• A novel architecture is proposed to solve traffic flow reconstruction problems • The architecture leverages graph neural networks and learned feature embeddings • A continuous estimation strategy is proposed to use the solution on real scenarios • The proposed approach is highly generalizable to previously unseen scenarios Traffic control and management play a crucial role within intelligent transportation systems. However, comprehensive traffic monitoring is often constrained by the limited availability of traffic sensors across urban road networks. This paper addresses the problem of Traffic Flow Reconstruction (TFR), which aims to estimate traffic conditions over an entire road network starting from a few sparse traffic sensor measurements. This approach does not require full road coverage by sensors, thereby reducing deployment costs and supporting sustainable urban mobility. In the present research, we propose a lightweight Graph Neural Network (GNN) architecture for real-time TFR using data from a few number of fixed sensors. The model exploits a carefully designed road graph, explicitly augmented to capture traffic propagation and backpropagation effects at junctions, and well-studied node features augmented though a learned encoder. Compared to classical Partial Differential Equations-based TFR solutions, the proposed method achieves satisfactory estimation accuracy while significantly reducing setup times by avoiding scenario-dependent optimizations and precomputation of traffic flow distributions at junctions. Moreover, it overcomes the limitations of existing GNN-based approaches, which often rely on complex architectures that are difficult to adapt to different urban contexts without reprocessing them. Extensive experiments have been conducted and presented in this paper taking into account different moments of the year, various road graph representations and sensors’ positioning, and multiple city environments. Results demonstrate the robustness, generalization capability, and computational efficiency of the proposed approach, making it suitable for large-scale and real-world mobility and transport applications.

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.

Navier-Stokes equations used to model fluid dynamic processes are fundamental to address several real-world problems related to energy production, aerospace applications , automotive design , industrial process, etc. However, since in most cases they do not admit any analytical solution, numerical simulations are required in industrial contexts to assess fluid dynamic behaviors in specific setups. Computational Fluid Dynamics (CFD) methods, like those using finite volume or element approaches, are exploited to find Navier-Stokes solutions and carry out simulations. However, such methods require expensive hardware resources, relevant computational times, and manual efforts for the definition of dense meshes on which equations are evaluated iteratively for each time step of the simulation. Physics-Informed Neural Networks (PINNs), which are deep neural networks where physical laws are directly embedded into the training process, offer a promising approach for solving Navier-Stokes equations, thus alleviating hardware and time requirements. PINNs bypass some CFD limitations by using neural networks to produce solutions based on governing equations, thus reducing the need for large datasets, dense meshing, and iterative estimation over time. This paper evaluates the application of PINNs in near real-world scenarios, while considering various geometries. The study focuses on the achieved accuracy, by comparing PINN estimates with CFD solutions obtained via OpenFOAM , and the required training times; this includes evaluating different neural network architectures , activation functions , and numbers of sampling points. Additionally, several training strategies such as fine-tuning, multi-resolution learning, and parametrized training are proposed to enhance efficiency and obtain speed up. Results demonstrate that PINNs can achieve comparable accuracy to CFD methods (with a velocity magnitude mean absolute error inferior to 10 − 2 ) and significantly reduce computational costs. Our findings demonstrated that with appropriate training techniques PINNs can be effectively used in industrial applications requiring rapid and accurate fluid dynamic simulations, thus paving the way for their broader adoption in practical engineering problems.

Climate change is a challenge of global relevance that requires in-depth understanding and immediate response. Urban CO 2 emissions are one of the main causes of climate change, and their estimation is crucial for planning actions aimed at creating more sustainable cities. Currently, models for estimating CO 2 emissions mainly focus on traffic patterns, energy consumption, or a limited set of socio-economic factors, often overlooking the increasing role of urban services in today’s cities. Moreover, these data are rarely accessible, which limits their usefulness for policy design. In the present paper, a general CO 2 emission estimation approach, based on a large range of often accessible open data as predictors, is presented. Such open data are related to human activity regarding services and (socio-economic) conditions arising in a given (urban) area. The proposed model focuses on fine-scale prediction to better understand the dynamics of emissions via machine learning approaches, while taking into account an innovative study based on open data sources including city services. The resulting best models have been based on XgBoost and GCN (graph convolutional network). The outcomes provided better precision (MAPE in the order of 8%) with respect to the state-of-the-art solutions. The goal has been to understand how specific predictors can contribute to or mitigate CO 2 emissions in the observed area. To this end, the impact of several features has been analyzed in order to identify the related key factors influencing emissions. A formal study has been conducted to perform feature relevance analysis by using eXplainable AI (XAI) approach. The proposed model is useful to define targeted policies reducing the pollutant impact of cities, promote a more ecologically sustainable urban lifestyle and improve sustainable urban planning. Both solutions and models have been assessed and improved, so as to be more flexible by using some transfer learning techniques. This research and its related results have been produced and validated by exploiting the Snap4City framework for smart city, mobility and transport and data analytics on CN MOST, national center on sustainable mobility. In particular, such results deal with the metropolitan cities of Florence and Bologna. © 2017 Elsevier Inc. All rights reserved.

