Services for data provisioning can be extremely valuable. Organizations wrestle with deciding whether to “expose or not to expose” their data through Web APIs and, depending on the nature of their business, mull over potential benefits and drawbacks. This is because data can be exploited and misused in unanticipated ways. Rate restriction techniques are frequently used to manage data access and protect back-end computing resources. The best choice for the maximum rate at which information can be “safely” provided to clients will determine its effectiveness. An important situation involves formal service-level agreements that govern the quality of services provided by public governments and private businesses. These firms might need to decide on a rate cap for their Web APIs that prevents unauthorized clients from accurately calculating service levels while still enabling the development of valuable value-added services. In this paper, we propose a statistical model for this problem and a technique, based on sampling tools, following brute-force, binary search, and heuristic search methods, to select an appropriate rate limit, and demonstrate its validity through a case study involving a large Italian bus company.

This paper focuses on the aspects of responsibility and ethics when using AI systems in the contexts of health and genomics. The present proposal also aims to address the important need to consider how the use of AI systems in the field of genomics affects human life and its implications. As AI systems increasingly participate in diagnostic and predictive decision-making, they introduce new challenges concerning moral agency, transparency, and human oversight. The discussion aims to examine how these technologies, while offering unprecedented analytical capabilities, simultaneously reshape the ethical landscape of medical practice and genomic research.

Recent years have witnessed a growing interest in automating labor-intensive and complex activities, i.e., those consisting of multiple atomic tasks, by deploying robots in dynamic and unpredictable environments such as industrial and agricultural settings. A key characteristic of these contexts is that activities are not predefined: while they involve a limited set of possible tasks, their combinations may vary depending on the situation. Moreover, despite recent advances in robotics, the ability for humans to monitor the progress of high-level activities - in terms of past, present, and future actions - remains fundamental to ensure the correct execution of safety-critical processes. In this paper, we introduce a general architecture that integrates Large Language Models (LLMs) with automated planning, enabling humans to specify high-level activities (also referred to as processes) using natural language, and to monitor their execution by querying a robot. We also present an implementation of this architecture using state-of-the-art components and quantitatively evaluate the approach in a real-world precision agriculture scenario.

The increasing adoption of Large Language Models (LLMs) has significantly lowered technical barriers across various domains, enabling tasks such as code generation, database querying, and service composition. This work explores the potential of LLMs to facilitate human access to heterogeneous data in Cyber-Physical Systems (CPS). The proposed system interprets natural language queries and, by leveraging in-context learning, dynamically synthesizes data integration pipelines that compose data services, thus enabling access to information from multiple sources. The execution of a pipeline generates a table that fulfills the input query, effectively integrating and structuring the retrieved data. This paper presents the design and implementation of the proposed solution and its evaluation using a real-world case study and the BIRD benchmark dataset. The results demonstrate its effectiveness in improving data retrieval, data service design, and execution in CPS.

Monitoring business processes within complex supply chains demands efficient data collection and analytics tailored to diverse phenomena. Traditional centralized solutions face limitations in adapting to the dynamic nature of supply chains. This calls for distributed solutions which break the usual architectural assumption to have a central entity in charge of collecting, integrating and offering tools for the analysis. This project, embedded in a larger initiative called MICS, proposes an inno-vative distributed monitoring solution integrating blockchain for a trustworthy and efficient data analytics strategy that preserves data sovereignty in complex collaborative environments. Leveraging the cloud -edge continuum, the solution aims to ensure secure data exchange, adherence to agreements, and real-time analytics. Expected outcomes include an innovative federated architecture, 5G slice management solutions, an adversarial analysis of supply chain security, and a proof-of-concept implementation of the blockchain-based data flow tracking system. These developments aim to enhance the reliability, security, and efficiency of supply chain monitoring in dynamic industrial environments.

Virtual Reality (VR) environments are used as a way to let humans experiment with algorithms, techniques, and situations in various application areas, including emergency management, serious games, smart manufacturing, and precision agriculture. They are especially relevant when experiments in the real world may be harmful to human operators. As VR environments are the closest possible faithful replicas of real environments, many recent works focus on the employment of such tools as a means to generate synthetic datasets that can be used for training machine and deep learning models, especially in situations where obtaining real datasets can be difficult. In this paper, we introduce a strategy to generate a dataset for pose estimation in the challenging scenario of precision agriculture. Finally, the quality of the generated dataset was evaluated.

