorithm like the Extended Kalman Filter and the Expectation Maximization for Gaussian Mixture Model in order to make the overall system robust to this type of problems

La messa alla prova per l'imputato maggiorenne. Una ricerca in Emilia-Romagna

Il contributo riporta i risultati di una ricerca realizzata dalle Universit\ue0 di Bologna e di Parma con gli Uffici di esecuzione penale esterna (UEPE) di Bologna, Modena e Reggio-Emilia. La ricerca ha inteso indagare le finalit\ue0 della c.d. probation, nonch\ue9 la conoscenza e l\u2019opinione degli operatori giuridici e sociali sulla \u2018messa alla prova\u2019, dispositivo penale esteso di recente ai maggiorenni (L. 67/2014), dopo essere stato appannaggio, per trent\u2019anni, del settore minorile. Lo studio ha preso avvio dalla constatazione del mutamento normativo, per cui il reato non viene pi\uf9 considerato (solo) come lesione dell\u2019ordine giuridico astratto, ma piuttosto dei beni e degli interessi concreti di una persona e della comunit\ue0 in cui vive. Si introduce cos\uec la formulazione di dispositivi che appartengono al modello della cosiddetta Giustizia riparativa (Restorative Justice), tramite cui l\u2019esecuzione penale pu\uf2 tendere (anche) al coinvolgimento diretto dell\u2019autore del reato, per considerare (finalmente) i vissuti e le aspettative di un soggetto finora rimasto \u201cdietro le quinte\u201d della scena processuale: la vittima. Il sistema della probation, com\u2019\ue8 noto, prevede la compresenza di tre elementi: la sospensione dell\u2019azione penale, l\u2019imposizione di oneri e prescrizioni all\u2019imputato e soprattutto l\u2019affiancamento di questo da parte dei Servizi sociali, durante la \u201cprova\u201d (l\u2019etimo probation fa riferimento appunto alla prova, da affrontare e valutare). La probation - e la messa alla prova, nello specifico - si presta quindi ad essere indagata per osservare le dinamiche che interessano l\u2019intreccio tra la metodologia del servizio sociale e le procedure giudiziarie. La ricerca ha permesso l\u2019analisi di 192 fascicoli di imputati adulti \u2018messi alla prova\u2019, realizzata tramite il Software SPSS; la somministrazione di un questionario standardizzato rivolto ai funzionari del Servizio Sociale degli UEPE dell\u2019Emilia-Romagna diretto a rilevare prassi e finalit\ue0 dell\u2019 operato dei servizi; e la conduzione di 13 interviste semi-strutturate con testimoni privilegiati (operatori giuridici e operatori sociali) focalizzate sulla riparazione, sulla mediazione e sull\u2019utilizzo del lavoro di pubblica utilit\ue0

Trials Supported By Smart Networks Beyond 5G: the TrialsNet Approach

TrialsNet is a project focused on improving European urban ecosystems through 13 innovative use cases in the three representative domains of Infrastructure, Transportation, Security and Safety; eHealth and Emergency; and Culture, Tourism, and Entertainment. These use cases will be implemented across different clusters in Italy, Spain, Greece, and Romania, involving real users. This paper provides an overview of the various use cases that will be trialled in different contexts through the platform and network solutions that will be deployed by the project based on advanced functionalities such as dynamic slicing management, NFV, MEC, AI/ML, and others. To this end, TrialsNet will develop assessment frameworks to measure the impact of use cases on a technical, socio-economic, and societal level through the definition and measurement of proper Key Performance Indicators (KPIs) and Key Value Indicators (KVIs). The project seeks to identify network limitations, optimize infrastructure, and define new requirements for next-generation mobile networks. Ultimately, TrialsNet aims to enhance livability in urban environments by driving advancements in various domains

5G-IANA - D2.1 Specifications of the 5G-IANA architecture

<p>This deliverable has the objective to provide the outcomes of the activities performed in Work Package (WP) 2 "Specifications". The activities included the design of the 5G-IANA Automotive Open Experimentation Platform (AOEP) and the requirements specification of each architecture layer. The specified 5G-IANA architecture capitalizes on the 5G prospect of being a unified multi-service platform by orchestrating Vertical Services based on virtualized network slices and coordination of distributed edge-to-cloud deployment.</p><p>The 5G-IANA AOEP aims to provide an open and flexible experimentation platform to third-parties developers (e.g., SMEs) that want to develop new 5G-based services devoted to the Automotive vertical. The availability of an easy-to-use experimentation environment can facilitate the launch of new services creating new market opportunities. Moreover, 5G-IANA will actively address the configuration of the 5G network (e.g., network slicing, edge resources, etc.) with the objective of supporting in the best way the requirements of the new services. In this way, it will be also possible to verify if the current 5G implementation can adequately satisfy the highly demanding performance requirements of Automotive services.</p&gt