Addressing mobility and transport problems is nowadays of paramount importance for any city due to the increasing urbanization. Traffic congestion, pollutant emissions, energy consumption are some of the problems related to urban mobility. Therefore, there is the need of tools able to support decision-makers in studying, evaluating, and planning sustainable urban evolutions. A few open-source and proprietary solutions are available requiring on-premises installations, large effort, and providing limited capabilities to actually handle real-time data (from data spaces, and standards). Moreover, they are limited in terms of analytic integration and do not offer automatic generation of suggestions. In practice they do not manage the explosion of complexity regarding computational and storage/models aspects. For these reasons, this paper presents a comprehensive architecture for a Smart City Digital Twin platform, specifically designed to support mobility and transportation decision-making through advanced what-if analysis and optimization. The platform, integrated within the Snap4City system, enables real-time data processing and complex analytics to create virtual urban environments for evaluating potential infrastructure changes. Through microservice architecture, the platform supports massive data ingestion, scenario creation, and predictive modelling, facilitating both short-term and long-term planning. The solution leverages artificial intelligence (AI), machine learning (ML), and reinforcement learning (RL) to optimize city operations and suggest actionable insights, aiding city planners in strategic and tactical decisions. This architecture has been validated through implementations in Italian cities, demonstrating scalability and flexibility to accommodate diverse urban needs and improve traffic flow, energy efficiency, and environmental impact. This work has been performed in the context of OPTIFaaS Flagship of CN MOST, the National Centre for Sustainable Mobility in Italy, and for CN HPC Big Data and Quantum Computing, ICSC.

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.

Traffic management and sustainable mobility are the central topics for intelligent transportation systems (ITS). By means of modern technologies, it is possible to collect real-time traffic flow data to extract useful information to monitor and control vehicular traffic. On the other hand, costs to obtain this piece of information are high. It requires either direct measures in the network road by installing large number of sensors (more precise data) or acquiring data from international providers supplying data coming from onboard units, mobile app, navigators, etc. In current paper, this problem has been addressed providing a solution granting traffic flow data in each road segment of the whole network by reconstructing the computation by means of data from few scattered traffic sensors in fixed positions of the road network. The proposed approach combines the solution of nonlinear Partial Differential Equations (PDEs) with machine learning for improving the state-of-the-art solutions of PDE. The result has been a higher precision with respect to PDE-based solutions, and a strongly reduced execution time. Several different machine learning models have been compared for such a purpose, demonstrating the general viability of the hybrid architecture proposed. The research result has been obtained in the framework of both the Sii-Mobility national project on transport systems, and MOST, the National Center on Sustainable mobility (both funded by the Italian Ministry of Research), by exploiting the Snap4City platform.

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.

The use of thermal imaging to detect the presence of people in indoor and outdoor environments is gaining an increasing attention given its wide applicability in the tourism, security, and mobility domains. However, due to the particular characteristics of different contexts, it is necessary to train/finetuning specifically object detectors for each scenario in order to obtain accurate results. This is due to changes in appearance caused by camera position, scene size, environmental factors, etc. In this paper, we present a data augmentation method that can improve both versatility and robustness of pedestrian detection models based on thermal images. Thanks to our solution, the trained model can deal with unseen thermal data from both indoor and outdoor environments, reliably detecting pedestrians regardless of their apparent size and position in the image, without any fine-tuning or transfer learning, therefore avoiding time consuming labeling activities to fine-tune and deploy the system in different scenarios.

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.

Abstract The modern mobile phones and the complete digitalization of the public and private transport networks have allowed to access useful information to understand the user’s mean of transportation. This enables a plethora of old and new applications in the fields of sustainable mobility, smart transportation, assistance, and e-health. The precise understanding of the travel means is at the basis of the development of a large range of applications. In this paper, a number of metrics has been identified to understand whether an individual on the move is stationary, walking, on a motorized private or public transport, with the aim of delivering to city users personalized assistance messages for: sustainable mobility, health, and/or for a better and enjoyable life, etc. Differently from the state-of-the-art solutions, the proposed approach has been designed to provide results, and thus collect metrics, in real operating conditions (imposed on the mobile phones as: a range of different mobile phone kinds, operating system constraints managing Applications, active battery consumption manager, etc.). The paper reports the whole experimentations and results. The solution has been developed in the context of Sii-Mobility Km4City Research Project infrastructure and tools, performed with the collaboration of public transport operators, and GDPR compliant. The same solution has been used in Snap4City mobile Apps with experiments performed in Antwerp and Helsinki.

The knowledge of the real time traffic flow status in each segment of a whole road network in a city or area is becoming fundamental for a large number of smart services such as: routing, planning, dynamic tuning services, healthy walk, etc. Rescue teams, police department, and ambulances need to know with high precision the status of the network in real time. On the other hand, the costs to obtain this information either with direct measures meant to add instruments on the whole network or acquiring data from international providers such as Google, TomTom, etc. is very high. The traditional modeling and computing approaches are not satisfactory since they are based on many assumptions that typically are doomed to change over time, as it occurs with traffic distribution at junctions; in short they cannot cover the whole network with the needed precision. In this paper, the above problem has been addressed providing a solution granting any traffic flow reconstruction with high precision and solving the indeterminacy of traffic distribution at junctions for large networks. The identified solution can be classified as a stochastic relaxation technique and resulted affordable on a parallel architecture based on GPU. The result has been obtained in the framework of the Sii-Mobility national project on smart city transport systems in Italy, a very large research project, and it is at present exploited in a number of cities/regions across Europe and by a number of research projects (Snap4City, TRAFAIR) of the European Commission.