Widespread use of IoT, like surveillance cameras, raises privacy concerns in citizens’ lives. However, limited studies explore AI-based automatic recognition of criminal incidents due to a lack of real data, constrained by legal and privacy regulations, preventing effective training and testing of deep learning models. To address dataset limitations, we propose using generative technology and virtual gaming data, such as the Grand Theft Auto (GTA-V) platform. However, it’s unclear if synthetic data accurately mirrors real-world videos for effective deep learning model performance. This research aims to explore the potential of identifying criminal scenarios using deep learning models based on gaming data. We propose a deep-learning violence detection framework using virtual gaming data. The 3-stage deep learning model focuses on person identification and violence activity recognition. We introduce a new dataset for supervised training and find virtual persons closely resembling real-world individuals. Our research demonstrates a 15% higher accuracy in identifying violent scenarios compared to three established real-world datasets, showcasing the effectiveness of a serious gaming approach.

Abstract The continuous evolution of digital technologies applied to the more traditional world of industrial automation led to Industry 4.0, which envisions production processes subject to continuous monitoring and able to dynamically respond to changes that can affect the production at any stage (resilient factory). The concept of agility, which is a core element of Industry 4.0, is defined as the ability to quickly react to breaks and quickly adapt to changes. Accurate approaches should be implemented aiming at managing, optimizing and improving production processes. In this vision paper, we show how process management (BPM) can benefit from the availability of raw data from the industrial internet of things to obtain agile processes by using a top-down approach based on automated synthesis and a bottom-up approach based on mining.

In recent decades, manufacturing practices have undergone a significant transformation, with the integration of computers and automation playing a central role. Concurrently, there has been a growing interest in utilizing intelligent techniques to effectively manage manufacturing processes. These processes entail the seamless integration of various activities across the supply chain. Given the diverse range of actors in a supply chain, each one with distinct characteristics such as cost, quality, and probability of failure, task assignment becomes a crucial challenge. In such a complex scenario, manual decision-making becomes impractical, necessitating the adoption of automated techniques to effectively address these challenges in a resilient and adaptive manner. This article proposes a service-oriented approach to model each manufacturing actor within the supply chain. Furthermore, it categorizes automated synthesis approaches for smart manufacturing on the basis ofi)the characteristics of each actor, which are retrieved by their Industrial API, andii)the goal(s) of the manufacturing process. Finally, the article evaluates three distinct approaches that implement automated synthesis techniques for composing services and generating operational plans.

Recent years witnessed a growing interest in the employment of intelligent techniques for the management of manufacturing processes in smart manufacturing. These processes may include tens of resources distributed among several different companies composing the supply chain. The status of the different resources evolves over time in terms of cost, quality, and probability of breakdunavailability, thus requiring the process to be adaptive and resilient to disruptions. Due to the high number of involved resources, making decisions manually soon become unfeasible, thus requiring automated techniques to solve the problem. In this paper, we discuss the potential and limitations of automated reasoning techniques for making modern supply chains adaptive and resilient by relying on the information provided by resources through their APIs.

Human activities represent a major source of information for smart home automation. While performing their daily activities, humans trigger sensors producing measurements that flow into a sensor log. Vast majority of techniques to recognize and exploit the occurrences of human activities are supervised, requiring the log to be manually labeled in correspondence of the onset and the end of each activity repetition. This task requires a considerable effort by the final user, resulting in imprecise labeling tampering the performance of algorithms. In this paper, we propose an unsupervised technique allowing to automatically segment smart home logs containing position sensor measurements. The proposed technique exploits information about the position of the human to automatically extract basic actions, which are then segmented on a temporal basis and clustered. The approach is evaluated against a state-of-the-art dataset.

Human-Robot Interaction (HRI) has become increasingly important as robots have been integrated into various daily life aspects. Gesture recognition plays a crucial role in non-verbal communication in HRI. Indeed, the real-time robot's interpretation of human gestures is essential for enhancing the overall user experience. To this aim, improving the accuracy and efficiency of gesture recognition in HRI could be achieved by integrating Virtual Reality (VR) technology. In this article, we describe the development of virtually-generated avatars and a gesture recognition system that uses machine learning techniques to allow ground robots to recognize gestures performed by both digital agents and actual humans. Nevertheless, we address some existing challenges by presenting a set of gesture definitions, data generation, and system evaluation using virtual reality simulation. Our findings and results highlight the importance of addressing these challenges to enable effective gesture recognition in HRI. In particular, we demonstrate that the adoption of VR can significantly increase the system's accuracy and efficiency, improving the overall user experience.

