The growing complexity and the increasing diffusion of applications exploiting both the Internet of Things (IoT) and the Web of Things (WoT) paradigms have led to the necessity of ensuring robust security and reciprocal trust in the management of huge amount of data from heterogeneous sources. The blockchain technology can satisfy these requirements, ensuring data integrity, univocity, and immutability of the data shared and saved in distributed IoT/IoW systems. In this paper, a deep analysis of requirements for Blockchain based IoT/WoT frameworks has been conducted. The main contributions include (i) formal implications of certifying integrity for data models and for the usage/certification of corresponding instances of entities/ devices and their time series messages in a platform that may present both non-certified and certified models, entities and data messages; (ii) automation of certification and verification processes, which implies mechanisms and formal rules; (iii) flexible certification of data messages enforcing efficiency and sustainability; (iv) support for multi-organization distributed solutions; (v) performance assessment . The solution has been validated with the interconnection to a private/permissioned blockchain developed using Hyperledger Fabric, while enforcing the solution on Snap4City federated network of platforms for applications in the domain of mobility. The research has been performed in the context of CN MOST, which is the national center on Sustainable Mobility in Italy, and within its flagship action OPTIFaaS.

Abstract Recently, Digital Twins solutions have attracted a growing interest as a fundamental paradigm for managing data driven processes on smart cities. They are complex modelling that should include 3D interactive representations of buildings and infrastructures, integrated with a wide range of data for Smart City cyber-physical ecosystem monitoring and controlling. This paper presents a framework for modelling, generating and distributing Digital Twin representations with 3D models from a various set of data, as well as its integration into the open-source Smart City framework, where many kinds of real time and historical data are available. The proposed solution offers a method for creating integrated data rendering of 3D city entities coupled with Smart City data (e.g., IoT Devices with time-series and historical data, heatmaps, geometries and shapes related to traffic flows, bus routes/stops, cycling paths). The solution for generating 3D representation is based on a number of computer vision and machine learning solutions, thus shortening the activities of passing from raw data (i.e., Lidar, shapes, patterns, etc.) to 3D representations. Implementation has been enforced into the quite widespread open-source Snap4City Smart City platform and has been validated by using hundreds of buildings in Florence city central area, Italy, plus hundreds of thousands of data as points of interest, IoT Devices, traffic flows, dynamic heatmaps, etc.

Abstract Tourism management plays an important role in the context of Smart Cities. In this work, we have used thermal cameras for the development of an Object Detection solution in pedestrian areas. The solution can classify people, bikes, strollers, and count people in Real-Time by using telephoto and wide-angle thermal cameras, in hot squares where there is a relevant number of people passing by. This work has improved FASTER-R-CNN and YOLOv5 architectures with new data sets and fine-tuning approaches to enhance mean average precision and flexibility whether compared to state of the art solutions. Both top-down and bottom-up training adaptation approaches have been assessed in order to demonstrate that the proposed bottom-up approach can provide better results. Results have overcome the state-of-the-art in terms of mean Average Precision in counting (i) for relevant number of people in the scene (removing the limitation of previous state-of-the-art solutions that were set to provide good precision up to 10 people) and (ii) in terms of flexibility with respect to different kinds of camera and resolutions. The resulting model can produce results also when executed on thermal camera and in Real-Time on industrial PC of mid-level. The proposed solution has been developed and validated in the framework of the Herit-Data EC project and it has exploited the Snap4City platform for the final collection of data results, monitoring and their publication on real time dashboards.

A range of new kind of applications are growing for the integration of different technologies and needs such as IoT/IoE (Internet of Things/Everything), WoT (web of things), Digital twins and smart data models, data spaces. These applications demand less attention to the storage modeling since the adopted big data stores are less structured. Therefore, the connection from data storage to visual representation can be more automated. All these aspects open the path for rapid prototyping. In this paper a fast-prototyping development lifecycle for that category of smart applications is proposed. The proposed solution works on heterogeneous data types, push and pull, wide interoperability, covering all aspects from data ingestion to front-end. It can be regarded as a specific declination of AGILE development paradigm in which several activities have been strongly shortened by exploiting a number of modelling facilities of the development environment and technologies. The proposed development lifecycle was defined and tuned for the Snap4City platform, which provides an integrated development environment for rapid prototyping of smart application based on low coding, including visual programming, and a number of visual tools for creating the applications. The solution has been validated in the context of multiple interconnected scenarios of several large-scale projects and pilots such as Herit-Data, Panacea, Resolute, Replicate, TRAFAIR, etc.