In recent years, there is an increasing attention on Air Quality and derived services. In most cases, the main objective consists in providing services independently on the number of sensors, and on any position of the users. On the other hand, sensors are collected in limited positions, then a dense grid is computed to satisfy the needs of services such as: conditional routing, alerting on data values for personal usage, general heatmap production for Dashboards in control room of the operators, and for web and mobile applications for the city users. The paper formalizes the development process and describes how it is possible to automatically integrate Data Analytics in data flow real time processes, called in the paper IoT Applications. Two interpolation methods have been compared and validated in order to assess the accuracy. Interpolation errors trends have been used to detected devices' dysfunctions on sensors. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City (https://www.snap4city.org) has been developed as a part Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

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.

Smart Cities are probably on the more complex environment for IOT data collection. IOT data could have different producers, sample rates, periodic and aperiodic, typical trends, structures and stacks, faults, etc. Thus, a strongly flexible and scalable solution is needed to avoid investing huge amount of resources in anomaly detection that has to be done in real time and has to be agnostic to the above-mentioned problems. This paper presents a solution for automatic detection of anomalies. The proposed approach scales seamlessly and integrates in different contexts, featuring different sensor types, protocols, and data formats, and computationally cheap. The research has been developed in the context of Snap4City PCP Select4Cities project and is presently implemented in the Https://www.snap4city.org solution adopted in several cities and regions.

In the context of Smart City, it is quite frequent the usage of Smart City API for providing services at web and mobile applications. Most of the solutions using Smart City APIs are focused on a single city. This means that passing from one city/area to another, the users must change application. This happens for the lack of interoperability among Smart City APIs and/or services. In this paper, the problem of federation of smart city services is addressed by proposing a solution for federating smart city APIs. To this end, a formal model has been proposed to federate API services, with efficiency, security, scalability, and capacity of managing overlapped areas of competence, distributed searches, etc. These features are typically not all satisfied by classic GIS solutions which federate the services at level of databases. The solution has been developed in the context of Snap4City European platform enhancing former Km4City API of Sii-Mobility national project with Snap4City (https://www.snap4city.org).

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.

Real time traffic flow data in terms of road-segments traffic densities are relevant information at the basis of several smart services, such as smart routing, smart planning for evacuations, planning of civil works on the city, etc. The traditional methods for traffic flow measured via sensors using data from navigator Apps (e.g., TomTom, Google map, Waze) could be very expensive to be acquired. For this reason, there is the space for low cost and fast solutions for dense traffic flow reconstruction from scattered data coming from a limited number of street sensors spread on the city. The proposed method is based on differential equations and physical constraints applied to a detailed street graph which is enriched of several features. A stochastic learning approach has been adopted to estimate the weights representing in certain sense the road-segments capacity at each time slot of the day. The proposed solution allows computing in real-time the traffic density reconstruction in unmeasured road-segments. The solution has been validated estimating the error in the places where the sensors are positioned, excluding each of them iteratively and reconstructing the flow without it. Then, it has been possible to estimate the error between the reconstructed traffic density and the measured values. This approach allowed setting up a converging algorithm for estimating the traffic density in the whole city graph from detailed parameters. The proposed reconstruction model has been created by exploiting open and real-time data in the context of Sii-Mobility research project by using Km4City infrastructure in the area of Florence, Italy, for its corresponding Smart City solution.

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.

The new challenges in the smart city context are mainly related to the stimulation of the city users towards taking more sustainable behaviors, in mobility and energy. The state of the art in this case is mainly focused on classical smart city solution for informing the city users and or for engaging them with specific wired rules toward virtuous models. And not using flexible languages and predictive models, pushing them towards a larger range of virtuous habits. On this regards, the main problems are the computation of user behavior via data analytic (semantic computing, machine learning), as well as the formalization of strategies via simple and well formalized language for producing engagements to the city users, which can be understood by city operators. In this paper, a solution for city users engagement is studied and implemented for Sii-Mobility Smart city national project in Italy has been presented. The solution has been implemented thanks to the exploitation of Km4City model and semantic computing. The paper also presents the validation of results about the effective usage of the solution by providing some statistical evidence about the efficient assessment of user behavior and of engagement rules acceptance rate.

The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs. Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented.

This work aims at investigating and quantifying the Urban Transport System (UTS) resilience enhancement enabled by the adoption of emerging technology such as Internet of Everything (IoE) and the new trend of the Connected Community (CC). A conceptual extension of Functional Resonance Analysis Method (FRAM) and its formalization have been proposed and used to model UTS complexity. The scope is to identify the system functions and their interdependencies with a particular focus on those that have a relation and impact on people and communities. Network analysis techniques have been applied to the FRAM model to identify and estimate the most critical community-related functions. The notion of Variability Rate (VR) has been defined as the amount of output variability generated by an upstream function that can be tolerated/absorbed by a downstream function, without significantly increasing of its subsequent output variability. A fuzzy-based quantification of the VR based on expert judgment has been developed when quantitative data are not available. Our approach has been applied to a critical scenario as flash flooding considering two cases: when UTS has CC and IoE implemented or not. However, the method can be applied in different scenarios and critical infrastructures. The results show a remarkable VR enhancement if CC and IoE are deployed.

The main technical issues regarding smart city solutions are related todata gathering, aggregation, reasoning, access, and service delivering via Smart City APIs (Application Program Interfaces). Aggregated and re-conciliated data (open and private, static and real time) should be exploitable by reasoning/smart algorithms for enabling sophisticated service delivering. Different kinds of Smart City APIs enable Smart City Services and Applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators. To this end, a comparison of the state of the art solutions for data aggregation was performed, by putting in evidence the needs of semantic interoperable aggregated data, to provide smart services. This paper presents the work performed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with services. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation and service production. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Finally, the API consumption related data in the recent period are presented.