Collaborative robots seamlessly share the space with humans in production scenarios such as those involved in smart manufacturing and agriculture, thus raising several human safety concerns. Since a collaboration between humans and robots is performed through communicative acts, applying accurate techniques for understanding them is of the utmost importance to guarantee the overall safety of the human. A preliminary classification of the communicative acts into categories is required to increase the accuracy of adopted methods and the promptness of the response. This paper evaluates a speech communicative act classification methodology in the challenging scenario of precision agriculture using Virtual Reality (VR). Our proposal can easily be applied to any production scenario involving collaborative robots.

The demand for protection and security of physical spaces and urban areas increased with the escalation of terroristic attacks in recent years. We envision with the proposed cyber–physical systems and spaces, a city that would indeed become a smarter urbanistic object, proactively providing alerts and being protective against any threat. This survey intend to provide a systematic multivocal literature survey comprised of an updated, comprehensive and timely overview of state of the art in counter-terrorism cyber–physical systems, hence aimed at the protection of cyber–physical spaces. Hence, provide guidelines to law enforcement agencies and practitioners providing a description of technologies and best practices for the protection of public spaces. We analyzed 112 papers collected from different online sources, both from the academic field and from websites and blogs ranging from 2004 till mid-2022. (a) There is no one single bullet-proof solution available for the protection of public spaces. (b) From our analysis we found three major active fields for the protection of public spaces: Information Technologies, Architectural approaches, Organizational field. (c) While the academic suggest best practices and methodologies for the protection of urban areas, the market did not provide any type of implementation of such suggested approaches, which shows a lack of fertilization between academia and industry. The overall analysis has led us to state that there is no one single solution available, conversely, multiple methods and techniques can be put in place to guarantee safety and security in public spaces. The techniques range from architectural design to rethink the design of public spaces keeping security into account in continuity, to emerging technologies such as AI and predictive surveillance.

Smart homes represent examples of cyber-physical environments realizing the paradigm known as ambient intelligence. An information system supporting ambient intelligence takes as input raw sensor measurements and analyzes them to eventually make decisions following final user preferences. Unfortunately, algorithms in this research area are mostly supervised, thus requiring a manual labeling of training instances usually involving final users in annoying and imprecise training sessions. In this paper, we propose a methodology allowing, given a sensor log, to automatically segment human habits by applying a bottom-up discretization strategy to the timestamp attribute of the sensor log. In particular, we show how classical quality measures, computed over Petri nets automatically mined from sensor logs filtered by timestamp, can be used as an heuristic to drive the discretization process, thus providing a likely subdivision of the day in human habits.

Abstract This paper proposes a general approach for using conversational interfaces such as chatbots to offer adaptive learning of business processes in an environment involving different actors. Adaptivity concerns both the content being proposed, the sequence of learning items, and the way the conversation is conducted. The original approach allows the development of sustainable chatbots and empowers various non-technical actors (authors, teachers, publishers, and learners) to control the chatbot features directly. The aCHAT-WF framework (adaptive CHATbot for WorkFlows), proposed in this paper for managing conversational interfaces, conceptually represents all the aspects related to a conversation about business processes, with different facets for the user, the conversation flow, and the conversation contents, combining them to obtain a flexible interaction with the user. The paper focuses on the different preparation phases for instructional material based on Business Process Modeling Notation (BPMN) models, separating the different roles involved in the construction of a chatbot for teaching business processes and with the possibility of defining different styles for the interaction with the users. The proposed method is configuration-driven, to facilitate the separation of the different aspects of the control of the interaction and the delivery of contents.