Fashion retail has a large and ever-increasing popularity and relevance, allowing customers to buy anytime finding the best offers and providing satisfactory experiences in the shops. Consequently, Customer Relationship Management solutions have been enhanced by means of several technologies to better understand the behaviour and requirements of customers, engaging and influencing them to improve their shopping experience, as well as increasing the retailers' profitability. Current solutions on marketing provide a too general approach, pushing and suggesting on most cases, the popular or most purchased items, losing the focus on the customer centricity and personality. In this paper, a recommendation system for fashion retail shops is proposed, based on a multi clustering approach of items and users' profiles in online and on physical stores. The proposed solution relies on mining techniques, allowing to predict the purchase behaviour of newly acquired customers, thus solving the cold start problems which is typical of the systems at the state of the art. The presented work has been developed in the context of Feedback project partially founded by Regione Toscana, and it has been conducted on real retail company Tessilform, Patrizia Pepe mark. The recommendation system has been validated in store, as well as online.

The field of 3D city modelling has attracted a growing interest for representing the city digital twin, providing interactive visualizations of building infrastructures integrated with a wide range of data typically produced in a Smart City environment. This paper presents a method for producing a 3D city model with photorealistic rooftop textures extracted from aerial images, as well as the integration of the 3D city model into an open-source Smart City framework. The proposed solution provides a smart visualization of 3D city entities integrated with a large variety of Smart City data (coming, for instance, from IoT Devices which generate time-series data, heatmaps, geometries and shapes related to traffic flows, bus routes, cycling paths etc.). The proposed method for rooftop detection and alignment follows a deep learning approach based on U-Net architecture, and it has been validated against a manually created ground-truth of 50 buildings scattered uniformly on the covered area. The solution is implemented in the open-source Snap4City Smart City platform. DEMO: The solution will be demonstrated and the readers and conference attendees will be capable to interact and work with the tools by using the links provided.

In the context of Smart Cities digital transformation, the field of 3D city modelling has attracted a growing interest for representing the city digital twin. This paper presents a method for producing a 3D city model with photorealistic rooftop textures extracted from aerial images, as well as the integration of the 3D city model into an open-source Smart City framework. The proposed solution provides a smart visualization of 3D city entities integrated with a large variety of Smart City data (coming, for instance, from IoT Devices which generate time-series data, heatmaps, geometries and shapes related to traffic flows, bus routes, cycling paths etc.). The proposed method for rooftop detection and alignment follows a deep learning approach based on U-Net architecture, and it has been validated against a manually created ground-truth of 50 buildings scattered uniformly on the covered area. The solution is implemented in the open-source Snap4City Smart City platform.

Smart cities are distributed heterogeneous systems of systems connected to each other via a variety of heterogeneous data streams involving multiple stakeholders and organizations. This complexity is reflected also in the data that have to be managed to provide a concrete and useful real time service to the citizens. The data ingestion phase is critical for the whole services, since it has to preserve the information, connect the new data with old data and establish right connections with city entities. This paper describes data ingestion and inspection in the Snap4City open source scalable Smart aNalytic APplication builder, with a specific focus on how heterogeneous data is represented, how its quality is inspected, and how to develop ingestion procedures in an efficient manner. The Snap4City ingestion processes are based on a semantic and unified data ingestion model, capable of aggregating different types of data. A performance comparison of different data ingestion modalities is presented.

Today most of the users on the move require contextualized local and georeferenced information. Several solutions aim to meet these trends, thus assisting users and satisfying their needs and preferences, such as virtual assistants and Location-Aware Recommender Systems (LARS), both in commercial and research literature. However, general purpose virtual assistants usually have to manage large domains, dealing with big amounts of data and online resources, losingfocus on more specific requirements and local information. On the other hand, traditional recommender systems are based on filtering techniques and contextual knowledge, and they usually do not rely on Natural Language Processing (NLP) features on users’ queries, which are useful to understand and contextualize users’ necessities on the spot. Therefore, comprehending the actual users’ information needs and other key information that can be included in the user query, such as geographical references, is a challenging task which is not yet fully accomplished by current state-of-the-art solutions. In this paper, we propose Paval (Location-Aware Virtual Personal Assistant 2 ), a semantic assisting engine for suggesting local points of interest (POIs) and services by analyzing users’ natural language queries, in order to estimate the information need and potential geographic references expressed by the users. The system exploits NLP and semantic techniques providing as output recommendations on local geolocated POIs and services which best match the users’ requests, retrieved by querying our semantic Km4City Knowledge Base. The proposed system is validated against the most popular virtual assistants, such as Google Assistant, Apple Siri and Microsoft Cortana, focusing the assessment on the request of geolocated POIs and services, showing very promising capabilities in successfully estimating the users’ information needs and multiple geographic references.