The complexity of Cloud infrastructures is increasing every year, requiring new concepts and tools to face off topics such as process configuration and reconfiguration, automatic scaling, elastic computing and healthiness control. This paper presents a Smart Cloud solution based on a Knowledge Base, KB, with the aim of modeling cloud resources, Service Level Agreements and their evolution, while enabling the reasoning on cloud structures and implementing strategies of efficient smart cloud management and intelligence. The solution proposed is composed of Smart Cloud Engine, SCE, the Knowledge Base, KB, and the Supervisor and Monitoring module for data acquisition. It can be easily integrated with any cloud configuration manager, cloud orchestra or, and monitoring tool, since the connections with these tools are performed by using REST calls and XML files.

Multimedia services of cultural institutions need to be supported by content, metadata and workflow management systems to efficiently manage huge amount of content items and metadata production. Online digital libraries and cultural heritage institutions, as well as portals of publishers need an integrated multimedia back office in order to aggregate content collection and provide them to national and international aggregators, with respect to Intellectual Property Rights, IPR. The aim of this paper is to formalize and discuss requirements, modeling, design and validation of an institutional aggregator for metadata and content, coping with IPR Models for conditional access and providing content towards Europeana, the European international aggregator. This paper presents the identification of the Content Aggregator requirements for content management and IPR, and thus the definition and realization of a corresponding distributed architecture and workflow solution satisfying them. The main contributions of this paper consist of the formalization of IPR Model that enable the shortening of the activities for the IPR resolution, and avoid the assignment of conflicting rights/permissions during IPR model formalization, and thus of licensing. The proposed solution, models and tools have been validated in the case of the ECLAP service and results are reported in the paper. ECLAP Content Aggregator has been established by the European Commission to serve Europeana for the thematic area of Performing Arts institutions.

The modeling of performing arts metadata is considered one of the most challenging problems, since performances add complexities related to events to the classical cultural heritage descriptors associated to physical objects. The most relevant lacks of the present models are related to the modeling of information connected to performers and performances, which are obviously distinguishing aspects of the performing arts and are essential for the preservation of our cultural heritage and literature, such aspects being strongly connected with performing arts. This paper presents the European Collected Library of Artistic Performance (ECLAP) semantic model that has been specifically defined for aggregating and enriching performing arts content coming from several content providers. ECLAP has been set up by the European Commission to play the role of content aggregator for Europeana. The proposed ECLAP semantic model addresses most of the identified problems. The proposed model has been compared with present standards and it is now supported by a graphic tool for user navigation among semantic relationships and Linked Open Data (LOD). The paper also describes the generation of LOD from the ECLAP semantic model and the mapping of ECLAP model to Europeana Data Model (EDM). The experience highlighted that some relevant elements produced, enriched and aggregated by ECLAP cannot be mapped into EDM, while the ECLAP model can address some of the details related to the performing arts which are not at present addressed by the available standards.

There are several education and training cases where multi-camera view is a traditional way to work: performing arts and news, medical surgical actions, sport actions, instruments playing, speech training, etc. In most cases, users need to interact with multi camera and multi audiovisual to create among audiovisual segments their own relations and annotations with the purpose of: comparing actions, gesture and posture; explaining actions; providing alternatives, etc. Most of the present solutions are based on custom players and/or specific applications which force to create custom streams from server side, thus leading to restrictions on the user activity as to establishing dynamically additional relations. Web based solutions would be more appreciated and are complex to be realized for the problems related to the video desynchronization. In this paper, MyStoryPlayer/ECLAP solution is presented. The major contributions to the state of the art are related to: (i) the semantic model to formalize the relationships and play among audiovisual determining synchronizations, (ii) the model and modality to save and share user experiences in navigating among lessons including several related and connected audiovisual, (iii) the design and development of algorithm to shorten the production of relationships among media, (iv) the design and development of the whole system including its user interaction model, and (v) the solution and algorithm to keep the desynchronizations limited among media in the event of low network bandwidth. The proposed solution has been developed for and it is in use within ECLAP (European Collected Library of Performing Arts) for accessing and commenting performing arts training content. The paper also reports validation results about performance assessment and tuning, and about the usage of tools on ECLAP services. In ECLAP, users may navigate in the audiovisual relationships, thus creating and sharing experience paths. The resulting solution includes a uniform semantic model, a corresponding semantic database for the knowledge, a distribution server for semantic knowledge and media, and the MyStoryPlayer Client for web applications.

Presently a very large number of public and private data sets are available around the local governments. In most cases, they are not semantically interoperable and a huge human effort is needed to create integrated ontologies and knowledge base for smart city. In this paper, a system for the ingestion of data for smart city related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows to manage a big volume of data coming from a variety of sources considering both static and dynamic data, these data are then mapped to a smart-city and mobility ontology and stored into an RDF-Store where this data are available for applications via SPARQL queries to provide new services to the users. The paper presents the knowledge base and the mechanisms adopted for the verification, reconciliation and validation. Some examples about the possible usage of the knowledge base produced are also offered and are accessible from the RDF-Store.