Abstract Nowadays, the role played by registries in monitoring and improving healthcare, including the quality of medical devices, is widely recognized. A well‐designed digital healthcare registry, in particular regarding data collection procedures and tools, can effectively support goals such as monitoring a (large) population subject to a specific condition, describing the natural history of diseases, supporting observational study methods, as well as evaluating the clinical effectiveness or cost effectiveness of healthcare products and services. This article describes the architecture of a new platform implementing a digital interoperable healthcare registry, the Italian Implantable Prostheses Registry (RIPI). One of the main goals of RIPI is to provide reliable and high‐quality data for monitoring surgery outcomes, performing survival analysis, assessing the safety of devices and procedures, and supporting the traceability of patients. The article focuses on the key aspects and choices that guided the design and implementation processes of the new platform. Most of the design choices came from specific requirements to fulfill, in particular concerning data quality, access policy, interoperability, extensibility and modularity. Overall, the article discusses the main challenges and the adopted solutions, proposing a design perspective and describing an experience of interest for computer scientists, engineers and practitioners, in particular in the area of healthcare information systems.

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.

The technological foundation of smart manufacturing consists of cyber-physical systems and the Internet-of-Things (IoT). Each IoT device in a smart factory can be coupled with a digital twin, that is, a dynamic virtual representation of the physical system across its life-cycle using real-time sensor data. Currently, the manufacturing process itself, the involved devices, and how they interact, is designed by human experts in a traditional way. We envision an architecture where humans can instead specify a goal and take advantage of technologies such as digital twins to automatically compose the corresponding physical processes, sharing some analogies with the notion of Web service composition.

Data-provisioning services can provide high value. Since data can be used and misused in unpredictable ways, organizations deal with the dilemma "to expose or not to expose" their data through Web APIs and - depending on the nature of their business - ponder possible opportunities and pitfalls. Rate limiting strategies are widely employed to control access to data and to safeguard back-end computational resources. However, their effectiveness depends on a good choice of the maximum allowed rate at which information can be "safely" released to users. A relevant scenario is represented by public administrations and private companies providing services whose quality is regulated by formal business agreements on the service levels. These organizations may need to choose a rate limit for their Web APIs that prevent unauthorized users to compute service levels with an high confidence while still allowing the creation of useful value-added services. In this paper, we propose a general statistical model for this problem and a technique, based on uniform sampling tools, to select an appropriate rate limit, and we demonstrate its validity through a case study involving a large bus company.

The increasing importance of data in business processes has led to the emergence of data-centric business process management, which deviates from the widely used activity-centric paradigm. Data-centric approaches set their focus on data, aiming at supporting data-intensive business processes and increased process flexibility. The objective of this article is to gain profound insights into the maturity of different data-centric approaches as well as their capabilities. In particular, this article will provide a framework for systematically evaluating and comparing data-centric approaches, with regard to the phases of the business process lifecycle. To this end, a systematic literature review (SLR) was conducted with the goal of evaluating the capabilities of data-centric process management approaches. The SLR comprises 38 primary studies which were thoroughly analyzed. The studies were categorized into different approaches, whose capabilities were thoroughly assessed. Special focus was put on the tooling and software of the approaches. The article provides the empirically grounded DALEC framework to evaluate and compare data-centric approaches. Furthermore, the results of the SLR offer insights into existing data-centric approaches and their capabilities. Data-centric approaches promise better support of loosely structured and data-intensive business processes, which may not be adequately represented by activity-centric paradigms.

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing techniques.

Summary Current smart spaces require more and more sophisticated sensors able to acquire the state of the environment in order to provide advanced and customized services. Among the most important environmental variables, locations of users and their identities represent a primary concern for smart home applications. Despite some years of investigation in indoor positioning, the availability of systems designed as components pluggable into complex home automation platforms is limited. We presentPeopleLocalizationandTracking forHomE Automation (PLaTHEA), a vision‐based indoor localization system specifically tailored for Ambient Assisted Living applications. PLaTHEA features a novel technique to acquire a stereo video stream from a couple of independent (and not synchronized) network‐attached cameras, thus easing its physical deployment. The input stream is processed by integrating well‐known techniques with a novel tracking approach targeted to indoor spaces. The system has a modular architecture that offers clear interfaces exposed as Web services, and it can run on off‐the‐shelf and cheap hardware (both in terms of sensing devices and computing units). We evaluated PLaTHEA in real usage conditions and reported the measured performance in terms of precision and accuracy. Low light, crowded and large monitored environments might slightly decrease the performance of the system; nevertheless, the results here presented show that it is perfectly suitable to be employed in the typical domestic day‐to‐day life settings. Copyright © 2014 John Wiley & Sons, Ltd.