The predictive capabilities of metrics based on Twitter data have been stressed in different fields: business, health, market, politics, etc. In specific cases, a deeper analysis is required to create useful metrics and models with predicting capabilities. In this paper, a set of metrics based on Twitter data have been identified and presented in order to predict the audience of scheduled television programmes, where the audience is highly involved such as it occurs with reality shows (i.e., X Factor and Pechino Express, in Italy). Identified suitable metrics are based on the volume of tweets, the distribution of linguistic elements, the volume of distinct users involved in tweeting, and the sentiment analysis of tweets. On this ground a number of predictive models have been identified and compared. The resulting method has been selected in the context of a validation and assessment by using real data, with the aim of building a flexible framework able to exploit the predicting capabilities of social media data. Further details are reported about the method adopted to build models which focus on the identification of predictors by their statistical significance. Experiments have been based on the collected Twitter data by using Twitter Vigilance platform, which is presented in this paper, as well.

The problem of assessing the mechanisms underlying the phenomenon of virality of social network posts is of great value for many activities, such as advertising and viral marketing, influencing and promoting, early monitoring and emergency response. Among the several social networks, Twitter.com is one of the most effective in propagating information in real time, and the propagation effectiveness of a post (i.e., tweet) is related to the number of times the tweet has been retweeted. Different models have been proposed in the literature to understand the retweet proneness of a tweet (tendency or inclination of a tweet to be retweeted). In this paper, a further step is presented, thus several features extracted from Twitter data have been analyzed to create predictive models, with the aim of predicting the degree of retweeting of tweets (i.e., the number of retweets a given tweet may get). The main goal is to obtain indications about the probable number of retweets a tweet may obtain from the social network. In the paper, the usage of the classification trees with recursive partitioning procedure for prediction has been proposed and the obtained results have been compared, in terms of accuracy and processing time, with respect to other methods. The Twitter data employed for the proposed study have been collected by using the Twitter Vigilance study and research platform of DISIT Lab in the last 18 months. The work has been developed in the context of smart city projects of the European Commission RESOLUTE H2020, in which the capacity of communicating information is fundamental for advertising, promoting alerts of civil protection, etc.

Despite the presence of many systems for developing and managing structured taxonomies and/or SKOS models for a given domain for which small documents set are accessible, the production and maintenance of these domain knowledge bases is still a very expensive and time consuming process. This paper proposes a solution for assisting expert users in the development and management of knowledge base, including SKOS and ontologies modeling structures and relationships. The proposed solution accelerates the knowledge production by crawling and exploiting different kinds of sources (in multiple languages and with several inconsistencies among them). The proposed tool supports the experts in defining relationships among the most recurrent concepts, reducing the time to SKOS production and allowing assisted production. The validity of the produced knowledge base has been assessed by using SPARQL query interface and a precision and recall model. The results have demonstrated better performance with respect to the state of the art. The solution has been developed for Open Space Innovative Mind project, with the aim of creating a portal to allow industries at posing semantic queries to discover potential competences in a large institution such as the University of Florence, in which several distinct domains are associated with its own departments.

In the area of music information retrieval (MIR), automatic music transcription is considered one of the most challenging tasks, for which many different techniques have been proposed. This paper presents a new method for polyphonic music transcription: a system that aims at estimating pitch, onset times, durations, and intensity of concurrent sounds in audio recordings, played by one or more instruments. Pitch estimation is carried out by means of a front-end that jointly uses a constant-Q and a bispectral analysis of the input audio signal; subsequently, the processed signal is correlated with a fixed 2-D harmonic pattern. Onsets and durations detection procedures are based on the combination of the constant-Q bispectral analysis with information from the signal spectrogram. The detection process is agnostic and it does not need to take into account musicological and instrumental models or other a priori knowledge. The system has been validated against the standard Real-World Computing (RWC)-Classical Audio Database. The proposed method has demonstrated good performances in the multipleF0 tracking task, especially for piano-only automatic transcription at MIREX 2009.