Digital rights management solutions are today quite widespread. Their cost is still quite expensive, and thus in many cases, their application is limited to specific business cases. On the other hand, the market still offers large cases where scalable DRM solutions would find their applicability, for example the management of complex cross media content such as the one used for the educational content, for electronic medical record, etc. In this paper, the focus is on reducing DRM costs by solving scalability problems lying behind the complexity of granting authorizations and performing verification for a large number of users, content and rights associated with them. The proposed solution is based on the exploitation of the DHT P2P network and storage to cope with verification and grant authorization. The paper reports the structure of the DRM solutions, the details for including the DHT P2P into the DRM architecture. The paper also reports details on how the proposed P2P DRM solution can be integrated into traditional DRM solutions. The provided experimental results have proved the reduction of costs, the scalability against the aforementioned cases. The studies and solutions reported in this paper have been worked out and validated on top of MPEG-21/AXMEDIS DRM solutions and tools. On the other hand, the solution is general enough to be adapted in other DRM solutions.

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 cultural heritage there is a strong push on aggregating content metadata from institutions (such as museums, university, archives, library, foundations, etc.) to make them widely accessible. This action is going to reduce fragmentation, allows aggregation and integrates valuable collections in a unique place. For example, European a (the so called European digital library) collects only metadata, while content files are referred via some URL. These URLs refer to the original content owner and/or to the Content Aggregator, facilitating the collection. That model leaves space to the Content Aggregator to provide additional services on their enriched models. The proposed Content Aggregation model attempts to satisfy specific requirements with a semantic model and tools providing support for executable aggregations such as: play lists, collections, e-learning courses, and media annotations/synchronizations. The produced aggregations may also be provided by mapping semantic concepts to European a. The paper also performs an analysis of semantics models mentioned and of their difficulties including some comments about the adoption of linked open data and media model. The results have been produced in the project ECLAP ICT PSP founded by the European Commission, http://www.eclap.eu.

The content technology needs to attain forms with more intelligence, flexibility and complete features than those being currently on the market or proposed by standards. In this paper, an analysis of the state of the art about intelligent and complex content models is presented. The analysis allowed identifying a number of topics and features which models and formats should evolve according to. The work has been used to extend AXMEDIS content model and format which in turn is grounded on MPEG-21, SMIL, HTML, and other standards. The Extended AXMEDIS format presents a set of new features among them: semantic descriptors, extended annotations, intelligent behavioral and semantic computing capabilities. The newly obtained format has been compared against NewsML which is one of the most widespread formats for news production and distribution. The management of news has some peculiarities such as container, production tools and players, that may take advantage of the intelligent content features and applications. Moreover, news have to be massively processed for ingestion and repurposing, and present relevant requirements on right control. Also these features may be satisfied by AXMEDIS tools. To this end, a comparative analysis of processing and modeling NewsML with AXMEDIS tools and format has been performed and reported to verify the usage. In addition, AXMEDIS format can be profitably used for a range of innovative applications of intelligent content.

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.

Possible solutions to the management of multichannel delivering, production on demand, and containment of sale prices in the digital multimedia content production could be the automating, accelerating and restructuring the production process. The proposed solution provides innovative methods and tools to speed up and optimize content production and distribution based on the GRID technology supported by a specific programming language that allow defining the automatic procedure for content processing, production, adaptation, protection, DRM managing, distribution, etc. This paper describes the GRID architecture of the AXMEDIS Content Processing Area and the language adopted to define algorithms executed into the GRID environment

Despite the relevance of the MPEG-21 standard, little has been done about the modeling of authoring tools and players for the production/consumption of MPEG-21 digital objects. The design and implementation of MPEG-21 tools present several critical points to be solved at the architectural level, so as to ensure security and provide a suitable support and the requested flexibility for manipulating any kind of digital resource, according to the spirit of MPEG-21 standard. This paper presents the AXMEDIS core model and components for MPEG-21 content authoring tools and players. The proposed architecture provides both data manipulation flexibility and a high level of security when digital resources are used. The same architecture can be used to develop any other MPEG-21 DI tools. The architecture presented has been adopted to build authoring and player tools developed for the AXMEDIS IST FP6 R&D European Commission project

The design and implementation of MPEG-21 players and authoring tools presents several critical points to be solved. One of the most relevant is the security level and protection processing in the players. This paper presents a solution for the realization of components in charge of enforcing Digital Rights Management in AXMEDIS tools for MPEG-21 digital content. The proposed architecture provides functionalities to create both trusted environment on the client side and dynamic protection and unprotection of digital content including digital resources and their organization and metadata. The same solution can be used to achieve the desired security level in any other MPEG-21 player or authoring tool. The architecture presented hereinafter has been adopted to enforce protection on authoring and player tools developed for the AXMEDIS IST FP6 R&D European Commission project

The paper on Comments on "An Interval Logic for Real-Time System Specification" presents some remarks on the comparison examples from TILCO and other logics and some slips on the related examples. This paper gives evidence that such issues have no impact on the validity of the TILCO theory of paper and provides some further clarifications about some aspects of the comparison

Over the last, years, software quality has become one of the most important requirements in the development of systems. Fault-proneness estimation could play a key role in quality control of software products. In this area, much effort has been spent in defining metrics and identifying models for system assessment. Using this metrics to assess which parts of the system are more fault-proneness is of primary importance. This paper reports a research study begun with the analysis of more than 100 metrics and aimed at producing suitable models for fault-proneness estimation and prediction of software modules/files. The objective has been to find a compromise between the fault-proneness estimation rate and the size of the estimation model in terms of number of metrics used in the model itself. To this end, two different methodologies have been used, compared, and some synergies exploited. The methodologies were the logistic regression and the discriminant analyses. The corresponding models produced for fault-proneness estimation and prediction have been based on metrics addressing different aspects of computer programming. The comparison has produced satisfactory results in terms of fault-proneness prediction. The produced models have been cross validated by using data sets derived from source codes provided by two application scenarios.

Currently, the digital-content market is growing very fast and it needs solutions of production, protection and distribution of massive content collections. This is a real challenge for many industries that are discovering the complexity of managing large digital content factories and distribution chains.

Manufacturing industries integrate different resources in their production pipelines: material, personnel, machine tools, robots, etc. The resource management has to optimize production costs by taking into account on-line changes. The purpose of OPTAMS (optimized all monitor system) is to integrate an on-line optimization tool with a resource management system and a control system. OPTAMS allows to monitor the production process in order to run the re-optimization, if the preceding planning has to be retuned or updated. Thanks to the optimization, the company is able to increase the production efficiency. The optimization is based on Tabu search and the functional consists in several terms allowing finding a compromise from optimizing, costs, resource distribution workload, and time to delivering, delay, etc. The whole system's architecture is based on a distributed control system which integrates CANbus and Ethernet devices. OPTAMS has been realized as a European Commission R&D IST project, and has been validated at SHELBOX factory; the company produces mobile houses.

Temporal logics are able to describe temporal constraints amongst events and actions, as invariance, precedence, periodicity, repeated occurrences, liveness and safety conditions. They are typically used to verify properties in the requirements analysis, in order to describe high level system behaviour and for test case generation. In some cases, temporal logics are used for the specification of real time systems and for their execution. In this paper a model and algorithm to execute specifications in TILCO first order temporal logic are presented. To this end, the TILCO specifications are translated into a model which can be directly, executed by a specific inference engine. An example is also presented.

Among the emerging applications of music scores we can see production and adoption of multimedia digital objects based on music score for distance learning/tuition of music, etc. In these applications, the music notation model has to provide distinct mechanisms for modeling symbolic and visual aspects of music and for defining the relationships among such aspects and in particular between music scores and audio files of real performance. This paper explains the capabilities of WEDELMUSIC regarding the synchronization and execution of music scores synchronous with audio files. The functionality is supported by a free music editor: www.wedelmusic.org. The tool has been developed during the WEDELMUSIC IST Research project funded by the European Commission.

In the last years, a system for joining metal sheets by using a cold press joining technique has been invented and called clinching. This technique has been adopted in the production of metal boards for civil construction. The quality of joint points is a critical aspect and the current approach for quality control is based on the capabilities of an expert to understand the nature of the defect by observing the shape of each clinched button. VISICON IST Research and Development project proposes a solution for realizing a distributed quality control system for the production of clinched metal boards. The solution integrates aspects of software engineering, process modeling, knowledge representation and computer vision.

As computer technologies and their potential emerging applications spread out, new needs have been detected for computer-based applications of music; cooperative music notation editing both in orchestras and music schools is one of them. This article is the only public document describing the details of cooperative work on music notation of MOODS (Music Object Oriented Distributed System). MOODS is a synchronous real-time cooperative editor for music scores. Its architecture includes mechanisms for troubleshooting conflicts in real-time, managing histories of commands and versioning, and for performing selective undo. The system also includes specific solutions in order to control the editing on the account of editing permission profiles. The most important aspects of MOODS associated with cooperative work on music notation scores are reported herein. The article highlights the general problems of cooperative systems and provides rationales for the solutions, which were found to build MOODS. The MOODS system has been implemented and validated thanks to the endeavor of several musicians in orchestras, music schools, and project partners. A prototype has been demonstrated in public at the Scala Theatre in Milan, Italy.

Formal techniques for the specification of real-time systems must be capable of describing a set of relationships expressing the temporal constraints among events and actions: properties of invariance, precedence, periodicity, liveness and safety conditions, etc. This paper describes CTILCO, an extension of TILCO (Temporal Interval Logic with Compositional Operators). CTILCO introduces the communication among components specified in TILCO and allows the adoption of decomposition/composition mechanisms. TILCO has been expressly designed for the specification of real-time systems. CTILCO is based on time intervals and can concisely express temporal constraints with time bounds, such as those needed to specify real-time systems. It can be used to verify the completeness and consistency of specifications, as well as to validate system behavior against its requirements and general properties. CTILCO has been formalized by using the theorem prover Isabelle/HOL. CTILCO specifications satisfying certain properties are executable. CTILCO is defined in terms of theorems and allows the system specification and the formal proof of properties including composition/decomposition with communications. An example of system specification and validation has been also included.

Temporal logics are typically used for the specification of real-time systems since they are capable of describing temporal constraints among events and actions: properties of invariance, precedence, periodicity, repeated occurrences, liveness and safety conditions, etc. This paper describes an evolution of the Temporal Interval Logic called TILCO. TILCO is based on time intervals and can concisely express temporal constraints with time bounds. In this paper, TILCO-X extension of TILCO is presented. TILCO-X proposes two new operators that strongly increase conciseness and readability of specifications to allow describing: the temporal ordering of events without distinction between past and future; and predicates depending on the number of occurrences of events in intervals. TILCO-X is capable to describe specifications by using a lower number of quantifications and of nesting levels between temporal operators. This paper also presents the semantics of TILCO-X and related examples showing the power of the model proposed in terms of conciseness.

All software systems, no matter how they are designed, are subject to continuous evolution and maintenance activities to eliminate defects and extend their functionalities. This is particularly true for object-oriented systems, where we may develop different software systems using the same internal library or framework. These systems may evolve in quite different directions in order to cover different functionalities. Typically, there is the need to analyze their evolution in order to redefine the library or framework boundaries. This is a typical problem of software reengineering analysis. In this paper, we describe metrics, based on duplication analysis, that contribute to the process of reengineering analysis of object-oriented systems. These metrics are the basic elements of a reengineering analysis method and tool. Duplication analyses at the file, class and method levels have been performed. A structural analysis using metrics that capture similarities in class structure has been also exploited. In order to identify the best approach for the reengineering analysis of object-oriented systems, a comparison between the two approaches is described. In this paper, a case study based on real cases is presented, in which the results obtained by using a reengineering process with and without the analysis tool are described. The purpose of this study is to discover which method is the most powerful and how much time reduction can be obtained by its use.

Most of the commonly used watermark applications are referred to color or black and white pictorial images. Watermarking techniques are adopted to hide codes that can be used to demonstrate the ownership of the digital objects in case of copyright infringement verification. A technique for watermarking music scores/sheets is presented. The solution has been defined to satisfy specific user requirements. These have been collected by using the expert user group of the European Project WEDELMUSIC. They took into account the capability of musicians in detecting changes in the music sheets. The approach has been validated by using experts and technical experiments.

Many software systems built in recent years have been developed using object-oriented technology and, in some cases, they already need adaptive maintenance in order to satisfy market and customer needs. In most cases, the estimation and prediction of maintenance effort is performed with difficulty due to the lack of metrics and suitable models. In this paper, a model and metrics for estimation/prediction of adaptive maintenance effort are presented and compared with some other solutions taken from the literature. The model proposed can be used as a general approach for adopting well-known metrics (typically used for the estimation of development effort) for the estimation/prediction of adaptive maintenance effort. The model and metrics proposed have been validated against real data by using multilinear regression analysis. The validation has shown that several well-known metrics can be profitably employed for the estimation/prediction of maintenance effort.

Formal techniques for the specification of real time systems must be capable of describing system behavior as a set of relationships expressing the temporal constraints among events and actions, including properties of invariance, precedence, periodicity, liveness, and safety conditions. The paper describes a Temporal-Interval Logic with Compositional Operators (TILCO) designed expressly for the specification of real time systems. TILCO is a generalization of classical temporal logics based on the operators, eventually and henceforth; it allows both qualitative and quantitative specification of time relationships. TILCO is based on time intervals and can concisely express temporal constraints with time bounds, such as those needed to specify real time systems. This approach can be used to verify the completeness and consistency of specifications, as well as to validate system behavior against its requirements and general properties. TILCO has been formalized by using the theorem prover Isabelle/HOL. TILCO specifications satisfying certain properties are executable by using a modified version of the Tableaux algorithm. The paper defines TILCO and its axiomatization, highlights the tools available for proving properties of specifications and for their execution, and provides an example of system specification and validation.

System composition/decomposition has to be analyzed in terms of temporal constraints associated with the components' external interface. The analysis is focused on the propagation of temporal constraints along the structural hierarchy, thus showing that in many cases missing temporal constraints can be evaluated on the basis of the temporal constraints known. A set of criteria for the verification of the temporal constraints in a compositional model is presented. A specific graphical notation is proposed. The application of the criteria to a real problem is illustrated by using TROL, an object oriented real time language.

External specification is currently approached by specification languages for describing and analyzing system requirements. The external specification can be defined during the early stages of the system development and can be very useful for: checking the class/system/subsystem requirements; checking the system composition; evaluating costs of reuse; defining validated reference requirements, histories, and traces for the final validation. The paper presents a collection of criteria in order to formally verify the external specification of reactive systems/subsystems. The verification criteria are grounded on the Tempo Reale object-oriented language (TROL) specification model for real-time systems. In TROL, the external specification is expressed in terms of ports and clauses with temporal constraints. The goal of the verification criteria presented is to check the completeness and consistency of the external specification with special attention to temporal constraints. These criteria can be applied to other real-time specification models and have been enforced in the tool object oriented machine state (TOOMS) tool. A practical example illustrates the verification process that embodies these criteria.

The automatic recognition of music scores is a key process for the electronic treatment of music information. In this paper we present the segmentation module of an OMR system. The proposed approach is based on the use of projection profiles for the location of elementary symbols that constitute the music notation. An extensive experimentation was made which the help of a tool developed to this purpose. Reported results shown a high efficiency in the correct location of elementary symbols.

Along production pipelines in manufacturing industries, several elaboration phases in which the movement of interpolated axes are present. These activities have to be synchronized with several other simpler activities along the pipeline. Computerized numerical controls for such systems have to be flexible and strongly expandable and reusable since pipelines are frequently reconfigured to realize differently arranged pipelines with different technical requirements. The assessment of such a flexible architecture is presented, on the basis of which the building of flexible distributed controls for pipelines is possible. The architecture has been defined in the ESPRIT HPCN (High Performance Computer Networking) project MUPAAC (Multi Processor Architecture for Automatic Control). MUPAAC architecture and prototype has been assessed in order to identify the most important performance indexes.

Object oriented modeling has been largely adopted in industry in the last few years. Several systems built 4 or 5 years ago may need an adaptive maintenance process in order to better satisfy market and customer needs. A model for effort estimation/prediction of the adaptive maintenance is presented. A selection of metrics for effort estimation has been applied to the general model for evaluating maintenance effort. The metrics presented have been validated against real data. The validation presented has shown that some metrics that can be profitably employed for effort estimation/prediction can be also used with success for the estimation/prediction of the maintenance effort. Moreover the results obtained give some guidelines for maintenance of control of relevant factors for adaptive maintenance.

A wide increment of object-oriented diffusion has been registered. This is accompanied by the needs of metrics and tools for assessing class reusability, maintainability, complexity, etc. Metrics have to produce confident results and have to be continuously revalidated on the basis of the specific purpose of their adoption. Tools for system assessment must be capable of satisfying the needs of each company by supporting the definition of more specific metrics and by providing appropriate views of software characteristics. These views are useful to give the developers as well as the managers immediately understandable representations of the system status. In this paper a new approach is proposed for system assessment. This approach is based on the adoption of new and well-known metrics together with a set of histograms and profiles that give a clear characterization of the system under development. The semantics of these histograms has been validated against several projects. A brief overview of the tool, TAC++, developed for system assessment is also included.

Owing to the growing diffusion of the object-oriented paradigm and the need to keep the process of software development under control, industries are looking for metrics/indicators capable of evaluating system evolution to control quality, reusability, maintainability, etc. Some new metrics are proposed for monitoring system development and maintenance. These metrics are used with a set of histograms to give a clear characterization of the system under development. Histograms call be profitably used to detect critical conditions during the system life-cycle. The semantics of these histograms has been validated against several projects: an example is also reported.

The Temporal Interval Logic with Compositional Operators (TILCO) has been especially designed for the specification and validation of real-time systems. TILCO extends the classical temporal logics based on the operators eventually, and henceforth by using time intervals in order to allow the specification of both qualitative and quantitative temporal relationships. The use of time intervals also supports an abstract and synthetic style for specifying real-time systems. The validation of TILCO specifications is supported by means of property proving; direct execution of specifications and model-checking of system histories are also possible. Therefore, the TILCO model can be considered as a dual approach. This paper describes TILCO and shows with an example its application for the specification and validation of real-time systems. Highlights about the TILCO axiomatization are given and the tools available for proving properties of specifications and for specifications execution are discussed.

Several approaches for optical flow estimation use partial differential equations to model changes in image brightness throughout time. A commonly used equation is the so-called optical flow constraint (OFC), which assumes that the image brightness is stationary with respect to time. More recently, a different constraint referred to as the extended optical flow constraint (EOFC) has been introduced, which also contains the divergence of the flow field of image brightness. There is no agreement in the literature about which of these constraints provides the best estimation of the velocity field. Two new solutions for optical flow computation are proposed, which are based on an approximation of the constraint equations. The two techniques have been used with both EOFC and OFC constraint equations. Results achieved by using these solutions have been compared with several well-known computational methods for optical flow estimation in different motion conditions. Estimation errors have also been measured and compared for different types of motion.

Different constraint equations have been proposed in the literature for the derivation of optical flow. Despite of the large number of papers dealing with computational techniques to estimate optical flow, only a few authors have investigated conditions under which these constraints exactly model the velocity field, that is, the perspective projection on the image plane of the true 3-D velocity. These conditions are analyzed under different hypotheses, and the departures of the constraint equations in modeling the velocity field are derived for different motion conditions. Experiments are also presented giving measures of these departures and of the induced errors in the estimation of the velocity field.

Retrieval by contents of images from pictorial databases can be effectively performed through visual icon-based systems. In these systems, the representation of pictures with 2D strings, which are derived from symbolic projections, provides an efficient and natural way to construct iconic indexes for pictures and is also an ideal representation for the visual query. With this approach, retrieval is reduced to matching two symbolic strings. However, using 2D-string representations, spatial relationships between the objects represented in the image might not be exactly specified. Ambiguities arise for the retrieval of images of 3D scenes. In order to allow the unambiguous description of object spatial relationships, in this paper, following the symbolic projections approach, images are referred to by considering spatial relationships in the 3D imaged scene. A representation language is introduced that expresses positional and directional relationships between objects in three dimensions, still preserving object spatial extensions after projections. Iconic retrieval from pictorial databases with 3D interfaces is discussed and motivated. A system for querying by example with 3D icons, which supports this language, is also presented.>

A method to determine the spatial orientation of a 3D planar-faced object from a single perspective view is presented. In this method the junctions, that is the concurrences of three object edges in a vertex, are considered as key features. A geometric constraint, related to the perspective projection of the object junctions, is shown and exploited to make the process faster and more efficient. In this way, the knowledge of only two parameters is sufficient to verify the object orientation in 3D space. A system of equations is proposed which define the orientations space for a junction as a subset of R/sup 2/, instead of R/sup 3/ as in the previous literature.>

A fully object-oriented approach to a general-purpose object-recognition system is presented. This, together with a blackboard architecture, provides a flexible and easily maintainable system. The knowledge information is distributed into two object-oriented databases: one, hierarchically structures into three layers according to visual abstractions, contains the object models (the knowledge source of the recognition process); the other, which reflects in the most natural way the problem domain knowledge, helps in the pruning of the decision trees and allows for the analysis of the behavior of the objects.>